[36] proposes data-driven stock forecasting models based on neural networks, such as recurrent NNs (RNNs), convolutional NNs, transformers, graph NNs (GNNs), generative adversarial networks (GANs), and large language models (LLMs). All models have advantages and disadvantages, such as the CNN...