Please use this identifier to cite or link to this item:
Title: An artificial neural network-based stock trading system using technical analysis and big data framework
Authors: Sezer, Ömer Berat
Özbayoğlu, Ahmet Murat
Doğdu, Erdoğan.
Keywords: Algorithmic trading
Artificial neural network
Multi layer perceptron
Stock market
Technical analysis
Issue Date: 13-Apr-2017
Publisher: Association for Computing Machinery, Inc.
Source: Sezer, O. B., Ozbayoglu, A. M., & Dogdu, E. (2017, April). An artificial neural network-based stock trading system using technical analysis and big data framework. In Proceedings of the SouthEast Conference (pp. 223-226). ACM.
Abstract: In this paper, a neural network-based stock price prediction and trading system using technical analysis indicators is presented. The model developed first converts the financial time series data into a series of buy-sell-hold trigger signals using the most commonly preferred technical analysis indicators. Then, a Multilayer Perceptron (MLP) artificial neural network (ANN) model is trained in the learning stage on the daily stock prices between 1997 and 2007 for all of the Dow30 stocks. Apache Spark big data framework is used in the training stage. The trained model is then tested with data from 2007 to 2017. The results indicate that by choosing the most appropriate technical indicators, the neural net- work model can achieve comparable results against the Buy and Hold strategy in most of the cases. Furthermore, fine tuning the technical indicators and/or optimization strategy can enhance the overall trading performance. Copyright 2017 ACM.
Description: ACM SouthEast Regional Conference (2017 : Kennesaw; United States)
ISBN: 978-145035024-2
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Show full item record

CORE Recommender


checked on Sep 23, 2022

Page view(s)

checked on Dec 5, 2022

Google ScholarTM



Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.