Forecasting in Big Data Environments: An Adaptable and Automated Shrinkage Estimation of Neural Networks (AAShNet)

Ali Habibnia () and Esfandiar Maasoumi
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Ali Habibnia: Virginia Tech

Journal of Quantitative Economics, 2021, vol. 19, issue 1, No 17, 363-381

Abstract: Abstract 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 a neural net with skip-layer connections. We expressly include both linear and nonlinear components. This is a high-dimensional learning approach including both sparsity $$L_1$$ L 1 and smoothness $$L_2$$ 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 univariate and multivariate models by actual portfolio performance.

Keywords: Nonlinear shrinkage estimation; Gradient-based hyperparameter optimization; High-dimensional nonlinear time Series; Neural networks (search for similar items in EconPapers)
JEL-codes: C45 C51 C52 C53 C61 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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Working Paper: Forecasting in Big Data Environments: an Adaptable and Automated Shrinkage Estimation of Neural Networks (AAShNet) (2019)
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DOI: 10.1007/s40953-021-00275-7

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