UCLA

This thesis investigates the use of deep learning and machine learning models for high-frequency stock price prediction and inference. By using models such as Long Short-Term Memory (LSTM) networks, Convolutional LSTMs (CLSTM), and Transformer architectures, this work evaluates the predictive performance of these models in both single-step and multi-step stock price prediction tasks. The models are trained on various datasets, including those with technical indicators, sentiment analysis, and the US Dollar Index, along with Fourier-transformed features for improved feature engineering. The results demonstrate that Transformer-based models, particularly those added with convolutional layers, outperform LSTM-based models in capturing long-term dependencies and making accurate predictions over extended time periods. Additionally, the Fourier-transformed features enhances overall models performance by revealing underlying periodic patterns in stock prices. This research contributes to the growing literature on stock price prediction and inference by offering insights into model architectures and feature engineering techniques that improve the accuracy of financial forecasting.