Deep Learning Models for Predicting Monthly TAIEX to Support Making Decisions in Index Futures Trading

Duy-An Ha, Chia-Hung Liao, Kai-Shien Tan and Shyan-Ming Yuan
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Duy-An Ha: EECS International Graduate Program, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
Chia-Hung Liao: Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
Kai-Shien Tan: Institute of Computer Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
Shyan-Ming Yuan: Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan

Mathematics, 2021, vol. 9, issue 24, 1-20

Abstract: Futures markets offer investors many attractive advantages, including high leverage, high liquidity, fair, and fast returns. Highly leveraged positions and big contract sizes, on the other hand, expose investors to the risk of massive losses from even minor market changes. Among the numerous stock market forecasting tools, deep learning has recently emerged as a favorite tool in the research community. This study presents an approach for applying deep learning models to predict the monthly average of the Taiwan Capitalization Weighted Stock Index (TAIEX) to support decision-making in trading Mini-TAIEX futures (MTX). We inspected many global financial and economic factors to find the most valuable predictor variables for the TAIEX, and we examined three different deep learning architectures for building prediction models. A simulation on trading MTX was then performed with a simple trading strategy and two different stop-loss strategies to show the effectiveness of the models. We found that the Temporal Convolutional Network (TCN) performed better than other models, including the two baselines, i.e., linear regression and extreme gradient boosting. Moreover, stop-loss strategies are necessary, and a simple one could be sufficient to reduce a severe loss effectively.

Keywords: stock market prediction; deep learning; machine learning; Taiwan Capitalization Weighted Stock Index; Mini-TAIEX futures; simulated trading (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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