Trading on Corporate Earnings News


Book Description

Profit from earnings announcements, by taking targeted, short-term option positions explicitly timed to exploit them! Based on rigorous research and huge data sets, this book identifies the specific earnings-announcement trades most likely to yield profits, and teaches how to make these trades—in plain English, with real examples! Trading on Corporate Earnings News is the first practical, hands-on guide to profiting from earnings announcements. Writing for investors and traders at all experience levels, the authors show how to take targeted, short-term option positions that are explicitly timed to exploit the information in companies’ quarterly earnings announcements. They first present powerful findings of cutting-edge studies that have examined market reactions to quarterly earnings announcements, regularities of earnings surprises, and option trading around corporate events. Drawing on enormous data sets, they identify the types of earnings-announcement trades most likely to yield profits, based on the predictable impacts of variables such as firm size, visibility, past performance, analyst coverage, forecast dispersion, volatility, and the impact of restructurings and acquisitions. Next, they provide real examples of individual stocks–and, in some cases, conduct large sample tests–to guide investors in taking advantage of these documented regularities. Finally, they discuss crucial nuances and pitfalls that can powerfully impact performance.







Option Strategies for Earnings Announcements


Book Description

By trading on corporate earnings, investors can reliably profit in both up and down markets, while avoiding market risk for nearly the entire quarter. In this book, two leading traders and portfolio managers present specific, actionable techniques anyone can use to capture these sizable profits. Ping Zhou and John Shon have performed an unprecedented empirical analysis of thousands of stocks, reviewing tens of millions of data points associated with option prices, earnings announcement returns, and fundamentals. Their massive analysis has identified consistent opportunities associated with focusing on the magnitude of the market’s reaction to earnings, not its direction. Option Trading Set-Ups for Corporate Earnings News offers concrete guidance for improving the likelihood of making correct forecasts, and managing the risks of incorrect forecasts. It introduces several ways to exploit option trading opportunities around earnings news, discuss crucial issues that most retail investors haven’t considered, and explore aspects of earnings-related option trading that have never been empirically examined and documented before. For example, they identify hidden patterns and potential opportunities based on valuation, industry, volatility, analyst forecasts, seasonality, and trades that immediately follow earnings announcements. Simply put, trading on earnings reports offers immense profit opportunities, if you know how. This book provides incontrovertible facts and detailed strategies, not just theories and anecdotes!




Why Investors Should Trade Options Around Earnings Announcements


Book Description

This Element is an excerpt from Trading on Corporate Earnings News: Profiting from Targeted, Short-Term Options Positions (9780137084920) by John Shon, Ph.D., and Ping Zhou, Ph.D. Available in print and digital formats. Two Line Description How to trade options before earning announcements — and profit whether the market raves or rages! Text Excerpt We’ve all seen perplexing market reactions to earnings announcements, but would you have guessed that this happens 40% of the time? Even if you predict the right direction of an earnings surprise, it’s still easy to lose money with a directional bet. So how can you profit from an earnings announcement? You use an options trading strategy called a “straddle.”




Informed Trading Behavior of Institutions and Individuals Around Earnings Announcements


Book Description

This study constructs the institutional- and individual-based probability of informed trading (PIN) by adjusting Easley, Hvidkjaer and O'Hara (2002) and investigates the impact of the informed trading behaviors of institutions and individuals on the post-announcement drift around the earnings announcement. The differences between this study and the previous literatures lie in that the investor types of informed traders are distinguished as institutions and individuals. Besides, the trading date effect is considered to examine the informed trading behaviors. The findings show that the informed trading behaviors of institutions and individuals can be distinguished. If there are informed traders involves in the stocks, the cumulative abnormal returns after the earnings announcement may be higher than the other stocks with no informed traders. Some individuals may possess relevant information that may prompt them to trade prior to or after the earnings announcement. The findings of the study may contribute to the government regulations and portfolio selections.




Individual Investor Trading Around Earnings Announcements


Book Description

This paper studies whether individual investors have information advantage before earnings announcements on an emerging market using a unique data set of TWSE. Consistent with existing research on American market, it is surprising that pre-event individual investor trading is also positively correlated with stock returns on and after earnings announcements dates in Taiwan. However, the sign of correlation between individual investor trading and stock return around earnings announcements shows weak evidence of noise trading rather than information advantage, which is opposite to that of American stock market.







Individual and Institutional Informed Trading in Competing Firms Around Earnings Announcements


Book Description

This study investigates individual and institutional trading activities in competing firms to infer informed trading. We find evidence for individual and institutional informed trading in competing firms around earnings announcements. The evidence is stronger prior to announcements than after announcements. Magnitude of institutional (individual) net order flow coefficient decreases (increases) with lag length, suggesting that institutional trading captures information faster than individual trading. Individual net order flow transmit information cross-stock when competitor is a small firm while institutional net order flow conveys information cross-stock irrespective of firm size. Our results will be informative for regulators with regard to insider trading laws and provide insights for market participants on the impact of individual and institutional trading on cross-stock price discovery process.