---
title: Expected Returns and Stock Performance
url: https://www.ml-quant.com/papers/ssrn/5244033/
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 5244033
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5244033
featured: 2025-05-07
citations: unknown
topic: Asset Pricing & Factors
---


# Expected Returns and Stock Performance

A few stocks significantly influence the performance of cross-sectional asset pricing anomalies, indicating that a large part of the returns may be due to mispricing.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5244033
- Identifier: SSRN 5244033
- Released: 2025-05-06
- First featured: Quant Letter No. 96 (2025-05-07): https://www.ml-quant.com/issues/2025-05-07/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Asset Pricing & Factors

## Related

- [NUMOSIM: A Synthetic Mobility Dataset with Anomaly Detection Benchmarks](https://www.ml-quant.com/papers/arxiv/2409.03024/): A Synthetic Mobility Dataset: The paper presents NUMOSIM, a synthetic mobility dataset for testing anomaly detection techniques, simulating realistic mobility scenarios and anomalies to improve geospatial mobility analysis.
- [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.
- [Anomaly Persistence](https://www.ml-quant.com/papers/ssrn/5276723/): The article introduces a method for testing asset pricing anomalies, showing that multiple paths on the same dataset lead to high outcome correlations, significantly affecting inference.
- [FearBased Pricing](https://www.ml-quant.com/papers/ssrn/5127501/): The article introduces a new fear-based model for returns, arguing that it could have predicted most anomalies and factors in the past 50 years.
- [Do Chinese Retail and Institutional Investors Trade on Anomalies?](https://www.ml-quant.com/papers/ssrn/5112567/): The study shows that retail investors in China trade against anomaly prescriptions, while institutions trade in line with anomalies, influenced by lottery stock preference and return extrapolation.
- [Anomalies and Market Return Predictability](https://www.ml-quant.com/papers/ssrn/5101577/): A link between cross-sectional anomalies and timeseries market return predictability in an international context has been found, leading to the creation of three new market efficiency measures.
