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This paper examines what orders informed traders use before quarterly earnings announcements. In particular, we investigate whether informed traders prefer median orders and market orders right before quarterly earnings announcements. Quarterly earnings announcements are anticipated events. Because informed traders expect their information advantage will disappear after the announcements, this information event provides a unique opportunity to test whether informed traders become more impatient and use more aggressive orders when the announcement is approaching. Our results show that when the information will be released soon but there is still enough time for the execution (from day -10 to day -6), informed investors use small orders and limit orders to trade stealthily and reduce price risk. Within five days right before the announcements, informed investors trade more aggressively. They start using large market orders to ensure the execution and high profits. Our findings that informed traders change their preference for order type and order size over time shed new light on the ongoing debate on the order submission strategies by informed traders.
This study examines the revealed preference of informed traders to infer the extent to which earnings announcements are informative of subsequent stock price responses. From 2011 to 2015, a cartel of sophisticated traders illegally obtained early access to firm press releases prior to publication and traded over 1,000 earnings announcements. I study their constrained profit maximization: which earnings announcements they chose to trade vs. which ones they forwent trading. Consistent with theory, these traders targeted more liquid earnings announcements with larger subsequent stock price movement. Despite earning large profits overall, the informed traders enjoyed only mixed success in identifying the biggest profit opportunities. Controlling for liquidity differences, only 31% of their trades were in the most extreme announcement period return deciles. I model the informed traders' tradeoff between liquidity and expected returns. From this model, I recover an average signal-to-noise ratio of 0.4. I further explore two potential economic sources of this noise: (i) ambiguous market expectations of earnings announcements and (ii) heterogeneous interpretations of earnings information by the marginal investor. Empirically, I document that the informed traders avoided noisier earnings announcements as measured by both sources of noise.
Abstract: Black and Scholes (1973) implied volatilities tend to be systematically related to the option's exercise price and time to expiration. Derman and Kani (1994), Dupire (1994), and Rubinstein (1994) attribute this behavior to the fact that the Black-Scholes constant volatility assumption is violated in practice. These authors hypothesize that the volatility of the underlying asset's return is a deterministic function of the asset price and time and develop the deterministic volatility function (DVF) option valuation model, which has the potential of fitting the observed cross-section of option prices exactly. Using a sample of S & P 500 index options during the period June 1988 through December 1993, we evaluate the economic significance of the implied deterministic volatility function by examining the predictive and hedging performance of the DV option valuation model. We find that its performance is worse than that of an ad hoc Black-Scholes model with variable implied volatilities.
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.
Written by one of the leading authorities in market microstructure research, this book provides a comprehensive guide to the theoretical work in this important area of finance.
We provide a synthesis of the empirical evidence on market liquidity. The liquidity measurement literature has established standard measures of liquidity that apply to broad categories of market microstructure data. Specialized measures of liquidity have been developed to deal with data limitations in specific markets, to provide proxies from daily data, and to assess institutional trading programs. The general liquidity literature has established local cross-sectional patterns, global cross-sectional patterns, and time-series patterns.
The U.S. stock market has been transformed over the last twenty-five years. Once a market in which human beings traded at human speeds, it is now an electronic market pervaded by algorithmic trading, conducted at speeds nearing that of light. High-frequency traders participate in a large portion of all transactions, and a significant minority of all trade occurs on alternative trading systems known as “dark pools.” These developments have been widely criticized, but there is no consensus on the best regulatory response to these dramatic changes. The New Stock Market offers a comprehensive new look at how these markets work, how they fail, and how they should be regulated. Merritt B. Fox, Lawrence R. Glosten, and Gabriel V. Rauterberg describe stock markets’ institutions and regulatory architecture. They draw on the informational paradigm of microstructure economics to highlight the crucial role of information asymmetries and adverse selection in explaining market behavior, while examining a wide variety of developments in market practices and participants. The result is a compelling account of the stock market’s regulatory framework, fundamental institutions, and economic dynamics, combined with an assessment of its various controversies. The New Stock Market covers a wide range of issues including the practices of high-frequency traders, insider trading, manipulation, short selling, broker-dealer practices, and trading venue fees and rebates. The book illuminates both the existing regulatory structure of our equity trading markets and how we can improve it.