In deciding to invest in stocks traded in the capital market, investors need to predict which stocks provide the prospect of return and the risks to be faced. This paper aims to predict the return and risk of stock asymmetry using a time series model approach. Predicting stock returns and risk is based on the Autoregressive Integrated Moving Average-Glosten Jagannatan Runkle-Generalized Autoregressive Conditional Heteroscedasticity (ARIMA-GJR-GARCH) model. In contrast, the largest risk potential measurement is performed using the Value-at-Risk (VaR) model. The data analyzed are the best ten stocks according to the criteria that apply on the IDX, the period between 17 December 2018 to 14 December 2021, which includes the names of stock BBCA, BBNI, BBRI, BMRI, ASII, ICBP, PGAS, PTBA, TLKM, and UNVR. The analysis results show that of the best ten stocks, based on the ratio between the predicted values of the average return and Value-at-Risk, those with relatively better performance are PTBA, TLKM, UNVR and BBCA stocks. Based on the results of this analysis, it can be used as a reference in making investment decisions for investors, specifically investing in the ten stocks analyzed.

The DSH Meat Kiosk is a kiosk that sells one of the foodstuffs, namely Beef. This DSH Meat Kiosk has been around for more than 20 years. However, as long as this kiosk is established, the manager still finds it difficult to analyze the profit from the sale. Therefore, this Profit and Loss Financial Statement is intended to assist traders in managing the profits generated. In this report a financial analysis is made in November 2021 and February 2022. The method used in making this report is to use primary data by collecting data in the form of interviews with kiosk owners relating to things needed in making profit and loss statements such as assets that owned, total revenue, operating costs and others. The results of this report show that sales in February 2022 decreased by 17.88% compared to November 2021. With this report, it is hoped that this report will help and make it easier to manage the profits generated and make decisions to generate the best profits.The discussion of the selected questions will look for what actuarial obligations are and what normal costs are based on the data provided. The purpose of this discussion is to know, understand, and be able to perform actuarial calculations regarding the unit credit method used. The unit credit method used is the traditional unit credit and the projected unit credit. The formula used for each question is as follows. andThe result of solving the first problem shows that the total actuarial liability on 1/1/95 is IDR 405,339.095. While the results of the second question show that the normal cost for 2021 on 1/1/2021 was IDR 1,071.43. From these results, users can find out how much actuarial obligations are and what normal costs are based on the data that has been provided.

Stocks are one of the most widely used financial market instruments by investors in investing. The most important component of any investment is volatility. Volatility is a conditional measure of variance in stock returns and is important for risk management. In addition to volatility, the important things in investing are return and risk. Risk can be measured using Value-at-Risk (VaR) and can estimate the maximum loss that occurs. The purpose of this study is to determine VaR using the Autoregressive Moving Average-Glosten Jagannatan Runkle-Generalized Autoregressive Conditional Heteroscedasticity (ARMA-GJR-GARCH) model. The stages of data analysis used are estimating the ARMA model and the GARCH model, then estimating the GJR-GARCH model by looking at the heteroscedasticity and asymmetric effects on the GARCH model. Next, determine the VaR value from the estimated mean and variance (volatility) using the ARMA-GJR-GARCH model. The results of the model estimator obtained are based on the return data for the four stocks analyzed, namely the ARMA (5,5)-GJR-GARCH (1,1) model for ICBP stocks and ARMA (1,2)-GJR-GARCH (1,1) for PGAS shares. The Value-at-Risk values of each stock are 0.060427 and 0.024724. This research can be used by investors as a consideration in making investment decisions.

Investment is an allocation of money, stocks, mutual funds, or other valuable resources provided by someone at the present time and held from being used until a specified period to get a profit (return). The higher the return received, the higher the risk. This study studied the Mean-Variance investment portfolio optimization model without risk-free assets to obtain the optimum portfolio. Five shares are used, namely BMRI, AMRT, SSMS, MLPT, and ANTM. The research results obtained optimal portfolio stocks with respective weights BMRI = 0.45741; AMRT=0.17852; SSMS=0.23300; MLPT=0.08475 and ANTM=0.04632. An optimal portfolio composition produces an average return = 0.00207 and variance = 0.00020.

Stocks are investment instruments that provide returns but tend to be risky. The most important component of investing is volatility, where volatility is identical to the standard conditional deviation of stock price return. The important thing in investing in addition to return is a risk. Value-at-Risk (VaR) is a statistical method of estimating maximum losses. To evaluate the quality of VaR estimates, models should always be back-tested with appropriate methods. Back-testing is a statistical procedure in which actual gains and losses are systematically compared to appropriate VaR estimates. To evaluate the quality of VaR estimates, models should always be back-tested with appropriate methods. Back-testing is a statistical procedure in which actual gains and losses are systematically compared to appropriate VaR estimates. The goal of the study was to estimate the Autoregressive Moving Average-Generalized Conditional Heteroscedastic (ARMA-GARCH) model to determine Value-at-Risk and back-testing. ARMA is a combination of AR and MA models, while GARCH is a time series model with symmetrical properties. The method in this study is systematic browsing of libraries. Systematic library tracing is an attempt to identify, evaluate, and interpret all research relevant to a particular phenomenon.