Local Football Sentiment and Returns from Stocks: Evidence from Europe
Consideration of additional variables based on investor sentiment that affects market performance on national stock exchanges. General characteristics of statistical evidence of the presence of local football sentiment, familiarity with the features.
| Рубрика | Экономика и экономическая теория |
| Вид | курсовая работа |
| Язык | английский |
| Дата добавления | 07.12.2019 |
| Размер файла | 83,8 K |
Отправить свою хорошую работу в базу знаний просто. Используйте форму, расположенную ниже
Студенты, аспиранты, молодые ученые, использующие базу знаний в своей учебе и работе, будут вам очень благодарны.
Conclusion
As the result, the primary goal of this research has been achieved. Stocks' abnormal returns were analyzed on the various sample of football matches on the presence of local football sentiment. Particularly, each company was linked to the football club that based in the same city, where this company is headquartered. Using two-step modified Edmans (2007) approach the panel data regressions were built to test the main hypotheses.
The results suggest that there exists local football sentiment: firms' returns are affected by the outcome of local football team match and the strength of football sentiment depends from the type of company (either small cap or big cap) and from the country economic level (either developed or developing). However, study failed to formulate the general rule of dependence between match outcome (win/loss) with the corresponding market reaction (positive/negative) as the results have shown different variations of dependency. Although there was an attempt to answer all of the raised questions, generally the study provides the basis for future research.
Bibliography
1.Ashton, JK., Gerrard, B., Hudson, R., (2003), "Economic impact of national sporting success: evidence from the London stock exchange", Applied Economics Letters, Vol. 10, pp. 783-785
2.Banz, RW., (1981), "THE RELATIONSHIP BETWEEN RETURN AND MARKET VALUE OF COMMON-STOCKS', Journal of Financial Economics, Vol. 9, pp. 3-18
3.Bauer, R., Cosemans, M., Schotman, PC., (2010), "Conditional Asset Pricing and Stock Market Anomalies in Europe", European Financial Management, Vol. 16, pp. 165-190
4.Berument, M., Ceylan, NB., Onar, B., (2013), "Football and the Risk-Return Relationship for a Stock Market: Borsa Istanbul", Emerging Markets Finance and Trade, Vol. 49, pp. 19-30
5.Bizman, A., Yinon, Y., (2002), "Engaging in distancing tactics among sport fans: Effects on self-esteem and emotional responses", Journal of Social Psychology, Vol. 142, pp. 381-392
6.Cao, M., Wei, J., (2005), "Stock market returns: A note on temperature anomaly", Journal of Banking & Finance, Vol. 29, pp. 1559-1573
7.Chang, S.C., Chen, S.S., Chou, R.K., Lin, Y.H., (2012). “Local sports sentiment and returns of locally headquartered stocks: a firm-level analysis”, Journal of Empirical Finance, Vol. 19, pp. 309-318
8.Coval, JD., Moskowitz, TJ., (1999), "Home bias at home: Local equity preference in domestic portfolios", Journal of Finance, Vol. 54, pp. 2045-2073
9.DeBondt, WFM., Thaler, R., (1985), "DOES THE STOCK-MARKET OVERREACT", Journal of Finance, Vol. 40, pp. 793-805
10.Edmans, A., Garcia, D., Norli, O., (2007), "Sports sentiment and stock returns", Journal of Finance, Vol. 62, pp. 1967-1998
11.Eker, G., Berument, H., Dogan, B., (2007), "Football and exchange rates: Empirical support for behavioral economics", Psychological Reports, Vol. 101, pp. 643-654
12.Etzioni, A., (1988), "NORMATIVE-AFFECTIVE FACTORS TOWARD A NEW DECISION-MAKING MODEL", Journal of Economic Psychology, Vol. 9, pp. 125-150
13.Forgas, JP., (1998), "On being happy and mistaken: Mood effects on the fundamental attribution error", Journal of Personality and Social Psychology, Vol. 75, pp. 318-331
14.Fung, KWT., Demir, E., Lau, CKM., Chan, KH., (2015), "Reexamining sports-sentiment hypothesis: Microeconomic evidences from Borsa Istanbul", Journal of International Financial Markets Institutions & Money, Vol. 34, pp 337-355
15.Guven, C., (2009), "Weather and Financial Risk-Taking: Is Happiness the Channel?", SOEPpaper No. 218
16.Hirshleifer, D., (2001), "Investor psychology and asset pricing", Journal of Finance, Vol. 56, pp. 1533-1597
17.Hirshleifer, D., Shumway, T., (2003) "Good day sunshine: Stock returns and the weather", Journal of Finance, Vol. 58, pp. 1009-1032
18.Kahneman, D., Knetsch, JL., Thaler, R., (1991), "ANOMALIES - THE ENDOWMENT EFFECT, LOSS AVERSION, AND STATUS-QUO BIAS", Journal of Economic Perspectives, Vol. 5, pp. 193-206
19.Kahneman, D., Lovallo, D., (1993), "TIMID CHOICES AND BOLD FORECASTS - A COGNITIVE PERSPECTIVE ON RISK-TAKING", Management Science, Vol. 39, pp. 17-31
20.Kahneman, D., Tversky, A., (1979), “PROSPECT THEORY - ANALYSIS OF DECISION UNDER RISK”, Econometrica, Vol. 47, pp. 263 - 291
Applications
Attachment 1
Appendix A
Table 1. The detailed description of football-company linking in the sample:
|
country |
city |
club |
# of games in domestic championship |
# of games in Europa League |
# of games in Champions League |
total number of games |
|
|
Russia |
Moscow |
CSKA |
197 |
8 |
46 |
251 |
|
|
Lokomotiv |
197 |
20 |
0 |
217 |
|||
|
Spartak |
197 |
6 |
14 |
217 |
|||
|
Dynamo |
167 |
18 |
0 |
185 |
|||
|
Saint-Petersburg |
Zenit |
197 |
0 |
44 |
241 |
||
|
Kazan |
Rubin |
197 |
36 |
4 |
237 |
||
|
Ufa |
Ufa |
137 |
0 |
0 |
137 |
||
|
Volgograd |
Volga |
60 |
0 |
0 |
60 |
||
|
Poland |
Warsaw |
Legia |
235 |
38 |
26 |
299 |
|
|
Polonia |
30 |
0 |
0 |
30 |
|||
|
Krakow |
Wisla |
235 |
0 |
0 |
235 |
||
|
Cracovia |
205 |
2 |
0 |
207 |
|||
|
Wroclaw |
Slask |
235 |
12 |
4 |
251 |
||
|
Poznan |
Lech |
235 |
28 |
4 |
267 |
||
|
Gdynia |
Arka |
94 |
0 |
0 |
94 |
||
|
Turkey |
Istanbul |
Besiktas |
221 |
24 |
18 |
263 |
|
|
Galatasaray |
221 |
4 |
30 |
255 |
|||
|
Fenerbahce |
221 |
40 |
12 |
273 |
|||
|
Istanbul BB |
187 |
12 |
4 |
203 |
|||
|
Kasimpasa |
221 |
0 |
0 |
221 |
|||
|
Karabuk |
Karabukspor |
170 |
4 |
0 |
174 |
||
|
Bursa |
Bursaspor |
221 |
8 |
0 |
229 |
||
|
England |
London |
Arsenal |
250 |
14 |
54 |
318 |
|
|
Chelsea |
250 |
9 |
55 |
314 |
|||
|
Tottenham |
250 |
46 |
14 |
310 |
|||
|
Fulham |
98 |
0 |
0 |
98 |
|||
|
QPR |
76 |
0 |
0 |
76 |
|||
|
West Ham |
250 |
10 |
0 |
260 |
|||
|
Crystal Palace |
212 |
0 |
0 |
212 |
|||
|
Germany |
Frankfurt |
Eintracht |
222 |
10 |
0 |
232 |
|
|
Hamburg |
Hamburg |
204 |
0 |
0 |
204 |
||
|
Hannover |
Hannover 96 |
188 |
12 |
0 |
200 |
||
|
Leverkusen |
Bayer |
222 |
12 |
42 |
276 |
||
|
Munich |
Bayern |
222 |
0 |
86 |
308 |
||
|
Stuttgart |
Stuttgart |
188 |
16 |
0 |
204 |
||
|
Wolfsburg |
Wolfsburg |
222 |
12 |
10 |
244 |
||
|
Berlin |
Hertha |
188 |
8 |
0 |
196 |
||
|
Dortmund |
Borussia |
222 |
20 |
53 |
295 |
||
|
Koln |
Koln |
136 |
6 |
0 |
142 |
||
|
country |
city |
club |
# of games in domestic championship |
# of games in Europa League |
# of games in Champions League |
total number of games |
|
|
France |
Paris |
PSG |
246 |
0 |
56 |
302 |
|
|
Saint-Йtienne |
Saint-Йtienne |
246 |
36 |
0 |
282 |
||
|
Bordeaux |
Bordeaux |
246 |
30 |
0 |
276 |
||
|
Lyon |
Lyon |
246 |
42 |
26 |
314 |
Attachment 2
Appendix B
The variety of can be described on the example of the exact club. Let's took, for example, FC Arsenal from London. For this club the set of dummy variables would be the following:
- ARSW - Arsenal wins the match in its national division (English Premier League).
- ARSL - Arsenal loses the match in its national division.
- ARSCLW - Arsenal wins the match in UEFA Champions League.
- ARSCLL - Arsenal loses the match in UEFA Champions League.
- ARSELW - Arsenal wins the match in UEFA Europe League.
- ARSELL - Arsenal loses the match in UEFA Europe League.
- ARSCLQUALW - Arsenal wins the match in qualification round of UEFA Champions League.
- ARSCLQUALL - Arsenal loses the match in qualification round of UEFA Champions League.
- ARSCLGRW - Arsenal wins the match in group stage of UEFA Champions League.
- ARSCLGRL - Arsenal loses the match in group stage of UEFA Champions League.
- ARSCLFLW - Arsenal wins the match in final stage of UEFA Champions League.
- ARSCLFLL - Arsenal loses the match in final stage of UEFA Champions League.
- ARSCLFLSUC - Arsenal successfully goes to the next round of final stage of UEFA Champions League.
- ARSCLFLFAIL - Arsenal fails to go to the next round of final stage of UEFA Champions League.
- ARSELQUALW - Arsenal wins the match in qualification round of UEFA Europe League.
- ARSELQUALL - Arsenal loses the match in qualification round of UEFA Europe League.
- ARSELGRW - Arsenal wins the match in group stage of UEFA Europe League.
- ARSELGRL - Arsenal loses the match in group stage of UEFA Europe League.
- ARSELFLW - Arsenal wins the match in final stage of UEFA Europe League.
- ARSELFLL - Arsenal loses the match in final stage of UEFA Europe League.
- ARSELFLSUC - Arsenal successfully goes to the next round of final stage of UEFA Europe League.
- ARSELFLFAIL - Arsenal fails to go to the next round of final stage of UEFA Europe League.
Attachment 3
Appendix C
The following tables represent the regression on datasets that don't show any significance:
Table 2. Regression results: Small Cap England companies
|
resid |
Coef. |
St.Err. |
t-value |
p-value |
[95% Conf |
Interval] |
Sig |
|
|
ARSW |
-3.140 |
7.481 |
-0.42 |
0.675 |
-17.804 |
11.524 |
||
|
ARSL |
-3.420 |
10.130 |
-0.34 |
0.736 |
-23.276 |
16.435 |
||
|
CHEW |
-0.816 |
7.489 |
-0.11 |
0.913 |
-15.496 |
13.864 |
||
|
CHEL |
2.759 |
10.759 |
0.26 |
0.798 |
-18.329 |
23.848 |
||
|
CRYW |
-1.382 |
9.009 |
-0.15 |
0.878 |
-19.041 |
16.276 |
||
|
CRYL |
1.895 |
8.082 |
0.23 |
0.815 |
-13.946 |
17.736 |
||
|
TOTW |
-0.977 |
7.808 |
-0.13 |
0.900 |
-16.282 |
14.327 |
||
|
TOTL |
1.439 |
10.376 |
0.14 |
0.890 |
-18.899 |
21.777 |
||
|
WHW |
-1.086 |
9.032 |
-0.12 |
0.904 |
-18.789 |
16.618 |
||
|
WHL |
-0.670 |
8.487 |
-0.08 |
0.937 |
-17.305 |
15.965 |
||
|
QPRW |
-0.007 |
18.954 |
0.00 |
1.000 |
-37.158 |
37.144 |
||
|
QPRL |
6.540 |
10.639 |
0.61 |
0.539 |
-14.313 |
27.393 |
||
|
FULW |
5.427 |
13.863 |
0.39 |
0.695 |
-21.745 |
32.599 |
||
|
FULL |
1.262 |
9.454 |
0.13 |
0.894 |
-17.268 |
19.793 |
||
|
ARSCLFRW |
28.490 |
40.492 |
0.70 |
0.482 |
-50.876 |
107.856 |
||
|
ARSCLFRL |
-4.014 |
22.760 |
-0.18 |
0.860 |
-48.624 |
40.597 |
||
|
ARSGRCLW |
1.579 |
15.284 |
0.10 |
0.918 |
-28.379 |
31.537 |
||
|
ARSGRCLL |
3.946 |
24.623 |
0.16 |
0.873 |
-44.316 |
52.207 |
||
|
ARSELW |
1.009 |
28.669 |
0.04 |
0.972 |
-55.184 |
57.202 |
||
|
ARSELL |
-0.590 |
43.115 |
-0.01 |
0.989 |
-85.099 |
83.918 |
||
|
CHECLGRW |
-8.434 |
15.513 |
-0.54 |
0.587 |
-38.839 |
21.972 |
||
|
CHECLGRL |
15.479 |
26.124 |
0.59 |
0.554 |
-35.725 |
66.684 |
||
|
CHECLFRW |
0.809 |
26.939 |
0.03 |
0.976 |
-51.994 |
53.611 |
||
|
CHECLFRL |
8.909 |
24.258 |
0.37 |
0.713 |
-38.639 |
56.457 |
||
|
CHEELW |
-16.079 |
25.909 |
-0.62 |
0.535 |
-66.862 |
34.705 |
||
|
CHEELL |
-10.416 |
48.154 |
-0.22 |
0.829 |
-104.801 |
83.969 |
||
|
TOTCLW |
3.102 |
22.866 |
0.14 |
0.892 |
-41.716 |
47.920 |
||
|
TOTCLL |
4.407 |
30.005 |
0.15 |
0.883 |
-54.403 |
63.217 |
||
|
TOTELGRW |
-17.103 |
15.883 |
-1.08 |
0.282 |
-48.235 |
14.029 |
||
|
TOTELGRL |
-28.971 |
43.102 |
-0.67 |
0.501 |
-113.453 |
55.510 |
||
|
TOTELFRW |
12.712 |
33.694 |
0.38 |
0.706 |
-53.329 |
78.754 |
||
|
TOTELFRL |
-3.848 |
22.747 |
-0.17 |
0.866 |
-48.434 |
40.738 |
||
|
Constant |
0.296 |
1.631 |
0.18 |
0.856 |
-2.902 |
3.494 |
||
|
Mean dependent var |
0.000 |
SD dependent var |
235.770 |
|||||
|
R-squared |
0.000 |
Number of obs |
26294.000 |
|||||
|
F-test |
0.145 |
Prob > F |
1.000 |
|||||
|
Akaike crit. (AIC) |
361944.482 |
Bayesian crit. (BIC) |
362214.326 |
|||||
|
*** p<0.01, ** p<0.05, * p<0.1 |
Table 3. Regression results: Small Cap Russia companies
|
resid |
Coef. |
St.Err. |
t-value |
p-value |
[95% Conf |
Interval] |
Sig |
|
|
LOKW |
-0.320 |
0.489 |
-0.66 |
0.512 |
-1.278 |
0.637 |
||
|
LOKL |
-0.104 |
0.625 |
-0.17 |
0.868 |
-1.328 |
1.121 |
||
|
SPAW |
0.067 |
0.492 |
0.14 |
0.891 |
-0.898 |
1.033 |
||
|
SPAL |
-0.075 |
0.623 |
-0.12 |
0.904 |
-1.297 |
1.147 |
||
|
CSKW |
0.207 |
0.462 |
0.45 |
0.654 |
-0.699 |
1.113 |
||
|
CSKL |
0.567 |
0.656 |
0.86 |
0.387 |
-0.719 |
1.854 |
||
|
DYNW |
0.078 |
0.560 |
0.14 |
0.890 |
-1.021 |
1.176 |
||
|
DYNL |
-0.170 |
0.599 |
-0.28 |
0.777 |
-1.343 |
1.004 |
||
|
ZENW |
-0.061 |
0.441 |
-0.14 |
0.890 |
-0.924 |
0.803 |
||
|
ZENL |
-0.466 |
0.768 |
-0.61 |
0.544 |
-1.971 |
1.039 |
||
|
UFAW |
0.249 |
1.449 |
0.17 |
0.864 |
-2.591 |
3.089 |
||
|
UFAL |
-0.041 |
1.226 |
-0.03 |
0.973 |
-2.444 |
2.361 |
||
|
VOLW |
-0.839 |
2.933 |
-0.29 |
0.775 |
-6.588 |
4.910 |
||
|
VOLL |
0.134 |
1.680 |
0.08 |
0.936 |
-3.159 |
3.427 |
||
|
LOKOMW |
0.427 |
4.171 |
0.10 |
0.918 |
-7.747 |
8.602 |
||
|
LOKOML |
1.317 |
2.453 |
0.54 |
0.591 |
-3.491 |
6.124 |
||
|
LOKOFAIL |
-0.338 |
2.485 |
-0.14 |
0.892 |
-5.208 |
4.532 |
||
|
SPAMW |
-0.446 |
2.860 |
-0.16 |
0.876 |
-6.052 |
5.160 |
||
|
SPAML |
-0.491 |
1.913 |
-0.26 |
0.798 |
-4.240 |
3.259 |
||
|
SPASUC |
-0.638 |
4.304 |
-0.15 |
0.882 |
-9.075 |
7.799 |
||
|
SPAFAIL |
-0.211 |
2.709 |
-0.08 |
0.938 |
-5.520 |
5.098 |
||
|
CSKAMW |
-0.253 |
1.778 |
-0.14 |
0.887 |
-3.738 |
3.232 |
||
|
CSKAML |
-0.830 |
1.202 |
-0.69 |
0.490 |
-3.185 |
1.525 |
||
|
CSKASUC |
0.542 |
1.701 |
0.32 |
0.750 |
-2.793 |
3.876 |
||
|
CSKAFAIL |
-1.167 |
2.443 |
-0.48 |
0.633 |
-5.955 |
3.620 |
||
|
DYNSUC |
-0.359 |
2.110 |
-0.17 |
0.865 |
-4.494 |
3.777 |
||
|
DYNFAIL |
0.361 |
2.819 |
0.13 |
0.898 |
-5.164 |
5.886 |
||
|
ZENMW |
-0.721 |
1.694 |
-0.43 |
0.670 |
-4.041 |
2.598 |
||
|
ZENML |
0.530 |
1.868 |
0.28 |
0.777 |
-3.132 |
4.192 |
||
|
ZENSUC |
-0.139 |
2.592 |
-0.05 |
0.957 |
-5.219 |
4.942 |
||
|
ZENFAIL |
0.265 |
3.427 |
0.08 |
0.938 |
-6.453 |
6.982 |
||
|
Constant |
0.003 |
0.064 |
0.04 |
0.964 |
-0.123 |
0.129 |
||
|
Mean dependent var |
-0.000 |
SD dependent var |
10.536 |
|||||
|
R-squared |
0.000 |
Number of obs |
29875.000 |
|||||
|
F-test |
0.100 |
Prob > F |
1.000 |
|||||
|
Akaike crit. (AIC) |
225521.372 |
Bayesian crit. (BIC) |
225787.125 |
|||||
|
*** p<0.01, ** p<0.05, * p<0.1 |
Table 4. Regression results: Small Cap France companies
|
resid |
Coef. |
St.Err. |
t-value |
p-value |
[95% Conf |
Interval] |
Sig |
|
|
PSGW |
0.020 |
0.123 |
0.16 |
0.872 |
-0.221 |
0.261 |
||
|
PSGL |
0.160 |
0.355 |
0.45 |
0.652 |
-0.537 |
0.857 |
||
|
LYOW |
0.168 |
0.419 |
0.40 |
0.689 |
-0.654 |
0.990 |
||
|
LYOL |
-0.176 |
0.598 |
-0.29 |
0.768 |
-1.349 |
0.996 |
||
|
BORW |
0.110 |
0.348 |
0.32 |
0.751 |
-0.571 |
0.792 |
||
|
BORL |
0.088 |
0.386 |
0.23 |
0.820 |
-0.669 |
0.845 |
||
|
STEW |
-0.178 |
0.483 |
-0.37 |
0.713 |
-1.124 |
0.768 |
||
|
STEL |
-0.055 |
0.613 |
-0.09 |
0.929 |
-1.256 |
1.147 |
||
|
PSGCLFRW |
0.356 |
0.655 |
0.55 |
0.586 |
-0.927 |
1.639 |
||
|
PSGCLFRL |
0.257 |
0.869 |
0.30 |
0.767 |
-1.445 |
1.960 |
||
|
PSGSUCCESS |
-0.806 |
0.865 |
-0.93 |
0.351 |
-2.502 |
0.889 |
||
|
PSGFAIL |
0.478 |
0.966 |
0.50 |
0.621 |
-1.415 |
2.370 |
||
|
BELQUALWIN |
-0.869 |
1.521 |
-0.57 |
0.568 |
-3.850 |
2.113 |
||
|
BELQUALL |
0.691 |
2.150 |
0.32 |
0.748 |
-3.523 |
4.904 |
||
|
BELFRW |
1.755 |
4.805 |
0.36 |
0.715 |
-7.663 |
11.173 |
||
|
BELFRL |
0.090 |
3.399 |
0.03 |
0.979 |
-6.572 |
6.751 |
||
|
SELQUALWIN |
0.251 |
1.820 |
0.14 |
0.890 |
-3.316 |
3.817 |
||
|
SELQUALL |
-0.802 |
2.405 |
-0.33 |
0.739 |
-5.516 |
3.912 |
||
|
SELGROUPW |
0.195 |
2.152 |
0.09 |
0.928 |
-4.023 |
4.412 |
||
|
SELGROUPL |
-1.074 |
3.399 |
-0.32 |
0.752 |
-7.736 |
5.587 |
||
|
SELFRW |
-0.548 |
4.805 |
-0.11 |
0.909 |
-9.966 |
8.870 |
||
|
SELFRL |
0.018 |
2.776 |
0.01 |
0.995 |
-5.423 |
5.460 |
||
|
LYCLFAIL |
0.566 |
4.805 |
0.12 |
0.906 |
-8.852 |
9.984 |
||
|
LYELGRW |
0.120 |
1.454 |
0.08 |
0.934 |
-2.729 |
2.969 |
||
|
LYELMW |
-0.036 |
1.338 |
-0.03 |
0.979 |
-2.658 |
2.586 |
||
|
LYELML |
0.254 |
1.523 |
0.17 |
0.868 |
-2.732 |
3.240 |
||
|
Constant |
-0.005 |
0.033 |
-0.16 |
0.873 |
-0.070 |
0.059 |
||
|
Mean dependent var |
-0.000 |
SD dependent var |
4.801 |
|||||
|
R-squared |
0.000 |
Number of obs |
24201.000 |
|||||
|
F-test |
0.148 |
Prob > F |
1.000 |
|||||
|
Akaike crit. (AIC) |
144645.614 |
Bayesian crit. (BIC) |
144864.156 |
|||||
|
*** p<0.01, ** p<0.05, * p<0.1 |
Table 5. Regression results: Big Cap England companies
|
resid |
Coef. |
St.Err. |
t-value |
p-value |
[95% Conf |
Interval] |
Sig |
|
|
ARSW |
-0.002 |
0.003 |
-0.56 |
0.576 |
-0.008 |
0.004 |
||
|
ARSL |
-0.001 |
0.004 |
-0.23 |
0.815 |
-0.009 |
0.007 |
||
|
CHEW |
-0.003 |
0.003 |
-0.86 |
0.389 |
-0.009 |
0.003 |
||
|
CHEL |
0.003 |
0.005 |
0.64 |
0.522 |
-0.006 |
0.012 |
||
|
CRYW |
-0.002 |
0.004 |
-0.55 |
0.584 |
-0.011 |
0.006 |
||
|
CRYL |
0.000 |
0.004 |
0.02 |
0.984 |
-0.007 |
0.008 |
||
|
TOTW |
-0.002 |
0.003 |
-0.80 |
0.423 |
-0.008 |
0.004 |
||
|
TOTL |
0.000 |
0.004 |
-0.05 |
0.957 |
-0.008 |
0.008 |
||
|
WHW |
-0.001 |
0.004 |
-0.34 |
0.734 |
-0.009 |
0.007 |
||
|
WHL |
-0.002 |
0.004 |
-0.44 |
0.662 |
-0.009 |
0.006 |
||
|
QPRW |
-0.001 |
0.009 |
-0.13 |
0.899 |
-0.019 |
0.017 |
||
|
QPRL |
0.005 |
0.005 |
0.96 |
0.336 |
-0.005 |
0.015 |
||
|
FULW |
0.003 |
0.007 |
0.47 |
0.641 |
-0.010 |
0.016 |
||
|
FULL |
0.000 |
0.005 |
0.07 |
0.946 |
-0.009 |
0.009 |
||
|
ARSCLFRW |
0.010 |
0.013 |
0.73 |
0.467 |
-0.017 |
0.036 |
||
|
ARSCLFRL |
-0.003 |
0.012 |
-0.26 |
0.796 |
-0.026 |
0.020 |
||
|
CHECLFRW |
-0.001 |
0.021 |
-0.07 |
0.947 |
-0.042 |
0.039 |
||
|
CHECLFRL |
0.003 |
0.014 |
0.24 |
0.812 |
-0.024 |
0.031 |
||
|
TOTCLFRL |
0.010 |
0.029 |
0.35 |
0.728 |
-0.047 |
0.068 |
||
|
Constant |
0.001 |
0.001 |
0.83 |
0.405 |
-0.001 |
0.002 |
||
|
Mean dependent var |
0.000 |
SD dependent var |
0.128 |
|||||
|
R-squared |
0.000 |
Number of obs |
31594.000 |
|||||
|
F-test |
0.424 |
Prob > F |
0.999 |
|||||
|
Akaike crit. (AIC) |
-40129.743 |
Bayesian crit. (BIC) |
-39962.528 |
|||||
|
*** p<0.01, ** p<0.05, * p<0.1 |
stock evidence football
Table 6. Regression results: Big Cap Russia companies
|
resid |
Coef. |
St.Err. |
t-value |
p-value |
[95% Conf |
Interval] |
Sig |
|
|
LOKOW |
-51.491 |
55.051 |
-0.94 |
0.350 |
-159.394 |
56.412 |
||
|
LOKOL |
-18.906 |
70.267 |
-0.27 |
0.788 |
-156.631 |
118.820 |
||
|
SPARTAKW |
3.784 |
55.039 |
0.07 |
0.945 |
-104.094 |
111.662 |
||
|
SPARTAKL |
-16.186 |
68.679 |
-0.24 |
0.814 |
-150.800 |
118.427 |
||
|
CSKAW |
30.850 |
51.162 |
0.60 |
0.547 |
-69.429 |
131.129 |
||
|
CSKAL |
80.127 |
73.904 |
1.08 |
0.278 |
-64.728 |
224.982 |
||
|
DYNAMOW |
6.071 |
64.930 |
0.09 |
0.926 |
-121.194 |
133.335 |
||
|
DYNAMOL |
-26.211 |
67.376 |
-0.39 |
0.697 |
-158.271 |
105.849 |
||
|
RUBINW |
3.982 |
190.782 |
0.02 |
0.983 |
-369.958 |
377.921 |
||
|
RUBINL |
-1.434 |
207.873 |
-0.01 |
0.994 |
-408.872 |
406.004 |
||
|
ZENITW |
21.206 |
115.897 |
0.18 |
0.855 |
-205.955 |
248.368 |
||
|
ZENITL |
-3.638 |
213.153 |
-0.02 |
0.986 |
-421.425 |
414.150 |
||
|
LOKOELFRW |
3.616 |
451.509 |
0.01 |
0.994 |
-881.356 |
888.589 |
||
|
LOKOELFRL |
156.801 |
257.344 |
0.61 |
0.542 |
-347.603 |
661.205 |
||
|
SPACLW |
-19.581 |
307.959 |
-0.06 |
0.949 |
-623.192 |
584.030 |
||
|
SPACLL |
-56.552 |
205.935 |
-0.28 |
0.784 |
-460.192 |
347.088 |
||
|
CSKACLW |
-29.379 |
194.170 |
-0.15 |
0.880 |
-409.959 |
351.201 |
||
|
CSKACLL |
-109.793 |
139.110 |
-0.79 |
0.430 |
-382.453 |
162.867 |
||
|
DYNSUC |
-117.595 |
238.323 |
-0.49 |
0.622 |
-584.717 |
349.527 |
||
|
DYNFAIL |
2.734 |
324.080 |
0.01 |
0.993 |
-632.475 |
637.943 |
||
|
ZENGRW |
-29.399 |
399.184 |
-0.07 |
0.941 |
-811.814 |
753.016 |
||
|
ZENGRL |
-30.976 |
477.559 |
-0.07 |
0.948 |
-967.008 |
905.057 |
||
|
Constant |
0.241 |
10.053 |
0.02 |
0.981 |
-19.463 |
19.945 |
||
|
Mean dependent var |
-0.000 |
SD dependent var |
1713.219 |
|||||
|
R-squared |
0.000 |
Number of obs |
33549.000 |
|||||
|
F-test |
0.132 |
Prob > F |
1.000 |
|||||
|
Akaike crit. (AIC) |
594854.836 |
Bayesian crit. (BIC) |
595065.355 |
|||||
|
*** p<0.01, ** p<0.05, * p<0.1 |
Table 7. Regression results: Big Cap Poland companies
|
resid |
Coef. |
St.Err. |
t-value |
p-value |
[95% Conf |
Interval] |
Sig |
|
|
LEGW |
0.010 |
0.015 |
0.65 |
0.517 |
-0.019 |
0.039 |
||
|
LEGL |
-0.016 |
0.025 |
-0.66 |
0.508 |
-0.064 |
0.032 |
||
|
POLW |
0.000 |
0.057 |
-0.00 |
0.999 |
-0.112 |
0.112 |
||
|
POLL |
-0.028 |
0.060 |
-0.46 |
0.645 |
-0.145 |
0.090 |
||
|
SLAW |
0.012 |
0.079 |
0.15 |
0.883 |
-0.143 |
0.167 |
||
|
SLAL |
0.012 |
0.080 |
0.15 |
0.882 |
-0.145 |
0.169 |
||
|
LEGQUALW |
-0.041 |
0.051 |
-0.80 |
0.423 |
-0.141 |
0.059 |
||
|
LEGQUALL |
-0.025 |
0.168 |
-0.15 |
0.879 |
-0.354 |
0.303 |
||
|
LEGGROUPW |
0.067 |
0.157 |
0.43 |
0.668 |
-0.241 |
0.376 |
||
|
LEGGROUPL |
0.031 |
0.084 |
0.38 |
0.707 |
-0.132 |
0.195 |
||
|
LEGELQUALW |
0.004 |
0.056 |
0.07 |
0.943 |
-0.106 |
0.114 |
||
|
LEGELQUALL |
0.115 |
0.155 |
0.74 |
0.458 |
-0.188 |
0.418 |
||
|
LEGELGROUP |
-0.028 |
0.063 |
-0.43 |
0.665 |
-0.152 |
0.097 |
||
|
LEGELGROUPL |
-0.019 |
0.058 |
-0.32 |
0.751 |
-0.133 |
0.096 |
||
|
SLAELQUALW |
0.040 |
0.315 |
0.13 |
0.899 |
-0.578 |
0.658 |
||
|
SLAELQUALL |
0.009 |
0.254 |
0.03 |
0.973 |
-0.489 |
0.507 |
||
|
Constant |
0.000 |
0.004 |
-0.00 |
0.997 |
-0.008 |
0.008 |
||
|
Mean dependent var |
0.000 |
SD dependent var |
0.683 |
|||||
|
R-squared |
0.000 |
Number of obs |
31994.000 |
|||||
|
F-test |
0.179 |
Prob > F |
1.000 |
|||||
|
Akaike crit. (AIC) |
66430.830 |
Bayesian crit. (BIC) |
66573.176 |
|||||
|
*** p<0.01, ** p<0.05, * p<0.1 |
Table 8. Regression results: Big Cap France companies
|
resid |
Coef. |
St.Err. |
t-value |
p-value |
[95% Conf |
Interval] |
Sig |
|
|
PSGW |
0.000 |
0.001 |
-0.10 |
0.923 |
-0.003 |
0.003 |
||
|
PSGL |
0.001 |
0.004 |
0.34 |
0.731 |
-0.006 |
0.009 |
||
|
PSGWCL |
-0.001 |
0.004 |
-0.35 |
0.729 |
-0.008 |
0.006 |
||
|
PSGLCL |
-0.002 |
0.008 |
-0.25 |
0.807 |
-0.019 |
0.015 |
||
|
PSGCLFRW |
0.005 |
0.007 |
0.70 |
0.486 |
-0.009 |
0.019 |
||
|
PSGCLFRL |
0.006 |
0.010 |
0.59 |
0.552 |
-0.014 |
0.025 |
||
|
Constant |
0.000 |
0.000 |
0.30 |
0.766 |
-0.001 |
0.001 |
||
|
Mean dependent var |
0.000 |
SD dependent var |
0.064 |
|||||
|
R-squared |
0.000 |
Number of obs |
24210.000 |
|||||
|
F-test |
0.212 |
Prob > F |
1.000 |
|||||
|
Akaike crit. (AIC) |
-64537.685 |
Bayesian crit. (BIC) |
... |
Подобные документы
The stock market and economic growth: theoretical and analytical questions. Analysis of the mechanism of the financial market on the efficient allocation of resources in the economy and to define the specific role of stock market prices in the process.
дипломная работа [5,3 M], добавлен 07.07.2013Mergers and acquisitions: definitions, history and types of the deals. Previous studies of post-merger performance and announcement returns and Russian M&A market. Analysis of factors driving abnormal announcement returns and the effect of 2014 events.
дипломная работа [7,0 M], добавлен 02.11.2015General characteristic of the LLC DTEK Zuevskaya TPP and its main function. The history of appearance and development of the company. Characteristics of the organizational management structure. Analysis of financial and economic performance indicators.
отчет по практике [4,2 M], добавлен 22.05.2015Issues about housing prices formation process. Analytical model of housing prices. Definition a type of relationship between the set of independent variables and housing prices. The graph of real housing prices of all Russian regions during the period.
курсовая работа [1,6 M], добавлен 23.09.2016Law of demand and law of Supply. Elasticity of supply and demand. Models of market and its impact on productivity. Kinds of market competition, methods of regulation of market. Indirect method of market regulation, tax, the governmental price control.
реферат [8,7 K], добавлен 25.11.2009Natural gas market overview: volume, value, segmentation. Supply and demand Factors of natural gas. Internal rivalry & competitors' overview. Outlook of the EU's energy demand from 2007 to 2030. Drivers of supplier power in the EU natural gas market.
курсовая работа [2,0 M], добавлен 10.11.2013State intervention in the economy. Assessment and the role of teaching Veblen. Economic development of the society. Process of long-term loan and the inclusion of investor-banker in industrial production. Negative aspects of American institucionalism.
реферат [27,4 K], добавлен 14.11.2012Transition of the Chinese labor market. Breaking the Iron Rice Bowl. Consequences for a Labor Force in transition. Labor market reform. Post-Wage Grid Wage determination, government control. Marketization Process. Evaluating China’s industrial relations.
курсовая работа [567,5 K], добавлен 24.12.2012Economic entity, the conditions of formation and functioning of the labor market as a system of social relations, the hiring and use of workers in the field of social production. Study of employment and unemployment in the labor market in Ukraine.
реферат [20,3 K], добавлен 09.05.2011Assessment of the rate of unemployment in capitalist (the USA, Germany, England, France, Japan) and backward countries (Russia, Turkey, Pakistan, Afghanistan). Influence of corruption, merges of business and bureaucracy on progress of market economy.
реферат [15,5 K], добавлен 12.04.2012A theoretic analysis of market’s main rules. Simple Supply and Demand curves. Demand curve shifts, supply curve shifts. The problem of the ratio between supply and demand. Subsidy as a way to solve it. Effects of being away from the Equilibrium Point.
курсовая работа [56,3 K], добавлен 31.07.2013Concept and program of transitive economy, foreign experience of transition. Strategic reference points of long-term economic development. Direction of the transition to an innovative community-oriented type of development. Features of transitive economy.
курсовая работа [29,4 K], добавлен 09.06.2012Stereotypes that influence on economic relations between the European Union countries and Russia. Consequences of influence of stereotypes on economic relations between EU and Russia. Results of first attempts solving problem. General conclusion.
реферат [19,0 K], добавлен 19.11.2007Basic rules of social protection in USA. Maintenance of legal basis, development and regular updating of general(common) methodological principles of state guarantees and methodical development in sphere of work. Features of payment of work by worker.
курсовая работа [29,4 K], добавлен 12.04.2012The first stage of market reforms in Kazakhstan is from 1992 to 1997. The second phase is in 1998 after the adoption of the Strategy "Kazakhstan-2030". The agricultural, education sectors. The material and technical foundation of the medical institutions.
презентация [455,3 K], добавлен 15.05.2012The levers of management of a national economy, regions and enterprises. The prices for the goods. Taxes to the proceeds from realization of commodity production. Proceeds from realization of services to the population, establishments and organizations.
реферат [18,7 K], добавлен 12.04.2012Models and concepts of stabilization policy aimed at reducing the severity of economic fluctuations in the short run. Phases of the business cycle. The main function of the stabilization policy. Deviation in the system of long-term market equilibrium.
статья [883,7 K], добавлен 19.09.2017Financial bubble - a phenomenon on the financial market, when the assessments of people exceed the fair price. The description of key figures of financial bubble. Methods of predicting the emergence of financial bubbles, their use in different situations.
реферат [90,0 K], добавлен 14.02.2016Project background and rationales. Development methodology, schedule planning. Company mission and vision. Organization of staff and company structure. Procurement system target market. Implementation of procurement system. Testing, user manual.
дипломная работа [6,8 M], добавлен 28.11.2013Calculation of accounting and economic profits. The law of diminishing returns. Short-Run production relationships and production costs, it's graphic representation. The long-run cost curve. Average fixed, variable, total costs and marginal costs.
презентация [66,7 K], добавлен 19.10.2016
