Automated ANN alerts : one step ahead with mobile support

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

Testing Market Response to Auditor Change Filings: a comparison of machine learning classifiers

The use of textual information contained in company filings with the Securities Exchange Commission (SEC), including annual reports on Form 10-K, quarterly reports on Form 10-Q, and current reports on Form 8-K, has gained the increased attention of finance and accounting researchers. In this paper we use a set of machine learning methods to predict the market response to changes in a firm\u27s auditor as reported in public filings. We vectorize the text of 8-K filings to test whether the resulting feature matrix can explain the sign of the market response to the filing. Specifically, using classification algorithms and a sample consisting of the Item 4.01 text of 8-K documents, which provides information on changes in auditors of companies that are registered with the SEC, we predict the sign of the cumulative abnormal return (CAR) around 8-K filing dates. We report the correct classification performance and time efficiency of the classification algorithms. Our results show some improvement over the naïve classification method

The Effects of Twitter Sentiment on Stock Price Returns

Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-know micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known "event study" from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the "event study" methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1-2%), but the dependence is statistically significant for several days after the events