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

IF 3.2 Q1 BUSINESS, FINANCE Quantitative Finance and Economics Pub Date : 2023-01-01 DOI:10.3934/qfe.2023028
Yimeng Wang, Keyue Yan
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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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基于机器学习的美国市场不同时间间隔的定量交易策略
<abstract>< >股票是最常见的金融投资产品,吸引着全球众多投资者。然而,对于个人投资者来说,股价波动通常是不可控制和不可预测的。本研究旨在应用不同的机器学习模型,从个人投资者的角度捕捉股票价格趋势。我们考虑了六种传统的机器学习预测模型:决策树、支持向量机、自举聚合、随机森林、自适应增强和分类增强。此外,我们提出了一个框架,该框架使用回归模型来获得不同移动平均变化的预测值,并将其转换为分类问题以生成最终的预测结果。利用该方法,基于支持向量机模型的移动平均线20天变化得到了最好的平均精度0.9031。此外,我们通过模拟交易实验来评估该预测框架的性能,并获得了29.57%的最高平均年化收益率。</p></abstract>
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来源期刊
CiteScore
0.30
自引率
1.90%
发文量
14
审稿时长
12 weeks
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