Mr. Jason

3. Research Methodology

3.1. Sample

The sample consists of product launching event of Microsoft for the years 2006 to 2015. We take into account of all the announcements of new products launch, any related press conferences of the product, the initial sale of those products, any upgrades of the operating system for existing phones or laptops and any new softwares launched. We collect the data of share prices correspond to all the events we included in the sample. We calculate the mean of abnormal return before, at and after the launching events.

3.2. Dependent variable

The dependent variable of our regression model is the abnormal return or called market-adjusted return on the share price. Brown and Warner (1985) showed that the market-adjusted returns method is efficient in detecting abnormal returns associated with specific events. This method subtracted the firm’s returns by the returns attributable to the stock market’s overal movements. Standard & Poor’s 500-stock index (S&P 500 index) is used in measuring the average market rate of return as it was usually used by Financial economists (Schweitzer, 1989). The market-adjusted returns are calculated by the following equation:

ARit = Rit - Rmt

where:

ARit     abnormal return on the share i on day t

Rit    return on share i on day t

Rmt    average market rate of return in period t

3.3. Control variable

3.3.1. Innovativeness (IN)

Holak and Lehmann (1990) stated the innovativeness of a new product is a consistently important determinant of accelerated consumer adoption rate. Booz, Allen and Hamilton (1982) found that the level of innovativeness of a new product can be classified into two level. The first being new to the company innovations while the second being new to the market innovations. Research from Chaney, Devinney and Winter (1991) has shown that original inventions provide a 3-day excess return over product updates. Srinivasan et. al. (2009) also suggested that pioneering innovations (new to the market) have an impact on the stock return that is seven times greater than minor updates. Gielens and Steenkamp (2007) suggested that this difference is probably because pioneering products provide a stronger platform for growth.

3.3.2. Earning per share (EPS)

Chang et. al. (2008) suggested that stock price will moves with EPS in long run based on the samples from Taiwan listed electronic industires. Al-Tamini, Alwan and Abdel Rahman (2011) also studied and confirmed the positive and significant association between EPS and the level of stock price. The results of their study also indicated that EPS was the most influencing factor on stock price. Sharma (2011) also supported their view and concluded that EPS is the strongest determinants of share price.