Article Per Year (5 Year)
p-Index From 2020 - 2025
0.751
P-Index
This Author published in this journals
All Journal International Journal of Global Operations Research International Journal of Quantitative Research and Modeling International Journal of Mathematics, Statistics, and Computing
Maharani, Asthie Zaskia
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Environmental Science Mathematics Physics

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Bankruptcy Prediction Analysis of General Insurance Companies Using the Ohlson Model Maharani, Asthie Zaskia; Bisyarah, Sania
International Journal of Global Operations Research Vol. 5 No. 4 (2024): International Journal of Global Operations Research (IJGOR), November 2024
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v5i4.336

Abstract

General insurance companies play an important role in maintaining economic stability by transferring financial risks from individuals and companies to insurance companies. However, insurance companies are not immune to the risk of bankruptcy that can arise due to factors such as inability to manage claims, premium fluctuations, and insufficient capital. Early detection of potential bankruptcy becomes very important to prevent greater losses. This study aims to analyze the prediction of bankruptcy in general insurance companies in Indonesia using the Ohlson Model. The Ohlson model is based on logistic regression, taking into account several financial variables such as leverage, profitability, and company size to estimate the probability of bankruptcy. The results of the study are expected to provide insights for insurance company management and regulators in identifying bankruptcy risks and taking appropriate preventive measures. In addition, this study contributes to enriching the literature related to the application of bankruptcy prediction models in the context of the insurance industry in emerging markets. From the analysis, it was found that out of 13 general insurance companies listed on the Indonesia Stock Exchange (IDX), the Ohlson value for all companies is below 0.38, which indicates that the sampled companies still have fairly good financial stability. The research results are expected to provide insights for insurance company management and regulators in identifying bankruptcy risks and taking appropriate preventive measures. In addition, this study contributes to enriching the literature related to the application of bankruptcy prediction models in the context of the insurance industry in emerging markets.
Analysis of Aggregated Claim Numbers with Geometric Distribution and Claim Sizes with Weibull Distribution Using Convolution Method Maharani, Asthie Zaskia
International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 1 (2025): International Journal of Mathematics, Statistics, and Computing
Publisher : Communication In Research And Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijmsc.v3i1.178

Abstract

An insurance claim is a form of claim from the insured party to the insurer, in this case the insurance company, which is submitted when a disaster or event that causes loss occurs. This claim is based on an agreement contract in the form of an insurance policy that has been agreed upon by both parties. Claims that arise every time a risk occurs are known as individual claims, while the total of individual claims that occur during a certain insurance period is called an aggregate claim. Aggregate loss refers to the total loss that must be borne by the insurance company due to claims filed by policyholders in a certain period. This study aims to estimate the total aggregate claim (aggregate loss) by modeling the number of claims using the Geometric distribution and the size of the claim using the Weibull distribution. The research was conducted using simulated data from PT Insurance XYZ. The method used in this research is the convolution method, which allows the calculation of the distribution of total aggregated claims based on the pairwise multiplication of the probability density function. To support the analysis, Easyfit and R Studio software were used in data processing and simulation. The results showed that the estimated total aggregate claim (aggregate loss) for a 12-month period on the simulated data was IDR2,809,454,000 using the Geometric distribution for the number of claims and the Weibull distribution for the size of the claim. In addition, the variance value obtained from the simulation results is 5.051215e-06. These findings provide an important overview of the estimation of potential losses that must be borne by insurance companies and can be used as a reference in risk management and the establishment of a more optimal financial strategy.
Investment Portfolio Optimization Using Ant Colony Optimization (ACO) Based on Fama-French Three Factor Model on IDX High Dividend 20 Stocks Maharani, Asthie Zaskia; Susanti, Dwi; Riaman, Riaman
International Journal of Quantitative Research and Modeling Vol 6, No 2 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i2.978

Abstract

Stock investment is one of the investment options that provides both profit and risk for investors. In an effort to maximize profits and minimize risks, investors need an optimal portfolio. The optimal portfolio is a portfolio selected from a collection of efficient portfolios. To form an optimal portfolio, this study combines the Fama-French Three Factor Model (FF3FM) for stock selection and Ant Colony Optimization (ACO) for stock weight optimization in the portfolio. FF3FM considers more factors resulting in more comprehensive stock selection than other methods. While ACO has the ability to explore the solution space widely and efficiently, minimizing the risk of getting stuck on a local solution. The performance of the optimal portfolio is measured using the Sharpe Ratio which considers total risk, thus providing an overview of overall investment efficiency. The research object used is quarterly stock data on IDX High Dividend 20 from the Indonesia Stock Exchange (IDX) for the period 2020-2023. Of the 20 stocks, 12 stocks were selected that were consistently included in the index during the 2020-2023 period. By selecting stocks using the FF3FM method, 10 efficient stocks were selected, namely ADRO, ASII, BBCA, BBNI, BBRI, INDF, ITMG, PTBA, TLKM, and UNTR. Portfolio optimization using ACO produces a portfolio return of 0.0473 and a risk of 0.0257 with the weight of each ADRO stock of 6.90%, BBCA of 17.24%, BBNI of 10.34%, BBRI of 27.59%, INDF of 3.45%, ITMG of 27.59%, TLKM of 3.45%, and UNTR of 3.45%. The results showed that the integration of FF3FM and ACO was able to form a portfolio with optimal performance with a Sharpe Ratio value of 1.41868, which means that the portfolio return is greater than the portfolio risk.
Investment Portfolio Optimization Using Ant Colony Optimization (ACO) Based on Fama-French Three Factor Model on IDX High Dividend 20 Stocks Maharani, Asthie Zaskia; Susanti, Dwi; Riaman, Riaman
International Journal of Quantitative Research and Modeling Vol. 6 No. 2 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i2.978

Abstract

Stock investment is one of the investment options that provides both profit and risk for investors. In an effort to maximize profits and minimize risks, investors need an optimal portfolio. The optimal portfolio is a portfolio selected from a collection of efficient portfolios. To form an optimal portfolio, this study combines the Fama-French Three Factor Model (FF3FM) for stock selection and Ant Colony Optimization (ACO) for stock weight optimization in the portfolio. FF3FM considers more factors resulting in more comprehensive stock selection than other methods. While ACO has the ability to explore the solution space widely and efficiently, minimizing the risk of getting stuck on a local solution. The performance of the optimal portfolio is measured using the Sharpe Ratio which considers total risk, thus providing an overview of overall investment efficiency. The research object used is quarterly stock data on IDX High Dividend 20 from the Indonesia Stock Exchange (IDX) for the period 2020-2023. Of the 20 stocks, 12 stocks were selected that were consistently included in the index during the 2020-2023 period. By selecting stocks using the FF3FM method, 10 efficient stocks were selected, namely ADRO, ASII, BBCA, BBNI, BBRI, INDF, ITMG, PTBA, TLKM, and UNTR. Portfolio optimization using ACO produces a portfolio return of 0.0473 and a risk of 0.0257 with the weight of each ADRO stock of 6.90%, BBCA of 17.24%, BBNI of 10.34%, BBRI of 27.59%, INDF of 3.45%, ITMG of 27.59%, TLKM of 3.45%, and UNTR of 3.45%. The results showed that the integration of FF3FM and ACO was able to form a portfolio with optimal performance with a Sharpe Ratio value of 1.41868, which means that the portfolio return is greater than the portfolio risk.
Co-Authors Bisyarah, Sania Dwi Susanti Riaman Riaman, Riaman