International Journal of Advances in Applied Sciences (IJAAS) Vol. 14, No. 1, March 2025, pp. 151~163 ISSN: 2252-8814, DOI: 10.11591/ijaas.v14.i1.pp151-163  151 Journal homepage: http://ijaas.iaescore.com Stock’s selection and trend prediction using technical analysis and artificial neural network Ignatius Wiseto Prasetyo Agung 1,2 , Toni Arifin 1,2 , Erfian Junianto 1,2 , Muhammad Ihsan Rabbani 1,3 , Ariefa Diah Mayangsari 1,2 1 ARS Digital Research and Innovation (ADRI), Universitas Adhirajasa Reswara Sanjaya (ARS University), Bandung, Indonesia 2 Informatics Study Program, Faculty of Information Technology, Universitas Adhirajasa Reswara Sanjaya (ARS University), Bandung, Indonesia 3 Information System Study Program, Faculty of Information Technology, Universitas Adhirajasa Reswara Sanjaya (ARS University), Bandung, Indonesia Article Info ABSTRACT Article history: Received Jul 30, 2024 Revised Oct 29, 2024 Accepted Dec 26, 2024 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. Keywords: Fortune 500 Long short-term memory Stock prediction Swing trading Technical analysis This is an open access article under the CC BY-SA license. Corresponding Author: Ignatius Wiseto Prasetyo Agung ARS Digital Research and Innovation (ADRI) Informatics Study Program, Faculty of Information Technology Universitas Adhirajasa Reswara Sanjaya (ARS University) Sekolah Internasional St. No. 1-2, Bandung, Indonesia Email: wiseto.agung@ars.ac.id 1. INTRODUCTION Stock trading involves the buying and selling of shares with the aim of generating short-term profits. Trades completed over days or weeks fall under the category of swing trading [1]. A study conducted by Gallagher et al. [2] indicated that engaging in short-term swing trading can be lucrative, with an average return of 2.72% observed in one swing sequence. Nevertheless, stock traders face two challenges. The first challenge is selecting potential stocks to buy from thousands of available options. The second challenge is deciding when to buy, which involves predicting future prices, where the accuracy of the prediction directly influences the potential profit. These challenges can be addressed by analyzing overall market conditions and examining the performance of the companies whose stocks are being traded. There are two types of analysis commonly