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ItemWhat do they mean? Using Media Richness as an Indicator for the Information Value of Stock Analyst Opinion regarding post-earnings Firm Performance( 2017-01-04)In this research the impact of media-richness on the investor reaction to earnings announcements is investigated. To this end, unstructured (high-richness) sources of analyst opinion are subjected to text-mining and combined with structured (low-richness) sources of analyst opinion, as well as other commonly used structured data relevant to company performance. Results indicate that equivocality is a major problem faced by investors, while uncertainty as understood by media-richness theory appears to be less dominant.
ItemPossibilistic Clustering for Crisis Prediction: Systemic Risk States and Membership Degrees( 2017-01-04)Research on understanding and predicting systemic financial \ risk has been of increasing importance in the recent \ years. A common approach is to build predictive models \ based on macro-financial vulnerability indicators to \ identify systemic risk at an early stage. In this article, we \ outline an approach for identifying different systemic risk \ states through possibilistic fuzzy clustering. Instead of directly \ using a supervised classification method, we aim at \ identifying coherent groups of vulnerability with macrofinancial \ indicators for pre-crisis data, and determine the \ level of risk for a new observation based on its similarity \ to the identified groups. The approach allows for differentiating \ among different possible pre-crisis states, and \ using this information for estimating the possibility of systemic \ risk. In this work, we compare different fuzzy clustering \ methods, as well as conduct an empirical exercise \ for European systemic banking crises.
ItemHow The Market Can Detect Its Own Mispricing - A Sentiment Index To Detect Irrational Exuberance( 2017-01-04)The emergence of big data analytics enables real \ time news analysis. Such analysis offers the possibility to instantly \ extract the sentiment conveyed by any newly published, \ textual information source. This paper investigates the existence \ of a causal relationship between news sentiment and stock \ prices. As such, we apply news sentiment analysis for unstructured, \ textual data to extract sentiment scores and utilize \ the Granger-causality test to determine the causal relationship \ between daily news sentiment scores and the corresponding \ stock market returns. Upon successfully identifying such a \ causal relationship with a time lag, we develop a real-time \ news sentiment index. This news sentiment index serves as \ a decision-support system in detecting a potential over- or \ undervaluation of stock prices given the news sentiment of \ available news sources. Thus, as a novelty, the news sentiment \ index serves as an early-warning system to detect irrational \ exuberance.