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APE-1: News effects
1Michigan State University; 2Rutgers Business School; 3Prime Quantitative Research LLC
We decompose daily stock returns into news- and non-news-driven components, using a comprehensive sample of intraday firm-level news arrivals matched with high-frequency movements of their stock prices. We find that, consistent with prior literature, non-news returns precede a reversal. For news-driven returns, however, we find strong evidence of return continuation without subsequent reversals. A strategy of news momentum that buys stocks with high news returns and sells stocks with low news returns generates an annualized return of 40.08% in the following week, with a four-factor alpha of 40.44%, controlling for the market, size, value, and momentum. The strategy’s profitability is driven by positive serial correlations in individual stock returns, and is particularly pronounced for overnight and weekend news and among small firms with low analyst coverage, high volatility, and low liquidity. These results suggest that investor under-reaction to news, coupled with limits to arbitrage, drives news momentum.
Temperature Shocks and Industry Earnings News
Cornell University, United States of America
Climate scientists project a rise in both average temperatures and the frequency of temperature extremes. We study how extreme temperatures affect companies’ earnings. We combine granular daily data on temperatures across the continental U.S. with locations of public companies’ establishments to build a panel of quarterly firm-level temperature exposures (1990-2015). Extreme temperatures significantly impact earnings in over 40% of industries. Some industries are harmed by temperature shocks while others benefit. Analysts and investors do not immediately react to observable intra-quarter temperature shocks, but earnings forecasts account for temperature effects by quarter-end in many, though not all, industries.
News as sources of jumps in stock returns: Evidence from 21 million news articles for 9000 companies
1Ted Rogers School of Management, Ryerson University; 2Rotman School of Management, University of Toronto; 3McDonough School of Business, Georgetown University
Stock prices exhibit large, discrete movements, typically labelled as "jumps''. A potential important source of jumps in stock returns can be material news events, such as earnings surprises. Explicitly modelling the impact of news on return jumps requires firm-level news data. In this paper, we collect 21 million news articles associated with more than 9000 publicly-traded companies from the Factiva database and use textual analysis to derive measures summarizing those news, including news frequency, tone and uncertainty. We find that these measures of news flow content are significantly related to nonparametric measures of jump intensity and jump size distributions and explain an important fraction of variations in the jumps across individual companies. Further, those nonparametric analyzes provide input for our time-series modelling of firm-level news processes. By modelling the observable news process explicitly and jointly with a latent jump process, we find that news are important drivers of the jumps in stock returns. Consequently, we are able to enrich the economic content of the widely-used econometric models of jumps used for applications such as option pricing and risk management where stock return jump models are frequently used.
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