SUSTAINABLE ECONOMIC GROWTH THROUGH OPTIMIZED STOCK FORECASTING: A HYBRID ARIMA-LSTM ENSEMBLE WITH ADAPTIVE RANDOM SEARCH

Abstract

Stock price prediction remains a complex challenge due to market volatility and non-linear dynamics. This study introduces a novel hybrid framework, AdapRandOpt_ARIMA-LSTM, which integrates an upgraded ARIMA model with a tailored Long Short-Term Memory (LSTM) network. The model employs Adaptive Random Search Optimization (AdapRandOpt) to precisely calibrate hyperparameters and determine optimal weight distributions for the ensemble. The framework was evaluated on sixteen leading NSE-listed companies: ASIANPAINT, BEL, CIPLA, DMART, ETERNAL, FACT, GLAXO, HINDLCO, INDIGO, JSWENERGY, KOTAKBANK, LTFOODS, MANKIND, NESTLEIND, RELIANCE, and TATAMOTORS. Beyond standard Root Mean Square Error (RMSE) metrics, the model's efficacy was validated through profitability ratios and directional accuracy, ensuring its practical utility for traders. Results confirm that the hybrid ensemble significantly outperforms standalone models, demonstrating that AdapRandOpt effectively enhances forecasting robustness and predictive precision. This approach provides a computationally efficient, high-accuracy solution for navigating the intricacies of the Indian financial market. The AdapRandOpt_ARIMA-LSTM framework supports SDG 8 (Decent Work and Economic Growth) by providing a high-precision tool that fosters financial stability and informed decision-making within the Indian equity market. Furthermore, by integrating profitability ratios and directional accuracy, the model promotes SDG 12 (Responsible Consumption and Production) through the encouragement of sustainable investment practices and transparent financial resource management.