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Azahra, Astrid Sulistya

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Basic Concepts of Stock Option Pricing Models Traded in the Capital Market Ibrahim, Riza Andrian; Azahra, Astrid Sulistya; Kalfin; Saputra, Moch Panji Agung
International Journal of Mathematics, Statistics, and Computing Vol. 2 No. 4 (2024): 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.v2i4.141

An option, in the world of capital markets, is a right based on an agreement to buy or sell a commodity, financial securities, or a foreign currency at an agreed price at any time within a three-month contract period. Factors that determine the value of an option include the current price of the stock, intrinsic value, expiration time or time value, volatility, interest rate, and cash dividends paid. Some options pricing models use this parameter to determine the fair market value of an option. This paper aims to learn the basic concepts of option pricing. The method used in studying the pricing of options is a literature review, which is an activity to collect scientific data, especially in the form of theories, methods, or research that has been carried out previously, either in the form of books, manuscripts, journals, and others that already exist in the library. Based on the results of the study, concepts, scientific findings, and method innovations that have been achieved previously are obtained, which are very relevant and useful for understanding the determination of stock option prices. An option, in the world of capital markets, is a right based on an agreement to buy or sell a commodity, financial securities, or a foreign currency at an agreed price at any time within a three-month contract period. Factors that determine the value of an option include the current price of the stock, intrinsic value, expiration time or time value, volatility, interest rate, and cash dividends paid. Some options pricing models use this parameter to determine the fair market value of an option. This paper aims to learn the basic concepts of option pricing. The method used in studying the pricing of options is a literature review, which is an activity to collect scientific data, especially in the form of theories, methods, or research that has been carried out previously, either in the form of books, manuscripts, journals, and others that already exist in the library. Based on the results of the study, concepts, scientific findings, and method innovations that have been achieved previously are obtained, which are very relevant and useful for understanding the determination of stock option prices.

Stock Price Prediction of PT. Pertamina Geothermal Energy Tbk Using Gated Recurrent Unit (GRU) Model Saputra, Renda Sandi; Hasan, Mohammad Tanzil; Azahra, Astrid Sulistya
International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 2 (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.v3i2.203

This study aims to predict the stock price of PT. Pertamina Geothermal Energy Tbk (PGEO.JK) using the Gated Recurrent Unit (GRU) model, a neural network architecture in the Recurrent Neural Network (RNN) category that is known to be effective in handling time series data. The data used is historical stock price data from 2022 to 2024 taken from Yahoo Finance. The GRU method was chosen because of its ability to remember long-term information and overcome the vanishing gradient problem. In the research process, the data was divided into two parts, namely training data and testing data. The GRU model was trained without adjusting hyperparameters to measure its performance by default. Model evaluation was carried out using the Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R²) metrics. The results of the study indicate that the GRU model is able to provide good prediction results with an RMSE value of 0.0271, MAE of 0.0180, MAPE of 22.25%, and an R² value of 0.9112. These values indicate that the GRU model is quite accurate in predicting the price of PGEO.JK shares. These findings indicate that GRU is a potential method in stock prediction analysis, especially in the renewable energy sector.

Comparison of Activation Functions in Recurrent Neural Network for Litecoin Cryptocurrency Price Prediction Saputra, Moch Panji Agung; Azahra, Astrid Sulistya; Pirdaus, Dede Irman
International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 3 (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.v3i3.233

The rapid advancement of information technology and digitalization has significantly transformed the financial sector, particularly with the emergence of cryptocurrencies characterized by high price volatility and complex movement patterns. Accurate price prediction of these crypto assets is essential to support investment decision-making and risk management. This study aims to compare the performance of six activation functions ReLU, Tanh, Sigmoid, Softplus, Swish, and Mish in a Simple Recurrent Neural Network (RNN) model for predicting the price of Litecoin, a widely traded cryptocurrency. Using historical daily closing price data from May 2020 to April 2025, the data were preprocessed through Min-Max normalization and sliding window sequence formation to fit the RNN input requirements. Each activation function was applied in the RNN model under consistent training conditions, and model performance was evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R²). Results indicate that the Swish activation function outperforms others by achieving the lowest RMSE of 4.58 and the highest R² score of 0.9578, demonstrating superior prediction accuracy and stable convergence. Tanh also showed competitive results, while Sigmoid and Softplus performed less effectively. In conclusion, Swish is recommended as the most suitable activation function for RNN-based cryptocurrency price forecasting due to its balance of accuracy and computational efficiency.

Co-Authors Agung Prabowo Ahmad Riyansyah, Ahmad Apriva, Rizki binti Abdullah Suhaimi, Nurnisaa Fadhilah, Dila Nur Hasan, Mohammad Tanzil Hidayana, Rizki Apriva Ibrahim, Riza Andrian Irman, Dede kalfin Kalfin Kalfin, Kalfin Kristina Wijayanti Laksito, Grida Saktian Munahefi, Detalia Noriza Pirdaus, Dede Irman Saefullah, Rifki Sandi, Renda Saputra, Jumadil Saputra, Renda Sandi Sidiq, Fahmi Suhaimi, Nurnisaa binti Abdullah Syahla, Raynita Ulyana, Yaza Azzahara Wahid, Alim Jaizul Yasmin, Arla Aglia Yuningsih, Siti Hadiaty

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