Risk analysis of five stocks indexed by LQ45 using credible value at risk and credible expected tail loss

Sulistianingsih, Evy; Rosadi, Dedi; Abdurakhman, Abdurakhman

doi:10.1088/1742-6596/1918/4/042023

Cited by 5 publications

(5 citation statements)

References 7 publications

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“…En esta medida, el autor hace aportes para superar estas carencias y recomienda el uso del VaR entrópico (EVaR) con el fin de obtener valores más confiables en la gestión del riesgo, para ello sugiere que este indicador se pueda aplicar en estudios de mercado diarios, unas horas antes de que inicie el horario de negociación para determinar cómo debe decidir el inversionista u operador de bolsa con relación al próximo riesgo del mercado. Sulistianingsih et al (2021) se refiere a las limitantes que tiene el VaR, porque aunque el indicador puede pronosticar la máxima pérdida cuando el inversionista tiene un solo activo, el inversionista opta con normalidad por tener un portafolio de activos y esto hace que pueda crear un riesgo compartido entre los activos que componen el portafolio, pues un activo puede contagiarse de las pérdidas que pueden experimentar otros activos inmersos en dicho portafolio y el VaR no puede dar información sobre las pérdidas de cola, por lo que se requiere la medida de perdida de cola esperada (ETL por sus siglas en inglés). De esta manera, los autores proponen la medida del valor creíble en el riesgo (CredVaR) y la perdida esperada de cola Credible (CredETL), donde se obtiene como resultado que ambos métodos son potentes y pueden detectar un riesgo de inversión que supera la falencia del VaR al no tener en cuenta el riesgo de los activos dentro del portafolio.…”

Section: Referentes Teóricosunclassified

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Análisis del índice precio-beneficio ajustado cíclicamente en portafolios del mercado accionario brasileño, 2011-2019

Contreras¹,

Amado²,

Santamaria³

et al. 2022

Semest. Econ.

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Este artículo evalúa las bondades del indicador precio-beneficio ajustado cíclicamente para la construcción de portafolios de inversión en el mercado accionario brasileño para el periodo 2011-2019. Para cumplir este objetivo se tomó información del valor de las acciones de treinta y tres empresas que cotizan en la bolsa de valores de Brasil y se les aplica el índice para la construcción de portafolios eficientes. El comportamiento de los activos financieros que componen dichos portafolios se comparó con el índice Bovespa, y luego se procedió a calcular el valor del riesgo, con el fin de generar portafolios de inversión con un riesgo equivalente al Bovespa. A pesar de que existen estudios de aplicación de este indicador en diversos mercados, son pocos los que se enfocan en el precio-beneficio ajustado cíclicamente para la construcción de portafolios de inversión y no se evidencia la existencia de análisis de este tipo enfocados en el mercado latinoamericano, de aquí la importancia de este trabajo. Como resultado, se observó que el rendimiento de los portafolios construidos con esta metodología supera al Bovespa en seis de los nueve años analizados, además, entre 2011 y 2019 los portafolios construidos generaron un rendimiento 3,27 veces superior al Bovespa.

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Section: Referentes Teóricosunclassified

“…Por otro lado, en contraposición a lo descrito por autores como Sulistianingsih et al (2021), con respecto a las limitaciones del VaR, la metodología utilizada en el mercado brasileño si arrojó resultados positivos al momento de construir portafolios de inversión de largo plazo.…”

Section: Conclusionesunclassified

Análisis del índice precio-beneficio ajustado cíclicamente en portafolios del mercado accionario brasileño, 2011-2019

Contreras¹,

Amado²,

Santamaria³

et al. 2022

Semest. Econ.

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“…The formation of an efficient and optimal portfolio is the goal of stock diversification, namely placing several funds in various investment alternatives so that the funds can generate optimal returns. The placement and merging of several shares are then referred to as a portfolio (Sulistianingsih & Rosadi, 2021).…”

Section: Introductionmentioning

confidence: 99%

Investment Portfolio Optimization Model Using The Markowitz Model

Sirait¹,

Salih²,

Hidayana³

2022

ijqrm

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The stock portfolio is related to how someone allocates several shares in various types of investments so that the results achieve maximum profit. By implementing a diversification system or portfolio optimization on several stocks, investors can reduce the level of risk and simultaneously optimize the expected rate of return. This study aims to determine which stocks listed on the Indonesia Stock Exchange (IDX) and included in the portfolio for the 2021-2022 period are eligible to be included in the optimal portfolio and to determine the proportion of funds for each share in the formation of the optimal portfolio. The population in this study are all shares included in the Indonesia Stock Exchange (IDX) listed on the Indonesia Stock Exchange (IDX) for the 2021-2022 period. The sample of this research is five stocks that are candidate portfolios. The sampling method uses a purposive sampling method with the criteria of 5 stocks with the highest positive ratio. The population in this study was all 30 companies included in the IDX30, while the samples were five companies. Data were analyzed using a mean-variant optimization model with a research duration between May 2021 and May 2022. Based on the results of the investment portfolio optimization analysis on the 5 (five) selected stocks, this study shows that, out of 23 stocks, five stocks are eligible to enter the optimal portfolio with their respective proportions, namely PT Adaro Energy Indonesia Tbk (ADRO) 20%, PT Astra International Tbk (ASII) 26%, PT Merdeka Copper Gold Tbk (MDKA) 10%, PT XL Axiata Tbk (EXCL) 19%, PT Bukit Asam Tbk (PTBA) 25%. The portfolio of these stocks generates an expected return of 0.00217 at a risk level of 0.00022. It is hoped that this research can be helpful to add to the literature on investment optimization models, especially the concentration of Mathematics in Finance, and serve as an additional reference for further research, as well as an alternative for investors in optimizing investment portfolios.

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“…Investment risk is the loss experienced by investors in investing. There are several types of investment risk, namely business risk, financial risk, inflation risk, liquidity risk, country risk, currency risk, market risk, and interest risk (Sulistianingsih, et al, 2021). In financial markets, the measure of risk that determines the potential for loss can be Value-at-Risk.…”

Section: Introductionmentioning

confidence: 99%

Determination of Risk Value Using the ARMA-GJR-GARCH Model on BCA Stocks and BNI Stocks

Hidayana¹,

Napitupulu²,

Saputra³

2021

Orics

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Stocks are common investments that are in great demand by investors. Stocks are also an investment instrument that provides returns but tends to be riskier. The return time series is easier to handle than the price time series. In investment activities, there are the most important components, namely volatility and risk. All financial evaluations require accurate volatility predictions. Volatility is identical to the conditional standard deviation of stock price returns. The most frequently used risk calculation is Value-at-Risk (VaR). Mathematical models can be used to predict future stock prices, the model that will be used is the Glosten Jagannathan Runkle-generalized autoregressive conditional heteroscedastic (GJR-GARCH) model. The purpose of this study was to determine the value of the risk obtained by using the time series model. GJR-GARCH is a development of GARCH by including the leverage effect. The effect of leverage is related to the concept of asymmetry. Asymmetry generally arises because of the difference between price changes and value volatility. The method used in this study is a literature and experimental study through secondary data simulations in the form of daily data from BCA shares and BNI shares. Data processing by looking at the heteroscedasticity of the data, then continued by using the GARCH model and seeing whether there is an asymmetry in the data. If there is an asymmetric effect on the processed data, then it is continued by using the GJR-GARCH model. The results obtained on the two stocks can be explained that the analyzed stock has a stock return volatility value for the leverage effect because the GJR-GARCH coefficient value is > 0. So, the risk value obtained by using VaR measurements on BCA stocks is 0.047247 and on BNI stocks. is 0.037355. Therefore, the ARMA-GJR-GARCH model is good for determining the value of stock risk using VaR.

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Risk analysis of five stocks indexed by LQ45 using credible value at risk and credible expected tail loss

Cited by 5 publications

References 7 publications

Análisis del índice precio-beneficio ajustado cíclicamente en portafolios del mercado accionario brasileño, 2011-2019

Análisis del índice precio-beneficio ajustado cíclicamente en portafolios del mercado accionario brasileño, 2011-2019

Investment Portfolio Optimization Model Using The Markowitz Model

Determination of Risk Value Using the ARMA-GJR-GARCH Model on BCA Stocks and BNI Stocks

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