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I analyze whether or not market-wide investor sentiment induces stock mispricing, by affecting the boldness of predictions of firms' long-term earnings growth. I predict that bullish market-wide sentiment induces investors to aggressively separate firms with high growth futures from others, and that this excessive boldness results in a high level of mispricing. Consistent with my prediction, I observe an excessively large dispersion in consensus growth forecasts when proxies for investor sentiment are high at the beginning of the period. Furthermore, stocks with higher-predicted growth experience more negative forecast revisions and lower subsequent stock returns, especially following periods of high investor sentiment.
We study how stock market mispricing might influence individual firms' investment decisions. We find a positive relation between investment and a number of proxies for mispricing, controlling for investment opportunities and financial slack, suggesting that overpriced (underpriced) firms tend to overinvest (underinvest). Consistent with the predictions of our model, we find that investment is more sensitive to our mispricing proxies for firms with higher R & D intensity suggesting longer periods of information asymmetry and thus mispricing) or share turnover (suggesting that the firms' shareholders are short-term investors). We also find that firms with relatively high (low) investment subsequently have relatively low (high) stock returns, after controlling for investment opportunities and other characteristics linked to return predictability. These patterns are stronger for firms with higher R & D intensity or higher share turnover.
This study investigates the mispricing of market-wide investor sentiment by exploring the relation between sentiment and investor expectations of future earnings. Prior research argues that sentiment-driven mispricing should be most pronounced for hard-to-value firms, such as those reporting losses (Baker and Wurgler 2006). Using investor expectations of future earnings, we provide empirical results consistent with this behavioral finance theory. We predict and find that investors perceive losses to be more (less) persistent during periods of low (high) sentiment; that (in contrast) investors perceive profit persistence to be lower (higher) during periods of low (high) sentiment; and that the effects appear stronger for loss firms relative to profit firms. We also document predictable cross-sectional variation within losses, with the mispricing mitigated for losses associated with activities expected to generate future benefits: R&D, growth, large negative special items, and severe financial distress. Overall, our results document a new and important channel--investor expectations of future earnings--to explain sentiment-driven mispricing.
"Recent evidence shows that investor sentiment is a contrarian predictor of stock returns with speculative stocks earning lower (higher) future returns than safe stocks following high (low) sentiment states. We extend this argument by conditioning expected stock returns on sentiment dynamics and show that the mispricing of speculative and safe stocks worsens with sentiment continuations but is corrected with sentiment transitions, consistent with the view that the mispricing of these stocks is sentiment-driven. We show that the unconditional contrarian return predictability of sentiment, at least in the short-run, is due to the returns of stocks in sentiment transitions. Results show that ex post, sentiment is a momentum predictor if subsequent sentiment continues; and a contrarian predictor if subsequent sentiment transitions. We also show that the MAX effect can either be positive or negative contingent on sentiment dynamics. The absence of a negative MAX effect following Low sentiment states suggested by prior studies is due to the completely offsetting negative MAX effect when sentiment continues in a Low state and the positive MAX effect when sentiment transitions from a High to a Low state. Keywords: Investor sentiment, sentiment dynamics, MAX effect, cross-sectional returns"--Page [ii].
Learn how to profit from information about insider trading. The term insider trading refers to the stock transactions of the officers, directors, and large shareholders of a firm. Many investors believe that corporate insiders, informed about their firms' prospects, buy and sell their own firm's stock at favorable times, reaping significant profits. Given the extra costs and risks of an active trading strategy, the key question for stock market investors is whether the publicly available insider-trading information can help them to outperform a simple passive index fund. Basing his insights on an exhaustive data set that captures information on all reported insider trading in all publicly held firms over the past twenty-one years—over one million transactions!—H. Nejat Seyhun shows how investors can use insider information to their advantage. He documents the magnitude and duration of the stock price movements following insider trading, determinants of insiders' profits, and the risks associated with imitating insider trading. He looks at the likely performance of individual firms and of the overall stock market, and compares the value of what one can learn from insider trading with commonly used measures of value such as price-earnings ratio, book-to-market ratio, and dividend yield.
Since the advent of Markov chain Monte Carlo (MCMC) methods in the early 1990s, Bayesian methods have been proposed for a large and growing number of applications. One of the main advantages of Bayesian inference is the ability to deal with many different sources of uncertainty, including data, models, parameters and parameter restriction uncertainties, in a unified and coherent framework. This book contributes to this literature by collecting a set of carefully evaluated contributions that are grouped amongst two topics in financial economics. The first three papers refer to macro-finance issues for real economy, including the elasticity of factor substitution (ES) in the Cobb–Douglas production function, the effects of government public spending components, and quantitative easing, monetary policy and economics. The last three contributions focus on cryptocurrency and stock market predictability. All arguments are central ingredients in the current economic discussion and their importance has only been further emphasized by the COVID-19 crisis.
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Using a unique data set consisting of more than 36.5 million submitted retail investor orders over the course of five years, Matthias Burghardt constructs an innovative retail investor sentiment index. He shows that retail investors’ trading decisions are correlated, that retail investors are contrarians, and that a profitable trading strategy can be based on these aggregated sentiment measures.