The Capital Structure of Venture Capital Firms in Indonesia

Venture capital (VC) is an important fund source for small and medium enterprises (SMEs) and start up, particularly to deliver its main product of equity participation. Therefore, capital structure and factors that affect it are very crucial. This study aims to analyze the capital structure of VC firms in Indonesia using econometric model of panel data regression. This study utilizes secondary data of six years period (2009-2014) monthly financial statements of 27 samples out of 58 VC firms to form 1,944 observations. The study reveals that capital structure of VC firms in Indonesia is dominated by debt/loan rather than capital with DER on average is 136.95%. In addition, the research confirms that VC firms\u27 capital structure is affected simultaneously by financial aspects which are asset size, profitability, liquidity, asset/investment quality, and earning asset structure. The attentions to financial aspects that affect the VC firms\u27 capital structure as well as other initiatives related to capital increases are necessary so that the VC firms could carry out its role effectively

Industri Perbankan Indonesia Periode 2001-2014: Deteksi Konsentrasi Pasar dan Prestasi Alma

This descriptive study aims to explain the condition of credits market and deposit market of Indonesian banking in the period of 2001-2014 includes the achievement of ALMA and CAMEL based on the secondary data and financial statements of 97 Indonesian banks. This graph result and tabulation and financial ratio show the information that Indonesian banking market is in the competitive condition (CR4 index and HHI index decrease and the market is classified in loose oligopoly condition), however all main variables of ALMA show the high ranking

Liberalisasi Keuangan dan Pengaruhnya terhadap Nilai Q-tobin Sektor Industri Dasar dan Kimia dan Perbankan

This research attempts to analyze the effect of financial liberalization to Q-Tobin ratio of Basic and Chemical industry and Banking sectors. Using annual data of 52 listed company's financial report from 2002 to 2009, the results show that the financial liberalization variables i.e. Foreign Direct Investment (FDI) and Investment Portfolio has negative effect on Q-Tobin of Basic and Chemical Industry and Banking sectors. The increase of the financial deepening variables has positive effect on Q-Tobin of Basic and Chemical Industry and Banking sectors. SBI (Sertifikat Bank Indonesia) and Money Supply has negative impact on Q-Tobin, while loan interest rates has positive impact on both sectors. The average of net fixed asset investment of two sectors has the same pattern of Q-Tobin values, and increased from 2002 to 2009, while at the year of 2008, Q-Tobin of all sectors experienced decreasing due to financial crisis. Furthermore, there should be a corporate financial performance indicator such as leverage ratio, to prevent short term investment of FDI. Capital Market's regulation, should be considerate a sectoral policy in portfolio investment, to prevent from financial global crisis. Corporation of two sectors could give more attention on capital structure while analyzing the company's investment decision

Model Sistem Bisnis Intelijen dalam Pengambilan Keputusan Persaingan Teknologi Informasi Perbankan

The objective of this study is to provide a structured modelling of business intelligence system for helping decision makers in banking IT management to extract external competitive information, to anticipate business environment changes, and to make appropriate and effective business decisions. Using a system perspective where the focus is on the interaction between dependent and independent variables, this study shows how IT management could use neural network to help improve the strategy formulation process. Combined with a business intelligence roadmap, the system has an ability to process internal and external data as well as providing intelligence analysis to determine IT strategy to anticipate competition. Expert system and artificial neural network were used in this competition submodel to predict bank's transaction cost strategies which gives some priority actions for management to decide the best IT cost strategy such as focus, differentiation, and overall cost leadership