Strategic Decision Support in Financial Management Using Deep Learning-Based Stock Price Prediction Models

The Strategic decision support creates a strong and smart decision support system for financial management through the correct forecast of stock prices with deep learning. Statistical models and shallow machine learning techniques tend to be ineffective in modeling the nonlinear relationships, sequential interdependencies, and time-dependent volatility typical of financial data; consequently, poor prediction quality and untrustworthy investment choices. To overcome these constraints, introduce a new hybrid deep learning architecture based on the Temporal Fusion Transformer (TFT) combined with Bidirectional Long Short-Term Memory (BiLSTM) networks. The proposed hybrid model is expected to complement time-series forecasting by simultaneously utilizing attention mechanisms for explainability and sequence memory functions for richer temporal understanding. The model is trained and tested with the publicly available Stock Market Dataset on Kaggle, which includes stock history from various companies. The whole process is carried out on the Python platform using TensorFlow along with relevant libraries for data preprocessing, feature scaling, and model training. The new TFT-BiLSTM model surpasses conventional models through an accuracy level of 93.4% and an F1-score of 94.2%, demonstrating its precision and generalization power. The system provides strategic benefits in financial planning and risk management. Financial analysts, investors, fintech, and portfolio managers may take advantage of our prediction system to make rational buy/sell judgments, minimize risks, and maximize asset allocations. By synthesizing state-of-the-art deep learning models and public financial data, our framework illustrates that accurate stock price prediction can be an effective mechanism for supporting decision-making in financial markets.

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