An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework

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 indi...

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Bibliographic Details
Published in:arXiv.org 2017-12
Main Authors: Sezer, O B, Ozbayoglu, M, Dogdu, E
Format: Article
Language:English
Subjects:
Artificial neural networks
Big Data
Data management
Indicators
Learning theory
Model testing
Multilayer perceptrons
Neural networks
Optimization
Online Access:Get full text
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Summary: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 network 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.
ISSN:2331-8422
DOI:10.48550/arxiv.1712.09592