Chin Soon KuSelva Dhanush R CharanHoong-Cheng SoongLim Jit TheamOoi Joo OnZeng-Wei Hong2026-10-142026-10-142025-08-02979833152700610.1109/ICSGRC65918.2025.11159810https://dspace-cris.utar.edu.my/handle/123456789/11735This comprehensive review examines advancements in stock market trend prediction using advanced computational techniques, focusing on deep learning methods, such as Long Short-Term Memory (LSTM) and technical analysis. From an initial pool of 5, 1 0 4 scholarly articles, 5 5 studies were rigorously selected based on predefined inclusion and exclusion criteria for in-depth analysis. LSTM appeared in approximately 40% of these studies, reflecting their capability in managing sequential time-series data and mitigating the vanishing gradient problem. The review also highlights a growing interest in Although less frequently employed, Transformer models possess significant potential for handling large datasets and modeling long-term dependencies. Many studies combined these advanced models with technical indicators, including Moving Averages and Relative Strength Index, to improve predictive accuracy. However, only 35.3% of the studies evaluated the profitability of these forecasting models, revealing a gap between predictive accuracy and real-world financial performance. The review advocates for a holistic approach encompassing diverse datasets, advanced trading strategies, and a heightened emphasis on profitability to better synchronize model outcomes with tangible financial objectives. © 2025 IEEE.en-USDeep LearningMachine LearningStock Market ForecastSystematic ReviewTransformer ModelCommerceData handlingElectronic tradingFinancial marketsForecastingInformation managementLarge datasetsLearning systemsPrediction modelsProfitabilityDeep learningMachine-learningMarket forecastMarket trendsPredictive accuracyShort term memoryStock market forecastStock market forecastingTransformer modelingLong short-term memorytext::conference output::conference proceedings::conference paper