Indonesia Stock Exchange provides Islamic stocks for Muslim investors who want toinvest, with the first Islamic stock index in Indonesia being Jakarta Islamic Index or JIIthat consists of thirty of the most liquid Islamic stocks. The market capitalization of JIItends to increase every year. This paper examines the presence of herding behavior inemerging Islamic stock market of Indonesia using daily return of Indonesia CompositeIndex and JII from October 6, 2000 to October 5, 2018. Herding behavior could generallytrigger shifting market prices from equilibrium values. Herding behavior may beidentified from the relation between stock return dispersion and market return. Stockreturn dispersion is measured using Cross Sectional Absolute Deviation or CSAD.Generalized Auto Regressive Conditional Heteroskedasticity or GARCH method isused to detect herding behavior. GARCH does not see heteroskedasticity as a problem,instead uses it to make a model. The result indicates that herding behavior exist inIslamic stock market of Indonesia. Asymmetric herding occurs in Indonesia Islamicstock market where herding behavior exists during falling market condition only.
Dividend policy is one of the most important functions for corporate finance and has influence with various company stakeholders. Dividend policy reflects the quality and reputation of the company, namely the company's ability to manage its business processes to generate profits well or vice versa. In practice, companies experience difficulties in determining and deciding dividend policies, namely the decision to withhold profits to be used as company operational development or to distribute dividends to shareholders to increase investor confidence in the company. The difference in interests that occurs in dividend policy is called agency theory. This study aims to determine the effect of collateralizable assets (COLLAS), growth in net assets, liquidity (CR), leverage (DER), and profitabilitas (ROE) on dividend policy (DPR) in non-financial service companies listed on the Indonesia Stock Exchange in 2016-2019. The data used in this study was obtained from financial report data taken from the official website of the Indonesia Stock Exchange. The population in this study are non-financial service companies listed on the Indonesia Stock Exchange in 2016-2019. The sampling technique used is perposive sampling and obtained 31 firms with a research period of 4 years, thus obtaining 124 sample data. The method of data analysis in this study is panel data regression analysis using software Eviews 11.0. The results showed that simultaneous collateralizable assets (COLLAS), growth in net assets, liquidity (CR), leverage (DER), and profitabilitas (ROE) had a significant effect on dividend policy (DPR). Meanwhile, partially Collateralizable Assets (COLLAS) has a significant effect in a positive direction on dividend policy. Growth In Net Assets has no significant effect in a negative direction on dividend policy. Liquidity (CR) does not have a significant negative effect on dividend policy. Leverage (DER) does not have a significant negative effect on dividend policy. Profitability (ROE) has a significant effect in a positive direction on dividend policy.
In this study we are going to discuss about optimal dynamic portfolio strategy given the new information of the market to the investor. The objective is to find the optimal strategy that maximizes the expected total hyperbolic absolute risk aversion (HARA)-utility of investor weight portfolio over finite life time. There are two assets that take place in to the dynamic portfolio model, risky asset and risk-free bond with constant interest rate. The underlying stock price is obtained under binomial process of Markov chain approximation of diffusion process. The stochastic dynamic programming is used as the approach to solve the problem. In contrast to the continuoustime counterpart, the optimal trading strategies are found to be time-dependent in recursive manners. Sufficient conditions for short selling are given in terms of physical and martingale probabilities of the stock price.
Value at Risk (VaR) is a tool to predict the greater loss less than the certain confidence level over a period of time. Value at Risk Historical Simulation produce reliable value of VaR because of the historical data and measure the skewness of the observe data. So, Value at Risk well used by investors to determine the risk to be faced on their investment. To calculate VAR it is better to use maximum likelihood, which has been considered for estimating from historical data and also available for estimating nonlinear model. It is also a mathematic function that can approximate return. From the maximum likelihood function with normal distribution, we can draw the normal curve at one tail test. This research conducted to calculate Value at Risk using maximum likelihood. The normal curve will be compared with data return at each bank (Bank Mandiri, Bank BRI and Bank BNI). Empirical results demonstrated that Bank BNI in 2009, Bank BRI in 2010 and Bank BNI in 2011, had less value of VaR by historical simulation in each year. It is concluded that by using maximum likelihood method in the estimation of VaR, has certain appropriates compared with the normal curve.
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