Forecasting stock market return with nonlinearity: a genetic programming approach

Shusheng Ding,Tianxiang Cui,Xihan Xiong,Ruibin Bai

doi:10.1007/s12652-020-01762-0

Abstract

The issue whether return in the stock market is predictable remains ambiguous. This paper attempts to establish new return forecasting models in order to contribute on addressing this issue. In contrast to existing literatures, we first reveal that the model forecasting accuracy can be improved through better model specification without adding any new variables. Instead of having a unified return forecasting model, we argue that stock markets in different countries shall have different forecasting models. Furthermore, we adopt an evolutionary procedure called Genetic programming (GP), to develop our new models with nonlinearity. Our newly-developed forecasting models are testified to be more accurate than traditional AR-family models. More importantly, the trading strategy we propose based on our forecasting models has been verified to be highly profitable in different types of stock markets in terms of stock index futures trading.

Highlights

A crucial question for open discussions in finance is whether future stock returns are predictable, and this issue is controversial (e.g. Ang and Bekaert 2006)
The periodic averaged Mean Absolute Error (MAE) can be defined as: 1T M AET = T |Observedt − P redictedt|, t=1 where T represents the number of observations embedded in the forecasting period, Observedt presents the observed variance from the market and P redictedt presents the variance predicted from the models
(Observedt − P redictedt)2, t=1 where T represents the number of observations embedded in the forecasting period, Observedt presents the observed variance from the market and P redictedt presents the variance predicted from the models

Summary

Introduction

A crucial question for open discussions in finance is whether future stock returns are predictable (see Fama 1970), and this issue is controversial (e.g. Ang and Bekaert 2006). We unveil that the model forecasting accuracy can be improved through better model specification without adding any new variables. Seeking the relevant variables for forecasting future returns has been witnessed in burgeoning literatures (see Fama and French 1988; Nelson and Kim 1993; Campbell and Shiller 1988). In these aforementioned works, they only focus on demonstrating the potential of different variables in forecasting stock market returns. It might be complementary to existing literatures that better model specification could be equivalently vital as including new variables, which reinforces predictive power of return forecasting model

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Results

Conclusion