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Predicting Stock Returns

Author

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  • Chakradhara Panda

    (Chakradhara Panda (corresponding author) is Assistant Professor, Economics Department, Faculty of Business and Economics, Addis Ababa University, P.O. Box: 1176, Ethiopia. E-mail: chdpanda@yahoo.com)

  • V. Narasimhan

    (V. Narasimhan is Reader, Department of Economics, University of Hyderabad, Central University, Hyderabad, 500 046, India. E-mail: vnss@uohyd.ernet.in)

Abstract

In this article, we use the artificial neural network in the forecasting of daily Bombay Stock Exchange (BSE) Sensitive Index (Sensex) returns. We compare the performance of the neural network with performances of random walk and linear autoregressive models by using six performance measures. The major findings are that neural network out-performs linear autoregressive and random walk models by all performance measures in both in-sample and out-of-sample forecasting of daily BSE Sensex returns. The findings suggest that stock markets do not follow a random walk and there exists a possibility of predicting stock returns. The superiority of the neural network model over linear autoregressive and random walk models in forecasting daily BSE Sensex returns indicates that neural network is able to capture non-linearities contained in stock returns.

Suggested Citation

  • Chakradhara Panda & V. Narasimhan, 2006. "Predicting Stock Returns," South Asia Economic Journal, Institute of Policy Studies of Sri Lanka, vol. 7(2), pages 205-218, September.
    • Handle: RePEc:sae:soueco:v:7:y:2006:i:2:p:205-218
    DOI: 10.1177/139156140600700203
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Huang, Alex YiHou, 2012. "Asymmetric dynamics of stock price continuation," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1839-1855.
    2. Carolina Fugazza & Massimo Guidolin & Giovanna Nicodano, 2009. "Time and Risk Diversification in Real Estate Investments: Assessing the Ex Post Economic Value," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 37(3), pages 341-381, September.
    3. Leite, Paulo & Cortez, Maria Céu, 2014. "Style and performance of international socially responsible funds in Europe," Research in International Business and Finance, Elsevier, vol. 30(C), pages 248-267.
    4. Lee, Jen-Sin & Huang, Gow-Liang & Kuo, Chin-Tai & Lee, Liang-Chien, 2012. "The momentum effect on Chinese real estate stocks: Evidence from firm performance levels," Economic Modelling, Elsevier, vol. 29(6), pages 2392-2406.
    5. Shanken, Jay & Tamayo, Ane, 2012. "Payout yield, risk, and mispricing: A Bayesian analysis," Journal of Financial Economics, Elsevier, vol. 105(1), pages 131-152.
    6. Jonathan Fletcher, 2011. "An Examination of Dynamic Trading Stategies in UK and US Stock Returns," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 38(9-10), pages 1290-1310, November.
    7. Lubos Pastor & Pietro Veronesi, 2009. "Learning in Financial Markets," Annual Review of Financial Economics, Annual Reviews, vol. 1(1), pages 361-381, November.
    8. Banegas, Ayelen & Gillen, Ben & Timmermann, Allan & Wermers, Russ, 2013. "The cross section of conditional mutual fund performance in European stock markets," Journal of Financial Economics, Elsevier, vol. 108(3), pages 699-726.
    9. Henkel, Sam James & Martin, J. Spencer & Nardari, Federico, 2011. "Time-varying short-horizon predictability," Journal of Financial Economics, Elsevier, vol. 99(3), pages 560-580, March.
    10. Chang, Kuang-Liang, 2009. "Do macroeconomic variables have regime-dependent effects on stock return dynamics? Evidence from the Markov regime switching model," Economic Modelling, Elsevier, vol. 26(6), pages 1283-1299, November.
    11. Cosemans, M. & Frehen, R.G.P. & Schotman, P.C. & Bauer, R.M.M.J., 2009. "Efficient Estimation of Firm-Specific Betas and its Benefits for Asset Pricing Tests and Portfolio Choice," MPRA Paper 23557, University Library of Munich, Germany.
    12. Avramov, Doron & Kosowski, Robert & Naik, Narayan Y. & Teo, Melvyn, 2011. "Hedge funds, managerial skill, and macroeconomic variables," Journal of Financial Economics, Elsevier, vol. 99(3), pages 672-692, March.
    13. Phan, Dinh Hoang Bach & Sharma, Susan Sunila & Narayan, Paresh Kumar, 2015. "Stock return forecasting: Some new evidence," International Review of Financial Analysis, Elsevier, vol. 40(C), pages 38-51.
    14. Massimo Guidolin & Giovanna Nicodano, 2010. "Ex Post Portfolio Performance with Predictable Skewness and Kurtosis," Carlo Alberto Notebooks 191, Collegio Carlo Alberto.
    15. Kim, J.W. & Leatham, D.J. & Bessler, D.A., 2007. "REITs' dynamics under structural change with unknown break points," Journal of Housing Economics, Elsevier, vol. 16(1), pages 37-58, March.
    16. Han, Yufeng, 2012. "State uncertainty in stock markets: How big is the impact on the cost of equity?," Journal of Banking & Finance, Elsevier, vol. 36(9), pages 2575-2592.
    17. Bangassa, Kenbata & Su, Chen & Joseph, Nathan L., 2012. "Selectivity and timing performance of UK investment trusts," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(5), pages 1149-1175.

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    More about this item

    Keywords

    JEL: C45; Feedforward Neural Network; Training; Generalization; In-sample Prediction; Out-of-Sample Prediction;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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