Authors : Priyadharshini Sekaran; R. Dhamotharan

Volume/Issue : Volume 10 - 2025, Issue 3 - March

Google Scholar : https://tinyurl.com/5n9yur8e

Scribd : https://tinyurl.com/577nmjps

DOI : https://doi.org/10.38124/ijisrt/25mar1247

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Abstract : While many investors participate in stock markets with profit motives, most struggle due to insufficient understanding of price behavior and analytical techniques. This study develops an enhanced prediction framework combining LSTM-Random Forest algorithms to improve forecasting reliability. The integrated model processes both sequential price patterns and key market indicators to generate more accurate predictions. Evaluation results demonstrate that the combined LSTM-Random Forest approach achieves better performance than individual models, with measurable improvements in prediction error reduction and trend explanation. The system effectively balances temporal pattern recognition with robust feature analysis. Future extensions of this work will focus on three directions: operational deployment for real-time analysis, incorporation of qualitative market sentiment, and enhancement of sequential processing capabilities. This research provides traders with an advanced analytical tool while emphasizing that market predictions should complement, rather than replace, informed decision-making and risk awareness.

Keywords : Hybrid Forecasting, Sequential Pattern Recognition, Ensemble Market Analysis, Adaptive Technical Indicators, Probabilistic Trading Insights.

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