Optimization Analysis of Multivariate Stock Price Prediction using LSTM Deep Learning Networks

Abstract

Stock market price prediction is a challenging task because financial time-series data are highly nonlinear, dynamic, noisy, and influenced by multiple interacting market factors. Traditional forecasting approaches may have limitations in capturing complex temporal dependencies among different financial variables. To address this challenge, this research proposes an “Optimization Analysis of Multivariate Stock Price Prediction using LSTM Deep Learning Networks.” The proposed approach utilizes a multivariate dataset containing important stock-market parameters such as Open, High, Low, Close, Trading Volume, and selected technical indicators. Data preprocessing techniques, including missing-value handling, normalization, feature preparation, and time-series sequence generation, are applied before model training. A Long Short-Term Memory (LSTM) deep learning network is developed to learn both short-term and long-term temporal dependencies within the multivariate stock-market data. Unlike conventional neural networks, LSTM uses memory cells and gating mechanisms to retain relevant historical information and reduce the difficulty of learning long-term dependencies. To improve forecasting performance, the proposed methodology incorporates model optimization through appropriate hyperparameter selection, including the number of LSTM layers, hidden units, learning rate, batch size, sequence length, and dropout rate. Different configurations can be systematically analyzed to identify an effective LSTM architecture for stock-price prediction. The proposed research aims to develop an accurate and optimized deep learning framework capable of forecasting future stock prices from historical multivariate financial data. The resulting model can provide useful analytical information for stock-market trend analysis, financial forecasting, and data-driven investment research. Future extensions may incorporate attention mechanisms, Transformer architectures, sentiment data, macroeconomic indicators, and metaheuristic optimization techniques to further investigate the robustness and generalization of stock-price forecasting models.