Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/1995
Title: A Deep Neural-Network Based Stock Trading System Based on Evolutionary Optimized Technical Analysis Parameters
Authors: Sezer, Ömer Berat
Özbayoğlu, Ahmet Murat
Doğdu, Erdoğan
142991
Keywords: Stock Trading
Stock Market
Deep Neural-Network
Evolutionary Algorithms
Technical Analysis
Issue Date: 2017
Publisher: ELSEVIER Science BV
Source: Sezer, O. B., Ozbayoglu, M., & Dogdu, E. (2017). A Deep neural-network based stock trading system based on evolutionary optimized technical analysis parameters. Procedia computer science, 114, 473-480.
Abstract: In this study, we propose a stock trading system based on optimized technical analysis parameters for creating buy-sell points using genetic algorithms. The model is developed utilizing Apache Spark big data platform. The optimized parameters are then passed to a deep MLP neural network for buy-sell-hold predictions. Dow 30 stocks are chosen for model validation. Each Dow stock is trained separately using daily close prices between 1996-2016 and tested between 2007-2016. The results indicate that optimizing the technical indicator parameters not only enhances the stock trading performance but also provides a model that might be used as an alternative to Buy and Hold and other standard technical analysis models. (c) 2017 The Authors. Published by Elsevier B.V.
Description: Complex Adaptive Systems Conference on Engineering Cyber Physical Systems (CAS) (2017 : Chicago, IL)
URI: https://www.sciencedirect.com/science/article/pii/S1877050917318252?via%3Dihub
https://hdl.handle.net/20.500.11851/1995
ISSN: 1877-0509
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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