![]() This allows us to investigate the relationships between the news sentiment and abnormal, i.e., idiosyncratic component of returns. This paper investigates how stock-specific and market-wide news sentiments, obtained from Thomson Reuters News Analytics, affect abnormal returns of S&P 500 stocks.Using factor models of Fama and French and of Carhart, we remove the influence of the fundamental factors from S&P500 stock returns. We conclude that that the public arrivals of firm-specific news play a significant role in explaining the IVOL puzzle. When we restrict the firm-specific news to value-relevant news, the positive relation disappears. This finding is robust to numerous model specifications, and the inclusion of firm-level characteristics, liquidity risk and information risk. Using the stock return data from the Center for Research in Security Prices (CRSP) database and the news database from the RavenPack News Analytics over the period from 2000 to 2011, we show that the strength of the positive relation is reduced systematically by 50% after accounting for the arrivals of good and bad news releases, which are defined by their sentiment scores. We postulate that the perceived negative IVOL-expected return relation could be the artifact of the confounding effect of public news arrivals. This paper analyzes the effects of news and its sentiment on the idiosyncratic volatility (IVOL) - expected return relation. In addition, corporate financial communication should avoid impetuous communication via social media channels as this could have deterrent effects on the market valuation of a listed company. SEC in the USA) regarding insider-trading and the publication of market-relevant information. The findings show that Twitter communication needs to be well considered in light of strict market regulations (e.g. ![]() The study is a response to recent discussions about the legitimacy of Twitter communication by CEOs or representatives of listed companies. Eventually, Twitter accounts of media visible companies and personalities, such as Tesla and its CEO Elon Musk, have been found to be useful market information sources for day traders and shareholders to trade at a profit. ![]() Financial online news instead seems to heavily rely on Elon Musk’s attention-triggering news to sustain its 24-h airtime with a variety of reporting tools, keeping the highly demanded audience engaged. The analysis covers a period of four days, encompassing the announcement and introduction of the new battery pack for Model S and X by Tesla as well as the accompanying and follow-up reporting by the financial news media.įindings show that market reactions are driven by business events and expectations among the market rather than the follow-up reporting by financial news media. Using a multi-method case study approach, combining quantitative intraday event studies with a qualitative text analysis of financial online news and tweets by Elon Musk and Twitter, the authors shed light on the complex interaction between market events, financial information and stock market reactions. In so doing, the study provides insights into the nature of market-moving information and the role of financial news flows in shaping market reactions in today’s high-frequency news and information environment. Tesla’s tweets about a new product) as well as the framing of both the event itself and the market reactions therewith in the news media influence the formation of the share price of the respective company over time. The purpose of this paper is to research how corporate communication regarding a specific corporate event (i.e. Alternatively, investors can enhance performance of existing earnings momentum strategies by either combining this with a news flow signal or by trying to forecast which companies are likely to see analyst revisions post news announcements. Those that can react quickly can benefit from the short-term momentum following particular news items and gain an information advantage by incorporating news flow ahead of analyst revisions. Our results show that news-flow-based strategies can add value to investors. Here we look at higher frequency information contained within corporate news flow as a leading indicator of analyst revisions to understand what type of information causes analysts to revise their earnings expectations, how the informational content of the signal varies according to the news catalyst, and whether investors can use news flow signals as input into their models. Earnings momentum strategies that the majority of systematic equity investors employ typically do not identify the piece of information that initially triggers the change in analyst forecasts.
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