--- title: Asset Pricing & Factors url: https://www.ml-quant.com/topics/asset-pricing-factors/ site: ML-Quant (https://www.ml-quant.com) updated: 2026-10-09 license: Summaries CC BY 4.0; links go to the original sources index: https://www.ml-quant.com/llms.txt --- # Asset Pricing & Factors Factor models, anomalies, the cross-section of returns and what survives publication. 269 papers featured; 40 in the last 12 months. Papers featured per quarter: 2023 Q2 9, 2023 Q3 24, 2023 Q4 23, 2024 Q1 35, 2024 Q2 24, 2024 Q3 25, 2024 Q4 22, 2025 Q1 31, 2025 Q2 31, 2025 Q3 5, 2025 Q4 8, 2026 Q1 0, 2026 Q2 0, 2026 Q3 12, 2026 Q4 20 ## Most cited - [Bubble economics](https://www.ml-quant.com/papers/arxiv/2311.03638/): 43 citations. Nonstationary Phenomenon: The article discusses the theory of rational asset price bubbles, highlighting that bubbles linked to real assets like stocks and housing are nonstationary phenomena tied to unbalanced growth. - [NUMOSIM: A Synthetic Mobility Dataset with Anomaly Detection Benchmarks](https://www.ml-quant.com/papers/arxiv/2409.03024/): 24 citations. 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. - [In Search of the True Greenium](https://www.ml-quant.com/papers/ssrn/4744608/): 21 citations. 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. - [Leverage, Endogenous Unbalanced Growth, and Asset Price Bubbles](https://www.ml-quant.com/papers/arxiv/2211.13100/): 10 citations. The paper introduces a new theoretical framework to understand asset price bubbles in dividend-paying assets, examining a macro-finance model with a positive feedback loop between capital investment and land price. - [HireVAE: An Online and Adaptive Factor Model Based on Hierarchical and Regime-Switch VAE](https://www.ml-quant.com/papers/arxiv/2306.02848/): 8 citations. HireVAE is a deep learning-based model that outperforms previous methods in terms of active returns in stock market benchmarks. - [The Cross-Section of Factor Returns](https://www.ml-quant.com/papers/ssrn/4441376/): 7 citations. Most of the 150 equity factors examined show positive returns but fail to deliver excess returns after accounting for risk, especially in downturns. - [When can weak latent factors be statistically inferred?](https://www.ml-quant.com/papers/arxiv/2407.03616/): 7 citations. 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. - [Exploratory Control with Tsallis Entropy for Latent Factor Models](https://www.ml-quant.com/papers/arxiv/2211.07622/): 7 citations. The research uses Tsallis Entropy in models with latent factors to optimally control and explore the state space, proving that the optimal state distribution is q-Gaussian, which can be used in creating robust statistical arbitrage trading strategies. - [Network Momentum across Asset Classes](https://www.ml-quant.com/papers/arxiv/2308.11294/): 6 citations. The article discusses network momentum, a trading signal from asset momentum spillover, and its use in a multi-asset investment strategy that yielded a 22% annual return from 2000 to 2022. - [Interpretable Machine Learning for Asset Pricing](https://www.ml-quant.com/papers/ssrn/4473746/): 5 citations. The paper utilizes deep neural networks to more accurately estimate equity risk premia over time, enhancing the interpretability of machine learning in economics. - [Design choices, machine learning, and the cross-section of stock returns](https://www.ml-quant.com/papers/ssrn/5031755/): 5 citations. The performance of machine learning models in predicting stock returns is greatly influenced by their design choices, with nonstandard errors in portfolio returns surpassing standard errors by 59%. - [Does Peer-Reviewed Research Help Predict Stock Returns?](https://www.ml-quant.com/papers/arxiv/2212.10317/): 5 citations. The research suggests that the predictability of cross-sectional return predictors decreases by half in post-sample scenarios, indicating that theory doesn't improve prediction and peer-review often misinterprets mispricing as risk. ## Latest - [FactorBench: A Portfolio-Aware Benchmark for Automated Factor Mining](https://www.ml-quant.com/papers/arxiv/2610.06947/) (2026-10-09): A portfolio-aware benchmark comparing five thousand factors from nine automated mining methods across five equity markets finds that no discovery paradigm consistently dominates in signal quality or portfolio performance. - [AlphaPADI: Formulaic Alpha Discovery via Pool-Aware Hierarchical Discrete Diffusion](https://www.ml-quant.com/papers/arxiv/2610.04959/) (2026-10-09): Introduces a hierarchical discrete diffusion framework that generates pools of formulaic alphas by reconstructing complete candidate pools under current context and maximizing joint predictive performance and inner diversity. - [On the Boundary of Admission Gates: An Injected-Truth Study of Falsification-First Selection in Quantitative Strategy Research](https://www.ml-quant.com/papers/arxiv/2610.07701/) (2026-10-09): An injected-truth protocol reveals that statistical admission gates eliminate false discoveries in weak-signal regimes but cut adoption rates to 1–7 percent, while criteria computed on absolute rather than excess returns reject all candidates. - [Measuring What is Repeated and Novel in Earnings Disclosures Using Optimal Transport](https://www.ml-quant.com/papers/arxiv/2610.04094/) (2026-10-09): Decomposing earnings press releases and conference calls into shared and unique textual components shows that unique call content predicts returns better than repeated material. - [The Three Faces of Fed Surprise: Rate-Path, Information and Tone Shocks in the Equity Cross-section](https://www.ml-quant.com/papers/ssrn/7560218/) (2026-10-09): The study decomposes Federal Reserve announcements into three orthogonal shocks and traces each through the equity cross-section, finding rate-path surprises dominate while standard balance-sheet characteristics carry no additional power beyond beta and size. - [Supplying Safety to an Aging World: Demographics, Debt, and Risk Premia](https://www.ml-quant.com/papers/ssrn/7570178/) (2026-10-09): A two-country model attributes 251 basis points of U.S. equity premium gains and 71 basis points of global rate decline since 1980 to demographic aging. - [Cross-Sectional Corporate Bond Returns](https://www.ml-quant.com/papers/ssrn/7566162/) (2026-10-09): Constructs a characteristics-based systematic risk measure using rate jumps that predicts out-of-sample corporate bond returns, with high-minus-low decile portfolio earning 9.12% annually and 7.80% alpha. - [The Price of Conviction](https://www.ml-quant.com/papers/ssrn/7571098/) (2026-10-09): The study shows that high-conviction beliefs exert outsized influence on prices, with a long-short portfolio exploiting conviction-weighted disagreement earning 13.2 percent annualized six-factor alpha. - [Artificial Intelligence Exposure and Financial Stability: Cross-Industry Common Risk and Connectedness in U.S. Equity Markets](https://www.ml-quant.com/papers/ssrn/7577948/) (2026-10-09): The study measures AI exposure across 284 U.S. firms and finds that an AI-factor explains only 18% variance alongside conventional factors, suggesting AI is a distinct cross-industry risk lens separate from traditional spillovers. - [Risk Appetite and Monetary Transmission](https://www.ml-quant.com/papers/repec/fip-fedfwp-103796/) (2026-10-09): The research shows Fed policy affects risk asset prices primarily through risk appetite shifts, not interest rates; transmission via risk appetite dwarfs rate-channel effects. - [A Currency Premium Puzzle](https://www.ml-quant.com/papers/repec/nbr-nberwo-35572/) (2026-10-09): The paper proves that asset pricing models solving equity and risk-free rate puzzles fail to generate observed interest-rate differentials between risky and safe currencies. - [The Implied Equity Term Structure](https://www.ml-quant.com/papers/repec/fip-fednsr-103730/) (2026-10-09): Inferring expected returns from stock prices and cash flows reveals an upward-sloping equity term structure, but value and speculative-grade firms show flat or downward slopes. - [A Model of Global Firms and Capital Markets](https://www.ml-quant.com/papers/repec/nbr-nberwo-35652/) (2026-10-09): A quantified general-equilibrium model of 23,000 firms across 48 countries shows financial and trade liberalization concentrate activity in largest firms with larger gains for emerging economies. - [Across the Capital Structure: Bond Signals and Equity Returns *](https://www.ml-quant.com/papers/ssrn/7527218/) (2026-10-02): Signals extracted from corporate bond portfolios predict next-month equity returns of same issuers at 21 basis points higher after controlling for stock characteristics. - [The Ant Group IPO Halt as a Natural Experiment on Chinese Fintech Regulation, Firm Valuations, and Shadow-Lending Contraction: A High-Frequency Event Study with Synthetic-Control Validation](https://www.ml-quant.com/papers/ssrn/7545235/) (2026-10-02): The study exploits Ant Group's suspended IPO in November 2020 as a natural experiment, finding that highly exposed firms suffered roughly 21 percentage point abnormal returns and experienced a 42% contraction in shadow-loan balances. - [Why Most Stocks Underperform the Index: Idiosyncratic Variance Drag and the Geometry of Compounding](https://www.ml-quant.com/papers/ssrn/7546579/) (2026-10-02): A lognormal model shows that individual stocks carry an idiosyncratic variance penalty making most underperform the index, with about one-third beating the market over five years. - [An Auditable Public-Data U.S. Equity Research Pipeline: Point-in-Time Factors, Asset Pricing, Portfolio Construction, and Walk-Forward Machine Learning](https://www.ml-quant.com/papers/ssrn/7528419/) (2026-10-02): A reproducible pipeline for factor research finds no model reliably beats simple approaches after accounting for transaction costs in point-in-time factor tests. - [Filing Discipline: Regulatory Disclosure Behavior and the Cross-Section of Stock Returns](https://www.ml-quant.com/papers/ssrn/7536681/) (2026-10-02): Filing discipline characteristics built from SEC timestamp data alone predict cross-sectional stock returns with a net Sharpe ratio of 1.27, concentrated in smaller and more volatile stocks. - [Corporate bond pricing in the AI era](https://www.ml-quant.com/papers/repec/bdi-opques-qef-1057-26/) (2026-10-02): Using ChatGPT's launch as a natural experiment, the paper finds that hyperscalers saw borrowing costs decline while software firms faced worse terms as debt markets repriced AI winners and losers. - [The Innovator's Risk Premium: Sticky Hurdle Rates, the Cost of Capital, and Creative Destruction](https://www.ml-quant.com/papers/repec/bde-wpaper-2628e/) (2026-10-02): The research shows that firms' hurdle rates exceed their financial cost of capital due to innovation risk and imperfect pledgeability, explaining weak productivity growth and declining business dynamism. - [Trust, Rule of Law, and the Size Premium: Evidence from a Meta-Analysis](https://www.ml-quant.com/papers/arxiv/2609.26212/) (2026-09-25): Meta-analysis of 1,613 size-premium estimates across 31 countries finds that stronger rule of law is associated with larger size premia, contrary to intuition. - [From D&I to D&I: European Capital Markets' Regime Shift from Diversity and Inclusion to Defence and Infrastructure](https://www.ml-quant.com/papers/ssrn/7477998/) (2026-09-25): European defence stocks repriced sharply starting November 2021, two to three months before Russia's invasion, delivering 26% alpha and reflecting release of ESG-exclusion constraints. - [Speculative Leverage and Factor Momentum](https://www.ml-quant.com/papers/ssrn/7512099/) (2026-09-25): Factor momentum strategies earn 49 basis points per month extra return following quarters of rapid margin-debt growth, a predictability that persists after publication and reflects limits to arbitrage correction. - [Firm-Specific Price Delay and Momentum](https://www.ml-quant.com/papers/ssrn/7518623/) (2026-09-25): Momentum profits concentrate among firms with high price delay, a measure of information friction, directly supporting theories that gradual information incorporation drives momentum. - [Crossing the Zero Lower Bound: Negative Interest Rates and Corporate Valuation](https://www.ml-quant.com/papers/ssrn/7512572/) (2026-09-25): Comparing firms across the ECB's 2014 negative rate adoption shows treated European firms had higher valuations but reduced leverage, suggesting cash-flow and discount-rate channels dominate tax-shield effects. - [Skewness Risk Premia and the Cross-Section of Currency Returns](https://www.ml-quant.com/papers/repec/cpr-ceprdp-20587/) (2026-09-25): Using model-free skewness measures from currency options, the study shows that skewness risk is priced in currency returns and explains variation across a broad cross-section of currency portfolios. - [Asset Embeddings](https://www.ml-quant.com/papers/repec/cpr-ceprdp-20082/) (2026-09-25): The paper shows that portfolio holdings contain all information needed for asset pricing and develops asset embeddings analogous to word embeddings to represent firms and predict valuations. - [Pricing Risk Globally: Intermediary Constraints, the Dollar, and the Global Financial Cycle](https://www.ml-quant.com/papers/repec/fip-fedgif-103716/) (2026-09-25): A two-country model shows that uncertainty shocks tighten intermediary constraints, widening credit spreads, appreciating the dollar, and raising currency risk premia globally. - [Carry Trade and Currency Crash Risk](https://www.ml-quant.com/papers/repec/cpr-ceprdp-20745/) (2026-09-25): Focusing on dollar-lira trading, the paper shows that higher crash risk significantly increases carry trade expected returns, accounting for 46–77% of compensation through Shapley decomposition. - [Rate Risk and Rate Insurance](https://www.ml-quant.com/papers/repec/nbr-nberwo-35636/) (2026-09-25): Stock returns are dampened by rate insurance: falling rates cushion payoff risk in bad times while rising rates in good times hedge duration exposure.