Paper Title
Literature Review: Advancements in Stock Market Prediction Models Through Deep Learning

Abstract
Stock market prediction has long been a subject of interest due to its potential to improve financial decision-making. While traditional models such as ARIMA and GARCH have offered insights, they often fall short in capturing the complex, non-linear relationships inherent in financial data. This paper reviews the development of deep learning (DL) techniques in stock market prediction, including Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and hybrid models. We explore the strengths and limitations of these models, highlighting their ability to process large datasets and detect intricate patterns while acknowledging challenges such as overfitting, computational costs, and the “black-box” nature of DL. Emerging trends, including reinforcement learning (RL), transfer learning, and the use of alternative data sources like social media sentiment, are also discussed. The paper also delves into ongoing debates, particularly the trade-off between prediction accuracy and model interpretability, and considers ethical concerns such as market manipulation. Lastly, the review identifies key areas for future research, including improving explainability and integrating alternative data into DL models for stock market prediction. Keywords - Stock Market Prediction, Deep Learning, RNN, LSTM, CNN, Hybrid Models, Reinforcement Learning, Transfer Learning, Sentiment Analysis, Model Interpretability, Market Manipulation.