---
title: Growth, Value, and Prices
url: https://www.ml-quant.com/papers/ssrn/4810330/
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 4810330
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4810330
featured: 2024-05-01
citations: unknown
topic: Asset Pricing & Factors
---


# Growth, Value, and Prices

The article suggests three linked solutions to the absence of a comprehensive asset pricing theory, including a state-space representation, explanatory gain decomposition, and a shift in research from asset returns to asset prices.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4810330
- Identifier: SSRN 4810330
- Released: 2023-03-03
- First featured: Quant Letter No. 47 (2024-05-01): https://www.ml-quant.com/issues/2024-05-01/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Asset Pricing & Factors

## Related

- [Macro Strikes Back: Term Structure of Risk Premia and Market Segmentation](https://www.ml-quant.com/papers/ssrn/4752696/): A unified framework reveals that most macroeconomic factors have increasing unconditional term structures with large risk premia at business cycle horizons.
- [In Search of the True Greenium](https://www.ml-quant.com/papers/ssrn/4744608/): The study introduces a robust green score and expected returns to calculate the greenium, the expected return of green securities compared to brown, which is found to be more negative in greener countries and over time.
- [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.
- [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.