Working papers

The Anomaly Zoo Has Many Alphas but Few Dimensions with George Chalamandaris, and Nikolaos Topaloglou

Do stock market anomalies make investors better off? We test whether any of 153 anomalies improves the return distribution available to risk-averse investors using a joint spanning test based on second-order stochastic dominance. The anomaly zoo is economically redundant: the largest improvement in bad-state outcomes is 1.3 basis points per week—roughly one-tenth of a typical significant alpha—and statistically insignificant. The test has power: reducing the benchmark from eight to seven factors causes spanning to collapse. Cross-sectional evidence shows that tail improvements and pricing errors co-move. The anomaly zoo contains many alphas but few new dimensions of downside protection.

Open-Source Activities and Stock Returns: Evidence from GitHub Engagement with Cagri Berk Onuk and Yunfei Zhao

We examine whether GitHub engagement predicts stock returns. Using data linking S&P 1500 firms to GitHub activity (2010–2022), we construct a market-equity scaled PCA measure of engagement based on GitHub stars, forks, watchers, and repositories. Portfolios sorted on this composite measure yield significant abnormal returns: the high-minus-low spread is 1.45% monthly, compounding to 52.48% over five years, with robust Fama-French and q-factor alphas. Predictability strengthens for hard-to-fake metrics and survives fake-star cleansing. GitHub engagement predicts returns at intermediate horizons (peaking at a five-month lag) but not at short horizons, contrasting with traditional innovation proxies that predict at immediate lags, consistent with a production-side signal that materializes through integration, deployment, and adoption cycles. A decomposition shows GitHub stars reflect future profitability and intangible value. These findings suggest open-source engagement signals organization capital that investors underweight, contributing to literatures on alternative data and intangible assets.

Wrapped Stablecoins and Systemic Risk in DeFi with Pairie Koh

Wrapped stablecoins convert base stablecoins into receipt tokens that can be pledged repeatedly across DeFi lending protocols, implementing a form of shadow banking on-chain. We develop a model in which this recursive collateral reuse generates an endogenous liquidity multiplier that expands leverage in normal times but amplifies liquidation cascades when collateral constraints tighten. Using novel on-chain data from Aave deployments across eight blockchain networks, we document three findings. First, using regulatory announcements as instruments, we show that wrapped stablecoin creation causally increases recursive leverage: a one-standard-deviation increase raises the liquidity multiplier by 7\% relative to its mean. Second, higher pre-event recursive leverage amplifies cryptocurrency volatility during stress episodes, with economically large effects concentrated in equally-weighted rather than value-weighted measures. Third, cross-chain bridge connectivity, not chain size, determines contagion speed: doubling bridge connections reduces time to stress arrival by 67\% during the March 2023 USDC depeg. Wallet-level evidence confirms that agents execute the deposit-borrow-redeposit loops predicted by our model. Our results highlight a fundamental tradeoff: deeper collateral reuse improves funding efficiency but increases exposure to systemic liquidation cascades.

Agnostic Stochastic Discount Factors in Small Samples: Theory, Momentum, and Cross-Market Evidence with Richard Roll and Junbo Wang

The first version with Richard Roll, "An Agnostic and Practically Useful Estimator of the Stochastic Discount Factor"
The revised version with Richard Roll and Junbo Wang, "An Agnostic and Practically Useful Estimator of the Stochastic Discount Factor"
New York University, Louisiana State University, Texas Tech, World Finance 2021, LSU (2021), MFA 2022
The 2nd prize, best paper award, The 14th SME International Conference

We study stochastic discount factor estimation when the cross section is large and the relevant time window is short. We develop a return-only estimator that recovers the pricing kernel directly from traded returns, without macroeconomic proxies, preference restrictions, or an imposed factor structure. The estimator admits a finite-state interpretation and a conditional lag-state representation that links discount-factor estimation to return dynamics. In one application, the lag-state SDF produces a momentum signal with time-varying lag weights and positive average payoffs. In another, the agnostic SDF outperforms Kim–Korajczyk in both domestic and international pricing, while also clarifying how finite-sample noise shapes pricingerror comparisons.

Climate Risk Preparedness and the Cross Section with Siddhartha Chib, Leifei Lyu, and Khaled Obaid

FinTech HK Conference 2024
What we found is distinct from climate risk exposure!!

We develop a theoretical framework to examine the relationship between a firm’s climate risk preparedness and its investment returns within a two-date stochastic general equilibrium production-based asset pricing model. Our analysis shows that, in equilibrium with an interior solution, a firm’s initial climate preparedness is positively correlated with the expected return on equity. We empirically test this theoretical model using a comprehensive set of assets, including stocks, ETFs, anomalies, and portfolios sorted by characteristics. The empirical findings support our theoretical predictions, highlighting the significant role that the constructed preparedness factor plays in explaining the cross-sectional variation in asset returns.

Forecasting the Term Structure of Equity Betas : Implications for Valuation with Niklas Augustin

Estimating firm discount rates remains a fundamental challenge in asset pricing and corporate finance. We propose a novel approach that forecasts discrete elements of the beta term structure. Our results show that firm betas exhibit predictable variation across horizons and can be reliably forecasted for at least ten years. Incorporating expected betas into factor models reveals substantial dispersion in costs of equity, with an annual difference of approximately five percentage points between the top and bottom deciles of firms. Adjusting for these forward-looking costs of equity in discounted cash flow models reduces pricing errors by 10\% relative to standard methods. Furthermore, accounting for changes in expected betas is necessary to construct truly market-neutral long-term portfolios. These findings highlight the importance of horizon-specific beta forecasts for improving the estimation of discount rates, valuation, and investment decisions.

Just Look Knowing Peers with Image Representation with Tomasz Kaczmarek.

The Third Crowell Prize Winner, 2025, PanAgora Asset Management; direct download here
University of Massachusette, Lowell March 2026, Technical University of Munich (TUM) School of Management, 2nd Workshop on Advances in NLP and Generative AI in Finance and Management, Munich, Germany, May 19-20, 2025, University of Missouri Columbia, Missouri State University, University of Missouri St. Louis, the Fields Institute for Research in Mathematical Sciences at University of Toronto 2022, Poznan University, Blackrock 2023, FinTech City University of Hong Kong 2023, Northern Finance Association meeting 2023, Depaul University in November 2025, MFA 2026,

We present a novel approach to assess firm similarity by analyzing two million images. We leverage machine learning techniques to identify graphical objects that best represent companies’ operations, forming Image Industry Classifications (IIC). IIC mirrors investor-defined peer groups and, akin to the brain’s visual processing superiority, outperforms SIC, GICS, NAICS, and text-based similarity in delivering the greatest diversification benefits and industry momentum profits. This success is attributed to high investor agreement within IIC, leading to substantial aggregated demand and supply effects on stock prices. IIC excels in industries characterized by expectation-driven value, growth, and intangibility.

The Magnificent Ten Equity Factor Model with Argyro Kofina, Ioannis Psaradellis, and Nikolas Topaloglou

A ten-factor SSD-based sparse model remains optimal and consistently outperforms all leading benchmarks.
The concept based on stochastic dominance considering all welfare.

We propose a novel asset pricing factor model employing a new estimation method based on sparse second-order stochastic dominance (SSD), implemented via a greedy algorithm combined with linear programming. Initially drawing from a prominent set of 24 candidate factors featured extensively in the asset pricing literature, we find statistically and economically that no incremental benefit emerges from including more than ten factors. To further ensure robustness and validate our results, we extend our factor universe to a comprehensive set of 177 factors—153 factors from Jensen et al. (2023) plus the original 24. Remarkably, our empirical results again demonstrate that a ten-factor SSD-based sparse model remains optimal and consistently outperforms all leading benchmarks. This SSD-based sparse factor model significantly generalizes the mean-variance Arbitrage Pricing Theory (APT) framework by explicitly accommodating broader classes of investor preferences, thus providing enhanced theoretical rigor and clear economic interpretation. Comprehensive parametric and non-parametric tests, conducted in-sample and out-of-sample on cross-sectional and time-series equity returns, reveal that our proposed model consistently dominates prominent benchmark models, including LASSO-based machine learning approaches. These robust findings emphasize the considerable advantages of explicitly integrating nvestor welfare considerations into factor selection, thereby delivering rigorous theoretical insights and practical asset pricing relevance.

Changing Expected Returns Can Induce Non-zero but Unprofitable (Illusory) Return Autocorrelation with Richard Roll and Avanidhar Subrahmanyam

6th JAAF India Symposium (January 2022), Auckland University of Technology, The 14th SME International Conference

Changing expected returns can induce spurious autocorrelation in returns. We show why this happens with some simple examples and then investigate its prevalence in actual equity data. Using different measures of expected returns based on realized means, options markets, factor models, and analysts’ price targets we find evidence that supports our premise. Specifically, absolute shifts in expected returns are strongly and positively related to measured autocorrelations in the cross-section of individual stocks, as predicted by our analysis. Well-studied risk factors show no evidence of spurious components. We also show how our analysis implies spurious cross-autocorrelation and find supporting evidence for this phenomenon as well.

Transmission Bias in Financial News with Khaled Obaid

University of Arkansas, Missouri State University, AI and Finance Conference at Concordia University in Montreal 2024

We examine how financial news is distorted as it spreads across different news outlets, akin to the “telephone game”. Using a sample of exclusive articles from the Wall Street Journal, we use ChatGPT-4 to quantify the distortion introduced in the information environment as competing news outlets retell these exclusive stories. We find strong evidence that retelling articles tend to be more opinionated and negative and less factual and appealing compared to the original story. Factors that influence the distortion include whether the stories are retold by news outlets that are less specialized, the time-lapse between the original story and its retelling, and the presence of competing narratives from other news outlets. Our findings indicate that media distortion affects asset prices, influences trading behaviors, and leads to disagreement among analysts.

Major Revision

Animating Stock Markets by Tomasz Kaczmarek and Kuntara Pukthuanthong

Even wonder if we can use graphs to predict graphs in a form of animation. You got it. We can! Coming soon.
Finalist 2024, Crowell Prize, PanAgora Asset Management
MFA 2024; Future Finance Info 2024

Our study presents a revolutionary method called Variational Recurrent Neural Networks (VRNNs) that utilizes a series of graphs to predict future stock price trends. It works like an animated movie about price trends. We analyze data from the S&P500 index constituents, which are known to be less predictable than other traded stocks, between 1993 and 2021. Our model generates a Sharpe ratio of 2.94 for equally weighted portfolios and 2.47 for value-weighted portfolios. Even after taking into account a 10 basis points transaction cost, our Sharpe ratio remains approximately twice as high as that of Jiang, Kelly, and Xiu, 2020. By adopting our graph-based approach, we achieve a substantial alpha of 55 basis points per week after controlling for established risk factors (FF3, FF5, FF6, Q5, and DHS). Furthermore, our prediction of price changes, after adjusting for various price trend strategies and firm traits, strongly forecasts the weekly returns of firms.

Cracking the Code: The Networking Matrix of Finance Academia with Sujiao (Emma) Zhao

Bank of Portugal (July 2023); FMA (2023)

This research delves into social networks among finance academics from 1980 to the present. Engagement in social networks positively correlates with productivity, with co-authors and student-advisor networks having the most significant impact, followed by post-Ph.D. and Ph.D. networks. Focusing on existing relationships rather than expanding connections and connecting with prominent researchers has proven more effective. Scholars with strong networks produce higher-quality research and receive better compensation. Our findings on editorial favoritism are complex. Co-authors of editors and Ph.D. colleagues are less likely to have their papers accepted, while student advisors are more likely. There is no evidence to support bias toward female scholars, but discrimination against Asian and Hispanic females and favoritism towards White females and Hispanic males is evident.

Average and Marginal Tobin’s q as Indicators of Future Growth Opportunities, Expected Return, and Risk with Richard Roll

Contrary to popular opinion, average Tobin’s q is a better indicator of future growth opportunities than marginal Tobin’s q. We derive a curious relation between average and marginal q: the more profitable a new investment opportunity, the smaller will be the increase in average q when the opportunity is undertaken. Average q is inversely related to the cost of equity capital, so it represents an inverse measure of risk. The closely- related book/market ratio is also a measure of risk in the cross-section.

Rational Apathy: Unveiling the Hidden Consequences of Workplace Automation with Zhihua Wei, Aoran Zhang, and Yunfei Zhao

FMA (2022)

We investigate the influence of utilizing automated capital to replace human workers in U.S. public firms. Firms that can replace human capital with automated capital need better records on employee related corporate social responsibility (employee CSR) and workplace safety. Our results remain robust after addressing the endogeneity concern. We further document that the negative link is more powerful for firms adopting more aggressive financial policies, incurring higher labor-related costs, and locating in less religious areas. Our analysis also unveils an ethical conundrum. The compromise on employees’ well-being among firms that are highly subjective to workplace automation comes at the cost of reputation damage; however, such firms are associated with better financial performance. Overall, we document that workplace automation creates a moral hazard: firms expect to increase productivity with automated capital while paying less attention to current employees’ welfare.

Foreign Exchange Exposure – No Returns, only risk with Heikki Lehkonen and Khaled Obaid
We use generative AI to capture the foreign exposure. Returns of portfolios formed based on US dollar foreign exchange exposure are found to have a negative relationship with the absolute value of the foreign exchange exposure. The result is state-dependent and visible during the appreciation periods of the US dollar. We relate this effect to mispricing where investors fail to consider to true impacts of the exchange rate changes on firm values and report several results supporting this claim.

Permanent Working Papers

A Generalized Machine Learning Framework for Linear Factor Model Test with Christopher Jones, Jinchi Lv and Junbo Wang

LSU (2020); SOFIE UCSD 2021; NBER-NSF 2021

We introduce a generalized statistical learning method, sparse orthogonal factor regression (SOFAR), in testing linear factor models with both large numbers of factors and testing assets. Our approach encompasses most of the existing methods in the literature and can be used in many other scenarios with large data sets. Applying SOFAR, we can select the PC factors from the whole swath of 219 candidate factors proposed by the literature simultaneously, identify test assets associated with the selected PC factors, and interpret them. We can also select the PC factors and correlated characteristics in the IPCA framework without bootstrapping. Without firm characteristics instrumenting, we find that four PC factors (market, investment, intangible, and frictions) are relevant to the covariance of asset returns and three types of factors (profitability, asset liquidity, and liquidity bets) price assets in cross-section. With characteristics as instruments, we only identify one factor, and the correlated characteristics are beta, size, momentum, and liquidity.

AI Narrative and Stock Mispricing with Arka Bandyopadhyay and Dat Mai

https://alphaarchitect.com/2023/06/ai-exposures/
https://www.barrons.com/articles/ai-nvidia-tech-winners-has-beens-e0dec10

We apply advanced natural language processing to develop a dynamic dictionary of artificial intelligence (AI). Using this dictionary, we construct a real-time index of AI attention from more than 3,000,000 New York Times articles. Firms having high exposures to AI have higher returns one month ahead and lower returns five to seven months ahead, suggesting initial overreactions to AI news and subsequent corrections. The connection between AI exposures and future returns is concentrated among non-big stocks, indicating that small AI stocks are more difficult to value. A long-short AI exposure portfolio among non-big stocks generates significant annual alphas of 3% against benchmark multifactor models.

Diversity Narrative and Equity in Firm Leadership with Arka Bandyopadhyay and Dat Mai

We provide causal evidence that the narrative of diversity from the New York Times articles has nudged the corporations to choose female CEOs to be equitable in terms of gender of firm leadership. This channel is independent of the board gender diversity, which was mandated in 2017 in California and later repealed in 2022. Surprisingly, the diversity narrative channel fails to explain the election of Indian CEOs in several HiTech firms over the last few years. We argue that the election of Indian CEOs was motivated to create a favorable image of Tech firms to Indian and Chinese labor. We conclude that providing equity in firm leadership in terms of ethnic diversity is harder compared to gender diversity.

Interest Rates and Real Estate Values: The Divergence Effects of Real Rates and Expected Inflation with Richard Roll
A common belief is that higher interest rates reduce real estate values, but corroborating evidence is underwhelming. A sensible explanation suggests heterogeneity across countries is that mortgage interest deductibility for the U.S. personal income tax makes much of the real value of the mortgage principal tax deductible when inflation is high. This implies that nominal mortgage rates could positively affect U.S. house prices. We find evidence consistent with this possibility; the inflation component of nominal interest is associated positively with U.S. real estate prices but negatively with those in Canada, a country without mortgage interest deductibility.

An Unambiguous Statement of Interest Rate Parity with Lee R. Thomas III
Interest rate parity (IRP) states that the difference between the interest rates of two currencies equals the expected percentage change in the associated exchange rate. Unfortunately, the IRP is ambiguous. There are two, inconsistent values of the expected percentage change in the exchange rate, depending on which currency is treated as the numeraire. We derive a corrected IRP condition that is unambiguous but subtly different. The equilibrium interest rate differential equals the percentage difference between the spot exchange rate and the geometric mean of the future exchange rate distribution, not its expected value.