Analysts’ Momentum Recommendations

We document that the likelihood of analyst recommendations following past stock returns decreased abruptly in 2003, coinciding with the Global Settlement and other regulatory changes designed to restrain analysts’ conflicts of interest. We also document that the likelihood of recommendations following past stock returns is abnormally high for recommendations issued after negative stock returns (but not for those issued after positive stock returns), among inexperienced and inaccurate analysts, among large brokerage houses, and for companies with high share turnover. Moreover, the recommendations that are more likely to follow past stock returns are accompanied by earnings forecast revisions that are larger in magnitude and less accurate ex post. Overall, our findings suggest that analysts with conflicts of interest and limited ability are more likely to base their recommendations on past stock returns. Finally, we document that the recommendations that are more likely to follow past stock returns (especially those that were issued before 2003 and those that are issued after negative stock returns) contribute to existing price momentum by generating incrementally stronger short‐term and long‐term stock returns.     

Non‐operating earnings and firm risk*

We find that non‐operating earnings reduce total earnings volatility, stock price volatility, idiosyncratic risk, and crash risk. The risk‐reducing effects of non‐operating earnings are higher than those of operating earnings for risk measures based on stock market data. Non‐operating earnings serve to mitigate risks among firms with operating losses, high financial leverage, high growth uncertainty, and low‐ability managers.     

Information misweighting and the cross-section of stock recommendations

This paper provides evidence that analysts whose earnings forecast revisions showed signs of greater exaggeration in the past make recommendation changes that lead to lower abnormal returns than their peers. Interpreting stock recommendations as a forecast of future abnormal returns, I show that this evidence is consistent with the hypothesis that analysts who typically exaggerate or overstate the weight of their private information when issuing forecasts also do so when making recommendations. The paper also shows that past earnings forecasts provide incremental information about analysts' recommending behavior beyond that contained in past recommendations.     

Earnings seasonality,management earnings forecasts and stock returns

We examine whether management earnings forecasts (MEFs) help reduce the stock return seasonality associated with earnings seasonality around earnings announcements (EAs) in Chinese A-share markets. We find that firms in historically low earnings seasons outperform firms in high earnings seasons by 2.1% around MEFs. Firms in low earnings seasons also have higher trading volume and return volatility than their counterparts around EAs and MEFs. MEFs significantly reduce the ability of historical seasonal earnings rankings to negatively predict announcement returns, volume and volatility around EAs. The reduction effects are stronger when MEFs are voluntary or made closer to EAs. The evidence suggests that MEFs facilitate the correction of investors’ tendency to extrapolate earnings seasonality and its resulted stock mispricing.     

Regime shift,speculation, and stock price

We investigate the impacts of speculation on stock price and return volatility in a framework with regime shifting. We find that greater difference in beliefs about the probability of bad state leads to higher stock prices. The intuition is that in periods of higher dispersion of beliefs, the investors perceive greater speculative opportunities, leading to increased demands and valuations of the stock. When investors agree with each other on the state of dividend growth, they have a stronger incentive to invest in the riskless bond, when becoming more pessimistic about the dividend growth. As a result, the demand and the valuations of stock decrease. Moreover, higher level of heterogeneity in beliefs gives rise to higher volatility of the stock returns, even in the absence of dividend shocks. Furthermore, with homogeneous beliefs, return volatility with respect to investors’ beliefs follows an inverted-U shape.     

R&D, knowledge spillovers and stock volatility

Firms improve their know‐how not only by innovations (producing new knowledge), but also by knowledge spillovers (learning from others). The objective of this study is to test for two major hypotheses developed from a theoretical model explaining the relationship between R&D, knowledge spillovers and stock volatility. Analytically, the model suggests that asymmetric information caused by R&D activities with uncertain future output increases stock volatility, even though dividends and consumptions remain unchanged. However, interfirm knowledge spillovers have a negative impact on stock volatility by reducing the degree of asymmetric information. Both hypotheses are supported by empirical evidence from this study.     

Jump risk,stock returns,and slope of implied volatility smile

In the presence of jump risk, expected stock return is a function of the average jump size, which can be proxied by the slope of option implied volatility smile. This implies a negative predictive relation between the slope of implied volatility smile and stock return. For more than four thousand stocks ranked by slope during 1996–2005, the difference between the risk-adjusted average returns of the lowest and highest quintile portfolios is 1.9% per month. Although both the systematic and idiosyncratic components of slope are priced, the idiosyncratic component dominates the systematic component in explaining the return predictability of slope. The findings are robust after controlling for stock characteristics such as size, book-to-market, leverage, volatility, skewness, and volume. Furthermore, the results cannot be explained by alternative measures of steepness of implied volatility smile in previous studies.     

R&D Progress, stock price volatility, and post-announcement drift: An empirical investigation into biotech firms

I investigate the effects of R&D progress on the dynamics of stock price volatility and the post announcement drift to provide insights into whether or not and how capital markets react to corporate R&D progress in the context of the biotech industry. I find both stock price volatility and the post announcement drift decrease in R&D progress. More importantly, the decrease is proportional to the increase in the drug development success rate driven by R&D progress. Findings suggest that R&D progress conveys useful risk-relevant information, and plays an important role in explaining stock price volatility change and market anomalies.     

Stock investment strategy combining earnings power index and machine learning

We propose an intermediate-term stock investment strategy based on fundamental analysis and machine learning. The approach uses predictors from the Earnings Power Index (EPI) as input variables derived from cross-sectional and time-series data from a company’s financial statements. The analytical methods of machine learning allow us to validate the link between financial factors and excess returns directly. We then select stocks for which returns are likely to increase at the time of the next disclosed financial statement. To verify the proposed approach’s usefulness, we use company data listed publicly on the Korean stock market from 2013 to 2019. We examine the profitability of trading strategy based on ten machine-learning techniques by forming long, short, and hedge portfolios with three different measures. As a result, most portfolios, including EPI-related variables, present positive returns regardless of the period. Especially, the neural network of the two layers with sigmoid function presents the best performance for the period of 3 months and 6 months, respectively. Our results show that incorporating machine learning is useful for mid-term stock investment. Further research into the possible convergence of financial statement analysis and machine-learning techniques is warranted.     

Institutional cross-ownership and stock price crash risk

Using manually collected data of Chinese listed firms during the period 2007–2018, we provide strong and robust evidence that institutional cross-ownership is negatively associated with firm-specific stock price crash risk. Building on China’s institutional settings, we document that the negative relation is more pronounced for firms located in provinces with higher political uncertainty, or state-owned enterprises. This paper also conducts several mechanisms analyses and has confirmed three potential influencing mechanisms, such as information advantage, governance improvement and anticompetitive incentives, in explaining the effect of institutional cross-ownership on stock price crash risk. Overall, this paper develops a new perspective to investigate the ways to alleviate stock price crash risk in emerging markets.     

Managerial ability,investment efficiency and stock price crash risk

We examine empirically the effect of managerial ability on firm-level investment efficiency and how this affects future stock price crash risk. Using a managerial ability measure developed by Demerjian et al. (2012), the paper documents consistent evidence that the more able managers over-invest compared to their not-so-able counterparts, even after controlling for the effects of financial reporting quality and other firm specific determinants of investment efficiency. This evidence is robust to alternative proxies for investment efficiency. The empirical evidence also suggests that crash risk increases for firms with more able managers, primarily through the investment inefficiency channel. Overall, the study contributes to a better understanding of the influence of managerial ability on investment decisions in the context of diverging opinions regarding manager-specific effects on organizational outcomes.     

Terrorism,country attributes,and the volatility of stock returns

This study investigates the interplay between terrorism and finance, focusing on the stock return volatility of American firms targeted by terrorist attacks. We find terrorism risk is an important factor in explaining the volatility of stock returns, which should be taken into account when modelling volatility. Using a volatility event-study approach and a new bootstrapping technique, we find volatility increases on the day of the attack and remain significant for at least fifteen days following the day of the attack. Cross-sectional analysis of the abnormal volatility indicates that the impact of terrorist attacks differs according to the country characteristics in which the incident occurred. We find that firms operating in wealthier, or more democratic countries, face greater volatility in stock returns relative to firms operating in developing countries. Firm exposure varies with the nature of country location, with country wealth and level of democracy playing an important role in explaining the likelihood of a terrorist attack. Our results show that despite significant terrorist events this past decade, stock markets in developed countries have not taken terrorist risk into sufficient consideration.     

Option prices and stock market momentum: evidence from China

Option prices tend to be correlated to past stock market returns due to market imperfections. We unprecedentedly examine this issue on the SSE 50 ETF option in the Chinese derivatives market. To measure the price pressure in the options market, we construct an implied volatility spread based on pairs of the SSE 50 ETF option with identical expiration dates and strike prices. By regressing the implied volatility spread on past stock returns, we find that past stock returns exert a strong influence on the pricing of index options. Specifically, we find that SSE 50 ETF calls are significantly overvalued relative to SSE 50 ETF puts after stock price increases and the reverse is also true after the stock price decreases. Moreover, we validate the momentum effects in the underlying stock market to be responsible for the price pressure. These findings are both economically and statistically significant and have important implications.     

Earnings volatility,ambiguity, and crisis-period stock returns

Financial crises are marked by substantial increases in ambiguity where prices appear to decouple from fundamentals. Consistent with ambiguity-based asset pricing theories, we find that ambiguity concerns are more severe for firms with higher earnings volatility, causing investors to demand a higher ambiguity premium for such firms. While there is no relation between earnings volatility and stock returns under normal conditions, there is a significant negative relation between crisis-period stock returns and prior earnings volatility. The effect is stronger in firms with low institutional ownership and low analyst following, consistent with ambiguity concerns being greatest amongst firms with unsophisticated investors.     

Local speculative culture and stock price crash risk

Using unique city gambling conviction data in China as a proxy for a local speculative culture, we examine the impact of such a culture on stock price crash risk. We find that firms in regions with a stronger speculative culture are more likely to experience future stock price crash risk. The results are consistent after using 2SLS regression analysis (IV) and staggered difference-in-difference (DID) analysis to mitigate endogeneity concerns. Further analysis shows that overinvestment, excessive debt, accounting conservatism and charitable donations are the main channels through which local speculative culture affects stock price crash risk. We also find that the positive relationship between local speculative culture and stock price crash risk is more salient for small firms and firms with managers with a cultural backgrounds similar to the local culture. Our study implies that the local culture plays an important role in the practice of corporate governance.     

Board diversity and stock price crash risk

We show how board diversity influences stock price crash risk. By classifying board diversity into relation-oriented diversity (gender and age) and task-oriented diversity (tenure and education), we find that greater diversity on board can lower the risk of future stock crash. Additional analyses show that the effect of board diversity on future crash risk is stronger for firms with high information opacity and low institutional ownership. Overall, our findings provide new insights and suggest for more diverse boards to improve corporate governance practices.     

Disentangling Managers’ and Analysts’ Non‐GAAP Reporting

Researchers frequently proxy for managers’ non‐GAAP disclosures using performance metrics available through analyst forecast data providers (FDPs), such as I/B/E/S. The extent to which FDP‐provided earnings are a valid proxy for managers’ non‐GAAP reporting, however, has been debated extensively. We explore this important question by creating the first large‐sample data set of managers’ non‐GAAP earnings disclosures, which we directly compare to I/B/E/S data. Although we find a substantial overlap between the two data sets, we also find that they differ in systematic ways because I/B/E/S (1) excludes managers’ lower quality non‐GAAP numbers and (2) sometimes provides higher quality non‐GAAP measures that managers do not explicitly disclose. Our results indicate that using I/B/E/S to identify managers’ non‐GAAP disclosures significantly underestimates the aggressiveness of their reporting choices. We encourage researchers interested in managers’ non‐GAAP reporting to use our newly available data set of manager‐disclosed non‐GAAP metrics because it more accurately captures managers’ reporting choices.     

Operating cash flow opacity and stock price crash risk

We examine the relation between operating cash flow (OCF) opacity and stock price crash risk. We find that OCF opacity is positively associated with future stock price crash risk after controlling for accruals opacity and other determinants known to influence crash risk. This finding suggests that OCF opacity facilitates bad news hoarding and enables managerial resource diversion, which in turn increases crash risk. We also find that the positive relation between OCF opacity and crash risk is more pronounced when external monitoring is weak, information asymmetry is high, OCF importance is low, and cost of accruals management is high. Overall, our evidence highlights the severe consequence of OCF opacity in that it boosts crash risk; our study should alert the researchers, investors, and regulators to pay more attention to OCF management.     

Exploring the predictability of range‐based volatility estimators using recurrent neural networks

We investigate the predictability of several range‐based stock volatility estimates and compare them with the standard close‐to‐close estimate, which is most commonly acknowledged as the volatility. The patterns of volatility changes are analysed using long short‐term memory recurrent neural networks, which are a state‐of‐the‐art method of sequence learning. We implement the analysis on all current constituents of the Dow Jones Industrial Average index and report averaged evaluation results. We find that the direction of changes in the values of range‐based estimates are more predictable than that of the estimate from daily closing values only.     

Systematic risk, and oil price and exchange rate sensitivities in Asia-Pacific stock markets

This paper examines the relationship between beta risk and realized stock index return in the presence of oil and exchange rate sensitivities for 15 countries in the Asia-Pacific region using the international factor model. Thirteen of the 15 countries have the expected beta signs and show significant sensitivity to domestic risk when the world stock market is in both up and down modes. In terms of oil sensitivity, only the Philippines and South Korea are oil-sensitive to changes in the oil price in the short run, when the price is expressed in local currency only. Basically no country shows sensitivity to oil price measured in US dollar regardless whether the oil market is up or down. Nine countries are affected by changes in the exchange rate. In terms of relative factor sensitivity distribution, one is willing to conclude that these stock markets are more conditionally sensitive to local currency oil price changes than to beta risk wherever the relationships are significant.