# The Adaptive Multi-Factor Model and the Financial Market

arXiv: https://arxiv.org/abs/2107.14410 · HTML: https://mlsanity.com/paper/2107.14410/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 74 references that are in the map.
- First posted on arXiv 30 Jul 2021.

## Builds on (most cited first, 12 of 12)

- [Exact Post-Selection Inference for Sequential Regression Procedures](https://mlsanity.com/paper/1401.3889/) (2014)
- [Form 10-Q Itemization](https://mlsanity.com/paper/2104.11783/) (2021)
- [In Defense of the Indefensible: A Very Naive Approach to High-Dimensional Inference](https://mlsanity.com/paper/1705.05543/) (2017)
- [High-Dimensional Estimation, Basis Assets, and the Adaptive Multi-Factor Model](https://mlsanity.com/paper/1804.08472/) (2018)
- [Time-Invariance Coefficients Tests with the Adaptive Multi-Factor Model](https://mlsanity.com/paper/2011.04171/) (2020)
- [A News-based Machine Learning Model for Adaptive Asset Pricing](https://mlsanity.com/paper/2106.07103/) (2021)
- [FrequentNet: A Novel Interpretable Deep Learning Model for Image Classification](https://mlsanity.com/paper/2001.01034/) (2020)
- [Clustering Structure of Microstructure Measures](https://mlsanity.com/paper/2107.02283/) (2021)
- [A general framework for estimation and inference from clusters of features](https://mlsanity.com/paper/1511.07839/) (2015)
- [An Efficient Group-based Search Engine Marketing System for E-Commerce](https://mlsanity.com/paper/2106.12700/) (2021)
- [The Low-volatility Anomaly and the Adaptive Multi-Factor Model](https://mlsanity.com/paper/2003.08302/) (2020)
- [A Subspace-based Approach for Dimensionality Reduction and Important Variable Selection](https://mlsanity.com/paper/2106.01584/) (2021)

## Built on by (most cited first, 3 of 3)

- [Finding Lookalike Customers for E-Commerce Marketing](https://mlsanity.com/paper/2301.03147/) (2023)
- [U-Net Convolutional Network for Recognition of Vessels and Materials in Chemistry Lab](https://mlsanity.com/paper/2109.05585/) (2021)
- [Two ways towards combining Sequential Neural Network and Statistical Methods to Improve the Prediction of Time Series](https://mlsanity.com/paper/2110.00082/) (2021)

## Abstract (arXiv)

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.