IDEAS home Printed from https://ideas.repec.org/a/ods/journl/v8y2019i2p71-77.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.jami.org.ua/Papers/JAMI_8_2_2019_71-77.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. Kent Daniel & David Hirshleifer & Avanidhar Subrahmanyam, 1998. "Investor Psychology and Security Market Under- and Overreactions," Journal of Finance, American Finance Association, vol. 53(6), pages 1839-1885, December.
    5. 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.
    6. Barberis, Nicholas & Shleifer, Andrei & Vishny, Robert, 1998. "A model of investor sentiment," Journal of Financial Economics, Elsevier, vol. 49(3), pages 307-343, September.
    7. 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.
    8. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Devarakonda, S. & Chittineni, J., 2019. "Does Insurance Promote Economic Growth? Evidence from BRICS Countries," Journal of Applied Management and Investments, Department of Business Administration and Corporate Security, International Humanitarian University, vol. 8(3), pages 135-146, September.
    2. 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.
    3. Mihir Dash, 2020. "Testing the Binomial Model in the Indian Stock Market," Journal of Applied Management and Investments, Department of Business Administration and Corporate Security, International Humanitarian University, vol. 9(1), pages 22-27, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mihir Dash, 2020. "Testing the Binomial Model in the Indian Stock Market," Journal of Applied Management and Investments, Department of Business Administration and Corporate Security, International Humanitarian University, vol. 9(1), pages 22-27, March.
    2. Lesmond, David A. & Schill, Michael J. & Zhou, Chunsheng, 2004. "The illusory nature of momentum profits," Journal of Financial Economics, Elsevier, vol. 71(2), pages 349-380, February.
    3. A. Malliaris & Mary Malliaris, 2014. "N-tuple S&P patterns across decades, 1950–2011," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 22(2), pages 339-353, June.
    4. Benjamin Chabot & Eric Ghysels & Ravi Jagannathan, 2009. "Momentum Cycles and Limits to Arbitrage Evidence from Victorian England and Post-Depression US Stock Markets," NBER Working Papers 15591, National Bureau of Economic Research, Inc.
    5. Ashish Kumar Garg & Pankaj Varshney, 2015. "Momentum Effect in Indian Stock Market: A Sectoral Study," Global Business Review, International Management Institute, vol. 16(3), pages 494-510, June.
    6. Benjamin Chabot & Eric Ghysels & Ravi Jagannathan, 2008. "Price Momentum In Stocks: Insights From Victorian Age Data," NBER Working Papers 14500, National Bureau of Economic Research, Inc.
    7. Tobias J. Moskowitz & Mark Grinblatt, 2002. "What Do We Really Know About the Cross-Sectional Relation Between Past and Expected Returns?," Yale School of Management Working Papers ysm259, Yale School of Management.
    8. Hou, Yang & Meng, Jiayin, 2018. "The momentum effect in the Chinese market and its relationship with the simultaneous and the lagged investor sentiment," MPRA Paper 94838, University Library of Munich, Germany.
    9. Kang, Joseph & Liu, Ming-Hua & Ni, Sophie Xiaoyan, 2002. "Contrarian and momentum strategies in the China stock market: 1993-2000," Pacific-Basin Finance Journal, Elsevier, vol. 10(3), pages 243-265, June.
    10. Kobana Abukari & Isaac Otchere, 2020. "Dominance of hybrid contratum strategies over momentum and contrarian strategies: half a century of evidence," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 34(4), pages 471-505, December.
    11. repec:esx:essedp:770 is not listed on IDEAS
    12. Stefano DellaVigna & Joshua M. Pollet, 2005. "Attention, Demographics, and the Stock Market," NBER Working Papers 11211, National Bureau of Economic Research, Inc.
    13. Gao, Bin & Liu, Xihua, 2020. "Intraday sentiment and market returns," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 48-62.
    14. Stefano DellaVigna, 2009. "Psychology and Economics: Evidence from the Field," Journal of Economic Literature, American Economic Association, vol. 47(2), pages 315-372, June.
    15. Nicholas Apergis & Vasilios Plakandaras & Ioannis Pragidis, 2022. "Industry momentum and reversals in stock markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3093-3138, July.
    16. Narayan, Paresh Kumar & Sharma, Susan Sunila, 2011. "New evidence on oil price and firm returns," Journal of Banking & Finance, Elsevier, vol. 35(12), pages 3253-3262.
    17. Peng, Lin & Xiong, Wei, 2006. "Investor attention, overconfidence and category learning," Journal of Financial Economics, Elsevier, vol. 80(3), pages 563-602, June.
    18. Cakici, Nusret & Tang, Yi & Yan, An, 2016. "Do the size, value, and momentum factors drive stock returns in emerging markets?," Journal of International Money and Finance, Elsevier, vol. 69(C), pages 179-204.
    19. Lee, King Fuei, 2021. "An Anomaly within an Anomaly: The Halloween Effect in the Long-term Reversal Anomaly," MPRA Paper 110859, University Library of Munich, Germany.
    20. Yang, Haijun & Ge, Hengshun & Gao, Xinpeng, 2022. "An information diffusion model for momentum effect based on investor wealth," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    21. Hao, Ying & Chou, Robin K. & Ko, Kuan-Cheng & Yang, Nien-Tzu, 2018. "The 52-week high, momentum, and investor sentiment," International Review of Financial Analysis, Elsevier, vol. 57(C), pages 167-183.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ods:journl:v:8:y:2019:i:2:p:71-77. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Anatoliy G. Goncharuk (email available below). General contact details of provider: https://edirc.repec.org/data/dmonaua.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.