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Irrational fads, short-term memory emulation, and asset predictability

  • Bekiros, Stelios D.

Opponents of the efficient markets hypothesis argue that predictability reflects the psychological factors and “fads” of irrational investors in a speculative market. In that, conventional time series analysis often fails to give an accurate forecast for financial processes due to inherent noise patterns, fat tails, and nonlinear components. A recent stream of literature on behavioral finance has revealed that boundedly rational agents using simple rules of thumb for their decisions under uncertainty provides a more realistic description of human behavior than perfect rationality with optimal decision rules. Consequently, the application of technical analysis in trading could produce high returns. Machine learning techniques have been employed in economic systems in modeling nonlinearities and simulating human behavior. In this study, we expand the literature that evaluates return sign forecasting ability by introducing a recurrent neural network approach that combines heuristic learning and short-term memory emulation, thus mimicking the decision-making process of boundedly rational agents. We investigate the relative direction-of-change predictability of the neural network structure implied by the Lee–White–Granger test as well as compare it to other well-established models for the DJIA index. Moreover, we examine the relationship between stock return volatility and returns. Overall, the proposed model presents high profitability, in particular during “bear” market periods.

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Article provided by Elsevier in its journal Review of Financial Economics.

Volume (Year): 22 (2013)
Issue (Month): 4 ()
Pages: 213-219

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Handle: RePEc:eee:revfin:v:22:y:2013:i:4:p:213-219
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  1. Shleifer, Andrei & Summers, Lawrence H, 1990. "The Noise Trader Approach to Finance," Journal of Economic Perspectives, American Economic Association, vol. 4(2), pages 19-33, Spring.
  2. Po-Hsuan Hsu & Chung-Ming Kuan, 2005. "Reexamining the Profitability of Technical Analysis with Data Snooping Checks," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 3(4), pages 606-628.
  3. Fama, Eugene F & French, Kenneth R, 1995. " Size and Book-to-Market Factors in Earnings and Returns," Journal of Finance, American Finance Association, vol. 50(1), pages 131-55, March.
  4. Paul R. Krugman, 1987. "Trigger Strategies and Price Dynamics in Equity and Foreign Exchange Markets," NBER Working Papers 2459, National Bureau of Economic Research, Inc.
  5. Lee, Tae-Hwy & White, Halbert & Granger, Clive W. J., 1993. "Testing for neglected nonlinearity in time series models : A comparison of neural network methods and alternative tests," Journal of Econometrics, Elsevier, vol. 56(3), pages 269-290, April.
  6. Chen, Son-Nan, 1982. "An Examination of Risk-Return Relationship in Bull and Bear Markets Using Time-Varying Betas," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 17(02), pages 265-286, June.
  7. Robert J. Shiller, 2003. "From Efficient Markets Theory to Behavioral Finance," Journal of Economic Perspectives, American Economic Association, vol. 17(1), pages 83-104, Winter.
  8. Christie, Andrew A., 1982. "The stochastic behavior of common stock variances : Value, leverage and interest rate effects," Journal of Financial Economics, Elsevier, vol. 10(4), pages 407-432, December.
  9. Levich, Richard M. & Thomas, Lee III, 1993. "The significance of technical trading-rule profits in the foreign exchange market: a bootstrap approach," Journal of International Money and Finance, Elsevier, vol. 12(5), pages 451-474, October.
  10. Pesaran, M.H. & Timmermann, A., 1990. "A Simple, Non-Parametric Test Of Predictive Performance," Cambridge Working Papers in Economics 9021, Faculty of Economics, University of Cambridge.
  11. Hommes, Cars H., 2006. "Heterogeneous Agent Models in Economics and Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 23, pages 1109-1186 Elsevier.
  12. Fabozzi, Frank J & Francis, Jack Clark, 1977. "Stability Tests for Alphas and Betas over Bull and Bear Market Conditions," Journal of Finance, American Finance Association, vol. 32(4), pages 1093-99, September.
  13. Gencay, Ramazan, 1998. "Optimization of technical trading strategies and the profitability in security markets," Economics Letters, Elsevier, vol. 59(2), pages 249-254, May.
  14. Asger Lunde & Allan Timmermann, 2000. "Duration Dependence in Stock Prices: An Analysis of Bull and Bear Markets," Econometric Society World Congress 2000 Contributed Papers 1216, Econometric Society.
  15. Henriksson, Roy D & Merton, Robert C, 1981. "On Market Timing and Investment Performance. II. Statistical Procedures for Evaluating Forecasting Skills," The Journal of Business, University of Chicago Press, vol. 54(4), pages 513-33, October.
  16. Pesaran, M Hashem & Timmermann, Allan, 1995. " Predictability of Stock Returns: Robustness and Economic Significance," Journal of Finance, American Finance Association, vol. 50(4), pages 1201-28, September.
  17. Bekaert, Geert & Wu, Guojun, 2000. "Asymmetric Volatility and Risk in Equity Markets," Review of Financial Studies, Society for Financial Studies, vol. 13(1), pages 1-42.
  18. G. Wenchi Kao & Christopher K. Ma, 1992. "Memories, heteroscedasticity, and price limit in Currency futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 12(6), pages 679-692, December.
  19. Black, Fischer, 1986. " Noise," Journal of Finance, American Finance Association, vol. 41(3), pages 529-43, July.
  20. Cheol-Ho Park & Scott H. Irwin, 2007. "What Do We Know About The Profitability Of Technical Analysis?," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 786-826, 09.
  21. Peter F. Christoffersen & Francis X. Diebold, 2003. "Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics," NBER Working Papers 10009, National Bureau of Economic Research, Inc.
  22. Rafael La Porta & Josef Lakonishok & Andrei Shleifer & Robert Vishny, 1995. "Good News for Value Stocks: Further Evidence on Market Efficiency," NBER Working Papers 5311, National Bureau of Economic Research, Inc.
  23. Teo Jasic & Douglas Wood, 2004. "The profitability of daily stock market indices trades based on neural network predictions: case study for the S&P 500, the DAX, the TOPIX and the FTSE in the period 1965-1999," Applied Financial Economics, Taylor & Francis Journals, vol. 14(4), pages 285-297.
  24. Andrew Lo & Harry Mamaysky & Jiang Wang, 1999. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Computing in Economics and Finance 1999 402, Society for Computational Economics.
  25. Fernandez-Rodriguez, Fernando & Gonzalez-Martel, Christian & Sosvilla-Rivero, Simon, 2000. "On the profitability of technical trading rules based on artificial neural networks:: Evidence from the Madrid stock market," Economics Letters, Elsevier, vol. 69(1), pages 89-94, October.
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