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Changing Expected Returns can Induce Spurious Serial Correlation with Richard Roll and Avanidhar Subrahmanyam
- 6th JAAF India Symposium
Slope Factors Outperform: Evidence from a Large Comparative Study with Siddhartha Chib, Yi Chun Li and Xiaming Zeng
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.
- Washington University (2021)
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.
Agnostic Tests of Stochastic Discount Factor with Amit Goyal, Richard Roll and Junbo Wang
- The firs 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
Testing Asset Pricing Model with Non-Traded Factors: A New Method to Resolve (Measurement/Econometric) Issues in Factor-Mimicking Portfolio with Richard Roll, Junbo Wang, and Tengfei Wang (Internet Appendix)
We relate factor-mimicking portfolios (FMPs) to the beta-pricing model and propose that each FMP should minimize the mispricing component of its underlying factor. We also examine FMP construction when the underlying factor contains noise and offer a new method to resolve this issue. For both classical and our newly proposed methods, we recommend enhanced necessary criteria. FMPs of several macroeconomic factors constructed by our method satisfy these criteria. We find that equities are priced by consumption growth, inflation, and the unemployment rate; and corporate bonds are priced by consumption growth, industrial production, and the default spread.
- Finalist of Crowell Prize 2019, PanAgora Asset Management
- World Finance Conference 2021
We relate factor-mimicking portfolios (FMPs) to the beta-pricing model and propose that each FMP should minimize the mispricing component of its underlying factor. We also examine FMP construction when the underlying factor contains noise and offer a new method to resolve this issue. For both classical and our newly proposed methods, we recommend enhanced necessary criteria. FMPs of several macroeconomic factors constructed by our method satisfy these criteria. We find that equities are priced by consumption growth, inflation, and the unemployment rate; and corporate bonds are priced by consumption growth, industrial production, and the default spread.
Economic Narratives and Market Outcomes: A Semi-supervised Topic Modeling Approach with Dat Mai
- Chicago Quantitative Alliance (2nd prize winner)
- EFA 2021 (PhD symposium), Texas Finance PhD symposium (2021), NFA (2021), FMA (2021), SFA (2021), FRA (2021), AFA PhS poster session (2022), MFA (2022)
- Long Tail Alpha presentation (2021)
Blaze New Trails for Others to Follow: Evidence from Scanner Data with Ruixiang Wang
- FMA 2019, SWFA 2020, AFA 2022
- Twinbeech Capital (2021)
Convenience Yields of Collectibles, with Elroy Dimson and Matthew Vorsatz
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.
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.
Asset Prices and Partisanship: Evidence from Daily Shopper Data with Jialu Shen and Ruixiang Wang
We propose a novel consumption measure that has a daily frequency and is based on real-time shopping data. Our measure explains the joint equity-premium–risk-free rate puzzle with a risk aversion coefficient much lower than any other consumption measures. It encompasses other consumption measures in explaining the cross-sectional variation of expected returns on various portfolio and is the only consumption measure that passes Kleibergen and Zhan (Journal of Finance, 2020) robust tests. Our model decomposes consumption shocks into different frequency of volatility and shows that ignoring short-term dynamics and intra-annual fluctuations explains the much higher risk aversion from low-frequency consumption measures. At state-level daily consumption, (a) consumption in blue states suggests higher risk aversion than that in red states; (b) only Democratic consumption beta explains a variation of cross-sectional returns, and is more sensitive to overall industry performance.
Are stock market anomalies anomalous after all with George Chalamandaris, and Nikolaos Topaloglou
HUMAN CAPITAL VALUATION
On Valuing Human Capital And Relating it to Macro-Economic Conditions with Dave Berger and Richard Roll
Human capital is the largest component of aggregate wealth, but its relation to other macroeconomic variables is murky due to the lack of market prices. Valuing human capital using historical costs or expected income is characterized by substantial measurement error. We develop a human capital index using slave prices and relate its dynamics to that of other indicators including equities, GDP, real estate and interest rates. The human capital values are extrapolated from the 19th Century to the modern era. Their evolution has substantial implications for our understanding of the human capital dynamics, with applications to growth and portfolio allocation.
ECONOMICS
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, one that 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 be positively related to 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.
- The data of mimicking portfolio of our consumption growth is here.
- Midwest Finance Association meeting 2021; FutFinInfo 2021; World Finance Conference 2021; FMA 2021
We propose a novel consumption measure that has a daily frequency and is based on real-time shopping data. Our measure explains the joint equity-premium–risk-free rate puzzle with a risk aversion coefficient much lower than any other consumption measures. It encompasses other consumption measures in explaining the cross-sectional variation of expected returns on various portfolio and is the only consumption measure that passes Kleibergen and Zhan (Journal of Finance, 2020) robust tests. Our model decomposes consumption shocks into different frequency of volatility and shows that ignoring short-term dynamics and intra-annual fluctuations explains the much higher risk aversion from low-frequency consumption measures. At state-level daily consumption, (a) consumption in blue states suggests higher risk aversion than that in red states; (b) only Democratic consumption beta explains a variation of cross-sectional returns, and is more sensitive to overall industry performance.
Are stock market anomalies anomalous after all with George Chalamandaris, and Nikolaos Topaloglou
- World Finance Conference 2021
HUMAN CAPITAL VALUATION
On Valuing Human Capital And Relating it to Macro-Economic Conditions with Dave Berger and Richard Roll
- Semi-Finalist of Best Paper Award 2016, Financial Management Association Meeting
- World Finance Conference 2021
Human capital is the largest component of aggregate wealth, but its relation to other macroeconomic variables is murky due to the lack of market prices. Valuing human capital using historical costs or expected income is characterized by substantial measurement error. We develop a human capital index using slave prices and relate its dynamics to that of other indicators including equities, GDP, real estate and interest rates. The human capital values are extrapolated from the 19th Century to the modern era. Their evolution has substantial implications for our understanding of the human capital dynamics, with applications to growth and portfolio allocation.
ECONOMICS
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, one that 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 be positively related to 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 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
Richard Thaler proposes two questions to test efficient market hypothesis: (1) can we beat the market, and (2) are the prices correct? We introduce a novel methodology, machine learning classification methods, and apply them to answer these questions. First, our classification portfolios beat the market in time series and cross-sections. Our models capture the information about return state transition and have time invariant applicability. Second, the performance is attributed to the prediction accuracy both in sample (IS) and out of sample (OOS). The accuracy implies that our classification models can generate information about future return states with historical observations. Our study suggests the current prices may not fully reflect all historical information.
- The 3rd winner of Crowell Prize 2021, Panagora Asset Management
- niversity of Miami Winter Research Conference on Machine Learning and Business 2021; AFA PhD poster 2021; World Finance Conference 2021
- Mentioned here
Richard Thaler proposes two questions to test efficient market hypothesis: (1) can we beat the market, and (2) are the prices correct? We introduce a novel methodology, machine learning classification methods, and apply them to answer these questions. First, our classification portfolios beat the market in time series and cross-sections. Our models capture the information about return state transition and have time invariant applicability. Second, the performance is attributed to the prediction accuracy both in sample (IS) and out of sample (OOS). The accuracy implies that our classification models can generate information about future return states with historical observations. Our study suggests the current prices may not fully reflect all historical information.
Media Tone Goes Viral: Global Evidence from the Currency Market with Kari Heimonen and Heikki Lehkonen
- FMA 2021, NEU (brownbag)
VC ownership post-IPO: When, why, and how do VCs exit? with Anup Basnet, Harry Turtle and Thomas Walker
- FMA 2020
