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Found 5 papers, 2 papers with code
Deep Partial Least Squares for Empirical Asset Pricing
This non-linear factor structure is extracted by using projected least squares to jointly project firm characteristics and asset returns on to a subspace of latent factors and using deep learning to learn the non-linear map from the factor loadings to the asset returns.
Industrial Forecasting with Exponentially Smoothed Recurrent Neural Networks
Time series modeling has entered an era of unprecedented growth in the size and complexity of data which require new modeling approaches.
Deep Fundamental Factor Models
Deep fundamental factor models are developed to automatically capture non-linearity and interaction effects in factor modeling.
Sequence Classification of the Limit Order Book using Recurrent Neural Networks
Our results demonstrate the ability of the RNN to capture the non-linear relationship between the near-term price-flips and a spatio-temporal representation of the limit order book.
Trading and Market Microstructure
Deep Learning for Spatio-Temporal Modeling: Dynamic Traffic Flows and High Frequency Trading
Deep learning applies hierarchical layers of hidden variables to construct nonlinear high dimensional predictors.
