Abstract:
Investors have the ability, using financial ratios, to analyze the performance of their firm in relation to other companies operating in the same industry. The link that exists between two or more aspects of financial statements can be measured using financial ratios such as earning per share, price earning ratio, price to book value, net profit margin, return on asset, enterprise value to ebitda, and enterprise value to earning. The comparison of the findings obtained across a number of time intervals is the most fruitful application for their use. In this research, the hypothesis is examined using a regression model with multiple independent variables such as earning per share, price earning ratio, price to book value, net profit margin, return on asset, enterprise value to ebitda, and enterprise value to earning. This type of model is referred to as descriptive statistical analysis, panel data regression, classical assumption, and multiple regression, and hypothesis testing with the EViews 12 as statistical tools. Sample of this research is 55 panel data observation, which consists of 11 property and real estate companies with 5 years annual data from 2015 until 2019. The result of the research also found that there are five significant variables of companies towards stock price in between 2015-2019 listed on IDX. The research results indicate that independent variables, simultaneously, 95.4% influences the stock price. Earning per share, price to book value, net profit margin, enterprise value to ebitda and enterprise value to earning is the factor with the most significant influence, and price earning ratio and return on asset insignificantly influence the stock price. This research can also serve as a resource for identifying other industrial company sectors with high profits, as it can influence the stock price of the company.
