Applied Stochastic Models in Business and Industry · 2016 · 672 citations · 13 references
BusinessComputer ScienceFinanceFinancial AnalyticsFinancial EconomicsMachine LearningAsset Return ModelingDeep LearningFinancial ForecastingFinancial MathematicsFinancial RiskNeural NetworksDeep Learning DetectionDeep Learning For ForecastingDeep Learning-based ForecastingPrediction
Financial prediction problems such as designing and pricing securities, constructing portfolios, and risk management involve large, complex data sets that current economic models cannot fully capture, and deep learning can detect and exploit interactions that are invisible to existing financial theory. The study explores using deep learning hierarchical models for financial prediction and classification tasks. They apply deep learning hierarchical models to address these financial prediction and classification problems. Deep learning methods yield more useful results than standard finance methods for these problems. © 2016 John Wiley & Sons, Ltd.
We explore the use of deep learning hierarchical models for problems in financial prediction and classification. Financial prediction problems – such as those presented in designing and pricing securities, constructing portfolios, and risk management – often involve large data sets with complex data interactions that currently are difficult or impossible to specify in a full economic model. Applying deep learning methods to these problems can produce more useful results than standard methods in finance. In particular, deep learning can detect and exploit interactions in the data that are, at least currently, invisible to any existing financial economic theory. Copyright © 2016 John Wiley & Sons, Ltd.
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