Developing a model to identify performance of leisure sector stocks.

Abstract

The amount of investment that has gone into new developments and expansions in tourism are immense and has come under much scrutiny of both local and foreign investors. However, the performance of tourism industry is sensitive to both internal and external factors which have made it much difficult to forecast the future performances. This study attempts to develop the out-performing stock performances using binary logistic regression. Qualitative factors which are affected to the stock performance in hotel and travel industry namely, competition, infrastructure development and international proceeding and three financial ratios such as Percentage change of Net Sales (NS), Price to Cash Earnings per Share (PECEPS) and Price to Book value (PEBV) are considered as independent variables to develop this model. The stock performance which is calculated using annual stock return of each company from 2010 to 2014. The companies are categorized in to two classes based on their performance using annual stock return. The sample consists of the 21 listed companies in Colombo Stock Exchange and data collected from 2010 to 2014. The fitted model with mentioned independent variables is able to classify the companies up to 71.4% level of accuracy in to two categories, “good” or “poor”, based on annual stock return. This study can be used by investors, fund managers and investment companies in the leisure sector to enhance their ability to choose out-performing stocks.