This paper explores the application of quantum-inspired deep learning techniques for processing high-dimensional financial data, specifically focusing on predicting stock price movement using Apple Inc. (AAPL) stock data. We investigate the effectiveness of a quantum-inspired model in comparison to a standard Long Short-Term Memory (LSTM) network. The study incorporates various technical indicators as features and evaluates model performance using standard classification metrics and visualizations. The challenges of high-dimensional data in finance are discussed, and the potential benefits of quantum-inspired approaches in this domain are explored.