id	sid	tid	token	lemma	pos
fbem-28239	1	1	frontiers	frontier	NOUN
fbem-28239	1	2	in	in	ADP
fbem-28239	1	3	business	business	NOUN
fbem-28239	1	4	,	,	PUNCT
fbem-28239	1	5	economics	economic	NOUN
fbem-28239	1	6	and	and	CCONJ
fbem-28239	1	7	management	management	NOUN
fbem-28239	1	8	issn	issn	PROPN
fbem-28239	1	9	:	:	PUNCT
fbem-28239	1	10	2766	2766	NUM
fbem-28239	1	11	-	-	PUNCT
fbem-28239	1	12	824x	824x	NUM
fbem-28239	1	13	|	|	ADJ
fbem-28239	1	14	vol	vol	NOUN
fbem-28239	1	15	.	.	PROPN
fbem-28239	1	16	17	17	NUM
fbem-28239	1	17	,	,	PUNCT
fbem-28239	1	18	no	no	INTJ
fbem-28239	1	19	.	.	NOUN
fbem-28239	1	20	3	3	NUM
fbem-28239	1	21	,	,	PUNCT
fbem-28239	1	22	2024	2024	NUM
fbem-28239	1	23	109	109	NUM
fbem-28239	1	24	the	the	DET
fbem-28239	1	25	combination	combination	NOUN
fbem-28239	1	26	of	of	ADP
fbem-28239	1	27	traditional	traditional	ADJ
fbem-28239	1	28	asset	asset	NOUN
fbem-28239	1	29	pricing	pricing	NOUN
fbem-28239	1	30	models	model	NOUN
fbem-28239	1	31	and	and	CCONJ
fbem-28239	1	32	machine	machine	NOUN
fbem-28239	1	33	learning	learn	VERB
fbem-28239	1	34	-starting	-starte	VERB
fbem-28239	1	35	from	from	ADP
fbem-28239	1	36	multiple	multiple	ADJ
fbem-28239	1	37	linear	linear	ADJ
fbem-28239	1	38	regression	regression	NOUN
fbem-28239	1	39	models	model	NOUN
fbem-28239	1	40	and	and	CCONJ
fbem-28239	1	41	recurrent	recurrent	ADJ
fbem-28239	1	42	neural	neural	ADJ
fbem-28239	1	43	networks	network	NOUN
fbem-28239	1	44	(	(	PUNCT
fbem-28239	1	45	rnns	rnns	PROPN
fbem-28239	1	46	)	)	PUNCT
fbem-28239	1	47	yanqing	yanqing	NOUN
fbem-28239	1	48	ma	ma	PROPN
fbem-28239	1	49	school	school	PROPN
fbem-28239	1	50	of	of	ADP
fbem-28239	1	51	finance	finance	NOUN
fbem-28239	1	52	,	,	PUNCT
fbem-28239	1	53	city	city	PROPN
fbem-28239	1	54	university	university	PROPN
fbem-28239	1	55	of	of	ADP
fbem-28239	1	56	macau	macau	PROPN
fbem-28239	1	57	,	,	PUNCT
fbem-28239	1	58	macau	macau	PROPN
fbem-28239	1	59	,	,	PUNCT
fbem-28239	1	60	999078	999078	NUM
fbem-28239	1	61	,	,	PUNCT
fbem-28239	1	62	china	china	PROPN
fbem-28239	1	63	f21090102549@cityu.edu.mo	f21090102549@cityu.edu.mo	PROPN
fbem-28239	1	64	abstract	abstract	NOUN
fbem-28239	1	65	:	:	PUNCT
fbem-28239	1	66	this	this	DET
fbem-28239	1	67	article	article	NOUN
fbem-28239	1	68	combines	combine	VERB
fbem-28239	1	69	the	the	DET
fbem-28239	1	70	capm	capm	PROPN
fbem-28239	1	71	theory	theory	NOUN
fbem-28239	1	72	with	with	ADP
fbem-28239	1	73	the	the	DET
fbem-28239	1	74	concept	concept	NOUN
fbem-28239	1	75	of	of	ADP
fbem-28239	1	76	beta	beta	ADJ
fbem-28239	1	77	coefficient	coefficient	NOUN
fbem-28239	1	78	,	,	PUNCT
fbem-28239	1	79	proposes	propose	VERB
fbem-28239	1	80	a	a	DET
fbem-28239	1	81	multiple	multiple	ADJ
fbem-28239	1	82	linear	linear	ADJ
fbem-28239	1	83	regression	regression	NOUN
fbem-28239	1	84	model	model	NOUN
fbem-28239	1	85	and	and	CCONJ
fbem-28239	1	86	recurrent	recurrent	ADJ
fbem-28239	1	87	neural	neural	ADJ
fbem-28239	1	88	network	network	NOUN
fbem-28239	1	89	(	(	PUNCT
fbem-28239	1	90	rnn	rnn	PROPN
fbem-28239	1	91	)	)	PUNCT
fbem-28239	1	92	,	,	PUNCT
fbem-28239	1	93	and	and	CCONJ
fbem-28239	1	94	predicts	predict	VERB
fbem-28239	1	95	the	the	DET
fbem-28239	1	96	stock	stock	NOUN
fbem-28239	1	97	of	of	ADP
fbem-28239	1	98	representative	representative	ADJ
fbem-28239	1	99	apple	apple	NOUN
fbem-28239	1	100	company	company	NOUN
fbem-28239	1	101	in	in	ADP
fbem-28239	1	102	sp500	sp500	PROPN
fbem-28239	1	103	.	.	PUNCT
fbem-28239	2	1	the	the	DET
fbem-28239	2	2	multiple	multiple	ADJ
fbem-28239	2	3	linear	linear	PROPN
fbem-28239	2	4	models	model	NOUN
fbem-28239	2	5	introduce	introduce	VERB
fbem-28239	2	6	the	the	DET
fbem-28239	2	7	concept	concept	NOUN
fbem-28239	2	8	of	of	ADP
fbem-28239	2	9	capm	capm	PROPN
fbem-28239	2	10	(	(	PUNCT
fbem-28239	2	11	beta	beta	NOUN
fbem-28239	2	12	)	)	PUNCT
fbem-28239	2	13	and	and	CCONJ
fbem-28239	2	14	is	be	AUX
fbem-28239	2	15	based	base	VERB
fbem-28239	2	16	on	on	ADP
fbem-28239	2	17	multiple	multiple	ADJ
fbem-28239	2	18	linear	linear	ADJ
fbem-28239	2	19	regression	regression	NOUN
fbem-28239	2	20	.	.	PUNCT
fbem-28239	3	1	it	it	PRON
fbem-28239	3	2	determines	determine	VERB
fbem-28239	3	3	the	the	DET
fbem-28239	3	4	values	value	NOUN
fbem-28239	3	5	of	of	ADP
fbem-28239	3	6	these	these	DET
fbem-28239	3	7	coefficients	coefficient	NOUN
fbem-28239	3	8	by	by	ADP
fbem-28239	3	9	minimizing	minimize	VERB
fbem-28239	3	10	the	the	DET
fbem-28239	3	11	error	error	NOUN
fbem-28239	3	12	between	between	ADP
fbem-28239	3	13	actual	actual	ADJ
fbem-28239	3	14	and	and	CCONJ
fbem-28239	3	15	predicted	predict	VERB
fbem-28239	3	16	values	value	NOUN
fbem-28239	3	17	.	.	PUNCT
fbem-28239	4	1	the	the	DET
fbem-28239	4	2	linearregression	linearregression	NOUN
fbem-28239	4	3	class	class	NOUN
fbem-28239	4	4	in	in	ADP
fbem-28239	4	5	the	the	DET
fbem-28239	4	6	sklearn	sklearn	ADJ
fbem-28239	4	7	library	library	NOUN
fbem-28239	4	8	i	i	PRON
fbem-28239	4	9	s	s	AUX
fbem-28239	4	10	used	use	VERB
fbem-28239	4	11	to	to	PART
fbem-28239	4	12	train	train	VERB
fbem-28239	4	13	and	and	CCONJ
fbem-28239	4	14	predict	predict	VERB
fbem-28239	4	15	data	datum	NOUN
fbem-28239	4	16	related	relate	VERB
fbem-28239	4	17	to	to	ADP
fbem-28239	4	18	apple	apple	PROPN
fbem-28239	4	19	inc	inc	PROPN
fbem-28239	4	20	.	.	PROPN
fbem-28239	4	21	recurrent	recurrent	ADJ
fbem-28239	4	22	neural	neural	ADJ
fbem-28239	4	23	network	network	NOUN
fbem-28239	4	24	(	(	PUNCT
fbem-28239	4	25	rnn	rnn	PROPN
fbem-28239	4	26	)	)	PUNCT
fbem-28239	4	27	is	be	AUX
fbem-28239	4	28	used	use	VERB
fbem-28239	4	29	to	to	PART
fbem-28239	4	30	predict	predict	VERB
fbem-28239	4	31	the	the	DET
fbem-28239	4	32	stock	stock	NOUN
fbem-28239	4	33	price	price	NOUN
fbem-28239	4	34	of	of	ADP
fbem-28239	4	35	apple	apple	PROPN
fbem-28239	4	36	inc	inc	PROPN
fbem-28239	4	37	.	.	PROPN
fbem-28239	4	38	and	and	CCONJ
fbem-28239	4	39	combines	combine	VERB
fbem-28239	4	40	the	the	DET
fbem-28239	4	41	beta	beta	ADJ
fbem-28239	4	42	coefficient	coefficient	NOUN
fbem-28239	4	43	calculated	calculate	VERB
fbem-28239	4	44	from	from	ADP
fbem-28239	4	45	market	market	NOUN
fbem-28239	4	46	index	index	NOUN
fbem-28239	4	47	data	datum	NOUN
fbem-28239	4	48	to	to	PART
fbem-28239	4	49	enhance	enhance	VERB
fbem-28239	4	50	the	the	DET
fbem-28239	4	51	performance	performance	NOUN
fbem-28239	4	52	of	of	ADP
fbem-28239	4	53	the	the	DET
fbem-28239	4	54	model	model	NOUN
fbem-28239	4	55	.	.	PUNCT
fbem-28239	5	1	the	the	DET
fbem-28239	5	2	results	result	NOUN
fbem-28239	5	3	show	show	VERB
fbem-28239	5	4	that	that	SCONJ
fbem-28239	5	5	the	the	DET
fbem-28239	5	6	predicted	predict	VERB
fbem-28239	5	7	values	value	NOUN
fbem-28239	5	8	are	be	AUX
fbem-28239	5	9	very	very	ADV
fbem-28239	5	10	close	close	ADJ
fbem-28239	5	11	to	to	ADP
fbem-28239	5	12	the	the	DET
fbem-28239	5	13	actual	actual	ADJ
fbem-28239	5	14	values	value	NOUN
fbem-28239	5	15	.	.	PUNCT
fbem-28239	6	1	in	in	ADP
fbem-28239	6	2	addition	addition	NOUN
fbem-28239	6	3	,	,	PUNCT
fbem-28239	6	4	this	this	DET
fbem-28239	6	5	article	article	NOUN
fbem-28239	6	6	also	also	ADV
fbem-28239	6	7	compared	compare	VERB
fbem-28239	6	8	and	and	CCONJ
fbem-28239	6	9	demonstrated	demonstrate	VERB
fbem-28239	6	10	the	the	DET
fbem-28239	6	11	different	different	ADJ
fbem-28239	6	12	prediction	prediction	NOUN
fbem-28239	6	13	results	result	NOUN
fbem-28239	6	14	of	of	ADP
fbem-28239	6	15	multiple	multiple	ADJ
fbem-28239	6	16	linear	linear	ADJ
fbem-28239	6	17	regression	regression	NOUN
fbem-28239	6	18	models	model	NOUN
fbem-28239	6	19	and	and	CCONJ
fbem-28239	6	20	recurrent	recurrent	ADJ
fbem-28239	6	21	neural	neural	ADJ
fbem-28239	6	22	networks	network	NOUN
fbem-28239	6	23	(	(	PUNCT
fbem-28239	6	24	rnn	rnn	PROPN
fbem-28239	6	25	)	)	PUNCT
fbem-28239	6	26	on	on	ADP
fbem-28239	6	27	whether	whether	SCONJ
fbem-28239	6	28	to	to	PART
fbem-28239	6	29	introduce	introduce	VERB
fbem-28239	6	30	capm	capm	PROPN
fbem-28239	6	31	related	relate	VERB
fbem-28239	6	32	concepts	concept	NOUN
fbem-28239	6	33	(	(	PUNCT
fbem-28239	6	34	beta	beta	NOUN
fbem-28239	6	35	)	)	PUNCT
fbem-28239	6	36	.	.	PUNCT
fbem-28239	7	1	the	the	DET
fbem-28239	7	2	results	result	NOUN
fbem-28239	7	3	showed	show	VERB
fbem-28239	7	4	that	that	SCONJ
fbem-28239	7	5	the	the	DET
fbem-28239	7	6	citation	citation	NOUN
fbem-28239	7	7	of	of	ADP
fbem-28239	7	8	related	related	ADJ
fbem-28239	7	9	capm	capm	NOUN
fbem-28239	7	10	concepts	concept	NOUN
fbem-28239	7	11	is	be	AUX
fbem-28239	7	12	very	very	ADV
fbem-28239	7	13	necessary	necessary	ADJ
fbem-28239	7	14	,	,	PUNCT
fbem-28239	7	15	and	and	CCONJ
fbem-28239	7	16	it	it	PRON
fbem-28239	7	17	is	be	AUX
fbem-28239	7	18	particularly	particularly	ADV
fbem-28239	7	19	strong	strong	ADJ
fbem-28239	7	20	in	in	ADP
fbem-28239	7	21	recurrent	recurrent	ADJ
fbem-28239	7	22	neural	neural	ADJ
fbem-28239	7	23	network	network	NOUN
fbem-28239	7	24	(	(	PUNCT
fbem-28239	7	25	rnn	rnn	PROPN
fbem-28239	7	26	)	)	PUNCT
fbem-28239	7	27	models	model	NOUN
fbem-28239	7	28	.	.	PUNCT
fbem-28239	8	1	afterwards	afterwards	ADV
fbem-28239	8	2	,	,	PUNCT
fbem-28239	8	3	this	this	DET
fbem-28239	8	4	article	article	NOUN
fbem-28239	8	5	diverged	diverge	VERB
fbem-28239	8	6	from	from	ADP
fbem-28239	8	7	this	this	DET
fbem-28239	8	8	result	result	NOUN
fbem-28239	8	9	and	and	CCONJ
fbem-28239	8	10	further	far	ADV
fbem-28239	8	11	demonstrated	demonstrate	VERB
fbem-28239	8	12	from	from	ADP
fbem-28239	8	13	both	both	CCONJ
fbem-28239	8	14	positive	positive	ADJ
fbem-28239	8	15	and	and	CCONJ
fbem-28239	8	16	negative	negative	ADJ
fbem-28239	8	17	perspectives	perspective	NOUN
fbem-28239	8	18	whether	whether	SCONJ
fbem-28239	8	19	more	more	ADJ
fbem-28239	8	20	parameters	parameter	NOUN
fbem-28239	8	21	would	would	AUX
fbem-28239	8	22	bring	bring	VERB
fbem-28239	8	23	better	well	ADJ
fbem-28239	8	24	predictive	predictive	ADJ
fbem-28239	8	25	results	result	NOUN
fbem-28239	8	26	to	to	ADP
fbem-28239	8	27	the	the	DET
fbem-28239	8	28	model	model	NOUN
fbem-28239	8	29	.	.	PUNCT
fbem-28239	9	1	keywords	keyword	NOUN
fbem-28239	9	2	:	:	PUNCT
fbem-28239	10	1	capm	capm	NOUN
fbem-28239	10	2	,	,	PUNCT
fbem-28239	10	3	stock	stock	NOUN
fbem-28239	10	4	price	price	NOUN
fbem-28239	10	5	prediction	prediction	NOUN
fbem-28239	10	6	,	,	PUNCT
fbem-28239	10	7	multiple	multiple	ADJ
fbem-28239	10	8	linear	linear	PROPN
fbem-28239	10	9	regression	regression	NOUN
fbem-28239	10	10	model	model	NOUN
fbem-28239	10	11	,	,	PUNCT
fbem-28239	10	12	structural	structural	ADJ
fbem-28239	10	13	information	information	NOUN
fbem-28239	10	14	of	of	ADP
fbem-28239	10	15	recurrent	recurrent	ADJ
fbem-28239	10	16	neural	neural	ADJ
fbem-28239	10	17	network	network	NOUN
fbem-28239	10	18	(	(	PUNCT
fbem-28239	10	19	rnn	rnn	PROPN
fbem-28239	10	20	)	)	PUNCT
fbem-28239	10	21	model	model	NOUN
fbem-28239	10	22	.	.	PUNCT
fbem-28239	11	1	1	1	X
fbem-28239	11	2	.	.	X
fbem-28239	11	3	introduction	introduction	NOUN
fbem-28239	11	4	at	at	ADP
fbem-28239	11	5	present	present	ADJ
fbem-28239	11	6	,	,	PUNCT
fbem-28239	11	7	traditional	traditional	ADJ
fbem-28239	11	8	asset	asset	NOUN
fbem-28239	11	9	pricing	pricing	NOUN
fbem-28239	11	10	models	model	NOUN
fbem-28239	11	11	(	(	PUNCT
fbem-28239	11	12	capm	capm	NOUN
fbem-28239	11	13	,	,	PUNCT
fbem-28239	11	14	three	three	NUM
fbem-28239	11	15	factor	factor	NOUN
fbem-28239	11	16	,	,	PUNCT
fbem-28239	11	17	etc	etc	X
fbem-28239	11	18	.	.	X
fbem-28239	11	19	)	)	PUNCT
fbem-28239	11	20	are	be	AUX
fbem-28239	11	21	widely	widely	ADV
fbem-28239	11	22	used	use	VERB
fbem-28239	11	23	in	in	ADP
fbem-28239	11	24	finance	finance	NOUN
fbem-28239	11	25	and	and	CCONJ
fbem-28239	11	26	other	other	ADJ
fbem-28239	11	27	fields	field	NOUN
fbem-28239	11	28	,	,	PUNCT
fbem-28239	11	29	but	but	CCONJ
fbem-28239	11	30	they	they	PRON
fbem-28239	11	31	also	also	ADV
fbem-28239	11	32	have	have	VERB
fbem-28239	11	33	significant	significant	ADJ
fbem-28239	11	34	limitations	limitation	NOUN
fbem-28239	11	35	,	,	PUNCT
fbem-28239	11	36	leading	lead	VERB
fbem-28239	11	37	to	to	ADP
fbem-28239	11	38	biased	biased	ADJ
fbem-28239	11	39	prediction	prediction	NOUN
fbem-28239	11	40	results	result	NOUN
fbem-28239	11	41	.	.	PUNCT
fbem-28239	12	1	traditional	traditional	ADJ
fbem-28239	12	2	asset	asset	NOUN
fbem-28239	12	3	pricing	pricing	NOUN
fbem-28239	12	4	theory	theory	NOUN
fbem-28239	12	5	assumes	assume	VERB
fbem-28239	12	6	that	that	SCONJ
fbem-28239	12	7	investors	investor	NOUN
fbem-28239	12	8	are	be	AUX
fbem-28239	12	9	completely	completely	ADV
fbem-28239	12	10	rational	rational	ADJ
fbem-28239	12	11	,	,	PUNCT
fbem-28239	12	12	while	while	SCONJ
fbem-28239	12	13	psychological	psychological	ADJ
fbem-28239	12	14	research	research	NOUN
fbem-28239	12	15	shows	show	VERB
fbem-28239	12	16	that	that	SCONJ
fbem-28239	12	17	people	people	NOUN
fbem-28239	12	18	often	often	ADV
fbem-28239	12	19	have	have	VERB
fbem-28239	12	20	various	various	ADJ
fbem-28239	12	21	"	"	PUNCT
fbem-28239	12	22	irrational	irrational	ADJ
fbem-28239	12	23	"	"	PUNCT
fbem-28239	12	24	emotions	emotion	NOUN
fbem-28239	12	25	when	when	SCONJ
fbem-28239	12	26	making	make	VERB
fbem-28239	12	27	decisions	decision	NOUN
fbem-28239	12	28	,	,	PUNCT
fbem-28239	12	29	especially	especially	ADV
fbem-28239	12	30	when	when	SCONJ
fbem-28239	12	31	making	make	VERB
fbem-28239	12	32	decisions	decision	NOUN
fbem-28239	12	33	under	under	ADP
fbem-28239	12	34	complex	complex	ADJ
fbem-28239	12	35	and	and	CCONJ
fbem-28239	12	36	uncertain	uncertain	ADJ
fbem-28239	12	37	conditions	condition	NOUN
fbem-28239	12	38	[	[	X
fbem-28239	12	39	1	1	NUM
fbem-28239	12	40	]	]	PUNCT
fbem-28239	12	41	.	.	PUNCT
fbem-28239	13	1	the	the	DET
fbem-28239	13	2	traditional	traditional	ADJ
fbem-28239	13	3	asset	asset	NOUN
fbem-28239	13	4	pricing	pricing	NOUN
fbem-28239	13	5	model	model	NOUN
fbem-28239	13	6	assumes	assume	VERB
fbem-28239	13	7	that	that	SCONJ
fbem-28239	13	8	all	all	DET
fbem-28239	13	9	participating	participate	VERB
fbem-28239	13	10	investors	investor	NOUN
fbem-28239	13	11	have	have	VERB
fbem-28239	13	12	equal	equal	ADJ
fbem-28239	13	13	information	information	NOUN
fbem-28239	13	14	,	,	PUNCT
fbem-28239	13	15	which	which	PRON
fbem-28239	13	16	is	be	AUX
fbem-28239	13	17	almost	almost	ADV
fbem-28239	13	18	impossible	impossible	ADJ
fbem-28239	13	19	in	in	ADP
fbem-28239	13	20	practice	practice	NOUN
fbem-28239	13	21	.	.	PUNCT
fbem-28239	14	1	investors	investor	NOUN
fbem-28239	14	2	with	with	ADP
fbem-28239	14	3	more	more	ADJ
fbem-28239	14	4	and	and	CCONJ
fbem-28239	14	5	faster	fast	ADJ
fbem-28239	14	6	information	information	NOUN
fbem-28239	14	7	clearly	clearly	ADV
fbem-28239	14	8	have	have	VERB
fbem-28239	14	9	a	a	DET
fbem-28239	14	10	greater	great	ADJ
fbem-28239	14	11	advantage	advantage	NOUN
fbem-28239	14	12	.	.	PUNCT
fbem-28239	15	1	the	the	DET
fbem-28239	15	2	traditional	traditional	ADJ
fbem-28239	15	3	asset	asset	NOUN
fbem-28239	15	4	pricing	pricing	NOUN
fbem-28239	15	5	model	model	NOUN
fbem-28239	15	6	(	(	PUNCT
fbem-28239	15	7	e.g.	e.g.	ADV
fbem-28239	15	8	capm	capm	NOUN
fbem-28239	15	9	,	,	PUNCT
fbem-28239	15	10	ff	ff	NOUN
fbem-28239	15	11	)	)	PUNCT
fbem-28239	15	12	only	only	ADV
fbem-28239	15	13	introduces	introduce	VERB
fbem-28239	15	14	a	a	DET
fbem-28239	15	15	few	few	ADJ
fbem-28239	15	16	factors	factor	NOUN
fbem-28239	15	17	and	and	CCONJ
fbem-28239	15	18	has	have	VERB
fbem-28239	15	19	a	a	DET
fbem-28239	15	20	limited	limited	ADJ
fbem-28239	15	21	scope	scope	NOUN
fbem-28239	15	22	of	of	ADP
fbem-28239	15	23	consideration	consideration	NOUN
fbem-28239	15	24	.	.	PUNCT
fbem-28239	16	1	the	the	DET
fbem-28239	16	2	traditional	traditional	ADJ
fbem-28239	16	3	asset	asset	NOUN
fbem-28239	16	4	pricing	pricing	NOUN
fbem-28239	16	5	model	model	NOUN
fbem-28239	16	6	is	be	AUX
fbem-28239	16	7	built	build	VERB
fbem-28239	16	8	on	on	ADP
fbem-28239	16	9	a	a	DET
fbem-28239	16	10	static	static	ADJ
fbem-28239	16	11	basis	basis	NOUN
fbem-28239	16	12	:	:	PUNCT
fbem-28239	16	13	that	that	PRON
fbem-28239	16	14	is	be	AUX
fbem-28239	16	15	,	,	PUNCT
fbem-28239	16	16	investors	investor	NOUN
fbem-28239	16	17	'	'	PART
fbem-28239	16	18	preferences	preference	NOUN
fbem-28239	16	19	and	and	CCONJ
fbem-28239	16	20	risk	risk	NOUN
fbem-28239	16	21	attitudes	attitude	NOUN
fbem-28239	16	22	remain	remain	VERB
fbem-28239	16	23	unchanged	unchanged	ADJ
fbem-28239	16	24	in	in	ADP
fbem-28239	16	25	a	a	DET
fbem-28239	16	26	short	short	ADJ
fbem-28239	16	27	period	period	NOUN
fbem-28239	16	28	of	of	ADP
fbem-28239	16	29	time	time	NOUN
fbem-28239	16	30	.	.	PUNCT
fbem-28239	17	1	but	but	CCONJ
fbem-28239	17	2	as	as	ADP
fbem-28239	17	3	the	the	DET
fbem-28239	17	4	market	market	NOUN
fbem-28239	17	5	changes	change	NOUN
fbem-28239	17	6	,	,	PUNCT
fbem-28239	17	7	the	the	DET
fbem-28239	17	8	above	above	ADJ
fbem-28239	17	9	may	may	AUX
fbem-28239	17	10	change	change	VERB
fbem-28239	17	11	over	over	ADP
fbem-28239	17	12	time	time	NOUN
fbem-28239	17	13	.	.	PUNCT
fbem-28239	18	1	the	the	DET
fbem-28239	18	2	fluctuation	fluctuation	NOUN
fbem-28239	18	3	of	of	ADP
fbem-28239	18	4	stock	stock	NOUN
fbem-28239	18	5	prices	price	NOUN
fbem-28239	18	6	affects	affect	VERB
fbem-28239	18	7	the	the	DET
fbem-28239	18	8	hearts	heart	NOUN
fbem-28239	18	9	of	of	ADP
fbem-28239	18	10	countless	countless	ADJ
fbem-28239	18	11	investors	investor	NOUN
fbem-28239	18	12	,	,	PUNCT
fbem-28239	18	13	and	and	CCONJ
fbem-28239	18	14	accurately	accurately	ADV
fbem-28239	18	15	predicting	predict	VERB
fbem-28239	18	16	stock	stock	NOUN
fbem-28239	18	17	prices	price	NOUN
fbem-28239	18	18	has	have	AUX
fbem-28239	18	19	always	always	ADV
fbem-28239	18	20	been	be	AUX
fbem-28239	18	21	a	a	DET
fbem-28239	18	22	major	major	ADJ
fbem-28239	18	23	challenge	challenge	NOUN
fbem-28239	18	24	in	in	ADP
fbem-28239	18	25	the	the	DET
fbem-28239	18	26	financial	financial	ADJ
fbem-28239	18	27	sector	sector	NOUN
fbem-28239	18	28	.	.	PUNCT
fbem-28239	19	1	in	in	ADP
fbem-28239	19	2	today	today	NOUN
fbem-28239	19	3	's	's	PART
fbem-28239	19	4	complex	complex	ADJ
fbem-28239	19	5	and	and	CCONJ
fbem-28239	19	6	ever	ever	ADV
fbem-28239	19	7	-	-	PUNCT
fbem-28239	19	8	changing	change	VERB
fbem-28239	19	9	financial	financial	ADJ
fbem-28239	19	10	markets	market	NOUN
fbem-28239	19	11	,	,	PUNCT
fbem-28239	19	12	traditional	traditional	ADJ
fbem-28239	19	13	capm	capm	PROPN
fbem-28239	19	14	theory	theory	NOUN
fbem-28239	19	15	and	and	CCONJ
fbem-28239	19	16	emerging	emerge	VERB
fbem-28239	19	17	machine	machine	NOUN
fbem-28239	19	18	learning	learning	NOUN
fbem-28239	19	19	technologies	technology	NOUN
fbem-28239	19	20	have	have	AUX
fbem-28239	19	21	brought	bring	VERB
fbem-28239	19	22	new	new	ADJ
fbem-28239	19	23	hope	hope	NOUN
fbem-28239	19	24	for	for	ADP
fbem-28239	19	25	stock	stock	NOUN
fbem-28239	19	26	price	price	NOUN
fbem-28239	19	27	prediction	prediction	NOUN
fbem-28239	19	28	.	.	PUNCT
fbem-28239	20	1	in	in	ADP
fbem-28239	20	2	this	this	DET
fbem-28239	20	3	article	article	NOUN
fbem-28239	20	4	,	,	PUNCT
fbem-28239	20	5	we	we	PRON
fbem-28239	20	6	mainly	mainly	ADV
fbem-28239	20	7	selected	select	VERB
fbem-28239	20	8	two	two	NUM
fbem-28239	20	9	models	model	NOUN
fbem-28239	20	10	:	:	PUNCT
fbem-28239	20	11	multiple	multiple	ADJ
fbem-28239	20	12	linear	linear	ADJ
fbem-28239	20	13	regression	regression	NOUN
fbem-28239	20	14	model	model	NOUN
fbem-28239	20	15	and	and	CCONJ
fbem-28239	20	16	recurrent	recurrent	ADJ
fbem-28239	20	17	neural	neural	ADJ
fbem-28239	20	18	network	network	NOUN
fbem-28239	20	19	(	(	PUNCT
fbem-28239	20	20	rnn	rnn	PROPN
fbem-28239	20	21	)	)	PUNCT
fbem-28239	20	22	,	,	PUNCT
fbem-28239	20	23	and	and	CCONJ
fbem-28239	20	24	additionally	additionally	ADV
fbem-28239	20	25	supplemented	supplement	VERB
fbem-28239	20	26	the	the	DET
fbem-28239	20	27	relevant	relevant	ADJ
fbem-28239	20	28	concepts	concept	NOUN
fbem-28239	20	29	of	of	ADP
fbem-28239	20	30	capm	capm	PROPN
fbem-28239	20	31	(	(	PUNCT
fbem-28239	20	32	beta	beta	NOUN
fbem-28239	20	33	)	)	PUNCT
fbem-28239	20	34	to	to	PART
fbem-28239	20	35	make	make	VERB
fbem-28239	20	36	the	the	DET
fbem-28239	20	37	models	model	NOUN
fbem-28239	20	38	more	more	ADV
fbem-28239	20	39	complete	complete	ADJ
fbem-28239	20	40	.	.	PUNCT
fbem-28239	21	1	after	after	ADP
fbem-28239	21	2	establishing	establish	VERB
fbem-28239	21	3	the	the	DET
fbem-28239	21	4	model	model	NOUN
fbem-28239	21	5	,	,	PUNCT
fbem-28239	21	6	divide	divide	VERB
fbem-28239	21	7	the	the	DET
fbem-28239	21	8	test	test	NOUN
fbem-28239	21	9	set	set	VERB
fbem-28239	21	10	and	and	CCONJ
fbem-28239	21	11	training	training	NOUN
fbem-28239	21	12	set	set	NOUN
fbem-28239	21	13	,	,	PUNCT
fbem-28239	21	14	and	and	CCONJ
fbem-28239	21	15	finally	finally	ADV
fbem-28239	21	16	obtain	obtain	VERB
fbem-28239	21	17	the	the	DET
fbem-28239	21	18	final	final	ADJ
fbem-28239	21	19	result	result	NOUN
fbem-28239	21	20	and	and	CCONJ
fbem-28239	21	21	evaluate	evaluate	VERB
fbem-28239	21	22	the	the	DET
fbem-28239	21	23	two	two	NUM
fbem-28239	21	24	models	model	NOUN
fbem-28239	21	25	.	.	PUNCT
fbem-28239	22	1	in	in	ADP
fbem-28239	22	2	addition	addition	NOUN
fbem-28239	22	3	,	,	PUNCT
fbem-28239	22	4	this	this	DET
fbem-28239	22	5	article	article	NOUN
fbem-28239	22	6	also	also	ADV
fbem-28239	22	7	compared	compare	VERB
fbem-28239	22	8	the	the	DET
fbem-28239	22	9	different	different	ADJ
fbem-28239	22	10	results	result	NOUN
fbem-28239	22	11	of	of	ADP
fbem-28239	22	12	capm	capm	PROPN
fbem-28239	22	13	related	relate	VERB
fbem-28239	22	14	concepts	concept	NOUN
fbem-28239	22	15	(	(	PUNCT
fbem-28239	22	16	beta	beta	NOUN
fbem-28239	22	17	)	)	PUNCT
fbem-28239	22	18	in	in	ADP
fbem-28239	22	19	the	the	DET
fbem-28239	22	20	predictions	prediction	NOUN
fbem-28239	22	21	of	of	ADP
fbem-28239	22	22	the	the	DET
fbem-28239	22	23	two	two	NUM
fbem-28239	22	24	models	model	NOUN
fbem-28239	22	25	.	.	PUNCT
fbem-28239	23	1	similarly	similarly	ADV
fbem-28239	23	2	,	,	PUNCT
fbem-28239	23	3	line	line	NOUN
fbem-28239	23	4	charts	chart	NOUN
fbem-28239	23	5	,	,	PUNCT
fbem-28239	23	6	mae	mae	PROPN
fbem-28239	23	7	,	,	PUNCT
fbem-28239	23	8	mse	mse	PROPN
fbem-28239	23	9	,	,	PUNCT
fbem-28239	23	10	rmse	rmse	NOUN
fbem-28239	23	11	,	,	PUNCT
fbem-28239	23	12	and	and	CCONJ
fbem-28239	23	13	r2	r2	PROPN
fbem-28239	23	14	reference	reference	NOUN
fbem-28239	23	15	indicators	indicator	NOUN
fbem-28239	23	16	are	be	AUX
fbem-28239	23	17	used	use	VERB
fbem-28239	23	18	.	.	PUNCT
fbem-28239	24	1	2	2	X
fbem-28239	24	2	.	.	X
fbem-28239	24	3	theory	theory	NOUN
fbem-28239	24	4	2.1	2.1	NUM
fbem-28239	24	5	.	.	PUNCT
fbem-28239	25	1	capm	capm	PROPN
fbem-28239	25	2	basic	basic	ADJ
fbem-28239	25	3	theoretical	theoretical	ADJ
fbem-28239	25	4	model	model	NOUN
fbem-28239	25	5	according	accord	VERB
fbem-28239	25	6	to	to	ADP
fbem-28239	25	7	the	the	DET
fbem-28239	25	8	traditional	traditional	ADJ
fbem-28239	25	9	capm	capm	PROPN
fbem-28239	25	10	model	model	NOUN
fbem-28239	25	11	,	,	PUNCT
fbem-28239	25	12	the	the	DET
fbem-28239	25	13	expected	expect	VERB
fbem-28239	25	14	return	return	NOUN
fbem-28239	25	15	on	on	ADP
fbem-28239	25	16	assets	asset	NOUN
fbem-28239	25	17	is	be	AUX
fbem-28239	25	18	directly	directly	ADV
fbem-28239	25	19	proportional	proportional	ADJ
fbem-28239	25	20	to	to	ADP
fbem-28239	25	21	the	the	DET
fbem-28239	25	22	beta	beta	ADJ
fbem-28239	25	23	coefficient	coefficient	NOUN
fbem-28239	25	24	,	,	PUNCT
fbem-28239	25	25	it	it	PRON
fbem-28239	25	26	implies	imply	VERB
fbem-28239	25	27	that	that	SCONJ
fbem-28239	25	28	there	there	PRON
fbem-28239	25	29	is	be	VERB
fbem-28239	25	30	a	a	DET
fbem-28239	25	31	linear	linear	ADJ
fbem-28239	25	32	relationship	relationship	NOUN
fbem-28239	25	33	between	between	ADP
fbem-28239	25	34	assets	asset	NOUN
fbem-28239	25	35	expected	expect	VERB
fbem-28239	25	36	return	return	NOUN
fbem-28239	25	37	and	and	CCONJ
fbem-28239	25	38	its	its	PRON
fbem-28239	25	39	quantity	quantity	NOUN
fbem-28239	25	40	of	of	ADP
fbem-28239	25	41	market	market	NOUN
fbem-28239	25	42	risk	risk	NOUN
fbem-28239	25	43	.	.	PUNCT
fbem-28239	26	1	the	the	DET
fbem-28239	26	2	quantity	quantity	NOUN
fbem-28239	26	3	of	of	ADP
fbem-28239	26	4	market	market	NOUN
fbem-28239	26	5	risk	risk	NOUN
fbem-28239	26	6	is	be	AUX
fbem-28239	26	7	measured	measure	VERB
fbem-28239	26	8	with	with	ADP
fbem-28239	26	9	the	the	DET
fbem-28239	26	10	so	so	ADV
fbem-28239	26	11	called	call	VERB
fbem-28239	26	12	market	market	NOUN
fbem-28239	26	13	beta	beta	NOUN
fbem-28239	26	14	(	(	PUNCT
fbem-28239	26	15	β	β	NOUN
fbem-28239	26	16	)	)	PUNCT
fbem-28239	27	1	[	[	X
fbem-28239	27	2	2	2	NUM
fbem-28239	27	3	]	]	PUNCT
fbem-28239	27	4	,	,	PUNCT
fbem-28239	27	5	which	which	PRON
fbem-28239	27	6	c2	c2	PROPN
fbem-28239	27	7	measures	measure	VERB
fbem-28239	27	8	how	how	SCONJ
fbem-28239	27	9	much	much	ADJ
fbem-28239	27	10	of	of	ADP
fbem-28239	27	11	the	the	DET
fbem-28239	27	12	returns	return	NOUN
fbem-28239	27	13	are	be	AUX
fbem-28239	27	14	on	on	ADP
fbem-28239	27	15	a	a	DET
fbem-28239	27	16	given	give	VERB
fbem-28239	27	17	asset	asset	NOUN
fbem-28239	27	18	move	move	NOUN
fbem-28239	27	19	together	together	ADV
fbem-28239	27	20	(	(	PUNCT
fbem-28239	27	21	co	co	NOUN
fbem-28239	27	22	movements	movement	NOUN
fbem-28239	27	23	)	)	PUNCT
fbem-28239	27	24	with	with	ADP
fbem-28239	27	25	the	the	DET
fbem-28239	27	26	market	market	NOUN
fbem-28239	27	27	.	.	PUNCT
fbem-28239	28	1	fama	fama	PROPN
fbem-28239	28	2	(	(	PUNCT
fbem-28239	28	3	1968	1968	NUM
fbem-28239	28	4	)	)	PUNCT
fbem-28239	28	5	provides	provide	VERB
fbem-28239	28	6	the	the	DET
fbem-28239	28	7	well	well	ADV
fbem-28239	28	8	-	-	PUNCT
fbem-28239	28	9	known	know	VERB
fbem-28239	28	10	beta	beta	NOUN
fbem-28239	28	11	form	form	NOUN
fbem-28239	28	12	of	of	ADP
fbem-28239	28	13	the	the	DET
fbem-28239	28	14	core	core	NOUN
fbem-28239	28	15	capm	capm	NOUN
fbem-28239	28	16	:	:	PUNCT
fbem-28239	29	1	e	e	X
fbem-28239	29	2	(	(	PUNCT
fbem-28239	29	3	ri)=rf+β	ri)=rf+β	NOUN
fbem-28239	29	4	i	i	PRON
fbem-28239	29	5	(	(	PUNCT
fbem-28239	29	6	e	e	X
fbem-28239	29	7	(	(	PUNCT
fbem-28239	29	8	rm	rm	NOUN
fbem-28239	29	9	)	)	PUNCT
fbem-28239	29	10	rf	rf	PROPN
fbem-28239	29	11	)	)	PUNCT
fbem-28239	29	12	,	,	PUNCT
fbem-28239	29	13	where	where	SCONJ
fbem-28239	29	14	e	e	X
fbem-28239	29	15	(	(	PUNCT
fbem-28239	29	16	ri	ri	NOUN
fbem-28239	29	17	)	)	PUNCT
fbem-28239	29	18	is	be	AUX
fbem-28239	29	19	the	the	DET
fbem-28239	29	20	expected	expect	VERB
fbem-28239	29	21	asset	asset	NOUN
fbem-28239	29	22	return	return	NOUN
fbem-28239	29	23	rate	rate	NOUN
fbem-28239	29	24	.	.	PUNCT
fbem-28239	30	1	rf	rf	PRON
fbem-28239	30	2	is	be	AUX
fbem-28239	30	3	the	the	DET
fbem-28239	30	4	risk	risk	NOUN
fbem-28239	30	5	-	-	PUNCT
fbem-28239	30	6	free	free	ADJ
fbem-28239	30	7	rate	rate	NOUN
fbem-28239	30	8	,	,	PUNCT
fbem-28239	30	9	e	e	X
fbem-28239	30	10	(	(	PUNCT
fbem-28239	30	11	rm	rm	NOUN
fbem-28239	30	12	)	)	PUNCT
fbem-28239	30	13	is	be	AUX
fbem-28239	30	14	the	the	DET
fbem-28239	30	15	expected	expect	VERB
fbem-28239	30	16	return	return	NOUN
fbem-28239	30	17	rate	rate	NOUN
fbem-28239	30	18	of	of	ADP
fbem-28239	30	19	the	the	DET
fbem-28239	30	20	market	market	NOUN
fbem-28239	30	21	portfolio	portfolio	NOUN
fbem-28239	30	22	,	,	PUNCT
fbem-28239	30	23	and	and	CCONJ
fbem-28239	30	24	β	β	X
fbem-28239	30	25	i	i	PRON
fbem-28239	30	26	is	be	AUX
fbem-28239	30	27	the	the	DET
fbem-28239	30	28	risk	risk	NOUN
fbem-28239	30	29	coefficient	coefficient	NOUN
fbem-28239	30	30	.	.	PUNCT
fbem-28239	31	1	in	in	ADP
fbem-28239	31	2	this	this	DET
fbem-28239	31	3	article	article	NOUN
fbem-28239	31	4	,	,	PUNCT
fbem-28239	31	5	although	although	SCONJ
fbem-28239	31	6	not	not	PART
fbem-28239	31	7	entirely	entirely	ADV
fbem-28239	31	8	a	a	DET
fbem-28239	31	9	standard	standard	ADJ
fbem-28239	31	10	capm	capm	NOUN
fbem-28239	31	11	implementation	implementation	NOUN
fbem-28239	31	12	,	,	PUNCT
fbem-28239	31	13	concepts	concept	NOUN
fbem-28239	31	14	related	relate	VERB
fbem-28239	31	15	to	to	ADP
fbem-28239	31	16	capm	capm	PROPN
fbem-28239	31	17	are	be	AUX
fbem-28239	31	18	introduced	introduce	VERB
fbem-28239	31	19	.	.	PUNCT
fbem-28239	32	1	the	the	DET
fbem-28239	32	2	capm	capm	PROPN
fbem-28239	32	3	theory	theory	NOUN
fbem-28239	32	4	holds	hold	VERB
fbem-28239	32	5	that	that	SCONJ
fbem-28239	32	6	the	the	DET
fbem-28239	32	7	expected	expect	VERB
fbem-28239	32	8	return	return	NOUN
fbem-28239	32	9	on	on	ADP
fbem-28239	32	10	an	an	DET
fbem-28239	32	11	asset	asset	NOUN
fbem-28239	32	12	is	be	AUX
fbem-28239	32	13	the	the	DET
fbem-28239	32	14	risk	risk	NOUN
fbem-28239	32	15	-	-	PUNCT
fbem-28239	32	16	free	free	ADJ
fbem-28239	32	17	rate	rate	NOUN
fbem-28239	32	18	plus	plus	CCONJ
fbem-28239	32	19	the	the	DET
fbem-28239	32	20	risk	risk	NOUN
fbem-28239	32	21	premium	premium	NOUN
fbem-28239	32	22	of	of	ADP
fbem-28239	32	23	the	the	DET
fbem-28239	32	24	asset	asset	NOUN
fbem-28239	32	25	relative	relative	ADJ
fbem-28239	32	26	to	to	ADP
fbem-28239	32	27	the	the	DET
fbem-28239	32	28	market	market	NOUN
fbem-28239	32	29	portfolio	portfolio	NOUN
fbem-28239	32	30	.	.	PUNCT
fbem-28239	33	1	in	in	ADP
fbem-28239	33	2	this	this	DET
fbem-28239	33	3	article	article	NOUN
fbem-28239	33	4	,	,	PUNCT
fbem-28239	33	5	we	we	PRON
fbem-28239	33	6	attempt	attempt	VERB
fbem-28239	33	7	to	to	PART
fbem-28239	33	8	capture	capture	VERB
fbem-28239	33	9	the	the	DET
fbem-28239	33	10	risk	risk	NOUN
fbem-28239	33	11	characteristics	characteristic	NOUN
fbem-28239	33	12	of	of	ADP
fbem-28239	33	13	apple	apple	NOUN
fbem-28239	33	14	's	's	PART
fbem-28239	33	15	stock	stock	NOUN
fbem-28239	33	16	relative	relative	ADJ
fbem-28239	33	17	to	to	ADP
fbem-28239	33	18	the	the	DET
fbem-28239	33	19	market	market	NOUN
fbem-28239	33	20	by	by	ADP
fbem-28239	33	21	calculating	calculate	VERB
fbem-28239	33	22	the	the	DET
fbem-28239	33	23	beta	beta	ADJ
fbem-28239	33	24	coefficient	coefficient	NOUN
fbem-28239	33	25	of	of	ADP
fbem-28239	33	26	its	its	PRON
fbem-28239	33	27	return	return	NOUN
fbem-28239	33	28	on	on	ADP
fbem-28239	33	29	spy	spy	NOUN
fbem-28239	33	30	(	(	PUNCT
fbem-28239	33	31	usually	usually	ADV
fbem-28239	33	32	considered	consider	VERB
fbem-28239	33	33	a	a	DET
fbem-28239	33	34	representative	representative	NOUN
fbem-28239	33	35	of	of	ADP
fbem-28239	33	36	the	the	DET
fbem-28239	33	37	market	market	NOUN
fbem-28239	33	38	portfolio	portfolio	NOUN
fbem-28239	33	39	)	)	PUNCT
fbem-28239	33	40	.	.	PUNCT
fbem-28239	34	1	the	the	DET
fbem-28239	34	2	beta	beta	ADJ
fbem-28239	34	3	coefficient	coefficient	NOUN
fbem-28239	34	4	measures	measure	VERB
fbem-28239	34	5	the	the	DET
fbem-28239	34	6	systematic	systematic	ADJ
fbem-28239	34	7	risk	risk	NOUN
fbem-28239	34	8	of	of	ADP
fbem-28239	34	9	an	an	DET
fbem-28239	34	10	asset	asset	NOUN
fbem-28239	34	11	,	,	PUNCT
fbem-28239	34	12	i.e.	i.e.	X
fbem-28239	34	13	the	the	DET
fbem-28239	34	14	sensitivity	sensitivity	NOUN
fbem-28239	34	15	between	between	ADP
fbem-28239	34	16	asset	asset	NOUN
fbem-28239	34	17	returns	return	NOUN
fbem-28239	34	18	and	and	CCONJ
fbem-28239	34	19	market	market	NOUN
fbem-28239	34	20	returns	return	NOUN
fbem-28239	34	21	.	.	PUNCT
fbem-28239	35	1	if	if	SCONJ
fbem-28239	35	2	the	the	DET
fbem-28239	35	3	beta	beta	ADJ
fbem-28239	35	4	coefficient	coefficient	NOUN
fbem-28239	35	5	is	be	AUX
fbem-28239	35	6	greater	great	ADJ
fbem-28239	35	7	than	than	ADP
fbem-28239	35	8	1	1	NUM
fbem-28239	35	9	,	,	PUNCT
fbem-28239	35	10	it	it	PRON
fbem-28239	35	11	indicates	indicate	VERB
fbem-28239	35	12	that	that	SCONJ
fbem-28239	35	13	the	the	DET
fbem-28239	35	14	risk	risk	NOUN
fbem-28239	35	15	of	of	ADP
fbem-28239	35	16	the	the	DET
fbem-28239	35	17	asset	asset	NOUN
fbem-28239	35	18	is	be	AUX
fbem-28239	35	19	higher	high	ADJ
fbem-28239	35	20	than	than	ADP
fbem-28239	35	21	the	the	DET
fbem-28239	35	22	market	market	NOUN
fbem-28239	35	23	average	average	ADJ
fbem-28239	35	24	risk	risk	NOUN
fbem-28239	35	25	;	;	PUNCT
fbem-28239	35	26	if	if	SCONJ
fbem-28239	35	27	the	the	DET
fbem-28239	35	28	beta	beta	ADJ
fbem-28239	35	29	coefficient	coefficient	NOUN
fbem-28239	35	30	is	be	AUX
fbem-28239	35	31	less	less	ADJ
fbem-28239	35	32	than	than	ADP
fbem-28239	35	33	1	1	NUM
fbem-28239	35	34	,	,	PUNCT
fbem-28239	35	35	it	it	PRON
fbem-28239	35	36	indicates	indicate	VERB
fbem-28239	35	37	that	that	SCONJ
fbem-28239	35	38	the	the	DET
fbem-28239	35	39	risk	risk	NOUN
fbem-28239	35	40	of	of	ADP
fbem-28239	35	41	the	the	DET
fbem-28239	35	42	asset	asset	NOUN
fbem-28239	35	43	is	be	AUX
fbem-28239	35	44	lower	low	ADJ
fbem-28239	35	45	than	than	ADP
fbem-28239	35	46	the	the	DET
fbem-28239	35	47	market	market	NOUN
fbem-28239	35	48	average	average	ADJ
fbem-28239	35	49	risk	risk	NOUN
fbem-28239	35	50	.	.	PUNCT
fbem-28239	36	1	110	110	NUM
fbem-28239	36	2	2.2	2.2	NUM
fbem-28239	36	3	.	.	PUNCT
fbem-28239	37	1	recurrent	recurrent	ADJ
fbem-28239	37	2	neural	neural	ADJ
fbem-28239	37	3	network	network	NOUN
fbem-28239	37	4	(	(	PUNCT
fbem-28239	37	5	rnn	rnn	PROPN
fbem-28239	37	6	)	)	PUNCT
fbem-28239	37	7	recurrent	recurrent	ADJ
fbem-28239	37	8	neural	neural	ADJ
fbem-28239	37	9	network	network	NOUN
fbem-28239	37	10	is	be	AUX
fbem-28239	37	11	a	a	DET
fbem-28239	37	12	type	type	NOUN
fbem-28239	37	13	of	of	ADP
fbem-28239	37	14	directed	direct	VERB
fbem-28239	37	15	recurrent	recurrent	ADJ
fbem-28239	37	16	neural	neural	ADJ
fbem-28239	37	17	network	network	NOUN
fbem-28239	37	18	composed	compose	VERB
fbem-28239	37	19	of	of	ADP
fbem-28239	37	20	units	unit	NOUN
fbem-28239	37	21	connected	connect	VERB
fbem-28239	37	22	in	in	ADP
fbem-28239	37	23	a	a	DET
fbem-28239	37	24	chain	chain	NOUN
fbem-28239	37	25	.	.	PUNCT
fbem-28239	38	1	through	through	ADP
fbem-28239	38	2	the	the	DET
fbem-28239	38	3	connections	connection	NOUN
fbem-28239	38	4	of	of	ADP
fbem-28239	38	5	each	each	DET
fbem-28239	38	6	unit	unit	NOUN
fbem-28239	38	7	,	,	PUNCT
fbem-28239	38	8	recursive	recursive	ADJ
fbem-28239	38	9	neural	neural	ADJ
fbem-28239	38	10	networks	network	NOUN
fbem-28239	38	11	can	can	AUX
fbem-28239	38	12	store	store	VERB
fbem-28239	38	13	the	the	DET
fbem-28239	38	14	relationship	relationship	NOUN
fbem-28239	38	15	between	between	ADP
fbem-28239	38	16	the	the	DET
fbem-28239	38	17	input	input	NOUN
fbem-28239	38	18	of	of	ADP
fbem-28239	38	19	neurons	neuron	NOUN
fbem-28239	38	20	at	at	ADP
fbem-28239	38	21	the	the	DET
fbem-28239	38	22	current	current	ADJ
fbem-28239	38	23	moment	moment	NOUN
fbem-28239	38	24	and	and	CCONJ
fbem-28239	38	25	the	the	DET
fbem-28239	38	26	output	output	NOUN
fbem-28239	38	27	of	of	ADP
fbem-28239	38	28	neurons	neuron	NOUN
fbem-28239	38	29	at	at	ADP
fbem-28239	38	30	the	the	DET
fbem-28239	38	31	previous	previous	ADJ
fbem-28239	38	32	moment	moment	NOUN
fbem-28239	38	33	,	,	PUNCT
fbem-28239	38	34	which	which	PRON
fbem-28239	38	35	is	be	AUX
fbem-28239	38	36	superior	superior	ADJ
fbem-28239	38	37	to	to	ADP
fbem-28239	38	38	other	other	ADJ
fbem-28239	38	39	types	type	NOUN
fbem-28239	38	40	of	of	ADP
fbem-28239	38	41	neural	neural	ADJ
fbem-28239	38	42	networks	network	NOUN
fbem-28239	38	43	in	in	ADP
fbem-28239	38	44	processing	processing	NOUN
fbem-28239	38	45	time	time	NOUN
fbem-28239	38	46	series	series	PROPN
fbem-28239	38	47	data	data	PROPN
fbem-28239	38	48	[	[	X
fbem-28239	38	49	3	3	NUM
fbem-28239	38	50	]	]	PUNCT
fbem-28239	38	51	.	.	PUNCT
fbem-28239	39	1	recurrent	recurrent	ADJ
fbem-28239	39	2	neural	neural	ADJ
fbem-28239	39	3	network	network	NOUN
fbem-28239	39	4	is	be	AUX
fbem-28239	39	5	a	a	DET
fbem-28239	39	6	type	type	NOUN
fbem-28239	39	7	of	of	ADP
fbem-28239	39	8	neural	neural	ADJ
fbem-28239	39	9	network	network	NOUN
fbem-28239	39	10	specifically	specifically	ADV
fbem-28239	39	11	designed	design	VERB
fbem-28239	39	12	for	for	ADP
fbem-28239	39	13	processing	process	VERB
fbem-28239	39	14	sequential	sequential	ADJ
fbem-28239	39	15	data	datum	NOUN
fbem-28239	39	16	.	.	PUNCT
fbem-28239	40	1	its	its	PRON
fbem-28239	40	2	characteristic	characteristic	NOUN
fbem-28239	40	3	is	be	AUX
fbem-28239	40	4	that	that	SCONJ
fbem-28239	40	5	there	there	PRON
fbem-28239	40	6	is	be	VERB
fbem-28239	40	7	a	a	DET
fbem-28239	40	8	hidden	hidden	ADJ
fbem-28239	40	9	state	state	NOUN
fbem-28239	40	10	at	at	ADP
fbem-28239	40	11	each	each	DET
fbem-28239	40	12	time	time	NOUN
fbem-28239	40	13	step	step	NOUN
fbem-28239	40	14	,	,	PUNCT
fbem-28239	40	15	which	which	PRON
fbem-28239	40	16	receives	receive	VERB
fbem-28239	40	17	the	the	DET
fbem-28239	40	18	input	input	NOUN
fbem-28239	40	19	of	of	ADP
fbem-28239	40	20	the	the	DET
fbem-28239	40	21	current	current	ADJ
fbem-28239	40	22	time	time	NOUN
fbem-28239	40	23	step	step	NOUN
fbem-28239	40	24	and	and	CCONJ
fbem-28239	40	25	the	the	DET
fbem-28239	40	26	hidden	hidden	ADJ
fbem-28239	40	27	state	state	NOUN
fbem-28239	40	28	of	of	ADP
fbem-28239	40	29	the	the	DET
fbem-28239	40	30	previous	previous	ADJ
fbem-28239	40	31	time	time	NOUN
fbem-28239	40	32	step	step	NOUN
fbem-28239	40	33	as	as	ADP
fbem-28239	40	34	inputs	input	NOUN
fbem-28239	40	35	,	,	PUNCT
fbem-28239	40	36	thus	thus	ADV
fbem-28239	40	37	capturing	capture	VERB
fbem-28239	40	38	the	the	DET
fbem-28239	40	39	temporal	temporal	ADJ
fbem-28239	40	40	dependencies	dependency	NOUN
fbem-28239	40	41	in	in	ADP
fbem-28239	40	42	the	the	DET
fbem-28239	40	43	sequence	sequence	NOUN
fbem-28239	40	44	.	.	PUNCT
fbem-28239	41	1	2.2.1	2.2.1	NUM
fbem-28239	41	2	.	.	PUNCT
fbem-28239	41	3	neuronal	neuronal	ADJ
fbem-28239	41	4	structure	structure	NOUN
fbem-28239	41	5	the	the	DET
fbem-28239	41	6	basic	basic	ADJ
fbem-28239	41	7	units	unit	NOUN
fbem-28239	41	8	of	of	ADP
fbem-28239	41	9	rnn	rnn	NOUN
fbem-28239	41	10	are	be	AUX
fbem-28239	41	11	like	like	ADP
fbem-28239	41	12	neurons	neuron	NOUN
fbem-28239	41	13	in	in	ADP
fbem-28239	41	14	traditional	traditional	ADJ
fbem-28239	41	15	neural	neural	ADJ
fbem-28239	41	16	networks	network	NOUN
fbem-28239	41	17	,	,	PUNCT
fbem-28239	41	18	but	but	CCONJ
fbem-28239	41	19	with	with	ADP
fbem-28239	41	20	additional	additional	ADJ
fbem-28239	41	21	self	self	NOUN
fbem-28239	41	22	connections	connection	NOUN
fbem-28239	41	23	that	that	PRON
fbem-28239	41	24	allow	allow	VERB
fbem-28239	41	25	it	it	PRON
fbem-28239	41	26	to	to	PART
fbem-28239	41	27	retain	retain	VERB
fbem-28239	41	28	memory	memory	NOUN
fbem-28239	41	29	of	of	ADP
fbem-28239	41	30	past	past	ADJ
fbem-28239	41	31	information	information	NOUN
fbem-28239	41	32	.	.	PUNCT
fbem-28239	42	1	this	this	DET
fbem-28239	42	2	self	self	NOUN
fbem-28239	42	3	connection	connection	NOUN
fbem-28239	42	4	allows	allow	VERB
fbem-28239	42	5	information	information	NOUN
fbem-28239	42	6	to	to	PART
fbem-28239	42	7	be	be	AUX
fbem-28239	42	8	transmitted	transmit	VERB
fbem-28239	42	9	over	over	ADP
fbem-28239	42	10	time	time	NOUN
fbem-28239	42	11	,	,	PUNCT
fbem-28239	42	12	enabling	enable	VERB
fbem-28239	42	13	the	the	DET
fbem-28239	42	14	network	network	NOUN
fbem-28239	42	15	to	to	PART
fbem-28239	42	16	handle	handle	VERB
fbem-28239	42	17	temporal	temporal	ADJ
fbem-28239	42	18	dependencies	dependency	NOUN
fbem-28239	42	19	in	in	ADP
fbem-28239	42	20	sequential	sequential	ADJ
fbem-28239	42	21	data	datum	NOUN
fbem-28239	42	22	.	.	PUNCT
fbem-28239	43	1	each	each	DET
fbem-28239	43	2	neuron	neuron	NOUN
fbem-28239	43	3	receives	receive	VERB
fbem-28239	43	4	the	the	DET
fbem-28239	43	5	input	input	NOUN
fbem-28239	43	6	of	of	ADP
fbem-28239	43	7	the	the	DET
fbem-28239	43	8	current	current	ADJ
fbem-28239	43	9	time	time	NOUN
fbem-28239	43	10	step	step	NOUN
fbem-28239	43	11	and	and	CCONJ
fbem-28239	43	12	the	the	DET
fbem-28239	43	13	hidden	hidden	ADJ
fbem-28239	43	14	state	state	NOUN
fbem-28239	43	15	of	of	ADP
fbem-28239	43	16	the	the	DET
fbem-28239	43	17	previous	previous	ADJ
fbem-28239	43	18	time	time	NOUN
fbem-28239	43	19	step	step	NOUN
fbem-28239	43	20	as	as	ADP
fbem-28239	43	21	inputs	input	NOUN
fbem-28239	43	22	and	and	CCONJ
fbem-28239	43	23	generates	generate	VERB
fbem-28239	43	24	the	the	DET
fbem-28239	43	25	output	output	NOUN
fbem-28239	43	26	of	of	ADP
fbem-28239	43	27	the	the	DET
fbem-28239	43	28	current	current	ADJ
fbem-28239	43	29	time	time	NOUN
fbem-28239	43	30	step	step	NOUN
fbem-28239	43	31	and	and	CCONJ
fbem-28239	43	32	the	the	DET
fbem-28239	43	33	updated	update	VERB
fbem-28239	43	34	hidden	hide	VERB
fbem-28239	43	35	state	state	NOUN
fbem-28239	43	36	through	through	ADP
fbem-28239	43	37	an	an	DET
fbem-28239	43	38	activation	activation	NOUN
fbem-28239	43	39	function	function	NOUN
fbem-28239	43	40	.	.	PUNCT
fbem-28239	44	1	2.2.2	2.2.2	X
fbem-28239	44	2	.	.	NOUN
fbem-28239	44	3	time	time	NOUN
fbem-28239	44	4	unfolding	unfold	VERB
fbem-28239	44	5	to	to	PART
fbem-28239	44	6	process	process	VERB
fbem-28239	44	7	sequential	sequential	ADJ
fbem-28239	44	8	data	datum	NOUN
fbem-28239	44	9	,	,	PUNCT
fbem-28239	44	10	rnn	rnn	PROPN
fbem-28239	44	11	can	can	AUX
fbem-28239	44	12	be	be	AUX
fbem-28239	44	13	unfolded	unfold	VERB
fbem-28239	44	14	over	over	ADP
fbem-28239	44	15	time	time	NOUN
fbem-28239	44	16	,	,	PUNCT
fbem-28239	44	17	treating	treat	VERB
fbem-28239	44	18	it	it	PRON
fbem-28239	44	19	as	as	ADP
fbem-28239	44	20	a	a	DET
fbem-28239	44	21	sequence	sequence	NOUN
fbem-28239	44	22	composed	compose	VERB
fbem-28239	44	23	of	of	ADP
fbem-28239	44	24	multiple	multiple	ADJ
fbem-28239	44	25	identical	identical	ADJ
fbem-28239	44	26	neurons	neuron	NOUN
fbem-28239	44	27	,	,	PUNCT
fbem-28239	44	28	with	with	ADP
fbem-28239	44	29	each	each	DET
fbem-28239	44	30	time	time	NOUN
fbem-28239	44	31	step	step	NOUN
fbem-28239	44	32	corresponding	correspond	VERB
fbem-28239	44	33	to	to	ADP
fbem-28239	44	34	a	a	DET
fbem-28239	44	35	neuron	neuron	NOUN
fbem-28239	44	36	.	.	PUNCT
fbem-28239	45	1	in	in	ADP
fbem-28239	45	2	this	this	DET
fbem-28239	45	3	way	way	NOUN
fbem-28239	45	4	,	,	PUNCT
fbem-28239	45	5	rnn	rnn	PROPN
fbem-28239	45	6	can	can	AUX
fbem-28239	45	7	process	process	VERB
fbem-28239	45	8	each	each	DET
fbem-28239	45	9	element	element	NOUN
fbem-28239	45	10	in	in	ADP
fbem-28239	45	11	the	the	DET
fbem-28239	45	12	sequence	sequence	NOUN
fbem-28239	45	13	and	and	CCONJ
fbem-28239	45	14	pass	pass	VERB
fbem-28239	45	15	information	information	NOUN
fbem-28239	45	16	from	from	ADP
fbem-28239	45	17	one	one	NUM
fbem-28239	45	18	time	time	NOUN
fbem-28239	45	19	step	step	NOUN
fbem-28239	45	20	to	to	ADP
fbem-28239	45	21	the	the	DET
fbem-28239	45	22	next	next	ADJ
fbem-28239	45	23	.	.	PUNCT
fbem-28239	46	1	by	by	ADP
fbem-28239	46	2	unfolding	unfold	VERB
fbem-28239	46	3	over	over	ADP
fbem-28239	46	4	time	time	NOUN
fbem-28239	46	5	,	,	PUNCT
fbem-28239	46	6	rnns	rnns	PROPN
fbem-28239	46	7	can	can	AUX
fbem-28239	46	8	learn	learn	VERB
fbem-28239	46	9	long	long	ADJ
fbem-28239	46	10	-	-	PUNCT
fbem-28239	46	11	term	term	NOUN
fbem-28239	46	12	dependencies	dependency	NOUN
fbem-28239	46	13	in	in	ADP
fbem-28239	46	14	a	a	DET
fbem-28239	46	15	sequence	sequence	NOUN
fbem-28239	46	16	,	,	PUNCT
fbem-28239	46	17	namely	namely	ADV
fbem-28239	46	18	the	the	DET
fbem-28239	46	19	impact	impact	NOUN
fbem-28239	46	20	of	of	ADP
fbem-28239	46	21	past	past	ADJ
fbem-28239	46	22	inputs	input	NOUN
fbem-28239	46	23	on	on	ADP
fbem-28239	46	24	current	current	ADJ
fbem-28239	46	25	and	and	CCONJ
fbem-28239	46	26	future	future	ADJ
fbem-28239	46	27	outputs	output	NOUN
fbem-28239	46	28	.	.	PUNCT
fbem-28239	47	1	in	in	ADP
fbem-28239	47	2	this	this	DET
fbem-28239	47	3	article	article	NOUN
fbem-28239	47	4	,	,	PUNCT
fbem-28239	47	5	the	the	DET
fbem-28239	47	6	simplernn	simplernn	NOUN
fbem-28239	47	7	layer	layer	NOUN
fbem-28239	47	8	is	be	AUX
fbem-28239	47	9	used	use	VERB
fbem-28239	47	10	,	,	PUNCT
fbem-28239	47	11	which	which	PRON
fbem-28239	47	12	is	be	AUX
fbem-28239	47	13	a	a	DET
fbem-28239	47	14	basic	basic	ADJ
fbem-28239	47	15	rnn	rnn	NOUN
fbem-28239	47	16	layer	layer	NOUN
fbem-28239	47	17	.	.	PUNCT
fbem-28239	48	1	the	the	DET
fbem-28239	48	2	structure	structure	NOUN
fbem-28239	48	3	of	of	ADP
fbem-28239	48	4	the	the	DET
fbem-28239	48	5	model	model	NOUN
fbem-28239	48	6	consists	consist	VERB
fbem-28239	48	7	of	of	ADP
fbem-28239	48	8	two	two	NUM
fbem-28239	48	9	simplernn	simplernn	NOUN
fbem-28239	48	10	layers	layer	NOUN
fbem-28239	48	11	,	,	PUNCT
fbem-28239	48	12	with	with	ADP
fbem-28239	48	13	two	two	NUM
fbem-28239	48	14	dropout	dropout	NOUN
fbem-28239	48	15	layers	layer	NOUN
fbem-28239	48	16	in	in	ADP
fbem-28239	48	17	between	between	ADP
fbem-28239	48	18	to	to	PART
fbem-28239	48	19	prevent	prevent	VERB
fbem-28239	48	20	overfitting	overfitting	NOUN
fbem-28239	48	21	,	,	PUNCT
fbem-28239	48	22	and	and	CCONJ
fbem-28239	48	23	finally	finally	ADV
fbem-28239	48	24	a	a	DET
fbem-28239	48	25	fully	fully	ADV
fbem-28239	48	26	connected	connected	ADJ
fbem-28239	48	27	dense	dense	ADJ
fbem-28239	48	28	layer	layer	NOUN
fbem-28239	48	29	to	to	PART
fbem-28239	48	30	output	output	VERB
fbem-28239	48	31	the	the	DET
fbem-28239	48	32	predicted	predict	VERB
fbem-28239	48	33	values	value	NOUN
fbem-28239	48	34	.	.	PUNCT
fbem-28239	49	1	stock	stock	NOUN
fbem-28239	49	2	price	price	NOUN
fbem-28239	49	3	data	datum	NOUN
fbem-28239	49	4	is	be	AUX
fbem-28239	49	5	a	a	DET
fbem-28239	49	6	typical	typical	ADJ
fbem-28239	49	7	time	time	NOUN
fbem-28239	49	8	series	series	PROPN
fbem-28239	49	9	data	datum	NOUN
fbem-28239	49	10	with	with	ADP
fbem-28239	49	11	temporal	temporal	ADJ
fbem-28239	49	12	dependence	dependence	NOUN
fbem-28239	49	13	.	.	PUNCT
fbem-28239	50	1	for	for	ADP
fbem-28239	50	2	example	example	NOUN
fbem-28239	50	3	,	,	PUNCT
fbem-28239	50	4	the	the	DET
fbem-28239	50	5	stock	stock	NOUN
fbem-28239	50	6	prices	price	NOUN
fbem-28239	50	7	of	of	ADP
fbem-28239	50	8	the	the	DET
fbem-28239	50	9	past	past	ADJ
fbem-28239	50	10	few	few	ADJ
fbem-28239	50	11	days	day	NOUN
fbem-28239	50	12	may	may	AUX
fbem-28239	50	13	have	have	VERB
fbem-28239	50	14	an	an	DET
fbem-28239	50	15	impact	impact	NOUN
fbem-28239	50	16	on	on	ADP
fbem-28239	50	17	future	future	ADJ
fbem-28239	50	18	prices	price	NOUN
fbem-28239	50	19	.	.	PUNCT
fbem-28239	51	1	rnn	rnn	PROPN
fbem-28239	51	2	can	can	AUX
fbem-28239	51	3	utilize	utilize	VERB
fbem-28239	51	4	this	this	DET
fbem-28239	51	5	temporal	temporal	ADJ
fbem-28239	51	6	dependency	dependency	NOUN
fbem-28239	51	7	to	to	PART
fbem-28239	51	8	predict	predict	VERB
fbem-28239	51	9	future	future	ADJ
fbem-28239	51	10	prices	price	NOUN
fbem-28239	51	11	by	by	ADP
fbem-28239	51	12	learning	learn	VERB
fbem-28239	51	13	patterns	pattern	NOUN
fbem-28239	51	14	from	from	ADP
fbem-28239	51	15	historical	historical	ADJ
fbem-28239	51	16	stock	stock	NOUN
fbem-28239	51	17	price	price	NOUN
fbem-28239	51	18	data	datum	NOUN
fbem-28239	51	19	.	.	PUNCT
fbem-28239	52	1	3	3	X
fbem-28239	52	2	.	.	X
fbem-28239	52	3	empirical	empirical	ADJ
fbem-28239	52	4	research	research	NOUN
fbem-28239	52	5	and	and	CCONJ
fbem-28239	52	6	result	result	VERB
fbem-28239	52	7	analysis	analysis	NOUN
fbem-28239	52	8	3.1	3.1	NUM
fbem-28239	52	9	.	.	PUNCT
fbem-28239	53	1	data	datum	NOUN
fbem-28239	53	2	preprocessing	preprocesse	VERB
fbem-28239	53	3	the	the	DET
fbem-28239	53	4	data	datum	NOUN
fbem-28239	53	5	selected	select	VERB
fbem-28239	53	6	in	in	ADP
fbem-28239	53	7	this	this	DET
fbem-28239	53	8	article	article	NOUN
fbem-28239	53	9	comes	come	VERB
fbem-28239	53	10	from	from	ADP
fbem-28239	53	11	the	the	DET
fbem-28239	53	12	s&p	s&p	PROPN
fbem-28239	53	13	500	500	NUM
fbem-28239	53	14	index	index	NOUN
fbem-28239	53	15	and	and	CCONJ
fbem-28239	53	16	apple	apple	PROPN
fbem-28239	53	17	inc	inc	PROPN
fbem-28239	53	18	.	.	PROPN
fbem-28239	53	19	data	data	PROPN
fbem-28239	53	20	from	from	ADP
fbem-28239	53	21	december	december	PROPN
fbem-28239	53	22	2009	2009	NUM
fbem-28239	53	23	to	to	ADP
fbem-28239	53	24	january	january	PROPN
fbem-28239	53	25	2019	2019	NUM
fbem-28239	53	26	.	.	PUNCT
fbem-28239	54	1	firstly	firstly	ADV
fbem-28239	54	2	,	,	PUNCT
fbem-28239	54	3	the	the	DET
fbem-28239	54	4	data	datum	NOUN
fbem-28239	54	5	was	be	AUX
fbem-28239	54	6	randomly	randomly	ADV
fbem-28239	54	7	sampled	sample	VERB
fbem-28239	54	8	for	for	ADP
fbem-28239	54	9	authenticity	authenticity	NOUN
fbem-28239	54	10	testing	testing	NOUN
fbem-28239	54	11	,	,	PUNCT
fbem-28239	54	12	and	and	CCONJ
fbem-28239	54	13	it	it	PRON
fbem-28239	54	14	was	be	AUX
fbem-28239	54	15	found	find	VERB
fbem-28239	54	16	to	to	PART
fbem-28239	54	17	be	be	AUX
fbem-28239	54	18	completely	completely	ADV
fbem-28239	54	19	consistent	consistent	ADJ
fbem-28239	54	20	with	with	ADP
fbem-28239	54	21	yahoo	yahoo	PROPN
fbem-28239	54	22	finance	finance	NOUN
fbem-28239	54	23	.	.	PUNCT
fbem-28239	55	1	after	after	ADP
fbem-28239	55	2	processing	processing	NOUN
fbem-28239	55	3	,	,	PUNCT
fbem-28239	55	4	it	it	PRON
fbem-28239	55	5	includes	include	VERB
fbem-28239	55	6	date	date	NOUN
fbem-28239	55	7	,	,	PUNCT
fbem-28239	55	8	open	open	ADJ
fbem-28239	55	9	,	,	PUNCT
fbem-28239	55	10	high	high	ADJ
fbem-28239	55	11	,	,	PUNCT
fbem-28239	55	12	low	low	ADJ
fbem-28239	55	13	,	,	PUNCT
fbem-28239	55	14	close	close	ADJ
fbem-28239	55	15	,	,	PUNCT
fbem-28239	55	16	adj	adj	X
fbem-28239	55	17	close	close	NOUN
fbem-28239	55	18	,	,	PUNCT
fbem-28239	55	19	and	and	CCONJ
fbem-28239	55	20	vo2ume	vo2ume	NOUN
fbem-28239	55	21	as	as	ADP
fbem-28239	55	22	indicators	indicator	NOUN
fbem-28239	55	23	.	.	PUNCT
fbem-28239	56	1	the	the	DET
fbem-28239	56	2	data	data	NOUN
fbem-28239	56	3	volume	volume	NOUN
fbem-28239	56	4	for	for	ADP
fbem-28239	56	5	both	both	PRON
fbem-28239	56	6	the	the	DET
fbem-28239	56	7	s&p	s&p	PROPN
fbem-28239	56	8	500	500	NUM
fbem-28239	56	9	index	index	NOUN
fbem-28239	56	10	and	and	CCONJ
fbem-28239	56	11	apple	apple	PROPN
fbem-28239	56	12	inc	inc	PROPN
fbem-28239	56	13	.	.	PROPN
fbem-28239	56	14	is	be	AUX
fbem-28239	56	15	2282	2282	NUM
fbem-28239	56	16	rows	row	NOUN
fbem-28239	56	17	*	*	PUNCT
fbem-28239	56	18	7	7	NUM
fbem-28239	56	19	columns	column	NOUN
fbem-28239	56	20	3.2	3.2	NUM
fbem-28239	56	21	.	.	PUNCT
fbem-28239	57	1	multiple	multiple	ADJ
fbem-28239	57	2	linear	linear	PROPN
fbem-28239	57	3	regression	regression	NOUN
fbem-28239	57	4	model	model	NOUN
fbem-28239	57	5	use	use	VERB
fbem-28239	57	6	pandas	panda	NOUN
fbem-28239	57	7	'	'	PART
fbem-28239	57	8	read_csv	read_csv	PROPN
fbem-28239	57	9	function	function	VERB
fbem-28239	57	10	to	to	PART
fbem-28239	57	11	read	read	VERB
fbem-28239	57	12	csv	csv	ADJ
fbem-28239	57	13	data	datum	NOUN
fbem-28239	57	14	files	file	NOUN
fbem-28239	57	15	from	from	ADP
fbem-28239	57	16	apple	apple	PROPN
fbem-28239	57	17	inc	inc	PROPN
fbem-28239	57	18	.	.	PROPN
fbem-28239	57	19	(	(	PUNCT
fbem-28239	57	20	aapl	aapl	NOUN
fbem-28239	57	21	)	)	PUNCT
fbem-28239	57	22	and	and	CCONJ
fbem-28239	57	23	standard	standard	ADJ
fbem-28239	57	24	poor	poor	ADJ
fbem-28239	57	25	’s	’s	PART
fbem-28239	57	26	500	500	NUM
fbem-28239	57	27	(	(	PUNCT
fbem-28239	57	28	spy	spy	NOUN
fbem-28239	57	29	)	)	PUNCT
fbem-28239	57	30	separately	separately	ADV
fbem-28239	57	31	and	and	CCONJ
fbem-28239	57	32	store	store	VERB
fbem-28239	57	33	the	the	DET
fbem-28239	57	34	data	datum	NOUN
fbem-28239	57	35	in	in	ADP
fbem-28239	57	36	a	a	DET
fbem-28239	57	37	dataframe	dataframe	ADJ
fbem-28239	57	38	object	object	NOUN
fbem-28239	57	39	.	.	PUNCT
fbem-28239	58	1	extract	extract	VERB
fbem-28239	58	2	independent	independent	ADJ
fbem-28239	58	3	variables	variable	NOUN
fbem-28239	58	4	from	from	ADP
fbem-28239	58	5	aapl	aapl	PROPN
fbem-28239	58	6	data	datum	NOUN
fbem-28239	58	7	,	,	PUNCT
fbem-28239	58	8	including	include	VERB
fbem-28239	58	9	opening	opening	NOUN
fbem-28239	58	10	price	price	NOUN
fbem-28239	58	11	,	,	PUNCT
fbem-28239	58	12	highest	high	ADJ
fbem-28239	58	13	price	price	NOUN
fbem-28239	58	14	,	,	PUNCT
fbem-28239	58	15	lowest	low	ADJ
fbem-28239	58	16	price	price	NOUN
fbem-28239	58	17	,	,	PUNCT
fbem-28239	58	18	and	and	CCONJ
fbem-28239	58	19	trading	trading	NOUN
fbem-28239	58	20	volume	volume	NOUN
fbem-28239	58	21	,	,	PUNCT
fbem-28239	58	22	as	as	ADV
fbem-28239	58	23	well	well	ADV
fbem-28239	58	24	as	as	ADP
fbem-28239	58	25	the	the	DET
fbem-28239	58	26	dependent	dependent	ADJ
fbem-28239	58	27	variable	variable	ADJ
fbem-28239	58	28	closing	closing	NOUN
fbem-28239	58	29	price	price	NOUN
fbem-28239	58	30	.	.	PUNCT
fbem-28239	59	1	and	and	CCONJ
fbem-28239	59	2	calculate	calculate	VERB
fbem-28239	59	3	the	the	DET
fbem-28239	59	4	daily	daily	ADJ
fbem-28239	59	5	returns	return	NOUN
fbem-28239	59	6	of	of	ADP
fbem-28239	59	7	aapl	aapl	NOUN
fbem-28239	59	8	and	and	CCONJ
fbem-28239	59	9	spy	spy	NOUN
fbem-28239	59	10	by	by	ADP
fbem-28239	59	11	calculating	calculate	VERB
fbem-28239	59	12	the	the	DET
fbem-28239	59	13	percentage	percentage	NOUN
fbem-28239	59	14	change	change	NOUN
fbem-28239	59	15	in	in	ADP
fbem-28239	59	16	closing	closing	NOUN
fbem-28239	59	17	price	price	NOUN
fbem-28239	59	18	.	.	PUNCT
fbem-28239	60	1	then	then	ADV
fbem-28239	60	2	,	,	PUNCT
fbem-28239	60	3	delete	delete	ADJ
fbem-28239	60	4	rows	row	NOUN
fbem-28239	60	5	containing	contain	VERB
fbem-28239	60	6	null	null	ADJ
fbem-28239	60	7	values	value	NOUN
fbem-28239	60	8	to	to	PART
fbem-28239	60	9	ensure	ensure	VERB
fbem-28239	60	10	data	datum	NOUN
fbem-28239	60	11	integrity	integrity	NOUN
fbem-28239	60	12	.	.	PUNCT
fbem-28239	61	1	use	use	VERB
fbem-28239	61	2	the	the	DET
fbem-28239	61	3	statsmodels	statsmodel	NOUN
fbem-28239	61	4	library	library	NOUN
fbem-28239	61	5	to	to	PART
fbem-28239	61	6	calculate	calculate	VERB
fbem-28239	61	7	the	the	DET
fbem-28239	61	8	beta	beta	ADJ
fbem-28239	61	9	coefficient	coefficient	NOUN
fbem-28239	61	10	of	of	ADP
fbem-28239	61	11	aapl	aapl	PROPN
fbem-28239	61	12	return	return	VERB
fbem-28239	61	13	on	on	ADP
fbem-28239	61	14	spy	spy	NOUN
fbem-28239	61	15	return	return	NOUN
fbem-28239	61	16	.	.	PUNCT
fbem-28239	62	1	firstly	firstly	ADV
fbem-28239	62	2	,	,	PUNCT
fbem-28239	62	3	a	a	DET
fbem-28239	62	4	constant	constant	ADJ
fbem-28239	62	5	term	term	NOUN
fbem-28239	62	6	is	be	AUX
fbem-28239	62	7	added	add	VERB
fbem-28239	62	8	to	to	ADP
fbem-28239	62	9	the	the	DET
fbem-28239	62	10	return	return	NOUN
fbem-28239	62	11	data	datum	NOUN
fbem-28239	62	12	of	of	ADP
fbem-28239	62	13	spy	spy	NOUN
fbem-28239	62	14	,	,	PUNCT
fbem-28239	62	15	and	and	CCONJ
fbem-28239	62	16	then	then	ADV
fbem-28239	62	17	ordinary	ordinary	ADJ
fbem-28239	62	18	least	least	ADJ
fbem-28239	62	19	squares	square	NOUN
fbem-28239	62	20	(	(	PUNCT
fbem-28239	62	21	ols	ol	NOUN
fbem-28239	62	22	)	)	PUNCT
fbem-28239	62	23	is	be	AUX
fbem-28239	62	24	used	use	VERB
fbem-28239	62	25	for	for	ADP
fbem-28239	62	26	regression	regression	NOUN
fbem-28239	62	27	fitting	fitting	ADJ
fbem-28239	62	28	.	.	PUNCT
fbem-28239	63	1	the	the	DET
fbem-28239	63	2	second	second	ADJ
fbem-28239	63	3	value	value	NOUN
fbem-28239	63	4	in	in	ADP
fbem-28239	63	5	the	the	DET
fbem-28239	63	6	obtained	obtain	VERB
fbem-28239	63	7	model	model	NOUN
fbem-28239	63	8	parameters	parameter	NOUN
fbem-28239	63	9	is	be	AUX
fbem-28239	63	10	the	the	DET
fbem-28239	63	11	beta	beta	ADJ
fbem-28239	63	12	coefficient	coefficient	NOUN
fbem-28239	63	13	.	.	PUNCT
fbem-28239	64	1	use	use	VERB
fbem-28239	64	2	standardscaler	standardscaler	NOUN
fbem-28239	64	3	to	to	PART
fbem-28239	64	4	standardize	standardize	VERB
fbem-28239	64	5	the	the	DET
fbem-28239	64	6	independent	independent	ADJ
fbem-28239	64	7	variable	variable	ADJ
fbem-28239	64	8	data	datum	NOUN
fbem-28239	64	9	of	of	ADP
fbem-28239	64	10	aapl	aapl	NOUN
fbem-28239	64	11	,	,	PUNCT
fbem-28239	64	12	so	so	SCONJ
fbem-28239	64	13	that	that	SCONJ
fbem-28239	64	14	it	it	PRON
fbem-28239	64	15	has	have	VERB
fbem-28239	64	16	a	a	DET
fbem-28239	64	17	distribution	distribution	NOUN
fbem-28239	64	18	with	with	ADP
fbem-28239	64	19	a	a	DET
fbem-28239	64	20	mean	mean	NOUN
fbem-28239	64	21	of	of	ADP
fbem-28239	64	22	0	0	NUM
fbem-28239	64	23	and	and	CCONJ
fbem-28239	64	24	a	a	DET
fbem-28239	64	25	standard	standard	ADJ
fbem-28239	64	26	deviation	deviation	NOUN
fbem-28239	64	27	of	of	ADP
fbem-28239	64	28	1	1	NUM
fbem-28239	64	29	.	.	PUNCT
fbem-28239	65	1	this	this	PRON
fbem-28239	65	2	helps	help	VERB
fbem-28239	65	3	to	to	PART
fbem-28239	65	4	improve	improve	VERB
fbem-28239	65	5	the	the	DET
fbem-28239	65	6	performance	performance	NOUN
fbem-28239	65	7	of	of	ADP
fbem-28239	65	8	linear	linear	ADJ
fbem-28239	65	9	regression	regression	NOUN
fbem-28239	65	10	models	model	NOUN
fbem-28239	65	11	and	and	CCONJ
fbem-28239	65	12	avoid	avoid	VERB
fbem-28239	65	13	certain	certain	ADJ
fbem-28239	65	14	features	feature	NOUN
fbem-28239	65	15	having	have	VERB
fbem-28239	65	16	a	a	DET
fbem-28239	65	17	significant	significant	ADJ
fbem-28239	65	18	impact	impact	NOUN
fbem-28239	65	19	on	on	ADP
fbem-28239	65	20	the	the	DET
fbem-28239	65	21	model	model	NOUN
fbem-28239	65	22	due	due	ADP
fbem-28239	65	23	to	to	ADP
fbem-28239	65	24	large	large	ADJ
fbem-28239	65	25	numerical	numerical	ADJ
fbem-28239	65	26	ranges	range	NOUN
fbem-28239	65	27	afterwards	afterwards	ADV
fbem-28239	65	28	,	,	PUNCT
fbem-28239	65	29	divide	divide	VERB
fbem-28239	65	30	the	the	DET
fbem-28239	65	31	aapl	aapl	PROPN
fbem-28239	65	32	data	datum	NOUN
fbem-28239	65	33	into	into	ADP
fbem-28239	65	34	a	a	DET
fbem-28239	65	35	training	training	NOUN
fbem-28239	65	36	set	set	NOUN
fbem-28239	65	37	and	and	CCONJ
fbem-28239	65	38	a	a	DET
fbem-28239	65	39	testing	testing	NOUN
fbem-28239	65	40	set	set	NOUN
fbem-28239	65	41	.	.	PUNCT
fbem-28239	66	1	the	the	DET
fbem-28239	66	2	data	datum	NOUN
fbem-28239	66	3	from	from	ADP
fbem-28239	66	4	the	the	DET
fbem-28239	66	5	previous	previous	ADJ
fbem-28239	66	6	(	(	PUNCT
fbem-28239	66	7	total	total	ADJ
fbem-28239	66	8	number	number	NOUN
fbem-28239	66	9	of	of	ADP
fbem-28239	66	10	data	datum	NOUN
fbem-28239	66	11	300	300	NUM
fbem-28239	66	12	)	)	PUNCT
fbem-28239	66	13	days	day	NOUN
fbem-28239	66	14	is	be	AUX
fbem-28239	66	15	used	use	VERB
fbem-28239	66	16	as	as	ADP
fbem-28239	66	17	the	the	DET
fbem-28239	66	18	training	training	NOUN
fbem-28239	66	19	set	set	VERB
fbem-28239	66	20	for	for	ADP
fbem-28239	66	21	training	train	VERB
fbem-28239	66	22	the	the	DET
fbem-28239	66	23	linear	linear	ADJ
fbem-28239	66	24	regression	regression	NOUN
fbem-28239	66	25	model	model	NOUN
fbem-28239	66	26	;	;	PUNCT
fbem-28239	66	27	the	the	DET
fbem-28239	66	28	data	datum	NOUN
fbem-28239	66	29	from	from	ADP
fbem-28239	66	30	the	the	DET
fbem-28239	66	31	last	last	ADJ
fbem-28239	66	32	300	300	NUM
fbem-28239	66	33	days	day	NOUN
fbem-28239	66	34	will	will	AUX
fbem-28239	66	35	be	be	AUX
fbem-28239	66	36	used	use	VERB
fbem-28239	66	37	as	as	ADP
fbem-28239	66	38	the	the	DET
fbem-28239	66	39	test	test	NOUN
fbem-28239	66	40	set	set	VERB
fbem-28239	66	41	to	to	PART
fbem-28239	66	42	evaluate	evaluate	VERB
fbem-28239	66	43	the	the	DET
fbem-28239	66	44	performance	performance	NOUN
fbem-28239	66	45	of	of	ADP
fbem-28239	66	46	the	the	DET
fbem-28239	66	47	model	model	NOUN
fbem-28239	66	48	.	.	PUNCT
fbem-28239	67	1	create	create	VERB
fbem-28239	67	2	a	a	DET
fbem-28239	67	3	linear	linear	ADJ
fbem-28239	67	4	regression	regression	NOUN
fbem-28239	67	5	model	model	NOUN
fbem-28239	67	6	object	object	NOUN
fbem-28239	67	7	and	and	CCONJ
fbem-28239	67	8	train	train	VERB
fbem-28239	67	9	it	it	PRON
fbem-28239	67	10	using	use	VERB
fbem-28239	67	11	the	the	DET
fbem-28239	67	12	training	training	NOUN
fbem-28239	67	13	set	set	VERB
fbem-28239	67	14	data	datum	NOUN
fbem-28239	67	15	.	.	PUNCT
fbem-28239	68	1	by	by	ADP
fbem-28239	68	2	minimizing	minimize	VERB
fbem-28239	68	3	the	the	DET
fbem-28239	68	4	error	error	NOUN
fbem-28239	68	5	between	between	ADP
fbem-28239	68	6	actual	actual	ADJ
fbem-28239	68	7	and	and	CCONJ
fbem-28239	68	8	predicted	predict	VERB
fbem-28239	68	9	values	value	NOUN
fbem-28239	68	10	,	,	PUNCT
fbem-28239	68	11	adjusting	adjust	VERB
fbem-28239	68	12	the	the	DET
fbem-28239	68	13	parameters	parameter	NOUN
fbem-28239	68	14	of	of	ADP
fbem-28239	68	15	the	the	DET
fbem-28239	68	16	model	model	NOUN
fbem-28239	68	17	,	,	PUNCT
fbem-28239	68	18	the	the	DET
fbem-28239	68	19	optimal	optimal	ADJ
fbem-28239	68	20	linear	linear	ADJ
fbem-28239	68	21	relationship	relationship	NOUN
fbem-28239	68	22	between	between	ADP
fbem-28239	68	23	the	the	DET
fbem-28239	68	24	independent	independent	ADJ
fbem-28239	68	25	and	and	CCONJ
fbem-28239	68	26	dependent	dependent	ADJ
fbem-28239	68	27	variables	variable	NOUN
fbem-28239	68	28	is	be	AUX
fbem-28239	68	29	established	establish	VERB
fbem-28239	68	30	.	.	PUNCT
fbem-28239	69	1	use	use	VERB
fbem-28239	69	2	a	a	DET
fbem-28239	69	3	trained	train	VERB
fbem-28239	69	4	linear	linear	NOUN
fbem-28239	69	5	regression	regression	NOUN
fbem-28239	69	6	model	model	NOUN
fbem-28239	69	7	to	to	PART
fbem-28239	69	8	predict	predict	VERB
fbem-28239	69	9	the	the	DET
fbem-28239	69	10	test	test	NOUN
fbem-28239	69	11	set	set	VERB
fbem-28239	69	12	data	datum	NOUN
fbem-28239	69	13	and	and	CCONJ
fbem-28239	69	14	obtain	obtain	VERB
fbem-28239	69	15	the	the	DET
fbem-28239	69	16	predicted	predict	VERB
fbem-28239	69	17	closing	closing	NOUN
fbem-28239	69	18	price	price	NOUN
fbem-28239	69	19	calculate	calculate	NOUN
fbem-28239	69	20	the	the	DET
fbem-28239	69	21	evaluation	evaluation	NOUN
fbem-28239	69	22	metrics	metric	NOUN
fbem-28239	69	23	of	of	ADP
fbem-28239	69	24	the	the	DET
fbem-28239	69	25	linear	linear	ADJ
fbem-28239	69	26	regression	regression	NOUN
fbem-28239	69	27	model	model	NOUN
fbem-28239	69	28	on	on	ADP
fbem-28239	69	29	the	the	DET
fbem-28239	69	30	test	test	NOUN
fbem-28239	69	31	set	set	NOUN
fbem-28239	69	32	,	,	PUNCT
fbem-28239	69	33	including	include	VERB
fbem-28239	69	34	mean	mean	ADJ
fbem-28239	69	35	square	square	ADJ
fbem-28239	69	36	error	error	NOUN
fbem-28239	69	37	(	(	PUNCT
fbem-28239	69	38	mse	mse	NOUN
fbem-28239	69	39	)	)	PUNCT
fbem-28239	69	40	,	,	PUNCT
fbem-28239	69	41	root	root	NOUN
fbem-28239	69	42	mean	mean	VERB
fbem-28239	69	43	square	square	ADJ
fbem-28239	69	44	error	error	NOUN
fbem-28239	69	45	(	(	PUNCT
fbem-28239	69	46	rmse	rmse	NOUN
fbem-28239	69	47	)	)	PUNCT
fbem-28239	69	48	,	,	PUNCT
fbem-28239	69	49	mean	mean	VERB
fbem-28239	69	50	absolute	absolute	ADJ
fbem-28239	69	51	error	error	NOUN
fbem-28239	69	52	(	(	PUNCT
fbem-28239	69	53	mae	mae	PROPN
fbem-28239	69	54	)	)	PUNCT
fbem-28239	69	55	,	,	PUNCT
fbem-28239	69	56	and	and	CCONJ
fbem-28239	69	57	coefficient	coefficient	NOUN
fbem-28239	69	58	of	of	ADP
fbem-28239	69	59	determination	determination	NOUN
fbem-28239	69	60	(	(	PUNCT
fbem-28239	69	61	r²	r²	NOUN
fbem-28239	69	62	)	)	PUNCT
fbem-28239	69	63	.	.	PUNCT
fbem-28239	70	1	these	these	DET
fbem-28239	70	2	indicators	indicator	NOUN
fbem-28239	70	3	are	be	AUX
fbem-28239	70	4	used	use	VERB
fbem-28239	70	5	to	to	PART
fbem-28239	70	6	measure	measure	VERB
fbem-28239	70	7	the	the	DET
fbem-28239	70	8	predictive	predictive	ADJ
fbem-28239	70	9	accuracy	accuracy	NOUN
fbem-28239	70	10	and	and	CCONJ
fbem-28239	70	11	fitting	fitting	ADJ
fbem-28239	70	12	degree	degree	NOUN
fbem-28239	70	13	of	of	ADP
fbem-28239	70	14	the	the	DET
fbem-28239	70	15	model	model	NOUN
fbem-28239	70	16	.	.	PUNCT
fbem-28239	71	1	use	use	VERB
fbem-28239	71	2	matplotlib	matplotlib	PROPN
fbem-28239	71	3	to	to	PART
fbem-28239	71	4	draw	draw	VERB
fbem-28239	71	5	a	a	DET
fbem-28239	71	6	line	line	NOUN
fbem-28239	71	7	comparison	comparison	NOUN
fbem-28239	71	8	chart	chart	NOUN
fbem-28239	71	9	between	between	ADP
fbem-28239	71	10	the	the	DET
fbem-28239	71	11	actual	actual	ADJ
fbem-28239	71	12	closing	closing	NOUN
fbem-28239	71	13	price	price	NOUN
fbem-28239	71	14	and	and	CCONJ
fbem-28239	71	15	the	the	DET
fbem-28239	71	16	predicted	predict	VERB
fbem-28239	71	17	closing	closing	NOUN
fbem-28239	71	18	price	price	NOUN
fbem-28239	71	19	,	,	PUNCT
fbem-28239	71	20	visually	visually	ADV
fbem-28239	71	21	demonstrating	demonstrate	VERB
fbem-28239	71	22	the	the	DET
fbem-28239	71	23	predictive	predictive	ADJ
fbem-28239	71	24	performance	performance	NOUN
fbem-28239	71	25	of	of	ADP
fbem-28239	71	26	the	the	DET
fbem-28239	71	27	model	model	NOUN
fbem-28239	71	28	.	.	PUNCT
fbem-28239	72	1	figure	figure	NOUN
fbem-28239	72	2	1	1	NUM
fbem-28239	72	3	.	.	PUNCT
fbem-28239	72	4	test	test	NOUN
fbem-28239	72	5	results	result	NOUN
fbem-28239	72	6	of	of	ADP
fbem-28239	72	7	multiple	multiple	ADJ
fbem-28239	72	8	linear	linear	ADJ
fbem-28239	72	9	regression	regression	NOUN
fbem-28239	72	10	model	model	NOUN
fbem-28239	72	11	the	the	DET
fbem-28239	72	12	multiple	multiple	ADJ
fbem-28239	72	13	linear	linear	ADJ
fbem-28239	72	14	regression	regression	NOUN
fbem-28239	72	15	equation	equation	NOUN
fbem-28239	72	16	is	be	AUX
fbem-28239	72	17	expressed	express	VERB
fbem-28239	72	18	as	as	ADP
fbem-28239	72	19	:	:	PUNCT
fbem-28239	72	20	y=99.8291	y=99.8291	PROPN
fbem-28239	72	21	+	+	PROPN
fbem-28239	72	22	-27.4244	-27.4244	PROPN
fbem-28239	72	23	*	*	ADJ
fbem-28239	72	24	open+39.1100	open+39.1100	NUM
fbem-28239	72	25	*	*	PUNCT
fbem-28239	72	26	high+35.1811	high+35.1811	PUNCT
fbem-28239	72	27	*	*	PUNCT
fbem-28239	72	28	low+0.0155	low+0.0155	PUNCT
fbem-28239	72	29	*	*	PUNCT
fbem-28239	72	30	volume	volume	NOUN
fbem-28239	72	31	.	.	PUNCT
fbem-28239	73	1	the	the	DET
fbem-28239	73	2	coefficients	coefficient	NOUN
fbem-28239	73	3	and	and	CCONJ
fbem-28239	73	4	intercepts	intercept	NOUN
fbem-28239	73	5	in	in	ADP
fbem-28239	73	6	the	the	DET
fbem-28239	73	7	equation	equation	NOUN
fbem-28239	73	8	are	be	AUX
fbem-28239	73	9	rounded	round	VERB
fbem-28239	73	10	to	to	ADP
fbem-28239	73	11	four	four	NUM
fbem-28239	73	12	decimal	decimal	ADJ
fbem-28239	73	13	places	place	NOUN
fbem-28239	73	14	,	,	PUNCT
fbem-28239	73	15	and	and	CCONJ
fbem-28239	73	16	the	the	DET
fbem-28239	73	17	variable	variable	ADJ
fbem-28239	73	18	names	name	NOUN
fbem-28239	73	19	are	be	AUX
fbem-28239	73	20	derived	derive	VERB
fbem-28239	73	21	from	from	ADP
fbem-28239	73	22	the	the	DET
fbem-28239	73	23	feature	feature	NOUN
fbem-28239	73	24	names	name	NOUN
fbem-28239	73	25	of	of	ADP
fbem-28239	73	26	the	the	DET
fbem-28239	73	27	original	original	ADJ
fbem-28239	73	28	data	datum	NOUN
fbem-28239	73	29	.	.	PUNCT
fbem-28239	74	1	111	111	NUM
fbem-28239	74	2	3.3	3.3	NUM
fbem-28239	74	3	.	.	PUNCT
fbem-28239	75	1	recurrent	recurrent	ADJ
fbem-28239	75	2	neural	neural	ADJ
fbem-28239	75	3	network	network	NOUN
fbem-28239	75	4	(	(	PUNCT
fbem-28239	75	5	rnn	rnn	PROPN
fbem-28239	75	6	)	)	PUNCT
fbem-28239	75	7	use	use	VERB
fbem-28239	75	8	pandas	panda	NOUN
fbem-28239	75	9	'	'	PART
fbem-28239	75	10	read_csv	read_csv	PROPN
fbem-28239	75	11	function	function	VERB
fbem-28239	75	12	to	to	PART
fbem-28239	75	13	read	read	VERB
fbem-28239	75	14	apple	apple	PROPN
fbem-28239	75	15	inc	inc	PROPN
fbem-28239	75	16	.	.	PROPN
fbem-28239	75	17	stock	stock	PROPN
fbem-28239	75	18	data	datum	NOUN
fbem-28239	75	19	files	file	NOUN
fbem-28239	75	20	and	and	CCONJ
fbem-28239	75	21	market	market	NOUN
fbem-28239	75	22	index	index	NOUN
fbem-28239	75	23	data	datum	NOUN
fbem-28239	75	24	files	file	NOUN
fbem-28239	75	25	,	,	PUNCT
fbem-28239	75	26	in	in	ADP
fbem-28239	75	27	preparation	preparation	NOUN
fbem-28239	75	28	for	for	ADP
fbem-28239	75	29	subsequent	subsequent	ADJ
fbem-28239	75	30	data	datum	NOUN
fbem-28239	75	31	processing	processing	NOUN
fbem-28239	75	32	and	and	CCONJ
fbem-28239	75	33	analysis	analysis	NOUN
fbem-28239	75	34	.	.	PUNCT
fbem-28239	76	1	divide	divide	VERB
fbem-28239	76	2	training	training	NOUN
fbem-28239	76	3	and	and	CCONJ
fbem-28239	76	4	testing	testing	NOUN
fbem-28239	76	5	sets	set	NOUN
fbem-28239	76	6	:	:	PUNCT
fbem-28239	76	7	divide	divide	VERB
fbem-28239	76	8	the	the	DET
fbem-28239	76	9	training	training	NOUN
fbem-28239	76	10	and	and	CCONJ
fbem-28239	76	11	testing	testing	NOUN
fbem-28239	76	12	sets	set	NOUN
fbem-28239	76	13	from	from	ADP
fbem-28239	76	14	the	the	DET
fbem-28239	76	15	stock	stock	NOUN
fbem-28239	76	16	data	datum	NOUN
fbem-28239	76	17	of	of	ADP
fbem-28239	76	18	apple	apple	PROPN
fbem-28239	76	19	inc	inc	PROPN
fbem-28239	76	20	.	.	PUNCT
fbem-28239	77	1	the	the	DET
fbem-28239	77	2	training	training	NOUN
fbem-28239	77	3	set	set	NOUN
fbem-28239	77	4	contains	contain	VERB
fbem-28239	77	5	data	datum	NOUN
fbem-28239	77	6	from	from	ADP
fbem-28239	77	7	earlier	early	ADJ
fbem-28239	77	8	times	time	NOUN
fbem-28239	77	9	,	,	PUNCT
fbem-28239	77	10	while	while	SCONJ
fbem-28239	77	11	the	the	DET
fbem-28239	77	12	test	test	NOUN
fbem-28239	77	13	set	set	VERB
fbem-28239	77	14	consists	consist	NOUN
fbem-28239	77	15	of	of	ADP
fbem-28239	77	16	data	datum	NOUN
fbem-28239	77	17	from	from	ADP
fbem-28239	77	18	300	300	NUM
fbem-28239	77	19	days	day	NOUN
fbem-28239	77	20	later	later	ADV
fbem-28239	77	21	.	.	PUNCT
fbem-28239	78	1	this	this	DET
fbem-28239	78	2	partitioning	partitioning	PROPN
fbem-28239	78	3	method	method	NOUN
fbem-28239	78	4	helps	help	VERB
fbem-28239	78	5	evaluate	evaluate	VERB
fbem-28239	78	6	the	the	DET
fbem-28239	78	7	model	model	NOUN
fbem-28239	78	8	's	's	PART
fbem-28239	78	9	generalization	generalization	NOUN
fbem-28239	78	10	ability	ability	NOUN
fbem-28239	78	11	on	on	ADP
fbem-28239	78	12	unseen	unseen	ADJ
fbem-28239	78	13	data	datum	NOUN
fbem-28239	78	14	.	.	PUNCT
fbem-28239	79	1	calculate	calculate	NOUN
fbem-28239	79	2	return	return	NOUN
fbem-28239	79	3	and	and	CCONJ
fbem-28239	79	4	beta	beta	ADJ
fbem-28239	79	5	coefficient	coefficient	NOUN
fbem-28239	79	6	:	:	PUNCT
fbem-28239	79	7	first	first	ADV
fbem-28239	79	8	,	,	PUNCT
fbem-28239	79	9	calculate	calculate	VERB
fbem-28239	79	10	the	the	DET
fbem-28239	79	11	return	return	NOUN
fbem-28239	79	12	of	of	ADP
fbem-28239	79	13	apple	apple	PROPN
fbem-28239	79	14	inc	inc	PROPN
fbem-28239	79	15	.	.	PROPN
fbem-28239	79	16	stock	stock	PROPN
fbem-28239	79	17	and	and	CCONJ
fbem-28239	79	18	market	market	NOUN
fbem-28239	79	19	index	index	NOUN
fbem-28239	79	20	,	,	PUNCT
fbem-28239	79	21	use	use	VERB
fbem-28239	79	22	the	the	DET
fbem-28239	79	23	pct_change	pct_change	NOUN
fbem-28239	79	24	function	function	NOUN
fbem-28239	79	25	to	to	PART
fbem-28239	79	26	calculate	calculate	VERB
fbem-28239	79	27	the	the	DET
fbem-28239	79	28	percentage	percentage	NOUN
fbem-28239	79	29	change	change	NOUN
fbem-28239	79	30	,	,	PUNCT
fbem-28239	79	31	and	and	CCONJ
fbem-28239	79	32	then	then	ADV
fbem-28239	79	33	use	use	VERB
fbem-28239	79	34	dropna	dropna	NOUN
fbem-28239	79	35	to	to	PART
fbem-28239	79	36	remove	remove	VERB
fbem-28239	79	37	rows	row	NOUN
fbem-28239	79	38	with	with	ADP
fbem-28239	79	39	missing	miss	VERB
fbem-28239	79	40	values	value	NOUN
fbem-28239	79	41	.	.	PUNCT
fbem-28239	80	1	next	next	ADV
fbem-28239	80	2	,	,	PUNCT
fbem-28239	80	3	by	by	ADP
fbem-28239	80	4	performing	perform	VERB
fbem-28239	80	5	linear	linear	ADJ
fbem-28239	80	6	regression	regression	NOUN
fbem-28239	80	7	on	on	ADP
fbem-28239	80	8	market	market	NOUN
fbem-28239	80	9	returns	return	NOUN
fbem-28239	80	10	and	and	CCONJ
fbem-28239	80	11	apple	apple	NOUN
fbem-28239	80	12	stock	stock	NOUN
fbem-28239	80	13	returns	return	NOUN
fbem-28239	80	14	,	,	PUNCT
fbem-28239	80	15	the	the	DET
fbem-28239	80	16	beta	beta	ADJ
fbem-28239	80	17	coefficient	coefficient	NOUN
fbem-28239	80	18	is	be	AUX
fbem-28239	80	19	calculated	calculate	VERB
fbem-28239	80	20	.	.	PUNCT
fbem-28239	81	1	after	after	ADP
fbem-28239	81	2	reshaping	reshape	VERB
fbem-28239	81	3	the	the	DET
fbem-28239	81	4	market	market	NOUN
fbem-28239	81	5	return	return	NOUN
fbem-28239	81	6	data	datum	NOUN
fbem-28239	81	7	into	into	ADP
fbem-28239	81	8	a	a	DET
fbem-28239	81	9	shape	shape	NOUN
fbem-28239	81	10	suitable	suitable	ADJ
fbem-28239	81	11	for	for	ADP
fbem-28239	81	12	linear	linear	PROPN
fbem-28239	81	13	regression	regression	NOUN
fbem-28239	81	14	,	,	PUNCT
fbem-28239	81	15	use	use	VERB
fbem-28239	81	16	linearregression	linearregression	NOUN
fbem-28239	81	17	for	for	ADP
fbem-28239	81	18	fitting	fitting	ADJ
fbem-28239	81	19	,	,	PUNCT
fbem-28239	81	20	and	and	CCONJ
fbem-28239	81	21	finally	finally	ADV
fbem-28239	81	22	obtain	obtain	VERB
fbem-28239	81	23	the	the	DET
fbem-28239	81	24	beta	beta	ADJ
fbem-28239	81	25	coefficient	coefficient	NOUN
fbem-28239	81	26	from	from	ADP
fbem-28239	81	27	the	the	DET
fbem-28239	81	28	fitting	fitting	ADJ
fbem-28239	81	29	result	result	NOUN
fbem-28239	81	30	.	.	PUNCT
fbem-28239	82	1	the	the	DET
fbem-28239	82	2	beta	beta	ADJ
fbem-28239	82	3	coefficient	coefficient	NOUN
fbem-28239	82	4	reflects	reflect	VERB
fbem-28239	82	5	the	the	DET
fbem-28239	82	6	risk	risk	NOUN
fbem-28239	82	7	characteristics	characteristic	NOUN
fbem-28239	82	8	of	of	ADP
fbem-28239	82	9	stocks	stock	NOUN
fbem-28239	82	10	relative	relative	ADJ
fbem-28239	82	11	to	to	ADP
fbem-28239	82	12	the	the	DET
fbem-28239	82	13	market	market	NOUN
fbem-28239	82	14	and	and	CCONJ
fbem-28239	82	15	is	be	AUX
fbem-28239	82	16	used	use	VERB
fbem-28239	82	17	as	as	ADP
fbem-28239	82	18	an	an	DET
fbem-28239	82	19	additional	additional	ADJ
fbem-28239	82	20	feature	feature	NOUN
fbem-28239	82	21	in	in	ADP
fbem-28239	82	22	subsequent	subsequent	ADJ
fbem-28239	82	23	models	model	NOUN
fbem-28239	82	24	.	.	PUNCT
fbem-28239	83	1	data	datum	NOUN
fbem-28239	83	2	normalization	normalization	NOUN
fbem-28239	83	3	:	:	PUNCT
fbem-28239	83	4	use	use	VERB
fbem-28239	83	5	minmaxscaler	minmaxscaler	NOUN
fbem-28239	83	6	to	to	PART
fbem-28239	83	7	normalize	normalize	VERB
fbem-28239	83	8	the	the	DET
fbem-28239	83	9	opening	opening	NOUN
fbem-28239	83	10	price	price	NOUN
fbem-28239	83	11	data	datum	NOUN
fbem-28239	83	12	of	of	ADP
fbem-28239	83	13	the	the	DET
fbem-28239	83	14	training	training	NOUN
fbem-28239	83	15	and	and	CCONJ
fbem-28239	83	16	testing	testing	NOUN
fbem-28239	83	17	sets	set	NOUN
fbem-28239	83	18	,	,	PUNCT
fbem-28239	83	19	mapping	map	VERB
fbem-28239	83	20	the	the	DET
fbem-28239	83	21	data	datum	NOUN
fbem-28239	83	22	to	to	ADP
fbem-28239	83	23	a	a	DET
fbem-28239	83	24	range	range	NOUN
fbem-28239	83	25	of	of	ADP
fbem-28239	83	26	0	0	NUM
fbem-28239	83	27	to	to	PART
fbem-28239	83	28	1	1	NUM
fbem-28239	83	29	.	.	PUNCT
fbem-28239	83	30	normalization	normalization	NOUN
fbem-28239	83	31	can	can	AUX
fbem-28239	83	32	improve	improve	VERB
fbem-28239	83	33	the	the	DET
fbem-28239	83	34	training	training	NOUN
fbem-28239	83	35	effectiveness	effectiveness	NOUN
fbem-28239	83	36	and	and	CCONJ
fbem-28239	83	37	stability	stability	NOUN
fbem-28239	83	38	of	of	ADP
fbem-28239	83	39	the	the	DET
fbem-28239	83	40	model	model	NOUN
fbem-28239	83	41	,	,	PUNCT
fbem-28239	83	42	making	make	VERB
fbem-28239	83	43	data	datum	NOUN
fbem-28239	83	44	with	with	ADP
fbem-28239	83	45	different	different	ADJ
fbem-28239	83	46	features	feature	NOUN
fbem-28239	83	47	have	have	VERB
fbem-28239	83	48	the	the	DET
fbem-28239	83	49	same	same	ADJ
fbem-28239	83	50	scale	scale	NOUN
fbem-28239	83	51	.	.	PUNCT
fbem-28239	84	1	building	build	VERB
fbem-28239	84	2	training	training	NOUN
fbem-28239	84	3	and	and	CCONJ
fbem-28239	84	4	testing	testing	NOUN
fbem-28239	84	5	set	set	VERB
fbem-28239	84	6	data	datum	NOUN
fbem-28239	84	7	:	:	PUNCT
fbem-28239	84	8	initialize	initialize	VERB
fbem-28239	84	9	a	a	DET
fbem-28239	84	10	list	list	NOUN
fbem-28239	84	11	used	use	VERB
fbem-28239	84	12	to	to	PART
fbem-28239	84	13	store	store	VERB
fbem-28239	84	14	input	input	NOUN
fbem-28239	84	15	features	feature	NOUN
fbem-28239	84	16	and	and	CCONJ
fbem-28239	84	17	labels	label	NOUN
fbem-28239	84	18	for	for	ADP
fbem-28239	84	19	the	the	DET
fbem-28239	84	20	training	training	NOUN
fbem-28239	84	21	set	set	VERB
fbem-28239	84	22	and	and	CCONJ
fbem-28239	84	23	input	input	NOUN
fbem-28239	84	24	features	feature	NOUN
fbem-28239	84	25	and	and	CCONJ
fbem-28239	84	26	labels	label	NOUN
fbem-28239	84	27	for	for	ADP
fbem-28239	84	28	the	the	DET
fbem-28239	84	29	testing	testing	NOUN
fbem-28239	84	30	set	set	NOUN
fbem-28239	84	31	.	.	PUNCT
fbem-28239	85	1	then	then	ADV
fbem-28239	85	2	,	,	PUNCT
fbem-28239	85	3	build	build	VERB
fbem-28239	85	4	the	the	DET
fbem-28239	85	5	training	training	NOUN
fbem-28239	85	6	and	and	CCONJ
fbem-28239	85	7	testing	testing	NOUN
fbem-28239	85	8	sets	set	NOUN
fbem-28239	85	9	of	of	ADP
fbem-28239	85	10	data	datum	NOUN
fbem-28239	85	11	through	through	ADP
fbem-28239	85	12	a	a	DET
fbem-28239	85	13	loop	loop	NOUN
fbem-28239	85	14	.	.	PUNCT
fbem-28239	86	1	for	for	ADP
fbem-28239	86	2	the	the	DET
fbem-28239	86	3	training	training	NOUN
fbem-28239	86	4	set	set	NOUN
fbem-28239	86	5	,	,	PUNCT
fbem-28239	86	6	add	add	VERB
fbem-28239	86	7	the	the	DET
fbem-28239	86	8	opening	opening	NOUN
fbem-28239	86	9	price	price	NOUN
fbem-28239	86	10	and	and	CCONJ
fbem-28239	86	11	beta	beta	ADJ
fbem-28239	86	12	coefficient	coefficient	NOUN
fbem-28239	86	13	of	of	ADP
fbem-28239	86	14	60	60	NUM
fbem-28239	86	15	consecutive	consecutive	ADJ
fbem-28239	86	16	days	day	NOUN
fbem-28239	86	17	as	as	SCONJ
fbem-28239	86	18	input	input	NOUN
fbem-28239	86	19	features	feature	NOUN
fbem-28239	86	20	to	to	ADP
fbem-28239	86	21	the	the	DET
fbem-28239	86	22	x_train	x_train	PROPN
fbem-28239	86	23	list	list	NOUN
fbem-28239	86	24	,	,	PUNCT
fbem-28239	86	25	and	and	CCONJ
fbem-28239	86	26	add	add	VERB
fbem-28239	86	27	the	the	DET
fbem-28239	86	28	corresponding	corresponding	ADJ
fbem-28239	86	29	opening	opening	NOUN
fbem-28239	86	30	price	price	NOUN
fbem-28239	86	31	of	of	ADP
fbem-28239	86	32	the	the	DET
fbem-28239	86	33	61st	61st	ADJ
fbem-28239	86	34	day	day	NOUN
fbem-28239	86	35	as	as	ADP
fbem-28239	86	36	a	a	DET
fbem-28239	86	37	label	label	NOUN
fbem-28239	86	38	to	to	ADP
fbem-28239	86	39	the	the	DET
fbem-28239	86	40	y_train	y_train	NOUN
fbem-28239	86	41	list	list	NOUN
fbem-28239	86	42	.	.	PUNCT
fbem-28239	87	1	similarly	similarly	ADV
fbem-28239	87	2	,	,	PUNCT
fbem-28239	87	3	perform	perform	VERB
fbem-28239	87	4	similar	similar	ADJ
fbem-28239	87	5	operations	operation	NOUN
fbem-28239	87	6	on	on	ADP
fbem-28239	87	7	the	the	DET
fbem-28239	87	8	test	test	NOUN
fbem-28239	87	9	set	set	VERB
fbem-28239	87	10	.	.	PUNCT
fbem-28239	88	1	random	random	ADJ
fbem-28239	88	2	shuffling	shuffling	NOUN
fbem-28239	88	3	and	and	CCONJ
fbem-28239	88	4	array	array	VERB
fbem-28239	88	5	transformation	transformation	NOUN
fbem-28239	88	6	:	:	PUNCT
fbem-28239	88	7	set	set	VERB
fbem-28239	88	8	a	a	DET
fbem-28239	88	9	random	random	ADJ
fbem-28239	88	10	seed	seed	NOUN
fbem-28239	88	11	to	to	PART
fbem-28239	88	12	ensure	ensure	VERB
fbem-28239	88	13	that	that	SCONJ
fbem-28239	88	14	the	the	DET
fbem-28239	88	15	random	random	ADJ
fbem-28239	88	16	shuffling	shuffling	NOUN
fbem-28239	88	17	of	of	ADP
fbem-28239	88	18	data	datum	NOUN
fbem-28239	88	19	is	be	AUX
fbem-28239	88	20	repeatable	repeatable	ADJ
fbem-28239	88	21	,	,	PUNCT
fbem-28239	88	22	and	and	CCONJ
fbem-28239	88	23	then	then	ADV
fbem-28239	88	24	randomly	randomly	ADV
fbem-28239	88	25	shuffle	shuffle	VERB
fbem-28239	88	26	x_train	x_train	PROPN
fbem-28239	88	27	and	and	CCONJ
fbem-28239	88	28	y_train	y_train	NOUN
fbem-28239	88	29	separately	separately	ADV
fbem-28239	88	30	.	.	PUNCT
fbem-28239	89	1	finally	finally	ADV
fbem-28239	89	2	,	,	PUNCT
fbem-28239	89	3	convert	convert	VERB
fbem-28239	89	4	the	the	DET
fbem-28239	89	5	list	list	NOUN
fbem-28239	89	6	of	of	ADP
fbem-28239	89	7	training	training	NOUN
fbem-28239	89	8	sets	set	NOUN
fbem-28239	89	9	into	into	ADP
fbem-28239	89	10	a	a	DET
fbem-28239	89	11	numpy	numpy	NOUN
fbem-28239	89	12	array	array	NOUN
fbem-28239	89	13	for	for	ADP
fbem-28239	89	14	subsequent	subsequent	ADJ
fbem-28239	89	15	processing	processing	NOUN
fbem-28239	89	16	and	and	CCONJ
fbem-28239	89	17	input	input	NOUN
fbem-28239	89	18	into	into	ADP
fbem-28239	89	19	the	the	DET
fbem-28239	89	20	model	model	NOUN
fbem-28239	89	21	.	.	PUNCT
fbem-28239	90	1	data	datum	NOUN
fbem-28239	90	2	reshaping	reshape	VERB
fbem-28239	90	3	:	:	PUNCT
fbem-28239	90	4	first	first	ADV
fbem-28239	90	5	,	,	PUNCT
fbem-28239	90	6	print	print	VERB
fbem-28239	90	7	the	the	DET
fbem-28239	90	8	shape	shape	NOUN
fbem-28239	90	9	of	of	ADP
fbem-28239	90	10	the	the	DET
fbem-28239	90	11	training	training	NOUN
fbem-28239	90	12	set	set	VERB
fbem-28239	90	13	data	datum	NOUN
fbem-28239	90	14	before	before	ADP
fbem-28239	90	15	reshaping	reshape	VERB
fbem-28239	90	16	.	.	PUNCT
fbem-28239	91	1	then	then	ADV
fbem-28239	91	2	,	,	PUNCT
fbem-28239	91	3	reshape	reshape	VERB
fbem-28239	91	4	the	the	DET
fbem-28239	91	5	training	training	NOUN
fbem-28239	91	6	set	set	VERB
fbem-28239	91	7	data	datum	NOUN
fbem-28239	91	8	into	into	ADP
fbem-28239	91	9	an	an	DET
fbem-28239	91	10	intermediate	intermediate	ADJ
fbem-28239	91	11	shape	shape	NOUN
fbem-28239	91	12	and	and	CCONJ
fbem-28239	91	13	add	add	VERB
fbem-28239	91	14	a	a	DET
fbem-28239	91	15	dimension	dimension	NOUN
fbem-28239	91	16	for	for	ADP
fbem-28239	91	17	subsequent	subsequent	ADJ
fbem-28239	91	18	processing	processing	NOUN
fbem-28239	91	19	.	.	PUNCT
fbem-28239	92	1	next	next	ADV
fbem-28239	92	2	,	,	PUNCT
fbem-28239	92	3	according	accord	VERB
fbem-28239	92	4	to	to	ADP
fbem-28239	92	5	the	the	DET
fbem-28239	92	6	input	input	NOUN
fbem-28239	92	7	requirements	requirement	NOUN
fbem-28239	92	8	of	of	ADP
fbem-28239	92	9	the	the	DET
fbem-28239	92	10	model	model	NOUN
fbem-28239	92	11	,	,	PUNCT
fbem-28239	92	12	further	far	ADV
fbem-28239	92	13	adjust	adjust	VERB
fbem-28239	92	14	the	the	DET
fbem-28239	92	15	shape	shape	NOUN
fbem-28239	92	16	and	and	CCONJ
fbem-28239	92	17	transform	transform	VERB
fbem-28239	92	18	the	the	DET
fbem-28239	92	19	data	datum	NOUN
fbem-28239	92	20	into	into	ADP
fbem-28239	92	21	the	the	DET
fbem-28239	92	22	form	form	NOUN
fbem-28239	92	23	of	of	ADP
fbem-28239	92	24	[	[	PUNCT
fbem-28239	92	25	number	number	NOUN
fbem-28239	92	26	of	of	ADP
fbem-28239	92	27	samples	sample	NOUN
fbem-28239	92	28	,	,	PUNCT
fbem-28239	92	29	time	time	NOUN
fbem-28239	92	30	steps	step	NOUN
fbem-28239	92	31	,	,	PUNCT
fbem-28239	92	32	number	number	NOUN
fbem-28239	92	33	of	of	ADP
fbem-28239	92	34	features	feature	NOUN
fbem-28239	92	35	]	]	PUNCT
fbem-28239	92	36	.	.	PUNCT
fbem-28239	93	1	perform	perform	VERB
fbem-28239	93	2	a	a	DET
fbem-28239	93	3	similar	similar	ADJ
fbem-28239	93	4	reshaping	reshaping	NOUN
fbem-28239	93	5	operation	operation	NOUN
fbem-28239	93	6	on	on	ADP
fbem-28239	93	7	the	the	DET
fbem-28239	93	8	test	test	NOUN
fbem-28239	93	9	set	set	NOUN
fbem-28239	93	10	.	.	PUNCT
fbem-28239	94	1	build	build	VERB
fbem-28239	94	2	and	and	CCONJ
fbem-28239	94	3	compile	compile	NOUN
fbem-28239	94	4	model	model	NOUN
fbem-28239	94	5	:	:	PUNCT
fbem-28239	94	6	build	build	VERB
fbem-28239	94	7	a	a	DET
fbem-28239	94	8	sequential	sequential	ADJ
fbem-28239	94	9	model	model	NOUN
fbem-28239	94	10	that	that	PRON
fbem-28239	94	11	includes	include	VERB
fbem-28239	94	12	two	two	NUM
fbem-28239	94	13	simplernn	simplernn	NOUN
fbem-28239	94	14	layers	layer	NOUN
fbem-28239	94	15	,	,	PUNCT
fbem-28239	94	16	two	two	NUM
fbem-28239	94	17	dropout	dropout	NOUN
fbem-28239	94	18	layers	layer	NOUN
fbem-28239	94	19	,	,	PUNCT
fbem-28239	94	20	and	and	CCONJ
fbem-28239	94	21	a	a	DET
fbem-28239	94	22	fully	fully	ADV
fbem-28239	94	23	connected	connected	ADJ
fbem-28239	94	24	dense	dense	ADJ
fbem-28239	94	25	layer	layer	NOUN
fbem-28239	94	26	.	.	PUNCT
fbem-28239	95	1	the	the	DET
fbem-28239	95	2	first	first	ADJ
fbem-28239	95	3	simplernn	simplernn	NOUN
fbem-28239	95	4	layer	layer	NOUN
fbem-28239	95	5	has	have	VERB
fbem-28239	95	6	80	80	NUM
fbem-28239	95	7	hidden	hide	VERB
fbem-28239	95	8	units	unit	NOUN
fbem-28239	95	9	and	and	CCONJ
fbem-28239	95	10	returns	return	VERB
fbem-28239	95	11	the	the	DET
fbem-28239	95	12	output	output	NOUN
fbem-28239	95	13	for	for	ADP
fbem-28239	95	14	each	each	DET
fbem-28239	95	15	time	time	NOUN
fbem-28239	95	16	step	step	NOUN
fbem-28239	95	17	,	,	PUNCT
fbem-28239	95	18	while	while	SCONJ
fbem-28239	95	19	the	the	DET
fbem-28239	95	20	second	second	ADJ
fbem-28239	95	21	simplernn	simplernn	NOUN
fbem-28239	95	22	layer	layer	NOUN
fbem-28239	95	23	has	have	VERB
fbem-28239	95	24	100	100	NUM
fbem-28239	95	25	hidden	hide	VERB
fbem-28239	95	26	units	unit	NOUN
fbem-28239	95	27	.	.	PUNCT
fbem-28239	96	1	dropout	dropout	NOUN
fbem-28239	96	2	layer	layer	NOUN
fbem-28239	96	3	is	be	AUX
fbem-28239	96	4	used	use	VERB
fbem-28239	96	5	to	to	PART
fbem-28239	96	6	prevent	prevent	VERB
fbem-28239	96	7	overfitting	overfitting	NOUN
fbem-28239	96	8	.	.	PUNCT
fbem-28239	97	1	compile	compile	NOUN
fbem-28239	97	2	the	the	DET
fbem-28239	97	3	model	model	NOUN
fbem-28239	97	4	using	use	VERB
fbem-28239	97	5	adam	adam	PROPN
fbem-28239	97	6	optimizer	optimizer	NOUN
fbem-28239	97	7	and	and	CCONJ
fbem-28239	97	8	mean	mean	VERB
fbem-28239	97	9	square	square	ADJ
fbem-28239	97	10	error	error	NOUN
fbem-28239	97	11	loss	loss	NOUN
fbem-28239	97	12	function	function	NOUN
fbem-28239	97	13	.	.	PUNCT
fbem-28239	98	1	model	model	NOUN
fbem-28239	98	2	saving	saving	NOUN
fbem-28239	98	3	and	and	CCONJ
fbem-28239	98	4	callback	callback	NOUN
fbem-28239	98	5	functions	function	NOUN
fbem-28239	98	6	:	:	PUNCT
fbem-28239	98	7	set	set	VERB
fbem-28239	98	8	the	the	DET
fbem-28239	98	9	path	path	NOUN
fbem-28239	98	10	for	for	ADP
fbem-28239	98	11	saving	save	VERB
fbem-28239	98	12	the	the	DET
fbem-28239	98	13	model	model	NOUN
fbem-28239	98	14	.	.	PUNCT
fbem-28239	99	1	if	if	SCONJ
fbem-28239	99	2	there	there	PRON
fbem-28239	99	3	is	be	VERB
fbem-28239	99	4	already	already	ADV
fbem-28239	99	5	a	a	DET
fbem-28239	99	6	saved	save	VERB
fbem-28239	99	7	model	model	NOUN
fbem-28239	99	8	,	,	PUNCT
fbem-28239	99	9	load	load	VERB
fbem-28239	99	10	the	the	DET
fbem-28239	99	11	model	model	NOUN
fbem-28239	99	12	weights	weight	NOUN
fbem-28239	99	13	.	.	PUNCT
fbem-28239	100	1	define	define	VERB
fbem-28239	100	2	a	a	DET
fbem-28239	100	3	callback	callback	NOUN
fbem-28239	100	4	function	function	NOUN
fbem-28239	100	5	to	to	PART
fbem-28239	100	6	save	save	VERB
fbem-28239	100	7	the	the	DET
fbem-28239	100	8	best	good	ADJ
fbem-28239	100	9	model	model	NOUN
fbem-28239	100	10	weights	weight	NOUN
fbem-28239	100	11	during	during	ADP
fbem-28239	100	12	training	training	NOUN
fbem-28239	100	13	and	and	CCONJ
fbem-28239	100	14	monitor	monitor	VERB
fbem-28239	100	15	them	they	PRON
fbem-28239	100	16	based	base	VERB
fbem-28239	100	17	on	on	ADP
fbem-28239	100	18	the	the	DET
fbem-28239	100	19	validation	validation	NOUN
fbem-28239	100	20	set	set	VERB
fbem-28239	100	21	loss	loss	NOUN
fbem-28239	100	22	.	.	PUNCT
fbem-28239	101	1	model	model	NOUN
fbem-28239	101	2	training	training	NOUN
fbem-28239	101	3	:	:	PUNCT
fbem-28239	101	4	train	train	VERB
fbem-28239	101	5	the	the	DET
fbem-28239	101	6	model	model	NOUN
fbem-28239	101	7	using	use	VERB
fbem-28239	101	8	the	the	DET
fbem-28239	101	9	training	training	NOUN
fbem-28239	101	10	and	and	CCONJ
fbem-28239	101	11	testing	testing	NOUN
fbem-28239	101	12	sets	set	NOUN
fbem-28239	101	13	,	,	PUNCT
fbem-28239	101	14	set	set	VERB
fbem-28239	101	15	the	the	DET
fbem-28239	101	16	batch	batch	NOUN
fbem-28239	101	17	size	size	NOUN
fbem-28239	101	18	to	to	ADP
fbem-28239	101	19	64	64	NUM
fbem-28239	101	20	,	,	PUNCT
fbem-28239	101	21	train	train	VERB
fbem-28239	101	22	for	for	ADP
fbem-28239	101	23	50	50	NUM
fbem-28239	101	24	cycles	cycle	NOUN
fbem-28239	101	25	,	,	PUNCT
fbem-28239	101	26	validate	validate	NOUN
fbem-28239	101	27	on	on	ADP
fbem-28239	101	28	the	the	DET
fbem-28239	101	29	testing	testing	NOUN
fbem-28239	101	30	set	set	VERB
fbem-28239	101	31	at	at	ADP
fbem-28239	101	32	the	the	DET
fbem-28239	101	33	end	end	NOUN
fbem-28239	101	34	of	of	ADP
fbem-28239	101	35	each	each	DET
fbem-28239	101	36	cycle	cycle	NOUN
fbem-28239	101	37	,	,	PUNCT
fbem-28239	101	38	and	and	CCONJ
fbem-28239	101	39	save	save	VERB
fbem-28239	101	40	the	the	DET
fbem-28239	101	41	model	model	NOUN
fbem-28239	101	42	weights	weight	NOUN
fbem-28239	101	43	using	use	VERB
fbem-28239	101	44	callback	callback	NOUN
fbem-28239	101	45	functions	function	NOUN
fbem-28239	101	46	.	.	PUNCT
fbem-28239	102	1	model	model	NOUN
fbem-28239	102	2	summary	summary	PROPN
fbem-28239	102	3	and	and	CCONJ
fbem-28239	102	4	parameter	parameter	NOUN
fbem-28239	102	5	saving	saving	NOUN
fbem-28239	102	6	:	:	PUNCT
fbem-28239	102	7	print	print	VERB
fbem-28239	102	8	the	the	DET
fbem-28239	102	9	structure	structure	NOUN
fbem-28239	102	10	and	and	CCONJ
fbem-28239	102	11	parameter	parameter	NOUN
fbem-28239	102	12	information	information	NOUN
fbem-28239	102	13	of	of	ADP
fbem-28239	102	14	the	the	DET
fbem-28239	102	15	model	model	NOUN
fbem-28239	102	16	.	.	PUNCT
fbem-28239	103	1	save	save	VERB
fbem-28239	103	2	the	the	DET
fbem-28239	103	3	trainable	trainable	ADJ
fbem-28239	103	4	parameters	parameter	NOUN
fbem-28239	103	5	of	of	ADP
fbem-28239	103	6	the	the	DET
fbem-28239	103	7	model	model	NOUN
fbem-28239	103	8	to	to	ADP
fbem-28239	103	9	a	a	DET
fbem-28239	103	10	file	file	NOUN
fbem-28239	103	11	for	for	ADP
fbem-28239	103	12	subsequent	subsequent	ADJ
fbem-28239	103	13	analysis	analysis	NOUN
fbem-28239	103	14	or	or	CCONJ
fbem-28239	103	15	reloading	reloading	NOUN
fbem-28239	103	16	.	.	PUNCT
fbem-28239	104	1	draw	draw	VERB
fbem-28239	104	2	loss	loss	NOUN
fbem-28239	104	3	curves	curve	NOUN
fbem-28239	104	4	:	:	PUNCT
fbem-28239	104	5	draw	draw	VERB
fbem-28239	104	6	loss	loss	NOUN
fbem-28239	104	7	curves	curve	NOUN
fbem-28239	104	8	for	for	ADP
fbem-28239	104	9	the	the	DET
fbem-28239	104	10	training	training	NOUN
fbem-28239	104	11	and	and	CCONJ
fbem-28239	104	12	validation	validation	NOUN
fbem-28239	104	13	sets	set	NOUN
fbem-28239	104	14	to	to	PART
fbem-28239	104	15	observe	observe	VERB
fbem-28239	104	16	the	the	DET
fbem-28239	104	17	training	training	NOUN
fbem-28239	104	18	process	process	NOUN
fbem-28239	104	19	of	of	ADP
fbem-28239	104	20	the	the	DET
fbem-28239	104	21	model	model	NOUN
fbem-28239	104	22	.	.	PUNCT
fbem-28239	105	1	by	by	ADP
fbem-28239	105	2	comparing	compare	VERB
fbem-28239	105	3	the	the	DET
fbem-28239	105	4	training	training	NOUN
fbem-28239	105	5	loss	loss	NOUN
fbem-28239	105	6	and	and	CCONJ
fbem-28239	105	7	validation	validation	NOUN
fbem-28239	105	8	loss	loss	NOUN
fbem-28239	105	9	,	,	PUNCT
fbem-28239	105	10	the	the	DET
fbem-28239	105	11	overfitting	overfitting	NOUN
fbem-28239	105	12	and	and	CCONJ
fbem-28239	105	13	generalization	generalization	NOUN
fbem-28239	105	14	ability	ability	NOUN
fbem-28239	105	15	of	of	ADP
fbem-28239	105	16	the	the	DET
fbem-28239	105	17	model	model	NOUN
fbem-28239	105	18	can	can	AUX
fbem-28239	105	19	be	be	AUX
fbem-28239	105	20	evaluated	evaluate	VERB
fbem-28239	105	21	.	.	PUNCT
fbem-28239	106	1	prediction	prediction	NOUN
fbem-28239	106	2	and	and	CCONJ
fbem-28239	106	3	evaluation	evaluation	NOUN
fbem-28239	106	4	:	:	PUNCT
fbem-28239	106	5	use	use	VERB
fbem-28239	106	6	a	a	DET
fbem-28239	106	7	trained	train	VERB
fbem-28239	106	8	model	model	NOUN
fbem-28239	106	9	to	to	PART
fbem-28239	106	10	predict	predict	VERB
fbem-28239	106	11	the	the	DET
fbem-28239	106	12	test	test	NOUN
fbem-28239	106	13	set	set	NOUN
fbem-28239	106	14	,	,	PUNCT
fbem-28239	106	15	and	and	CCONJ
fbem-28239	106	16	then	then	ADV
fbem-28239	106	17	perform	perform	VERB
fbem-28239	106	18	inverse	inverse	ADJ
fbem-28239	106	19	normalization	normalization	NOUN
fbem-28239	106	20	between	between	ADP
fbem-28239	106	21	the	the	DET
fbem-28239	106	22	predicted	predict	VERB
fbem-28239	106	23	results	result	NOUN
fbem-28239	106	24	and	and	CCONJ
fbem-28239	106	25	the	the	DET
fbem-28239	106	26	actual	actual	ADJ
fbem-28239	106	27	results	result	NOUN
fbem-28239	106	28	.	.	PUNCT
fbem-28239	107	1	calculate	calculate	VERB
fbem-28239	107	2	and	and	CCONJ
fbem-28239	107	3	print	print	VERB
fbem-28239	107	4	the	the	DET
fbem-28239	107	5	mean	mean	ADJ
fbem-28239	107	6	square	square	NOUN
fbem-28239	107	7	error	error	NOUN
fbem-28239	107	8	,	,	PUNCT
fbem-28239	107	9	root	root	NOUN
fbem-28239	107	10	mean	mean	VERB
fbem-28239	107	11	square	square	NOUN
fbem-28239	107	12	error	error	NOUN
fbem-28239	107	13	,	,	PUNCT
fbem-28239	107	14	mean	mean	ADJ
fbem-28239	107	15	absolute	absolute	ADJ
fbem-28239	107	16	error	error	NOUN
fbem-28239	107	17	,	,	PUNCT
fbem-28239	107	18	and	and	CCONJ
fbem-28239	107	19	coefficient	coefficient	NOUN
fbem-28239	107	20	of	of	ADP
fbem-28239	107	21	determination	determination	NOUN
fbem-28239	107	22	to	to	PART
fbem-28239	107	23	evaluate	evaluate	VERB
fbem-28239	107	24	the	the	DET
fbem-28239	107	25	predictive	predictive	ADJ
fbem-28239	107	26	performance	performance	NOUN
fbem-28239	107	27	of	of	ADP
fbem-28239	107	28	the	the	DET
fbem-28239	107	29	model	model	NOUN
fbem-28239	107	30	.	.	PUNCT
fbem-28239	108	1	draw	draw	VERB
fbem-28239	108	2	a	a	DET
fbem-28239	108	3	comparison	comparison	NOUN
fbem-28239	108	4	curve	curve	NOUN
fbem-28239	108	5	between	between	ADP
fbem-28239	108	6	real	real	ADJ
fbem-28239	108	7	stock	stock	NOUN
fbem-28239	108	8	prices	price	NOUN
fbem-28239	108	9	and	and	CCONJ
fbem-28239	108	10	predicted	predict	VERB
fbem-28239	108	11	stock	stock	NOUN
fbem-28239	108	12	prices	price	NOUN
fbem-28239	108	13	to	to	PART
fbem-28239	108	14	visually	visually	ADV
fbem-28239	108	15	demonstrate	demonstrate	VERB
fbem-28239	108	16	the	the	DET
fbem-28239	108	17	predictive	predictive	ADJ
fbem-28239	108	18	performance	performance	NOUN
fbem-28239	108	19	of	of	ADP
fbem-28239	108	20	the	the	DET
fbem-28239	108	21	model	model	NOUN
fbem-28239	108	22	.	.	PUNCT
fbem-28239	109	1	these	these	DET
fbem-28239	109	2	evaluation	evaluation	NOUN
fbem-28239	109	3	indicators	indicator	NOUN
fbem-28239	109	4	can	can	AUX
fbem-28239	109	5	help	help	VERB
fbem-28239	109	6	measure	measure	VERB
fbem-28239	109	7	the	the	DET
fbem-28239	109	8	accuracy	accuracy	NOUN
fbem-28239	109	9	and	and	CCONJ
fbem-28239	109	10	reliability	reliability	NOUN
fbem-28239	109	11	of	of	ADP
fbem-28239	109	12	the	the	DET
fbem-28239	109	13	model	model	NOUN
fbem-28239	109	14	.	.	PUNCT
fbem-28239	110	1	figure	figure	NOUN
fbem-28239	110	2	2	2	NUM
fbem-28239	110	3	.	.	PUNCT
fbem-28239	110	4	structural	structural	ADJ
fbem-28239	110	5	information	information	NOUN
fbem-28239	110	6	of	of	ADP
fbem-28239	110	7	recurrent	recurrent	ADJ
fbem-28239	110	8	neural	neural	ADJ
fbem-28239	110	9	network	network	NOUN
fbem-28239	110	10	(	(	PUNCT
fbem-28239	110	11	rnn	rnn	PROPN
fbem-28239	110	12	)	)	PUNCT
fbem-28239	110	13	model	model	NOUN
fbem-28239	110	14	figure	figure	NOUN
fbem-28239	110	15	3	3	NUM
fbem-28239	110	16	.	.	NOUN
fbem-28239	110	17	line	line	NOUN
fbem-28239	110	18	graph	graph	NOUN
fbem-28239	110	19	of	of	ADP
fbem-28239	110	20	recurrent	recurrent	ADJ
fbem-28239	110	21	neural	neural	ADJ
fbem-28239	110	22	network	network	NOUN
fbem-28239	110	23	(	(	PUNCT
fbem-28239	110	24	rnn	rnn	PROPN
fbem-28239	110	25	)	)	PUNCT
fbem-28239	110	26	model	model	NOUN
fbem-28239	110	27	figure	figure	NOUN
fbem-28239	110	28	4	4	NUM
fbem-28239	110	29	.	.	PUNCT
fbem-28239	110	30	evaluation	evaluation	NOUN
fbem-28239	110	31	parameters	parameter	NOUN
fbem-28239	110	32	of	of	ADP
fbem-28239	110	33	recurrent	recurrent	ADJ
fbem-28239	110	34	neural	neural	ADJ
fbem-28239	110	35	network	network	NOUN
fbem-28239	110	36	(	(	PUNCT
fbem-28239	110	37	rnn	rnn	PROPN
fbem-28239	110	38	)	)	PUNCT
fbem-28239	110	39	3.4	3.4	NUM
fbem-28239	110	40	.	.	PUNCT
fbem-28239	110	41	evaluation	evaluation	NOUN
fbem-28239	110	42	results	result	NOUN
fbem-28239	110	43	and	and	CCONJ
fbem-28239	110	44	summary	summary	NOUN
fbem-28239	110	45	at	at	ADP
fbem-28239	110	46	this	this	DET
fbem-28239	110	47	point	point	NOUN
fbem-28239	110	48	,	,	PUNCT
fbem-28239	110	49	the	the	DET
fbem-28239	110	50	two	two	NUM
fbem-28239	110	51	basic	basic	ADJ
fbem-28239	110	52	models	model	NOUN
fbem-28239	110	53	have	have	AUX
fbem-28239	110	54	been	be	AUX
fbem-28239	110	55	constructed	construct	VERB
fbem-28239	110	56	,	,	PUNCT
fbem-28239	110	57	and	and	CCONJ
fbem-28239	110	58	from	from	ADP
fbem-28239	110	59	the	the	DET
fbem-28239	110	60	line	line	NOUN
fbem-28239	110	61	chart	chart	NOUN
fbem-28239	110	62	,	,	PUNCT
fbem-28239	110	63	there	there	PRON
fbem-28239	110	64	is	be	VERB
fbem-28239	110	65	not	not	PART
fbem-28239	110	66	a	a	DET
fbem-28239	110	67	significant	significant	ADJ
fbem-28239	110	68	difference	difference	NOUN
fbem-28239	110	69	between	between	ADP
fbem-28239	110	70	the	the	DET
fbem-28239	110	71	two	two	NUM
fbem-28239	110	72	models	model	NOUN
fbem-28239	110	73	.	.	PUNCT
fbem-28239	111	1	the	the	DET
fbem-28239	111	2	predicted	predict	VERB
fbem-28239	111	3	and	and	CCONJ
fbem-28239	111	4	actual	actual	ADJ
fbem-28239	111	5	lines	line	NOUN
fbem-28239	111	6	of	of	ADP
fbem-28239	111	7	both	both	PRON
fbem-28239	111	8	are	be	AUX
fbem-28239	111	9	relatively	relatively	ADV
fbem-28239	111	10	close	close	ADJ
fbem-28239	111	11	,	,	PUNCT
fbem-28239	111	12	indicating	indicate	VERB
fbem-28239	111	13	good	good	ADJ
fbem-28239	111	14	prediction	prediction	NOUN
fbem-28239	111	15	results	result	NOUN
fbem-28239	111	16	.	.	PUNCT
fbem-28239	112	1	the	the	DET
fbem-28239	112	2	specific	specific	ADJ
fbem-28239	112	3	parameters	parameter	NOUN
fbem-28239	112	4	are	be	AUX
fbem-28239	112	5	as	as	SCONJ
fbem-28239	112	6	shown	show	VERB
fbem-28239	112	7	above	above	ADV
fbem-28239	112	8	:	:	PUNCT
fbem-28239	112	9	multiple	multiple	ADJ
fbem-28239	112	10	linear	linear	PROPN
fbem-28239	112	11	regression	regression	NOUN
fbem-28239	112	12	model	model	NOUN
fbem-28239	112	13	:	:	PUNCT
fbem-28239	112	14	mean	mean	VERB
fbem-28239	112	15	squared	square	VERB
fbem-28239	112	16	error	error	NOUN
fbem-28239	112	17	:	:	PUNCT
fbem-28239	112	18	1.285559038132224	1.285559038132224	NUM
fbem-28239	112	19	112	112	NUM
fbem-28239	112	20	root	root	NOUN
fbem-28239	112	21	mean	mean	VERB
fbem-28239	112	22	squared	square	VERB
fbem-28239	112	23	error	error	NOUN
fbem-28239	112	24	:	:	PUNCT
fbem-28239	112	25	1.1338249592120575	1.1338249592120575	NUM
fbem-28239	112	26	mean	mean	NOUN
fbem-28239	112	27	absolute	absolute	ADJ
fbem-28239	112	28	error	error	NOUN
fbem-28239	112	29	:	:	PUNCT
fbem-28239	112	30	0.8434524191930256	0.8434524191930256	NUM
fbem-28239	112	31	r	r	X
fbem-28239	112	32	-	-	PUNCT
fbem-28239	112	33	squared	squared	ADJ
fbem-28239	112	34	:	:	PUNCT
fbem-28239	112	35	0.9971114301478347	0.9971114301478347	NUM
fbem-28239	112	36	recurrent	recurrent	ADJ
fbem-28239	112	37	neural	neural	ADJ
fbem-28239	112	38	network	network	NOUN
fbem-28239	112	39	(	(	PUNCT
fbem-28239	112	40	rnn	rnn	PROPN
fbem-28239	112	41	):	):	PUNCT
fbem-28239	112	42	mean	mean	NOUN
fbem-28239	112	43	squared	square	VERB
fbem-28239	112	44	error	error	NOUN
fbem-28239	112	45	:	:	PUNCT
fbem-28239	112	46	14.901471	14.901471	NUM
fbem-28239	112	47	root	root	NOUN
fbem-28239	112	48	mean	mean	VERB
fbem-28239	112	49	squared	square	VERB
fbem-28239	112	50	error	error	NOUN
fbem-28239	112	51	:	:	PUNCT
fbem-28239	112	52	3.860242	3.860242	NUM
fbem-28239	112	53	mean	mean	ADJ
fbem-28239	112	54	absolute	absolute	ADJ
fbem-28239	112	55	error	error	NOUN
fbem-28239	112	56	:	:	PUNCT
fbem-28239	112	57	3.003388	3.003388	NUM
fbem-28239	112	58	r	r	NOUN
fbem-28239	112	59	-	-	PUNCT
fbem-28239	112	60	squared	squared	ADJ
fbem-28239	112	61	:	:	PUNCT
fbem-28239	112	62	0.969658	0.969658	NUM
fbem-28239	112	63	in	in	ADP
fbem-28239	112	64	terms	term	NOUN
fbem-28239	112	65	of	of	ADP
fbem-28239	112	66	specific	specific	ADJ
fbem-28239	112	67	indicators	indicator	NOUN
fbem-28239	112	68	,	,	PUNCT
fbem-28239	112	69	at	at	ADP
fbem-28239	112	70	least	least	ADJ
fbem-28239	112	71	in	in	ADP
fbem-28239	112	72	the	the	DET
fbem-28239	112	73	last	last	ADJ
fbem-28239	112	74	300	300	NUM
fbem-28239	112	75	days	day	NOUN
fbem-28239	112	76	of	of	ADP
fbem-28239	112	77	data	datum	NOUN
fbem-28239	112	78	from	from	ADP
fbem-28239	112	79	december	december	PROPN
fbem-28239	112	80	31	31	NUM
fbem-28239	112	81	,	,	PUNCT
fbem-28239	112	82	2009	2009	NUM
fbem-28239	112	83	to	to	ADP
fbem-28239	112	84	january	january	PROPN
fbem-28239	112	85	25	25	NUM
fbem-28239	112	86	,	,	PUNCT
fbem-28239	112	87	2019	2019	NUM
fbem-28239	112	88	for	for	ADP
fbem-28239	112	89	apple	apple	PROPN
fbem-28239	112	90	inc	inc	PROPN
fbem-28239	112	91	.	.	PROPN
fbem-28239	112	92	in	in	ADP
fbem-28239	112	93	the	the	DET
fbem-28239	112	94	sp500	sp500	PROPN
fbem-28239	112	95	prediction	prediction	NOUN
fbem-28239	112	96	,	,	PUNCT
fbem-28239	112	97	the	the	DET
fbem-28239	112	98	multiple	multiple	ADJ
fbem-28239	112	99	linear	linear	ADJ
fbem-28239	112	100	regression	regression	NOUN
fbem-28239	112	101	model	model	NOUN
fbem-28239	112	102	has	have	VERB
fbem-28239	112	103	the	the	DET
fbem-28239	112	104	advantage	advantage	NOUN
fbem-28239	112	105	,	,	PUNCT
fbem-28239	112	106	with	with	ADP
fbem-28239	112	107	smaller	small	ADJ
fbem-28239	112	108	mse	mse	NOUN
fbem-28239	112	109	,	,	PUNCT
fbem-28239	112	110	rmse	rmse	NOUN
fbem-28239	112	111	,	,	PUNCT
fbem-28239	112	112	and	and	CCONJ
fbem-28239	112	113	mae	mae	PROPN
fbem-28239	112	114	,	,	PUNCT
fbem-28239	112	115	and	and	CCONJ
fbem-28239	112	116	r	r	X
fbem-28239	112	117	-	-	PUNCT
fbem-28239	112	118	squared	square	VERB
fbem-28239	112	119	leaning	lean	VERB
fbem-28239	112	120	more	more	ADJ
fbem-28239	112	121	towards	towards	ADP
fbem-28239	112	122	1	1	NUM
fbem-28239	112	123	.	.	PUNCT
fbem-28239	113	1	as	as	ADP
fbem-28239	113	2	for	for	ADP
fbem-28239	113	3	when	when	SCONJ
fbem-28239	113	4	the	the	DET
fbem-28239	113	5	scenario	scenario	NOUN
fbem-28239	113	6	is	be	AUX
fbem-28239	113	7	more	more	ADV
fbem-28239	113	8	suitable	suitable	ADJ
fbem-28239	113	9	for	for	ADP
fbem-28239	113	10	these	these	DET
fbem-28239	113	11	two	two	NUM
fbem-28239	113	12	models	model	NOUN
fbem-28239	113	13	,	,	PUNCT
fbem-28239	113	14	investors	investor	NOUN
fbem-28239	113	15	can	can	AUX
fbem-28239	113	16	adjust	adjust	VERB
fbem-28239	113	17	it	it	PRON
fbem-28239	113	18	according	accord	VERB
fbem-28239	113	19	to	to	ADP
fbem-28239	113	20	the	the	DET
fbem-28239	113	21	actual	actual	ADJ
fbem-28239	113	22	situation	situation	NOUN
fbem-28239	113	23	or	or	CCONJ
fbem-28239	113	24	choose	choose	VERB
fbem-28239	113	25	the	the	DET
fbem-28239	113	26	best	good	ADJ
fbem-28239	113	27	one	one	NUM
fbem-28239	113	28	after	after	ADP
fbem-28239	113	29	experimentation	experimentation	NOUN
fbem-28239	113	30	.	.	PUNCT
fbem-28239	114	1	3.5	3.5	NUM
fbem-28239	114	2	.	.	PUNCT
fbem-28239	115	1	the	the	DET
fbem-28239	115	2	necessity	necessity	NOUN
fbem-28239	115	3	of	of	ADP
fbem-28239	115	4	theoretical	theoretical	ADJ
fbem-28239	115	5	participation	participation	NOUN
fbem-28239	115	6	in	in	ADP
fbem-28239	115	7	capm	capm	PROPN
fbem-28239	115	8	model	model	NOUN
fbem-28239	115	9	afterwards	afterwards	ADV
fbem-28239	115	10	,	,	PUNCT
fbem-28239	115	11	we	we	PRON
fbem-28239	115	12	need	need	VERB
fbem-28239	115	13	to	to	PART
fbem-28239	115	14	demonstrate	demonstrate	VERB
fbem-28239	115	15	the	the	DET
fbem-28239	115	16	importance	importance	NOUN
fbem-28239	115	17	of	of	ADP
fbem-28239	115	18	the	the	DET
fbem-28239	115	19	capm	capm	PROPN
fbem-28239	115	20	model	model	NOUN
fbem-28239	115	21	,	,	PUNCT
fbem-28239	115	22	where	where	SCONJ
fbem-28239	115	23	the	the	DET
fbem-28239	115	24	beta	beta	ADJ
fbem-28239	115	25	coefficient	coefficient	NOUN
fbem-28239	115	26	is	be	AUX
fbem-28239	115	27	involved	involve	VERB
fbem-28239	115	28	in	in	ADP
fbem-28239	115	29	model	model	NOUN
fbem-28239	115	30	construction	construction	NOUN
fbem-28239	115	31	,	,	PUNCT
fbem-28239	115	32	by	by	ADP
fbem-28239	115	33	comparing	compare	VERB
fbem-28239	115	34	the	the	DET
fbem-28239	115	35	current	current	ADJ
fbem-28239	115	36	results	result	NOUN
fbem-28239	115	37	with	with	ADP
fbem-28239	115	38	those	those	PRON
fbem-28239	115	39	without	without	ADP
fbem-28239	115	40	the	the	DET
fbem-28239	115	41	participation	participation	NOUN
fbem-28239	115	42	of	of	ADP
fbem-28239	115	43	the	the	DET
fbem-28239	115	44	beta	beta	ADJ
fbem-28239	115	45	coefficient	coefficient	NOUN
fbem-28239	115	46	and	and	CCONJ
fbem-28239	115	47	evaluating	evaluate	VERB
fbem-28239	115	48	them	they	PRON
fbem-28239	115	49	using	use	VERB
fbem-28239	115	50	the	the	DET
fbem-28239	115	51	same	same	ADJ
fbem-28239	115	52	method	method	NOUN
fbem-28239	115	53	.	.	PUNCT
fbem-28239	116	1	the	the	DET
fbem-28239	116	2	specific	specific	ADJ
fbem-28239	116	3	steps	step	NOUN
fbem-28239	116	4	,	,	PUNCT
fbem-28239	116	5	except	except	SCONJ
fbem-28239	116	6	for	for	ADP
fbem-28239	116	7	adding	add	VERB
fbem-28239	116	8	beta	beta	ADJ
fbem-28239	116	9	related	relate	VERB
fbem-28239	116	10	code	code	NOUN
fbem-28239	116	11	,	,	PUNCT
fbem-28239	116	12	are	be	AUX
fbem-28239	116	13	basically	basically	ADV
fbem-28239	116	14	the	the	DET
fbem-28239	116	15	same	same	ADJ
fbem-28239	116	16	as	as	ADP
fbem-28239	116	17	those	those	PRON
fbem-28239	116	18	mentioned	mention	VERB
fbem-28239	116	19	earlier	early	ADV
fbem-28239	116	20	.	.	PUNCT
fbem-28239	117	1	therefore	therefore	ADV
fbem-28239	117	2	,	,	PUNCT
fbem-28239	117	3	we	we	PRON
fbem-28239	117	4	will	will	AUX
fbem-28239	117	5	not	not	PART
fbem-28239	117	6	go	go	VERB
fbem-28239	117	7	into	into	ADP
fbem-28239	117	8	too	too	ADV
fbem-28239	117	9	much	much	ADJ
fbem-28239	117	10	detail	detail	NOUN
fbem-28239	117	11	here	here	ADV
fbem-28239	117	12	and	and	CCONJ
fbem-28239	117	13	will	will	AUX
fbem-28239	117	14	directly	directly	ADV
fbem-28239	117	15	provide	provide	VERB
fbem-28239	117	16	the	the	DET
fbem-28239	117	17	final	final	ADJ
fbem-28239	117	18	results	result	NOUN
fbem-28239	117	19	(	(	PUNCT
fbem-28239	117	20	the	the	DET
fbem-28239	117	21	figures	figure	NOUN
fbem-28239	117	22	show	show	VERB
fbem-28239	117	23	the	the	DET
fbem-28239	117	24	results	result	NOUN
fbem-28239	117	25	of	of	ADP
fbem-28239	117	26	beta	beta	ADJ
fbem-28239	117	27	value	value	NOUN
fbem-28239	117	28	participation	participation	NOUN
fbem-28239	117	29	and	and	CCONJ
fbem-28239	117	30	nonparticipation	nonparticipation	NOUN
fbem-28239	117	31	):	):	PUNCT
fbem-28239	117	32	3.5.1	3.5.1	NUM
fbem-28239	117	33	.	.	PUNCT
fbem-28239	118	1	multiple	multiple	ADJ
fbem-28239	118	2	linear	linear	PROPN
fbem-28239	118	3	regression	regression	NOUN
fbem-28239	118	4	model	model	NOUN
fbem-28239	118	5	beta	beta	ADJ
fbem-28239	118	6	participation	participation	NOUN
fbem-28239	118	7	figure	figure	NOUN
fbem-28239	118	8	5	5	NUM
fbem-28239	118	9	.	.	PUNCT
fbem-28239	118	10	line	line	NOUN
fbem-28239	118	11	graph	graph	NOUN
fbem-28239	118	12	of	of	ADP
fbem-28239	118	13	multiple	multiple	ADJ
fbem-28239	118	14	linear	linear	ADJ
fbem-28239	118	15	regression	regression	NOUN
fbem-28239	118	16	model	model	NOUN
fbem-28239	118	17	(	(	PUNCT
fbem-28239	118	18	beta	beta	ADJ
fbem-28239	118	19	participation	participation	NOUN
fbem-28239	118	20	)	)	PUNCT
fbem-28239	119	1	beta	beta	NOUN
fbem-28239	119	2	does	do	AUX
fbem-28239	119	3	not	not	PART
fbem-28239	119	4	participate	participate	VERB
fbem-28239	119	5	figure	figure	NOUN
fbem-28239	119	6	6	6	NUM
fbem-28239	119	7	.	.	PUNCT
fbem-28239	119	8	line	line	NOUN
fbem-28239	119	9	graph	graph	NOUN
fbem-28239	119	10	of	of	ADP
fbem-28239	119	11	multiple	multiple	ADJ
fbem-28239	119	12	linear	linear	ADJ
fbem-28239	119	13	regression	regression	NOUN
fbem-28239	119	14	model	model	NOUN
fbem-28239	119	15	(	(	PUNCT
fbem-28239	119	16	beta	beta	NOUN
fbem-28239	119	17	not	not	PART
fbem-28239	119	18	included	include	VERB
fbem-28239	119	19	)	)	PUNCT
fbem-28239	120	1	3.5.2	3.5.2	X
fbem-28239	120	2	.	.	PUNCT
fbem-28239	120	3	recurrent	recurrent	ADJ
fbem-28239	120	4	neural	neural	ADJ
fbem-28239	120	5	network	network	NOUN
fbem-28239	120	6	(	(	PUNCT
fbem-28239	120	7	rnn	rnn	PROPN
fbem-28239	120	8	)	)	PUNCT
fbem-28239	120	9	beta	beta	ADJ
fbem-28239	120	10	participation	participation	NOUN
fbem-28239	120	11	figure	figure	NOUN
fbem-28239	120	12	7	7	NUM
fbem-28239	120	13	.	.	NOUN
fbem-28239	120	14	line	line	NOUN
fbem-28239	120	15	graph	graph	NOUN
fbem-28239	120	16	of	of	ADP
fbem-28239	120	17	recurrent	recurrent	ADJ
fbem-28239	120	18	neural	neural	ADJ
fbem-28239	120	19	network	network	NOUN
fbem-28239	120	20	(	(	PUNCT
fbem-28239	120	21	rnn	rnn	PROPN
fbem-28239	120	22	)	)	PUNCT
fbem-28239	120	23	model	model	NOUN
fbem-28239	120	24	(	(	PUNCT
fbem-28239	120	25	beta	beta	ADJ
fbem-28239	120	26	participation	participation	NOUN
fbem-28239	120	27	)	)	PUNCT
fbem-28239	120	28	figure	figure	NOUN
fbem-28239	120	29	8	8	NUM
fbem-28239	120	30	.	.	PUNCT
fbem-28239	121	1	evaluation	evaluation	NOUN
fbem-28239	121	2	parameters	parameter	NOUN
fbem-28239	121	3	of	of	ADP
fbem-28239	121	4	recurrent	recurrent	ADJ
fbem-28239	121	5	neural	neural	ADJ
fbem-28239	121	6	network	network	NOUN
fbem-28239	121	7	(	(	PUNCT
fbem-28239	121	8	rnn	rnn	PROPN
fbem-28239	121	9	)	)	PUNCT
fbem-28239	121	10	(	(	PUNCT
fbem-28239	121	11	beta	beta	ADJ
fbem-28239	121	12	participation	participation	NOUN
fbem-28239	121	13	)	)	PUNCT
fbem-28239	122	1	beta	beta	NOUN
fbem-28239	122	2	does	do	AUX
fbem-28239	122	3	not	not	PART
fbem-28239	122	4	participate	participate	VERB
fbem-28239	122	5	figure	figure	NOUN
fbem-28239	122	6	9	9	NUM
fbem-28239	122	7	.	.	PUNCT
fbem-28239	122	8	structural	structural	ADJ
fbem-28239	122	9	information	information	NOUN
fbem-28239	122	10	of	of	ADP
fbem-28239	122	11	recurrent	recurrent	ADJ
fbem-28239	122	12	neural	neural	ADJ
fbem-28239	122	13	network	network	NOUN
fbem-28239	122	14	(	(	PUNCT
fbem-28239	122	15	rnn	rnn	PROPN
fbem-28239	122	16	)	)	PUNCT
fbem-28239	122	17	model	model	NOUN
fbem-28239	122	18	(	(	PUNCT
fbem-28239	122	19	beta	beta	NOUN
fbem-28239	122	20	does	do	AUX
fbem-28239	122	21	not	not	PART
fbem-28239	122	22	participate	participate	VERB
fbem-28239	122	23	)	)	PUNCT
fbem-28239	122	24	113	113	NUM
fbem-28239	122	25	figure	figure	NOUN
fbem-28239	122	26	10	10	NUM
fbem-28239	122	27	.	.	PUNCT
fbem-28239	122	28	line	line	NOUN
fbem-28239	122	29	graph	graph	NOUN
fbem-28239	122	30	of	of	ADP
fbem-28239	122	31	recurrent	recurrent	ADJ
fbem-28239	122	32	neural	neural	ADJ
fbem-28239	122	33	network	network	NOUN
fbem-28239	122	34	(	(	PUNCT
fbem-28239	122	35	rnn	rnn	PROPN
fbem-28239	122	36	)	)	PUNCT
fbem-28239	122	37	model	model	NOUN
fbem-28239	122	38	(	(	PUNCT
fbem-28239	122	39	beta	beta	NOUN
fbem-28239	122	40	not	not	PART
fbem-28239	122	41	involved	involve	VERB
fbem-28239	122	42	)	)	PUNCT
fbem-28239	122	43	figure	figure	NOUN
fbem-28239	122	44	11	11	NUM
fbem-28239	122	45	.	.	PUNCT
fbem-28239	123	1	evaluation	evaluation	NOUN
fbem-28239	123	2	parameters	parameter	NOUN
fbem-28239	123	3	of	of	ADP
fbem-28239	123	4	recurrent	recurrent	ADJ
fbem-28239	123	5	neural	neural	ADJ
fbem-28239	123	6	network	network	NOUN
fbem-28239	123	7	(	(	PUNCT
fbem-28239	123	8	rnn	rnn	PROPN
fbem-28239	123	9	)	)	PUNCT
fbem-28239	123	10	(	(	PUNCT
fbem-28239	123	11	beta	beta	NOUN
fbem-28239	123	12	does	do	AUX
fbem-28239	123	13	not	not	PART
fbem-28239	123	14	participate	participate	VERB
fbem-28239	123	15	)	)	PUNCT
fbem-28239	123	16	through	through	ADP
fbem-28239	123	17	the	the	DET
fbem-28239	123	18	longitudinal	longitudinal	ADJ
fbem-28239	123	19	comparison	comparison	NOUN
fbem-28239	123	20	of	of	ADP
fbem-28239	123	21	the	the	DET
fbem-28239	123	22	two	two	NUM
fbem-28239	123	23	models	model	NOUN
fbem-28239	123	24	themselves	themselves	PRON
fbem-28239	123	25	,	,	PUNCT
fbem-28239	123	26	observing	observe	VERB
fbem-28239	123	27	the	the	DET
fbem-28239	123	28	indicators	indicator	NOUN
fbem-28239	123	29	and	and	CCONJ
fbem-28239	123	30	charts	chart	NOUN
fbem-28239	123	31	,	,	PUNCT
fbem-28239	123	32	both	both	DET
fbem-28239	123	33	models	model	NOUN
fbem-28239	123	34	have	have	AUX
fbem-28239	123	35	improved	improve	VERB
fbem-28239	123	36	to	to	ADP
fbem-28239	123	37	varying	vary	VERB
fbem-28239	123	38	degrees	degree	NOUN
fbem-28239	123	39	after	after	ADP
fbem-28239	123	40	adding	add	VERB
fbem-28239	123	41	the	the	DET
fbem-28239	123	42	beta	beta	ADJ
fbem-28239	123	43	indicator	indicator	NOUN
fbem-28239	123	44	.	.	PUNCT
fbem-28239	124	1	this	this	PRON
fbem-28239	124	2	is	be	AUX
fbem-28239	124	3	sufficient	sufficient	ADJ
fbem-28239	124	4	to	to	PART
fbem-28239	124	5	demonstrate	demonstrate	VERB
fbem-28239	124	6	the	the	DET
fbem-28239	124	7	necessity	necessity	NOUN
fbem-28239	124	8	and	and	CCONJ
fbem-28239	124	9	importance	importance	NOUN
fbem-28239	124	10	of	of	ADP
fbem-28239	124	11	incorporating	incorporate	VERB
fbem-28239	124	12	the	the	DET
fbem-28239	124	13	capm	capm	PROPN
fbem-28239	124	14	theoretical	theoretical	PROPN
fbem-28239	124	15	foundation	foundation	NOUN
fbem-28239	124	16	into	into	ADP
fbem-28239	124	17	the	the	DET
fbem-28239	124	18	model	model	NOUN
fbem-28239	124	19	.	.	PUNCT
fbem-28239	125	1	3.6	3.6	NUM
fbem-28239	125	2	.	.	PUNCT
fbem-28239	126	1	sensitivity	sensitivity	NOUN
fbem-28239	126	2	analysis	analysis	NOUN
fbem-28239	126	3	of	of	ADP
fbem-28239	126	4	recurrent	recurrent	ADJ
fbem-28239	126	5	neural	neural	ADJ
fbem-28239	126	6	networks	network	NOUN
fbem-28239	126	7	(	(	PUNCT
fbem-28239	126	8	rnns	rnns	PROPN
fbem-28239	126	9	)	)	PUNCT
fbem-28239	126	10	and	and	CCONJ
fbem-28239	126	11	multiple	multiple	ADJ
fbem-28239	126	12	linear	linear	ADJ
fbem-28239	126	13	regression	regression	NOUN
fbem-28239	126	14	models	model	NOUN
fbem-28239	126	15	besides	besides	SCONJ
fbem-28239	126	16	,	,	PUNCT
fbem-28239	126	17	there	there	PRON
fbem-28239	126	18	is	be	VERB
fbem-28239	126	19	another	another	DET
fbem-28239	126	20	place	place	NOUN
fbem-28239	126	21	worth	worth	ADJ
fbem-28239	126	22	paying	pay	VERB
fbem-28239	126	23	attention	attention	NOUN
fbem-28239	126	24	to	to	ADP
fbem-28239	126	25	.	.	PUNCT
fbem-28239	127	1	that	that	PRON
fbem-28239	127	2	is	be	AUX
fbem-28239	127	3	to	to	PART
fbem-28239	127	4	say	say	VERB
fbem-28239	127	5	,	,	PUNCT
fbem-28239	127	6	the	the	DET
fbem-28239	127	7	improvement	improvement	NOUN
fbem-28239	127	8	of	of	ADP
fbem-28239	127	9	recurrent	recurrent	ADJ
fbem-28239	127	10	neural	neural	ADJ
fbem-28239	127	11	networks	network	NOUN
fbem-28239	127	12	(	(	PUNCT
fbem-28239	127	13	rnns	rnns	PROPN
fbem-28239	127	14	)	)	PUNCT
fbem-28239	127	15	is	be	AUX
fbem-28239	127	16	much	much	ADV
fbem-28239	127	17	greater	great	ADJ
fbem-28239	127	18	than	than	ADP
fbem-28239	127	19	that	that	PRON
fbem-28239	127	20	of	of	ADP
fbem-28239	127	21	multiple	multiple	ADJ
fbem-28239	127	22	linear	linear	ADJ
fbem-28239	127	23	regression	regression	NOUN
fbem-28239	127	24	models	model	NOUN
fbem-28239	127	25	.	.	PUNCT
fbem-28239	128	1	this	this	PRON
fbem-28239	128	2	can	can	AUX
fbem-28239	128	3	be	be	AUX
fbem-28239	128	4	seen	see	VERB
fbem-28239	128	5	from	from	ADP
fbem-28239	128	6	the	the	DET
fbem-28239	128	7	mse	mse	NOUN
fbem-28239	128	8	:	:	PUNCT
fbem-28239	128	9	the	the	DET
fbem-28239	128	10	mse	mse	NOUN
fbem-28239	128	11	of	of	ADP
fbem-28239	128	12	recurrent	recurrent	ADJ
fbem-28239	128	13	neural	neural	ADJ
fbem-28239	128	14	networks	network	NOUN
fbem-28239	128	15	(	(	PUNCT
fbem-28239	128	16	rnns	rnns	PROPN
fbem-28239	128	17	)	)	PUNCT
fbem-28239	128	18	decreased	decrease	VERB
fbem-28239	128	19	from	from	ADP
fbem-28239	128	20	about	about	ADP
fbem-28239	128	21	19.44	19.44	NUM
fbem-28239	128	22	to	to	ADP
fbem-28239	128	23	14.90	14.90	NUM
fbem-28239	128	24	,	,	PUNCT
fbem-28239	128	25	a	a	DET
fbem-28239	128	26	decrease	decrease	NOUN
fbem-28239	128	27	of	of	ADP
fbem-28239	128	28	about	about	ADV
fbem-28239	128	29	4.54	4.54	NUM
fbem-28239	128	30	points	point	NOUN
fbem-28239	128	31	,	,	PUNCT
fbem-28239	128	32	while	while	SCONJ
fbem-28239	128	33	the	the	DET
fbem-28239	128	34	mse	mse	NOUN
fbem-28239	128	35	of	of	ADP
fbem-28239	128	36	multiple	multiple	ADJ
fbem-28239	128	37	linear	linear	PROPN
fbem-28239	128	38	regression	regression	NOUN
fbem-28239	128	39	models	model	NOUN
fbem-28239	128	40	decreased	decrease	VERB
fbem-28239	128	41	from	from	ADP
fbem-28239	128	42	about	about	ADP
fbem-28239	128	43	1.2893	1.2893	NUM
fbem-28239	128	44	to	to	ADP
fbem-28239	128	45	about	about	ADP
fbem-28239	128	46	1.2855	1.2855	NUM
fbem-28239	128	47	,	,	PUNCT
fbem-28239	128	48	which	which	PRON
fbem-28239	128	49	is	be	AUX
fbem-28239	128	50	far	far	ADV
fbem-28239	128	51	less	less	ADV
fbem-28239	128	52	significant	significant	ADJ
fbem-28239	128	53	than	than	ADP
fbem-28239	128	54	that	that	PRON
fbem-28239	128	55	of	of	ADP
fbem-28239	128	56	recurrent	recurrent	ADJ
fbem-28239	128	57	neural	neural	ADJ
fbem-28239	128	58	networks	network	NOUN
fbem-28239	128	59	(	(	PUNCT
fbem-28239	128	60	rnns	rnns	PROPN
fbem-28239	128	61	)	)	PUNCT
fbem-28239	128	62	.	.	PUNCT
fbem-28239	129	1	other	other	ADJ
fbem-28239	129	2	parameters	parameter	NOUN
fbem-28239	129	3	are	be	AUX
fbem-28239	129	4	also	also	ADV
fbem-28239	129	5	the	the	DET
fbem-28239	129	6	same	same	ADJ
fbem-28239	129	7	,	,	PUNCT
fbem-28239	129	8	and	and	CCONJ
fbem-28239	129	9	readers	reader	NOUN
fbem-28239	129	10	can	can	AUX
fbem-28239	129	11	test	test	VERB
fbem-28239	129	12	them	they	PRON
fbem-28239	129	13	themselves	themselves	PRON
fbem-28239	129	14	.	.	PUNCT
fbem-28239	130	1	as	as	ADP
fbem-28239	130	2	for	for	ADP
fbem-28239	130	3	the	the	DET
fbem-28239	130	4	reasons	reason	NOUN
fbem-28239	130	5	,	,	PUNCT
fbem-28239	130	6	this	this	DET
fbem-28239	130	7	article	article	NOUN
fbem-28239	130	8	believes	believe	VERB
fbem-28239	130	9	that	that	SCONJ
fbem-28239	130	10	there	there	PRON
fbem-28239	130	11	may	may	AUX
fbem-28239	130	12	be	be	AUX
fbem-28239	130	13	the	the	DET
fbem-28239	130	14	following	follow	VERB
fbem-28239	130	15	factors	factor	NOUN
fbem-28239	130	16	:	:	PUNCT
fbem-28239	130	17	3.6.1	3.6.1	X
fbem-28239	130	18	.	.	PUNCT
fbem-28239	130	19	model	model	NOUN
fbem-28239	130	20	expression	expression	NOUN
fbem-28239	130	21	ability	ability	NOUN
fbem-28239	130	22	the	the	DET
fbem-28239	130	23	complexity	complexity	NOUN
fbem-28239	130	24	of	of	ADP
fbem-28239	130	25	neural	neural	ADJ
fbem-28239	130	26	networks	network	NOUN
fbem-28239	130	27	:	:	PUNCT
fbem-28239	130	28	neural	neural	ADJ
fbem-28239	130	29	networks	network	NOUN
fbem-28239	130	30	,	,	PUNCT
fbem-28239	130	31	especially	especially	ADV
fbem-28239	130	32	recurrent	recurrent	ADJ
fbem-28239	130	33	neural	neural	ADJ
fbem-28239	130	34	networks	network	NOUN
fbem-28239	130	35	(	(	PUNCT
fbem-28239	130	36	rnns	rnns	PROPN
fbem-28239	130	37	)	)	PUNCT
fbem-28239	130	38	,	,	PUNCT
fbem-28239	130	39	have	have	VERB
fbem-28239	130	40	powerful	powerful	ADJ
fbem-28239	130	41	expressive	expressive	ADJ
fbem-28239	130	42	power	power	NOUN
fbem-28239	130	43	.	.	PUNCT
fbem-28239	131	1	they	they	PRON
fbem-28239	131	2	can	can	AUX
fbem-28239	131	3	learn	learn	VERB
fbem-28239	131	4	complex	complex	ADJ
fbem-28239	131	5	nonlinear	nonlinear	ADJ
fbem-28239	131	6	relationships	relationship	NOUN
fbem-28239	131	7	in	in	ADP
fbem-28239	131	8	data	datum	NOUN
fbem-28239	131	9	,	,	PUNCT
fbem-28239	131	10	automatically	automatically	ADV
fbem-28239	131	11	extract	extract	VERB
fbem-28239	131	12	features	feature	NOUN
fbem-28239	131	13	,	,	PUNCT
fbem-28239	131	14	and	and	CCONJ
fbem-28239	131	15	perform	perform	VERB
fbem-28239	131	16	advanced	advanced	ADJ
fbem-28239	131	17	pattern	pattern	NOUN
fbem-28239	131	18	recognition	recognition	NOUN
fbem-28239	131	19	.	.	PUNCT
fbem-28239	132	1	in	in	ADP
fbem-28239	132	2	contrast	contrast	NOUN
fbem-28239	132	3	,	,	PUNCT
fbem-28239	132	4	multiple	multiple	ADJ
fbem-28239	132	5	linear	linear	ADJ
fbem-28239	132	6	regression	regression	NOUN
fbem-28239	132	7	is	be	AUX
fbem-28239	132	8	a	a	DET
fbem-28239	132	9	relatively	relatively	ADV
fbem-28239	132	10	simple	simple	ADJ
fbem-28239	132	11	model	model	NOUN
fbem-28239	132	12	that	that	PRON
fbem-28239	132	13	can	can	AUX
fbem-28239	132	14	only	only	ADV
fbem-28239	132	15	capture	capture	VERB
fbem-28239	132	16	linear	linear	ADJ
fbem-28239	132	17	relationships	relationship	NOUN
fbem-28239	132	18	.	.	PUNCT
fbem-28239	133	1	for	for	ADP
fbem-28239	133	2	complex	complex	ADJ
fbem-28239	133	3	time	time	NOUN
fbem-28239	133	4	series	series	NOUN
fbem-28239	133	5	data	datum	NOUN
fbem-28239	133	6	such	such	ADJ
fbem-28239	133	7	as	as	ADP
fbem-28239	133	8	stock	stock	NOUN
fbem-28239	133	9	prices	price	NOUN
fbem-28239	133	10	,	,	PUNCT
fbem-28239	133	11	there	there	PRON
fbem-28239	133	12	may	may	AUX
fbem-28239	133	13	be	be	AUX
fbem-28239	133	14	a	a	DET
fbem-28239	133	15	large	large	ADJ
fbem-28239	133	16	number	number	NOUN
fbem-28239	133	17	of	of	ADP
fbem-28239	133	18	nonlinear	nonlinear	ADJ
fbem-28239	133	19	relationships	relationship	NOUN
fbem-28239	133	20	,	,	PUNCT
fbem-28239	133	21	and	and	CCONJ
fbem-28239	133	22	neural	neural	ADJ
fbem-28239	133	23	networks	network	NOUN
fbem-28239	133	24	are	be	AUX
fbem-28239	133	25	more	more	ADV
fbem-28239	133	26	capable	capable	ADJ
fbem-28239	133	27	of	of	ADP
fbem-28239	133	28	capturing	capture	VERB
fbem-28239	133	29	these	these	DET
fbem-28239	133	30	complex	complex	ADJ
fbem-28239	133	31	patterns	pattern	NOUN
fbem-28239	133	32	,	,	PUNCT
fbem-28239	133	33	thereby	thereby	ADV
fbem-28239	133	34	improving	improve	VERB
fbem-28239	133	35	prediction	prediction	NOUN
fbem-28239	133	36	accuracy	accuracy	NOUN
fbem-28239	133	37	.	.	PUNCT
fbem-28239	134	1	adaptive	adaptive	ADJ
fbem-28239	134	2	feature	feature	NOUN
fbem-28239	134	3	learning	learning	NOUN
fbem-28239	134	4	:	:	PUNCT
fbem-28239	134	5	neural	neural	ADJ
fbem-28239	134	6	networks	network	NOUN
fbem-28239	134	7	automatically	automatically	ADV
fbem-28239	134	8	adjust	adjust	VERB
fbem-28239	134	9	weights	weight	NOUN
fbem-28239	134	10	and	and	CCONJ
fbem-28239	134	11	biases	bias	NOUN
fbem-28239	134	12	through	through	ADP
fbem-28239	134	13	backpropagation	backpropagation	NOUN
fbem-28239	134	14	algorithms	algorithm	NOUN
fbem-28239	134	15	to	to	PART
fbem-28239	134	16	minimize	minimize	VERB
fbem-28239	134	17	the	the	DET
fbem-28239	134	18	loss	loss	NOUN
fbem-28239	134	19	function	function	NOUN
fbem-28239	134	20	.	.	PUNCT
fbem-28239	135	1	during	during	ADP
fbem-28239	135	2	the	the	DET
fbem-28239	135	3	learning	learning	NOUN
fbem-28239	135	4	process	process	NOUN
fbem-28239	135	5	,	,	PUNCT
fbem-28239	135	6	it	it	PRON
fbem-28239	135	7	can	can	AUX
fbem-28239	135	8	adaptively	adaptively	ADV
fbem-28239	135	9	learn	learn	VERB
fbem-28239	135	10	which	which	PRON
fbem-28239	135	11	features	feature	NOUN
fbem-28239	135	12	are	be	AUX
fbem-28239	135	13	important	important	ADJ
fbem-28239	135	14	for	for	ADP
fbem-28239	135	15	prediction	prediction	NOUN
fbem-28239	135	16	,	,	PUNCT
fbem-28239	135	17	including	include	VERB
fbem-28239	135	18	the	the	DET
fbem-28239	135	19	complex	complex	ADJ
fbem-28239	135	20	interactions	interaction	NOUN
fbem-28239	135	21	between	between	ADP
fbem-28239	135	22	beta	beta	ADJ
fbem-28239	135	23	coefficients	coefficient	NOUN
fbem-28239	135	24	and	and	CCONJ
fbem-28239	135	25	other	other	ADJ
fbem-28239	135	26	input	input	NOUN
fbem-28239	135	27	features	feature	NOUN
fbem-28239	135	28	.	.	PUNCT
fbem-28239	136	1	however	however	ADV
fbem-28239	136	2	,	,	PUNCT
fbem-28239	136	3	multiple	multiple	ADJ
fbem-28239	136	4	linear	linear	ADJ
fbem-28239	136	5	regression	regression	NOUN
fbem-28239	136	6	models	model	NOUN
fbem-28239	136	7	have	have	VERB
fbem-28239	136	8	relatively	relatively	ADV
fbem-28239	136	9	fixed	fix	VERB
fbem-28239	136	10	processing	processing	NOUN
fbem-28239	136	11	of	of	ADP
fbem-28239	136	12	features	feature	NOUN
fbem-28239	136	13	,	,	PUNCT
fbem-28239	136	14	usually	usually	ADV
fbem-28239	136	15	assuming	assume	VERB
fbem-28239	136	16	a	a	DET
fbem-28239	136	17	linear	linear	ADJ
fbem-28239	136	18	relationship	relationship	NOUN
fbem-28239	136	19	between	between	ADP
fbem-28239	136	20	features	feature	NOUN
fbem-28239	136	21	and	and	CCONJ
fbem-28239	136	22	target	target	NOUN
fbem-28239	136	23	variables	variable	NOUN
fbem-28239	136	24	,	,	PUNCT
fbem-28239	136	25	and	and	CCONJ
fbem-28239	136	26	limited	limited	ADJ
fbem-28239	136	27	modeling	modeling	NOUN
fbem-28239	136	28	ability	ability	NOUN
fbem-28239	136	29	for	for	ADP
fbem-28239	136	30	the	the	DET
fbem-28239	136	31	interaction	interaction	NOUN
fbem-28239	136	32	between	between	ADP
fbem-28239	136	33	features	feature	NOUN
fbem-28239	136	34	.	.	PUNCT
fbem-28239	137	1	3.6.2	3.6.2	X
fbem-28239	137	2	.	.	PUNCT
fbem-28239	137	3	ability	ability	NOUN
fbem-28239	137	4	to	to	PART
fbem-28239	137	5	process	process	VERB
fbem-28239	137	6	time	time	NOUN
fbem-28239	137	7	series	series	PROPN
fbem-28239	137	8	data	data	PROPN
fbem-28239	137	9	memory	memory	NOUN
fbem-28239	137	10	and	and	CCONJ
fbem-28239	137	11	sequence	sequence	NOUN
fbem-28239	137	12	dependent	dependent	ADJ
fbem-28239	137	13	learning	learning	NOUN
fbem-28239	137	14	:	:	PUNCT
fbem-28239	137	15	rnn	rnn	NOUN
fbem-28239	137	16	is	be	AUX
fbem-28239	137	17	specifically	specifically	ADV
fbem-28239	137	18	designed	design	VERB
fbem-28239	137	19	for	for	ADP
fbem-28239	137	20	processing	processing	NOUN
fbem-28239	137	21	time	time	NOUN
fbem-28239	137	22	series	series	PROPN
fbem-28239	137	23	data	data	PROPN
fbem-28239	137	24	and	and	CCONJ
fbem-28239	137	25	has	have	VERB
fbem-28239	137	26	a	a	DET
fbem-28239	137	27	memory	memory	NOUN
fbem-28239	137	28	mechanism	mechanism	NOUN
fbem-28239	137	29	that	that	PRON
fbem-28239	137	30	can	can	AUX
fbem-28239	137	31	remember	remember	VERB
fbem-28239	137	32	past	past	ADJ
fbem-28239	137	33	information	information	NOUN
fbem-28239	137	34	and	and	CCONJ
fbem-28239	137	35	use	use	VERB
fbem-28239	137	36	it	it	PRON
fbem-28239	137	37	for	for	ADP
fbem-28239	137	38	current	current	ADJ
fbem-28239	137	39	predictions	prediction	NOUN
fbem-28239	137	40	.	.	PUNCT
fbem-28239	138	1	this	this	PRON
fbem-28239	138	2	is	be	AUX
fbem-28239	138	3	important	important	ADJ
fbem-28239	138	4	for	for	ADP
fbem-28239	138	5	stock	stock	NOUN
fbem-28239	138	6	price	price	NOUN
fbem-28239	138	7	forecasting	forecasting	NOUN
fbem-28239	138	8	because	because	SCONJ
fbem-28239	138	9	stock	stock	NOUN
fbem-28239	138	10	price	price	NOUN
fbem-28239	138	11	movements	movement	NOUN
fbem-28239	138	12	are	be	AUX
fbem-28239	138	13	usually	usually	ADV
fbem-28239	138	14	influenced	influence	VERB
fbem-28239	138	15	by	by	ADP
fbem-28239	138	16	factors	factor	NOUN
fbem-28239	138	17	and	and	CCONJ
fbem-28239	138	18	trends	trend	NOUN
fbem-28239	138	19	over	over	ADP
fbem-28239	138	20	a	a	DET
fbem-28239	138	21	longer	long	ADJ
fbem-28239	138	22	time	time	NOUN
fbem-28239	138	23	horizon[4	horizon[4	NOUN
fbem-28239	138	24	]	]	PUNCT
fbem-28239	138	25	.	.	PUNCT
fbem-28239	139	1	for	for	ADP
fbem-28239	139	2	example	example	NOUN
fbem-28239	139	3	,	,	PUNCT
fbem-28239	139	4	the	the	DET
fbem-28239	139	5	price	price	NOUN
fbem-28239	139	6	trends	trend	NOUN
fbem-28239	139	7	of	of	ADP
fbem-28239	139	8	the	the	DET
fbem-28239	139	9	past	past	ADJ
fbem-28239	139	10	few	few	ADJ
fbem-28239	139	11	days	day	NOUN
fbem-28239	139	12	may	may	AUX
fbem-28239	139	13	have	have	VERB
fbem-28239	139	14	a	a	DET
fbem-28239	139	15	significant	significant	ADJ
fbem-28239	139	16	impact	impact	NOUN
fbem-28239	139	17	on	on	ADP
fbem-28239	139	18	future	future	ADJ
fbem-28239	139	19	prices	price	NOUN
fbem-28239	139	20	.	.	PUNCT
fbem-28239	140	1	neural	neural	ADJ
fbem-28239	140	2	networks	network	NOUN
fbem-28239	140	3	can	can	AUX
fbem-28239	140	4	learn	learn	VERB
fbem-28239	140	5	long	long	ADJ
fbem-28239	140	6	-	-	PUNCT
fbem-28239	140	7	term	term	NOUN
fbem-28239	140	8	dependencies	dependency	NOUN
fbem-28239	140	9	in	in	ADP
fbem-28239	140	10	time	time	NOUN
fbem-28239	140	11	series	series	NOUN
fbem-28239	140	12	,	,	PUNCT
fbem-28239	140	13	while	while	SCONJ
fbem-28239	140	14	multiple	multiple	ADJ
fbem-28239	140	15	linear	linear	ADJ
fbem-28239	140	16	regression	regression	NOUN
fbem-28239	140	17	models	model	NOUN
fbem-28239	140	18	are	be	AUX
fbem-28239	140	19	relatively	relatively	ADV
fbem-28239	140	20	weaker	weak	ADJ
fbem-28239	140	21	in	in	ADP
fbem-28239	140	22	handling	handle	VERB
fbem-28239	140	23	this	this	DET
fbem-28239	140	24	characteristic	characteristic	NOUN
fbem-28239	140	25	of	of	ADP
fbem-28239	140	26	time	time	NOUN
fbem-28239	140	27	series	series	PROPN
fbem-28239	140	28	data	data	PROPN
fbem-28239	140	29	.	.	PUNCT
fbem-28239	141	1	dynamic	dynamic	ADJ
fbem-28239	141	2	modeling	modeling	NOUN
fbem-28239	141	3	:	:	PUNCT
fbem-28239	141	4	neural	neural	ADJ
fbem-28239	141	5	networks	network	NOUN
fbem-28239	141	6	can	can	AUX
fbem-28239	141	7	continuously	continuously	ADV
fbem-28239	141	8	update	update	VERB
fbem-28239	141	9	their	their	PRON
fbem-28239	141	10	internal	internal	ADJ
fbem-28239	141	11	states	state	NOUN
fbem-28239	141	12	and	and	CCONJ
fbem-28239	141	13	weights	weight	NOUN
fbem-28239	141	14	with	with	ADP
fbem-28239	141	15	the	the	DET
fbem-28239	141	16	input	input	NOUN
fbem-28239	141	17	of	of	ADP
fbem-28239	141	18	new	new	ADJ
fbem-28239	141	19	data	datum	NOUN
fbem-28239	141	20	,	,	PUNCT
fbem-28239	141	21	thus	thus	ADV
fbem-28239	141	22	being	be	AUX
fbem-28239	141	23	able	able	ADJ
fbem-28239	141	24	to	to	PART
fbem-28239	141	25	adapt	adapt	VERB
fbem-28239	141	26	to	to	ADP
fbem-28239	141	27	the	the	DET
fbem-28239	141	28	dynamic	dynamic	ADJ
fbem-28239	141	29	changes	change	NOUN
fbem-28239	141	30	in	in	ADP
fbem-28239	141	31	data	datum	NOUN
fbem-28239	141	32	.	.	PUNCT
fbem-28239	142	1	in	in	ADP
fbem-28239	142	2	the	the	DET
fbem-28239	142	3	stock	stock	NOUN
fbem-28239	142	4	market	market	NOUN
fbem-28239	142	5	,	,	PUNCT
fbem-28239	142	6	prices	price	NOUN
fbem-28239	142	7	are	be	AUX
fbem-28239	142	8	influenced	influence	VERB
fbem-28239	142	9	by	by	ADP
fbem-28239	142	10	various	various	ADJ
fbem-28239	142	11	factors	factor	NOUN
fbem-28239	142	12	,	,	PUNCT
fbem-28239	142	13	and	and	CCONJ
fbem-28239	142	14	market	market	NOUN
fbem-28239	142	15	conditions	condition	NOUN
fbem-28239	142	16	are	be	AUX
fbem-28239	142	17	constantly	constantly	ADV
fbem-28239	142	18	changing	change	VERB
fbem-28239	142	19	.	.	PUNCT
fbem-28239	143	1	neural	neural	ADJ
fbem-28239	143	2	networks	network	NOUN
fbem-28239	143	3	can	can	AUX
fbem-28239	143	4	better	well	ADV
fbem-28239	143	5	adapt	adapt	VERB
fbem-28239	143	6	to	to	ADP
fbem-28239	143	7	this	this	DET
fbem-28239	143	8	dynamism	dynamism	NOUN
fbem-28239	143	9	.	.	PUNCT
fbem-28239	144	1	multiple	multiple	ADJ
fbem-28239	144	2	linear	linear	PROPN
fbem-28239	144	3	regression	regression	NOUN
fbem-28239	144	4	models	model	NOUN
fbem-28239	144	5	typically	typically	ADV
fbem-28239	144	6	assume	assume	VERB
fbem-28239	144	7	that	that	SCONJ
fbem-28239	144	8	data	datum	NOUN
fbem-28239	144	9	relationships	relationship	NOUN
fbem-28239	144	10	are	be	AUX
fbem-28239	144	11	static	static	ADJ
fbem-28239	144	12	and	and	CCONJ
fbem-28239	144	13	not	not	PART
fbem-28239	144	14	easily	easily	ADV
fbem-28239	144	15	adaptable	adaptable	ADJ
fbem-28239	144	16	to	to	ADP
fbem-28239	144	17	dynamic	dynamic	ADJ
fbem-28239	144	18	changes	change	NOUN
fbem-28239	144	19	in	in	ADP
fbem-28239	144	20	data	datum	NOUN
fbem-28239	144	21	.	.	PUNCT
fbem-28239	145	1	3.6.3	3.6.3	NUM
fbem-28239	145	2	.	.	PUNCT
fbem-28239	146	1	the	the	DET
fbem-28239	146	2	utilization	utilization	NOUN
fbem-28239	146	3	of	of	ADP
fbem-28239	146	4	beta	beta	ADJ
fbem-28239	146	5	coefficient	coefficient	NOUN
fbem-28239	146	6	nonlinear	nonlinear	ADJ
fbem-28239	146	7	fusion	fusion	NOUN
fbem-28239	146	8	:	:	PUNCT
fbem-28239	146	9	in	in	ADP
fbem-28239	146	10	neural	neural	ADJ
fbem-28239	146	11	networks	network	NOUN
fbem-28239	146	12	,	,	PUNCT
fbem-28239	146	13	beta	beta	ADJ
fbem-28239	146	14	coefficients	coefficient	NOUN
fbem-28239	146	15	can	can	AUX
fbem-28239	146	16	be	be	AUX
fbem-28239	146	17	fused	fuse	VERB
fbem-28239	146	18	with	with	ADP
fbem-28239	146	19	other	other	ADJ
fbem-28239	146	20	input	input	NOUN
fbem-28239	146	21	features	feature	NOUN
fbem-28239	146	22	in	in	ADP
fbem-28239	146	23	a	a	DET
fbem-28239	146	24	non	non	ADJ
fbem-28239	146	25	-	-	ADJ
fbem-28239	146	26	linear	linear	ADJ
fbem-28239	146	27	manner	manner	NOUN
fbem-28239	146	28	.	.	PUNCT
fbem-28239	147	1	neural	neural	ADJ
fbem-28239	147	2	networks	network	NOUN
fbem-28239	147	3	can	can	AUX
fbem-28239	147	4	learn	learn	VERB
fbem-28239	147	5	the	the	DET
fbem-28239	147	6	complex	complex	ADJ
fbem-28239	147	7	nonlinear	nonlinear	ADJ
fbem-28239	147	8	relationship	relationship	NOUN
fbem-28239	147	9	between	between	ADP
fbem-28239	147	10	beta	beta	ADJ
fbem-28239	147	11	coefficients	coefficient	NOUN
fbem-28239	147	12	and	and	CCONJ
fbem-28239	147	13	stock	stock	NOUN
fbem-28239	147	14	prices	price	NOUN
fbem-28239	147	15	,	,	PUNCT
fbem-28239	147	16	thereby	thereby	ADV
fbem-28239	147	17	better	well	ADV
fbem-28239	147	18	utilizing	utilize	VERB
fbem-28239	147	19	this	this	DET
fbem-28239	147	20	additional	additional	ADJ
fbem-28239	147	21	information	information	NOUN
fbem-28239	147	22	to	to	PART
fbem-28239	147	23	improve	improve	VERB
fbem-28239	147	24	prediction	prediction	NOUN
fbem-28239	147	25	accuracy	accuracy	NOUN
fbem-28239	147	26	.	.	PUNCT
fbem-28239	148	1	in	in	ADP
fbem-28239	148	2	multiple	multiple	ADJ
fbem-28239	148	3	linear	linear	PROPN
fbem-28239	148	4	regression	regression	NOUN
fbem-28239	148	5	,	,	PUNCT
fbem-28239	148	6	the	the	DET
fbem-28239	148	7	beta	beta	ADJ
fbem-28239	148	8	coefficient	coefficient	NOUN
fbem-28239	148	9	is	be	AUX
fbem-28239	148	10	usually	usually	ADV
fbem-28239	148	11	only	only	ADV
fbem-28239	148	12	added	add	VERB
fbem-28239	148	13	as	as	ADP
fbem-28239	148	14	a	a	DET
fbem-28239	148	15	linear	linear	ADJ
fbem-28239	148	16	term	term	NOUN
fbem-28239	148	17	to	to	ADP
fbem-28239	148	18	the	the	DET
fbem-28239	148	19	model	model	NOUN
fbem-28239	148	20	,	,	PUNCT
fbem-28239	148	21	and	and	CCONJ
fbem-28239	148	22	its	its	PRON
fbem-28239	148	23	contribution	contribution	NOUN
fbem-28239	148	24	to	to	ADP
fbem-28239	148	25	prediction	prediction	NOUN
fbem-28239	148	26	is	be	AUX
fbem-28239	148	27	relatively	relatively	ADV
fbem-28239	148	28	limited	limited	ADJ
fbem-28239	148	29	.	.	PUNCT
fbem-28239	149	1	feature	feature	NOUN
fbem-28239	149	2	extraction	extraction	NOUN
fbem-28239	149	3	and	and	CCONJ
fbem-28239	149	4	abstraction	abstraction	NOUN
fbem-28239	149	5	:	:	PUNCT
fbem-28239	149	6	neural	neural	ADJ
fbem-28239	149	7	networks	network	NOUN
fbem-28239	149	8	can	can	AUX
fbem-28239	149	9	gradually	gradually	ADV
fbem-28239	149	10	extract	extract	VERB
fbem-28239	149	11	and	and	CCONJ
fbem-28239	149	12	abstract	abstract	ADJ
fbem-28239	149	13	input	input	NOUN
fbem-28239	149	14	features	feature	VERB
fbem-28239	149	15	through	through	ADP
fbem-28239	149	16	multi	multi	ADJ
fbem-28239	149	17	-	-	ADJ
fbem-28239	149	18	layer	layer	ADJ
fbem-28239	149	19	structures	structure	NOUN
fbem-28239	149	20	,	,	PUNCT
fbem-28239	149	21	transforming	transform	VERB
fbem-28239	149	22	raw	raw	ADJ
fbem-28239	149	23	data	datum	NOUN
fbem-28239	149	24	into	into	ADP
fbem-28239	149	25	higher	high	ADJ
fbem-28239	149	26	-	-	PUNCT
fbem-28239	149	27	level	level	NOUN
fbem-28239	149	28	representations	representation	NOUN
fbem-28239	149	29	.	.	PUNCT
fbem-28239	150	1	the	the	DET
fbem-28239	150	2	beta	beta	ADJ
fbem-28239	150	3	coefficient	coefficient	NOUN
fbem-28239	150	4	can	can	AUX
fbem-28239	150	5	be	be	AUX
fbem-28239	150	6	integrated	integrate	VERB
fbem-28239	150	7	with	with	ADP
fbem-28239	150	8	other	other	ADJ
fbem-28239	150	9	features	feature	NOUN
fbem-28239	150	10	into	into	ADP
fbem-28239	150	11	more	more	ADV
fbem-28239	150	12	meaningful	meaningful	ADJ
fbem-28239	150	13	feature	feature	NOUN
fbem-28239	150	14	representations	representation	NOUN
fbem-28239	150	15	during	during	ADP
fbem-28239	150	16	this	this	DET
fbem-28239	150	17	process	process	NOUN
fbem-28239	150	18	,	,	PUNCT
fbem-28239	150	19	providing	provide	VERB
fbem-28239	150	20	stronger	strong	ADJ
fbem-28239	150	21	support	support	NOUN
fbem-28239	150	22	for	for	ADP
fbem-28239	150	23	prediction	prediction	NOUN
fbem-28239	150	24	.	.	PUNCT
fbem-28239	151	1	the	the	DET
fbem-28239	151	2	multiple	multiple	ADJ
fbem-28239	151	3	linear	linear	PROPN
fbem-28239	151	4	regression	regression	NOUN
fbem-28239	151	5	model	model	NOUN
fbem-28239	151	6	lacks	lack	VERB
fbem-28239	151	7	the	the	DET
fbem-28239	151	8	ability	ability	NOUN
fbem-28239	151	9	to	to	PART
fbem-28239	151	10	extract	extract	VERB
fbem-28239	151	11	and	and	CCONJ
fbem-28239	151	12	abstract	abstract	ADJ
fbem-28239	151	13	features	feature	NOUN
fbem-28239	151	14	and	and	CCONJ
fbem-28239	151	15	can	can	AUX
fbem-28239	151	16	only	only	ADV
fbem-28239	151	17	directly	directly	ADV
fbem-28239	151	18	use	use	VERB
fbem-28239	151	19	the	the	DET
fbem-28239	151	20	original	original	ADJ
fbem-28239	151	21	features	feature	NOUN
fbem-28239	151	22	for	for	ADP
fbem-28239	151	23	linear	linear	ADJ
fbem-28239	151	24	combination	combination	NOUN
fbem-28239	151	25	to	to	PART
fbem-28239	151	26	make	make	VERB
fbem-28239	151	27	predictions	prediction	NOUN
fbem-28239	151	28	.	.	PUNCT
fbem-28239	152	1	4	4	X
fbem-28239	152	2	.	.	X
fbem-28239	152	3	summary	summary	NOUN
fbem-28239	152	4	so	so	ADV
fbem-28239	152	5	,	,	PUNCT
fbem-28239	152	6	in	in	ADP
fbem-28239	152	7	summary	summary	NOUN
fbem-28239	152	8	,	,	PUNCT
fbem-28239	152	9	we	we	PRON
fbem-28239	152	10	can	can	AUX
fbem-28239	152	11	reasonably	reasonably	ADV
fbem-28239	152	12	infer	infer	VERB
fbem-28239	152	13	that	that	SCONJ
fbem-28239	152	14	if	if	SCONJ
fbem-28239	152	15	more	more	ADJ
fbem-28239	152	16	coefficient	coefficient	NOUN
fbem-28239	152	17	parameters	parameter	NOUN
fbem-28239	152	18	are	be	AUX
fbem-28239	152	19	given	give	VERB
fbem-28239	152	20	to	to	ADP
fbem-28239	152	21	the	the	DET
fbem-28239	152	22	recurrent	recurrent	ADJ
fbem-28239	152	23	neural	neural	ADJ
fbem-28239	152	24	network	network	NOUN
fbem-28239	152	25	(	(	PUNCT
fbem-28239	152	26	rnn	rnn	PROPN
fbem-28239	152	27	)	)	PUNCT
fbem-28239	152	28	,	,	PUNCT
fbem-28239	152	29	the	the	DET
fbem-28239	152	30	model	model	NOUN
fbem-28239	152	31	can	can	AUX
fbem-28239	152	32	have	have	VERB
fbem-28239	152	33	more	more	ADJ
fbem-28239	152	34	parameters	parameter	NOUN
fbem-28239	152	35	to	to	PART
fbem-28239	152	36	adapt	adapt	VERB
fbem-28239	152	37	to	to	ADP
fbem-28239	152	38	the	the	DET
fbem-28239	152	39	complexity	complexity	NOUN
fbem-28239	152	40	of	of	ADP
fbem-28239	152	41	the	the	DET
fbem-28239	152	42	data	datum	NOUN
fbem-28239	152	43	.	.	PUNCT
fbem-28239	153	1	if	if	SCONJ
fbem-28239	153	2	these	these	DET
fbem-28239	153	3	coefficients	coefficient	NOUN
fbem-28239	153	4	can	can	AUX
fbem-28239	153	5	effectively	effectively	ADV
fbem-28239	153	6	capture	capture	VERB
fbem-28239	153	7	key	key	ADJ
fbem-28239	153	8	features	feature	NOUN
fbem-28239	153	9	and	and	CCONJ
fbem-28239	153	10	patterns	pattern	NOUN
fbem-28239	153	11	in	in	ADP
fbem-28239	153	12	the	the	DET
fbem-28239	153	13	data	datum	NOUN
fbem-28239	153	14	,	,	PUNCT
fbem-28239	153	15	the	the	DET
fbem-28239	153	16	model	model	NOUN
fbem-28239	153	17	may	may	AUX
fbem-28239	153	18	better	well	ADV
fbem-28239	153	19	fit	fit	VERB
fbem-28239	153	20	the	the	DET
fbem-28239	153	21	training	training	NOUN
fbem-28239	153	22	data	datum	NOUN
fbem-28239	153	23	,	,	PUNCT
fbem-28239	153	24	thereby	thereby	ADV
fbem-28239	153	25	improving	improve	VERB
fbem-28239	153	26	prediction	prediction	NOUN
fbem-28239	153	27	accuracy	accuracy	NOUN
fbem-28239	153	28	to	to	ADP
fbem-28239	153	29	a	a	DET
fbem-28239	153	30	certain	certain	ADJ
fbem-28239	153	31	extent	extent	NOUN
fbem-28239	153	32	,	,	PUNCT
fbem-28239	153	33	and	and	CCONJ
fbem-28239	153	34	even	even	ADV
fbem-28239	153	35	surpassing	surpass	VERB
fbem-28239	153	36	the	the	DET
fbem-28239	153	37	prediction	prediction	NOUN
fbem-28239	153	38	accuracy	accuracy	NOUN
fbem-28239	153	39	of	of	ADP
fbem-28239	153	40	multiple	multiple	ADJ
fbem-28239	153	41	linear	linear	ADJ
fbem-28239	153	42	regression	regression	NOUN
fbem-28239	153	43	in	in	ADP
fbem-28239	153	44	this	this	DET
fbem-28239	153	45	article	article	NOUN
fbem-28239	153	46	.	.	PUNCT
fbem-28239	154	1	in	in	ADP
fbem-28239	154	2	addition	addition	NOUN
fbem-28239	154	3	,	,	PUNCT
fbem-28239	154	4	more	more	ADJ
fbem-28239	154	5	coefficients	coefficient	NOUN
fbem-28239	154	6	mean	mean	VERB
fbem-28239	154	7	that	that	SCONJ
fbem-28239	154	8	the	the	DET
fbem-28239	154	9	model	model	NOUN
fbem-28239	154	10	has	have	VERB
fbem-28239	154	11	higher	high	ADJ
fbem-28239	154	12	flexibility	flexibility	NOUN
fbem-28239	154	13	and	and	CCONJ
fbem-28239	154	14	can	can	AUX
fbem-28239	154	15	better	well	ADV
fbem-28239	154	16	adapt	adapt	VERB
fbem-28239	154	17	to	to	ADP
fbem-28239	154	18	different	different	ADJ
fbem-28239	154	19	data	datum	NOUN
fbem-28239	154	20	distributions	distribution	NOUN
fbem-28239	154	21	and	and	CCONJ
fbem-28239	154	22	changes	change	NOUN
fbem-28239	154	23	.	.	PUNCT
fbem-28239	155	1	this	this	PRON
fbem-28239	155	2	is	be	AUX
fbem-28239	155	3	particularly	particularly	ADV
fbem-28239	155	4	important	important	ADJ
fbem-28239	155	5	for	for	ADP
fbem-28239	155	6	handling	handle	VERB
fbem-28239	155	7	complex	complex	ADJ
fbem-28239	155	8	time	time	NOUN
fbem-28239	155	9	series	series	PROPN
fbem-28239	155	10	data	data	PROPN
fbem-28239	155	11	,	,	PUNCT
fbem-28239	155	12	as	as	SCONJ
fbem-28239	155	13	time	time	NOUN
fbem-28239	155	14	series	series	PROPN
fbem-28239	155	15	data	datum	NOUN
fbem-28239	155	16	such	such	ADJ
fbem-28239	155	17	as	as	ADP
fbem-28239	155	18	stock	stock	NOUN
fbem-28239	155	19	prices	price	NOUN
fbem-28239	155	20	often	often	ADV
fbem-28239	155	21	have	have	VERB
fbem-28239	155	22	high	high	ADJ
fbem-28239	155	23	nonlinearity	nonlinearity	NOUN
fbem-28239	155	24	and	and	CCONJ
fbem-28239	155	25	dynamism	dynamism	NOUN
fbem-28239	155	26	.	.	PUNCT
fbem-28239	156	1	114	114	NUM
fbem-28239	156	2	of	of	ADP
fbem-28239	156	3	course	course	NOUN
fbem-28239	156	4	,	,	PUNCT
fbem-28239	156	5	apart	apart	ADV
fbem-28239	156	6	from	from	ADP
fbem-28239	156	7	favorable	favorable	ADJ
fbem-28239	156	8	factors	factor	NOUN
fbem-28239	156	9	,	,	PUNCT
fbem-28239	156	10	there	there	PRON
fbem-28239	156	11	are	be	VERB
fbem-28239	156	12	also	also	ADV
fbem-28239	156	13	some	some	DET
fbem-28239	156	14	unknown	unknown	ADJ
fbem-28239	156	15	factors	factor	NOUN
fbem-28239	156	16	.	.	PUNCT
fbem-28239	157	1	adding	add	VERB
fbem-28239	157	2	too	too	ADV
fbem-28239	157	3	many	many	ADJ
fbem-28239	157	4	coefficients	coefficient	NOUN
fbem-28239	157	5	can	can	AUX
fbem-28239	157	6	make	make	VERB
fbem-28239	157	7	the	the	DET
fbem-28239	157	8	model	model	NOUN
fbem-28239	157	9	more	more	ADV
fbem-28239	157	10	complex	complex	ADJ
fbem-28239	157	11	,	,	PUNCT
fbem-28239	157	12	thereby	thereby	ADV
fbem-28239	157	13	increasing	increase	VERB
fbem-28239	157	14	the	the	DET
fbem-28239	157	15	risk	risk	NOUN
fbem-28239	157	16	of	of	ADP
fbem-28239	157	17	overfitting	overfitte	VERB
fbem-28239	157	18	.	.	PUNCT
fbem-28239	158	1	at	at	ADP
fbem-28239	158	2	the	the	DET
fbem-28239	158	3	same	same	ADJ
fbem-28239	158	4	time	time	NOUN
fbem-28239	158	5	,	,	PUNCT
fbem-28239	158	6	it	it	PRON
fbem-28239	158	7	will	will	AUX
fbem-28239	158	8	also	also	ADV
fbem-28239	158	9	reduce	reduce	VERB
fbem-28239	158	10	the	the	DET
fbem-28239	158	11	interpretability	interpretability	NOUN
fbem-28239	158	12	of	of	ADP
fbem-28239	158	13	the	the	DET
fbem-28239	158	14	model	model	NOUN
fbem-28239	158	15	,	,	PUNCT
fbem-28239	158	16	increase	increase	VERB
fbem-28239	158	17	computational	computational	ADJ
fbem-28239	158	18	costs	cost	NOUN
fbem-28239	158	19	,	,	PUNCT
fbem-28239	158	20	and	and	CCONJ
fbem-28239	158	21	other	other	ADJ
fbem-28239	158	22	unknown	unknown	ADJ
fbem-28239	158	23	risks	risk	NOUN
fbem-28239	158	24	.	.	PUNCT
fbem-28239	159	1	complex	complex	ADJ
fbem-28239	159	2	models	model	NOUN
fbem-28239	159	3	may	may	AUX
fbem-28239	159	4	be	be	AUX
fbem-28239	159	5	difficult	difficult	ADJ
fbem-28239	159	6	to	to	PART
fbem-28239	159	7	explain	explain	VERB
fbem-28239	159	8	how	how	SCONJ
fbem-28239	159	9	their	their	PRON
fbem-28239	159	10	predicted	predict	VERB
fbem-28239	159	11	results	result	NOUN
fbem-28239	159	12	are	be	AUX
fbem-28239	159	13	generated	generate	VERB
fbem-28239	159	14	,	,	PUNCT
fbem-28239	159	15	making	make	VERB
fbem-28239	159	16	it	it	PRON
fbem-28239	159	17	difficult	difficult	ADJ
fbem-28239	159	18	for	for	SCONJ
fbem-28239	159	19	people	people	NOUN
fbem-28239	159	20	to	to	PART
fbem-28239	159	21	trust	trust	VERB
fbem-28239	159	22	and	and	CCONJ
fbem-28239	159	23	apply	apply	VERB
fbem-28239	159	24	these	these	DET
fbem-28239	159	25	models	model	NOUN
fbem-28239	159	26	.	.	PUNCT
fbem-28239	160	1	being	be	AUX
fbem-28239	160	2	too	too	ADV
fbem-28239	160	3	complex	complex	ADJ
fbem-28239	160	4	is	be	AUX
fbem-28239	160	5	also	also	ADV
fbem-28239	160	6	not	not	PART
fbem-28239	160	7	conducive	conducive	ADJ
fbem-28239	160	8	to	to	ADP
fbem-28239	160	9	widespread	widespread	ADJ
fbem-28239	160	10	application	application	NOUN
fbem-28239	160	11	and	and	CCONJ
fbem-28239	160	12	promotion	promotion	NOUN
fbem-28239	160	13	.	.	PUNCT
fbem-28239	161	1	in	in	ADP
fbem-28239	161	2	summary	summary	NOUN
fbem-28239	161	3	,	,	PUNCT
fbem-28239	161	4	providing	provide	VERB
fbem-28239	161	5	more	more	ADJ
fbem-28239	161	6	coefficients	coefficient	NOUN
fbem-28239	161	7	to	to	ADP
fbem-28239	161	8	the	the	DET
fbem-28239	161	9	recurrent	recurrent	ADJ
fbem-28239	161	10	neural	neural	ADJ
fbem-28239	161	11	network	network	NOUN
fbem-28239	161	12	does	do	AUX
fbem-28239	161	13	not	not	PART
fbem-28239	161	14	necessarily	necessarily	ADV
fbem-28239	161	15	lead	lead	VERB
fbem-28239	161	16	to	to	ADP
fbem-28239	161	17	better	well	ADJ
fbem-28239	161	18	prediction	prediction	NOUN
fbem-28239	161	19	results	result	NOUN
fbem-28239	161	20	.	.	PUNCT
fbem-28239	162	1	in	in	ADP
fbem-28239	162	2	practical	practical	ADJ
fbem-28239	162	3	applications	application	NOUN
fbem-28239	162	4	,	,	PUNCT
fbem-28239	162	5	it	it	PRON
fbem-28239	162	6	is	be	AUX
fbem-28239	162	7	necessary	necessary	ADJ
fbem-28239	162	8	to	to	PART
fbem-28239	162	9	carefully	carefully	ADV
fbem-28239	162	10	select	select	VERB
fbem-28239	162	11	and	and	CCONJ
fbem-28239	162	12	adjust	adjust	VERB
fbem-28239	162	13	the	the	DET
fbem-28239	162	14	number	number	NOUN
fbem-28239	162	15	of	of	ADP
fbem-28239	162	16	coefficients	coefficient	NOUN
fbem-28239	162	17	in	in	ADP
fbem-28239	162	18	the	the	DET
fbem-28239	162	19	model	model	NOUN
fbem-28239	162	20	,	,	PUNCT
fbem-28239	162	21	and	and	CCONJ
fbem-28239	162	22	determine	determine	VERB
fbem-28239	162	23	the	the	DET
fbem-28239	162	24	most	most	ADV
fbem-28239	162	25	suitable	suitable	ADJ
fbem-28239	162	26	model	model	NOUN
fbem-28239	162	27	structure	structure	NOUN
fbem-28239	162	28	and	and	CCONJ
fbem-28239	162	29	parameter	parameter	NOUN
fbem-28239	162	30	settings	setting	NOUN
fbem-28239	162	31	through	through	ADP
fbem-28239	162	32	experiments	experiment	NOUN
fbem-28239	162	33	and	and	CCONJ
fbem-28239	162	34	evaluations	evaluation	NOUN
fbem-28239	162	35	to	to	PART
fbem-28239	162	36	balance	balance	VERB
fbem-28239	162	37	the	the	DET
fbem-28239	162	38	accuracy	accuracy	NOUN
fbem-28239	162	39	,	,	PUNCT
fbem-28239	162	40	generalization	generalization	NOUN
fbem-28239	162	41	ability	ability	NOUN
fbem-28239	162	42	,	,	PUNCT
fbem-28239	162	43	and	and	CCONJ
fbem-28239	162	44	computational	computational	ADJ
fbem-28239	162	45	cost	cost	NOUN
fbem-28239	162	46	of	of	ADP
fbem-28239	162	47	the	the	DET
fbem-28239	162	48	model	model	NOUN
fbem-28239	162	49	.	.	PUNCT
fbem-28239	163	1	references	reference	NOUN
fbem-28239	163	2	[	[	X
fbem-28239	163	3	1	1	NUM
fbem-28239	163	4	]	]	PUNCT
fbem-28239	163	5	chen	chen	PROPN
fbem-28239	163	6	z	z	PROPN
fbem-28239	163	7	y	y	PROPN
fbem-28239	163	8	,	,	PUNCT
fbem-28239	163	9	&	&	CCONJ
fbem-28239	163	10	li	li	PROPN
fbem-28239	163	11	c	c	PROPN
fbem-28239	163	12	q.	q.	PROPN
fbem-28239	163	13	(	(	PUNCT
fbem-28239	163	14	2017	2017	NUM
fbem-28239	163	15	)	)	PUNCT
fbem-28239	163	16	.	.	PUNCT
fbem-28239	164	1	media	medium	NOUN
fbem-28239	164	2	reporting	reporting	NOUN
fbem-28239	164	3	and	and	CCONJ
fbem-28239	164	4	asset	asset	NOUN
fbem-28239	164	5	pricing	pricing	NOUN
fbem-28239	164	6	:	:	PUNCT
fbem-28239	164	7	a	a	DET
fbem-28239	164	8	review	review	NOUN
fbem-28239	164	9	.	.	PUNCT
fbem-28239	165	1	foreign	foreign	ADJ
fbem-28239	165	2	economics	economic	NOUN
fbem-28239	165	3	and	and	CCONJ
fbem-28239	165	4	management	management	NOUN
fbem-28239	165	5	,	,	PUNCT
fbem-28239	165	6	39(3	39(3	NUM
fbem-28239	165	7	)	)	PUNCT
fbem-28239	165	8	,	,	PUNCT
fbem-28239	165	9	16	16	NUM
fbem-28239	165	10	.	.	PUNCT
fbem-28239	166	1	[	[	X
fbem-28239	166	2	2	2	NUM
fbem-28239	166	3	]	]	X
fbem-28239	166	4	melissa	melissa	PROPN
fbem-28239	166	5	vergara	vergara	PROPN
fbem-28239	166	6	-	-	PUNCT
fbem-28239	166	7	fernández	fernández	PROPN
fbem-28239	166	8	,	,	PUNCT
fbem-28239	166	9	conrad	conrad	PROPN
fbem-28239	166	10	heilmann	heilmann	PROPN
fbem-28239	166	11	,	,	PUNCT
fbem-28239	166	12	marta	marta	PROPN
fbem-28239	166	13	szymanowska	szymanowska	PROPN
fbem-28239	166	14	,	,	PUNCT
fbem-28239	166	15	describing	describe	VERB
fbem-28239	166	16	model	model	NOUN
fbem-28239	166	17	relations	relation	NOUN
fbem-28239	166	18	:	:	PUNCT
fbem-28239	166	19	the	the	DET
fbem-28239	166	20	case	case	NOUN
fbem-28239	166	21	of	of	ADP
fbem-28239	166	22	the	the	DET
fbem-28239	166	23	capital	capital	NOUN
fbem-28239	166	24	asset	asset	NOUN
fbem-28239	166	25	pricing	pricing	NOUN
fbem-28239	166	26	model	model	NOUN
fbem-28239	166	27	(	(	PUNCT
fbem-28239	166	28	capm	capm	PROPN
fbem-28239	166	29	)	)	PUNCT
fbem-28239	166	30	family	family	NOUN
fbem-28239	166	31	in	in	ADP
fbem-28239	166	32	financial	financial	ADJ
fbem-28239	166	33	economics	economic	NOUN
fbem-28239	166	34	,	,	PUNCT
fbem-28239	166	35	studies	study	NOUN
fbem-28239	166	36	in	in	ADP
fbem-28239	166	37	history	history	NOUN
fbem-28239	166	38	and	and	CCONJ
fbem-28239	166	39	philosophy	philosophy	NOUN
fbem-28239	166	40	of	of	ADP
fbem-28239	166	41	science	science	NOUN
fbem-28239	166	42	,	,	PUNCT
fbem-28239	166	43	volume	volume	NOUN
fbem-28239	166	44	97	97	NUM
fbem-28239	166	45	,	,	PUNCT
fbem-28239	166	46	2023	2023	NUM
fbem-28239	166	47	,	,	PUNCT
fbem-28239	166	48	[	[	X
fbem-28239	166	49	3	3	X
fbem-28239	166	50	]	]	X
fbem-28239	166	51	chen	chen	PROPN
fbem-28239	166	52	yiying	yiying	PROPN
fbem-28239	166	53	,	,	PUNCT
fbem-28239	166	54	zhang	zhang	PROPN
fbem-28239	166	55	zexing	zexing	PROPN
fbem-28239	166	56	,	,	PUNCT
fbem-28239	166	57	&	&	CCONJ
fbem-28239	166	58	li	li	PROPN
fbem-28239	166	59	wenbin	wenbin	PROPN
fbem-28239	166	60	(	(	PUNCT
fbem-28239	166	61	2014	2014	NUM
fbem-28239	166	62	)	)	PUNCT
fbem-28239	166	63	.	.	PUNCT
fbem-28239	167	1	a	a	DET
fbem-28239	167	2	neural	neural	ADJ
fbem-28239	167	3	network	network	NOUN
fbem-28239	167	4	-	-	PUNCT
fbem-28239	167	5	based	base	VERB
fbem-28239	167	6	stock	stock	NOUN
fbem-28239	167	7	price	price	NOUN
fbem-28239	167	8	prediction	prediction	NOUN
fbem-28239	167	9	model	model	NOUN
fbem-28239	167	10	computer	computer	NOUN
fbem-28239	167	11	applications	application	NOUN
fbem-28239	167	12	and	and	CCONJ
fbem-28239	167	13	software	software	NOUN
fbem-28239	167	14	,	,	PUNCT
fbem-28239	167	15	31	31	NUM
fbem-28239	167	16	(	(	PUNCT
fbem-28239	167	17	5	5	NUM
fbem-28239	167	18	)	)	PUNCT
fbem-28239	167	19	,	,	PUNCT
fbem-28239	167	20	4	4	NUM
fbem-28239	167	21	[	[	SYM
fbem-28239	167	22	4	4	X
fbem-28239	167	23	]	]	X
fbem-28239	167	24	cao	cao	PROPN
fbem-28239	167	25	aiqing	aiqing	PROPN
fbem-28239	167	26	,	,	PUNCT
fbem-28239	167	27	&	&	CCONJ
fbem-28239	167	28	wu	wu	PROPN
fbem-28239	167	29	miao	miao	PROPN
fbem-28239	167	30	.	.	PUNCT
fbem-28239	168	1	(	(	PUNCT
fbem-28239	168	2	2022	2022	NUM
fbem-28239	168	3	)	)	PUNCT
fbem-28239	168	4	.	.	PUNCT
fbem-28239	169	1	daily	daily	ADJ
fbem-28239	169	2	stock	stock	NOUN
fbem-28239	169	3	price	price	NOUN
fbem-28239	169	4	prediction	prediction	NOUN
fbem-28239	169	5	based	base	VERB
fbem-28239	169	6	on	on	ADP
fbem-28239	169	7	long	long	ADJ
fbem-28239	169	8	short	short	ADJ
fbem-28239	169	9	-	-	PUNCT
fbem-28239	169	10	term	term	NOUN
fbem-28239	169	11	memory	memory	NOUN
fbem-28239	169	12	neural	neural	ADJ
fbem-28239	169	13	network	network	NOUN
fbem-28239	169	14	.	.	PUNCT
fbem-28239	170	1	information	information	NOUN
fbem-28239	170	2	and	and	CCONJ
fbem-28239	170	3	computers	computer	NOUN
fbem-28239	170	4	(	(	PUNCT
fbem-28239	170	5	034	034	NUM
fbem-28239	170	6	-	-	PUNCT
fbem-28239	170	7	001	001	NUM
fbem-28239	170	8	)	)	PUNCT
fbem-28239	170	9	.	.	PUNCT