A Comparative Analysis of Deep Learning Models for Short- Term Stock Price Prediction

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Elma Sibonghanoy Groenewald, Kifah Sami Hussein, Inam Abass Hamidi, Juhi Vinod Mehta


Short-term stock price prediction is a critical task in financial markets, influencing investment decisions and risk management strategies. Deep learning models have gained traction in recent years for their ability to capture complex patterns and temporal dependencies in time-series data. This paper presents a comparative analysis of various deep learning models for short-term stock price prediction. We investigate the performance of recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and convolutional neural networks (CNNs) on historical stock price data. The study explores the effectiveness of different input features, including technical indicators, sentiment analysis, and news articles, in conjunction with deep learning models. Experimental results demonstrate the capabilities of each deep learning architecture in capturing market dynamics and predicting short-term stock price movements. Furthermore, insights into the strengths and limitations of each model are provided, along with practical considerations for deploying deep learning-based stock price prediction systems in real-world trading environments. This research contributes to the existing literature by offering a systematic comparison of deep learning models and input features for short-term stock price prediction. The findings have implications for investors, traders, and financial institutions seeking to enhance their decision-making processes and improve the accuracy of stock price forecasts. Additionally, the study highlights areas for future research and development in deep learning-based financial forecasting, aiming to advance the state-of-the-art techniques for predicting short-term stock price movements.

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