Return to search

Analyzing an acquisition model and optimizing stock abnormal return using simulation techniques

The relative economic efficiency of acquisitions as a means of restructuring financially distressed firms is investigated. Yearly accounting and daily stock price data are extracted for the period between 1979 and 1998 on firms entering financial distress The behaviour and performance of these firms were traced for a five year period following their entry into distress or until their shares were no longer trading. These collected data forms the basis for analyzing the returns acquired from investing in potential takeover targets.
Survival analysis is used to analyze the hazard rate for both the acquisition and bankruptcy of distressed firms. The results of the analysis indicate that the ZSCORE, a predictor of the probability of failure, and SPCSRM, the rating by Standard and Poor's, can be used as financial indicators in the screening mechanisms for financially distressed firms.
A multinomial-logit acquisition model is used to predict three outcomes of financially distressed firms: survival, acquisition and failure. This model is tested using two methods by simulating the probability of acquisition. The first uses to compare the predicted versus the actual corporate events to maximize the predicted acquisition event. The second uses to compute abnormal return to maximize portfolio return over a given time period, continual on the ZSCORE, probability of acquisition, and the length of holding period. The predictive model of the acquisition probability is applied as a stock entry rule in a buy-sell system. The success of the model will serve two purposes. One is to predict the economic value of acquisition. The other is to provide successful strategies for investing in stocks.

Identiferoai:union.ndltd.org:WATERLOO/oai:uwspace.uwaterloo.ca:10012/960
Date January 2003
CreatorsLiu, Ying
PublisherUniversity of Waterloo
Source SetsUniversity of Waterloo Electronic Theses Repository
LanguageEnglish
Detected LanguageEnglish
TypeThesis or Dissertation
Formatapplication/pdf, 1567955 bytes, application/pdf
RightsCopyright: 2003, Liu, Ying. All rights reserved.

Page generated in 0.0019 seconds