Random walks and market efficiency tests: evidence on US, Chinese and European capital markets within the context of the global Covid-19 pandemic

• Authors: Rui Dias , Nuno Teixeira , Veronika Machova , Pedro Pardal , Jakub Horak , Marek Vochozka

Abstracts

EN

Research background: Covid-19 has affected the global economy and has had an inevitable impact on capital markets. In the week of February 24-28, 2020, stock markets crashed. The index FTSE 100 decreased 13%, while the indices DJIA and S&P 500 fell 11-12%, the biggest drop since the 2007-2008 financial and economic crisis. It is therefore of interest to test the random walk hypothesis in developed capital markets, European and also non-European, in order to understand the different predictabilities between them. Purpose of the article: The aim is to analyze capital market efficiency, in its weak form, through the stock market indices of Belgium (index BEL 20), France (index CAC 40), Germany (index DAX 30), USA (index DOW JONES), Greece (index FTSE Athex 20), Spain (index IBEX 35), Ireland (index ISEQ), Portugal (index PSI 20) and China (index SSE) for the period from December 2019 to May 2020. Methods: Panel unit root tests of Breitung (2000), Levin et al. (2002) and Hadri (2002) were used to assess the time series stationarity. The test of Clemente et al. (1998) is used to detect structural breaks. The tests for the random walk hypothesis follows the variance ratio methodology proposed by Lo and MacKinlay (1988). Findings & Value added: In general, we found mixed confirmation about the EMH (efficient market hypothesis). Taking into account the conclusions of the rank variance test, the random walk hypothesis was rejected in the case of stock indices: Dow Jones, SSE and PSI 20, partially rejected in the case indices: BEL 20, CAC 40, FTSTE Athex 20 and DEX 30, but accepted for indices: IBEX 35 and ISEQ. The results also show that prices do not fully reflect the information available and that changes in prices are not independent and identically distributed. This situation has consequences for investors, since some returns can be expected, creating opportunities for arbitrage and for abnormal returns, contrary to the assumptions of random walk and information efficiency.

Keywords

EN

COVID-19; capital market; random walk hypothesis; efficient market hypothesis; arbitration; portfolio diversification

• Publisher: Instytut Badań Gospodarczych
• Journal: Oeconomia Copernicana
• Year: 2020
• Volume: 11
• Issue: 4
• Pages: 585-608

Dates

published

2020

Contributors

author

Rui Dias

• Polytechnic Institute of Setúbal

• https://orcid.org/0000000261383098

author

Nuno Teixeira

• Polytechnic Institute of Setúbal

• https://orcid.org/0000000179067731

author

Veronika Machova

• Institute of Technology and Business in České Budějovice

• https://orcid.org/0000000319089407

author

Pedro Pardal

• Polytechnic Institute of Setúbal

• https://orcid.org/0000000331873472

author

Jakub Horak

• Institute of Technology and Business in České Budějovice

• https://orcid.org/0000000163649745

author

Marek Vochozka

• Institute of Technology and Business in České Budějovice

• https://orcid.org/0000000219386957

References

• Abakah, E. J. A., Alagidede, P., Mensah, L., & Ohene-Asare, K. (2018). Non-linear approach to random walk test in selected African countries. International Journal of Managerial Finance, 14(3), 362-376. doi: 10.1108/IJMF-10-2017-0235.
• Aggarwal, D. (2018). Random walk model and asymmetric effect in Korean composite stock price index. Afro-Asian Journal of Finance and Accounting, 8(1). doi: 10.1504/aajfa.2018.10009906.
• Assaf, A., & Charif, H. (2017). Market efficiency in the MENA equity markets: Evidence from newly developed tests and regime change. Journal of Reviews on Global Economics, 6, 15-32. doi: 10.6000/1929-7092.2017.06.02.
• Breitung, J. (2000). The Local Power of Some Unit Root Tests for Panel Data. Advances in Econometrics, 15, 161-178.
• Caporale, G. M., Gil-Alana, L. A., & Poza, C. (2020). High and low prices and the range in the European stock markets: a long-memory approach. Research in International Business and Finance, 52. 101126. doi: 10.1016/j.ribaf.2019.101 126.
• Clemente, J., Monta?és, A., & Reyes, M. (1998). Testing for a unit root in variables with a double change in the mean. Economics Letters, 59(2), 175-182. doi: 10.1016/S0165-1765(98)00052-4.
• Dickey, D., & Fuller, W. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49(4), 1057-1072. doi: 10.2307/1912517.
• Dsouza, J. J., & Mallikarjunappa, T. (2015). Does the Indian stock market exhibit random walk? Paradigm, 19(1), 1-20. doi: 10.1177/0971890715585197.
• Durusu-Ciftci, D., Ispir, M. S., & Kok, D. (2019). Do stock markets follow a random walk? New evidence for an old question. International Review of Economics and Finance, 64, 165-175. doi: 10.1016/j.iref.2019.06.002.
• El Khamlichi, A., Sarkar, K., Arouri, M., & Teulon, F. (2014). Are Islamic equity indices more efficient than their conventional counterparts? Evidence from major global index families. Journal of Applied Business Research, 30(4), 1137-1150. doi: 10.19030/jabr.v30i4.8660.
• Fama, E. F., & French, K. R. (1988). Dividend yields and expected stock returns. Journal of Financial Economics, 22(1), 3-25. doi: 10.1016/0304-405X(88)90 020-7.
• Ferreira, P., & Dionísio, A. (2016). How long is the memory of the US stock market? Physica A: Statistical Mechanics and Its Applications, 451, 502-506. doi: 10.1016/j.physa.2016.01.080.
• Groda, B., & Vrbka, J. (2017). Prediction of stock price developments using the Box-Jenkins method. In J. Vachal, M. Vochozka & J. Horák (Eds.). SHS web of conferences - innovative economic symposium 2017: strategic partnership in international trade. Les Ulis: EDP Sciences. doi: 10.1051/shsconf/2017 3901007.
• Hadri, K. (2000). Testing for stationarity in heterogeneous panel data. Econometrics Journal, 3(2),148-161.
• Hamid, K., Suleman, M. T., Ali Shah, S. Z., & Imdad Akash, R. S. (2017). Testing the weak form of efficient market hypothesis: empirical evidence from Asia-Pacific markets. SSRN Electronic Journal, 58(58), 121-133. doi: 10.2139/ ssrn.2912908.
• Inclán, C., & Tiao, G. C. (1994). Use of cumulative sums of squares for retrospective detection of changes of variance. Journal of the American Statistical Association, 89(427), 913-923. doi: 10.1080/01621459.1994.10476 824.
• Jarque, C. M., & Bera, A. K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters, 6(3), 255-259. doi: 10.1016/0165-1765(80)90024-5.
• Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shinb, Y. (1992). Testing the null hypothesis of stationary against the alternative of a unit root. Journal of Econometrics, 54(1), 159-178. doi: 10.1016/0304-4076(92)90104-Y.
• Levin, A., Lin, F., & Chu, C. (2002). Unit root tests in panel data: asymptotic and finite-sample properties. Journal of Econometrics 108, 1-24.
• Liu, H., Manzoor, A., Wang, C., Zhang, L., & Manzoor, Z. (2020). The COVID-19 outbreak and affected countries stock markets response. International Journal of Environmental Research and Public Health, 17(8). doi: 10.3390/ijerph1708 2800.
• Lo, A. W., & Mackinlay, A. C. (1988). The society for financial studies stock market prices do not follow random walks: evidence from a simple specification test. Review of Financial Studies, 1(1), 41-66. doi: 66.10.1093 /rfs/1.1.41.
• Malafeyev, O., Awasthi, A., Kambekar, K. S., & Kupinskaya, A. (2019). Random walks and market efficiency in Chinese and Indian equity markets. Statistics, Optimization & Information Computing, 7(1), 1-25. doi: 10.19139/soic. v7i1.499.
• Mehla, S., & Goyal, S. K. (2013). Empirical evidence on weak form of efficiency in Indian stock market. Asia-Pacific Journal of Management Research and Innovation, 8(1), 59-68. doi: 10.1177/2319510x1200800107.
• Milos, L. R., Hatiegan, C., Milos, M. C., Barna, F. M., & Botoc, C. (2020). Multifractal detrended fluctuation analysis (MF-DFA) of stock market indexes. Empirical evidence from seven central and eastern european markets. Sustainability, 12(2). doi: 10.3390/su12020535.
• Mohti, W., Dionísio, A., Ferreira, P., & Vieira, I. (2018). Frontier markets? efficiency: mutual information and detrended fluctuation analyses. Journal of Economic Interaction and Coordination, 14(3), 551-572. doi: 10.1007/s11403-018-0224-9.
• Moore, C., & Kolencik, J. (2020). Acute depression, extreme anxiety, and prolonged stress among COVID-19 frontline healthcare workers. Psychosociological Issues in Human Resource Management 8(1), 55-60. doi: 10.22381/PIHRM8120209.
• Ngene, G., Tah, K. A., & Darrat, A. F. (2017). The random-walk hypothesis revisited: new evidence on multiple structural breaks in emerging markets. Macroeconomics and Finance in Emerging Market Economies, 10(1), 88-106. doi: 10.1080/17520843.2016.1210189.
• Nisar, S., & Hanif, M. (2012). Testing weak form of efficient market hypothesis: empirical evidence from South-Asia. World Applied Sciences Journal, 17(4), 414-427.
• Perron, P., & Phillips, P. C. B. (1988). Testing for a unit root in a time series regression. Biometrika, 2(75), 335-346. doi: 10.1080/07350015.1992.1050 9923.
• Popescu Ljungholm, D., & Olah, M. L. (2020). Mental health consequences of the COVID-19 crisis on frontline healthcare professionals: psychological impairments as a result of work-related stress. Psychosociological Issues in Human Resource Management, 8(1), 31-36. doi: 10.22381/PIHRM8120205.
• Rehman, S., Chhapra, I. U., Kashif, M., & Rehan, R. (2018). Are stock prices a random walk? An empirical evidence of asian stock markets. Etikonomi, 17(2), 237-252. doi: 10.15408/etk.v17i2.7102.
• Richards, A. J. (1997). Winner-loser reversals in national stock market indices: Can they be explained? Journal of Finance, 52(5), 2129-2144. doi: 10.2307/2329478.
• Rosenthal, L. (1983). An empirical test of the efficiency of the ADR market. Journal of Banking & Finance, 7(1), 17-29. doi: 10.1016/0378-4266(83)90 053-5.
• Rounaghi, M. M., & Nassir Zadeh, F. (2016). Investigation of market efficiency and financial stability between S&P 500 and London Stock Exchange: monthly and yearly forecasting of time series stock returns using ARMA model. Physica A: Statistical Mechanics and Its Applications, 456, 10-21. doi: 10.1016/j.physa. 2016.03.006.
• Sadat, A. R., & Hasan, M. E. (2019). Testing weak form of market efficiency of DSE based on random walk hypothesis model: a parametric test approach. International Journal of Accounting and Financial Reporting, 9(1), 400-413. doi: 10.5296/ijafr.v9i1.14454.
• Segers, C. (2020). Psychological resilience, burnout syndrome, and stress-related psychiatric disorders among healthcare professionals during the COVID-19 crisis. Psychosociological Issues in Human Resource Management, 8(1), 7-12. doi: 10.22381/PIHRM8120201.
• Sensoy, A., & Tabak, B. M. (2015). Time-varying long term memory in the European Union stock markets. Physica A: Statistical Mechanics and Its Applications, 436, 147-158. doi: 10.1016/j.physa.2015.05.034.
• Shirvani, H., & Delcoure, N. V. (2016). The random walk in the stock prices of 18 OECD countries: some robust panel-based integration and cointegration tests. Journal of Economic Studies, 43(4), 598-608. doi: 10.1108/JES-03-2015-0053.
• Singh, D. S. K., & Kumar, L. (2018). Market efficiency in Malaysia: an empirical study of random walk hypothesis of Kuala Lumpur stock market (composite index) Bursa Malaysia. SSRN Electronic Journal. doi: 10.2139/ssrn.3095176.
• Tiwari, A. K., & Kyophilavong, P. (2014). New evidence from the random walk hypothesis for BRICS stock indices: a wavelet unit root test approach. Economic Modelling, 43, 38?41. doi: 10.1016/j.econmod.2014.07.005.
• Thompson, D. (2020). Psychological trauma symptoms and mental conditions of medical staff during the COVID-19 pandemic: severe stress, elevated anxiety, and clinically significant depression. Psychosociological Issues in Human Resource Management, 8(1), 25-30. doi: 10.22381/PIHRM8120204.
• Tsay, R. S. (2005). Analysis of financial time series. Willey. doi: 10.1198/tech. 2006.s405.
• Vochozka, M., Horak, J., & Krulicky, T. (2020). Innovations in management forecast: time development of stock prices with neural networks. Marketing and Management of Innovations, 2, 324-339. doi: 10.21272/mmi.2020.2-24.
• Vrbka, J., & Rowland, Z. (2017). Stock price development forecasting using neural networks. In J. Vachal; M. Vochozka & J. Horák (Eds.). SHS web of conferences - innovative economic symposium 2017: strategic partnership in international trade. Les Ulis: EDP Sciences. doi: 10.1051/shsconf/201739010 32.
• Worthington, A. C., & Higgs, H. (2013). Tests of random walks and market efficiency in Latin American stock markets: an empirical note. Pathogens and Global Health, 107(8), 493. doi: 10.1179/204777213X13869290853977.
• Zeren, F., & Hizarci, A. (2020). The impact of Covid-19 coronavirus on stock markets: evidence from selected countries. Muhasebe ve Finans ?ncelemeleri Dergisi, 3(1), 78-84. doi: 10.32951/mufider.706159.

Identifiers

DOI

10.24136/oc.2020.024

Biblioteka Nauki

19233559