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

By Omer Berat Sezer, A. Ozbayoglu, Erdogan Dogdu. 2017

66 citations recorded by the source corpus.

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.

Subfields: Finance, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Finance

1. M. Ugur Gudelek
2. A. Ozbayoglu
3. S. Arda Boluk
4. Omer Berat Sezer
5. Weiwei Jiang
6. Longbing Cao

In Management Science and Operations Research

1. M. Ugur Gudelek
2. A. Ozbayoglu
3. S. Arda Boluk
4. Omer Berat Sezer
5. Weiwei Jiang
6. Longbing Cao

Citations in the corpus

• A deep learning based stock trading model with 2-D CNN trend detection (2017, methodology): “The approaches that use technical analysis have been split into several sub-categories: classical machine learning [7], recently popularized deep learning [8], evolutionary algorithms [9] or fuzzy-logic based systems [6].”