A model of active trading by using the properties of chaos

This study introduces a research path to obtain alternative trading rules by using nonlinear dynamical analysis of stock returns. We examine the daily return data of Istanbul Stock Exchange index and Shenzhen Index B-Shares. Both stock returns series are shown to exhibit chaotic behavior and associated maximal Lyapunov exponents (LE) are computed. A new prediction method which bases on the properties of detected chaotic behavior is proposed to perform one-week out-of-sample prediction of the stock returns. Finally we develop a nonlinear model of active trading, in which traders rely only on their heterogeneous forecasts of future periods' maximum and minimum returns. The model motivates active trading under chaotic behavior.