Recommendation Algorithm of Industry Stock Trading Model with TODIM

Dongdong Lv, Yingli Gong, Jianting Chen and Yang Xiang
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Dongdong Lv: School of Cyber Science and Engineering, Ningbo University of Technology, Ningbo 315211, P. R. China
Jianting Chen: College of Electronics and Information Engineering, Tongji University, Shanghai 201804, P. R. China
Yang Xiang: College of Electronics and Information Engineering, Tongji University, Shanghai 201804, P. R. China

International Journal of Information Technology & Decision Making (IJITDM), 2024, vol. 23, issue 03, 1301-1334

Abstract: In stock trading, a common phenomenon is that the trends of stocks in the same industry are very similar. In contrast, the movements of stocks in different industries are often different. Therefore, applying the same model to all stock trading is inappropriate without distinguishing the industries in which the stocks belong. However, recommending an optimal industry stock trading model is very challenging based on performance evaluation indicators. First, the indicators of the trading model are diverse. Second, the ranking of multiple indicators is often inconsistent. In the paper, we model the problem to be solved as a multi-criteria decision-making process. Therefore, we first divide stock dataset into nine industries according to their main business. Then, we apply several machine learning algorithms as candidate models to generate trading signals. Second, we conduct daily trading backtesting based on the trading signals to obtain multiple performance evaluation indicators. Third, we propose an optimal recommendation algorithm for the industry stock trading model with TODIM. The experimental results in the US stock market and China’s A-share market show that the proposed algorithm can get a better trading model out-of-sample industry stock. Moreover, we effectively evaluate the generalization ability of the algorithm based on the proposed metrics. Finally, the proposed long–short portfolios based on the algorithm have achieved returns exceeding the benchmark on most out-of-sample datasets.

Keywords: Stock trading; recommendation algorithm; TODIM; trading strategy (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0219622023500402

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