Time series forecasting is of fundamental importance for financial market prediction and, consequently, for portfolio allocation strategies. However, non-stationarity and non-linearity of most financial time series often make these tasks difficult to perform. In this paper, we propose a methodology based on chaos and dynamical systems theory for non-linear time series forecasting and investment strategy development, which is able to correctly make predictions at long time horizons. We construct Constant Chaoticity Portfolios (CCP) and evaluate their performances on the survival components of the STOXX Europe 50 index and the Hang-Seng index. Results show that the CCP overwhelms several competing alternatives, both in terms of net profits and risk-return profiles. Our findings are confirmed by a sensitivity analysis on the parameters of the underlying model and over different choices of forecast horizons.
Chaos based portfolio selection: A nonlinear dynamics approach
Pagnottoni P.
2022-01-01
Abstract
Time series forecasting is of fundamental importance for financial market prediction and, consequently, for portfolio allocation strategies. However, non-stationarity and non-linearity of most financial time series often make these tasks difficult to perform. In this paper, we propose a methodology based on chaos and dynamical systems theory for non-linear time series forecasting and investment strategy development, which is able to correctly make predictions at long time horizons. We construct Constant Chaoticity Portfolios (CCP) and evaluate their performances on the survival components of the STOXX Europe 50 index and the Hang-Seng index. Results show that the CCP overwhelms several competing alternatives, both in terms of net profits and risk-return profiles. Our findings are confirmed by a sensitivity analysis on the parameters of the underlying model and over different choices of forecast horizons.
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