Dynamic Logistic Velocity-Acceleration Model Algorithm for Stock Price Prediction.
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| Title: | Dynamic Logistic Velocity-Acceleration Model Algorithm for Stock Price Prediction. |
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| Authors: | Noviantri, Viska1 viskanoviantri@binus.ac.id, Nariswari, Rinda2 rinda.nariswari@binus.ac.id, Saputra, Wisnowan Hendy3 wisnowan.saputra@binus.ac.id, Yolandito, Richy Vernando4 richy.yolandito@binus.ac.id |
| Source: | IAENG International Journal of Computer Science. Jun2026, Vol. 53 Issue 6, p2155-2164. 10p. |
| Subjects: | Stock price forecasting, Mathematical models, Market volatility, Computer simulation, Forecasting methodology |
| Abstract: | This study discusses modeling stock price movements using a differential equation-based logistic model by examining two main components of market dynamics: the rate of change and the acceleration of stock price. Unlike conventional logistic approaches, the parameters in this model are dynamic, so their values change at each observation time according to the characteristics of the actual data. This dynamic nature requires a specialized solution algorithm, as parameter estimates must be updated iteratively as the data evolves. The main contribution of this study lies in the development and presentation of a comprehensive fitting and forecasting algorithm, which is an advantage over previous studies that generally focused only on mathematical formulations without providing implementation procedures. To see the model's performance, this study uses IDX Composite data during the 2024 Indonesian Presidential Election as a case study. This data was chosen because this period represents market conditions with high dynamics and volatility. Numerical simulations were performed to analyze the model's behavior, and the evaluation results indicated that the Mean Average Percentage Error (MAPE) during the fitting stage was 0.775%. Furthermore, the forecasting results showed that the MAPE after the election was higher than before. This is reasonable because market uncertainty and volatility increase in the post-election period. However, overall, it can be concluded that this model has high accuracy with MAPE less than 1%. This study also complements numerical simulations to support a more in-depth analysis of the model's behavior. Evaluation results using MAPE indicate that the proposed model accurately represents stock price movements. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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