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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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    Cited by:

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    3. 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.
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    5. 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.
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    7. Massimo Guidolin & Giovanna Nicodano, 2010. "Ex Post Portfolio Performance with Predictable Skewness and Kurtosis," Carlo Alberto Notebooks 191, Collegio Carlo Alberto.
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    14. 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.
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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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