COPULA FRANK UNTUK PERHITUNGAN VALUE AT RISK PORTOFOLIO BIVARIAT PADA MODEL EXPONENTIAL GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY

Stocks are one type of investment that promises return for investors but often carries a high risk. Value at Risk (VaR) is a measuring tool that can calculate the amount of the worst loss that occurs in a stock portfolio with a certain level of confidence and within a certain time period. In general, financial data have a high volatility value, which causes the residuals are not normally distributed. ARCH/GARCH modoel is used to solve the heteroscedasticity problem. If the data also have an asymmetric effect, it is modelled with Exponential GARCH model. Copula-Frank is part of the Archimedian copula which is used to solve empirical cases. The data on this study were BBCA and KLBF stock price return data in the observation period 30 December 2011 – 6 December 2019. Furthermore, to test the validity of the VaR model, a backtesting test will be carried out using the Kupiec Test. The results showed that the best model used for BBCA stocks was ARIMA (1,0,1) EGARCH (1,1) and for KLBF stocks was ARIMA (1,0,1) EGARCH (1,2). The amount of risk with a 95% confidence level used a combination of the EGARCH and Copula-Frank models was 2.233% of today's investment. Based on the backtesting test used the Kupiec Test, the VaR model of the portfolio obtained was declared valid.

Analysis Investor Index Indonesia with Capital Asset Pricing Model (CAPM)

This study aimed to compare composition of the optimal portfolio of stocks, the proportion of funds in each of these stocks and calculate risk and return portfolio from Investor33 (INV33) Index and Jakarta Islamic Index (JII) in research period January 2016-December 2018. The method used in this research is a quantitative descriptive method. Sample in this study using purposive sampling were 24 stock from INV33 Index and 17 stock from JII Index. The results of the study were as follows : (1) The optimal portfolio of stocks by using capital asset pricing model from INV33 Index are CPIN (Charoen Pokphand Indonesia Tbk), ITMG (Indo Tambangraya Megah Tbk), BBCA (Bank Central Asia Tbk), UNTR (United Tractor Tbk), (TLKM) Telekomunikasi Indonesia (Persero) Tbk, ICBP (Indofood CBP Sukses Makmur Tbk), BBTN (Bank Tabungan Negara Persero Tbk and from JII Index are ADRO (Adaro Energy Tbk), ICBP (Indofood CBP Sukses Makmur Tbk), INCO (Vale Indonesia Tbk), INDF (Indofood Sukses Makmur Tbk), TLKM (Telekomunikasi Indonesia Persero Tbk), UNTR (United Tractor Tbk). (2) The composition of the proportion of funds in optimal portfolio formed by INV33 Index are BBCA (46,49%), CPIN (20,11%), ICBP (12,78%), ITMG (8,59%), UNTR (6,95%), TLKM (4,11%) and BBTN (0,97%) and from JII Index are ICBP (34,96%), ADRO (19,47%), UNTR (16,26%), INCO (10,88%), TLKM (10,43%) and INDF (8,00%). (3) The optimal portfolio of stocks return from INV33 Index was greater than stock portfolio return from JII Index and the optimal portfolio of stocks risk from INV33 Index was lower than stock portfolio risk from JII Index.

The COVID-19 Outbreak and Risk–Return Spillovers between Main and SME Stock Markets in the MENA Region

This study investigates return and asymmetric volatility spillovers and dynamic correlations between the main and small and medium-sized enterprise (SME) stock markets in Saudi Arabia and Egypt for the periods before and during the COVID-19 pandemic. Return and volatility spillovers are modelled using a VAR-asymmetric BEKK–GARCH (1,1) model, while a VAR-asymmetric DCC–GARCH (1,1) model is employed to model the dynamic conditional correlations between these markets, which are then used to determine and explore portfolio design and hedging implications. The results show that while bidirectional return spillovers between the main and SME stock markets are limited to Saudi Arabia, shock and volatility spillovers have different characteristics and dynamics in both main–SME market pairs. In addition, the dynamic correlations between the main and SME markets are mostly positive and have notably increased during the COVID-19 pandemic, particularly in Saudi Arabia, suggesting that adding SME stocks to a main stock portfolio enhances its risk-adjusted return, especially during tranquil market phases. One practical implication of our results is that the development of SME stock markets can indirectly contribute to economic development via the main market channel and provide an avenue for portfolio diversification and risk management.

RISK ASSESSMENT OF STOCKS PORTFOLIO THROUGH ENSEMBLE ARMA-GARCH AND VALUE AT RISK (CASE STUDY: INDF.JK AND ICBP.JK STOCK PRICE)

Stocks portfolio is a form of investment that can be used to minimize the risk of loss. In a stock portfolio, the Value at Risk (VaR) can be predicted through the portfolio return. If portfolio return variance is heteroskedastic risk prediction can be done by using VaR with ARIMA-GARCH or Ensemble ARIMA-GARCH model approach. Furthermore, the accuracy of VaR is tested through Backtesting test. In this study, the portfolio is formed from PT Indofood CBP Sukses Makmur (ICBP.JK) and PT Indofood Sukses Makmur Tbk (INDF.JK) stocks from 01/01/2018 to 07/30/2021. The results showed that the best model is Ensemble ARMA-GARCH with MSE 1.3231×10-6. At confidence level of 95% and 1 day holding period, the VaR of the Ensemble ARMA-GARCH was -0.0213. Based on the Backtesting test, it is proven to be very accurate to predict the value of loss risk because the value of the Violation Ratio (VR) is equal to 0.

AHP–TOPSIS Methodology for Stock Portfolio Investments

This paper presents a methodology for making decisions in the stock market using the AHP-TOPSIS multi-criteria technique. The problem is related to the stock market’s investment process considering the criteria of liquidity, risk, and profitability. The proposed methodology includes integrating economic and financial theories of investment in equity portfolios with the AHP-TOPSIS multi-criteria technique, which allows for evaluating a finite number of alternatives hierarchically under qualitative and quantitative criteria. The methodology has been tested in a real case of selecting a portfolio of high and medium marketability stocks for the Colombian market from April 2012 to April 2017. The computational results show the importance and efficiency of successfully integrating traditional equity portfolio investment criteria and multi-criteria methodologies to find an appropriate balance between profitability and risk in the investment decision-making process in shares in the Colombian stock market. The proposed methodology could be applied to other emerging markets, similar to Colombia.

USE OF TRADING TO INCREASE PROFITABILITY OF SECURITIES PORTFOLIO IN INVESTMENT FUNDS

It is determined that in investment processes, each percentage of returns is important. The hypothesis was considered that active management of the stock portfolio through intensive trading is a potential way of significantly improving the level of efficiency of investments in the stock market. The purpose of the work was to study the feasibility of using trading to increase the profitability of the securities portfolio and, in particular, for institutional investors. Trading of shares (intensive purchase and sale) is considered as a factor in increasing the profitability of investments in shares. The shortcomings of the intensification of trading are specified, which consists in an increase in taxes, brokerage commissions and lost profits due to the expectation of a better date for entering the transaction. As a research method, modeling based on the data of a three-year period of dynamics of Microsoft shares and hypothetical companies was used. The corresponding modeling made it possible to draw the following conclusions: the increase in trading intensity does not guarantee an increase in the level of investment efficiency; the increase in trading intensity leads to an increase in the tax burden and risk level, which ultimately neutralizes efforts on intensive trading. Investment funds which are actively managed and use intensive trading in activities do not have a significant advantage over funds that have passive management. The basis of the efficiency of investment funds is the minimization of overhead costs, including by minimizing taxes due to the reduction of the level of trading intensity to zero. It is important to pre-envisage promising shares for purchase, and keep them in their own portfolio for a long period of time with a minimum level of portfolio balancing intensity. Rebalancing the stock portfolio on the principle of profit fixing leads to an increase in tax payments and neutralizes capital growth opportunities due to the sale of shares with high growth potential. Keywords: trading, shares, investment fund, rebalancing of securities portfolio. JEL Сlassification G11, G14, G20 Formulas: 0; fig.: 4; tabl.: 5; bibl.: 14.

Analysis and implementation of realtime stock prediction using reinforcement frameworks

Real time stock prediction is interesting research topic due to the risk involved with volatile scenarios. Modelling of the stocks by reducing the overestimation in ANN model, due to rapid fluctuations in the market guide fund managers risky decisions while building stock portfolio. This paper builds real time framework for stock prediction using deep reinforcement learning to buy, sell or hold the stocks. This paper models the transformed stock tick data and technical indicators using Transformed Deep-Q Learning. Our framework is cost reduced and transaction time optimized to get real time stock prediction using GPU and Memory containers. Stock predictor is architected using GRPC based clean architecture which has the benefits of easy updates, addition of new services with reduced integration costs. Data archive features of the cloud will give benefit of reduced cost of the new stock predictor framework.