A Comparative Analysis of Deep Learning Models in Stock Market Forecasting

In the context of guiding investors with reliable insights into the stock market, the efficacy of stock price prediction holds paramount importance. The stock market, being influenced by a myriad of complex factors, poses a formidable challenge in achieving accurate predictions. This paper presents a novel approach to stock prediction through the integration of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) in a hybrid model termed CNN-LSTM. Employing an end-to-end network structure, the model leverages CNN for uncovering profound features within the data and LSTM for capturing temporal patterns. Experimental validation is conducted using Tesla Inc. (stock code: TSLA) as a benchmark. Comparative analyses of experimental predictions and evaluation metrics serve to authenticate the effectiveness and feasibility of the CNN-LSTM network model in the domain of stock forecasting.