Stock Market Prediction with Gaussian Naïve Bayes Machine Learning Algorithm
DOI:
https://doi.org/10.31449/inf.v45i2.3407Abstract
The stock market is one of the key sectors of a country’s economy. It provides investors with an opportunity to invest and gain returns on their investment. Predicting the stock market is a very challenging task and has attracted serious interest from researchers from many fields such as statistics, artificial intelligence, economics, and finance. An accurate prediction of the stock market reduces investment risk in the market. Different approaches have been used to predict the stock market. The performances of Machine learning (ML) models are typically superior to those of statistical and econometric models. The ability of Gaussian Naïve Bayes ML algorithm to predict stock price movement has not been addressed properly in the existing literature, hence this work attempt to fill that gap by evaluating the performance of GNB algorithm when combined with different feature scaling and feature extraction techniques in stock price movement prediction. The performance of the GNB models set up were ranked using the Kendall’s test of concordance for the various evaluation metrics used. The results indicated that, the predictive model based on integration of GNB algorithm and Linear Discriminant Analysis (GNB_LDA) outperformed all the other models of GNB considered in three of the four evaluation metrics (i.e., accuracy, F1-score, and AUC). Similarly, the predictive model based on integration of GNB algorithm, Min-Max scaling, and PCA produced the best rank using the specificity results. In addition, GNB produced better performance with Min-Max scaling technique than it does with standardization scaling techniquesReferences
Fama E. F, Fisher L, Jensen M, Roll R (1969) The adjustment of stock price to new information. Int Eco Rev 10(1):1–21
Yeh, I.-C., & Hsu, T.-K. (2014). Exploring the dynamic model of the returns from value stocks and growth stocks using time series mining. Expert Systems with Applications, 41, 7730–7743.
Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2, 1–8.
Smith, V. L. (2003). Constructivist and ecological rationality in economics. American Economic Review, 93, 465–508.
Huang, C.-J., Yang, D.-X., & Chuang, Y.-T. (2008). Application of wrapper approach and composite classifier to the stock trend prediction. Expert Systems with Applications, 34(4), 2870–2878.
Huang, W., Nakamori, Y., &Wang, S.-Y. (2005). Forecasting stock market movement direction with support vector machine. Computers & Operations Research, 32(10), 2513–2522.
Maragoudakis M., Serpanos D. (2015). Exploiting Financial News and Social Media Opinions for Stock Market Analysis using MCMC Bayesian Inference. Computational Economics. DOI 10.1007/s10614-015-9492-9
Hsu, M.-W., Lessmann, S., Sung, M.-C., Ma, T., & Johnson, J. E. (2016). Bridging the di- vide in financial market forecasting: Machine learners vs. financial economists. Expert Systems with Applications, 61, 215–234.
Weng, B., Ahmed, M. A., & Megahed, F. M. (2017). Stock market one-day ahead movement prediction using disparate data sources. Expert Systems with Applications, 79, 153–163.
Zhang, Y., & Wu, L. (2009). Stock market prediction of s&p 500 via combination of improved bco approach and bp neural network. Expert Systems with Applications, 36 (5), 8849–8854.
Meesad, P., & Rasel, R. I. (2013). Predicting stock market price using support vector regression. In Informatics, electronics & vision (iciev), 2013 international conference on informatics. IEEE, 2013, 1–6.
Jahromi, A. H. Taheri M. A non-parametric mixture of Gaussian naive Bayes classifiers based on local independent features. In 2017 Artificial Intelligence and Signal Processing Conference (AISP). IEEE, 2017, 209-212
Kumar, M., & Thenmozhi,M. (2006). Forecasting Stock index movement: A comparison of support vector machines and random forest. SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, January 24, 2006.
Ou, P., & Wang, H. (2009). Prediction of stock market index movement by ten data mining techniques. Modern Applied Science, 3(12), 28.
Subha, M. V., & Nambi, S. T. (2012). Classification of Stock Index movement using K-nearest neighbours (k-NN) algorithm. WSEAS Transactions on Information Science & Applications, 9(9), 261–270. P259.
De Oliveira, F. A., Nobre, C. N., & Zárate, L. E. (2013). Applying artificial neural networks to prediction of stock price and improvement of the directional prediction index – Case study of PETR4, Petrobras, Brazil. Expert Systems with Applications, 40, 7596–7606.
Zikowski, K. (2015). Using volume weighted support vector machines with walk forward testing and feature selection for the purpose of creating stock trading strategy. Expert Systems with Applications, 42, 1797–1805.
Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques. Expert Systems with Applications, 42, 259–268.
Sun, S., Wei, Y., & Wang, S. (2018). AdaBoost-LSTM Ensemble Learning for Financial Time Series Forecasting. Computational Science – ICCS 2018, 590–597. doi:10.1007/978-3-319-93713-7_55
Saranya C. Manikandan G. A study on normalization techniques for privacy preserving data mining. International Journal of Engineering and Technology (IJET). 2013, 5(3):2701-2714
Abdi H. Williams L. J. Principal component analysis. Wiley interdisciplinary reviews: computational statistics. 2010, 2(4):433-459
Bro R. Smilde A. K. Principal component analysis. Analytical Methods. 2014;6(9):2812-2831
Tharwat A. Gaber T. Ibrahim A. Hassanien A. E. Linear discriminant analysis: A detailed tutorial. AI communications. 2017, 30(2):169-190
Maskey R. Fei J. Nguyen H. O. Use of exploratory factor analysis in maritime research. The Asian journal of shipping and logistics. 2018, 34(2):91-111
Downloads
Published
How to Cite
Issue
Section
License
I assign to Informatica, An International Journal of Computing and Informatics ("Journal") the copyright in the manuscript identified above and any additional material (figures, tables, illustrations, software or other information intended for publication) submitted as part of or as a supplement to the manuscript ("Paper") in all forms and media throughout the world, in all languages, for the full term of copyright, effective when and if the article is accepted for publication. This transfer includes the right to reproduce and/or to distribute the Paper to other journals or digital libraries in electronic and online forms and systems.
I understand that I retain the rights to use the pre-prints, off-prints, accepted manuscript and published journal Paper for personal use, scholarly purposes and internal institutional use.
In certain cases, I can ask for retaining the publishing rights of the Paper. The Journal can permit or deny the request for publishing rights, to which I fully agree.
I declare that the submitted Paper is original, has been written by the stated authors and has not been published elsewhere nor is currently being considered for publication by any other journal and will not be submitted for such review while under review by this Journal. The Paper contains no material that violates proprietary rights of any other person or entity. I have obtained written permission from copyright owners for any excerpts from copyrighted works that are included and have credited the sources in my article. I have informed the co-author(s) of the terms of this publishing agreement.
Copyright © Slovenian Society Informatika
