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In this paper, I examine the potential of mobile alerting services empowering investors to react quickly to critical market events. Therefore, an analysis of short-term (intraday) price effects is performed. I find abnormal returns to company announcements which are completed within a timeframe of minutes. To make use of these findings, these price effects are predicted using pre-defined external metrics and different estimation methodologies. Compared to previous research, the results provide support that artificial neural networks and multiple linear regression are good estimation models for forecasting price effects also on an intraday basis. As most of the price effect magnitude and effect delay can be estimated correctly, it is demonstrated how a suitable mobile alerting service combining a low level of user-intrusiveness and timely information supply can be designed.
This paper examines intraday stock price effects and trading activity caused by ad hoc disclosures in Germany. The evidence suggests that the observed stock prices react within 90 minutes after the ad hoc disclosures. Trading volumes take even longer to adjust. We find no evidence for abnormal price reactions or abnormal trading volume before announcements. The bigger the company that announces an ad hoc disclosure, the less severe is the abnormal price effect following the announcement. The number of analysts is negatively correlated to the trading volume effect before the ad hoc disclosure. The higher the trading volume on the last trading day before the announcement, the greater is the price effect after the ad hoc disclosures and the greater the trading volume effect. Keywords: ad hoc disclosure rules, intraday stock price adjustments, market efficiency.
AS A MATTER OF COURSE, QUANTITATIVE DATA SUCH AS TIME SERIES AND QUARTERLY FIGURES ARE FREQUENTLY USED IN FINANCIAL ANALYSIS. SUCH DATA CAN BE PROCESSED AUTOMATICALLY AND INTERPRETED RATHER EFFICIENTLY. HOWEVER, A SIGNIFICANT PERCENTAGE OF RELEVANT INFORMATION ORIGINATES FROM UNSTRUCTURED SOURCES, PRIMARILY TEXTUAL DATA, WHICH REQUIRE MANUAL (HUMAN) INTERPRETATION. WE EXPLORE EMPIRICALLY HOW MACHINE LEARNING TECHNIQUES CAN PROVIDE SUPPORT FOR ANALYZING AND INTERPRETING SUCH TEXTUAL DATA SOURCES.