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Do Security Analysts Overreact?
... This forecast inefficiency typically results in systematic underreaction, where similar earnings surprises tend to follow earnings announcements (Mendenhall 1991;Ali et al. 1992;Abarbanell and Bernard 1992). However, De Bondt and Thaler (1990) document that financial analysts overestimate new information, which generates systematic overreaction. Other studies find that the overreaction and the underreaction depend on the nature of the information, in the sense that analysts tend to respond excessively to favorable information and insufficiently to unfavorable information. ...
... Therefore, this result does not align with the conclusions of systematic underreaction as presented in Mendenhall (1991), Abarbanell and Bernard (1992), Ali et al. (1992), Elliott et al. (1995), and the related literature. However, it does support the findings of general overreaction as illustrated by De Bondt and Thaler (1990), Amir andGanzach (1998), andElkemali (2023). −0.285 (−12.446 ...
... This perspective assumes that financial analysts' forecasts swiftly and impartially integrate all new data. However, recent research indicates that cognitive biases, such as representativeness, anchoring, and leniency, lead analysts to exhibit, respectively, patterns of overreaction and underreaction to new information (Abarbanell and Bernard 1992;De Bondt and Thaler 1990;Amir and Ganzach 1998). These findings seem contradictory to each other and do not align with the concept of rational forecasting. ...
Several prior studies indicate that financial analysts exhibit systematic underreaction to information; others illustrate systematic overreaction. We assume that cognitive biases influence analysts’ behavior and that these misreactions are not systematic, but they depend on the nature of news. As cognitive biases intensify in situations of high ambiguity, we distinguish between bad and good news and investigate the impact of intangible assets—synonymous with high uncertainty and risk—on financial analysts’ reactions. We explore the effect of information conveyed by prior-year earnings announcements on the current-year forecast error. Our findings in the Saudi financial market reveal a tendency for overreaction to positive prior-year earnings change (good performance) and positive prior-year forecast errors (good surprise). Conversely, there is an underreaction to the negative prior-year earnings change (bad performance) and negative prior-year forecast error (bad surprise). Notably, analysts exhibit systematic optimism rather than systematic underreaction or overreaction. The results also highlight that the simultaneous phenomena of overreaction and underreaction is more pronounced in high intangible asset firms compared to low intangible asset firms.
... Linear models are widely used in empirical research and the OLS estimator has been intensely exploited. Nobel Price Richard Thaler run a linear regression of current earnings changes over current forecast bias in earnings and find statistical evidence of optimism and overreaction in financial analysts' forecasts [5]. Following [6], [7] use a two-stage least squares (2SLS) regression and find statistical evidence of a relationship between education and formal employment. ...
Dealing with uncertainty about the true data generating process requires a differentiated perspective of the distributions in hypothesis testing. In particular, the realizations, or the observed data, generated by interactions that are naturally ordered in time, posits a need for a differentiated analysis with respect to the standard statistics available for hypothesis testing. The Functional Central Limit Theorem provides a framework that enables the researcher to build a statistic that fits his data and hypothesis at hand. In this paper I show some of the necessary conditions under which the popular t − statistic properly condenses the information of the underlying distribution as well as the additional tools available when then t distribution is not suitable for hypothesis testing.
... Additionally, investors' earnings forecasts play a crucial role in shaping stock market valuations. Due to the bounded rationality of investors, these forecasts often carry biases, as discussed by Bondt and Thaler (1990). These biases also lead to significant overvaluation in bull markets and undervaluation in bear markets Thaler 1985 andThaler 1989). ...
Academic research on trend-following investing has almost exclusively focused on testing various trading rules’ profitability. However, all existing trend-following rules are essentially ad hoc, lacking a solid theoretical justification for their optimality. This paper aims to address this gap in the literature. Specifically, we examine the optimal trend-following when the returns follow a two-state process, randomly switching between bull and bear markets. We show that if a Markov model governs the return process, it is optimal to follow the trend using the Exponential Moving Average rule. However, the Markov model is unrealistic because it does not represent the bull and bear market duration times correctly. It is more sensible to model the return process by a semi-Markov model where the state termination probability increases with age. Under this framework, the optimal trend-following rule resembles the Moving Average Convergence/Divergence rule. We confirm the validity of the semi-Markov model with an empirical study demonstrating that the theoretically optimal trading rule outperforms the popular 10-month Simple Moving Average and 12-month Momentum rules across a universe of international markets.
This study examines the day-of-the-week effect on the returns of different classifications of South African REITs. Ordinary least squares regression (OLS), generalized autoregressive conditional heteroskedasticity (GARCH) (1,1) (2,1), and Kruskal–Wallis (KW) tests were performed on data obtained from the IRESS Expert database from 2013 to 2021. We found statistical differences in the day-of-the-week effects for SAREITs; the best day to invest in office REITs is Friday, for diversified REITs Thursday, and for industrial REITs Friday. Generally, Wednesday was found to be the least profitable day to invest in all REIT classifications because it had the least average daily return. Tuesdays were the most profitable days for all REIT classifications, with the highest average daily return. REITs traded the most on Fridays, while REITs traded the least on Mondays. Returns were the most volatile on Monday, while volume was the least volatile on Thursday. The KW test revealed a statistically significant difference between the median returns across days of the week. Based on the above, profitability is expressed on Tuesdays in South African REITs. By recognizing the day-of-the-week effect, investors can buy and sell South African REITs more effectively. This study, apart from being the first in the context of South African REITs, provides updated evidence of the contested calendar anomaly issues.
We develop an economic model demonstrating that firms can benefit from committing to underinvestment. The model considers a firm's IPO, secondary‐market trading and subsequent investment decision. We analyse the conditions under which underinvestment can paradoxically be advantageous despite reducing the fundamental value of the firm. The benefit of committing to underinvest post‐IPO is expressed in reduced underpricing and thus a higher valuation during the IPO. We furthermore show that the firm may commit to an inefficient investment policy by appointing a manager with biased expectations or risk aversion. Our findings imply that, under certain conditions, firms are better off relying on biased managers when their initial outlook is poor, but risk‐averse managers when their initial outlook is good.
Comments or suggestions from credible experts will give the stock greater momentum, with a big number of positive comments providing the stock a stronger upward momentum over time and a large number of negative comments giving the stock a stronger downward momentum. Therefore, it is necessary to investigate the link between the stock price movement after analyst recommendations and analyst remarks, as well as the length of analyst comments. The report utilizes electric vehicle (EV) firms Tesla and Nio as a case study for significant comment date and subsequent share price fluctuations. There is a positive correlation between comments and share price, and the longer the length, the greater the impact.
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