Download or read book Analyst Forecast Skewness and Cross Section Stock Returns written by Cai Zhu and published by . This book was released on 2015 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the paper, we show a significant economic linkage between analyst EPS forecast skewness and cross section stock returns. The effect on stock return of our skewness measure is quite different from that based on skewness calculated from options or high frequency data. Literature shows that, using such skewness as a signal, trading profit is generated mostly from over-valued stocks with high positive skewness, which is consistent with Barberis and Huang (2008)'s lottery arguments. However, we find that for our analyst forecast skewness, trading profit mainly comes from those stocks with negative skewness. Long-short strategy purchasing stocks with low forecast skewness and shorting those with high forecast skewness earns annualized abnormal returns 11% with sharpe ratio 0.64. Our study suggests that negative skewness stocks tend to be undervalued (risk-adjusted returns for negative skewness stocks are significantly positive), while stocks with high positive skewness have fair prices (risk-adjusted returns for positive skewness stocks are not significant). Our empirical results are closely related with investors learning behavior and consistent with Veronesi (1999) theory. In the model, Veronesi shows that when investors cannot observe cash flow growth rate, they tend to overreact to bad news, push current stock price down, such behavior will lead to higher future stock returns. Our results also hold when using robust skewness defined as the gap between analyst EPS forecast mean and median.
Download or read book Option Implied Volatility Skewness and Kurtosis and the Cross Section of Expected Stock Returns written by Turan G. Bali and published by . This book was released on 2019 with total page 67 pages. Available in PDF, EPUB and Kindle. Book excerpt: We develop an ex-ante measure of expected stock returns based on analyst price targets. We then show that ex-ante measures of volatility, skewness, and kurtosis implied from stock option prices are positively related to the cross section of ex-ante expected stock returns. While expected returns are related to both the systematic and unsystematic components of volatility, only the unsystematic components of skewness and kurtosis are related to the cross section of expected stock returns after controlling for other variables known to be related to the cross section of expected stock returns or analyst forecast bias.
Download or read book Skewness and Dispersion of Opinion and the Cross Section of Stock Returns written by Jinghan Meng and published by . This book was released on 2015 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt: We show that the degree of dispersion and asymmetry of analysts' earnings forecasts is related to future stock returns. When skewness is negative, future returns are decreasing in the degree of dispersion of analysts' earnings forecasts; when skewness is positive, future returns are increasing in the degree of dispersion of analysts earnings forecasts. We develop a model that incorporates dispersion and asymmetry in agents' beliefs that can account for these empirical facts.
Download or read book Forecasting Crashes written by Joseph Chen and published by . This book was released on 2000 with total page 66 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper is an investigation into the determinants of asymmetries in stock returns. We develop a series of cross-sectional regression specifications which attempt to forecast skewness in the daily returns of individual stocks. Negative skewness is most pronounced in stocks that have experienced: 1) an increase in trading volume relative to trend over the prior six months; and 2) positive returns over the prior thirty-six months. The first finding is consistent with the model of Hong and Stein (1999), which predicts that negative asymmetries are more likely to occur when there are large differences of opinion among investors. The latter finding fits with a number of theories, most notably Blanchard and Watson's (1982) rendition of stock-price bubbles. Analogous results also obtain when we attempt to forecast the skewness of the aggregate stock market, though our statistical power in this case is limited.
Download or read book Analyst Forecasts and the Cross Section of European Stock Returns written by Steven K. Todd and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We examine revisions to earnings forecasts by equity analysts and their role in predicting stock returns. We provide evidence that European stocks with net upward revised forecasts earn higher future returns than otherwise similar stocks. This effect is not concentrated in small stocks, stocks with low analyst coverage, or stocks with low book-to-market ratios. We find differences in the return continuation patterns of stocks with upward versus downward revisions, namely, bad news travels quickly, but good news travels slowly. This result is consistent with investors' attaching greater significance to poor earnings forecasts, but adopting a wait-and-see approach to good news.
Download or read book Analyst Price Target Expected Returns and Option Implied Risk written by Turan G. Bali and published by . This book was released on 2015 with total page 71 pages. Available in PDF, EPUB and Kindle. Book excerpt: Motivated by the nature of asset pricing models, we investigate the cross-sectional relation between the market's ex-ante view of a stock's risk and the stock's ex-ante expected return. We demonstrate that an ex-ante measure of expected returns based on analyst price targets is highly related to the market's required rate of return. Using this measure, we show that ex-ante measures of volatility, skewness, and kurtosis derived from option prices are positively related to ex-ante expected returns. We then decompose the risk measures into systematic and unsystematic components and find that while expected returns are related to both systematic and unsystematic variance risk, only the unsystematic components of skewness and kurtosis are important for explaining the cross-section of expected stock returns. The results are consistent using two different approaches to measuring ex-ante risk and robust to controls for other variables related to stock returns and analyst bias.
Download or read book Opposite Sides of a Skewed Bet written by Christian L. Goulding and published by . This book was released on 2018 with total page 77 pages. Available in PDF, EPUB and Kindle. Book excerpt: I test the predictions of a new asset pricing model regarding the interaction of ex-ante return skewness and the dispersion of analysts' earnings forecasts on a sample of U.S. stocks. I present evidence that skewness and forecast dispersion have an interactive pricing impact, that forecast dispersion has no marginal impact unless stocks exhibit ex-ante skewness, and that higher risk or risk aversion is associated with a deepening of their joint effect. The averagereturn gap between stocks in the 5th and 95th percentiles by skewness and dispersion is 1.61% monthly (19.3% annualized). These otherwise anomalous discoveries comprise new cross-sectional features of expected stock returns.
Download or read book Empirical Asset Pricing written by Turan G. Bali and published by John Wiley & Sons. This book was released on 2016-02-26 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: “Bali, Engle, and Murray have produced a highly accessible introduction to the techniques and evidence of modern empirical asset pricing. This book should be read and absorbed by every serious student of the field, academic and professional.” Eugene Fama, Robert R. McCormick Distinguished Service Professor of Finance, University of Chicago and 2013 Nobel Laureate in Economic Sciences “The empirical analysis of the cross-section of stock returns is a monumental achievement of half a century of finance research. Both the established facts and the methods used to discover them have subtle complexities that can mislead casual observers and novice researchers. Bali, Engle, and Murray’s clear and careful guide to these issues provides a firm foundation for future discoveries.” John Campbell, Morton L. and Carole S. Olshan Professor of Economics, Harvard University “Bali, Engle, and Murray provide clear and accessible descriptions of many of the most important empirical techniques and results in asset pricing.” Kenneth R. French, Roth Family Distinguished Professor of Finance, Tuck School of Business, Dartmouth College “This exciting new book presents a thorough review of what we know about the cross-section of stock returns. Given its comprehensive nature, systematic approach, and easy-to-understand language, the book is a valuable resource for any introductory PhD class in empirical asset pricing.” Lubos Pastor, Charles P. McQuaid Professor of Finance, University of Chicago Empirical Asset Pricing: The Cross Section of Stock Returns is a comprehensive overview of the most important findings of empirical asset pricing research. The book begins with thorough expositions of the most prevalent econometric techniques with in-depth discussions of the implementation and interpretation of results illustrated through detailed examples. The second half of the book applies these techniques to demonstrate the most salient patterns observed in stock returns. The phenomena documented form the basis for a range of investment strategies as well as the foundations of contemporary empirical asset pricing research. Empirical Asset Pricing: The Cross Section of Stock Returns also includes: Discussions on the driving forces behind the patterns observed in the stock market An extensive set of results that serve as a reference for practitioners and academics alike Numerous references to both contemporary and foundational research articles Empirical Asset Pricing: The Cross Section of Stock Returns is an ideal textbook for graduate-level courses in asset pricing and portfolio management. The book is also an indispensable reference for researchers and practitioners in finance and economics. Turan G. Bali, PhD, is the Robert Parker Chair Professor of Finance in the McDonough School of Business at Georgetown University. The recipient of the 2014 Jack Treynor prize, he is the coauthor of Mathematical Methods for Finance: Tools for Asset and Risk Management, also published by Wiley. Robert F. Engle, PhD, is the Michael Armellino Professor of Finance in the Stern School of Business at New York University. He is the 2003 Nobel Laureate in Economic Sciences, Director of the New York University Stern Volatility Institute, and co-founding President of the Society for Financial Econometrics. Scott Murray, PhD, is an Assistant Professor in the Department of Finance in the J. Mack Robinson College of Business at Georgia State University. He is the recipient of the 2014 Jack Treynor prize.
Download or read book Differences of Opinion and the Cross Section of Stock Returns written by Karl Diether and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We provide evidence that stocks with higher dispersion in analysts' earnings forecasts earn lower future returns than otherwise similar stocks. This effect is most pronounced in small stocks, and stocks that have performed poorly over the past year. Interpreting dispersion in analysts' forecasts as a proxy for differences in opinion about a stock, we show that this evidence is consistent with the hypothesis that prices will reflect the optimistic view whenever investors with the lowest valuations do not trade. By contrast, our evidence is inconsistent with a view that dispersion in analysts' forecasts proxies for risk.
Download or read book Preference for Positive Skewness and Expected Stock Returns written by Turan G. Bali and published by . This book was released on 2015 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper investigates the role of skewness preference in cross-sectional pricing of NYSE, AMEX, and NASDAQ stocks over the long sample period of January 1926-December 2005 as well as two subsamples. Portfolio-level analyses and the firm-level cross-sectional regressions indicate a negative and significant relation between total skewness and expected stock returns. After controlling for size, book-to-market, momentum, liquidity, and idiosyncratic volatility, the negative relation between total skewness and expected returns remains economically and statistically significant. These results hold for the NYSE stocks, after screening for size, price, and liquidity, and they are also robust across different sample periods. We decompose total skewness into idiosyncratic and systematic components and find a significantly negative relation between idiosyncratic skewness and the cross-section of expected returns, whereas there is no evidence for a significant link between systematic skewness and average stock returns.
Download or read book Temporal Influences on Cross sectional Stock Return Predictabilities written by Zhenmei Zhu and published by . This book was released on 2012 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, I examine the following three temporal influences on the cross-section of stock returns: disclosure and analyst regulations, the subprime credit crisis, and time-varying investor sentiment. The thesis consists of three essays. The first essay deals with the influence of regulation. Between 2000 and 2003 a series of disclosure and analyst regulations curbing abusive financial reporting and analyst behavior were enacted to strengthen the information environment of U.S. capital markets. I investigate whether these regulations benefited investors by increasing stock market efficiency. After the regulations, I find a significant reduction in short-term stock price continuation following analyst forecast revisions and past stock returns. The effect was more pronounced among higher information uncertainty firms, where I expect security valuation to be most sensitive to the regulations. Further analysis shows that analyst forecast accuracy improved in these firms, consistent with reduced mispricing being due to an improved corporate information environment following the regulations. My findings are robust to controlling for time trends, trading activity, the recent financial crisis, and changes in firms' analyst coverage status and delistings. In the second essay, I examine whether the value premium survived the recent subprime credit crisis. I find that value stocks underperformed growth stocks during the crisis, resulting in a value discount, while the value premium was significantly positive before the crisis. This is consistent with value stocks being riskier than growth stocks because they are more vulnerable during bad times. The value premium reversal during the crisis worked primarily through financially constrained firms, suggesting that the effect was due to the adverse influence of the crisis rather than confounding effects. The results are robust to controlling for common risk factors and alternative financial constraint proxies. The third essay is related to time-varying investor sentiment. Recent literature in financial economics has examined whether investor sentiment affects asset pricing. An open question is whether an investor sentiment effect reflects mispricing or risk compensation. Currently, the literature supports the former view by documenting that investor sentiment predicts realized stock returns beyond the explanatory power of state-of-the-art factor models. But, despite its popularity, estimating expected returns from realized returns has limitations. I re-examine the evidence on investor sentiment using accounting-based implied costs of capital (ICCs). I find that ICCs cannot explain the sentiment effect on stock returns. If ICCs are reliable expected return proxies, this suggests that the investor sentiment effect does not exist ex ante and confirms previous evidence that mispricing is the driving force behind the investor sentiment effect on stock returns.
Download or read book Cross sectional Skewness written by Simon Sangmin Oh and published by . This book was released on 2018 with total page 47 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper evaluates skewness in the cross-section of stock returns in light of predictions from a well-known class of models. Cross-sectional skewness in monthly returns far exceeds what the standard lognormal model of returns would predict. However, skewness in long-run returns substantially understates what the lognormal model would predict. Nonstationary share dynamics imply a breakdown in the distinction between market and idiosyncratic risk in the lognormal model. We present an alternative model that matches the skewness in the data and implies stationary wealth shares. In this model, idiosyncratic risk is the primary driver of growth in the economy.
Download or read book Essays on Predicting and Explaining the Cross Section of Stock Returns written by Xun Zhong and published by . This book was released on 2019 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: My dissertation consists of three chapters that study various aspects of stock return predictability. In the first chapter, I explore the interplay between the aggregation of information about stock returns and p-hacking. P-hacking refers to the practice of trying out various variables and model specifications until the result appears to be statistically significant, that is, the p-value of the test statistic is below a particular threshold. The standard information aggregation techniques exacerbate p-hacking by increasing the probability of the type I error. I propose an aggregation technique, which is a simple modification of 3PRF/PLS, that has an opposite property: the predictability tests applied to the combined predictor become more conservative in the presence of p-hacking. I quantify the advantages of my approach relative to the standard information aggregation techniques by using simulations. As an illustration, I apply the modified 3PRF/PLS to three sets of return predictors proposed in the literature and find that the forecasting ability of combined predictors in two cases cannot be explained by p-hacking. In the second chapter, I explore whether the stochastic discount factors (SDFs) of five characteristic-based asset pricing models can be explained by a large set of macroeconomic shocks. Characteristic-based factor models are linear models whose risk factors are returns on trading strategies based on firm characteristics. Such models are very popular in finance because of their superior ability to explain the cross-section of expected stock returns, but they are also criticized for their lack of interpretability. Each characteristic-based factor model is uniquely characterized by its SDF. To approximate the SDFs by a comprehensive set of 131 macroeconomic shocks without overfitting, I employ the elastic net regression, which is a machine learning technique. I find that the best combination of macroeconomic shocks can explain only a relatively small part of the variation in the SDFs, and the whole set of macroeconomic shocks approximates the SDFs not better than only few shocks. My findings suggest that behavioral factors and sentiment are important determinants of asset prices. The third chapter investigates whether investors efficiently aggregate analysts' earnings forecasts and whether combinations of the forecasts can predict announcement returns. The traditional consensus forecast of earnings used by academics and practitioners is the simple average of all analysts' earnings forecasts (Naive Consensus). However, this measure ignores that there exists a cross-sectional variation in analysts' forecast accuracy and persistence in such accuracy. I propose a consensus that is an accuracy-weighted average of all analysts' earnings forecasts (Smart Consensus). I find that Smart Consensus is a more accurate predictor of firms' earnings per share (EPS) than Naive Consensus. If investors weight forecasts efficiently according to the analysts' forecast accuracy, the market reaction to earnings announcements should be positively related to the difference between firms' reported earnings and Smart Consensus (Smart Surprise) and should be unrelated to the difference between firms' reported earnings and Naive Consensus (Naive Surprise). However, I find that market reaction to earnings announcements is positively related to both measures. Thus, investors do not aggregate forecasts efficiently. In addition, I find that the market reaction to Smart Surprise is stronger in stocks with higher institutional ownership. A trading strategy based on Expectation Gap, which is the difference between Smart and Naive Consensuses, generates positive risk-adjusted returns in the three-day window around earnings announcements.
Download or read book Analyst Forecasts and Stock Returns written by James S. Ang and published by . This book was released on 2001 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study seeks to determine the relation between stock returns and analyst forecast properties, specifically, the dispersion and error of annual earnings forecasts. The results of portfolio sorts, Fama-MacBeth cross-sectional regression models, and Fama and French (1993) factor models indicate firms with low dispersion or error outperform firms with high dispersion or error. Robustness tests show the results are not explained by liquidity, momentum, industry, post-earnings announcement drift, or traditional risk measures. An investment strategy based on forecast properties is shown to produce zero-cost returns of 13% per year, yielding positive returns in all 19 years using an error measure. The results are not attributable to several potential theories. Risk-related theories are eliminated as firms with low dispersion or error (quot;transparentquot;) outperform firms with high dispersion or error (quot;opaquequot;). This remains true even after controlling for volatility measures. Behavioral theories based on optimism are also eliminated as optimistic forecasts only explain a small part of the results. Finally, the results are not related to contrarian-value strategies as the transparent firms outperform in both up and down markets.
Download or read book Expectations in the Cross Section written by Johnathan Loudis and published by . This book was released on 2019 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper provides evidence that the market does not efficiently incorporate expected returns implied by analyst price targets into prices. I use a novel decomposition to extract information and bias components from these analyst-expected returns and develop an asset pricing framework that helps interpret price reactions to each component. A one-standard-deviation increase in the information (bias) component is associated with a five (one) percentage point increase in announcement-month returns. The positive reaction to bias implies the market does not fully debias analyst-expected returns before incorporating them into prices. Prices overreact to bias and reverse their initial reaction within three to six months. Prices underreact to information and returns drift an additional one percentage point beyond their initial reaction in the following 12 months. Announcement-window returns forecast future returns, which provides model-free evidence of underreaction, and that underreaction dominates overreaction. Trading against underreaction generates average monthly returns of 1.12% with a Sharpe ratio of 1.08, and the returns survive controlling for exposure to many standard factors.
Download or read book Forecasting Crashes written by Joseph Chen and published by . This book was released on 2009 with total page 47 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper is an investigation into the determinants of asymmetries in stock returns. We develop a series of cross-sectional regression specifications which attempt to forecast skewness in the daily returns of individual stocks. Negative skewness is most pronounced in stocks that have experienced: 1) an increase in trading volume relative to trend over the prior six months; and 2) positive returns over the prior thirty-six months. The first finding is consistent with the model of Hong and Stein (1999), which predicts that negative asymmetries are more likely to occur when there are large differences of opinion among investors. The latter finding fits with a number of theories, most notably Blanchard and Watson's (1982) rendition of stock-price bubbles. Analogous results also obtain when we attempt to forecast the skewness of the aggregate stock market, though our statistical power in this case is limited.
Download or read book Forecasting Skewness in Stock Returns written by Mariko Fujii and published by . This book was released on 2006 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt:
