---
title: Asset Returns: Auto Debiased ML
url: https://www.ml-quant.com/papers/ssrn/4632395/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4632395
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4632395
featured: 2023-11-15
citations: unknown
topic: Asset Pricing & Factors
---


# Asset Returns: Auto Debiased ML

Auto Debiased ML: A new machine learning method has been developed to identify risk factors in asset pricing, performing better than traditional methods by eliminating biased estimation and overfitting.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4632395
- Identifier: SSRN 4632395
- Released: 2022-09-28
- First featured: Quant Letter No. 26 (2023-11-15): https://www.ml-quant.com/issues/2023-11-15/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Asset Pricing & Factors

## Related

- [Machine Learning and the Cross-Section of Emerging Market Corporate Bond Returns](https://www.ml-quant.com/papers/ssrn/4632924/): Machine learning models considering nonlinearities and interactions offer better predictions of corporate bond behavior in emerging markets with high transaction costs, with key predictors tied to low-risk macro and momentum factors.
- [Bubble economics](https://www.ml-quant.com/papers/arxiv/2311.03638/): Nonstationary Phenomenon: The article discusses the theory of rational asset price bubbles, highlighting that bubbles linked to real assets like stocks and housing are nonstationary phenomena tied to unbalanced growth.
- [Does Peer-Reviewed Research Help Predict Stock Returns?](https://www.ml-quant.com/papers/arxiv/2212.10317/): The research suggests that the predictability of cross-sectional return predictors decreases by half in post-sample scenarios, indicating that theory doesn't improve prediction and peer-review often misinterprets mispricing as risk.
- [A Deep Structural Model for Empirical Asset Pricing](https://www.ml-quant.com/papers/ssrn/4602537/): ML and Theory Integration: The article introduces a new model that merges deep learning and structural models for better prediction of equity returns and covariances, leading to higher returns and sharpe ratios.
- [Generalized Autoregressive Conditional Betas: A New Multivariate Score-Driven Filter](https://www.ml-quant.com/papers/ssrn/4602060/): New: A new asset pricing model, the generalized ACB, is introduced, enhancing the autoregressive conditional beta model by driving dynamic interaction effects among beta coefficients.
- [Exploratory Control with Tsallis Entropy for Latent Factor Models](https://www.ml-quant.com/papers/arxiv/2211.07622/): The research uses Tsallis Entropy in models with latent factors to optimally control and explore the state space, proving that the optimal state distribution is q-Gaussian, which can be used in creating robust statistical arbitrage trading strategies.
