Please use this identifier to cite or link to this item:
Title: Comparison of Bayesian Estimation and Neural Network Model in Stock Market Trading
Authors: Özbayoğlu, Ahmet Murat
Bahadır, İsmet
Keywords: Neural network models
Stock markets
Issue Date: 2008
Publisher: ASME Press
Source: Ozbayoglu, A.M and I Bahadır. Comparison of Bayesian Estimation and Neural Network Model in Stock Market Trading. Intelligent Engineering Systems Through Artificial Neural Networks. ASME, pp. 581–586.
Abstract: In this study, a decision support system for stock market prediction is proposed. This model uses the historical data of 180K data points obtained from the 215 highest volume ETFs that are open for trade in NYSE. The data is analyzed with several different criteria such as next 1,2,3,4,5 days percent increase/decrease, percent moves with respect to 50/200 day Moving Averages, changes in RSI, MACD values, direction of movement within Bollinger Bands, etc. The next day prediction is made by statistical analysis on the data using a Bayesian Maximum Likelihood decision model and the best course of action (which ETF is most likely to increase its value) is identified. The training data for the model is the historical data of these ETFs between 1999 and 2006. With the trained network, 2007 data has been tested and the results are analyzed. In order to compare the performance of the model, a multilayer perceptron neural network is developed using the same training and testing data and the results are compared. For performance evaluation, both models analyze which ETF is most likely to create the best short term (1 day – 5 days) rate of return and perform buy/sell decisions accordingly. The results indicate that both models can be used in stock/ETF selection in short term stock market trading, however neural network model provided better results.
ISBN: 0791802823
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering

Files in This Item:
File Description SizeFormat 
annie08_proceeding_271155.pdf182.97 kBAdobe PDFThumbnail
Show full item record

CORE Recommender

Page view(s)

checked on Dec 5, 2022


checked on Dec 5, 2022

Google ScholarTM



This item is licensed under a Creative Commons License Creative Commons