Download or read book Analyst Forecast Accuracy Dispersion and Stock Returns Before and During Stock Market Crashes written by Xiaolei Wang and published by . This book was released on 2008 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 Quantitative analysis of large stock market crashes written by Victor Odour and published by GRIN Verlag. This book was released on 2014-02-05 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: Document from the year 2011 in the subject Business economics - Investment and Finance, grade: A, California State University, East Bay, language: English, abstract: The objective of this study is to structure a dependable model to forecast the timing of entry and exit from the stock markets by using multivariate linear regression analysis. The study uses major macroeconomic indicators such CPI, PPI, GDP, MEI as independent variables and the S&P 500 index value as the dependent variable. The sample consists of 30 years of monthly data. This study includes four different loss scenarios in the S&P 500 index value and analyzes the data to see if the losses can be absorbed or if further losses will occur. This report discusses the practical implications of using regression analysis and how it is used to predict the market movements. This paper concludes that our regression model can help an investor to anticipate market movements and thus make appropriate buy and sell decisions.
Download or read book Analysts Forecast Dispersion and Stock Market Anomalies written by Tingting Liu and published by . This book was released on 2020 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: We show that understanding the role of analysts' forecast bias is central to discovering the behavior that causes some stocks to have high analyst forecast dispersion. This finding is important because stocks with high analyst forecast dispersion contribute significantly to many important anomalies. We first explain how forecast bias produces significant negative future returns in the high dispersion portfolio. Next we examine the effect of these stocks on momentum returns, the profitability anomaly, and post-earnings announcement drift. Finally, we examine the performance of four asset pricing models focusing on the model's ability to explain the returns to these high dispersion stocks.
Download or read book Analyst Forecast Dispersion and Future Stock Return Volatility written by Madhu Kalimipalli and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we examine the relationship between analysts' forecast dispersion and future stock return volatility using monthly data for a cross section of 160 US firms from 1981 to 1996. We find that there is a strong and positive relationship between analysts' forecast dispersion and future return volatility. The dispersion measure has incremental information content even after accounting for market volatility. These results are robust across sub-sample periods and sub-samples based on based on number of analysts following a firm, forecast dispersion and market capitalization. There is also a strong seasonal relationship between the dispersion measure and future volatility. The importance of dispersion on future return volatility is high in January and the first few months of the year, and declines thereafter. Such information content of analysts' earnings forecast dispersion is of great importance for active portfolio management, option pricing and arbitrage trading strategies.
Download or read book Dispersion of Forecasts and Stock Returns written by Bilal Erturk and published by . This book was released on 2006 with total page 49 pages. Available in PDF, EPUB and Kindle. Book excerpt: Prior research has established that stocks with high dispersion of earnings forecasts yield lower subsequent returns. I offer a new explanation based on some analysts' reluctance to revise their forecasts downward. I show that analysts' sluggish and non-synchronous response to negative information results in dispersion of forecasts. The inertia in downward forecast revisions also leads to market underreaction to bad news. Therefore, the negative relationship between dispersion and subsequent returns may be partially attributable to some analysts' sluggish response to negative information. I also test whether dispersion of forecasts exacerbates overpricing (Miller (1977)), but find that when dispersion of forecasts increases, prices decrease.
Download or read book Further Evidence on the Relation Between Analysts Forecast Dispersion and Stock Returns written by Orie E. Barron and published by . This book was released on 2008 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: Prior research reports seemingly conflicting evidence and interpretations concerning the relation between dispersion in analysts' earnings forecasts and stock returns. Diether et al. (2002) and Johnson (2004) find a negative relation between levels of dispersion in analysts' forecasts and future stock returns. Yet, changes in forecast dispersion are negatively associated with contemporaneous stock returns (L'Her and Suret 1996). We demonstrate that levels and changes in dispersion reflect different theoretical constructs. Changes in dispersion primarily reflect changes in information asymmetry whereas levels of dispersion primarily reflect levels of uncertainty. Further, the uncertainty component of dispersion levels reflects idiosyncratic risk that is negatively associated with future stock returns. These findings provide support for Johnson's (2004) explanation that dispersion levels reflect idiosyncratic uncertainty that increases the option value of the firm and generally refute Diether et al.'s (2002) explanation that dispersion levels reflect information asymmetry.In addition, we reconcile L'Her and Suret's (1996) findings with the findings of Johnson (2004). We find that the negative association between changes in dispersion and contemporaneous stock returns is not due to increased uncertainty but rather increased information asymmetry.
Download or read book Analysts Forecast Dispersion and Stock Split Announcements written by Maria Chiara Iannino and published by . This book was released on 2016 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper is an empirical investigation of the relation between the dispersion on analysts' earnings forecasts and the future performance following a change in the nominal price of shares. On a sample of US splits occurred from 1993 to 2013, we observe a change in the distribution of analysts' forecasts after the announcement of the event. In particular, we observe an increase in forecasts' dispersion. We distinguish the two components of private and common information, and we find that asymmetric information significantly increases after the announcement of stock splits, while no change is evinced in uncertainty. While we do not observe any relationship between dispersion and future returns in our sample of stocks, we shed light on the literature on disagreement observing a negative relation between asymmetric information and both future returns and cumulative abnormal returns post-split. We conclude observing that stock splits have a stronger positive effect on future performance for shares with lower prior asymmetric information.
Download or read book Expectations and the Structure of Share Prices written by John G. Cragg and published by University of Chicago Press. This book was released on 2009-05-15 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt: John G. Cragg and Burton G. Malkiel collected detailed forecasts of professional investors concerning the growth of 175 companies and use this information to examine the impact of such forecasts on the market evaluations of the companies and to test and extend traditional models of how stock market values are determined.
Download or read book The Effect of Analysts Forecasts on Stock Market Returns written by Stefano Bonini and published by . This book was released on 2009 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stock returns forecasting is one of the major objectives of financial analysts. Equity Analysts' forecasts, on the other side, are one of the major sources of information used by less informed investors in their asset allocation decisions. Therefore, analysing which major drivers affect time series of stock returns could allow to shed light over the price revelation process in capital markets. In this paper we propose a model aimed at predicting stock market by combining both macroeconomic and microeconomic factors. We first develop a standard APT approach with multiple macroeconomic factors as regressors. We then integrate the model by explicitly including a metric for intrinsic equity value, basing upon a proxy derived by the weighted average of Stock Market Consensus Forecasts by equity analysts. Third, we complete the model by imposing an ARMA specification for the error term, which allows identifying stock returns' stationarity moving over time. The resulting model shows both a strong fitting capability when tested in the in-sample period and a good predictive capability when applied to an out-of-sample period of monthly Italian stock market returns. In particular, we employed specific estimation procedures based upon recently developed statistics aimed at testing for both factors' equal predicting power and forecast encompassing. As a major empirical finding, our model suggests that the information conveyed by analysts' forecasts is indeed a factor in determining future stock prices, even if there is the possibility that the information transferred could be biased.
Download or read book Dispersion in Analyst Forecasts and the Profitability of Earnings Momentum Strategies written by Andreas P. Dische and published by . This book was released on 2002 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is a well documented phenomenon that stock prices underreact to news about future earnings and drift in the direction suggested by revisions in analysts' earnings forecasts. This paper shows that the dispersion in analysts' consensus forecasts contains incremental information to predict future stock returns. Higher abnormal returns can be achieved by applying an earnings momentum strategy to stocks with a low dispersion. This finding supports one of the recent behavioral models in which investors focus too little on the weight of new evidence and conservatively update their beliefs in the right direction, but by too little in magnitude with respect to more objective information.
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 The Relation between Dispersion in Analysts Forecasts and Stock Returns written by Shuping Chen and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper investigates the conclusion in Diether, Malloy, and Scherbina (2002) that dispersion in analysts' forecasts proxies for differences in investor beliefs, and that prices reflect the beliefs of optimistic investors when dispersion is high. If this is the case, we expect to find higher earnings response coefficients (ERCs), related to negative earnings surprises, for high versus low dispersion firms. This follows because the negative earnings surprises are less consistent with the beliefs of optimists. However, we find smaller ERCs, which calls into question the optimism argument in DMS. Further, we find that the relatively low future returns earned by high forecast dispersion firms, documented in DMS, are explained by the well known post-earnings-announcement drift phenomena. Specifically, after sorting observations based on prior period standardized unexpected earnings (SUEs), which are associated with drift, the difference between the future returns of high versus low dispersion firms is not statistically significant.
Download or read book Financial Analysts Earnings Forecast Dispersion and Intraday Stock Price Variability Around Quarterly Earnings Announcements written by Gerald J. Lobo and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This study investigates the relationship between the dispersion of analysts' earnings forecasts and stock price variability around quarterly earnings announcements. Consistent with theoretical predictions, the empirical analysis shows that stock price variability at the time of earnings announcements is positively related to the degree of analysts' earnings forecast dispersion. The analysis also demonstrates that stock price variability is significantly greater from two days before to two days after the earnings announcement for firms ranked in the bottom third on the basis of analysts' forecast dispersion, whereas it is significantly greater from eight days prior to five days following the earnings announcement for firms in the top third. These results suggest that there is information about the earnings announcement that becomes available to at least a subset of investors prior to the earnings release. The increased level of price variability for five days following the earnings announcement suggests that market participants take different amounts of time to process the information conveyed by the earnings announcement.
Download or read book Financial Analysts Earnings Forecast Dispersion and Intraday Stock Price Variability Around Quarterly Earnings Announcements written by Samuel S. Tung and published by . This book was released on 2020 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study investigates the relationship between the dispersion of analysts? earnings forecasts and stock price variability around quarterly earnings announcements. Consistent with theoretical predictions, the empirical analysis shows that stock price variability at the time of earnings announcements is positively related to the degree of analysts? earnings forecast dispersion. The analysis also demonstrates that stock price variability is significantly greater from two days before to two days after the earnings announcement for firms ranked in the bottom third on the basis of analysts? forecast dispersion, whereas it is significantly greater from eight days prior to five days following the earnings announcement for firms in the top third. These results suggest that there is information about the earnings announcement that becomes available to at least a subset of investors prior to the earnings release. The increased level of price variability for five days following the earnings announcement suggests that market participants take different amounts of time to process the information conveyed by the earnings announcement.
Download or read book Up and Down written by Yun Liao and published by . This book was released on 2017 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper investigates the relationships among cross-sectional stock returns and analysts' forecast revisions, forecast dispersion and momentum. Market rewards the strategy in pursuit of revision up and away from revision down by 22.7% per annum over the 1983-2015 periods. I find that the negative dispersion-return relationships are robust in 1983-2015 periods. Revision up and revision down betas account for most of the momentum strategy and over half of forecast dispersion strategy profits. Moreover, the sub-periods analysis of cross-sectional stock return demonstrates that market generally overreacts to revision in good times than to revision in bad times.
Download or read book Aggregate Analyst Forecast Errors Stock Market Liquidity and the Economy written by Ji-Chai Lin and published by . This book was released on 2018 with total page 62 pages. Available in PDF, EPUB and Kindle. Book excerpt: We examine aggregate analyst forecast errors (AAFE) and find a systematic component, which is predictable using lagged stock market returns and macroeconomic variables. The evidence suggests that analysts do not fully take into account macroeconomic influences on individual firms' earnings in their forecasts, and that systematic biases in market expectations exist. Since informed investors may exploit over-optimistic (over-pessimistic) analyst earnings forecasts in their sells (buys), their trading affects stock prices, which induces uninformed investors to gradually revise their expectations and leave (enter) the market. As the number of uninformed investors decreases (increases), stock market liquidity deteriorates (improves). Based on this reasoning, we show that - predictable AAFE is a driving force of time-varying stock market liquidity - and also an important channel through which stock market liquidity incorporates macroeconomic information.
