International Journal of Electrical and Computer Engineering (IJECE) Vol. 15, No. 2, April 2025, pp. 2192∼2201 ISSN: 2088-8708, DOI: 10.11591/ijece.v15i2.pp2192-2201 ❒ 2192 A novel technique for selecting financial parameters and technical indicators to predict stock prices Sneha S. Bagalkot 1,2 , Dinesha H. A. 3 , Nagaraj Naik 4 1 Nagarjuna College of Engineering and Technology, Bengaluru and Visvesvaraya Technological University, Belagavi, India 2 Department of Computer and Science Engineering, B.M.S. College of Engineering, Bangalore, India 3 Department of Computer and Science Engineering, Shridevi Institute of Engineering and Technology, Tumkuru, India 4 Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education Manipal, Manipal, India Article Info Article history: Received May 30, 2024 Revised Nov 13, 2024 Accepted Nov 20, 2024 Keywords: Artificial neural network Deep neural network Financial parameters Recursive feature elimination XGBoost ABSTRACT Stock price predictions are crucial in financial markets due to their inherent volatility. Investors aim to forecast stock prices to maximize returns, but accu- rate predictions are challenging due to frequent price fluctuations. Most litera- ture focuses on technical indicators, which rely on historical data. This study integrates both financial parameters and technical indicators to predict stock prices. It involves three main steps: identifying essential financial parameters us- ing recursive feature elimination (RFE), selecting quality stocks with a decision tree (DT), and forecasting stock prices using artificial neural networks (ANN), deep neural networks (DNN), and extreme gradient boosting (XGBoost). The models’ performance is evaluated with root mean square error (RMSE) and mean absolute error (MAE) scores. ANN and DNN models showed superior performance compared to the XGBoost model. The experiments utilized Indian stock data. This is an open access article under the CC BY-SA license. Corresponding Author: Ngaaraj Naik Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education Manipal-576 104, India Email:nagaraj.naik@manipal.edu 1. INTRODUCTION The stock market attracts numerous investors and traders due to its potential for rapid and substantial gains [1], [2]. Both institutional and individual investors engage in stock trading [3], [4]. Institutional investors, being trained professionals, manage significant investment portfolios [5], [6] and typically focus on long-term investments. In contrast, retail investors often lack the trading expertise necessary for optimal buy or sell decisions [7]. Consequently, a robust stock forecasting model is essential for assisting retail investors. To identify stock price trends, investors utilize two primary methods of analysis. The first is technical analysis, which assesses stock trends using historical data such as opening, closing, high, and low prices, along with trading volume. While technical analysis is applicable for both short-term and long-term investments, its limitation lies in its exclusive reliance on historical data, often overlooking fundamental factors crucial for future price predictions [8]. This study, therefore, integrates both financial parameters and historical stock data to enhance forecasting accuracy. Journal homepage: http://ijece.iaescore.com