Optimizing Stock Price Prediction: Exploring LSTM Architectural Parameters in Financial Forecasting

Abstract

Accurate stock price prediction is a crucial concern in the field of finance, impacting investors, traders, and the broader economic landscape. Traditional time series analysis and statistical models often fall short in capturing the dynamic and nonlinear nature of financial markets. Financial market forecasting has advanced significantly with the advent of deep learning technology, especially Long Short-Term Memory (LSTM) networks. This study conducts an in-depth exploration of LSTM networks' critical architectural parameters and their impact on stock price prediction. The performance of the LSTM-based model is assessed using historical stock price data from Amazon, with particular attention paid to hyperparameters like the number of neurons, dropout rates, and LSTM layers. The research reveals that a well-balanced model architecture with moderate units per layer (e.g., 64), a single layer, and a dropout rate of 0.7 is key to achieving accurate predictions in financial markets. The outcomes of this research hold practical significance for both investors and financial analysts. Furthermore, they emphasize the promising potential of LSTM networks in the realm of financial forecasting, opening avenues for future studies in finance. This investigation deepens the comprehension of LSTM network architecture and its applicability in predicting stock prices, providing valuable insights that extend beyond the finance sector.