Working papers

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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.​
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Timing Anomalies Through Investor Bias with Jonas Frey and Denis Mokanov



​Motivated by a theoretical framework that links time-varying expectation bias to capital market anomaly returns, we propose a return predictor based on the difference between analysts’ earnings growth forecasts and unbiased machine learning forecasts. Using a sample of 179 anomalies, we show that, out-of-sample, the bias measure outperforms the benchmark of mean historical returns for up to 30% of anomalies at investment horizons beyond twelve months, consistent with the view that a notable fraction of anomalies reflect mispricing. Restricting the analysis to anomalies for which our bias measure delivers superior predictive performance in a validation sample further improves forecasting performance. In line with our framework, the persistence of expectation bias determines whether an anomaly reflects the build-up or resolution of mispricing. Most anomalies alternate between build-up and resolution, which challenges approaches that unconditionally classify anomalies into a single category.
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Forecasting the Term Structure of Equity Betas: Implications for Valuation with Niklas Augustin 

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


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  • 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.
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  • The Third Crowell Prize Winner,  2025, PanAgora Asset Management; direct download here
  • (Scheduled) 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
​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.
Changing Expected Returns Can Induce Non-zero but Unprofitable (Illusory) Return Autocorrelation with Richard Roll and Avanidhar Subrahmanyam 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  ​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
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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.
Agnostic Tests of Stochastic Discount Factor with Richard Roll and Junbo Wang We propose an estimator for the stochastic discount factor (SDF) that does not require macroeconomic proxies or preference assumptions. It depends only on observed asset returns yet is immune to the form of the multivariate return distribution. It does not depend on factor structure, thus making our SDF ideal for asset pricing test. We find that SDF is correlated with a large number of the factor/firm characteristics, which confirms Kozak, Nagel, and Santosh (2018)’s finding. Moreover, assets within the equity market are not fully integrated as SDFs constructed from different asset groups are uncorrelated.
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.
Are Stock Market Anomalies Anomalous After All? with George Chalamandaris, and Nikolaos Topaloglou
  • World Finance Conference 2021
We propose a stochastic spanning to evaluate whether anomalies are genuine under factor-model framework. Our approach is nonparametric and does not rely on any assumption of return distribution and investor risk preferences. It depends on the whole distribution of returns, rather than only on the first two moments. Of the anomalies considered, only a few expand the opportunity set of the risk-averter and have real economic content. Our approach is consistent in identifying genuine anomalies in and out of samples. This is in contrast to mean-variance (MV) spanning tests where anomalies identified in-sample, not out-of-sample.
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
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.
Slope Factors Outperforms? Evidence from a Large Comparative Study with Siddhartha Chib, Yi Chun Li and Xiaming Zeng​
  • Washington University (2021); CICF (2022); SOFIE- New York University Shanghai (2022)
  • This paper compare slope, rank and differential factors altogether. ​
Does the method used to construct long-short factors from firm-level characteristics change the assessment of the factor risks (risk-factors) that are incorporated in the cross-section of expected returns? Is the method of construction important for the pricing performance of those risk- factors? We compare factors constructed by three methods: pure-play slope factors (estimated OLS slopes from cross-sectional regressions), differential factors (difference in returns of 2 by K sorts of stocks) and rank factors (normalized rank weighted returns). Starting from 62 firm level characteristics, we construct 62 such factors from each method. For each set of 62 factors plus the market factor, we examine the posterior distributions of the SDF coefficients to infer risk-factors, and apply a Bayesian pricing criteria to determine the evidence in favor of pricing for a large collection of portfolios, ETFs and stocks. The evidence shows that the slope risk-factors strongly dominate, with important implications for empirical finance.
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.