Comparative Analysis of CNN Pre-trained Model for Stock Market Trend Prediction

This research offers an in-depth comparative analysis of various pre-trained Convolutional Neural Network (CNN) models such as VGG16, ResNet50, InceptionV3, MobileNetV2, and Xception to predict stock market trends. Our approach involves the conversion of time-series financial data into 2D image-like structures through the application of two distinct techniques: the Gramian Angular Field (GAF) and the Markov Transition Field (MTF). By applying this transformation, we leverage the power of CNNs. We utilize the ideas of transfer learning and try to evaluate the performance of each model using several measures including predictive accuracy, precision, recall, F1-score, and computational efficiency. The analysis highlights the unique advantages and limitations of each model, thereby offering valuable insights into their suitability for stock market prediction tasks. This study is a significant contribution to the current body of literature on financial time series forecasting, providing a novel perspective on using pre-trained CNN models in the Indian Financial Sector. It carries important implications for future work and practitioners in the finance and investment sectors, offering a tool for more e-market predictions.