---
title: Linear Factor Model Properties
url: https://www.ml-quant.com/papers/ssrn/4933856/
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 4933856
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4933856
featured: 2024-08-28
citations: unknown
topic: Asset Pricing & Factors
---


# Linear Factor Model Properties

The study of conditional linear factor models in asset pricing shows that the efficient portfolio of an unbalanced panel can be represented by low-dimensional factor portfolios, focusing on conditional means and covariances.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4933856
- Identifier: SSRN 4933856
- Released: 2024-08-22
- First featured: Quant Letter No. 63 (2024-08-28): https://www.ml-quant.com/issues/2024-08-28/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Asset Pricing & Factors

## Related

- [Fundamental Properties of Linear Factor Models](https://www.ml-quant.com/papers/arxiv/2409.02521/): The article examines conditional linear factor models in asset pricing. It explores the relationships and characteristics of returns and factors using conditional means and covariances. This research lays the groundwork for defining and estimating these models.
- [Block-diagonal idiosyncratic covariance estimation in high-dimensional factor models for financial time series](https://www.ml-quant.com/papers/doi/10-1016-j-jocs-2024-102348/): The research proposes a method for estimating high-dimensional covariance matrices in latent factor models by clustering residual series, focusing on the idiosyncratic component.
- [When can weak latent factors be statistically inferred?](https://www.ml-quant.com/papers/arxiv/2407.03616/): The article introduces a new theory for principal component analysis (PCA) under the weak factor model. This theory accounts for cross-sectional dependent components and provides finite-sample characterizations for estimation error and statistical inference uncertainty level, improving upon previous research.
- [Unveiling True Talent: The Soccer Factor Model for Skill Evaluation](https://www.ml-quant.com/papers/arxiv/2412.05911/): The Soccer Factor Model (SFM) is a new method for evaluating soccer players' performance independently from their team's influence, allowing for more accurate comparisons.
- [Commodity Futures Characteristics and Asset Pricing Models](https://www.ml-quant.com/papers/ssrn/4746258/): The article shows that a latent-factor model using the Instrumented Principal Component Analysis methodology surpasses existing models in explaining variations in commodity futures returns, with momentum, expected shortfall, and idiosyncratic volatility as key factors.
- [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.
