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





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


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.


Download data is not yet available.


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.

DOI: https://doi.org/10.1108/IJMF-10-2017-0235
View in Google Scholar

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.

DOI: https://doi.org/10.1504/AAJFA.2018.10009906
View in Google Scholar

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.

DOI: https://doi.org/10.6000/1929-7092.2017.06.02
View in Google Scholar

Breitung, J. (2000). The Local Power of Some Unit Root Tests for Panel Data. Advances in Econometrics, 15, 161-178.

DOI: https://doi.org/10.1016/S0731-9053(00)15006-6
View in Google Scholar

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.

DOI: https://doi.org/10.1016/j.ribaf.2019.101126
View in Google Scholar

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.

DOI: https://doi.org/10.1016/S0165-1765(98)00052-4
View in Google Scholar

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.

DOI: https://doi.org/10.2307/1912517
View in Google Scholar

Dsouza, J. J., & Mallikarjunappa, T. (2015). Does the Indian stock market exhibit random walk? Paradigm, 19(1), 1-20. doi: 10.1177/0971890715585197.

DOI: https://doi.org/10.1177/0971890715585197
View in Google Scholar

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.

DOI: https://doi.org/10.1016/j.iref.2019.06.002
View in Google Scholar

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.

DOI: https://doi.org/10.19030/jabr.v30i4.8660
View in Google Scholar

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.

DOI: https://doi.org/10.1016/0304-405X(88)90020-7
View in Google Scholar

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.

DOI: https://doi.org/10.1016/j.physa.2016.01.080
View in Google Scholar

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.

DOI: https://doi.org/10.1051/shsconf/20173901007
View in Google Scholar

Hadri, K. (2000). Testing for stationarity in heterogeneous panel data. Econometrics Journal, 3(2),148-161.

DOI: https://doi.org/10.1111/1368-423X.00043
View in Google Scholar

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.

DOI: https://doi.org/10.2139/ssrn.2912908
View in Google Scholar

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.

DOI: https://doi.org/10.1080/01621459.1994.10476824
View in Google Scholar

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.

DOI: https://doi.org/10.1016/0165-1765(80)90024-5
View in Google Scholar

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.

DOI: https://doi.org/10.1016/0304-4076(92)90104-Y
View in Google Scholar

Levin, A., Lin, F., & Chu, C. (2002). Unit root tests in panel data: asymptotic and finite-sample properties. Journal of Econometrics 108, 1-24.

DOI: https://doi.org/10.1016/S0304-4076(01)00098-7
View in Google Scholar

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.

DOI: https://doi.org/10.3390/ijerph17082800
View in Google Scholar

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.

DOI: https://doi.org/10.1093/rfs/1.1.41
View in Google Scholar

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.

DOI: https://doi.org/10.19139/soic.v7i1.499
View in Google Scholar

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.

DOI: https://doi.org/10.1177/2319510X1200800107
View in Google Scholar

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.

DOI: https://doi.org/10.3390/su12020535
View in Google Scholar

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.

DOI: https://doi.org/10.1007/s11403-018-0224-9
View in Google Scholar

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.

DOI: https://doi.org/10.22381/PIHRM8120209
View in Google Scholar

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.

DOI: https://doi.org/10.1080/17520843.2016.1210189
View in Google Scholar

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.
View in Google Scholar

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.

DOI: https://doi.org/10.1093/biomet/75.2.335
View in Google Scholar

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.

DOI: https://doi.org/10.22381/PIHRM8120205
View in Google Scholar

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.

DOI: https://doi.org/10.15408/etk.v17i2.7102
View in Google Scholar

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.

DOI: https://doi.org/10.1111/j.1540-6261.1997.tb02755.x
View in Google Scholar

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.

DOI: https://doi.org/10.1016/0378-4266(83)90053-5
View in Google Scholar

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.

DOI: https://doi.org/10.1016/j.physa.2016.03.006
View in Google Scholar

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.

DOI: https://doi.org/10.5296/ijafr.v9i1.14454
View in Google Scholar

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.

DOI: https://doi.org/10.22381/PIHRM8120201
View in Google Scholar

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.

DOI: https://doi.org/10.1016/j.physa.2015.05.034
View in Google Scholar

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.

DOI: https://doi.org/10.1108/JES-03-2015-0053
View in Google Scholar

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.

DOI: https://doi.org/10.2139/ssrn.3095176
View in Google Scholar

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.

DOI: https://doi.org/10.1016/j.econmod.2014.07.005
View in Google Scholar

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.

DOI: https://doi.org/10.22381/PIHRM8120204
View in Google Scholar

Tsay, R. S. (2005). Analysis of financial time series. Willey. doi: 10.1198/tech. 2006.s405.

DOI: https://doi.org/10.1002/0471746193
View in Google Scholar

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.

DOI: https://doi.org/10.21272/mmi.2020.2-24
View in Google Scholar

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.
View in Google Scholar

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.

DOI: https://doi.org/10.1179/204777213X13869290853977
View in Google Scholar

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.

DOI: https://doi.org/10.32951/mufider.706159
View in Google Scholar




How to Cite

Dias, R., Teixeira, N., Machova, V., Pardal, P., Horak, J., & Vochozka, M. (2020). Random walks and market efficiency tests: evidence on US, Chinese and European capital markets within the context of the global Covid-19 pandemic. Oeconomia Copernicana, 11(4), 585–608. https://doi.org/10.24136/oc.2020.024




Similar Articles

1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.