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All Journal International Journal of Advances in Applied Sciences Manager : Jurnal Ilmu manajemen
Rabbani, Muhammad Ihsan
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Earth & Planetary Sciences Education Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Stock’s selection and trend prediction using technical analysis and artificial neural network Agung, Ignatius Wiseto Prasetyo; Arifin, Toni; Junianto, Erfian; Rabbani, Muhammad Ihsan; Mayangsari, Ariefa Diah
International Journal of Advances in Applied Sciences Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i1.pp151-163

Abstract

Stock trading offers potential profits when traders buy low and sell high. To maximize profits, accurate analysis is essential for selecting the right stocks, timing purchases, and selling at peak prices. The authors propose a new method for selecting potential stocks that are highly likely to rise in price. The method has two stages. First, technical analysis, using moving averages and stochastic oscillators, filters stocks with downward trends, anticipating a reversal and subsequent rise. Second, for selected stocks, future price trends are predicted using artificial neural networks, specifically long short-term memory (LSTM) with adaptive moment estimation (Adam) optimizer. The second step ensures that only stocks with increasing prices will be chosen for trading. This study analyzes five hundred Fortune 500 stocks over three different periods, with 250 days of data each. Simulations conducted in Python showed that technical analysis could filter 5 to 6 candidate stocks. Subsequently, the LSTM model predicted that only 4 of these stocks would have an upward trend. Validation shows that trend predictions are correct, resulting in an average profit of 5.51% within 10 working days. This profit outperforms the profits generated by other existing methods.
THE EFFECT OF GOLD PRICE AND RUPIAH EXCHANGE RATE ON THE COMPOSITE STOCK PRICE INDEX ON THE INDONESIA STOCK EXCHANGE Rabbani, Muhammad Ihsan; Nurhayati, Immas; Yudiana
Manager : Jurnal Ilmu Manajemen Vol. 8 No. 4 (2025): Manager : Jurnal Ilmu Manajemen
Publisher : Universitas Ibn Khaldun Bogor

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Abstract

This research aims to examine the influence of gold prices and the rupiah exchange rate on Indonesia's Jakarta Composite Index (JCI). The study applies multiple linear regression analysis, using a t-test to assess the individual impact of each independent variable and an F-test to evaluate their combined effect on the dependent variable. Based on the t-test results, gold price has a significant negative relationship with the JCI, as shown by a significance level of 0.000 and a t-value of -6.912, indicating that higher gold prices tend to suppress the JCI. Conversely, the rupiah exchange rate does not show a significant partial impact on the JCI, with a significance value of 0.277 and a t-value of 1.099. However, the F-test reveals that gold prices and the exchange rate together significantly affect the JCI, with a significance level of 0.000 and an F-value of 87.957, exceeding the critical F-value of 3.16. Thus, it can be inferred that while only gold prices have a notable individual impact, both variables collectively influence the fluctuations in the JCI.
Co-Authors Erfian Junianto Ignatius Wiseto Prasetyo Agung Mayangsari, Ariefa Diah Nurhayati, Immas Toni Arifin Yudiana