​Note: Please click the title to see the paper. Your comments are welcomed
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Changing Expected Returns can Induce Spurious Serial Correlation 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.
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War Risk: Time Series and Cross-sectional Evidence from the Stock and Bond Markets with Dat Mai
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  • 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)
We employ seeded LDA to extract rare disaster and economic narratives discussed by Shiller (2019) from 7 million NYT articles over 150 years. The estimation scheme addresses look-ahead bias and changes in semantics. War and the narrative index positively predict market return in- and out-of-sample, while the economic narratives only have in-sample predictability. The predictability of War increases over time. A one-standard-deviation increase in War increases annualized excess returns by 3.80% in the next month, and the monthly in-sample R2 is 0.39%, while the respective numbers are over the past 20 years 9.83% and 3.39%. War presents time-varying risk aversion. Its predictability is robust when War is extracted from WSJ. Our single-factor model with innovations in War prices anomalies and characteristics-sorted portfolios, and performs better than some multifactor benchmarks when pricing nonlinear tree-based portfolios with an R2 of 54%.
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Asset Pricing with Slope Factors: Model and Evidence of Outperformance 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.
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

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.
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.
​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.
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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.
Blaze New Trails for Others to Follow: Evidence from Scanner Data with Ruixiang Wang​
  • FMA 2019, SWFA 2020, AFA 2022, NFA (scheduled September 2022)
  •  Twinbeech Capital (2021)
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.
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.
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

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.



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.
Media Tone Goes Viral: Global Evidence from the Currency Market with Kari Heimonen and Heikki Lehkonen

  • FMA 2021, NEU (brownbag)
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.
VC ownership post-IPO: When, why, and how do VCs exit? with Anup Basnet, Harry Turtle and Thomas Walker

  • FMA 2020
We examine how the ownership of lead venture capital firms (VCs) evolves after their portfolio companies (PCs) are publicly listed. The VC investment period before the IPO, the VC age, the PC age, and the percentage change in the post-IPO stock price all incentivize earlier VC exit. Lead VCs remain invested for a longer period when the PC is of better quality, when the VC has more experience in taking companies public, and when the VC holds positions in the company’s compensation committee. VCs with longer pre-IPO investment periods prefer share distributions (SDs) to continuous sales (C sales), perhaps because SD provides a more expeditious exit compared to C Sales and M&A. Older lead VCs prefer C Sales to M&A, VCs with prior M&A exit experiences prefer M&A exits, and VCs investing in younger PCs prefer SDs. 
​Machine Learning Classification Methods and Portfolio Allocation: An Examination of Market Efficiency with Yang Bai We design a novel framework to examine market efficiency through out-of-sample (OOS) predictability. We frame the asset pricing problem as a machine learning classification problem and construct classification models to predict return states. The prediction-based portfolios beat the market with significant OOS economic gains. We measure prediction accuracies directly. For each model, we introduce a novel application of binomial test to test the accuracy of 3.34 million return state predictions. The tests show that our models can extract useful contents from historical information to predict future return states. We provide unique economic insights about OOS predictability and machine learning models.
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.