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Deep Learning in Finance

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Ali Habibnia
added a research item
This paper considers improved forecasting in possibly nonlinear dynamic settings, with high-dimension predictors (big data environments). To overcome the curse of dimensionality and manage data and model complexity, we examine shrinkage estimation of a back-propagation algorithm of deep neural nets with skip-layer connections. We expressly include both linear and nonlinear components. This is a high-dimensional learning approach including both sparsity L 1 and smoothness L 2 penalties, allowing high-dimensionality and nonlinearity to be accommodated in one step. This approach selects significant predictors as well as the topology of the neural network. We estimate optimal values of shrinkage hyperparameters by incorporating a gradient-based optimization technique resulting in robust predictions with improved reproducibility. The latter has been an issue in some approaches. This is statistically interpretable and unravels some network structure, commonly left to a black box. An additional advantage is that the nonlinear part tends to get pruned if the underlying process is linear. In an application to forecasting equity returns, the proposed approach captures nonlinear dynamics between equities to enhance forecast performance. It offers an appreciable improvement over current uni-variate and multivariate models by RMSE and actual portfolio performance. https://arxiv.org/abs/1904.11145
Ali Habibnia
added 2 research items
Forecasting in Big Data Environments High-dimensional Nonlinear Time Series Analysis Nonlinear Forecasting Using a Large Number of Predictors Forecasting in Big Data Environments with a Shrinkage Estimation of Skip-layer Neural Networks Past, Present and Future of Testing for Nonlinearity in Time Series Motivation and Inspirations, Nonlinear Factor Model
Machine Learning & Deep Learning Applied to Trading
Ali Habibnia
added a research item
This study proposes a nonlinear generalization of factor models based on artificial neural networks for forecasting financial time series with many predictors. http://eprints.lse.ac.uk/62916/