Giving Content to Investor Sentiment: The Role of Media in the Stock Market
PAUL C. TETLOCK
I quantitatively measure the interactions between the media and the stock market using daily content from a popular Wall Street Journal column. I find that high media pessimism predicts downward pressure on market prices followed by a reversion to fundamentals, and unusually high or low pessimism predicts high market trading volume. These and similar results are consistent with theoretical models of noise and liquidity traders, and are inconsistent with theories of media content as a proxy for new information about fundamental asset values, as a proxy for market volatility, or as a sideshow with no relationship to asset markets.
Giving Content to Investor Sentiment: The Role of Media in the Stock Market – Introduction
One of the more fascinating sections of the WSJ is on the inside of the back page under the standing headline “Abreast of the Market.” There you can read each day what the market did yesterday, whether it went up, down or sideways as measured by indexes like the Dow Jones Industrial Average . . . . In that column, you can also read selected post-mortems from brokerage houses, stock analysts and other professional track watchers explaining why the market yesterday did whatever it did, sometimes with predictive nuggets about what it will do today or tomorrow. This is where the fascination lies. For no matter what the market did—up, down or sideways—somebody will have a ready explanation.
Vermont Royster (Wall Street Journal, “Thinking Things Over Abaft of the Market,” January 15, 1986)
CASUAL OBSERVATION SUGGESTS THAT THE CONTENT OF NEWS about the stock market could be linked to investor psychology and sociology. However, it is unclear whether the financial news media induces, amplifies, or simply reflects investors’ interpretations of stock market performance. This paper attempts to characterize the relationship between the content of media reports and daily stock market activity, focusing on the immediate influence of the Wall Street Journal’s (WSJ’s) “Abreast of the Market” column on U.S. stock market returns.
To my knowledge, this paper is the first to find evidence that news media content can predict movements in broad indicators of stock market activity. Using principal components analysis, I construct a simple measure of media pessimism from the content of the WSJ column. I then estimate the intertemporal links between this measure of media pessimism and the stock market using basic vector autoregressions (VARs). First and foremost, I find that high levels of media pessimism robustly predict downward pressure on market prices, followed by a reversion to fundamentals. Second, unusually high or low values of media pessimism forecast high market trading volume. Third, low market returns lead to high media pessimism. These findings suggest that measures of media content serve as a proxy for investor sentiment or noninformational trading. By contrast, statistical tests reject the hypothesis that media content contains new information about fundamental asset values and the hypothesis that media content is a sideshow with no relation to asset markets.
I use the General Inquirer (GI), a well-known quantitative content analysis program, to analyze daily variation in the WSJ “Abreast of the Market” column over the 16-year period 1984–1999. This column is a natural choice for a data source that both reflects and influences investor sentiment for three reasons. First, the WSJ has by far the largest circulation—over two million readers—of any daily financial publication in the United States, and Dow Jones Newswires, the preferred medium for electronic WSJ distribution, reaches over 325,000 finance and investment professionals.1 Second, the WSJ and Dow Jones Newswires, founded in 1889 and 1897, respectively, are extremely well established and have strong reputations with investors. Third, electronic texts of the WSJ “Abreast of the Market” column are accessible over a longer time horizon than are the texts of any other column about the stock market.
For each day in the sample, I gather newspaper data by counting the words in all 77 predetermined GI categories from the Harvard psychosocial dictionary. To mitigate measurement error and thereby enhance construct validity, I perform a principal components factor analysis of these categories. This process collapses the 77 categories into a single media factor that captures the maximum variance in the GI categories. Because this single media factor is strongly related to pessimistic words in the newspaper column, hereafter I refer to it as a pessimism factor.
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