The role of attribute selection in Deep ANNs learning framework for high‐frequency financial trading-Reference-Cited by-同舟云学术

The role of attribute selection in Deep ANNs learning framework for high‐frequency financial trading

Published:2020-03-12 Issue:2 Volume:27 Page:43-54
ISSN:1055-615X
Container-title:Intelligent Systems in Accounting, Finance and Management
language:en
Short-container-title:Intell Sys Acc Fin Mgmt

Author:

Aloud Monira Essa¹^ORCID

Affiliation:

1. Department of Management Information Systems, College of Business AdministrationKing Saud University Riyadh Saudi Arabia

Funder

OANDA and Olsen Ltd

Publisher

Wiley

Subject

Finance,General Business, Management and Accounting

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/isaf.1466

Reference51 articles.

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2. Using genetic algorithms to find technical trading rules1Helpful comments were made by Adam Dunsby, Lawrence Fisher, Steven Kimbrough, Paul Kleindorfer, Michele Kreisler, James Laing, Josef Lakonishok, George Mailath, and seminar participants at Institutional Investor, J.P. Morgan, the NBER Asset Pricing Program, Ohio State University, Purdue University, the Santa Fe Institute, Rutgers University, Stanford University, University of California, Berkeley, University of Michigan, University of Pennsylvania, University of Utah, Washington University (St. Louis), and the 1995 AFA Meetings in Washington, D.C. We are particularly grateful to Kenneth R. French (the referee), and G. William Schwert (the editor) for their suggestions. Financial support from the National Science Foundation is gratefully acknowledged by the first author and from the Academy of Finland by the second and from the Geewax-Terker Program in Financial Instruments by both. Correspondence should be addressed to Franklin Allen, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104-6367.1

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5. Exploring Trading Strategies and Their Effects in the Foreign Exchange Market

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