Li Jiaqi, Wang Xiaoyan, Ahmad Saleem, Huang Xiaobing, Khan Yousaf Ali
UNSW Business School, University of New South Wales (UNSW Sydney), Sydney, 2052, NSW, Australia.
Accounting Department, Hebei Vocational University of Technology and Engineering, Xingtai, 054000, Hebei Province, China.
Heliyon. 2023 May 11;9(5):e16155. doi: 10.1016/j.heliyon.2023.e16155. eCollection 2023 May.
The main objective of this research is to develop a sustainable stock quantitative investing model based on Machine Learning and Economic Value-Added techniques for optimizing investment strategies. Quantitative stock selection and algorithmic trading are the two features of the model. Principal component analysis and economic value-added criteria are used in quantitative stock model for efficiently stocks selection, which may repeatedly select valuable stocks. Machine learning techniques such as Moving Average Convergence, Stochastic Indicators and Long-Short Term Memory are used in algorithmic trading. One of the first attempts, the Economic Value-Added indicators are used to appraise stocks in this study. Furthermore, the application of EVA in stock selection is exposed. Illustration of the proposed model has been done on United States stock market and finding shows that Long-Short Term Memory (LSTM) networks can more accurately forecast future stock values. The proposed strategy is feasible in all market situations, with a return that is significantly larger than the market return. As a result, the proposed approach can not only assist the market in returning to rational investing, but also assist investors in obtaining significant returns that are both realistic and valuable.
本研究的主要目标是基于机器学习和经济增加值技术开发一种可持续的股票量化投资模型，以优化投资策略。量化选股和算法交易是该模型的两个特点。在量化股票模型中使用主成分分析和经济增加值标准来有效选股，该模型可能会反复选出有价值的股票。算法交易中使用了诸如移动平均线收敛、随机指标和长短期记忆等机器学习技术。本研究首次尝试使用经济增加值指标来评估股票。此外，还展示了经济增加值在选股中的应用。已在美国股票市场对所提出的模型进行了说明，结果表明长短期记忆（LSTM）网络能够更准确地预测未来股票价值。所提出的策略在所有市场情况下都是可行的，其回报显著高于市场回报。因此，所提出的方法不仅可以帮助市场回归理性投资，还可以帮助投资者获得既现实又有价值的可观回报。
