In this study I investigate the potential improvement in multiple-based valuations from using composite valuations based on price to earnings and price to book ratios against their respective individual ratios and actual price in terms of their predictive accuracy against future price. It is motivated by the popularity of accounting-based market multiples used by practitioners in valuation activities with little published research documenting the absolute and relative performance of composite multiples and its vulnerability to manipulation by biased analysts. First, I generate benchmark multiples using a multiple regression approach and in turn these benchmark multiples are used in the generation of composite valuations. Second, I incorporate firm characteristics such as anticipated growth and financial positions in the development of these composite valuations. Third, I investigate any further improvement in predictive accuracy from enterprise value to sales ratio which is less subjective to accounting policy choices and conservative accounting. The main results support the hypothesis that composite benchmark multiples lead to improved valuations over single multiples and further improvement is achieved by incorporating the potential growth rate and financial condition in the composite benchmark multiples. In particular, the three ratio regression-based composite multiples with the growth and the financial condition factor has the smallest mean and median absolute valuation errors. Findings remain unchanged when the analysis is based on December fiscal year end firms and using a parsimonious model in the estimation regression. However, the analysis of mispricing reveals that the valuation model might be useful in settings where market price is not available, such as initial public offerings and court valuation of private firms where a valuation is needed due to strong evidence that high positive pricing errors identify subsequent high returns.
| Identifer | oai:union.ndltd.org:ADTP/272620 |
| Date | January 2010 |
| Creators | Chan, Kelly, Australian Graduate School of Management, Australian School of Business, UNSW |
| Publisher | Awarded By:University of New South Wales. Australian Graduate School of Management |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | http://unsworks.unsw.edu.au/copyright, http://unsworks.unsw.edu.au/copyright |
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