Testing efficient market hypothesis in developing Eastern European countries
-
Author(s)Saša TokićLink to ORCID Index: https://orcid.org/0000-0001-9459-5831
,
Berislav BolfekLink to ORCID Index: https://orcid.org/0000-0001-6968-2319
,
Anita Radman PešaLink to ORCID Index: https://orcid.org/0000-0003-3767-0676
-
DOIhttp://dx.doi.org/10.21511/imfi.15(2).2018.25
-
Article InfoVolume 15 2018, Issue #2, pp. 281-291
- Cited by
- 1825 Views
-
347 Downloads
This work is licensed under a
Creative Commons Attribution-NonCommercial 4.0 International License
This paper analyzes financial markets in four developing countries (Croatia, Serbia, Slovenia, Slovakia) using daily returns of their respective stock market indices from January 1, 2006 till December 31, 2016, timeframe which was rarely analyzed. Analysis was conducted by various statistical tests, more precisely serial correlation test, runs test, Augmented Dickey-Fuller test, unit root test, variance ratio test and test of January effect. Results suggest that all analyzed indices, except BelexLine (Serbia), confirm weak form of efficient market hypothesis, while the results on the index BelexLine are mixed and it can be concluded that it does not follow weak form of efficient market hypothesis. Given these results, it can be said that not only passive approach to portfolio management is more appropriate on all indices, except BelexLine, but also additional test and more complex models are necessary in order to confirm this conclusion.
- Keywords
-
JEL Classification (Paper profile tab)G14, G15
-
References20
-
Tables6
-
Figures4
-
- Figure 1. CROBEX autocorrelation test results
- Figure 2. BelexLine autocorrelation test results
- Figure 3. SBITOP autocorrelation test results
- Figure 4. SAX autocorrelation test results
-
- Table 1. Correlation coefficients in serial correlation test
- Table 2. ADF test results
- Table 3. Runs test results
- Table 4. Variance ratio test results
- Table 5. January effect test results
- Table 6. All test result matrix
-
- Barbić, T. (2010). Testing Week Form of Efficient Market Hypothesis in Croatia’ S Equity Market [Testiranje slabog oblika hipoteze efikasnog tržišta na hrvatskom tržištu kapitala]. Zbornik Ekonomskog fakulteta u Zagrebu, 8 (1), 155-172.
- Bartlett, M. S. (1937). Properties of Sufficiency and Statistical Tests. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 160(901), 268-282.
- Brooks, C. (2008). Introductory Econometrics for Finance. Finance. http://doi.org/10.1111/1468- 0297.13911
- Charles, A., & Darné, O. (2009). Variance-ratio tests of random walk: An overview. Journal of Economic Surveys, 23(3), 503-527.
- Chung, H. (2006). Testing weak-form efficiency of the Chinese stock market (Working Paper).
- Deželan, S. (1999). Efficiency of the Slovenian Capital Market. Delovni Zvezki/Raziskovalni Center Ekonomske Fakultete, 91, 1-27.
- Dickey, D. A., & Fuller, W. A. (1979). Distribution of the Estimators for Autoregressive Time Series With a Unit Root. Journal of the American Statistical Association, 74(366), 427-431.
- Dragotă, V., & Ţilică, E. V. (2014). Market efficiency of the Post Communist East European stock markets. Central European Journal of Operations Research, 22(2), 307-337.
- Fama, E. F. (1965). Random Walks in Stock Market Prices. Financial Analysts Journal, 51(1), 75-80.
- Hančlova, J., & Rublikova, E. (2006). Testing the Weak Form of Efficiency on Czech and Slovak Stock Market, 1.
- Hawaldar, I. T., Rohit, B., & Pinto, P. (2017). Testing of weak form of efficient market hypothesis: evidence from the Bahrain Bourse. Investment Management and Financial Innovations, 14(2-2), 376-385.
- Heininen, P., & Puttonen, V. (2010). Stock Market Efficiency in the Transition Economies through the Lens of Calendar Anomalies. Paper presented at Moscow: EACES 10th bi-annual conference Patterns of Transition and New Approaches to Comparative Economics.
- John Y. Campbell, Andrew W. Lo, A. C. M. (1997). The Econometrics of Financial Markets.
- Magel, R. C., & Wibowo, S. H. (1997). Comparing the Powers of the Wald-Wolfowitz and Kolmogorov-Smirnov Tests. Biometrical Journal, 39(6), 665- 675.
- Marrett, G., & Worthington, A. (2011). The Month-of-the-year Effect in the Australian Stock Market: A Short Technical Note on the Market, Industry and Firm Size Impacts The Month-of-the-year Effect in the Australian Stock Market: A Short, 5(1), 117-123.
- Njuguna, J. (2016). Testing the efficient market hypothesis on the Nairobi Securities Exchange. Investment Management and Financial Innovations, 13(3), 75-83.
- Prorok, V., & Radović, D. (2014, July 5). Weak-form hypothesis testing of the Serbian capital market efficiency. Socioeconomica.
- Rukhin, A., Soto, J., Nechvatal, J., Miles, S., Barker, E., Leigh, S., … Vo, S. (2010). A statistical test suite for random and pseudorandom number generators for cryptographic applications. National Institute of Standards and Technology, 800(April), 131.
- Šonje, V., Alajbeg, D., & Bubaš, Z. (2011). Efficient Market Hypothesis : is the Croatian Stock Market as ( in ) Efficient as the U. S . Market. Financial Theory and Practice, 35(3), 301-326.
- Wolfowitz, A., & Wald, J. (1961). On A test Wherher Two Samples Are From the Same Population. Young, 37(3), 23.
-
Calendar anomalies in the Ukrainian stock market
Guglielmo Maria Caporale , Alex Plastun 
doi: http://dx.doi.org/10.21511/imfi.14(1).2017.11
Investment Management and Financial Innovations Volume 14, 2017 Issue #1 pp. 104-114 Views: 1617 Downloads: 398 TO CITE АНОТАЦІЯThis paper is a comprehensive investigation of calendar anomalies in the Ukrainian stock market. It employs various statistical techniques (average analysis, Student’s t-test, ANOVA, the Kruskal-Wallis test, and regression analysis with dummy variables) and a trading simulation approach to test for the presence of the following anomalies: day-of-the-week effect; turn-of-the-month effect; turn-of-the-year effect; month-of-the-year effect; January effect; holiday effect; Halloween effect. The results suggest that in general calendar anomalies are not present in the Ukrainian stock market, but there are a few exceptions, i.e. the turn-of-the-year and Halloween effect for the PFTS index, and the month-of-the-year effect for UX futures. However, the trading simulation analysis shows that only trading strategies based on the turn-of-the-year effect for the PFTS index and the month-of-the-year effect for the UX futures can generate exploitable profit opportunities that can be interpreted as evidence against market efficiency.
-
January effect: 200 years of evolution in the us stock market
Alex Plastun ,
Vyacheslav Plastun
doi: https://doi.org/10.21511/gg.02(1).2018.04
Geopolitics under Globalization Volume 2, 2018 Issue #1 pp. 27-33 Views: 1028 Downloads: 177 TO CITE АНОТАЦІЯThis paper is a comprehensive investigation of the January Effect evolution in the US stock market over the period 1791–2015. It employs various statistical techniques (average analysis, Student’s t-test, ANOVA, Mann-Whitney test) and a trading simulation approach to analyze the evolution of this anomaly. The results suggest that January effect during the XVIII–XXI century passed the way from rise to fall. The rise of the January Effect starts in the end of the XIX century and this anomaly mostly disappeared in middle of the XX century. Nowadays the January Effect is not present in the US stock market, but even today January stays one of the best months for purchases in the US stock market.
-
Testing of causality relationship between Indian and Australian mutual funds performance: standard vs customized benchmarks
B. R. Manjunath ,
J. K. Raju ,
M. Rehaman
doi: http://dx.doi.org/10.21511/imfi.17(3).2020.18
Investment Management and Financial Innovations Volume 17, 2020 Issue #3 pp. 231-245 Views: 645 Downloads: 90 TO CITE АНОТАЦІЯMost Australian domestic investors rely on fund managers, and in India, this is not the same as they are primarily in direct investment rather than indirect. The study attempts to investigate the causal relationship between the returns of the standard indices, namely BSE500 and ASX300, and customized indices, MIMF and MAMF, for both India and Australia. The study uses econometric tools and techniques such as unit root test, vector error correction model, Wald test, Johansen co-integration, and model efficacy assumptions on the historical closing NAV of the selected mutual fund schemes for the period from April 2008 to March 2018. The econometric investigation using Johansen’s Co-Integration test confirmed the co-integration between BSE500, ASX300 and customized indices. Empirical evidence suggests that the Australian customized MAMF index is not Granger-caused by the Indian customized index MIMF, and therefore the MIMF index value cannot be used to predict the future rate of index MAMF returns, and vice versa.
