Optimization of investment strategies through machine learning
Research on optimizing stock investment strategies using machine learning and Economic Value-Added techniques for better returns.
What it examines
This study aims to develop a sustainable quantitative stock investment model using Machine Learning and Economic Value-Added (EVA) techniques to optimize investment strategies, focusing on stock selection and algorithmic trading within the US stock market.
What it concludes
The research suggests that the proposed model can assist in rational investing and achieving significant returns. Future research should explore more criteria for the scoring model and apply the model to other markets. Potential applications include enhancing stock market stability and aiding investors in making informed decisions.
Evidence objects
The research suggests that the proposed model can assist in rational investing and achieving significant returns. Future research should explore more criteria for the scoring model and apply the model to other markets. Potential applications include enhancing stock market stability and aiding investors in making informed decisions.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
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.
Source row: 1506 · abstract type: unknown