The Adaptive Multi-Factor Model and the Financial Market
This addresses problems for financial analysts and traders dealing with complex market data, but it appears incremental as it builds on existing methodologies without specifying breakthroughs.
The paper tackles the challenges of high-dimensional, high-correlation, and time-varying financial data by developing techniques to improve interpretability, explanations, and predictions in financial markets.
Modern evolvements of the technologies have been leading to a profound influence on the financial market. The introduction of constituents like Exchange-Traded Funds, and the wide-use of advanced technologies such as algorithmic trading, results in a boom of the data which provides more opportunities to reveal deeper insights. However, traditional statistical methods always suffer from the high-dimensional, high-correlation, and time-varying instinct of the financial data. In this dissertation, we focus on developing techniques to stress these difficulties. With the proposed methodologies, we can have more interpretable models, clearer explanations, and better predictions.