Analyse de texte et finance computationnelle

Classify text data using a deep learning long short-term memory (LSTM) network.

Classify text data that has multiple independent labels.

Convert decimal strings to Roman numerals using a recurrent sequence-to-sequence encoder-decoder model with attention.

Create, train, and compare three deep learning networks for predicting credit default probability.

Train a credit risk for probability of default (PD) prediction using a deep neural network. The example also shows how to use the locally interpretable model-agnostic explanations (LIME) and Shapley values interpretability techniques to understand the predictions of the model. In addition, the example analyzes model predictions for out-of-sample values and performs a stress-testing analysis.

Outperform the traditional BSM approach using an optimal option hedging policy.