Financial disclosures serve as primary intermediaries between companies and investors. However, investors have different information processing skills and might easily be misled by noisy signals that lack a deeper meaning. In financial markets, this is formalized by the noise trader theory, which groups investors into two categories: (a) informed investors assumed to form rational decisions and (b) noise traders forming beliefs partly based on non-fundamental noise signals and news sentiment. Yet, little is known about how these groups actually interpret textual information in financial statements and how the resulting stock market reaction differs. This work extends previous research by unraveling the role of word choice and semantic orientation in financial disclosures for both investor types. For this purpose, we use Kalman filtering to decompose the stock market reaction following the publication of U.S. regulated Form 8-K filings into a fundamental price component and a noise residual. We then use LASSO regression to identify the statistically relevant words for informed investors and noise traders. According to our results, each investor type assigns significantly different interpretations and degrees of importance to individual words and documents. Keywords: Noise trader theory, Information processing, Decision-making, Financial markets, News sentiment, Kalman filter



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