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the claim

Log returns possess specific statistical and economic properties useful for financial modeling.

the verdict
SUPPORTED
the evidence backs this
Recorded sources
8 sources for · 0 against

Counts group repeated records of the same source within each side. They do not measure evidence strength or source independence.

Is it true that log returns possess specific statistical and economic properties useful for financial modeling?

Yes. Log returns exhibit critical statistical properties, such as non-Gaussian distributions, heavy tails, and volatility clustering, which are foundational for financial modeling, risk management, and asset pricing.

Verdict: SUPPORTED · 8 sources for, 0 against · Last checked

The analysis

Multiple papers discuss the use of financial log-returns and their specific statistical properties (such as heavy tails, volatility clustering, and non-Gaussian distributions) in financial modeling, risk management, and option pricing. There are no papers contradicting the utility of log returns in financial modeling.

Evidence for · 8
Deep Learning in Characteristics-Sorted Factor Models
2018
The study utilizes firm characteristics to model cross-sectional asset pricing and risk factors.
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Feng, G., He, J., Polson, N. G., & Xu, J. (2018). Deep Learning in Characteristics-Sorted Factor Models. doi.org. https://doi.org/10.1017/S0022109023000893

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More for · 7
Modeling and simulation of financial returns under non-Gaussian distributions
2023
The paper examines statistical properties of financial returns and explores their non-Gaussian distributions in risk management and option pricing.
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Domenico, F. D., Livan, G., Montagna, G., & Nicrosini, O. (2023). Modeling and simulation of financial returns under non-Gaussian distributions. doi.org. https://doi.org/10.1016/j.physa.2023.128886

Graph-based multi-factor asset pricing model
2021
The research proposes a latent multi-factor asset pricing model utilizing asset connectivity and firm characteristics.
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Son, B. & Lee, J. (2021). Graph-based multi-factor asset pricing model. doi.org. https://doi.org/10.1016/J.FRL.2021.102032

Entropy-Based Volatility Analysis of Financial Log-Returns Using Gaussian Mixture Models
2024
The study assesses volatility and risk measures using financial log-returns and Gaussian mixture models.
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Scrucca, L. (2024). Entropy-Based Volatility Analysis of Financial Log-Returns Using Gaussian Mixture Models. doi.org. https://doi.org/10.3390/e26110907

Modeling Stylized Facts in FX Markets with FINGAN-BiLSTM: A Deep Learning Approach to Financial Time Series
2025
The paper models stylized facts such as heavy-tailed behavior and volatility clustering in log returns for financial forecasting and risk assessment.
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Kim, D., Kim, D., & Choi, S. (2025). Modeling Stylized Facts in FX Markets with FINGAN-BiLSTM: A Deep Learning Approach to Financial Time Series. doi.org. https://doi.org/10.3390/e27060635

Evaluation Finite Moment Log-Stable Option Pricing by a Spectral Method
2018
The study evaluates fractional partial integral-differential equations for option pricing based on financial log-returns.
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sci, X. G. a. L. L. (2018). Evaluation Finite Moment Log-Stable Option Pricing by a Spectral Method. doi.org. https://doi.org/10.4208/NMTMA.2017-OA-0131

Non Gaussian returns and pension funds asset allocation
2016
The research analyzes the long-run statistical characteristics and mean-reversion properties of financial returns for asset allocation.
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Hamayon, S., Legros, F., & Pradat, Y. (2016). Non Gaussian returns and pension funds asset allocation. doi.org. https://doi.org/10.1108/RAF-01-2016-0005

Asset Allocation and Long-Term Returns: An Empirical Approach
2006
The study utilizes empirical log-return data and shortfall risk concepts to evaluate asset allocation models over long-term horizons.
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Coggeshall, S. & Wu, G. (2006). Asset Allocation and Long-Term Returns: An Empirical Approach. doi.org. https://doi.org/10.2139/ssrn.873184

What does the evidence say?

Multiple papers discuss the use of financial log-returns and their specific statistical properties (such as heavy tails, volatility clustering, and non-Gaussian distributions) in financial modeling, risk management, and option pricing. There are no papers contradicting the utility of log returns in financial modeling.

Receipt History
first checked05 Aug 2026
judged → SUPPORTED · 8605 Aug 2026
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