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Testing the Random Walk Hypothesis in the Indian Stock Market Using ARIMA Modelling

Author

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  • Dash, M.

    (Alliance University, India)

Abstract

The Random Walk Hypothesis (RWH) is a consequence of two foundational financial theories: the Geometric Brownian Motion (GBM) model and the Efficient Market Hypothesis (EMH). The paper examines the RWH for twenty major stocks from the Indian banking sector. The stock price data was collected from the National Stock Exchange (NSE). The study period selected was Apr. 1, 2017 to Mar. 31, 2018, a period of one year. The study uses the runs test, the Augmented Dickey-Fuller (ADF) unit root test, and Auto-regressive integrated moving average (ARIMA) modelling for the stock log-returns to test the RWH. While the results of runs test and ADF test support the RWH, the results of the Auto-regressive moving average (ARMA) modelling, however, provide some evidence against the RWH. However, as the R2 for the ARMA models were low, log-returns may largely be due to random stock price movements. Thus, though log-returns may not follow a pure random walk, there is some scope for randomness in log-returns series.

Suggested Citation

  • Dash, M., 2019. "Testing the Random Walk Hypothesis in the Indian Stock Market Using ARIMA Modelling," Journal of Applied Management and Investments, Department of Business Administration and Corporate Security, International Humanitarian University, vol. 8(2), pages 71-77, May.
    • Handle: RePEc:ods:journl:v:8:y:2019:i:2:p:71-77
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    References listed on IDEAS

    as
    1. Smidt, Seymour, 1968. "A New Look at the Random Walk Hypothesis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 3(3), pages 235-261, September.
    2. Vijai Anand & Tapal Dulababu, 2012. "The random walk hypothesis: a research study on selected banks," Journal of Applied Management and Investments, Department of Business Administration and Corporate Security, International Humanitarian University, vol. 1(1), pages 67-70.
    3. Yoon Hong & Ji-chul Lee & Guoping Ding, 2017. "Volatility Clustering, New Heavy-Tailed Distribution and the Stock Market Returns in South Korea," Journal of Applied Management and Investments, Department of Business Administration and Corporate Security, International Humanitarian University, vol. 6(3), pages 164-169, September.
    4. Ankita Mishra & Vinod Mishra & Russell Smyth, 2015. "The Random-Walk Hypothesis on the Indian Stock Market," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 51(5), pages 879-892, September.
    5. Barberis, Nicholas & Shleifer, Andrei & Vishny, Robert, 1998. "A model of investor sentiment," Journal of Financial Economics, Elsevier, vol. 49(3), pages 307-343, September.
    6. Harrison Hong & Jeremy C. Stein, 1999. "A Unified Theory of Underreaction, Momentum Trading, and Overreaction in Asset Markets," Journal of Finance, American Finance Association, vol. 54(6), pages 2143-2184, December.
    7. repec:bla:jfinan:v:53:y:1998:i:6:p:1839-1885 is not listed on IDEAS
    8. Jensen, Michael C & Bennington, George A, 1970. "Random Walks and Technical Theories: Some Additional Evidence," Journal of Finance, American Finance Association, vol. 25(2), pages 469-482, May.
    9. Evans, John L., 1968. "The Random Walk Hypothesis, Portfolio Analysis and the Buy-and-Hold Criterion*," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 3(3), pages 327-342, September.
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    Cited by:

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    3. Amit K. Sinha, 2021. "The reliability of geometric Brownian motion forecasts of S&P500 index values," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1444-1462, December.
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    5. Ma, Fei & Wang, Ping, 2024. "Understanding influence of fractal generative manner on structural properties of tree networks," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).

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    More about this item

    Keywords

    random walk; efficient market; unit root test; ARIMA modelling;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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