The nonlinear dynamics of stock prices

Abstract This paper evaluates the nonlinear dynamics of stock prices using a three-regime, nonlinear threshold random-walk model and daily, international data from the Cotation Assistee en Continu (CAC) 40, Deutscher Aktienindex (DAX) 30, Financial Times Stock Exchange (FTSE) 100, Nikkei 225, Standard & Poor’s (S&P) 500 and Toronto Stock Exchange (TSE) 300 stock-price indexes from January 1, 1970 through December 29, 2000. The idea behind a nonlinear, three-regime threshold model is that investors trade stocks in all three regimes, but they have the added incentive of selling (buying) stocks after unusually large positive (negative) returns. This model finds statistically-significant evidence that stock prices are consistent with a regime-reverting process where on average, stock prices in the two outer regimes revert to the middle regime.

[1]  Ton Vorst,et al.  A Threshold Error Correction Model for Intraday Futures and Index Returns , 1998 .

[2]  R. Thaler,et al.  Does the Stock Market Overreact , 1985 .

[3]  Simon M. Potter.  A Nonlinear Approach to U.S. GNP , 1993 .

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

[5]  Narasimhan Jegadeesh,et al.  Evidence of Predictable Behavior of Security Returns , 1990 .

[6]  W. Brock,et al.  Heterogeneous beliefs and routes to chaos in a simple asset pricing model , 1998 .

[7]  Guyonne Kalb,et al.  Bayesian Arbitrage Threshold Analysis , 1999 .

[8]  Peter R. Locke,et al.  Index arbitrage and nonlinear dynamics between the S&P 500 futures and cash , 1996 .

[9]  Simon M. Potter.  A Nonlinear Approach to US GNP , 1995 .

[10]  T. Willey.  Testing for nonlinear dependence in daily stock indices , 1992 .

[11]  James D. Hamilton.  A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle , 1989 .

[12]  Jay R. Ritter,et al.  Measuring abnormal performance: Do stocks overreact? , 1992 .

[13]  B. LeBaron,et al.  Nonlinear Dynamics and Stock Returns , 2021, Cycles and Chaos in Economic Equilibrium.

[14]  Min Qi,et al.  Nonlinear Predictability of Stock Returns Using Financial and Economic Variables , 1999 .

[15]  Blake LeBaron,et al.  A Dynamic Structural Model for Stock Return Volatility and Trading Volume , 1995 .

[16]  Gerald Kohers,et al.  Using nonlinear dynamics to test for market efficiency among the major U.S. stock exchanges , 1997 .

[17]  David Hsieh.  Chaos and Nonlinear Dynamics: Application to Financial Markets , 1991 .

[18]  Simon van Norden,et al.  Regime Switching in Stock Market Returns , 1997 .

[19]  Mark A. White,et al.  Disappearing Evidence of Chaos in Security Returns: A Simulation , 1996 .

[20]  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 .

[21]  V. Pandey,et al.  Deterministic nonlinearity in the stock returns of major European equity markets and the United States , 1998 .

[22]  Angelos Kanas,et al.  Neural network linear forecasts for stock returns , 2001 .

[23]  T. Lux.  Herd Behaviour, Bubbles and Crashes , 1995 .

[24]  R. Tsay.  Testing and modeling multivariate threshold models , 1998 .

[25]  Nicholas Sarantis,et al.  Nonlinearities, cyclical behaviour and predictability in stock markets: international evidence , 2001 .

[26]  Abhay Abhyankar,et al.  Nonlinear Dynamics in Real-Time Equity Market Indices: Evidence from the United Kingdom , 1995 .