An Effective Composite Investment Quantitative Strategy with Machine Learning Method

Abstract

In recent years, significant breakthroughs in AI and machine learning have garnered international attention. One such advancement is the development of key algorithms, including "machine self-learning and self-coding algorithms." These algorithms enable machines to continually learn from data and adjust parameters autonomously, without human intervention. Additionally, machines engage in "reinforcement learning," where algorithms achieve set goals through constant trial and error in specific environments. This has allowed current AI systems to adapt to more complex settings and learn more intricate content. In terms of data processing, AI has also made significant strides, particularly with "Recurrent Neural Networks" enabling current AI to handle sequential data more effectively like neural networks, natural language texts, and speech. Consequently, applying these technologies in the financial investment sector has become a hot topic in the industry. This paper outlines the limitations of traditional methods and proposes a new quantitative composite investment strategy. Our main approach involves designing an intelligent investment advisory model capable of self-learning, continuous self-improvement, and market adaptation. Our process includes analyzing industries, collecting extensive stock and securities data, and eliminating noise data. We then construct a model and assess its stability. Finally, accuracy is determined through an indicator. The results of our method show a high degree of similarity between the predicted stock prices and actual stock prices, indicating the effectiveness of our approach. Thus, our method offers a valuable reference for future applications.