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.

The Dynamic Effects of Anchoring on Portfolio Return Volatility; Are there any economic issues in the developing world?

This study aims to see the anchoring effect on portfolio return volatility in the case of KSE-30. Business anomalies such as overreaction and under-reaction are affected by a variety of psychological causes. The use of anchors or baseline values known as the anchoring effect causes market under-reaction and overreaction. This research used nearness to 52-week high and nearness to historical high as proxies for under and over-reaction, respectively, to analyze the psychological causes for under and over-reaction. On the KSE-30, the findings revealed that proximity to the 52-week peak positively predicts future returns, whereas proximity to the historical high negatively predicts future returns. KSE-30 was used for rigorous testing. Similarly, the three macroeconomic variables used as control variables are the exchange rate, inflation rate, and interest rate to provide a more robust model of strong prediction capacity. The findings revealed that proximity to the 52-week maximum and proximity to the historical high and other macroeconomic factors had a forecast capacity of around 62 percent. Similarly, focused on volatility clusters, the GARCH (1, 1) model was used to measure the association between potential and past returns. The results show that there is a first order autoregressive function in the GARCH (1, 1) model. The findings also show that their predictive capacity decreases when the study's individual variables are moved from every day to annual Periods.

Unleveraged Portfolios and Pure Allocation Return

In asset management, the portfolio leverage affects performance, and can be subject to constraints and operational limitations. Due to the possible leverage aversion of the investors, the comparison between portfolio performances can be incomplete or misleading. We propose a procedure to unleverage the mean-variance efficient portfolios to satisfy a leverage requirement. We obtain a class of unleveraged portfolios that are homogeneous in terms of leverage, so therefore properly comparable. The proposed unleverage procedure permits isolating the pure allocation return, i.e., the return component, due to the qualitative choice of portfolio allocation, from the return component due to the portfolio leverage. Theoretical analysis and empirical evidence on actual data show that efficient mean-variance portfolios, once unleveraged, uncover mean-variance dominance relations hidden by the leverage contribution to portfolio return. Our approach may be useful to practitioners proposing to take long positions on “short assets” (e.g. inverse ETF), thereby considering short positions as active investment choices, in contrast with the usual interpretation where are used to overweight long positions.

Treynor, Sharpe, and Jensen Ratio of Health Sector Companies on The Indonesia Stock Exchange Before and During Covid-19 Pandemic Period

This research aims to compare the return and risk in investment at the stock portfolio of Health Sector Companies in period before the COVID-19 pandemic and during the COVID-19 pandemic in Indonesia. This research conducted using quantitative method with descriptive approach with secondary data with samples of the stock of Health Sector Companies listed in the Indonesia Stock Exchange ("IDX") which then formulated into a stock portfolio. The research period used is the period March 2019-Feb 2020 for before COVID19 pandemic, and the period March 2020 - Feb 2021 for during the COVID19 pandemic. The stock portfolio return and risk measurement is measured by Sharpe, Treynor, and Jensen Ratio which then will be statistically tested to see if there are significant differences of ratios between the two conditions.

Portfolio Construction Using Financial Ratio Indicators and Classification through Machine Learning

Financial Ratios have been a major indicator for financial asset selection. It’s seen that the decision taken to construct a portfolio based on financial ratio indicators has been able to make better returns than the random asset allocation process in the portfolio. This research will show multiple classifications based on unsupervised machine learning processes to satisfactorily determine investable assets or securities for portfolio contribution. Our suggested portfolio would then be compared with a random portfolio for a specific time frame in order to determine portfolio return, Sharpe ratio, and portfolio performance.

Estimation of value at risk by using gjr-garch copula based on block maxima

This paper will discuss the risk estimation of a portfolio based on value at risk (VaR) using a copula-based asymmetric Glosten – Jagannathan – Runkle - Generalized Autoregressive Conditional Heteroskedasticity (GJR-GARCH). There is non-linear correlation for dependent model structure among the variables that lead to the inaccurate VaR estimation so that we use copula functions to model the joint probability of large market movements. Data is GEV distributed. Therefore, we use Block Maxima consisting of fitting an extreme value distribution as a tail distribution to count VaR. The results show VaR can estimate the risk of portfolio return reasonably because the model has captured the data properties. Data volatility can be accommodated by GJR-GARCH, Copula can capture dependence between stocks, and Block maxima can accommodate extreme tail behavior of the data.

Risk-Return Analysis on Optimum Portfolio Selection of Islamic Stocks

<p><em>This study aims to determine the optimum portfolio category and analyze the risk-return on a formed portfolio. Data was taken from eighteen listed companies indexed by Jakarta Islamic Index during 2015-2018. Stock returns are calculated based on the closing price at the end of each month in the period. Sharia Certificate of Bank Indonesia is a proxy of risk-free return, while the market return is measured by the value of the Jakarta Islamic Index. Stocks are sorted by the value of excess return to beta (ERB) from highest to lowest, and to obtain optimal stock portfolio candidates, and the ERB value must be compared with the cut-off rate value. Seven issuers qualify for forming the optimum portfolio of shares. The results show that the optimum portfolio return is greater than the expected return and the expected risk-free return. When compared between individual stock returns and portfolio stock returns, some individual stocks provide higher returns than portfolio returns. However, the risk of individual shares was also higher than the risk of the portfolio. This finding proves that risk can be reduced optimally in Islamic stocks selection by forming an optimum portfolio.</em></p>

A Probabilistic-Based Portfolio Resampling Under the Mean-Variance Criterion

An abundant amount of literature has documented the limitations of traditional unconstrained mean-variance optimization and Efficient Frontier (EF) considered as an estimation-error maximization that magnifies errors in parameter estimates. Originally introduced by Michaud (1998), empirical superiority of portfolio resampling supposedly lies in the addressing of parameter uncertainty by averaging forecasts that are based on a large number of bootstrap replications. Nevertheless, averaging over resampled portfolio weights in order to obtain the unique Resampled Efficient Frontier (REF, U.S. patent number 6,003,018) has been documented as a debated statistical procedure. Alternatively, we propose a probabilistic extension of the Michaud resampling that we introduce as the Probabilistic Resampled Efficient Frontier (PREF). The originality of this work lies in addressing the information loss in the REF by proposing a geometrical three-dimensional representation of the PREF in the mean-variance-probability space. Interestingly, this geometrical representation illustrates a confidence region around the naive EF associated to higher probabilities; in particular for simulated Global-Mean-Variance portfolios. Furthermore, the confidence region becomes wider with portfolio return, as is illustrated by the dispersion of simulated Maximum-Mean portfolios.

Analysis of Optimal Portfolio Formation Using a Single Index Model as a Basis for Investment Decision Making [Study on the Jakarta Islamic Index (JII) 2015-2017]

The aim of this research is to know the performance ofstocks that become the member ofJakarta Islamic Index (JII) and determine what stocks are part of the optimal portfolio. This research is a quantitative descriptive study. The technique of taking samples is using purpose sampling techniques, with determined criteria. The population needed in this study is 42 stocks. After doing the sampling purposes, the sample is 20 stocks. The analysis technique used is the Single Index Model. The results of this study reveal that there are 5 stocks which eligible to become optimal portfolio members. These shares are UNTR, AKRA, UNVR, TLKM, and ADRO. Then the proportion offunds in each consecutive share is 31.58%, 15.18%, 28.02%, 17.7% and 7.52%. While the portfolio of the portfolio formed (expected portfolio return) is 0. 0203 or 2. 03% with portfolio risk of0. 0006 or 0. 06%.