The main objective of every company is to maximize the assets or firm value. Maximizing firm value is essential for a company because it means increasing the wealth of shareholders as well. This study aims to determine if there is significant influence between the company's profile such as industry, company age and its profitability with the firm value using Tobin's Q model. The proponents selected 86 diversified companies in the Philippines by gathering and analyzing annual financial reports on 2014 in the Philippine Stock Exchange (PSE) to obtain the objective of the study and also employed predictive correlational design. Frequency, Mean and Multiple Regression were used to determine the significant influence between the independent and dependent variables. The multiple regression reveals that of the three factors assumed to influence value of the firm using the Tobin's Q, only profitability shows significant positive impact on the firm's value.

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Globalization and diversification are the trends of business nowadays. Companies’ diversification will lead to complexity of financial information and investment decision. The objective of this study is to determine the level of compliance with International Financial Reporting Standard (IFRS) 8 and to determine other related segment information to formulate investment strategies. The subjects of the research were the 100 diversified publicly-listed holding companies in the Philippines with 2010-2011 annual financial reports. Segment Disclosure Index (SDI) was developed to determine the level of compliance with IFRS 8. The study used firm size, company age, audit quality, profitability, leverage, growth and industry as predictors of the level of compliance. The result shows high overall level of compliance with IFRS 8 – Operating segment. Using multiple regression analysis, it was found out that only firm size predicts the level of compliance with IFRS 8. Large companies mostly comply with disclosure requirement stated in IFRS 8 compared to smaller companies. Most of the companies are engaged in real estate development and leasing business.Keywords: Business and Management, accounting, IFRS 8, disclosure, segment reporting,descriptive design, Philippines2

Over previous decades, market participants have been unsure about how to forecast the movement of the stock market. Coming up with a good forecasting model could lead stock market participants towards a less risky investment portfolio. This study attempts to explore the haphazard nature of Philippine Stock indices and the long-term relationship between Philippine Stock Exchange (PSE) sectoral indices and the PSE index. The data series used are the daily closing values of the PSE index and six sectoral indices between July 2010 to December 2019. This study employs an Augmented Dickey-Fuller (ADF) unit root test and a Johansen cointegration test. The results revealed that all indices are non-stationary at index level, but stationary at first difference. The results showed that all indices followed a random walk; however, no long-term relationships between the PSE index and five sectoral indices were found. Therefore, long-term investors are able to diversify their portfolio to reduce investment risk. Contribution/ Originality: This study contributes to the existing literature on employing a cointegration analysis on the Philippine stock indices. It provides investors with additional perspectives on forecasting the stock market movement at a macro level. The results offer long-term investors vital information they can use when managing their portfolio.

The financial markets provide a viable avenue for investors who wants to invest their idle resources. Investors need accurate information to minimize investment risk and make the right investment decision. This study attempted to test the predictability of the Philippine Stock Exchange (PSE) sectoral indices. The data used in this study are the daily closing price of the six sectoral indices from January 2010 to December 2019. Augmented Dickey-Fuller (ADF) for stationarity test and Johansen Cointegration and Granger Causality analysis were used to test the long-run and short-run relationship among the six sectoral indices. The results showed that all indices are not predictable at the index level (I(0)) but predictable at the first difference (I(1)). The study found no long-run relationships between sectoral indices. The result also revealed that the sectoral indices have a short-run relationship in both directions.