Kuntara Pukthuanthong
Working papers

War Discourse and the Cross-Section of Expected Stock Returns with David Hirshleifer and Dat Mai
- Revise and Resubmit, Journal of Finance

War Discourse and Disaster Premia: 160 Years of Evidence from Stock and Bond Markets with David Hirshleifer and Dat Mai
- Revise and Resubmit, Review of Financial Studies
- The War, PLS, and Economics indices are here. Codes and raw data are available upon request. Email me.
- Chicago Quantitative Alliance (2nd prize winner, 2021)
- EFA 2021 (PhD symposium), Texas Finance PhD symposium (2021), NFA (2021), FMA (2021), SFA (2021), FRA (2021), AFA PhD poster session (2022), MFA (2022), Texas PhD Symposium (2022), CFDM Conference at Boulder (2022), Bank of Portugal (2022), Center for Financial Research Cologne (CFR 2022)
- Long Tail Alpha presentation (2021)
Slope Factors Outperforms? Evidence from a Large Comparative Study with Siddhartha Chib, Yi Chun Li and Xiaming Zeng
- Reject and Resubmit, Management Science
- Washington University (2021); CICF (2022); SOFIE- New York University Shanghai (2022)
- This paper compare slope, rank and differential factors altogether.
VC ownership post-IPO: When, why, and how do VCs exit? with Anup Basnet, Harry Turtle and Thomas Walker
- Revise and Resubmit, Journal of Financial Research
- FMA 2020

Climate Risk Preparedness and the Cross Section with Sid Chib and Leifei Lyu
Our paper presents a theoretical model to analyze the relationship between a company’s expected returns and its preparation for climate risk. The model is based on a two-date investment framework and considers the impact of a climate change-induced event (CCIE) that may occur on the second date. The extent of damage caused by this event is dependent on the amount spent on climate preparedness on the first date. The company has an initial capital stock, profitability, and readiness. Our analysis demonstrates that the expected return on equity is directly proportional to the company’s preparedness for climate change, profitability, and size, but inversely proportional to investment. In addition, we have developed an empirical model based on five factors that can be used to price stocks, ETFs, anomalies, and characteristics sorted portfolios.
Our paper presents a theoretical model to analyze the relationship between a company’s expected returns and its preparation for climate risk. The model is based on a two-date investment framework and considers the impact of a climate change-induced event (CCIE) that may occur on the second date. The extent of damage caused by this event is dependent on the amount spent on climate preparedness on the first date. The company has an initial capital stock, profitability, and readiness. Our analysis demonstrates that the expected return on equity is directly proportional to the company’s preparedness for climate change, profitability, and size, but inversely proportional to investment. In addition, we have developed an empirical model based on five factors that can be used to price stocks, ETFs, anomalies, and characteristics sorted portfolios.

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.
Even wonder if we can use graphs to predict graphs in a form of animation. You got it. We can! Coming soon.
- MFA 2023

No One-Size-Fits-All Tale: The Diversity and Complexity in Asset Pricing across Global Markets with Zhuo Qiao and Yan Wang
We utilize a Bayesian approach developed by Chib, Zeng, and Zhao (2020) and Chib and Zeng (2020) to identify the best asset pricing models for international stock markets. Our findings reveal that models with Student-t distribution outperform models with Gaussian distribution in all markets. However, the best models vary depending on the specific market, and the factor strength fluctuates over time, highlighting the diversity and complexity of asset pricing in different markets. Although currency risk factors are crucial in international asset pricing, they were weakened during the global financial crisis in 2008. Additionally, we evaluate the efficiency of our local, regional, and global models in explaining various cross-sectional anomalies. Our research indicates that local factor models are the most successful, while regional and global models have similar outcomes.
We utilize a Bayesian approach developed by Chib, Zeng, and Zhao (2020) and Chib and Zeng (2020) to identify the best asset pricing models for international stock markets. Our findings reveal that models with Student-t distribution outperform models with Gaussian distribution in all markets. However, the best models vary depending on the specific market, and the factor strength fluctuates over time, highlighting the diversity and complexity of asset pricing in different markets. Although currency risk factors are crucial in international asset pricing, they were weakened during the global financial crisis in 2008. Additionally, we evaluate the efficiency of our local, regional, and global models in explaining various cross-sectional anomalies. Our research indicates that local factor models are the most successful, while regional and global models have similar outcomes.

Cracking the Code: The Networking Matrix of Finance Academia with Sujiao (Emma) Zhao
- Bank of Portugal (July 2023)
- FMA (2023)

- 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, and Northern Finance Association meeting 2023

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

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.
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.

- Commodity dependence and optimal asset allocation with Vianney Dequiedt, Mathieu Gomes, and Benjamin Williams-Rambaud
- FMA (2023)

Do robots hurt humans? Evidence from the dark side of workplace automation with Zhihua Wei, Aoran Zhang, and Yunfei Zhao
- FMA (2022)
Investor Sentiment and Asset Returns: Actions Speak Louder than Words with Dat Mai and Guofu Zhou
We analyze daily predictability of investor sentiment across four major asset classes and compare sentiment measures based on news and social media with those based on trade information. For the majority of assets, trade-based sentiment measures outperform their text-based equivalents for both in-sample and out-of-sample predictions. This outperformance is particularly noticeable in long-term forecasts. However, real-time mean-variance investors can only achieve economic gains using Bitcoin trade sentiment, suggesting the challenge of transforming sentiment into daily profitable trading strategies.
We analyze daily predictability of investor sentiment across four major asset classes and compare sentiment measures based on news and social media with those based on trade information. For the majority of assets, trade-based sentiment measures outperform their text-based equivalents for both in-sample and out-of-sample predictions. This outperformance is particularly noticeable in long-term forecasts. However, real-time mean-variance investors can only achieve economic gains using Bitcoin trade sentiment, suggesting the challenge of transforming sentiment into daily profitable trading strategies.
Human Capital Valuation, Asset Pricing, and Economic Development with Dave Berger and Richard Roll
- Semi-Finalist of Best Paper Award 2016, Financial Management Association Meeting World Finance Conference 2021
Changing Expected Returns can Induce Spurious Serial Correlation with Richard Roll and Avanidhar Subrahmanyam
- 6th JAAF India Symposium (January 2022), Auckland University of Technology, The 14th SME International Conference
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.
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.
A New Method for Factor-Mimicking Portfolio Construction with Richard Roll, Junbo Wang, and Tengfei Wang
- Finalist of Crowell Prize 2019, PanAgora Asset Management
- AFA (Ph.D. Poster), CICF, EFA, FMA, Louisiana State University, NFA, the Society of Financial Econometrics Conference (SoFiE), the World Finance Conference, and PanAgora Asset Management
Agnostic Tests of Stochastic Discount Factor 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

Asset Prices and Partisanship: Evidence from Daily Shopper Data with Jialu Shen and Ruixiang Wang
- The data of mimicking portfolio of our consumption growth is here.
- Midwest Finance Association meeting 2021; FutFinInfo 2021; World Finance Conference 2021; FMA 2021, CICF 2023, and AFA 2024
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.
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.

Blaze New Trails for Others to Follow: Evidence from Scanner Data with Ruixiang Wang
- FMA 2019, SWFA 2020, AFA 2022, NFA 2022
- Twinbeech Capital (2021)
Are stock market anomalies anomalous after all with George Chalamandaris, and Nikolaos Topaloglou
- World Finance Conference 2021
NEED MAJOR REVISIONS
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.
- 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.
Machine Learning Classification Methods and Portfolio Allocation: An Examination of Market Efficiency with Yang Bai
- Third prize winner of Crowell Prize, 2020 awarded by PanAgora Asset Management
Convenience Yields of Collectibles, with Elroy Dimson and Matthew Vorsatz.
Please also see the Appendix and additional materials.
We propose a novel method to estimate convenience yields of collectibles based on factor mimicking portfolios. Using up to 110 years of collectibles returns for 13 distinct asset classes, we apply machine learning techniques to address challenges from non-synchronous trading. We use these estimates to study how convenience yields affect equilibrium pricing. Convenience yield estimates for 24 of our 30 collectibles return series are positive, with an annualized mean (median) of 2.64% (2.53%). Despite various forms of underestimation, these results provide evidence that assets with positive emotional returns have lower equilibrium financial returns. This finding has important implications for ESG investing.
Please also see the Appendix and additional materials.
We propose a novel method to estimate convenience yields of collectibles based on factor mimicking portfolios. Using up to 110 years of collectibles returns for 13 distinct asset classes, we apply machine learning techniques to address challenges from non-synchronous trading. We use these estimates to study how convenience yields affect equilibrium pricing. Convenience yield estimates for 24 of our 30 collectibles return series are positive, with an annualized mean (median) of 2.64% (2.53%). Despite various forms of underestimation, these results provide evidence that assets with positive emotional returns have lower equilibrium financial returns. This finding has important implications for ESG investing.
NO BIG DEAL PAPERS
Is Media Tone just a Tone? Time-Series and Cross-Sectional Evidence from the Currency Market with Kari Heimonen and Heikki Lehkonen
- FMA 2021, NEU (brownbag)
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.
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.
Foreign Exchange Exposure – No Returns, only risk with Kari Heimonen and Heikki Lehkonen
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.
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.