Explore the evidence.
Understand the reach.

This study analyzes the effect of capital structure on the performance of telecommunications firms. The firm performance is measured by return on assets while capital structure is proxied with the ratio of debt total assets. Data of telecommunication firm in 62 countries period of year 2010-2020 proceed with a dynamic data panel regression model using the generalized method of moment approach. The empirical results indicated that capital structure of telecommunication firm has significantly impact to the firm performance in developed and developing countries as well as in integrated and wireless telecommunication firms.  The other factors that effect on firm performance are firm size and growth. The novelty of this study lies in the scope of analyses that cover world-wide telecommunication industry based on demographic group (developed and developing countries) and main businesses (integrated and wireless telecommunication).

The purpose of the study was to determine the determinants of the firms' capital structure concerning their maximum financial performance. To reach this aim, the data of the financial statements firms of Indonesia were used. As the first method, a Pearson correlation matrix was applied to determine a statistically significant correlation between capital structure indicator (debt-to-assets ratio) and financial performance and ownership of the firms. The analysis used the data panel multiple regression model to assess the effect of these independent and controlling variables on leverage. Some findings are that profitability has positive (ROA) and negative (ROE) effect on leverage. MBV and tangibility do not affect the capital structure, and firm size negatively impacts on it. In this panel analysis, it was confirmed that the managerial and institutional ownership impact on leverage negatively and positively, respectively. By decreasing the sales growth, the debt ratio entity rises, or they have a negative relationship. Based on these findings, it can be stated that financial performances influenced the capital structure.

Mutual fund is one of the investment instruments widely used as an investment option. There are two essential things to be considered by the Investment Manager as a mutual fund manager: the stock selection and the market timing ability. The Covid-19 pandemic has caused many investors to experience potential investment losses. Understanding stock selection and market timing will be needed for determining which Investment Managers perform well in managing equity mutual fund, especially while dealing with the Covid-19 pandemic. The objective of this study is to analyze the ability of Investment Managers to perform stock selection and market timing, the performance of equity mutual fund, and the effect of stock selection and market timing ability on the performance of equity mutual fund in Indonesia. This study applies the Fama net selectivity methods to assess stock selection ability and Treynor-Mazuy and Henriksson-Merton models to analyze market timing ability. Sharpe, Treynor, and Jensen's alpha methods are also used for measuring mutual fund performance. The data is processed per mutual fund to recognize each mutual fund's stock selection and market timing ability. Furthermore, the research spans two time periods: before and following the Covid-19 pandemic. A dataset of 104 equity mutual funds from 2016-2021 has been analyzed. The findings show that only 9 and 12 Investment Managers have stock selection ability prior to and following the Covid-19 pandemic, respectively. According to Treynor Mazuy model, there are only two Investment Managers with market timing ability prior to the Covid-19 pandemic, and only twelve Investment Managers have market timing ability during the Covid-19 pandemic, according to the Henriksson-Merton model. Every mutual fund with stock selection ability outperformed the benchmark. However, not all equity mutual fund with market timing ability outperformed the benchmark.

This study aims to analyze the development and soundness of banks from financial performance ratios in banking companies listed on the IDX in 2016-2021 using the RGEC approach; analyze the effect of the ratios used in RGEC on stock returns in banking companies listed on the IDX in 2016-2019 or before Covid19; analyzed the effect of the ratios used in RGEC on stock returns in banking companies listed on the IDX in 2020-2021 or during the Covid-19 pandemic. The sample consisted of the banking sub-sector listed on the IDX in the period 2016 to 2021. The data analyzed using the RGEC method. Based on the calculation of bank rating (bank soundness level), it can be seen that the key figures for the risk profile, GCG, earning and equity (RGEC) in the banking sub-sector listed on the IDX developed well in 2016-2021 in the banking sector on average from 39 banks studied. There are 11 Banks that are in Composite Rating 1 (Very Healthy), 25 Banks are on Composite Rating 2 (Healthy) and 3 Banks are on Composite Rating 3 (Sufficiently Healthy). Based on the results of panel regression tests in 2016-2019 (before Covid-19), bank rating (bank soundness level) which include risk profile (NPL and LDR), GCG, earnings (ROA and NIM) and equity (CAR) together have an effect of 28.89% of bank stock returns and 71.11% of stock returns are influenced by other factors outside the variables studied. However, based on the p-value results, the variables NPL, GCG, NIM and CAR have no significant effect on bank stock returns, while the LDR and ROA variables have a significant effect on stock returns. In general, the soundness of banks listed on the IDX is in a healthy condition. Whereas the results of panel regression tests in 2020-2021 (after Covid-19), bank rating (bank soundness level) which include risk profile (NPL and LDR), GCG, earnings (ROA and NIM) and equity (CAR) together have an effect of 4.93% of bank stock returns and 95.07% of stock returns are influenced by other factors outside the variables studied. However, based on the results of the p-value variables, NPL, LDR, GCG, ROA, NIM and CAR have no significant effect on bank stock returns. However, in general, the health level of banks listed on the IDX during the Covid-19 pandemic is in a healthy condition.

An increase in population with various activities and lifestyles of a community has an impact on increasing volume, type, and characteristics of waste. The increase in the amount of waste needs to be balanced with the government and the ability to manage waste, so it is necessary to involve the community. This study aims to measure community participation in managing household waste that will support the agricultural sector. This study was carried out in Takalar Regency because Takalar is one of Indonesia’s regencies that has been successfully awarded the Community-Based Total Sanitation (STBM) from the Indonesian Ministry of Health and the Sustainable STBM Award in waste management. The method used in this study was a descriptive analysis of questionnaire data which was filled by 1011 villages of Takalar Regency. The study found that most of the Takalar citizens have participated in the waste management program initiated by the government of Takalar Regency. However, only a small number of residents process waste into products that support the agricultural sector. This research provides implications for evaluating program effectiveness and finding opportunities for further improvement in regulations.

Mutual fund is one of the investment instruments widely used as an investment option. There are two essential things to be considered by the Investment Manager as a mutual fund manager: the stock selection and the market timing ability. The Covid-19 pandemic has caused many investors to experience potential investment losses. Understanding stock selection and market timing will be needed for determining which Investment Managers perform well in managing equity mutual fund, especially while dealing with the Covid-19 pandemic. The objective of this study is to analyze the ability of Investment Managers to perform stock selection and market timing, the performance of equity mutual fund, and the effect of stock selection and market timing ability on the performance of equity mutual fund in Indonesia. This study applies the Fama net selectivity methods to assess stock selection ability and Treynor-Mazuy and Henriksson-Merton models to analyze market timing ability. Sharpe, Treynor, and Jensen's alpha methods are also used for measuring mutual fund performance. The data is processed per mutual fund to recognize each mutual fund's stock selection and market timing ability. Furthermore, the research spans two time periods: before and following the Covid-19 pandemic. A dataset of 104 equity mutual funds from 2016-2021 has been analyzed. The findings show that only 9 and 12 Investment Managers have stock selection ability prior to and following the Covid-19 pandemic, respectively. According to Treynor Mazuy model, there are only two Investment Managers with market timing ability prior to the Covid-19 pandemic, and only twelve Investment Managers have market timing ability during the Covid-19 pandemic, according to the Henriksson-Merton model. Every mutual fund with stock selection ability outperformed the benchmark. However, not all equity mutual fund with market timing ability outperformed the benchmark.

An increase in population with various activities and lifestyles of a community has an impact on increasing volume, type, and characteristics of waste. The increase in the amount of waste needs to be balanced with the government and the ability to manage waste, so it is necessary to involve the community. This study aims to measure community participation in managing household waste that will support the agricultural sector. This study was carried out in Takalar Regency because Takalar is one of Indonesia’s regencies that has been successfully awarded the Community-Based Total Sanitation (STBM) from the Indonesian Ministry of Health and the Sustainable STBM Award in waste management. The method used in this study was a descriptive analysis of questionnaire data which was filled by 1011 villages of Takalar Regency. The study found that most of the Takalar citizens have participated in the waste management program initiated by the government of Takalar Regency. However, only a small number of residents process waste into products that support the agricultural sector. This research provides implications for evaluating program effectiveness and finding opportunities for further improvement in regulations.

This study aims to analyze the development and soundness of banks from financial performance ratios in banking companies listed on the IDX in 2016-2021 using the RGEC approach; analyze the effect of the ratios used in RGEC on stock returns in banking companies listed on the IDX in 2016-2019 or before Covid19; analyzed the effect of the ratios used in RGEC on stock returns in banking companies listed on the IDX in 2020-2021 or during the Covid-19 pandemic. The sample consisted of the banking sub-sector listed on the IDX in the period 2016 to 2021. The data analyzed using the RGEC method. Based on the calculation of bank rating (bank soundness level), it can be seen that the key figures for the risk profile, GCG, earning and equity (RGEC) in the banking sub-sector listed on the IDX developed well in 2016-2021 in the banking sector on average from 39 banks studied. There are 11 Banks that are in Composite Rating 1 (Very Healthy), 25 Banks are on Composite Rating 2 (Healthy) and 3 Banks are on Composite Rating 3 (Sufficiently Healthy). Based on the results of panel regression tests in 2016-2019 (before Covid-19), bank rating (bank soundness level) which include risk profile (NPL and LDR), GCG, earnings (ROA and NIM) and equity (CAR) together have an effect of 28.89% of bank stock returns and 71.11% of stock returns are influenced by other factors outside the variables studied. However, based on the p-value results, the variables NPL, GCG, NIM and CAR have no significant effect on bank stock returns, while the LDR and ROA variables have a significant effect on stock returns. In general, the soundness of banks listed on the IDX is in a healthy condition. Whereas the results of panel regression tests in 2020-2021 (after Covid-19), bank rating (bank soundness level) which include risk profile (NPL and LDR), GCG, earnings (ROA and NIM) and equity (CAR) together have an effect of 4.93% of bank stock returns and 95.07% of stock returns are influenced by other factors outside the variables studied. However, based on the results of the p-value variables, NPL, LDR, GCG, ROA, NIM and CAR have no significant effect on bank stock returns. However, in general, the health level of banks listed on the IDX during the Covid-19 pandemic is in a healthy condition.

This study analyzes the effect of capital structure on the performance of telecommunications firms. The firm performance is measured by return on assets while capital structure is proxied with the ratio of debt total assets. Data of telecommunication firm in 62 countries period of year 2010-2020 proceed with a dynamic data panel regression model using the generalized method of moment approach. The empirical results indicated that capital structure of telecommunication firm has significantly impact to the firm performance in developed and developing countries as well as in integrated and wireless telecommunication firms.  The other factors that effect on firm performance are firm size and growth. The novelty of this study lies in the scope of analyses that cover world-wide telecommunication industry based on demographic group (developed and developing countries) and main businesses (integrated and wireless telecommunication).

The purpose of the study was to determine the determinants of the firms' capital structure concerning their maximum financial performance. To reach this aim, the data of the financial statements firms of Indonesia were used. As the first method, a Pearson correlation matrix was applied to determine a statistically significant correlation between capital structure indicator (debt-to-assets ratio) and financial performance and ownership of the firms. The analysis used the data panel multiple regression model to assess the effect of these independent and controlling variables on leverage. Some findings are that profitability has positive (ROA) and negative (ROE) effect on leverage. MBV and tangibility do not affect the capital structure, and firm size negatively impacts on it. In this panel analysis, it was confirmed that the managerial and institutional ownership impact on leverage negatively and positively, respectively. By decreasing the sales growth, the debt ratio entity rises, or they have a negative relationship. Based on these findings, it can be stated that financial performances influenced the capital structure.