Market reaction to takeover rumour in internet discussion sites

We examine the market reaction to takeover rumour postings in the Hotcopper Internet Discussion Site (IDS). Results from the interday analysis show abnormal returns and trading volumes on the day before and the day of the posting. Results of the intraday analysis show abnormal returns and trading volumes during the 10 min posting interval and abnormal trading volume during the 10 min interval immediately preceding it. Sensitivity analyses indicate that the results are robust to concerns regarding potential confounds, credibility and bid–ask spread bias. Taken together, these findings are consistent with the market reacting to the posting of takeover rumours in IDS.

Rule extraction from technology IPOs in the US stock market

Machine learning techniques for prediction and rule extraction from artificial neural network methods are used. The hypothesis that market sentiment and IPO specific attributes are equally responsible for first-day IPO returns in the US stock market is tested. Machine learning methods used are Bayesian classifications, support vector machines, decision tree techniques, rule learners and artificial neural networks. The outcomes of the research are predictions and rules associated With first-day returns of technology IPOs. The hypothesis that first-day returns of technology IPOs are equally determined by IPO specific and market sentiment is rejected. Instead lower yielding IPOs are determined by IPO specific and market sentiment attributes, while higher yielding IPOs are largely dependent on IPO specific attributes.