Multiple Regresion for Market Capitalization

Topics: Regression analysis, Brand, Pearson product-moment correlation coefficient Pages: 8 (2882 words) Published: May 23, 2012
Multiple Regression Model for
Market Capitalisation
Duvan Lopez (Victoria University)
Yen Nguyen (Victoria University)
Meutia Iqbal (Victoria University)
Bayu Suropati (Victoria University)

Over the years, the importance of market capitalisation has been well acknowledged to value a certain company for its shareholders, future acquirers, and generally anyone related with the company. There are various methodologies that have been used to analyse market capitalisation, such as cash flow based analysis, real options and regression analysis. In this study, we used multiple regression analysis to determine which of the numerous important factors yield the best model for market capitalisation as the multiples approach to company valuation. The aim of this paper is to back-testing Ko’s (2009) work in his research paper, Multiple Regression Model For Market Capitalization, using the same, exact methodology he used. Although our work refers to what has been done by Ko (2009) in we found some differences with him in terms of the independent variables that have the most statistically significant relationship with the dependent variable, market capitalisation as we did not use the same number of companies that he used.

In the business world, it is well understood that the main goal of companies is to earn profit as high as possible. This is why every company need to be aware of its stock prices in the relation to profit. Market capitalisation is highly essential to companies, especially public companies, as it is directly proportional to stock price. Stockholders, future acquirers, or anyone who desires to attain a general knowledge of a certain company’s value could see it from that its market capitalisation. If a company wanted to acquire some other company, the first thing to be considered is its market capitalisation. If it has a good value, or is believed to have a greater value in the future, that company is likely to be acquired by another company. For instance, the recent attempt of Microsoft to acquire Skype, which is now already completed, highlighted the importance of acknowledging the value of a company in accordance to determine a fair acquisition price. Supposing Skype’s value was not high enough in Microsoft’s eyes, it probably would not be acquired. Several methods have been utilised in measuring company’s market capitalisation, or also known as value. For instance, options and real options theory, which is persist to be widely discussed topic (Jaggle, 1999; Wourner, Racheva-Iotova & Stoyanov, 2002). The other one is the cash flow based analysis, which is considered as a traditional method, still has been used in the real world (Glasgow, 2002; Samuel, 2003). Although there are other methods that can be used to measure market capitalisation with varying levels of success (Olsen, 2002), it is stated by Lie & Lie (2002) that using multiples of key factors is an effective way to value a company. Therefore, multiple regression analysis was chosen to be used in this paper (Ko, 2009). The aim of this paper is to find out, back-testing Ko’s methodology in developing multiple regression model by using a different total of number of companies used. It should be noted that even though this paper almost completely absorbed Ko’s study in Multiple Regression Model For Market Capitalization, there are some difference founded along the completion of this paper, i.e. in Ko’s work, Brand Value is the factor which has the most significant relationship with market capitalisation whereas in our finding it is the Earnings Per Share, not the Brand Value. We assumed that the reason why these differences occurred is the total number of companies used. Unlike Ko’s, who used 59 companies, we only used almost half of it, 30 companies.

In order to determine which of different factors produce the best model for market capitalisation, we used the multiple regression analysis....
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