Forecasting stock market return with nonlinearity: a genetic programming approach

Shusheng Ding, Tianxiang Cui, Xihan Xiong, Ruibin Bai

Research output: Journal PublicationArticlepeer-review

8 Citations (Scopus)
27 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)4927-4939
Number of pages13
JournalJournal of Ambient Intelligence and Humanized Computing
Issue number11
Publication statusPublished - Nov 2020


  • Genetic programming
  • Nonlinear models
  • Return forecasting

ASJC Scopus subject areas

  • General Computer Science


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