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Online

Introduction to AI

Classification and regression trees vs. neural networks (NN): pros and cons
How to train a NN: backpropagation and stochastic gradient descent
Types of NN: multilayer perceptron (MLP), recurrent neural network (RNN), convolutional neural network (CNN), and related variants
Discriminative vs. generative AI: a Bayesian perspective, and what generative models can do that discriminative models can’t
Exercise: Build, train, and apply an RNN to predict SPX returns with the help of a chatbot such as ChatGPT

Deep Autoregressive Models and Transformers

Deep Autoregressive Models and TransformersProbabilistic modeling of time series: reducing model complexity and dimensionality
Limitations of RNNs: slow training, vanishing/exploding gradients, high memory usage
Transformers to the rescue: Attention Is All You Need
Exercise: Build, fine-tune, and apply the Lag-Llama transformer to predict exchange rates

LLMs for Sentiment Analysis in Trading

BERT and FinBERT: fine-tuning a pre-trained LLM with financial text data; what pre-training and fine-tuning mean, and how to fine-tune
Embeddings: converting English text into numerical input for a NN
Exercise: Use FinBERT to compute sentiment scores on Fed Chair speech transcripts and backtest a trading model on SPY based on these scores