Working papers
Asset Pricing
Changing Expected Returns can Induce Spurious Serial Correlation with Richard Roll and Avanidhar Subrahmanyam
Abstract: 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.
Abstract: 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.
Agnostic Tests of Stochastic Discount Factor with Amit Goyal, Richard Roll and Junbo Wang
A Generalized Machine Learning Framework for Linear Factor Model Test with Christopher Jones, Jinchi Lv and Junbo Wang
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)
Abstract: 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.
Machine Learning Classification Methods and Portfolio Allocation: An Examination of Market Efficiency with Yang Bai
Abstract: 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.
Asset Prices and Partisanship: Evidence from Daily Shopper Data with Jialu Shen and Ruixiang Wang
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
Abstract: 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
Abstract: 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.
CURRENCY MARKET
Media Tone Goes Viral: Global Evidence from the Currency Market with Kari Heimonen and Heikki Lehkonen
Abstract: Using several million news and social media articles related to currencies, we examine the role of media tone in predicting the exchange rate returns of 12 developed and 24 emerging markets from 1998 to 2016. The text-based currency Media tone is a strong positive predictor of currency excess returns beyond fundamentals of one to three months ahead and six months cumulatively, with the average in-sample and out-of-sample R2s of 4.45% and 9.03% in the US. The one-month predictability is observed in four other developed markets and 18 emerging market currencies, with the latter showing a stronger pattern. This predictability encompasses previous month currency returns, currency factors, macro fundamentals, and market sentiment and is stronger for currencies that are freely floating, less liquid, difficult to value, costly to arbitrage, and which have high interest rates. The effect is channeled through expectation errors and driven by the forecasting component rather than risk. The predictability of Media tone is driven by non-mainstream financial media.
- The original version with RIchard Roll, "An Agnostic and Practically Useful Estimator of the Stochastic Discount Factor"
A Generalized Machine Learning Framework for Linear Factor Model Test with Christopher Jones, Jinchi Lv and Junbo Wang
- SOFIE UCSD 2021
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)
- Finalist of Crowell Prize 2019, PanAgora Asset Management
- World Finance Conference 2021
Abstract: 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.
Machine Learning Classification Methods and Portfolio Allocation: An Examination of Market Efficiency with Yang Bai
- The 3rd winner of Crowell Prize 2021, Panagora Asset Management
- AFA PhD poster 2021; World Finance Conference 2021
- Mentioned here
Abstract: 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.
Asset Prices and Partisanship: Evidence from Daily Shopper Data with Jialu Shen and Ruixiang Wang
- The mimicking portfolio of our consumption growth is here.
- Midwest Finance Association meeting 2021
- FutFinInfo 2021; World Finance Conference 2021
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
Abstract: 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
Abstract: 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.
CURRENCY MARKET
Media Tone Goes Viral: Global Evidence from the Currency Market with Kari Heimonen and Heikki Lehkonen
Abstract: Using several million news and social media articles related to currencies, we examine the role of media tone in predicting the exchange rate returns of 12 developed and 24 emerging markets from 1998 to 2016. The text-based currency Media tone is a strong positive predictor of currency excess returns beyond fundamentals of one to three months ahead and six months cumulatively, with the average in-sample and out-of-sample R2s of 4.45% and 9.03% in the US. The one-month predictability is observed in four other developed markets and 18 emerging market currencies, with the latter showing a stronger pattern. This predictability encompasses previous month currency returns, currency factors, macro fundamentals, and market sentiment and is stronger for currencies that are freely floating, less liquid, difficult to value, costly to arbitrage, and which have high interest rates. The effect is channeled through expectation errors and driven by the forecasting component rather than risk. The predictability of Media tone is driven by non-mainstream financial media.
Blaze New Trails for Others to Follow: Evidence from Scanner Data with Ruixiang Wang
Abstract: Tracking more than 100 billion weekly transactions of two million products at the barcode level from 2007 to 2017, we identify and categorize new products as pioneers, followers and improvers to study corporate exploratory and/or exploitative innovation strategies. Firms introducing “pioneer” products are associated with greater future profitability and stock returns than those introducing “improver” and “follower” products. Price elasticity of demand explains pioneering (exploratory) innovation’s operating success. Meanwhile, limited investor attention accounts for pioneering firms’ superior stock performance. We exploit two exogenous shocks to firms’ new product development decisions to address endogeneity concerns.
- FMA 2019, SWFA 2020, AFA 2022
Abstract: Tracking more than 100 billion weekly transactions of two million products at the barcode level from 2007 to 2017, we identify and categorize new products as pioneers, followers and improvers to study corporate exploratory and/or exploitative innovation strategies. Firms introducing “pioneer” products are associated with greater future profitability and stock returns than those introducing “improver” and “follower” products. Price elasticity of demand explains pioneering (exploratory) innovation’s operating success. Meanwhile, limited investor attention accounts for pioneering firms’ superior stock performance. We exploit two exogenous shocks to firms’ new product development decisions to address endogeneity concerns.
