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PEMILIHAN PORTFOLIO ROBUST DENGAN KLROBUST PORTFOLIO SELECTION WITH CLUSTERING BASED ON BUSINESS SECTOR OF STOCKS ASTERING BERDASARKAN SEKTOR USAHA SAHAM Gubu, La; Rosadi, Dedi; Abdurakhman, Abdurakhman
MEDIA STATISTIKA Vol 14, No 1 (2021): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.14.1.33-43

In recent years there have been numerous studies on portfolio selection using cluster analysis in conjunction with Markowitz model which used mean vectors and covariance matrix that are estimated from a highly volatile data. This study presents a more robust way of portfolio selection where stocks are grouped into clusters based on business sector of stocks. A representative from each cluster is selected from each cluster using Sharpe ratio to construct a portfolio and then optimized using robust FCMD and S-estimation. Calculation Sharpe ratio showed that this method works efficiently on large number of data while also robust against outlier in comparison to k-mean clustering. Implementation of this method on stocks listed on the Indonesia Stock Exchange, which included in the LQ-45 indexed for the period of August 2017 to July 2018 showed that portfolio performance obtained using clustering base on business sector of stocks combine with robust FMCD estimation is outperformed the other possible combination of the methods.

Peramalan Harga Saham PT. Bank Central Asia, Tbk dengan Menggunakan Metode ARIMA Gubu, La; Bakti Sadewa, Muhammad Alfian; Pimpi, La
Jurnal Derivat: Jurnal Matematika dan Pendidikan Matematika Vol. 11 No. 1 (2024): Jurnal Derivat (April 2024)
Publisher : Pendidikan Matematika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jderivat.v11i1.5329

The purpose of this research is to analyze the fluctuations and quantity of daily stock closing data of PT. Bank Central Asia Tbk (BCA) during the period from January 3, 2022, to December 30, 2022, and forecast the closing stock price in January 2023 using the ARIMA model. The research procedure was conducted step by step, including data collection, descriptive analysis, testing data stationarity, determining ARIMA model parameters, writing ARIMA equation models, conducting diagnostic tests on the best ARIMA model, and making predictions or forecasts. The analysis shows that the stock closing prices in 2022 fluctuated with an average range of Rp 7,214.29 - Rp 8,851.14 per share. The highest closing price occurred in November 2022, while the lowest occurred in July 2022. The forecast for the daily closing stock price of BCA from January 2 to January 31, 2023, using the ARIMA Model (1,1,0), ranges from Rp 8,550.00 to Rp 8,550.86. The MAPE value obtained from the forecasting result is 1.07%, indicating that the ARIMA model is highly effective in forecasting the closing stock price of BCA in January 2023. This research provides valuable insights into stock price fluctuations for stakeholders in the stock market. Keywords: fluctuations, ARIMA, stock closing prices, forecast.

Pembentukan Portofolio Optimal Saham Dengan Menggunakan Model Portofolio Mean-Variance-Skewness-Kurtosis Gubu, La; Rashif Hilmi, Muhamad
Jurnal Derivat: Jurnal Matematika dan Pendidikan Matematika Vol. 11 No. 2 (2024): Jurnal Derivat (Agustus 2024)
Publisher : Pendidikan Matematika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jderivat.v10i2.6218

This paper presents the development of Markowitz's classic Mean-Variance (MV) portfolio model, namely the Mean-Variance-Skewness-Kurtosis (MVSK) portfolio model. The MVSK portfolio model aims to overcome the fact that most stock returns in the capital market do not follow a normal distribution, and there are skewness and excessive kurtosis. The solution of the MVSK portfolio model is determined using the Newton-Raphson method. To see the advantages of the MVSK model, an empirical study was carried out on a portfolio construction using the four best stocks on the Indonesian Stock Exchange, which are included in the LQ45 index group for February-July 2023. The empirical study shows that for risk aversion the performance of portfolios formed using the MVSK model outperforms portfolios formed using the classical MV model, while for risk aversion the performance of portfolios formed using the classic MV model outperforms portfolios formed using the MVSK model. In addition, it was also found that for risk aversion , the weight and performance of the portfolio formed using the MVSK model were close to the weight and performance of the portfolio formed using the classic MV model. Keywods: portofolio, return, risk, portfolio performance, MVSK.

OPTIMASI PORTOFOLIO MEAN-VARIANCE DENGAN ANALISIS KLASTER FUZZY C-MEANS Gubu, La; Cahyono, Edi; Arman, Arman; Budiman, Herdi; Djafar, Muh. Kabil
Jurnal Gaussian Vol 12, No 4 (2023): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.12.4.593-604

Many studies have been carried out to solve and develop the Markowitz portfolio model. This was done to correct existing models in response to the changes in financial market dynamics and the needs of capital market practitioners. In this study, we provide Mean-Variance (MV) portfolio selection via cluster analysis. Fuzzy C-Means clustering is used to separate stocks into different categories. As a comparison, stocks categories were also carried out using K-Mean clustering. Based on the Sharpe ratio, a stock from each cluster is chosen as a cluster representative. The stocks chosen for each cluster have the greatest Sharpe ratio. The MV portfolio model is used to determine the best portfolio. For the empirical analysis, we examined the fundamental data and the daily return data of stocks that were included in the LQ-45 index from August 2022 to January 2023. The fundamental data of stocks are used to form clusters and the daily return of stocks are used to construct the best portfolio. The results of this study reveal that, for all given risk aversion values, portfolio performance created by Fuzzy C-Means clustering outperformed portfolio performance produced by K-Means clustering.

Peramalan Harga Saham PT. Bank Central Asia, Tbk dengan Menggunakan Metode ARIMA Gubu, La; Bakti Sadewa, Muhammad Alfian; Pimpi, La
Jurnal Derivat: Jurnal Matematika dan Pendidikan Matematika Vol. 11 No. 1 (2024): Jurnal Derivat (April 2024)
Publisher : Pendidikan Matematika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jderivat.v11i1.5329

The purpose of this research is to analyze the fluctuations and quantity of daily stock closing data of PT. Bank Central Asia Tbk (BCA) during the period from January 3, 2022, to December 30, 2022, and forecast the closing stock price in January 2023 using the ARIMA model. The research procedure was conducted step by step, including data collection, descriptive analysis, testing data stationarity, determining ARIMA model parameters, writing ARIMA equation models, conducting diagnostic tests on the best ARIMA model, and making predictions or forecasts. The analysis shows that the stock closing prices in 2022 fluctuated with an average range of Rp 7,214.29 - Rp 8,851.14 per share. The highest closing price occurred in November 2022, while the lowest occurred in July 2022. The forecast for the daily closing stock price of BCA from January 2 to January 31, 2023, using the ARIMA Model (1,1,0), ranges from Rp 8,550.00 to Rp 8,550.86. The MAPE value obtained from the forecasting result is 1.07%, indicating that the ARIMA model is highly effective in forecasting the closing stock price of BCA in January 2023. This research provides valuable insights into stock price fluctuations for stakeholders in the stock market. Keywords: fluctuations, ARIMA, stock closing prices, forecast.

Pembentukan Portofolio Optimal Saham Dengan Menggunakan Model Portofolio Mean-Variance-Skewness-Kurtosis Gubu, La; Rashif Hilmi, Muhamad
Jurnal Derivat: Jurnal Matematika dan Pendidikan Matematika Vol. 11 No. 2 (2024): Jurnal Derivat (Agustus 2024)
Publisher : Pendidikan Matematika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jderivat.v10i2.6218

This paper presents the development of Markowitz's classic Mean-Variance (MV) portfolio model, namely the Mean-Variance-Skewness-Kurtosis (MVSK) portfolio model. The MVSK portfolio model aims to overcome the fact that most stock returns in the capital market do not follow a normal distribution, and there are skewness and excessive kurtosis. The solution of the MVSK portfolio model is determined using the Newton-Raphson method. To see the advantages of the MVSK model, an empirical study was carried out on a portfolio construction using the four best stocks on the Indonesian Stock Exchange, which are included in the LQ45 index group for February-July 2023. The empirical study shows that for risk aversion the performance of portfolios formed using the MVSK model outperforms portfolios formed using the classical MV model, while for risk aversion the performance of portfolios formed using the classic MV model outperforms portfolios formed using the MVSK model. In addition, it was also found that for risk aversion , the weight and performance of the portfolio formed using the MVSK model were close to the weight and performance of the portfolio formed using the classic MV model. Keywods: portofolio, return, risk, portfolio performance, MVSK.

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