mlsanityData to 14 Oct 2026

The Adaptive Multi-Factor Model and the Financial Market

arXiv 2107.14410 ↗First posted 30 Jul 2021q-fin.ST cs.LG stat.MLcited by 3

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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.

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

12 of 74 references.

References · 12

  1. 2014Exact Post-Selection Inference for Sequential Regression Procedurescited by 48
  2. 2021Form 10-Q Itemizationcited by 10
  3. 2017In Defense of the Indefensible: A Very Naive Approach to High-Dimensional Inferencecited by 8
  4. 2018High-Dimensional Estimation, Basis Assets, and the Adaptive Multi-Factor Modelcited by 8
  5. 2020Time-Invariance Coefficients Tests with the Adaptive Multi-Factor Modelcited by 7
  6. 2021A News-based Machine Learning Model for Adaptive Asset Pricingcited by 7
  7. 2020FrequentNet: A Novel Interpretable Deep Learning Model for Image Classificationcited by 5
  8. 2021Clustering Structure of Microstructure Measurescited by 3
  9. 2015A general framework for estimation and inference from clusters of featurescited by 2
  10. 2021An Efficient Group-based Search Engine Marketing System for E-Commercecited by 2
  11. 2020The Low-volatility Anomaly and the Adaptive Multi-Factor Modelcited by 1
  12. 2021A Subspace-based Approach for Dimensionality Reduction and Important Variable Selectioncited by 1

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Cited by year

Citing papers per year: 2021: 2; 2022: 0; 2023: 1.

3 papers.

  1. 2023Finding Lookalike Customers for E-Commerce Marketingcited by 1
  2. 2021U-Net Convolutional Network for Recognition of Vessels and Materials in Chemistry Labcited by 0
  3. 2021Two ways towards combining Sequential Neural Network and Statistical Methods to Improve the Prediction of Time Seriescited by 0