One of the first studies to explore the relationship between media press and financial market activity is attributed to Cutler et al. (1989). The authors examine different kinds of news and its explanatory power for aggregate stock returns. Their findings, however, do not support the hypothesis that the variance of stock price movements can be explained by news related to macroeconomic, political or world events. A number of studies followed, which not only ana- lyzed the relation between financial markets and media press coverage (e.g., Chan, 2003; Fang and Peress, 2009) but also the media tone or sentiment expressed in these articles (e.g., Tetlock, 2007; García, 2013) or even both elements (Hillert et al., 2014). Shiller (2000) propagates the view that news media impacts on financial markets, even if the news is non-informative and just evoking a short hype. News content, thus, drives market sentiment in his opinion.
The most influential work on media sentiment is probably accounted to the work by Tetlock (2007). Tetlock (2007) states three distinctive hypothesis on the role of media sentiment in financial markets. First and the main hypothesis of his work, media sentiment and more spe- cifically media pessimism serves as a proxy for investor sentiment. In this hypothesis, the tim- ing of media sentiment is crucial due to the question of whether investor sentiment predicts media sentiment or reflects past media sentiment. In the former scenario, one might expect low returns following media pessimism in the short-run but high returns in the long-run. In the latter case, low returns follow media pessimism, but prices reverse to the fundamental value in the
This table provides a selected literature overview of news media and sentiment related stud- ies. News and returns describe the intertemporal relationship of news media and firm or mar- ket returns, where CT denotes the contemporaneous time period around the news release date (+/- 3 days), ST is the short-term horizon of up to 3 months, MLT is the mid- to long-term horizon between 3 and 36 months, + (-) describes a positive (negative) correlation in the time period, 0 characterizes a return reversal in the time period, +/0 depicts a partial return rever- sion in the time period, and N/A indicates a missing focus of the study on that time period.
News and returns
Authors and year Print media / Data sources
Time period
CT ST MLT
Liu, Smith & Syed (1990)
Wall Street Journal
"Heard on the Street" column
09/1982 - 09/1985
+ + N/A
Barber & Loeffler (1993)
Wall Street Journal "Dartboard" column 10/1988 - 10/1990 + +/0 N/A Tetlock (2007)
Wall Street Journal
"Abreast of the Market" col.
01/1984 - 09/1999
N/A 0 N/A
Tetlock, Saar-Tsechansky & Macskassy (2008)
Dow Jones News Service & Wall Street Journal
1980 - 2004 + + N/A García (2013) Two columns in New York Times
1905 - 2005
N/A +/0 N/A
Hillert, Jacobs & Müller (2014)
45 national and local US newspapers
1989 - 2010
N/A + 0
Hendershott, Livdan & Schürhoff (2015)
Thomson Reuters News Analytics (TRNA)
2003 - 2005
N/A N/A N/A
Bajo & Raimondo (2017) Factiva database (majority of US newspapers) 01/1995 - 12/2013 + N/A N/A
long-run. In the second hypothesis, the media press sentiment reflects information that is not yet fully incorporated into prices. This effect is in the spirit of underreaction and the assumption of gradual diffusion of information amongst newswatchers according to Hong and Stein (1999). The last hypothesis states that media press sentiment only conveys stale information and hence has no impact on financial markets. The main limitation of this study is that the theory only applies to negative sentiment or media pessimism. In a more recent study, García (2013) doc- uments also significant results for media optimism. The media sentiment effect reported in his study is most pronounced in times of economic recessions.
This dissertation frames the news media related topics around investor sentiment. The com- prehensive literature on news-related market efficiency tests or the impact of media coverage (excluding the tone of the content) is, thus, out of scope of this dissertation. Consequently, we
provide a brief but relevant literature overview of studies related to news media and investor sentiment. The main findings are summarized in Table 2-2.
Liu et al. (1990) examine the impact of low-cost analyst recommendations published in the “Heard-on-the-Street” column of the Wall Street Journal on stock returns. Their data overall consists of 852 recommendations, thereof 566 buy and 286 sell recommendations, in the time period between 1982 and 1985. Their findings indicate a symmetric impact of buy and sell recommendations on abnormal returns on the publication day. However, they also find signif- icant abnormal returns in the two days before publication. Applying the event study methodol- ogy, the results yield a cumulative abnormal return of 3% in the three-day time window [-2,0] relative to the publication date. The absence of return reversals in their results implicate the informativeness of recommendations published in news media. Yet, this study only refers to second/hand information from analysts and, thus, does not consider the opinion of investors expressed in news media.
In the highly regarded study on the interaction of media sentiment and stock markets, Tetlock (2007) studies the media content of the Wall Street Journal column “Abreast of the Market”. He specifically tests whether media pessimism predicts daily returns of the Dow Jones Indus- trial Average Index (DJIA). His US study covers the time period between 1984 and 1999 and extracts the fraction of negative words in a news column article with textual analysis based on the Harvard psychosocial dictionary. He documents an economic meaningful impact of media pessimism on the next day’s market return. The effect, however, reverses after four subsequent days.
In a following but more extensive study, Tetlock et al. (2008) analyze whether textual analysis can be applied to not only predict stock returns but also earnings on the individual firm level. The authors fall back to a US data set (1980 – 2004) consisting of a variety of news articles about S&P 500 firms covered by the Dow Jones News Service and in the Wall Street Journal. Following the study by Tetlock (2007), the study focuses on the fraction of negative words in news articles based on the Harvard-IV-4 psychosocial dictionary. The data includes more than 80 quarters of earnings and 6,000 days of returns data. In contrast to the study by Tetlock (2007), the authors rather find new support for the information hypothesis. In this hypothesis, news articles convey value-relevant information that is not yet fully incorporated into stock
prices. Their main finding suggests that news articles contain new information on firm earn- ings. In other words, news articles convey fundamental information and do not simply repeat stale information. Furthermore, the authors find weak evidence for a stock price underreaction where prices incorporate new information with a one-day delay. The predictive power for firm earnings and returns is even higher when specific news stories report on fundamental infor- mation. All in all, the findings by Tetlock et al. (2008) support the hypothesis that news media contributes to market efficiency.
In another study, García (2013) explores the interaction between the content of two columns in the New York Times and the aggregated US market return. His study falls back to news articles covering the time period between 1905 and 2005. The broad database counts 27,449 trading days in total, and media content was analyzed based on the Loughran and McDonald (2011) word dictionary. Other than the former presented studies, García (2013) stresses the symmetrical importance of positive and negative tones in news articles. The fraction of positive and negative words in news articles predict the next day aggregate return, followed by a partial reversal in the subsequent four days. Their results, in total, rather speak for the behavioral sentiment hypothesis than for the informational hypothesis with regard to the informational quality of the media content. The sentiment effect is found to be more pronounced in times of economic recessions, indicating a higher sensitivity to news in bad economic times.
Hillert et al. (2014) research on media coverage and sentiment from the momentum perspec- tive. The authors refer to news articles published in 45 national and local US newspapers be- tween 1989 and 2010, summing up to a total of more than 2.2 million news articles. In inves- tigating on a buy-and-hold-portfolio that goes long on winner stocks (top 30% stocks with highest returns in the past 6 months) and shorts loser stocks (least 30%, respectively) with high media coverage, the authors find a significant momentum effect in the first 10 months which reverses afterwards. The portfolio return fully diminishes after a time horizon of 36 months. When portfolios are, furthermore, sorted by media sentiment (in addition to coverage and past returns), the momentum effect is found to be even stronger.
The former studies primarily studied the impact of news media sentiment on stock markets. Hendershott et al. (2015), however, turn the perspective and analyze whether institutional in- vestors are already informed before the actual date of the news release. The authors, hence,
examine the order flow information from institutional investors and its predictive power for several news variables, including the respective news media sentiment. In their hypothesis, a positive order flow (buy volume > sell volume) forecasts positive media sentiment. For their data, the authors refer to the Thomson Reuters News Analytics (TRNA) database covering the time period between 2003 and 2005. Their analysis includes more than 1 million observations for 1,667 stocks on 755 trading days. In their results, the institutional order flow increases (decreases) five days prior to announcements of positive (negative) news. The order flow in- formation predicts the following sentiment of news announcements and stock returns on the announcement days. Hence, Hendershott et al. (2015) suggest that institutional investors are well-informed compared to news providers. Potential reasons that the authors mention could be related to the direct communication of institutional investors to publicly traded firms and brokerage firms, or the greater resources to process available information.
In an IPO-related study, Bajo and Raimondo (2017) research on the impact of news media sentiment on the level of IPO-underpricing, timing effects of news announcements and the associated reputation of the news provider. The authors build on US data that covers 2,814 IPOs and over 27,000 news articles recorded in the Factiva database in the time period between 1995 and 2013. Akin to other studies, news media sentiment was extracted with textual analysis tools based on the Loughran and McDonald (2011) word dictionary. Bajo and Raimondo (2017) document in their findings a positive relationship between positive news media senti- ment and IPO underpricing. The effect is more pronounced for news announcements close to the IPO date and for news published by more reputable news providers.
All the literature introduced above have in common that news media sentiment is somehow related to stock returns. Even though many studies find confirmation for the behavioral senti- ment hypothesis that investors react to non-informative news announcements, researchers can- not conclude with full evidence that news announcements do not convey fundamental or stale information. In reality, it is more likely that all three hypotheses (1. Behavioral sentiment hy- pothesis of non-informative news announcements, 2. Information hypothesis of underreaction to value relevant information that is not yet fully incorporated into prices, 3. Stale information hypothesis of old information that has no impact on financial markets) have proven their right to exist. The next section introduces the reader to the social media topic which significantly
gained in importance in recent years, not only in the general society but also in financial mar- kets.