Predicting Market Volatility: A Multimodal Deep Learning Approach with LSTMs and FinBERT

1. 1Build a dataset of historical stock prices and financial news articles
2. 2Preprocess the data using FinBERT for text embedding and normalization for numerical data
3. 3Configure an LSTM model to predict market volatility based on the preprocessed data
4. 4Train the model using a multimodal approach, combining text and numerical data
5. 5Evaluate the model's performance using metrics such as mean absolute error and R-squared

Quantitative analysts and machine learning engineers can benefit from this approach to predict market volatility and inform investment strategies

💡 Multimodal deep learning approaches can effectively predict market volatility by combining text and numerical data

Predict market volatility with LSTMs & FinBERT!