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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
refutedsupported
the weight of evidence
8 sources for · 0 against

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.

Evidence for · 8
Deep Learning in Characteristics-Sorted Factor Models
2018 · cited by 59
The study utilizes firm characteristics to model cross-sectional asset pricing and risk factors.
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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.

More for · 7
Modeling and simulation of financial returns under non-Gaussian distributions
2023 · cited by 19
The paper examines statistical properties of financial returns and explores their non-Gaussian distributions in risk management and option pricing.
Graph-based multi-factor asset pricing model
2021 · cited by 8
The research proposes a latent multi-factor asset pricing model utilizing asset connectivity and firm characteristics.
Entropy-Based Volatility Analysis of Financial Log-Returns Using Gaussian Mixture Models
2024 · cited by 7
The study assesses volatility and risk measures using financial log-returns and Gaussian mixture models.
Modeling Stylized Facts in FX Markets with FINGAN-BiLSTM: A Deep Learning Approach to Financial Time Series
2025 · cited by 4
The paper models stylized facts such as heavy-tailed behavior and volatility clustering in log returns for financial forecasting and risk assessment.
Evaluation Finite Moment Log-Stable Option Pricing by a Spectral Method
2018 · cited by 3
The study evaluates fractional partial integral-differential equations for option pricing based on financial log-returns.
Non Gaussian returns and pension funds asset allocation
2016 · cited by 2
The research analyzes the long-run statistical characteristics and mean-reversion properties of financial returns for asset allocation.
Asset Allocation and Long-Term Returns: An Empirical Approach
2006 · cited by 2
The study utilizes empirical log-return data and shortfall risk concepts to evaluate asset allocation models over long-term horizons.
Everything we examined (12)
  1. Deep Learning in Characteristics-Sorted Factor Modelspeer-reviewedsupports
  2. Modeling and simulation of financial returns under non-Gaussian distributionspeer-reviewedsupports
  3. Graph-based multi-factor asset pricing modelpeer-reviewedsupports
  4. Entropy-Based Volatility Analysis of Financial Log-Returns Using Gaussian Mixture Modelspeer-reviewedsupports
  5. Modeling Stylized Facts in FX Markets with FINGAN-BiLSTM: A Deep Learning Approach to Financial Time Seriespeer-reviewedsupports
  6. The myth of the Bayesian brain.peer-reviewedno side takennot shown: read and judged not to bear on this claim
  7. Revisiting the hazards of hazard ratios through simulations and case studies.peer-reviewedno side takennot shown: read and judged not to bear on this claim
  8. Evaluation Finite Moment Log-Stable Option Pricing by a Spectral Methodpeer-reviewedsupports
  9. Non Gaussian returns and pension funds asset allocationpeer-reviewedsupports
  10. Asset Allocation and Long-Term Returns: An Empirical Approachpeer-reviewedsupports
  11. Stochastic modeling of economic risk and net return distributions for feedlot steers marketed at alternative endpoints.peer-reviewedno side takennot shown: read and judged not to bear on this claim
  12. Modeling the Impact of Multicancer Early Detection Tests: A Review of Natural History of Disease Models.peer-reviewedno side takennot shown: read and judged not to bear on this claim
The paper trail · every fact has a biography
first checked05 Aug 2026
judged → SUPPORTED · 8605 Aug 2026
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