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http://www.dem.umk.pl/dem 

D Y N A M I C  E C O N O M E T R I C  M O D E L S  
DOI: http://dx.doi.org/10.12775/DEM.2017.001  Vol. 17 (2017) 5−18 

Submitted December 28, 2016  ISSN (online) 2450-7067 

Accepted October 11, 2017 ISSN (print) 1234-3862 

Tomasz Schabek
 *
, Henrique Castro  

“Sell not only in May”  
Seasonal Effects on Stock Markets 

A b s t r a c t. Described in literature stock market anomaly still remains unexplained. In long 

time series regressions and wide geographical spread research, “Halloween effect” is signifi-

cant on 19 amongst 73 markets, but also on 11 amongst 23 with long time series data. The 

data shows that abnormal returns could be realized also in strategies starting in October, 

November and December. We conclude that even with control of weather (sun hours), behav-

ioral (sentiment index, number of IPOs) and macroeconomic (industrial production) factors, 

the effect persists. 

K e y w o r d s: seasonal anomaly; behavioral factor; Halloween indicator; January effect; sell 

in May.  

J E L Classification: G15; Q47; G10; G14. 

Introduction 

Seasonal anomalies are widely discussed in financial literature because of 

their unknown nature and relative simplicity in application as market strate-

gies. “Halloween effect” as one of them is subject of many articles. It was 

analyzed and tested on broad range of markets in Bauman and Jacobsen 

(2002). Briefly speaking we can describe it as anomaly that is derived from 

old market saying: “Sell in May and go away”. Halloween indicator is an-

other name to similar strategy that is to have long position on the market 

                                                 
* Correspondence to: Tomasz Schabek, University of Lodz, Faculty of Economic and So-

ciology, 3/5 Polskiej Organizacji Wojskowej Street, 90-255 Lodz, Poland, e-mail: 

tschabek@uni.lodz.pl; Henrique Castro, School of Economics, Business and Accounting of 

the University of São Paulo, São Paulo, Brazil, e-mail: hcastro@usp.br. 

https://orcid.org/0000-0002-4362-8864


Tomasz Schabek, Henrique Castro 

DYNAMIC ECONOMETRIC MODELS 17 (2017) 5–18 

6 

from October 31 till April 30 each year – described in O'Higgins and 

Downes (1990). Although it has its beginnings only in market saying, nu-

merous of studies has proven that it is still profitable and valid investment 

strategy. In our paper we focus on finding possible explanations of the 

anomaly. We use set of the variables representing fundamental and behav-

ioral factors that could influence market participant’s behavior. We verify 

hypothesis about the existence of similar effects for all strategies starting in 

winter and finishing in summer months. Weather factors has been broadly 

discussed as the possible reason of the anomaly, we address also this issue. 

Our research brings an important contribution to existing literature because 

we test Halloween effect in the context of behavioral variables. It allows to 

directly check if changes in investor sentiment are responsible for seasonal 

fluctuations. We used sentiment measure proposed by Baker and Wurgler 

(2007) which is solely based on market factors. We also analyzed the role of 

consumer confidence and industrial production. Secondly we directly test 

influence of daylight in seasonal anomalies based strategies to confirm or 

reject the hypothesis of its influence on Halloween effect. Thirdly we con-

firm the existence of similar seasonal effects not only in end of October – 

end of April period but also from end of September (November) till end of 

March (May).  

1. Literature Review 

Numerous of studies: Andrade, Chhaochharia and Fuerst (2013), Jacobsen 

and others (2005), Jacobsen and Visaltanachoti (2009), Zarour (2007), Lean 

(2011) have confirmed the existence and significance of Sell in May or Hal-

loween effects. Researchers tried to explain the anomaly in many different 

ways. Bouman and Jacobsen (2002) in their research control for risk, Janu-

ary effect, changes in interest rates and volume and stated that even though 

the anomaly persists, they suggested summer holidays as the explanation of 

the anomaly (liquidity needs or changed risk aversion during vacations). 

Summer holiday as a reason of the anomaly is supported by Hong and Yu 

(2009) which concluded that trading activity drops in summer months, also 

recent findings of Kaustia and Rantapuska (2016) seems to be in favor of 

holiday’s hypothesis. Kamstra, Kramer and Levi (2003) postulated Seasonal 

Affective Disorder (SAD) as the main reason of Halloween anomaly, their 

research however was heavily criticized in Kelly, Meschke (2010), Jacobsen 

and Marquering (2008, 2009). Maberly and Pierce (2004) questioned the 

existence of Halloween effect as caused by outliers in October 1987 and 

August 1998 but Haggard and Witte (2010) acquire the results using robust 



“Sell Not Only in May”. Seasonal Effect on Stock Markets 

DYNAMIC ECONOMETRIC MODELS 17 (2017) 7–18 

7 

estimation technique and confirms significant Halloween anomaly. Jacobsen 

and others (2005) checked if Halloween effect is dependent on size, book to 

market or divided yield and conclude that it is not the case. Gerlach (2007) 

found that returns from October to December are higher because of macroe-

conomic announcements but Gugten (2010) reports that even with control of 

this factor Halloween anomaly still exists. Lucey and Zhao (2007) postulated 

that January effect is the reason, but those results were questioned by Hag-

gard and Witte (2010), because of short sub periods used in Lucey and Zhao 

(2007) tests. Doeswijk (2008) hypothesizes that investors behavior is driven 

by optimism cycles. He stated that size and value factor do not influence 

“sell in May” effect, but behavioral factor – initial returns from IPOs has 

explanatory power. In study of Jacobsen and Zhang (2012a) they use sample 

of 108 markets and very long time series (21 of those markets have more 

than 1000 monthly observations). They provide thorough and detailed analy-

sis of persistence and significance of the Halloween anomaly. Their findings 

confirm significant and strengthening Halloween effect. We rely on their 

studies therefore we will not replicate them in detailed way as Jacobsen and 

Zhang (2012a) to reaffirm existence of Halloween effect, instead we will 

focus on verification of possible explanations of the effect. In recent paper 

Jacobsen and Zhang (2012) analyze 317 years of United Kingdom stock 

prices data. Such long time data series allowed testing Halloween effect in 

100- and 50- years subsample intervals. They report significant 0,56% high-

er winter returns than summer returns. May effect is present in all of their 

100- and 50- years periods. 

2. Data 

In our study we divide used data into the following groups: 

a) Stock markets monthly rates of return. All rates of return are calculated 

using last stock market session of the month closing price (in local cur-

rency). Closing prices are acquired from Routers database. The longest 

period covered is: Jan 1964 to Jun 2012
1
. 

b) US market sentiment is measured by Baker and Wurgler (2007) monthly 

sentiment index and acquired directly from Wurgler web-page
2
. It co-

vers the period of July 1965 to Dec 2010. 

                                                 
1 Different markets are covered with different periods as the effect of the fact that some 

analyzed market have shorter time series of required data. 
2 http://www.stern.nyu.edu/~jwurgler/ 



Tomasz Schabek, Henrique Castro 

DYNAMIC ECONOMETRIC MODELS 17 (2017) 5–18 

8 

c) Number of Initial Public Offering (IPO) is calculated based on data ob-

tained from Reuters One database. The longest period starts from Jan 

1970 and finishes in Jun 2012. 

d) Industrial production – gathered from World Bank Global Economic 

Monitor (GEM) database and OECD. Covers period between Jan 1991 

to Jun 2012 depending on country. 

e) Consumer Confidence Indicator (CCI) – acquired from OECD database. 

The longest period covered is: Jan 1964 to Jun 2012. 

f) Sun hours – data comes from World Meteorological Organization, Hong 

Kong Observatory and national meteorological institutes of analyzed 

countries. 

We analyze 73 indexes that cover 68
3
 countries and 2 non-country indexes 

(MSCI World and CRB commodity index). The selection of countries was 

driven by maximum possible geographical coverage and data availability. 

3. Methodology 

First objective of this study is to find presence of “Sell in May and go away” 

(SiM) and Halloween (Hal) effects in a group of emerging and developed 

capital markets. For this purpose we used methodology similar to Bouman 

and Jacobsen (2002). Our second motive is to check hypothesis of signifi-

cant role of daylight, U.S. market sentiment, number of IPOs, and CCI as 

possible causes of this effect. We also examine investing strategies starting 

in months other than May. Our tests imitate strategy that could be utilized by 

investors on stock markets. This approach is very similar to buy-and-hold 

except that it keeps long position for six months of each year and during the 

rest of the year it takes form of passive “out of the market” strategy. We are 

examining if the starting month of the strategy plays any role and creates 

similar to SiM anomalies and if yes, could it be explained by set of non-

fundamental variables. This is the third motive of our study – to check if 

behavioral variables as postulated in Doeswijk (2008) are responsible for 

SiM/Hal and similar effects. To identify SiM and other month’s seasonal 

effects we run following time series regressions (1) or (2) for each market: 

  
                  

          
    
  ,  (1) 

if strategy include January, 

or: 

  
               

  ,  (2) 

                                                 
3 US and Chinese markets are represented by accordingly three and two indexes. 



“Sell Not Only in May”. Seasonal Effect on Stock Markets 

DYNAMIC ECONOMETRIC MODELS 17 (2017) 7–18 

9 

if strategy does not include January.  

Where:   
   is the monthly rate of return calculated for i-th market index 

(from 1 to 73),          is the dummy variable equal 1 when month t falling 

into the period of 6-months strategy initiated in the last day of month MON 

(except Jan) and finishing in last day of month MON + 6 (except Jan) and 0 

otherwise.         
  is the dummy variable equal 1 when (t=Jan), 0 other-

wise.      is the dummy variable equal 1 when month t falling into the 

period of 6-months strategy initiated in the last day of month MON and fin-

ishing in last day of month MON + 6 and 0 otherwise; t symbolize time and 

MON symbolize months (Jan, Feb, …, Dec).  

 For instance if we analyze strategy starting in last day of April (MON = 

Apr) regression (1) takes the following form:   
                   

  

and means that dummy variable          is equal 1 for months May till Oct 

and 0 otherwise
4
. In the case of strategy starting in last day of October 

(MON = Oct) regression (2) takes form of:   
                   

        
    

  and means that dummy variable          is equal 1 for 

months Nov, Dec, Feb, Mar, Apr and 0 otherwise. Dummy variable 

        
  is equal 1 for Jan and 0 otherwise.  

 In this part of the research we estimated 876 regressions (73 markets 

times 12 different starting months for our strategies). In these regressions we 

used estimation method introduced by Huber (1973) (function rlm() in R 

program)
5
. We run regressions (1) and (2) with controlling (when possible – 

i.e. on 30 markets due to unavailability of data for other markets) for growth 

rate of industrial production as macroeconomic measure of business cycle 

and Consumer Confidence Index – expressing consumer confidence that can 

be proxy of investor sentiment (as in e.g. Qiu, Welch,  2006). After identifi-

cation of seasonal effects we moved to examining possible explanations of 

them. For ten markets with longest available IPO history (US, UK, Japan, 

Canada, Australia, China, Hong Kong, India, South Korea and Taiwan) we 

can run time series regressions where number of IPOs, U.S. market senti-

ment, macroeconomic variable controlling for cyclical changes (industrial 

production) and dummy variable representing each month, describe rates of 

return of selected market indices. In this approach we assume ad hoc that 

                                                 
4 We analyze monthly rates of return, but strategy is named after the level of index in giv-

en month so when strategy starts in the last day of month MON, the first rate of return is for 

month MON+1. 
5 Relying on Jacobsen and Zhang (2012a) where coefficients obtained from GARCH and 

OLS models are similar we did not run GARCH regressions. 



Tomasz Schabek, Henrique Castro 

DYNAMIC ECONOMETRIC MODELS 17 (2017) 5–18 

10 

sentiment of U.S. investors affects other markets because of large capital 

flows and primary role of American stock exchange. There is no stock mar-

ket related
6
 sentiment index data for other than US countries publically 

available. We also used the proxy of stock market sentiment for other mar-

kets – Consumer Confidence Indicator, delivered by OECD. We could not 

conduct this analysis for all the markets in our sample due to not enough IPO 

observation and lack of sentiment data, therefore we chose only ten men-

tioned above markets. Ten regressions for each market used in this part of 

analysis take form of equation (3): 

   
 
       

 
       

     
           

 
  

                    
           

 
   
 
,  (3) 

where:   
 
 is the monthly rate of return calculated for j-th biggest market 

index (from 1 to 10) in month t,        is the dummy variable that takes 

value 1 when month of measured rate of return is equal respectively k = Jan, 

Feb, Mar, … Nov, otherwise 0.     
 
 symbolizes number of IPOs in month t 

for market j,      
   is the Baker-Wurgler sentiment index in month t, 

      
 
 is percentage change in industrial production in month t, for coun-

try j.  

 As mentioned above we run similar to (3) regressions but with Consumer 

Confidence Indicator (CCI) for each country. Symbolic representation of this 

regression is following: 

  
 
       

 
       

 

    

   

        
 
  

                   
     
         

 
   
 
,  (4) 

where: all symbols as previously except      
     

 which is CCI for j-th 

country.  

 Both (3) and (4) equations were tested for autocorrelation and possible 

ARCH effects – when found we applied accordingly MA and/or ARCH 

models, after that statistical properties of models was test again and this pro-

cedure was repeated till elimination of autocorrelation or ARCH effects. 

Results of those estimations are presented in tables 1 and 2. 

Next step in exploring possible reasons of seasonal effects is to include in 

regressions weather variable (sunlight). In this part we tested returns directly 

                                                 
6 Like in Baker and Wurgler sentiment index, that is based exclusively on market data. 



“Sell Not Only in May”. Seasonal Effect on Stock Markets 

DYNAMIC ECONOMETRIC MODELS 17 (2017) 7–18 

11 

from described previously 6-month strategies. In cross sectional regressions 

we regressed average returns from those strategies in the following manner
7
: 

          
                    

       
   

                                       
    

  ,  (5)
8
 

where:           
  is average rate of return for six month strategies starting at 

the end of month m and at the end of month n – six month later in sequence 

for each of i-indexes;             
  (             

 ) is average number of 

sunlight hours (standard deviation) for six month strategies starting at the 

end of month m and at the end of month n – six month later;   
 is the dum-

my variable with value 1 for the second month of pair (m, n); symbol m and 

n marks pairs of months [(Jan, Jul), (Feb, Aug), … (Jun, Dec)] with six 

months “distant” from each other.  

 We used pairs of data vectors to avoid multicollinearity due to the fact 

that we are using averages that in other regression settings would cause over-

lapping problem. We also wanted to compare averages between cold and hot 

halves of the year across the countries. We repeated calculations described in 

equation (5) but only for markets on which we found presence of SiM ef-

fect.
9
 

4. Results 

The outcomes of our analysis generally support existence of Sell in May and 

Halloween effect but also seasonal effects in other months. We can observe 

that
10

 estimated    coefficients from (1) or (2) are mostly negative for strate-

gies starting at the end of northern hemisphere spring months (Mar, Apr, 

May) and mostly positive for strategies beginning in last days of autumn 

months (Sep, Oct, Nov). Numbers of markets that can be characterized by 

SiM effect vary from 19 to 31 (11 to 21) when we use 0.10 (0.05) p-values 

for judging the significance of   . Comparing to the total number of ana-

                                                 
7 We cannot use longitudinal (time series cross sectional) regressions due to lack of publi-

cally available time series data for numbers of sun hours in each month of the particular year. 
8 We also run this regression with additional                

  part, where             
  is 

average number of IPOs but because of only few countries having those date appropriately 

frequent i.e. without too many “zeros”, we do not report those low power regressions, even if 

they have similar results. 
9 We also run regressions with control of latitude of cities where analyzed stock exchang-

es are located; those results are similar and not presented here, but available upon request. 
10 Exact can be provided upon request. 



Tomasz Schabek, Henrique Castro 

DYNAMIC ECONOMETRIC MODELS 17 (2017) 5–18 

12 

lyzed indices (73) those numbers seem to be small, but what is worth notic-

ing out of 10 stock exchanges which total capitalization corresponds to over 

70% of World Federation of Exchanges (WFE) global capitalization, seven 

have significant SiM effect
11

. Seasonal effects are valid for most important 

and with the longest data series stock market indices. We did not test pres-

ence of SiM effect in particular sub periods (due to relative short data sam-

ples) but according to the newest research of Jacobsen and Zhang (2012, 

2012a) we can conclude that SiM effect is persistent over time. Our results 

with respect to the emerging markets with relatively short data series are 

similar to mentioned in Jacobsen and Zhang (2012a) paper and we conclude 

that even if SiM effect is not present for all selected markets it can be due to 

lack of long time series data for them. One scratch on surface of hypothesis 

proving persistence of SiM effect is significantly lower number
12

 of SiM and 

Hal effects when we run our tests and start our samples just after Bouman 

and Jacobsen publication in 2002. Similar conclusion could be found in Ja-

cobsen and Zhang paper: only three markets
13

 are characterized by signifi-

cant “Halloween effect” in period 2001–2011. Again those results could 

mean the disappearing SiM and Hal anomalies after making them publicly 

known in scientific papers or it just could be caused by relatively short time 

series. Other additional and not less interesting question is about the pres-

ence and persistence of January effect and its share in Halloween effect. Our 

results suggest that January effect plays less important role in observed sea-

sonal anomalies. For six-months strategies that include January, only in eight 

to eleven (depending on the months used in estimates) regressions given by 

equation (1)    parameter is significant with p-value of 0.05.  

Another test conducted in this research checks if – in regressions based on 

time series data – seasonal effects still exist after controlling for number of 

IPOs, level of US sentiment index and changes of industrial production. In 

time series regression analysis we need to acquire reasonable amount of 

data. While both descriptive variables: US sentiment index (or Consumer 

Confidence Index) and industrial production have continued time series, the 

number of IPO for most of the markets is characterized by incomparably 

higher number of “zero” data due to not as often as in the largest markets 

occurrence of IPOs. We choose the following approach to solve this prob-

                                                 
11 Members of World Federation of Exchanges (WFE) are the biggest and most important 

stock exchanges in the world; data from WFE, December 2012. 
12 Results can be provided upon request. 
13 Table 5 in Jacobsen and Zhang (2012a). 



“Sell Not Only in May”. Seasonal Effect on Stock Markets 

DYNAMIC ECONOMETRIC MODELS 17 (2017) 7–18 

13 

lem: in regressions (3) and (4) we include only those markets that meet two 

following conditions: 

1. Has at least 50 observations 

2. Has 95% or more of non-zero IPO data (i.e. number of non-zero IPO 

months – counted from given month to end of analyzed period – divided 

by the number of months in analyzed period is equal or greater then 

0.95) 

This simple algorithm provides us ten indices that can be characterized by 

frequent IPOs for longer period of time. Outcomes of equation (3) and (4) 

presented in tables 1 and 2 are similar. We can find there that statistically 

significant seasonal effects – represented by coefficients related to particular 

months, exist. Tables 1 and 2 shows January effect existing in Korea, UK, 

Hong Kong and India
14

. Most of the statistically significant seasonal anoma-

lies, with negative coefficients, fall in the periods between April-September 

(or similarly: May–October) and it causes the cumulative rate of returns of 

6-months investing strategies performing better, when started in Autumn 

months. Even with behavioral and macroeconomic control variables we still 

observe seasonal effects in all (excluding Taiwan) markets. Significantly 

lower rates in winter months means falling prices of stock indexes that leads 

to lower cumulative returns in six months strategies. Results
15

 of estimation 

of equation (1) and (2) show us that higher rates of returns occur not only for 

strategies lasting from end of October till end of April (Hallowen effect) but 

the same is true for September – March and November – May. Such coinci-

dence with much different from each other seasons of the year could con-

vince us that it is the weather factor that cause seasonal effects. Indeed many 

authors like Kamstra, Kramer and Levi (2003), Cao and Wei (2005), Saun-

ders (1993), Hirshleifer and Shumway (2003) support such thesis. From the 

other hand there is broad literature that criticize weather factor as cause of 

SiM and Hal effects, for instance: Jacobsen and Marquering (2008), and 

more recently Kaustia and Rantapuska (2016). The final step in our analysis 

is to examine postulated in literature role of sunlight in seasonal anomalies. 

To check this, we run cross-sectional regressions described in equation (5). 

We tested 6-months-strategies rates of returns, with risk (average standard 

deviation for time of the strategy) as control variable
16

.  

                                                 
14 January effect disappear after controlling for Consumer Confidence indicator in the 

case of Korea and Hong Kong – see table 2. 
15 Results available upon request. 
16 We also run these regressions with additional average IPO factor but, due to lack of 

frequent IPOs in most countries we do not present these results. In case of behavioral varia-

bles, where we want to preserve the same source of data and methodology of calculations - 



Tomasz Schabek, Henrique Castro 

DYNAMIC ECONOMETRIC MODELS 17 (2017) 5–18 

14 

Table 1. Coefficients of estimation of equation (3) – d,   ,  ,  ,   for particular 

market 

Variable Australia Canada China Japan Korea 

JAN –0.01 –0.01 –0.04 –0.01 –0.06* 
FEB –0.01 –0.02 0.01 –0.02 –0.02 
MAR –0.02 –0.03* –0.02 0.01 –0.01 
APR –0.02 –0.02 –0.01 0.01 0.02 
MAY 0 0 0.02 –0.02 –0.03 
JUN –0.03* –0.03** –0.06 –0.01 –0.04 
JUL –0.01 –0.01 –0.06 –0.02 –0.02 
AUG –0.02 –0.02 –0.04 –0.01 –0.03 
SEP –0.03** –0.04** –0.03 –0.03 –0.03 
OCT –0.01 –0.01 –0.07 –0.03* –0.06* 
NOV –0.02 –0.02 –0.02 0 –0.01 

       –0.01* –0.01** 0.04** –0.02*** –0.01 
INDPR 0.72 –0.18 2.17*** 0.29 0.51* 

IPO 0 0 0 0 0.01** 
d 0.02 0.02 0.01 0.01 0.02 

Variable UK US Hong Kong India Taiwan 

JAN –0.03** –0.01 –0.04* –0.09** –0.04 
FEB –0.02 –0.02 0.01 –0.07* –0.01 
MAR –0.02 –0.02 –0.04** –0.05 –0.02 
APR –0.01 –0.01 0 –0.01 –0.01 
MAY –0.03** –0.01 0 –0.05 –0.01 
JUN –0.02 –0.03** –0.02 –0.06 –0.04 
JUL –0.01 –0.02 0 0 –0.01 
AUG –0.02 –0.02 –0.03 –0.03 –0.03 
SEP –0.03* –0.02 –0.02 0 –0.03 
OCT –0.01 –0.01 0 –0.07* –0.03 
NOV –0.01 –0.01 –0.01 –0.04 –0.02 

       –0.01* –0.01*** –0.02* –0.11*** –0.05** 
INDPR 0.36 –0.72*** 1.79*** 0.69 0.69*** 

IPO 0 0 0 0 0 
d 0.03** 0.02** 0.02 0.05 0.02 

Note: table contains estimations of coefficients from equation (3). Significance levels: *** – 0.01, ** – 

0.05, * – 0.10. Data cover maximum possible period for each market.      symbolizes number of IPOs 

in month for market j,       marks growth rate of industrial production for each market,        
symbolizes Baker-Wurgler sentiment index. 

  

                                                                                                                   
we could not include CCI factors in cross sectional regressions of 6 months strategies because 

CCI is calculated by OECD in the way that six months moving averages have the same val-

ues. 



“Sell Not Only in May”. Seasonal Effect on Stock Markets 

DYNAMIC ECONOMETRIC MODELS 17 (2017) 7–18 

15 

Table 2. Coefficients of estimation of equation (4) –           , for particular 

market 

Variable Australia Canada China Japan Korea 

JAN –0.01 –0.01 –0.03 –0.01 –0.04 

FEB –0.01 –0.01 0.01 –0.01 –0.01 

MAR –0.03* –0.03** –0.02 0 0.01 

APR –0.02 –0.02* –0.01 0 0.01 

MAY –0.01 –0.01 0.01 –0.03* –0.03 

JUN –0.03** –0.04*** –0.07* –0.02 –0.03 

JUL –0.01 –0.01 –0.06 –0.02 0 

AUG –0.02 –0.02 –0.04 –0.02 –0.02 

SEP –0.04** –0.04*** –0.03 –0.03* –0.01 

OCT 0 –0.01 –0.06 –0.03* –0.05* 

NOV –0.02 –0.02 –0.03 0 –0.01 

        0 0 0 0.01*** 0 

INDPR 0.44 –0.24 1.85*** 0.33* 0.56** 

IPO 0 0 0 –0.001* 0.01** 

g –0.36 –0.25 0.03 –0.88*** 0.27 

Variable UK US Hong Kong India Taiwan 

JAN –0.03** –0.01 –0.03 –0.07** –0.04 

FEB –0.02 –0.02 0 –0.06* –0.02 

MAR –0.02 –0.02 –0.04* –0.03 –0.04 

APR –0.01 –0.01 0 –0.01 –0.02 

MAY –0.03** –0.01 –0.01 –0.06* –0.03 

JUN –0.02* –0.03* –0.03 –0.05 –0.04 

JUL –0.01 –0.01 0 –0.02 –0.03 

AUG –0.02* –0.02* –0.03 –0.04 –0.05 

SEP –0.03** –0.02 –0.02 0.01 –0.03 

OCT –0.01 0 0 –0.05 –0.06 

NOV –0.01 –0.01 –0.02 –0.04 –0.02 

        0.01** 0 – – – 

INDPR 0.19 –0.74*** 1.96*** 0.33 0.59*** 

IPO –0.0006** 0 0 0 0 

g –0.35* –0.12 0.03 0.06** 0.04 

Note: table contains estimations of coefficients from equation (4). Significance levels: *** – 0.01, ** – 

0.05, * – 0.10. Data cover maximum possible period for each market.     symbolizes number of IPOs in 

month t for market j,       marks growth rate of industrial production for each market,         sym-
bolizes Consumer Confidence indicator for market j. For Hong Kong, India and Taiwan CCI data are not 

available. 

The results of these regressions prove that sunlight hours have no significant 

explanation power in the tested models
17

. Even if we limit our sample to 

                                                 
17 Results available upon request. 



Tomasz Schabek, Henrique Castro 

DYNAMIC ECONOMETRIC MODELS 17 (2017) 5–18 

16 

these markets on which SiM effect is present, sunlight is significant only in 

one of our regressions. 

Conclusions 

Financial market puzzle described by Bouman and Jacobsen (2002) is still 

unsolved. Controlling for macroeconomic conditions (industrial production), 

behavioral variables (Consumer Confidence Index, number of IPOs and 

Baker–Wurgler sentiment Index) and weather factor (sun hours) did not 

cause disappearing of “Sell in May” or “Halloween” effects. We can con-

clude that answer for those anomalies should not be searched in Sun cycles, 

industrial production changes or investors sentiment, as some studies sug-

gest. Existence of persisting for longer time anomalies is calling for new 

research, although after Bauman and Jacobsen publication the effect is fad-

ing – maybe because of exploitation of “Sell in May” and “Halloween” 

strategies or it is just short term disappearance of the anomaly. From the 

other hand more rigorous analysis shows that not only May but all 

spring/autumn months anomalies are present on many stock markets. We can 

conclude that old market saying is still able to deliver some positive rates of 

return to the investors who want to listen to it. 

References  

Andrade, S. C., Chhaochharia, V., Fuerst, M. (2013), "Sell in May and Go Away" Just Won't 

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“Sell not only in May”. Efekty sezonowe na rynkach akcji 

Z a r y s  t r e ś c i. Opisywana w literaturze anomalia sezonowa (upraszczając: „sprzedaj 

w maju, kupuj w październiku”) nadal pozostaje niewyjaśniona. W przeprowadzonych regre-

sjach, bazujących na długich szeregach czasowych oraz na licznej grupie indeksów giełdo-

wych, efekt Halloween jest istotny statystycznie w 19 spośród 73 badanych indeksów, ale 

także w 11 spośród 23 indeksów z najdłuższymi dostępnym szeregami czasowymi. Wyniki 

badań wskazują, że ponadprzeciętne zyski mogą zostać zrealizowane również w strategiach 

zaczynających się w innych miesiącach jesiennych. Stwierdzono, że także po włączeniu do 

testowanych modeli zmienny kontrolnych dotyczących pogody, sentymentu inwestorów, 

zmiennych makroekonomicznych – badana anomalia nadal istnieje. 

S ł o w a  k l u c z o w e: anomalie sezonowe; czynniki behawioralne; efekt Halloween; efekt 

stycznia. 


