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The Application of Deep Learning in the Financial Field

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-540-49487-4openalex-snapshot

Handbook on Information Technology in Finance

inferredcites →https://doi.org/10.1016/j.asoc.2020.106384openalex-snapshot

Deep learning for financial applications : A survey

inferredcites →https://doi.org/10.1016/j.eswa.2015.06.001openalex-snapshot

Genetic algorithms and Darwinian approaches in financial applications: A survey

inferredcites →https://doi.org/10.1016/j.irfa.2014.02.006openalex-snapshot

Textual sentiment in finance: A survey of methods and models

inferredcites →https://doi.org/10.1016/s0378-7206(98)00050-0openalex-snapshot

Neural network applications in finance: A review and analysis of literature (1990–1996)

inferredcites →https://doi.org/10.1016/s1574-0021(05)02024-1openalex-snapshot

Chapter 24 Agent-based Computational Finance

inferredcites →https://doi.org/10.1109/icoa.2018.8370600openalex-snapshot

Artificial neural networks applications in economics and management research: An exploratory literature review

inferredcites →https://doi.org/10.1109/iscid.2010.70openalex-snapshot

Applications of Artificial Neural Networks in Financial Economics: A Survey

inferredcites →https://doi.org/10.1109/tevc.2012.2196800openalex-snapshot

A Survey on Multiobjective Evolutionary Algorithms for the Solution of the Portfolio Optimization Problem and Other Finance and Economics Applications

inferredcites →https://doi.org/10.1145/2512962openalex-snapshot

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What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4460041openalex-extracted

Segmented Learning Architecture Model for Analytical Tax Revenue Forecasting Based on Electronic Invoice Information

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