Working papers      


New Risk Momentum: A New Class of Price Patterns

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

We uncover a new class of price pattern in terms of risk: the risk component, the component of stock returns explained by common factors, exhibits strong momentum intraday. Moreover, this risk momentum implies a return momentum: the long-short portfolio of stocks sorted by past risk exhibits a momentum in return, demonstrating that high systematic risk implies high return even intraday. In comparison with the extremely popular and extensively studied Jegadeesh and Titman (1993) monthly return momentum, our risk momentum generates a new return momentum that is stronger, holds across frequencies and asset classes, and has no crash risk.

Presented at MFA 2023, and SFS Cavalcade North America 2023 (to be presented)



New ETFs, Anomalies and Market Efficiency

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

We investigate the effect of ETF ownership on stock market anomalies and market efficiency. We find that low ETF ownership stocks exhibit higher returns, greater Sharpe ratios, and highly significant alphas in comparison to high ETF ownership stocks. We show that high ETF ownership stocks demonstrate more pronounced information flows than low ETF ownership stocks which reduces their mispricing as they are more informationally efficient. We find similar results when we match the two groups based on size, volume, book-to-market and momentum. Our results are robust to different matching methods and to a wide array of controls in Fama-MacBeth regressions.

SGF 2023 and WFA 2023 (to be presented)



New How Accurate Are Survey Forecasts on the Market?

with Songrun He and Jiaen Li (current Version: March, 2023).

In this paper, we provide a novel performance analysis of three widely used survey forecasts, by investigating how well they predict the stock market. Judging by their out-of-sample R-squares, a standard measure in the market predictability literature, we find that none of the survey forecasts outperforms a simple random walk forecast!, which predicts the future returns simply by their past sample mean. Our study raises important questions on how to well-guard the use of the survey forecasts and how to properly interpret the results from the large literature that rely on the survey forecasts. On the other hand, we find that the stock market short interest, which are profit-oriented and are backed with money, performs well in predicting the market, beating the random walk significantly. Our study suggests that the short interest is by far a much better measure of investor subjective beliefs than any other measures used today.



New Macroeconomic Trends and Equity Risk Premium

with Yufeng Han and Yueliang (Jacques) Lu (current Version: March, 2023).

We present the first evidence on how macroeconomic trends affect equity risk premium, going beyond the literature that rely on only the most recent values. Our results show that macro trends are important, and they contribute statistically and economically to the out-of-sample aggregate market return predictability. Moreover, we present novel evidence that nonlinearity matters in market return predictability by combining macro trends with neural networks, yielding an out-of-sample R2 statistic as high as 1.6%. We find that pooling time-series trends helps to track more closely the important macroeconomic fluctuations and to regulate more effectively the forecast variability, thereby generating superior and robust forecasting gains consistently over time.



New 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.



New Risk-based Momentum of Corporate Bonds

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

In this paper, we uncover the first momentum pattern of corporate bonds. In contrast to the popular stock momentum, originated by Jegadeesh and Titman (1993) but unextendable to bonds, our momentum is based on the risk components of the bonds rather than past returns. We find that bonds with high risks persistently earn greater average returns in the future than those with lower risks over time. Both bond and stock characteristics play a role in determining bond risk, and the risk itself possesses a momentum pattern too: high risk this month tends to predict high risk next month. The bond return momentum is driven by the risk momentum. Moreover, the average returns on the risk-based bond return momentum is about the same magnitude of the traditional stock momentum, yet it has no crash risk.



New Corporate Bond Moments and Predictability of Equity Returns

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

We document that the first and third cross-sectional moments of corporate bond returns significantly and positively predict future stock market returns both in- and out-of-sample. The predictability emerges from informed bond trading and gradual diffusion of information. Particularly, the moments contain information about future aggregate firm fundamentals and real economic activity. The lead-lag effect is more pronounced when the lack of integration between the two markets is more severe. Moreover, the predictive power extends to various size- and value-sorted portfolios and industries.



New Earnings Announcements: Ex-ante Risk Premia

with Hong Liu, Yingdong Mao and Xiaoxiao Tang (current Version: Jan., 2023).

In this paper, we provide an estimate of the ex-ante risk premia on earnings announcements based on the option market. We find that the risk premia are time-varying and have predictive power on future stock returns. The well-documented positive post-earnings-announcement drift (PEAD) is present only when the risk premia are high. After controlling for the announcement risk premia, the PEAD factor of the literature no longer has any abnormal returns. Moreover, while trading option straddles is not profitable in general, its performance improves substantially during announcements with high risk.



New Anomalies as New Hedge Fund Factors: A Machine Learning Approach

with Yong Chen, Sophia Zhengzi Li and Yushan Tang (current Version: Jan., 2023).

We identify nine factors out of a large set of anomalies and macroeconomic factors for explaining hedge fund returns by using machine-learning methods. The new factor model outperforms existing models both in-sample and out-of-sample. Moreover, the model leads to a significant reduction in hedge fund alphas compared with other models, while revealing substantial cross-sectional performance heterogeneity. Further subsample analysis provides evidence of style shifting in the hedge fund industry. Overall, the proposed factors quantify well strategies and risk exposures of hedge funds and can be used for fund performance evaluation.



New Unspanned Risk and Risk-Return Tradeoff

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

We show that the conditional risk estimation in the ICAPM model (Merton, 1973) should contain the unspanned uncertainty beyond stock market if the interest rate is not sufficient to describe the dynamic investment state. Borrowing an aggregated uncertainty measure that captures unspanned uncertainty beyond financial markets from Baker, Bloom, and Davis (2016), we detect a significant risk-return tradeoff in both aggregated market and stock cross-section, in both short and long run, and both in and out of sample. We find that about 80% of this tradeoff can be attributed to unspanned uncertainty. A zero-investment portfolio buying stocks in the top unspanned risk decile and selling stocks in the bottom decile can generate a Fama-French-Carhart alpha of 0.61% in the subsequent month and 8.4% in the next 12 months. Further analysis suggests that the unspanned uncertainty comes mainly from economic policy, fiscal policy, healthcare, government-entitled programs, national security, and non-financial regulations. This significant unspanned uncertainty-return tradeoff exists in seven Europe markets as well. Overall, our study suggests that uncertainty unspanned by capital market risks plays a key role in the positive risk–return tradeoff relationship.



New 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.



New Market Risk Premium Expectation: Combining Option Theory with Traditional Predictors

with Hong Liu, Yueliang (Jacques) Lu and Weike Xu (current Version: Jan., 2023).

The market risk premium is central in finance, and has been analyzed by numerous studies in the time-series predictability literature and by growing studies in the options literature. In this paper, we provide a novel link between the two literatures. Theoretically, we derive a lower bound on the market risk premium in terms of option prices and state variables. Empirically, we show that combining information from both options and investor sentiment significantly improves the out-of-sample predictability of the market risk premium versus using either type of information alone.



New 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.



New 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.



New Inflation Risk Premium for Commodity and Stock Market Returns

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

We propose a novel measure of the ex-ante inflation risk premium (IRP) for each commodity based on a term structure model of commodity futures. Our theory-based IRP, capturing forward-looking information in the futures markets, outperforms well-known characteristics in explaining the cross-section of commodity returns. The IRP factor – the low minus high portfolio constructed from sorting IRP – has the highest Sharpe ratio among existing factors, and none of the latter can explain it, implying it has substantial new information. Moreover, various aggregations of individual commodity IRP predict future stock market returns significantly, even after controlling for major economic predictors. The link between commodities and the stock market is stronger than previously thought.



New International Corporate Bond Market: Uncovering Risks Using Machine Learning

with Delong Li, Lei Lu and Zhen Qi (current Version: June, 2022).

In this paper, we explore what factors drive expected corporate bond returns all over the world. With a novel dataset, and utilizing machine learning models, we find there is strong predictability of corporate bond returns in international markets. However, the documented factors that drive bonds in the U.S. and non-U.S. developed markets are substantially different from factors that impact bonds in the emerging markets, where inflation, downside risk, duration, illiquidity, and volatility are more influential. Moreover, U.S.-based equity and bond factors do not contribute predictive power to non-U.S. corporate bonds, indicating that international corporate bond markets are not well integrated.



New Asset Pricing: Cross-section Predictability

with Paolo Zaffaroni (current Version: May, 2022).

We provide a selected review of the vast literature on cross-section predictability. We focus on the state of art methods used to forecast the cross-section of stock returns with man predictors and are primarily interested in the ideas, methods, and their applications. To understand the cross-section predictability, we also provide a review of factor models, which shed light on whether the predictability is due to mispricing or risk exposure.



New Heterogeneous Response: An Extension of the Fama-MacBeth Regression

with Xiaoxiao Tang and Xiwei Tang (current Version: January, 2022).

We propose an extension of the Fama-MacBeth regression by allowing stock returns to respond differently to firm characteristics. This heterogeneous model can potentially capture non-linearity and interactions. Empirical, applying it to a common set of fifteen firm characteristics, we find that the value-weighted long-short portfolio has an annualized Sharpe ratio of 0.97, doubling that of the usual homogenous model, and our model is not only easier to understand, but also performs better than existing machine learning models. We also propose a test detecting which risk exhibits heterogeneous reactions, and find that heterogeneity is significant for firm size, momentum, and stock volatility. Furthermore, we find that heterogeneous reactions are more pronounced during recession periods.



New Labor Flow Shocks Matter for Asset Pricing

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

(On-line Appendix)

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.



New Why Naive 1/N Diversification Is Not So Naive, and How to Beat It?

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

In this paper, we explain theoretically why the 1/N rule is very difficult to beat. we show that the usual estimated investment strategies are biased even asymptotically when the dimensionality is high relative to sample size, and the 1/N rule is optimal in a one-factor model with diversifiable risks as dimensionality increases, irrespective of the sample size. Moreover, we explore conditions under which the 1/N can be beaten. We find that combining the 1/N with the popular estimated rules can improve its performance when N is small, and combining it with anomaly or machine learning portfolios can also improve its performance when N is large.

Presented at 2022 CICF (China International Conference in Finance)



New 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.



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.



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

with Xiahu Deng, Lei Gao and Bo Hu (current Version: June, 2022).

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 2022 CICF (China International Conference in Finance)



Maximizing the Sharpe Ratio: A Genetic Programming Approach

with Yang Liu and Yingzi Zhu (current Version: June, 2022).

(On-line Appendix)

While common machine learning algorithms focus on minimizing the mean-square errors of model fit, we show that genetic programming, GP, is well-suited to maximize an economic objective, the Sharpe ratio of the usual spread portfolio in the cross-section of expected stock returns. In contrast to popular regression-based learning tools and the neural network, GP can double their performance in the US, and outperform them internationally. We find that, while the economic objective plays a role, GP captures nonlinearity in comparison with methods like the Lasso, and it requires smaller sample size than the neural network.

Presented at 2021 CICF (China International Conference in Finance)



Option Characteristics as Cross-Sectional Predictors

with Andreas Neuhierl, Xiaoxiao Tang and Rasmus Tangsgaard Varneskov (current Version: June, 2022).

We provide the first comprehensive analysis of option information for pricing the cross-section of stock returns by jointly examining extensive sets of firm and option characteristics. Using portfolio sorts and high-dimensional methods, we show that certain option measures have significant predictive power, even after controlling for firm characteristics, earning a Fama-French three-factor alpha in excess of 20% per annum. Our analysis further reveals that the strongest option characteristics are associated with information about asset mispricing and future tail return realizations. Our findings are consistent with models of informed trading and limits to arbitrage.

Presented at 2023 AFA in New Orleans



Mispricing and Market Efficiency: An Exogenous Shock to Short Selling from the Dividend Tax Law Change

with Yufeng Han, Yueliang (Jacques) Lu and Weike Xu (current Version: July, 2022).

We study the causal effect of short-sale constraints on market efficiency by examining an extensive set of 182 anomalies documented in the accounting, finance and economics literature. Our identification strategy relies on a persistent, robust and plausibly exogenous shock to short-selling supply induced by the dividend tax law change in the Job and Growth Tax Relief Reconciliation Act (JGTRRA) of 2003. We find that overpricing as opposed to underpricing becomes stronger following the dividend record months in the post-JGTRRA periods. While the shock magnifies returns to most anomaly types, we find that valuation anomalies seem unlikely to be driven by mispricing. Collectively, our results highlight the importance of tax policies on market efficiency.

Presented at 2021 SFS Cavalcade North America, 2021 AFA, and 2022 CICF (China International Conference in Finance)



Expected Stock Returns in the Cross-section: An Ensemble Approach

with Yufeng Han, Ai He and David Rapach (current Version: August, 2022).

We develop new methods for constructing and analyzing cross-sectional stock return forecasts. We propose an E-ENet approach that blends the elastic net, forecast combination, and forecast encompassing to implement shrinkage in a flexible manner designed to handle a large number of firm characteristics. We provide a cross-sectional out-of-sample R-squared statistic for assessing the accuracy of cross-sectional forecasts. Empirically, with presently the largest set of 193 firm characteristics applied in cross-sectional return forecasting, we find that our E-ENet forecast produces significant cross-sectional out-of-sample R-squared gains and generates substantial economic value consistently over time. We further find that many firm characteristics matter, instead of a dozen or so.

Presented at 2019 AFA in Atlanta



Pricing Error Reversal: A Diagnosis of Asset Pricing Models

with Ai He, Songrun He and Dashan Huang (current Version: January, 2023).

Based on the intuition that pricing errors from an asset pricing model cannot generate any tradable profits if the model is true, we propose an economic test that is applicable to almost any model. We apply it to six classic factor models and to sophisticated ones recently developed with machine learning. We find that all the models are rejected and their pricing errors share similar patterns, which lead to significant trading profits and cannot be explained by investor sentiment, limits-to-arbitrage, prospect theory, and expectation extrapolation. Our findings suggest that there is still a long way to go toward modeling well the cross section of stock returns.

Presented at 2020 AFA in San Diego; Winner of 2019 Chicago Quantitative Alliance Conference: 3rd Prize.



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.



Out-of-Sample Exchange Rate Prediction: A Machine Learning Perspective

with Ilias Filippou, David Rapach and Mark Taylor (current version: April, 2022)


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



Trend Factor in China: The Role of Large Individual Trading

with Yang Liu and Yingzi Zhu (current Version: June, 2022).


Because up to 80% of trading volume is driven by individual investors, price and volume trends play an important role in the Chinese stock market, which is the second largest in the world. In this paper, we propose a trend factor based on both price and volume information cross horizons, capturing price momentum and trading liquidity. We find that the volume contributes substantially to the trend factor in China than in the US, due to large market participation of retail investors. In terms of factor investing, the trend factor enhances the opportunity set substantially. Augmenting the 3-factor of Liu, Stambaugh, and Yuan (2019), we propose a 4-factor model– market, size, value and trend –for the Chinese stock market. We find that the 4-factor model explains well a number of stylized facts and a set of 59 representative anomalies of the Chinese stock market. Moreover, the trend factor, as a composite of both price and volume trends, performs well too in the global markets.

Presented at 2019 WRDS, 2020 FMA and 2021 AFA (poster session)



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).

(Factor Data: Mkt, Yield and Housing)

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 (China International Conference in Finance)



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).


The End