An Exploration of Different Strategies and Applications of Quantitative Investment under Different Models

Abstract

With the rapid development of financial markets and the surge in data volume, quantitative investment has gradually become an important tool for investment decision-making. Traditional investment strategies often rely on experience and qualitative analysis, which is difficult to cope with complex market environments. Therefore, exploring quantitative investment models based on machine learning and deep learning has become a hot topic of research. This study aims to analyze the different applications of various models in quantitative investment, focusing on areas such as factor models, statistical arbitrage, and sentiment analysis. Through a comprehensive analysis of relevant literature in recent years, the results show that the future development of quantitative investment will increasingly rely on advanced algorithms and big data analysis techniques. Under the application of different models, there are diverse ideas and tools for the analysis and decision-making of financial markets. The combination of these models will promote the diversity and flexibility of investment strategies. At the same time, policymakers and financial institutions need to pay attention to the transparency and interpretability of the model to reduce potential risks. Looking forward, further research can focus on the combination optimization of models and the improvement of real-time data processing capabilities to cope with the changing market environment and provide stronger support for the practice of quantitative investment.