Direction-of-change forecasting using a volatility-based recurrent neural network

This paper investigates the profitability of a trading strategy, based on recurrent neural networks, that attempts to predict the direction-of-change of the market in the case of the NASDAQ composite index. The sample extends over the period 2/8/1971 \u2013 4/7/1998, while the sub-period 4/8/1998 - 2/5/2002 has been reserved for out-of-sample testing purposes. We demonstrate that the incorporation in the trading rule of estimates of the conditional volatility changes strongly enhances its profitability during `bear' market periods. This improvement is being measured with respect to a nested model that does not include the volatility variable as well as to a buy & hold strategy. We suggest that our findings can be justified by invoking either the `volatility feedback' theory or the existence of portfolio insurance schemes in the equity markets. Our results are also consistent with the view that volatility dependence produces sign dependence.

[1]  S. Sosvilla‐Rivero,et al.  Dancing with bulls and bears: Nearest-neighbour forecasts for the Nikkei index , 1999 .

[2]  M. Hashem Pesaran,et al.  A Simple Nonparametric Test of Predictive Performance , 1992 .

[3]  F. Fabozzi,et al.  Stability Tests for Alphas and Betas Over Bull and Bear Market Conditions , 1977 .

[4]  A. Timmermann,et al.  Predictability of Stock Returns: Robustness and Economic Significance , 1995 .

[5]  Sonnan Chen,et al.  An Examination of Risk-Return Relationship in Bull and Bear Markets Using Time-Varying Betas , 1982, Journal of Financial and Quantitative Analysis.

[6]  A. Christie,et al.  The stochastic behavior of common stock variances: value , 1982 .

[7]  P. Giot.  Relationships Between Implied Volatility Indexes and Stock Index Returns , 2005 .

[8]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[9]  G. Schwert,et al.  Stock Volatility in the New Millennium: How Wacky is NASDAQ? , 2001 .

[10]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[11]  A. Timmermann,et al.  Duration Dependence in Stock Prices , 2003 .

[12]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[13]  Abhay Abhyankar,et al.  Uncovering nonlinear structure in real-time stock-market indexes: the S&P 500, the DAX, the Nikkei 225, and the FTSE-100 , 1997 .

[14]  E. Fama,et al.  Filter Rules and Stock-Market Trading , 1966 .

[15]  Robert E. Whaley,et al.  The Investor Fear Gauge , 2000 .

[16]  A. Lo,et al.  Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test , 1987 .

[17]  Ramazan Gençay,et al.  Optimization of technical trading strategies and the profitability in security markets , 1998 .

[18]  S. Sosvilla‐Rivero,et al.  On the profitability of technical trading rules based on artificial neural networks:: Evidence from the Madrid stock market , 2000 .

[19]  Pierre Giot,et al.  Implied Volatility Indices as Leading Indicators of Stock Index Returns? , 2002 .

[20]  Ramazan Gençay,et al.  The predictability of security returns with simple technical trading rules , 1998 .

[21]  P. D. Lima,et al.  Nuisance parameter free properties of correlation integral based statistics , 1996 .

[22]  L. Glosten,et al.  On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks , 1993 .

[23]  Francis X. Diebold,et al.  The Rodney L. White Center for Financial Research Financial Asset Returns, Direction-of-Change Forecasting and Volatility , 2003 .

[24]  E. Fama,et al.  Permanent and Temporary Components of Stock Prices , 1988, Journal of Political Economy.

[25]  P. Krugman,et al.  Trigger Strategies and Price Dynamics in Equity and Foreign Exchange Markets , 1987 .

[26]  Halbert White,et al.  Artificial Neural Networks: Approximation and Learning Theory , 1992 .

[27]  Thanasis Stengos,et al.  Moving average rules, volume and the predictability of security returns with feedforward networks , 1998 .

[28]  M. Hashem Pesaran,et al.  Forecasting stock returns an examination of stock market trading in the presence of transaction costs , 1994 .

[29]  Douglas M. Patterson,et al.  Nonlinear Dynamics, Chaos, And Instability , 1994 .

[30]  Chung-Ming Kuan,et al.  Reexamining the Profitability of Technical Analysis with Data Snooping Checks , 2005 .

[31]  A. Lo,et al.  When are Contrarian Profits Due to Stock Market Overreaction? , 1989 .

[32]  D. M. Titterington,et al.  Neural Networks: A Review from a Statistical Perspective , 1994 .

[33]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[34]  Daren E. Miller.  The Nasdaq Volatility Index during and after the Bubble , 2004 .

[35]  Guojun Wu,et al.  Asymmetric Volatility and Risk in Equity Markets , 1997 .