The Adaptive Multi-Factor Model and the Financial Market

TL;DR

This paper discusses the development of an adaptive multi-factor model that leverages advanced techniques to handle high-dimensional, correlated, and time-varying financial data, improving interpretability and prediction accuracy.

Contribution

It introduces novel methodologies to address challenges in modeling complex financial data, enhancing interpretability and predictive performance.

Findings

More interpretable financial models achieved

Enhanced prediction accuracy demonstrated

Effective handling of high-dimensional data

Abstract

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.