Forecasting stock market return with nonlinearity: a genetic programming approach

Ding, Shusheng, Cui, Tianxiang, Xiong, Xihan and Bai, Ruibin (2020) Forecasting stock market return with nonlinearity: a genetic programming approach. Journal of Ambient Intelligence and Humanized Computing . ISSN 1868-5137

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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.

Item Type: Article
Keywords: Return forecasting; Nonlinear models; Genetic programming
Schools/Departments: University of Nottingham Ningbo China > Faculty of Science and Engineering > School of Computer Science
Identification Number:
Depositing User: Wu, Cocoa
Date Deposited: 28 Apr 2020 02:09
Last Modified: 28 Apr 2020 02:09

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