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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.2017.009  Vol. 17 (2017) 147−159 

Submitted November 28, 2017  ISSN (online) 2450-7067 

Accepted December 28, 2017 ISSN (print) 1234-3862 

Dorota Witkowska , Krzysztof Kompa
*
 

How the Change of Governing Party Influences  
the Efficiency of Financial Market in Poland  

A b s t r a c t. Financial market seems to be sensitive to political changes, especially when the 

change of governing party is connected with essential changes of the economic development 

concepts. Such situation took place in Poland in 2015, as a result of the presidential and par-

liamentary elections. The aim of our research is to investigate the changes occurred on the 

market, represented by some stable growth open mutual funds, and stock indexes: WIG and 

TBSP. Analysis is provided applying single index and CAPM models, classical investment 

performance measures, and statistical interference. 

K e y w o r d s: stable growth open mutual funds, investment efficiency, Sharpe model, 

CAPM, Sharpe, Treynor and Jensen ratios. 

J E L Classification: G11; C12. 

Introduction 

Financial market seems to be sensitive to political changes, especially when 

the change of governing party is connected with essential changes of the 

concepts concerning economic development. Such situation took place in 

Poland at the end of 2015, as a result of the presidential and parliamentary 

elections that were won by the Law and Justice party (PiS) which was in an 

opposition to the government during two last terms. Now PiS is the largest 

                                                 
* Correspondence to: Krzysztof Kompa, Warsaw University of Life Sciences, Department 

of Econometrics and Statistics, 166 Nowoursynowska Street, 02-787 Warsaw, Poland, e-mail: 

krzysztof_kompa@sggw.pl; Dorota Witkowska, University of Lodz, Faculty of Management, 

Department of Finance and Strategic Management, 22/26 Matejki Street, 90-237 Łódź, Po-

land, e-mail: dorota.witkowska@uni.lodz.pl. 

https://orcid.org/0000-0001-9538-9589
http://orcid.org/0000-0002-2810-6654


Dorota Witkowska, Krzysztof Kompa 

DYNAMIC ECONOMETRIC MODELS 17 (2017) 147–159 

148 

party in the Polish parliament having majority in both chambers of parlia-

ment. The governing party introduced program called “good change” con-

sisting in populist movements such as decreasing of the retirement age or the 

500+ Familly Programme, etc. which burden the economy and may affect 

the financial market.  

 Therefore, here a question arises how the change of ruling party and their 

economic program influence the situation of Polish financial market. The 

answer is not easy especially that both sides i.e. governing and opposite par-

ties presented completely different arguments which had rather political than 

economic character. PiS was emphasizing social benefits of proposed pro-

grams while the opposition was highlighting the economic consequences and 

threats for the budget. Some economists even forecasted that financial mar-

ket in Poland may collapse since investors do not trust markets with high 

political risk which comes from social programs together with controversial 

economic proposals such as increasing taxes from the banking sector or su-

permarkets.  

 Therefore, the aim of our research is to find out how the change on the 

political scene affected the performance of equity and bond markets, togeth-

er with stable growth mutual funds (FIO), applying single index and capital 

assets pricing models together with classical investment performance 

measures and statistical interference.  

1. Data and Methodology 

Our investigation is carried out using daily logarithmic rates of returns from 

selected financial instruments:  

 Warsaw Stock Exchange Index – WIG, representing equity market, 

 Poland’s Official Treasury Bonds Index – TBSP.Index, representing 

bond market, 

 participation units of stable growth open mutual funds (FIO): Credit 

Agricole Stabilnego Wzrostu (denoted as CA), KBC Fundusz Stabilny  

(as KBC), Nationale-Nederlanden Stabilnego Wzrostu (as NN), Pioneer 

Stabilnego Wzrostu (as PIO) and PZU Stabilnego Wzrostu MAZUREK 

(as PZU). 

 The analysis is provided for the period from 10.10.2013 to 9.12.2016 

(the whole period including 826 observations). This time span is divided into 

five pairs of sub-periods according to the selected events which we take into 

consideration:  

 the presidential elections: the first round of election – 10.05.2015, the 

second round of election – 24.05.2015, 



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DYNAMIC ECONOMETRIC MODELS 17 (2017) 147–159 

149 

 the parliamentary election – 25.10.2015, 

 the new government appointment – 16.11.2015, 

 the entry into force 500+ Familly Program – 1.04.2016. 

 The subperiods are defined assuming that the last observation comes 

from 9.12.2017, and sharing date is a day when the distinguished event took 

place. It was our concern to obtain subsamples with similar number of ob-

servations (detailed information about sample sizes are in Table 2).  

 Investigation of returns and risk generated by the investment portfolios 

constructed by selected funds is conducted in several steps, beginning from 

the analysis of the basic parameters and applying statistical interference
1
  

(assuming the significance level 0.05). Denoting by: E(R) – expected returns, 

D
2
(R) – variance of returns, RWIG, RTBSP, RFIO – returns from WIG, TBSP and 

FIO respectively, ,  – parameters of Sharpe model or CAPM, Rbefore, Rafter, 

before, after – returns from the portfolio and beta coefficients before and after 

the considered event, respectively, we verify the null hypotheses concerning:  

1. rates of return levels, i.e.: E(RFIO) = 0; E(RWIG) = 0; E(RTBSP) = 0, 

2. parameters of Sharpe and CAPM models, i.e.:  = 0;  = 0, 

3. comparisons of parameters values in two considered sub-periods i.e.: 

E(Rbefore) = E(Rafter), D
2
(Rbefore) = D

2
(Rafter), before = after, before = after. 

We apply the classical tests for verification hypothesis of returns equity: 

 using the Cochran-Cox test statistics: 

  
     

 
  
 

  
 

  
 

  

 (1) 

 and using the following statistics: 

   
    

  
    (2) 

where for the k-th period,    – average logarithmic rate of return from the 

selected instrument,   
  – variance of return,    – number of observations, B 

– benchmark: B=0, B=R1 or B=R2. The comparison of returns in both period 

is provided using statistics (1) and (2). In the letter case benchmark B is de-

fined as an average value of returns obtained in the second considered period 

(k=1, 2) and the test is provided as two-way test.  

 Comparison of variances is provided using the test with Fisher statistics: 

  
    

 

    
  (3) 

                                                 
1 All formulas (1)-(5) are discussed in (Witkowska 2016, p. 29-55). 



Dorota Witkowska, Krzysztof Kompa 

DYNAMIC ECONOMETRIC MODELS 17 (2017) 147–159 

150 

where,     
      

  – maximal and minimal variance obtained for the both 

compared samples.  

 The shape of the probability distribution of logarithmic rates of return is 

examined on the basis of parametric tests verifying hypothesis that sym-

metry and kurtosis equal to zero, applying the following statistics: 

       
 

  
 
 

 

 
 (4) 

       
  

  
 
 

 

 
 (5) 

where, At, Kt – the third and the fourth central statistical moments of loga-

rithmic rates of returns. 

 The next step of our research is estimation of Sharpe and capital assets 

pricing models on the basis of daily logarithmic rates of return
2
: 

                 (6) 

                            (7) 

where for the t-th period, Rit – rate of return from participation units of the  

i-th stable growth open mutual fund; Rrt – rate of return from the market 

index (WIG); Rrt – rate of return from the risk-free instrument (TBSP); i, i 

– model parameters, it – random component; t – number of observation 

(t=1, 2, …, T). Parameters of both models are estimated using OLS method. 

 Analysis of parameter significance in the models is provided using the 

test statistics
3
: 

  
   

      
 and   

   

      
 (8) 

where,          – parameter estimates,               – standard estimation er-

rors from the models (6) and (7).  

 Comparison of parameters obtained in both comparable periods is made 

applying the test statistics: 

   
         

       
 and    

         

       
 (9a) 

   
         

       
 and    

         

       
 (9b) 

                                                 
2 Models are widely discussed in literature see for instance: (Zamojska 2012, p. 57-60), 

(Witkowska 2016, p. 41-48) 
3 These measures are discussed in (Witkowska 2016, p. 49-50). 



How the Change of Governing Party Influences the Efficiency of Financial Market…  

DYNAMIC ECONOMETRIC MODELS 17 (2017) 147–159 

151 

 The performance of mutual funds is provided using classical measures 

i.e. Sharpe, Treynor and Jensen ratios
4
. The first two measures estimate the 

obtained risk premium evaluated for the unit of risk and the decision about 

the efficiency is made by comparing the values of the ratios obtained by the 

mutual fund and the market index. Jensen ratio is the parameter estimate     

from CAPM, and investment is efficient if     is positive. 

 The comparison of the funds’ efficiency is provided applying measures 

mentioned above which are evaluated for all considered mutual funds in all 

analyzed time spans, assuming that WIG represents market index, and TBSP 

is the risk-free instrument. To recognize if the Sharpe ratios are equal, we 

apply Jobson-Korkie test (Jobson, Korkie, 1981) with Memmel correction 

(Memmel, 2003), using test statistics – see (Blitz, van Vliet, 2007), (Kurach, 

Papla, 2014): 

  
       

 
 

 
                 

     
                

    

 (10) 

where for the k-th period of analysis (k=1,2), WSi – Sharpe ratio, ρ12 – corre-

lation coefficient, (i=1, 2). 

2. Changes of the Equity and Bond Markets 

In the first step of our analysis we investigate daily rates of return of indexes 

WIG and TBSP. Tables 1–3 contain basic characteristics of logarithmic rates 

of return. Bold letters denote rejection of null hypotheses.  

Table 1. Basic characteristics of daily logarithmic rates of return from the both 

benchmarks evaluated for the whole period of analysis 

Basic parameters WIG TBSP Basic parameters WIG TBSP 

min –5.8250% –0.6366% range 8.8302% 1.2498% 
max 3.0052% 0.6133% standard deviation 0.9022% 0.1752% 

arithmetic mean –0.0026% 0.0148% coefficient of variability  343.12 11.80 
median 0.0000% 0.0091% interquartile deviation 0.9530% 0.1958% 
quartile I –0.4495% –0.0774% asymmetry –0.7278 –0.2519 
quartile III 0.5036% 0.1184% kurtosis 4.2876 1.0511 

Note: Bold letters denote rejection of null hypothesis. 

 It is visible (Table 1) that expected rate of returns from Treasury Bonds 

is significantly positive while average returns from equity market is negative 

                                                 
4 The description of the efficiency ratios, their application and discussion can be found in 

many publications. Good examples could be (Borkowski 2014), (Perez 2012), (Zamojska 

2012) and Witkowska (2016). 



Dorota Witkowska, Krzysztof Kompa 

DYNAMIC ECONOMETRIC MODELS 17 (2017) 147–159 

152 

although the null hypothesis cannot be rejected. TBSP can be treated as risk-

free instrument because its variability is very low. Time series of daily rates 

of return from WIG and TBSP are asymmetric with positive kurtosis thus 

they are not normally distributed. However according to the huge number of 

observation we assume that probability distribution is asymptotical normal. 

Table 2. Test statistics verifying the hypothesis about expected returns from both 

indexes in the considered periods H0: E(RWIG) = 0 and H0: E(RTBSP) = 0 

Number of 
observations 

Rates of returns from Number of 
observations 

Rates of returns from 
WIG TBSP TBSP TBSP 

Whole period for the presidential elections: 
10.10.2013–9.12.2016 

Whole period for the parliamentary elections: 
8.09.2014–9.12.2016 

826 –0.0597 1.7347 590 –0.3203 0.9267 

Period before the first round of presidential elec-
tions: 8.10.2013–10.05.2015 

Period after the first round of presidential elec-
tions: 11.05.2015–9.12.2016 

409 0.6326 3.0922 417 –0.6079 0.4903 

Period before the second round of presidential 
elections: 8.10.2013–22.05.2015 

Period after the second round of presidential 
elections: 25.05.2015–9.12.2016 

419 0.5652 2.9436 407 –0.5767 0.5054 

Period before the parliamentary elections: 
8.09.2014–23.10.2015 

Period after the parliamentary elections: 
26.10.2015–9.12.2016 

295 –0.4297 1.3199 295 –0.0541 –0.0376 

Period before the new government appointment: 
20.10.2014–13.11.2015 

Period after the new government appointment: 
16.11.2015–9.12.2016 

280 –0.5547 0.9040 280 0.2423 –0.0931 

Period before entry into force 500+ Familly Pro-
gram: 23.07.2015–31.03.2016 

Period after entry into force 500+Familly Program: 
1.04.2016–9.12.2016 

181 –0.3644 1.0793 181 0.2689 –0.5718 

Note: Bold letters denote rejection of null hypothesis. 

 Analyzing returns from both markets in distinguished 12 periods of con-

sideration, one may notice (Table 2) that only Treasury Bonds generated 

significantly positive rates of return in the periods before both rounds of 

presidential election and in the whole analyzed period. It is visible that 

change on the politic scene caused decline in the both markets. However, the 

performance of the equity market shows slight improvement in the periods 

after new government appointment and when 500+ Family Program entered 

into force. Such situation may be a result of investors emotions and expecta-

tions which revealed earlier and anticipated both events.  

 The obtained results are also validated by the tests which are used for 

comparison of returns in considered sub-periods (Table 3). Higher returns 

are observed only for TBSP before both rounds of presidential election. Eq-

uity market was characterized by the significant increase of risk after the 

government appointment, presidential and parliamentary elections however 



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DYNAMIC ECONOMETRIC MODELS 17 (2017) 147–159 

153 

the risk significantly decreased after the 500+ Family Program went into 

effect.  

Table 3. Test statistics verifying the hypothesis about expected returns and risk  

of equity and bond markets in considered periods E(Rbefore) = E(Rafter), 

D
2
(Rbefore) = D

2
(Rafter) 

Index 

Test statistics evaluated due to formulas  

for returns: for risk   for returns: for risk  

(2) (2)  (1) (3)  (2) (2)  (1) (3) 

 
presidential elections, the 1st round  presidential elections, the 2nd round 

WIG 1.3649 1.1186 0.8652 1.5179  1.2906 1.0112 0.7960 1.5823 
TBSP 2.5649 2.5592 1.8116 1.0241  2.4287 2.3494 1.6886 1.0381 

 
Parliamentary elections  Government appointment 

WIG –0.3663 –0.3125 –0.0574 1.3068  –0.8394 –0.7142 –0.1330 1.3810 
TBSP 1.3560 1.4143 0.2362 1.0040  0.9925 1.0440 0.1758 1.1066 

 
Introduction of 500+ Program  

WIG –0.5945 –0.6945 –0.1231 1.3649  
TBSP 1.5723 1.8236 0.3247 1.3452  

Note: positive values of test statistics denote that returns are bigger before the considered event than after. 
Italic letters denote that risk was smaller after the event than before, bold – rejection of null hypothesis.  

3. Mutual Funds Market 

The mutual fund market is represented by five selected stability growth mu-

tual funds. All these funds started their functioning in Poland in years 1999–

–2003, the “oldest” is FIO PZU, and the “youngest” FIO Credit Agricole. 

Analysis is provided for the sub-periods constructed around both rounds of 

the presidential election because in other sub-periods no essential changes 

was observed. 

Table 4. Values of test statistics (2) verifying the hypothesis about expected returns 

from mutual funds in considered periods H0: E(RFIO) = 0 

Period CA KBC NN PIO PZU 

Whole sample 0.8114 0.4773 0.2734 –0.3305 –0.1463 
Before the presidential elections 1st round 2.2363 1.4043 1.1328 0.4378 0.4042 
After the presidential elections 1st round –0.3498 –0.5631 –0.4387 –1.0512 –0.6572 
Before the presidential elections 2nd round 2.0612 1.2635 1.0595 0.3959 0.3531 
After the presidential elections 2nd round –0.2664 –0.4694 –0.4092 –1.0385 –0.6432 

Note: Bold letters denote rejection of null hypothesis. 

 Analyzing expected returns form participation units of mutual 

funds, it is visible that before both rounds of the presidential election 

mutual funds generated positive returns while after – the negative ones 



Dorota Witkowska, Krzysztof Kompa 

DYNAMIC ECONOMETRIC MODELS 17 (2017) 147–159 

154 

(Table 4). However, the null hypothesis is rejected only for FIO Credit 

Agricole (significantly positive returns are observed before the 

election). 

Tabele 5. Values of statistics (1)–(3) verifying the hypothesis about expected returns 

and risk in considered periods E(Rbefore) = E(Rafter), D
2
(Rbefore) = D

2
(Rafter) 

Period Parameter Formulas no. CA KBC NN PIO PZU 

presidential elections, the 
1st round 

returns (2) 2.6162 1.8575 1.6271 1.5188 1.0643 
(2) 2.2442 2.2198 1.4018 1.4663 1.0493 
(1) 1.7034 1.4246 1.0620 1.0549 0.7472 

risk (3) 1.3856 1.4007 1.3736 1.0939 1.0489 

presidential elections, the 
2nd round 

returns (2) 2.3801 1.6672 1.5480 1.5040 1.0250 
(2) 1.9594 1.9103 1.2780 1.3892 0.9671 
(1) 1.5127 1.2561 0.9856 1.0205 0.7034 

risk (3) 1.4333 1.3515 1.4252 1.1386 1.0912 

Note: Bold letters denote rejection of null hypothesis.   

Table 6. Parameter estimates and determination coefficients of Sharpe models  

Before  After  Before  After  The whole 
period presidential elections, the 1st round  presidential elections, the 2nd round  

beta alfa beta alfa  beta alfa beta alfa  beta alfa 

FIO Credit Agricole  

0.2445 0.0002 0.2405 0.0000  0.2441 0.0002 0.2409 0.0000  0.2425 0.0001 

 R2 0.6230 R2 0.6604  R2 0.6176 R2  0.6641  R2 0.6446 

FIO PZU 

0.4285 0.0000 0.3404 0.0000  0.4274 0.0000 0.3410 0.0000  0.3752 0.0000 

 R2 0.8493 R2 0.7756  R2 0.8444 R2 0.7792  R2 0.8010 

FIO Pioneer 

0.3892 0.0000 0.3347 –0.0001  0.3887 0.0000 0.3349 –0.0001  0.3563 0.0000 

 R2 0.8843 R2 0.9078  R2 0.8821 R2 0.9098  R2 0.8917 

FIO Nationale-Nederlanden 

0.3323 0.0001 0.3240 0.0000  0.3324 0.0001 0.3240 0.0000  0.3274 0.0001 

 R2 0.8672 R2 0.9107  R2 0.8651 R2 0.9125  R2 0.8924 

FIO KBC 
0.4163 0.0002 0.2730 0.0000  0.4159 0.0001 0.2732 0.0000  0.3300 0.0001 

 R2 0.7786 R2 0.7118  R2 0.7748 R2 0.7153  R2 0.7181 

Note: Bold letters denote statistically significant.  

 The better performance of analyzed funds before the election is also 

proved by the results presented in Table 5. As one can see, better perfor-

mance before the election was visible for FIO Credit Agricole and FIO KBC. 

FIO Credit Agricole and FIO Nationale-Nederlanden were characterized by 

significantly smaller risk before election however FIO KBC generated re-



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DYNAMIC ECONOMETRIC MODELS 17 (2017) 147–159 

155 

turns with smaller volatility after the election. Null hypotheses are not re-

jected for FIO Pioneer, FIO PZU and FIO Nationale-Nederlanden. 

 Beta parameters in the single index models and CAPM are significantly 

positive however the values of  parameter estimates are rather small (Ta-

bles 6–7). That is connected with the fact that portfolios of stable growth 

mutual funds contain a great share of bonds. At the end of January 2017, 

Credit Agricole stable growth fund’s portfolio contains only 25% of equity, 

FIO PZU – 30.3%, FIO Pioneer – 29.3%, FIO Nationale-Nederlanden – 

35.3% and FIO KBC –38.2%. The structure of the investment funds’ portfo-

lios is also visible when beta parameter estimates are analyzed since the 

biggest value is observed for FIO PZU and KBC while the smallest for FIO 

Credit Agricole.  

Table 7. Parameter estimates and determination coefficients of CAPM  

Before  After Before  After The whole 
period presidential elections, the 1st round presidential elections, the 2nd round 

beta alfa beta alfa beta alfa beta alfa beta alfa 

FIO Credit Agricole  

0.2171 0.0000 0.2072 0.0000 0.2168 0.0000 0.2074 0.0000 0.2112 0.0000 

R2 0.8023 R2 0.7528 R2 0.7998 R2 0.7543 R2 0.7727 

FIO PZU 

0.4054 –0.0002 0.3103 –0.0001 0.4042 –0.0002 0.3109 –0.0001 0.3485 –0.0001 
R2 0.9109 R2 0.8216 R2 0.9079 R2 0.8232 R2 0.8514 

FIO Pioneer 

0.3754 –0.0002 0.3121 –0.0001 0.3752 –0.0002 0.3121 –0.0001 0.3375 –0.0001 

R2 0.9083 R2 0.9258 R2 0.9069 R2 0.9262 R2 0.9088 

FIO Nationale-Nederlanden 

0.3166 –0.0001 0.3012 0.0000 0.3169 –0.0001 0.3010 0.0000 0.3073 –0.0001 

R2 0.9173 R2 0.9302 R2 0.9161 R2 0.9305 R2 0.9235 

FIO KBC 

0.3885 0.0000 0.2474 0.0000 0.3879 0.0000 0.2475 0.0000 0.3042 0.0000 

R2 0.8464 R2 0.7054 R2 0.8445 R2 0.7068 R2 0.7486 

Note: Bold letters denote statistically significant parameters. 

 Value and significance of the alpha parameter is important when capital 

assets pricing models are taken into consideration since this parameter is an 

efficiency measure i.e. Jensen ratio. In estimated models, none of alphas is 

significantly positive (Table 7). The best portfolio management can be no-

ticed for FIO Credit Agricole and KBC since alphas equaled zero. For the 

rest of funds Jensen ratios were significantly negative at least in the periods 

before election and for the whole period, it means that the mutual fund man-



Dorota Witkowska, Krzysztof Kompa 

DYNAMIC ECONOMETRIC MODELS 17 (2017) 147–159 

156 

agers did not earned enough return given the amount of risk they were tak-

ing. 

 In the next step, we compare betas from the models estimated before and 

after both rounds of the presidential election (Table 8). Since a positive value 

of the test statistics means that before the election the parameter was bigger 

than after the election, it is visible that the risk, measured by beta, signifi-

cantly lowered after the election for all mutual funds but FIO Credit 

Agricole. Taking into account the quality of management, we notice that it 

was significantly improved after the election by FIO PZU and FIO 

Nationale-Nederlanden, however one must realize that Jensen alphas re-

mained negative. 

Table 8. Value of statistics comparing betas estimated in both periods H0: before = 

after 

Funds Period Sharpe beta CAPM beta CAPM alpha 
t1 t2 t1 t2 t1 t2 

Credit Agricole I round 0.4255 0.4706 1.8703 1.7048 0.8368 0.6278 
II round 0.3411 0.3750 1.7730 1.5932 0.4965 0.3601 

PZU I round 9.8989 9.7889 15.1606 13.3466 –2.4622 –1.7842 
II round 9.6437 9.5620 14.8252 12.9878 –2.2242 –1.5727 

Pioneer I round 7.7857 10.2830 10.7358 14.5523 –1.5012 –1.6749 

II round 7.7518 10.2474 10.7422 14.3758 –1.1776 –1.2720 
NN I round 1.2969 1.6600 3.2745 3.7870 –2.3184 –2.2070 

II round 1.3137 1.6850 3.3972 3.8855 –2.1582 –1.9923 
KBC I round 13.0273 16.6628 17.2482 17.9215 0.7775 0.6650 

II round 13.0234 16.6094 17.2693 17.6845 0.4928 0.4073 

Note: Bold letters denote rejection of null hypothesis. 

 The last stage of our investigation consists in evaluation the classical 

efficiency measures, which are given in Tables 9 and 10. Treynor ratio uses 

beta as a measure of risk but some Authors apply beta estimated from the 

single index model – see (Domański 2011, p. 64), (Perez 2011, p.155), and 

other – take beta from CAPM – see (Borowski 2014, p. 20), (Białek 2009,  

p. 34). Therefore, we use both approaches in our analysis. The main conclu-

sion from our research is that the majority of portfolios were ineffective, 

except FIO Credit Agricole. Inefficiency appeared more often after than 

before the presidential election. After the election Sharpe and Treynor ratios 

show negative risk premium, and they usually decreased in comparison to 

the first analyzed time span, although Jensen alphas for FIO PZU and 

Nationale-Nederlanden increased in the samples containing observations 

after the presidential election.  



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DYNAMIC ECONOMETRIC MODELS 17 (2017) 147–159 

157 

Table 9. Values of the efficiency measures evaluated for mutual funds before and 

after both rounds of presidential election 

 Ratio: 

 Sharpe Treynor (β Sharpe) Treynor (β CAPM) Jensen alpha 

Fund  
or index 

Periods before rounds of presidential election 

1st round 2nd round 1st round 2nd round 1st round 2nd round 1st round 2nd round 

CA 0.00392 –0.00015 0.00004 0.00000 0.00004 0.00000 0.00001 0.00001 
PZU –0.05050 –0.05011 –0.00044 –0.00043 –0.00047 –0.00046 –0.00018 –0.00017 
PIO –0.05753 –0.05635 –0.00049 –0.00048 –0.00051 –0.00049 –0.00019 –0.00017 
NN –0.03614 –0.03591 –0.00031 –0.00031 –0.00033 –0.00032 –0.00010 –0.00009 
KBC –0.00056 –0.00459 –0.00001 –0.00004 –0.00001 –0.00004 0.00000 –0.00001 
WIG –0.00173 –0.00372 –0.00001 –0.00003 –0.00001 –0.00003 x x 

 Periods after rounds of presidential election 

CA –0.03148 –0.02775 –0.00038 –0.00034 –0.00045 –0.00040 –0.00002 –0.00001 
PZU –0.04315 –0.04270 –0.00049 –0.00048 –0.00053 –0.00053 –0.00006 –0.00006 
PIO –0.06353 –0.06314 –0.00066 –0.00066 –0.00071 –0.00071 –0.00037 –0.00012 
NN –0.03399 –0.03284 –0.00035 –0.00034 –0.00038 –0.00037 –0.00004 –0.00001 
KBC –0.04069 –0.03642 –0.00048 –0.00043 –0.00053 –0.00048 –0.00019 –0.00005 
WIG –0.03399 –0.02817 –0.00034 –0.00028 –0.00034 –0.00028 x x 

Note: Bold letters denote that Sharpe and Treynor ratios evaluated for mutual funds are bigger than the 

ones calculated for WIG and Jensen ratios are statistically significant. 

Table 10. Values of the efficiency measures evaluated for whole period 

 Sharpe Treynor (β Sharpe) Treynor (β CAPM) Jensen alpha 

CA –0.01485 –0.00017 –0.00019 0.00000 
PZU –0.04640 –0.00047 –0.00050 –0.00011 
PIO –0.05976 –0.00057 –0.00060 –0.00014 
NN –0.03413 –0.00033 –0.00035 –0.00005 

KBC –0.01894 –0.00020 –0.00022 –0.00001 
WIG –0.01938 –0.00017 –0.00017 x 

Note: Bold letters denote that Sharpe and Treynor ratios evaluated for mutual funds are bigger than the 

ones calculated for WIG and Jensen ratios are statistically significant. 

 Here the question arises if changes of efficiency measures observed in 

comparable periods are statistically significant. To verify such hypothesis 

the test with statistics (10) is used for Sharpe ratio, together with the test 

statistics (1) and (2), applied for average values of Sharpe and Treynor rati-

os, evaluated for all analyzed funds. As one may notice in Table 11, neither 

differences of Sharpe ratios between periods nor differences between mutual 

fund and market index are significant. However, if average values of 

Treynors ratios are compared the better investment performance before the 

election is proved (Table 12) since the higher risk premium was obtained. 
  



Dorota Witkowska, Krzysztof Kompa 

DYNAMIC ECONOMETRIC MODELS 17 (2017) 147–159 

158 

Table 11. Values of test statistics in Jobson – Korkie test 

Funds  
or index 

Comparison of Sharpe ratios 

in two sub-periods 
between FIO Credit Agricole and WIG 

1st round 2nd round 

CA –0.03148 –0.02775 1st round of presidential election 0.17623 
 PZU –0.04315 –0.04270 

PIO –0.06353 –0.06314 2nd round of presidential election 0.11181 
NN –0.03399 –0.03284 

KBC –0.04069 –0.03642 in the whole period of analysis 0.20728 
WIG –0.03399 –0.02817 

Table 12. Values of test statistics for average ratios 

No. of 
formula 

Sharpe Treynor (β Sharpe) Treynor (β CAPM) 

I round II round I round II round I round II round 

(2) 1.6922 1.4133 2.7795 2.5495 2.9429 2.8190 

(2) 3.9387 2.6980 5.4554 4.0513 5.5685 4.2752 
(1) 1.5547 1.2520 2.4765 2.1578 2.6019 2.3534 

Note: Bold letters denote rejection of null hypothesis. 

Conclusion 

 Our research show that in the whole period of analysis (covering more 

than three years), statistically significant and positive returns were generated 

only by the bond market, represented by index TBSP. The mutual stabile 

growth fund FIO Credit Agricole also obtained positive rates of return but 

only in the periods before both rounds of the presidential election. Taking 

into account other considered instruments we cannot reject the hypotheses 

about zero returns. 

 Comparison of the return and risk level let us conclude that: 

1. returns from Treasury Bonds significantly decreased after both rounds 

of the presidential election, 

2. risk of the bond market significantly decreased after entry into force 

500+ Family Program, 

3. returns from equity market did not significantly change but rates of re-

turn were lower after both rounds of the presidential election, 

4. equity market risk increased after both rounds of the presidential elec-

tion, the parliamentary election and appointment of the government, 

5. equity market risk decreased after the program 500+ started. 

  



How the Change of Governing Party Influences the Efficiency of Financial Market…  

DYNAMIC ECONOMETRIC MODELS 17 (2017) 147–159 

159 

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Jak wpływa zmiana partii rządzącej 
na efektywność rynku finansowego w Polsce 

Z a r y s  t r e ś c i. Rynek finansowy wydaje się być wrażliwym na zmiany polityczne, 

zwłaszcza gdy zmiana partii rządzącej wiąże się z zasadniczymi zmianami koncepcji rozwoju 

gospodarczego. Taka sytuacja miała miejsce w Polsce w 2015 roku w wyniku wyborów 

prezydenckich i parlamentarnych. Celem naszych badań jest analiza zmian zachodzących na 

rynku, reprezentowanym przez niektóre stabilne, otwarte fundusze inwestycyjne oraz indeksy 

giełdowe: WIG i TBSP. Przeprowadzono analizę, stosując modele jednoczynnikowe i CAPM, 

klasyczne miary efektywności inwestycji i wnioskowanie statystyczne. 

S ł o w a  k l u c z o w e: otwarte fundusze inwestycyjne stabilnego wzrostu; efektywność 

inwestycji; model Sharpe; CAPM; Sharpe, Treynor i Jensen.  

 


