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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan JAAF (Journal of Applied Accounting and Finance)
Theotista, Giovanny
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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DEVELOPMENT OF EXPECTED MONETARY VALUE USING BINOMIAL STATE PRICE IN DETERMINING STOCK INVESTMENT DECISIONS Theotista, Giovanny; Febe, Margareta; Marshelly, Yvone
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1703-1712

Abstract

Stock investment is an investment opportunity. This stock investment carries relatively high risk and therefore requires additional analysis to minimize losses and maximize profits. Expected Monetary Value (EMV) is a simple modeling method for estimating the value of an investment that will provide the greatest future return. The expected monetary value (EMV) method involves multiplying the total value of each scenario by the probability of that scenario occurring. However this method has weaknesses in terms of how many cases occur what is the value of each case and what is the probability of each case occurring. Binomial State Price is a method commonly used to calculate stock options and real options but includes the step of modeling the value of an investment in many situations and opportunities that arise in the future. In this paper, our objective is to develop the EMV method with the binomial state pricing model to determine the investment that offers the most favorable payoff. In short, we can develop the expected monetary value (EMV) method and the binomial state pricing model. It was found that this model always recommends stocks which have high dividens.
UNDERSTANDING LQ45 STOCKS (2021-2023) WITH K-MEANS CLUSTERING Febe, Margareta; Theotista, Giovanny; Winson, Winson
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp153-162

Abstract

The primary aim of this study is to examine the use of K-Means clustering in analyzing LQ45 stocks from 2021 to 2023, utilizing data obtained from the Yahoo Finance platform. The analysis delves into key performance measures such as the price-to-earnings ratio (PER), earnings per share (EPS), dividends, trading volume, and historical return on investment. This technique categorizes stocks with similar characteristics, providing financial analysts, money managers, and investors with valuable insights. The objective of the clustering analysis is to gain a deeper understanding of the relationship between intrinsic stock features and the inherent price volatility of companies. This is accomplished by using historical datasets to conduct stock feature analysis. Mathematics plays a crucial role in the K-Means model by providing the foundational algorithms and statistical methods used to categorize and analyze the data. The study contributes to the field of financial market analysis by demonstrating how understanding group-to-group dynamics can affect investment decisions and offering a more precise representation of large datasets in financial contexts. These findings provide significant insights for individuals involved in financial matters in the stock market, helping to identify potential investment opportunities and reduce risk more effectively.
ANALYSING MARKET DYNAMICS: REVEALING OBSCURED PATTERNS IN LQ45 STOCKS (2021-2023) USING WARD’S HIERARCHICAL CLUSTERING Theotista, Giovanny; Febe, Margareta; Ryan, Michael Sannova
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp163-172

Abstract

This study aimed to address the instability of the Indonesian stock market from 2021 to 2023 by analyzing the LQ45 index, a critical indicator of economic robustness and corporate performance. Hierarchical Ward clustering was employed to categorize LQ45 stocks based on fundamental metrics such as Return, Volume, Price, Price-Earnings Ratio (PER), Earnings Per Share (EPS), and Dividends. Data preprocessing involved feature creation, Max-Abs scaling for normalization, and binary encoding of categorical variables. The optimal number of clusters was identified using dendrograms, revealing two primary clusters: one focusing on core materials and the other on financial services, alongside other industry-specific clusters. This method, characterized by its ability to minimize variance within clusters and determine natural groupings without predefined assumptions, provided valuable insights for financial advisors, policymakers, and investors. The findings offer practical guidance for optimizing decision-making, minimizing risks, and leveraging opportunities within the Indonesian stock market during a period of significant economic uncertainty. By employing this strategy, investors and traders can gain a comprehensive understanding of the current condition of the stock market, offering a thorough comprehension of the connections between equities and the operational and financial issues currently under scrutiny.
The impact of return on assets (ROA), current ratio (CR), debt to assets ratio (DAR), and company size on effective tax rate (ETR) for the top 100 companies in Kompas in the years 2020-2023 Febe, Margareta; Novianti, Bella; Theotista, Giovanny
JAAF (Journal of Applied Accounting and Finance) Vol 8, No 2 (2024): JAAF (Journal of Applied Accounting and Finance)
Publisher : President University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jaaf.v8i2.5401

Abstract

This study aims to examine the impact of various financial circumstances on the effective tax rate (ETR) of manufacturing enterprises listed on the Indonesia Stock Exchange between 2020 and 2023. The sample size was 47 distinct industrial organizations, and a panel data regression analysis is implemented to achieve the investigation's objectives. The return on assets (ROA) and the debt-to-asset ratio (DAR) are both significant components in the process of determining the effective tax rate (ETR), as established by the research that was examined. Despite this, it is crucial to recognize that the efficiency transfer rate (ETR) does not seem to be significantly impacted by the current ratio (CR). It is recommended that organizations analyse the financial factors that affect the effective tax burden they are subjected to. The following findings serve as the foundation for this recommendation.
Co-Authors Febe, Margareta Marshelly, Yvone Novianti, Bella Ryan, Michael Sannova Winson, Winson