Intern Spotlight: 2022 HRT AI Labs Summer Projects

This post introduces the application of Large Language Models (LLMs) to financial news for sentiment analysis and prediction, detailing prompt engineering techniques and the impact of model size and examples on accuracy. It also explores the use of Transformer neural networks for modeling financial data at two scales: tick-by-tick order book events and minute-to-minute returns across a cross-section of instruments. For order book data, it discusses representation challenges and the integration of Variational Autoencoders (VAEs). For cross-sectional returns, it compares regression and classification objectives, highlighting the trade-offs in capturing correlations versus avoiding degenerate trajectories.