---
title: Evidence and Profitable Strategies in Linear Asset Pricing Models
url: https://www.ml-quant.com/papers/ssrn/4704766/
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 4704766
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4704766
featured: 2024-01-30
citations: unknown
topic: Asset Pricing & Factors
---


# Evidence and Profitable Strategies in Linear Asset Pricing Models

The author suggests a new measure of mispricing in asset pricing models, providing evidence of mispricing in U.S. equity data and proposing a profitable investment strategy based on this mispricing.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4704766
- Identifier: SSRN 4704766
- Released: 2023-12-10
- First featured: Quant Letter No. 35 (2024-01-30): https://www.ml-quant.com/issues/2024-01-30/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Asset Pricing & Factors

## Related

- [Correlated Demand Shocks and Asset Pricing](https://www.ml-quant.com/papers/ssrn/4705500/): The correlated demand shocks from institutional investors can increase risk in asset pricing, with stocks exposed to higher correlated demand showing higher market betas and risk premiums.
- [The Expected Returns on Machine-Learning Strategies](https://www.ml-quant.com/papers/ssrn/4702406/): Despite high turnover rates and the selection of hard-to-arbitrage stocks, machine learning strategies can predict profitable returns that common risk factors cannot explain, according to a study.
- [No Sparsity in Asset Pricing: Evidence from a Generic Statistical Test](https://www.ml-quant.com/papers/ssrn/4730259/): The paper introduces a statistical test to identify sparsity in high-dimensional factor models, concluding that less than ten factors can explain stock returns and dense models perform better than sparse ones.
- [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.
- [Empirical Asset Pricing Using Explainable Artificial Intelligence](https://www.ml-quant.com/papers/ssrn/4680571/): The paper demonstrates the use of explainable artificial intelligence in empirical asset pricing, showing enhanced predictive power and investment performance when incorporating insights from explainable AI into model refinement.
- [A Capital Asset Pricing Model with Idiosyncratic Tail Risk: Comovement of Momentum and Low Risk Anomalies](https://www.ml-quant.com/papers/ssrn/4680248/): Momentum and Low Risk Anomalies: The new model expands the traditional Capital Asset Pricing Model (CAPM) by factoring in idiosyncratic tail risk, explaining momentum in stock returns and low risk anomalies.