|Title:||An Investment Strategy for Prediction of Takeover Targets using High Frequency Data|
|Publisher:||University of Sydney.|
|Abstract:||Abstract The ability to identify likely takeover targets at an early stage should provide an investor with valuable information to profit from investing in potential target firms. Based on the hypothesis that agents with asymmetric information operate in the securities market, the objective of this study is to develop an investment strategy able to achieve high portfolio returns and reduce risks by investing in takeover targets. The analysis conducted on tick-by-tick data from shares traded on the Australian Securities Exchange (ASX) uses a range of models from the logistic, neural network, forecast combination, Autoregressive Conditional Duration (ACD), along with associated market timing rules. The first part of this thesis contributes to the takeover prediction literature by showing that the combination of probability forecasts as an alternative approach improves forecast accuracy in takeover prediction with improved economic return from portfolios made up of predicted targets. The second part investigates the joint impact of market microstructure variables on return volatility in the months prior to the public release of the takeover announcement. The last part introduces an innovative market timing approach to capture information from the intraday trading and to guide portfolio investments. The information content of each trade is analysed in the search for trading behaviour consistent with the use of privileged information before the takeover announcement. Three general conclusions come from the results. First, an investment in a portfolio comprising predicted targets is capable of achieving significant abnormal returns. Second, traders who may hold private information before the event affect the intraday trading behaviour in takeover targets. Finally, the proposed Forecast Range Strategy is shown to be successful in predicting market trends and providing an alternative method for reducing risk without sacrificing return.|
|Type of Work:||PhD Doctorate|
|Type of Publication:||Doctor of Philosophy Ph.D.|
|Appears in Collections:||Sydney Digital Theses (Open Access)|
|RODRIGUES_Bruno_thesis_with_copyright.pdf||4.87 MB||Adobe PDF|
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