Machine learning-based quantitative trading strategies across different time intervals in the American market

Yimeng Wang; Keyue Yan; Yimeng Wang; Keyue Yan

doi:10.3934/QFE.2023028

Quantitative Finance and Economics

2023, Volume 7, Issue 4: 569-594. doi: 10.3934/QFE.2023028

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Research article

Machine learning-based quantitative trading strategies across different time intervals in the American market

Yimeng Wang ^{1
,},
Keyue Yan ^{2
,
, ,}

1.
Department of Mathematics, University of Macau, Macau, China
2.
Choi Kai Yau College, University of Macau, Macau, China

Received: 31 July 2023 Revised: 31 October 2023 Accepted: 06 November 2023 Published: 13 November 2023
JEL Codes: C32, C63, E37

Stocks are the most common financial investment products and attract many investors around the world. However, stock price volatility is usually uncontrollable and unpredictable for the individual investor. This research aims to apply different machine learning models to capture the stock price trends from the perspective of individual investors. We consider six traditional machine learning models for prediction: decision tree, support vector machine, bootstrap aggregating, random forest, adaptive boosting, and categorical boosting. Moreover, we propose a framework that uses regression models to obtain predicted values of different moving average changes and converts them into classification problems to generate final predictive results. With this method, we achieve the best average accuracy of 0.9031 from the 20-day change of moving average based on the support vector machine model. Furthermore, we conduct simulation trading experiments to evaluate the performance of this predictive framework and obtain the highest average annualized rate of return of 29.57%.
- stock price prediction,
- machine learning,
- quantitative trading,
- moving average
Citation: Yimeng Wang, Keyue Yan. Machine learning-based quantitative trading strategies across different time intervals in the American market[J]. Quantitative Finance and Economics, 2023, 7(4): 569-594. doi: 10.3934/QFE.2023028

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Abstract

Stocks are the most common financial investment products and attract many investors around the world. However, stock price volatility is usually uncontrollable and unpredictable for the individual investor. This research aims to apply different machine learning models to capture the stock price trends from the perspective of individual investors. We consider six traditional machine learning models for prediction: decision tree, support vector machine, bootstrap aggregating, random forest, adaptive boosting, and categorical boosting. Moreover, we propose a framework that uses regression models to obtain predicted values of different moving average changes and converts them into classification problems to generate final predictive results. With this method, we achieve the best average accuracy of 0.9031 from the 20-day change of moving average based on the support vector machine model. Furthermore, we conduct simulation trading experiments to evaluate the performance of this predictive framework and obtain the highest average annualized rate of return of 29.57%.

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