Risk Momentum

with Sophia Zhengzi Li and Peixuan Yuan (current Version: October, 2024).

We uncover a new pattern: the stock risk component exhibits momentum. This risk momentum yields a return momentum: stocks sorted by risk have persistent positive returns. In comparison with the extremely popular and extensively studied Jegadeesh and Titman (1993) momentum sorted by return, which is valid only monthly and only for stocks, our risk-based return momentum holds intraday, daily, weekly, and monthly, and exists for not only stocks, but also for corporate bonds and other asset classes. Furthermore, our risk momentum, the strongest ever discovered, is different from the factor momentum of Ehsani and Linnainmaa (2022) sorted by factor performance.

Fear in the "Fearless" Treasury Market

with Tianyang Wang, Yuanzhi Wang and Qunzi Zhang (current Version: Nov., 2024).

This paper examines how fear affects the Treasury market and predicts Treasury bond returns. Using a text-based fear index from social and news media, we find that fear significantly predicts future Treasury returns, both in-sample and out-of-sample, and suggests the global transmission of fear. We also propose a model explaining that risk aversion shocks drive bond risk premia. Our paper further explores various dimensions of fear effects, such as term, magnitude, dynamics, and sources, and compares them with other sentiments. The results highlight the critical role of fear in Treasury market dynamics.

Equity Risk Premium Prediction: Return Decomposition and Noise Shrinkage

with Yanyan Lin, Chongfeng Wu and Shunwei Zhu (current Version: Nov., 2024).

We propose a novel decomposition of stock returns into a fundamental component (FC) and an unexpected capital gains component (UC). The FC, driven by firm's valuation ratios, reflects long-term growth and exhibits high persistence, while the UC, influenced by market trading prices, reflects short-term fluctuations and is more random. To predict the UC, we use a predictive regression model with an L multiplier to shrink noise for mitigating estimation errors. Among the 41 monthly predictors examined by Goyal, Welch and Zafirov (2024), we find 33 of them significantly outperform the historical average forecast, compared to only 5 with their method. Aggregating information across the predictors, we reaffirms the predictability of the equity risk premium.

ETFs, Anomalies and Market Efficiency

with Ilias Filippou, Songrun He and Sophia Zhengzi Li (current Version: Oct., 2023).

Investigating how ETF ownership impacts anomalies and market efficiency, we find that low ETF ownership stocks yield higher returns, greater Sharpe ratios, and more significant alphas versus high ETF ownership stocks. Moreover, high ETF ownership stocks exhibit more pronounced information flow, reducing mispricing and enhancing efficiency. These findings hold when we match the two groups by characteristics to have similar short-sales constraint, arbitrage costs, and information environment, and remain robust to controlling for those effects in Fama-MacBeth regressions. Using Russell index reconstitution as a natural experiment, we find causal evidence of ETF ownership attenuating anomaly profits.

Presented SGF 2023, and WFA 2023; would have been presented at TAU Finance Conference 2023.

✍ ^New ChatGPT, Stock Market Predictability and Links to the Macroeconomy

with Jian Chen, Guohao Tang and Wu Zhu (current Version: July, 2024).

We find that good news extracted by ChatGPT from the front pages of Wall Street Journal can predict the stock market and is related to macroeconomic conditions. Consistent with existing theories, investors tend to underreact to positive news, especially during periods of economic downturns, high information uncertainty and high novelty of news. In contrast, the negative news is only associated with contemporaneous returns. Traditional methods of textual analysis, such as word lists and large language models like BERT, can barely find any predictability. In short, ChatGPT appears the best AI in discerning economic-related news that drive the stock market.

Presented at 2024 SIF.

✍ ^New Unusual Financial Communication: ChatGPT, Earnings Calls, and the Financial Markets

with Lars Beckmann, Heiner Beckmeyer, Ilias Filippou and Stefan Menze (current Version: August, 2024).

We devise a prompting strategy for ChatGPT to detect and analyze unusual aspects of financial communication in earnings calls. We identify 25 dimensions across four categories: unusual communication by executives and analysts, unusual contents, and technical difficulties. Unusual financial communication is common, correlates with certain firm characteristics and fluctuates with the business cycle. Financial markets react to it, with a negative stock return, elevated trading activity, higher volatility and option-implied uncertainty, and downward revisions of next-quarter earnings forecasts by analysts. Our study demonstrates the potential of large language models to provide new insights into the interpretation of financial textual data.

Finalist for Crowell Memorial Prize (to be presented); Presented at CICF 2024.

What Drives the Earnings Announcement Risk?

with Hong Liu, Yingdong Mao and Xiaoxiao Tang (current Version: July, 2024).

We provide the first estimates of the ex-ante risk premia on earnings announcements using the forward-looking information in the options market. We find that the average earnings announcement risk premium is highly significant at 16 basis points, with substantial variation across firms and across time. Sorting by the ex-ante estimated risk premia generates a daily return spread of 40 bps between high and low terciles. Moreover, the ex-ante estimated risk premia provide new insights on what drives the well-documented positive post-earnings-announcement drift and yield profitable straddle strategies

A New Option Momentum: Compensation for Risk

with Heiner Beckmeyer and Ilias Filippou (current Version: June, 2024).

This paper introduces a novel momentum strategy in the options market based on the systematic component of option returns. Utilizing a latent factor model to decompose options returns, we demonstrate that the systematic component exhibits stronger momentum and subsumes the performance of conventional return-based momentum. With a six-month formation and one-month holding period, the strategy achieves an annualized Sharpe ratio of 2.23, compared to 1.08 for traditional momentum, and is highly profitable for various formation and holding periods. The superior performance is driven by time-varying risk compensation rather than investor biases, underscoring the economic rationale behind its success

Best paper awards, INQUIRE UK/Europe, 2024 and FMA Asset Mgt Consortium at Cambridge, 2024; Presented at EFA, 2024.

✍ ^New Option Expected Hedging Demand

with Xiaoxiao Tang and Zhaoque (Chosen) Zhou (current Version: Feb., 2024).

Options market makers' delta hedging has an increasing impact on underlying stock prices as both the option volume and the ratio of option volume to stock volume grow drastically in recent years. We introduce a novel approach utilizing real-time option information to calculate the spot elasticity of delta (ED) and expected hedging demand (EHD), and find that the EHD significantly predicts future stock returns in the cross section. The positive impact of EHD on stock prices lasts up to five trading days, and then a reversal follows. The empirical evidence of heterogeneous EHD-return relationship, influenced by ED, leads to varied option market maker behaviors, and is consistent with conventional economic theory. Moreover, we find that EHD has a little correlation with other popular firm characteristics, representing a new risk that is not captured by conventional factor models.

Seeing is Believing: Annual Report 'Graphicity' and Stock Returns Predictability

with Xiahu Deng, Lei Gao and Bo Hu (current Version: Sept., 2023).

Why do firms graphically enhance their annual reports that appear redundant to the 10-Ks? We develop a novel rational model to explain this. Using a large dataset, we report the first evidence that firms earn approximately 3.5% abnormal returns in the next 3 to 6 months after they initiate graphic annual reports. This is accompanied by an increase in institutional investors' holdings, consistent with our theory that firms create visuals to overcome investor inattention and help communicate subtle information to fundamental investors. This is also consistent with the fact that such firms tend to increase their R&D investments afterwards.

Presented at 2024 EFA.

Market Risk Premium Expectation: Combining Option Theory with Traditional Predictors

with Hong Liu, Yueliang (Jacques) Lu and Weike Xu (current Version: Oct., 2024).

We extend the Martin (2017) option bound by incorporating economic state variables, linking option-based bounds to the traditional predictability literature. Our state-dependent bounds (SDBs) significantly improve out-of-sample predictions of the market risk premium, outperforming models that rely solely on either option prices or traditional stock market predictors. Moreover, SDBs substantially increase portfolio Sharpe ratios and enhance investor utility. In a cross-sectional analysis of expected stock returns, we show that option-based information provides incremental value beyond conventional firm characteristics. Our novel findings highlight the importance of integrating information in both option prices and economic state variables.

Presented at AFA 2024.

Maximizing the Sharpe Ratio: A Genetic Programming Approach

with Yang Liu and Yingzi Zhu (current Version: Dec., 2023).

While existing studies focus on minimizing model errors, we consider maximizing the Sharpe ratio of investing in the usual spread portfolio. In contrast to popular machine learning methods, we find that GP can double their performance in the US, and outperform them internationally, because GP captures nonlinearity in comparison with linear methods like the LASSO and it requires smaller sample size than the nonlinear neural network. We also apply GP to maximize the Sharpe ratio of all the underlying stocks, and find that its value is 60% greater than before, indicating the loss of relying on spread portfolios can be substantial.

Presented at AFA 2024, and at 2021 CICF.

Which Expectation?

with Juhani T. Linnainmaa and Yingguang (Conson) Zhang (current Version: Dec., 2023).

We test a theory of two expectations in asset pricing: investors separately form beliefs on cash flow level and cash flow growth when valuing assets. Using 123 anomalies and analysts’ earnings term structure forecasts, we find strong evidence for the separability of the two beliefs. Forecast errors in cash flow level and cash flow growth are uncorrelated. Anomaly portfolios typically manifest biases in one belief or the other but not both. Anomalies with large (small) alphas often have the two biases amplifying (offsetting) each other. The first two principal components of anomaly returns are essentially a growth bias factor and a level bias factor. The two biases explain about 50\% of the anomaly portfolios' cross-sectional deviation from the CAPM. Level bias generates large initial alpha and growth bias generates persistent alpha. We also provide an explanation for the recent alpha decay with analysts’ improved forecast accuracy.

Presented at AFA 2024.

Myopic Expectations and Stock Market Mispricing

with Yingguang (Conson) Zhang and Yingzi Zhu (current Version: April, 2024).

Are expectations in financial markets myopic? Based on a new multi-horizon expectation framework and using data of U.S. stock analysts’ forecasts, we find that their forecasts are myopic, and their myopic expectations are associated with large price distortions even in recent periods. Our study distinguishes among different sources of myopic expectations, reconciles myopia with long-horizon belief overreaction, quantifies myopia effects across horizons, tests the role of information frictions, and assesses the economic significance in terms of trading profits. Our framework is generally applicable to other settings with multi-horizon expectations, providing a useful tool for future research.

✍ ^New Fama-MacBeth Regression with Asset Pricing Restriction

with Yuanqi Yang and Yifeng Zhu (current Version: May, 2024).

In this paper, we propose a modified Fama-MacBeth regression that incorporates asset pricing restrictions into the estimation. The restrictions require the model to explain both the time series and cross-sectional variations, and also to select factors for sparsity. Solving the estimation via a least angle regression-type algorithm, we find empirically that the new model outperforms existing factor selection methodologies in predicting the cross-sectional stock returns. In addition, we propose new interpretable characteristics-based factors, and our factors outperform classical factors models.

✍ ^New Pockets of Factor Pricing

with Sophia Zhengzi Li and Peixuan Yuan (current Version: December, 2023).

Current factor models assume certain pre-specified factors can price or explain asset returns with the same level of ability across time. In contrast with this conventional wisdom, we find that factor's pricing ability exhibit notable temporal variations, and it tends to cluster in certain periods referred to as "pockets." We propose a real-time approach to effectively identify the pockets, and apply it to a comprehensive set of firm characteristics. We find episodic and distinct dynamics of return predictability for different types of characteristics, contradicting the notion of continuous presence of the same factors with the same pricing ability. Exploiting factor's time-varying predictive power, we construct a composite predictor/factor that achieves a value-weighted hedge return of 3.94% per month with a high t-statistic of 13.87. Additionally, the composite factor pricing model, which incorporates a selection of factors with factor timing, demonstrates superior effectiveness in both explaining and predicting market anomalies. The factor also provides a comprehensive explanation for factor momentum, which is shown a consequence of the past performance of factor returns.

✍ ^New Did Retail Traders Take Over Wall Street? A Tick-by-Tick Analysis of GameStop's Price Surge

with Zhaoque (Chosen) Zhou (current Version: November, 2024).

GameStop’s stock price unprecedentedly surged by over 2800% in January 2021. Unlike previous studies, we utilize tick-by-tick data of both stock and option trades to show that this dramatic price rise was primarily driven by overnight trading and largely fueled by institutional orders rather than retail activity. Our analysis of option trading further provides evidence of a “gamma squeeze”. Theoretically, we extend the Brunnermeier and Pedersen (2005) model to explain several of our key findings. Overall, we conclude that it is because of the institutional backing that retail investors succeeded in driving the stock price bubble.

Presented at CICF 2024.

Economic Trends and Equity Risk Premium

with Yufeng Han and Yueliang (Jacques) Lu (current Version: November, 2024).

In this paper, we demonstrate that economic trends, a tool for guiding monetary policies, also play a significant role in forecasting the equity risk premium. Using a linear forecast combination method with a common set of 14 financial variables, we find that trends captured by moving averages outperform the current values of these variables, both statistically and economically, in predicting market returns. Moreover, the incorporation of machine learning methods, particularly neural networks, significantly enhances out-of-sample forecasts. Our study underscores the importance of economic trends in finance, aligning with the Federal Reserve's practice of prioritizing trends over lagged variables in their forecasting models. By accounting for economic trends and nonlinearity, our study further reveals that market return predictability is much greater than commonly believed, and the results hold not only for U.S. data but also for global equity markets.

✍ ^New Expected Index Option Return: What Can We Learn From Macro and Anomalies

with Heiner Beckmeyer and Guoshi Tong (current Version: Jan., 2024).

We provide the first study on whether the expected returns on stock index option is predictable and how, extending the large literature on the predictability of the stock market in a new direction. We find that the stock index option is predictable by common macroeconomic predictors whose predictive power on options is even stronger than on the underlying. We find also that, although stock market inefficiency, as captures by anomalies, explains the future option returns, option market inefficiency plays a greater role. The economic value of incorporating the option predictability versus ignoring it can be substantial.

Presented at CICF 2024.

Bottom Up vs Top Down: What Does Firm 10-K Tell Us?

with Landon Ross, Jim Horn, Mert Pilanci and Kaihong Luo (current Version: November, 2024).

In contrast to the recent increasing focus on large languages model, we propose a bottom-up approach that exploits the individual predictive power of each word. Our word dictionary is constructed by using a data-driven approach, and it is these selected words that are used to build the predictive model with lasso regularized regressions and large panels of word counts. We find that our approach effectively estimates the cross-section of stocks' expected returns, so that a factor that summarizes the information generates economically and statistically significant returns, and these returns are largely unexplained by standard factor models. However, an inspection of the factor dictionary indicates the element contains many words with possible risk-related interpretations, such as currency, oil, research, and restructuring, which increase a stock's expected return, while the words acquisition, completed, derivatives, and quality decrease the expected return.

No Sparsity in Asset Pricing: Evidence from a Generic Statistical Test

with Junnan He and Lingxiao Zhao (current Version: Feb., 2024).

We provide a generic statistical test to discern whether there is sparsity of in high-dimensional factor models. Applying the test to recent characteristic-based factor models, we find that the null hypothesis of fewer than ten factors capable of explaining the cross-section of stock returns is rejected. Moreover, a dense model representation outperforms sparse models both in pricing in the cross-section and as an investment strategy, which provides an economic explanation for the testing result. Overall, there is no sparsity in asset pricing in the large space of characteristic-based factors.

Useful Factors Are Fewer Than You Think After Accounting for False-Discovery

with Bin Chen and Qiyang Yu (current Version: February, 2024).

We examine how many factors out of a wide range of 207 that have incremental information in explaining cross-sectional stock returns. First, we find that the significance of each factor changes drastically over time. After accounting for false discovery rate (FDR), only 157 out of 207 factors are significant from 1967 to 2021, and only 56 from 2000 to 2021. Second, from 2000 to 2021, we find strikingly that only 3 clusters of factors that have incremental information. We further propose a new flexible time-varying latent factor model, and test in an alternative way on the number of factors that capture the information of the 56 significant factors while controlling for FDR, and find only 3, the market plus 2 latent ones, a number much fewer than widely believed.

Interpretable Factors of Firm Characteristics

with Yuxiao Jiao and Yingzi Zhu (current Version: February, 2024).

We propose a new approach to construct factors from firm characteristics. In contrast to existing studies, each of our factors comes from the same group of statistically related firm characteristics, making its economic interpretation possible. The number of groups is not chosen ad hocly, but rather determined by data. Applying our method to a set of 94 representative firm characteristics, we find that the factors chosen by our approach are not only easy to interpret economically, but they also outperforms typical machine learning models. We also apply our approach to the recent and highly effective IPCA model of Kelly, Pruitt and Su (2019), and find that our factors not only are well linked apparent economic risks, but also can price assets no worse than the standard IPCA model.

Presented at 2024 AFA (poster) and 2024 CICF.

Empirical Asset Pricing with Probability Forecasts

with Songrun He and Linying Lv (current Version: February, 2024).

We study probability forecasts in the context of cross-sectional asset pricing with a large number of firm characteristics. Empirically, we find that a simple probability forecast model can surprisingly perform as well as a sophisticated probability forecast model, and all of which deliver long-short portfolios whose Sharpe ratios are comparable to those of the widely used return forecasts. Moreover, we show that combining probability forecasts with return forecasts yields superior portfolio performance versus using each type of forecast individually, suggesting that probability forecasts provide valuable information beyond return forecasts for our understanding of the cross-section of stock returns.

to be Presented at 2025 AFA.

How Accurate Are Survey Forecasts on the Market?

with Songrun He, Jiaen Li and Linying Lv (current Version: July, 2024).

We provide a novel performance analysis of three widely used survey forecasts along with naive and analysts predictions. We find that none of the popular survey forecasts can predict the stock market out-of-sample, and the surveys are not very informative about investor¡¯s attitudes toward risk. Our study raises important questions on how to safeguard the use of the survey forecasts and how to properly interpret the results from the large literature that relies on them. On the other hand, we show that an naive Bayesian learning and forecasts using analysts¡¯ expectations can outperform the surveys, suggesting that the study on these can potentially be more important and deserve more attention than the study of survey forecasts.

Principal Portfolios: The Multi-Signal Case

with Songrun He and Ming Yuan (current Version: October, 2022).

In this paper, we extend Kelly, Malamud, and Pederson"s (2021) new asset pricing framework to allow incorporating multiple predictive signals into optimal principal portfolios. Empirically, we find that the multi-signal theory is valuable for combining signals, improving a naive combination of single signal principal portfolios.

Anomaly Returns and FOMC

with Lin Tan and Xiaoyan Zhang (current Version: April, 2023).

We find that anomaly returns are generally unchanged during FOMC days. The average return on the long- and short-leg, of a comprehensive set of 207 anomalies, increases by 26.3 bps and 28.8 bps, respectively, prior to the FOMC and reverses back afterwards. But for a small group of anomalies that do have substantial changes, their profitability tends to go down with absolute pricing errors greater than usual. Our evidence challenges existing studies that find the CAPM perform better during the FOMC period. Furthermore, we uncover that the less participation of retail investors contributes to the decline of profitability.

Presented at 2023 CICF.

Information Transmission from Corporate Bonds to the Aggregate Stock Market

with Sophia Zhengzi Li and Peixuan Yuan (current Version: November, 2024).

We provide perhaps the first empirical evidence that the corporate bond market leads the aggregate stock market: the term-structure slope of the bond returns predicts the stock market returns both in- and out-of-sample. This predictability arises from informed bond trading and gradual information diffusion due to market segmentation. Additionally, the bond slope contains valuable information about future firm fundamentals and real economic activity. The lead-lag relationship becomes more prominent when the two markets are less integrated. Moreover, the predictive power extends to stock portfolios sorted by size, value, and industries, particularly those with high credit risk exposures.

Presented at CICF 2024.

Anomalies as New Hedge Fund Factors: A Machine Learning Approach

with Yong Chen, Sophia Zhengzi Li and Yushan Tang (current Version: Feb, 2024).

We identify a parsimonious set of factors from a large set of candidates that can potentially explain hedge fund returns, ranging from equity market factor, anomaly factors, trend-following factors to macroeconomic factors. The resulting nine-factor model, including five anomaly factors, outperforms existing hedge fund models both in-sample and out-of-sample, with a significant reduction in alphas while maintaining substantial cross-sectional performance heterogeneity. Further analysis reveals evidence of strategy shifts by hedge funds over time, making necessary the addition of the anomaly factors. Our results suggest the importance of periodically updating factors for the hedge fund industry.

Unspanned Risk and Risk-Return Tradeoff

with Huacheng Zhang (current Version: Dec, 2023).

A major tenet of modern finance is the risk-return tradeoff, and yet there is a lack of empirical evidence supporting it. We provide an unspanned risk explanation, which, measured as uncertainty beyond financial markets, is well approximated by the macro uncertainty index of Baker, Bloom, and Davis (2016), 90% of which can be attributed to unspanned uncertainty. We find the first out-of-sample evidence that there is a positive risk-return tradeoff after all. In addition, we find that the unspanned risk matters at stock level too: a high-minus-low unspanned risk portfolio can generate an annualized return of 3.5%.

Hide in the Herd: Macroeconomic Uncertainty and Analyst Forecasts Dispersion

with Shen Zhao (current Version: Jan., 2023).

We uncover a negative correlation between macroeconomic uncertainty and security analyst earning forecasts dispersion, and explain it through herding behavior bias of the analysts. We find that the herding firms, whose analysts suffer the herding bias, have greater firm-level uncertainty than non-herding firms. The stock prices of the herding firms have stronger momentum and tend to under-react more to the both firm and macro news. Moreover, the herding firms' stocks are more likely to be overpriced and earn lower subsequent returns. Our study links the interaction between macro-uncertainty and micro-dispersion to the firms' characteristics and our findings support the notion that greater uncertainty leaves more room for psychological biases, which further leads to informational inefficiency.

Betting Against the Crowd: Option Trading and Market Risk Premium

with Jie Cao, Gang Li and Xintong Zhan (current Version: Dec., 2022).

We study how equity option trading affects the market risk premium. We find that a measure of aggregate call order imbalance (ACIB), defined as the cross-sectional average of the difference between open-buy and open-sell volume, negatively forecasts future stock market returns significantly from days to months. Moreover, ACIB represents an option-based investor sentiment measure that accounts for excess option buying or selling, and is highly correlated with the stock investor sentiment. Our findings shed new insights on the distinctions for call and put option trading, index and equity option trading, and cross-sectional and time-series predictions.

Presented at 2023 CICF.

Investor Sentiment and Asset Returns: Actions Speak Louder than Words

with Dat Mai and Kuntara Pukthuanthong (current Version: Nov., 2022).

We analyze the 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.

Commodity Inflation Risk Premium and Stock Market Returns

with Ai Jun Hou, Emmanouil Platanakis and Xiaoxia Ye (current Version: Dec, 2023).

We propose a novel measure of commodity inflation risk premium (cIRP) based on a term structure model of commodity futures. The cIRP, capturing forward-looking information in the futures markets, outperforms well-known characteristics in explaining the cross-section of commodity returns. The associated cIRP factor has the highest Sharpe ratio among the existing factors, and has substantial new information beyond them. Moreover, various aggregations of the individual cIRP predict stock market returns significantly, even after controlling for major economic predictors including the usual inflation measure. The link between commodities and the stock market is stronger than previously thought.

International Corporate Bond Returns: Uncovering Predictability Using Machine Learning

with Delong Li, Lei Lu and Zhen Qi (current Version: November, 2024).

This paper examines cross-sectional predictability of corporate bond returns using a novel international dataset and machine learning techniques. We find significant predictability in both U.S. and non-U.S. markets, with predicting factors differing substantially. Downside risk and illiquidity have a greater influence on corporate bond returns in non-U.S. markets. We further show that corporate bonds in developed economies, compared with those in emerging markets, are more integrated with the U.S. corporate bond market. Developed economies also have a stronger integration between corporate bonds and stocks. These findings shed light on bond pricing and diversification opportunities among international corporate bond markets.

Heterogeneous Responses in Financial Markets: Insights from AI Learning

with Xiaoxiao Tang and Xiwei Tang (current Version: November, 2024).

We propose an AI-based framework that extends the Fama-MacBeth regression to capture individual-level heterogeneity in stock returns, building on the group-level heterogeneity approach introduced by Patton and Weller (2022). Leveraging 15 firm characteristics from Lewellen (2015), our model nearly doubles the Sharpe ratio of long-short portfolios compared to the Fama-MacBeth model. Our model is more interpretable and outperforms other machine learning models, even in high-dimensional settings with 94 characteristics. Our findings emphasize the importance of stock-level heterogeneity, particularly during recessions, challenging state-of-the-art asset pricing models that assume homogeneity.

Presented at CICF 2024.

Asymmetry in Variance: Does It Matter to Stock Returns?

with Xiaoxiao Tang (current Version: December, 2023).

We propose a new measure, AVar, of asymmetry in variance of an asset return. Theoretically, we link the ranking of stocks by AVar based on the physical measure to that based on the risk-neutral measure, enabling us to use forward-looking information from the options market to estimate AVar. Empirically, we find that, in the cross section of stocks, the greater the AVar, the greater the stocks returns. The term structure of AVar also reflects future time variation in stock returns. Economically, we explain the compensation for bearing the asymmetry risk by interpreting AVar, under certain conditions, as a measure that effectively reflects the asymmetry in investors’ utility curvature between losses and gains, thus highlighting investors’ greater disutility from losses compared to equivalent gains.

Labor Flow Shocks Matter for Asset Pricing

with Jian Chen, Chunmian Ge, and Jiaquan Yao (current Version: Feb., 2023).

Using a novel dataset based on individual resumes of public firm employees, we propose a monthly index of labor flows and decompose it into an expected level and unexpected shock component. We find that shocks strongly predict short-term market excess returns, while levels of labor hiring have insignificant forecasting power in the short run. The substantial return predictability of hiring shocks remains out of the sample and delivers sizable economic value in asset allocation. Our findings cannot be explained by existing labor-related predictors and common economic variables. To exploit the underlying economic mechanism, we show that concerns about economic conditions are more likely to influence return predictability.

ESG and the Market Return

with with Liya Chu, Kent Wang and Bohui Zhang (current Version: Dec., 2022).

We propose an environmental, social, and governance (ESG) index. We find that it has significant power in predicting the stock market risk premium, both in- and out-of-sample, and delivers sizable economic gains for mean-variance investors in asset allocation. Although the index is extracted by using the PLS method, its predictability is robust to using alternative machine learning tools. We find further that the aggregate of environmental variables captures short-term forecasting power, while that of social or governance captures long-term. The predictive power of the ESG index stems from both cash flow and discount rate channels.

Presented at CICF 2024.

Does Compensation Matter? Evidence from CD&A Disclosures

with Xiumin Martin and Jie (Jane) Xu (current Version: April, 2021).

We study whether the similarity of firm disclosures on the Compensation Discussion and Analysis (CD&A) has predictability for future stock returns. We find that changes to the language and construction of the CD&As predict firms' future stock returns. A portfolio that longs the CD&A "non-changers" and shorts the "changers" earns a significant Fama-French 5-factor alpha of 5.86% (annualized), for the period of 2008-2020. We further find that companies with low CD&A similarities invest less in R&D, are more likely to be targeted by short-sellers, and have greater forced CEO turnovers. Our results provide new and strong evidence on the role of executive compensation in the cross-section of stock returns.

Lottery Preference and Anomalies

with Lei Jiang, Quan Wen, and Yifeng Zhu (current version: Nov., 2022).

We construct a lottery factor based on 13 commonly used lottery proxies and show that this factor adds significant explanatory power to prominent factor models for anomalies, especially for those in the skewness and value groups. We find that anomaly returns are significantly stronger among stocks with high lottery features and are mainly driven by the short leg of lottery stocks instead of financial distress. We find further that lottery stocks are often associated with low short volume and high shorting fees, indicating that retail investors' preference to hold lottery stocks leads to a low lendable supply of such shares.

Economic Fundamentals and Short-Run Exchange Rate Prediction: A Machine Learning Perspective

with Ilias Filippou, David Rapach and Mark Taylor (current version: Dec., 2023)

We establish the out-of-sample predictability of monthly exchange rates via machine learning techniques based on 70 predictors capturing country characteristics, global variables, and their interactions. To better guard against overfitting in our high-dimensional and noisy data environment, we adjust “off-the-shelf” implementations of machine learning techniques to induce adequate shrinkage. The resulting forecasts consistently outperform the no-change benchmark, which has proven difficult to beat. Variable importance analysis indicates that country characteristics are important for forecasting, once they interact with global variables. Machine learning forecasts also markedly improve the performance of a carry trade portfolio, especially since the Global Financial Crisis.

Presented at Vienna Symposium on Foreign Exchange Markets, 2021, and 5th Workshop in Financial Markets and Nonlinear Dynamics, 2021.

Fundamental Extrapolation and Stock Returns

with Dashan Huang, Huacheng Zhang and Yingzi Zhu (current version: January, 2022).

We propose an economic objective-driven pooling strategy to extrapolate multiple fundamentals simultaneously. This strategy outperforms naive extrapolation strategies that use a single fundamental variable and strategies that use past prices or analyst forecasts, and performs similarly as a machine learning-based pooling strategy. We propose a model to show that fundamental extrapolation has dual price effects: a cash flow effect that pushes stock price up relative to its fundamental value and a discount rate effect that depresses stock price via increasing the expected volatility. Our empirical results suggest that the discount rate effect dominates the cash flow effect.

Presented at AFA 2022 and EFA 2020.

Momentum, Reversal, and the Firm Fundamental Cycle

with Yufeng Han, Zhaodan Huang, and Weidong Tian (current version: April, 2021).

We propose a firm fundamental index (FFI) to summarize the firm’s broad range of business activities, and document that firm fundamentals experience economic cycles similar to business cycles of the economy. We find that FFIs explain well the momentum and long-term reversal: due to repeated positive (negative) shocks in fundamentals, investors continue to raise (lower) prices for winner (loser) firms, yielding momentum; but, due to cyclicality, the shocks decrease in magnitude over time and eventually reverse, generating the reversal pattern. We further show that they also explain an extensive list of momentum/reversal anomalies including return seasonality.

Best Paper Award, The World Finance Conference, 2019

Twin Momentum: Fundamental Trends Matter

with Dashan Huang, and Huacheng Zhang (current version: June, 2021).

Using trends in firm fundamentals, we find that there is a fundamental momentum in the stock market. Buying stocks in the top quintile of fundamental trends and selling stocks in the bottom earns a monthly average return of 0.85% comparable to price momentum. Combining both price and fundamental momentum produces a twin momentum, that earns an average return that exceeds their sum and is difficult to explain by short-sell impediment. Our results not only support the view that fundamental analysis is as important as technical analysis, but also indicate that trends contain incremental information beyond often used lagged fundamental predictors.

Sparse Macro Factors

with David Rapach (current version: January, 2021).

We use machine-learning techniques to estimate sparse principal components (PCs) for 120 monthly macroeconomic variables from the FRED-MD database. Each sparse PC is a sparse linear combination of the underlying macroeconomic variables, whose active weights allow for their economic interpretation. Innovations to the sparse PCs constitute a set of sparse macro factors. Robust tests indicate that sparse macro factors corresponding to yields and housing earn statistically and economically significant risk premia. A three-factor model comprised of the market factor and mimicking portfolio returns for the yields and housing factors performs well compared to leading multifactor models in explaining numerous anomalies.

Best paper award, Inquire UK and Inquire Europe, 2019

Investor Sentiment and the Cross-Section of Corporate Bond Returns

with Xu Guo, Hai Lin and Chunchi Wu (current version: January, 2021).

This paper constructs an investor sentiment measure at both individual bond and aggregate levels, uncovering the first evidence that investor sentiment has strong cross-sectional predictive power for corporate bond returns. High bond investor sentiment leads to low future returns. A portfolio that longs low sentiment bonds and shorts high sentiment ones generates an average monthly return of 0.87% for top-quality bonds and 1.48% for speculative-grade bonds. The results are robust to controlling for risk factors and bond characteristics. The cross-sectional predictability of bond returns is countercyclical, and the predictability appears to stem from its predictive power on macroeconomic conditions.

Presented at 2022 CICF.

An Information Factor: Can Informed Traders Make Abnormal Profits?

with Matthew Ma, Xiumin Martin and Matthew Ringgenberg (current Version: September, 2019).

We construct an information factor (INFO) using the informed stock buying of corporate insiders and the informed selling of short sellers and option traders. INFO strongly predicts future stock returns -- a long-short portfolio formed on INFO earns monthly alphas of 1.24%, substantially outperforming existing strategies including momentum. INFO explains hedge fund returns in the time-series and cross-section. Higher values of INFO are associated with increases in aggregate hedge fund value. Moreover, funds with higher covariation between their returns and INFO outperform by 0.28% per month. The results show information processing skill is an important source of return variation.

Corporate Activities and the Market Risk Premium

with Erik Lie, Bo Meng and Yiming Qian (current version: October, 2017).

While existing asset pricing studies focus on macroeconomic variables to predict stock market risk premium, we find that an aggregate index of corporate activities has substantially greater predictive power both in- and out-of sample, and yields much greater economic gain for a mean-variance investor. The predictive ability of the corporate index stems from its information content about future cash flows. Cross-sectionally, the corporate index performs particularly well for stocks with great information asymmetry.

Sentiment Across Asset Markets

with Dashan Huang, Heikki Lehkonen and Kuntara Pukthuanthong (current version: June, 2018)

In this paper, we study investor sentiment in five major asset markets: stocks, bonds, commodities, currencies, and housing. Based on Thomson Reuter's sentiment measures extracted from 235 news and social media sources, we find that each market is predicted by its own sentiment. Cross-markets, kitchen sink regressions reveal that the stock market is influenced only by bond sentiment, while bond market is affected just by currency market, which is largely unexplained by others; the commodities are related to currencies and housing, and housing can be predicted by stock and bond sentiment. In an efficient information aggregation by the partial least square (PLS), the predictability of each market increases substantially by using information of all markets vs using only its own sentiment.

Cost Behavior and Stock Returns

with Dashan Huang, Fuwei Jiang and Jun Tu (current version: April, 2017).

This paper shows that investors do not fully incorporate cost behavior information into valuation. Firms with higher growth in operating costs generate substantially lower future stock returns. A long-short spread portfolio earns an average return of about 12% per year after controlling for extant risk factors and firm characteristics. Mean-variance spanning tests show that an investor can benefit from investing in this spread portfolio in addition to well-known factors. Firms with high cost growth also suffer from deteriorations in future operating performance. The negative cost growth-return relation is much stronger around earnings announcement days, among firms with lower investor attention, higher idiosyncratic volatility, and higher transaction costs, suggesting that investor underreaction and limits to arbitrage mainly drive the effect.

Taming Momentum Crashes: A Simple Stop-loss Strategy

with Yufeng Han and Yingzi Zhu (current version: August, 2015).

In this paper, we propose a stop-loss strategy to limit the downside risk of the well-known momentum strategy. At a stop-level of 10%, we find, with data from January 1926 to December 2013, that the maximum monthly losses of the equal- and value-weighted momentum strategies go down from -49.79% to -11.36% and from -64.97% to -23.28%, while the Sharpe ratios are more than doubled at the same time. We also provide a general equilibrium model of stop-loss traders and non-stop traders and show that the market price differs from the price in the case of no stop-loss traders by a barrier option.

Which Hedge Fund Styles Hedge Against Bad Times?

with Charles Cao and David Rapach (current Version: February, 2015).

We examine hedge fund style performance in bad versus good times defined as (1) up and down equity market regimes derived from the 200-day moving average of the S&P 500 price index or (2) nonstressed and stressed financial market regimes determined endogenously using the Federal Reserve Bank of Kansas City Financial Stress Index and threshold estimation. We show that hedge fund styles often exhibit significant changes in risk factor exposures across good and bad times. For certain hedge fund styles, changes in factor exposures represent valuable hedges against bad times; in contrast, other hedge fund styles become more exposed to risk factors during bad times in a manner that magnifies downside risk exposure. In the context of “balanced” 40-30-30 portfolios that allocate across U.S. stocks, bonds, and individual hedge fund styles, we find that the Global Macro, Managed Futures, and Multi-Strategy styles provide investors with especially valuable hedges against bad times.

Forecasting Bond Risk Premia Using Technical Indicators

with Jeremy Goh, Fuwei Jiang, and Jun Tu (current Version: July, 2013).

While economic variables have been used extensively to forecast the U.S. bond risk premia, little attention has been paid to the use of technical indicators which are widely employed by practitioners. In this paper, we fill this gap by studying the predictive ability of using a variety of technical indicators vis-a-vis the economic variables. We find that the technical indicators have statistically and economically significant in- and out-of-sample forecasting power. Moreover, we find that utilizing information from both technical indicators and economic variables substantially increases the forecasting performances relative to using just economic variables.

Forecasting Stock Returns During Good and Bad Times

with Dashan Huang, Fuwei Jiang and Jun Tu (current version: May, 2015).

We show that stock returns can be significantly predicted by past realized returns in both good and bad times, in and out of sample. We extend the model in Fama and French (1988) to show that stock returns display mean reversion and momentum over time, which is dependent on the market state. Specifically, past stock returns predict future returns negatively in good times and positively in bad times, which is consistent consistent with the change and level effects in P´astor and Stambaugh (2009).

Hansen-Jagannathan Distance: Geometry and Exact Distribution

with Raymond Kan; November, 2002.

This paper provides an in-depth analysis of the Hansen-Jagannathan (HJ) distance, which is a measure that is widely used for diagnosis of asset pricing models, and also as a tool for model selection. In the mean and standard deviation space of portfolio returns, we provide a geometric interpretation of the HJ-distance. In relation to the traditional regression approach of testing asset pricing models, we show that the HJ-distance is a scaled version of the aggregate pricing errors, and it is closely related to Shanken's (1985) cross-sectional regression test (CSRT) statistic, with the only major difference in how the zero-beta rate is estimated. For the statistical properties, we provide the exact distribution of the sample HJ-distance and also a simple numerical procedure for computing its distribution function. In addition, we propose a new test of equality of HJ-distance for two nested models. Simulation evidence shows that the asymptotic distribution for sample HJ-distance is grossly inappropriate for typical number of test assets and time series observations, making the small sample analysis empirically relevant.

Toward a Better Understanding of the Beta Method and the Stochastic Discount Factor Method

with Raymond Kan; May, 2002.

In a standardized factor model, Kan and Zhou (1999) show the stochastic discount factor (SDF) method yields less efficient estimates than the beta method when both are based on the generalized method of moments (GMM). By modifying the common use of the SDF [via adding more moment conditions to the practice before the publication of Kan and Zhou (1999)], Jagannathan and Wang (2001) and Cochrane (2000a,b) find that the two methods have the same asymptotic variance for the new GMM estimator (which no longer admits analytical solution). Moreover, their analysis relies on a joint normality assumption of both the asset returns and factors. In this paper, we show that: 1) once the normality assumption is relaxed, the modified SDF method is highly sensitive to factor skewness and kurtosis whereas the beta method is not, implying that the SDF estimates can be less reliable in realistic situations where the factors are leptokurtic; 2) in conditional asset pricing models, the modified SDF is in general still strictly dominated by the beta method in terms of estimation accuracy; 3) while it is not well understood and almost never used in the SDF formulation of an asset pricing model, the maximum likelihood method is well defined and has both strictly more efficient estimates and more powerful tests than the SDF method; 4) the SDF tests can have much less power than the beta method in conditional asset pricing models. In short, while the SDF set-up is an elegant theoretical formation, empirical estimation and tests should pay as much attention to the beta method as to the SDF if not more (one more reason is that, as shown by Jagannathan and Wang (2001), estimated model pricing errors have smaller variance by using the beta method than the SDF one).