Stock Market Ontology-Based Knowledge Management for Forecasting Stock Trading

Abstract:

Today’s markets are rather matured and arbitrage opportunities remain for a very short time. The main objective of the paper is to devise a stock market ontology-based novel trading strategy employing machine learning to obtain maximum stock return with the highest stock ratio. The paper aims to create a dynamic portfolio to obtain high returns. In this work, the impact of the applied machine learning techniques on the Chinese market was studied. The problem of investing a particular total amount in a large universe of stocks is considered. The Chinese stocks traded on Shanghai Stock Exchange and Shenzhen Stock Exchange are chosen to be the entire universe. The inputs that are considered are fundamental data and company-specific technical indicators unlike the macroscopic factors considered in the existing systems. In the stock market document repository, ontological constructs with Word Sense Disambiguation (WSD) algorithm improve the conceptual relationships and reduce the ambiguities in Ontological construction. The machine learning techniques Kernel Regression and Recurrent Neural Networks are used to start the analysis. The predicted values of stock prices from the Artificial Neural Network provided quite accurate results with an accuracy level of 97.55%. In this study, the number of nodes will be selected based on Variance-Bias plots by tracking the error on the in-sample data set and the validation data set.

• Periodical: Advances in Science and Technology (Volume 124)
• Pages: 433-443
• DOI: https://doi.org/10.4028/p-02laqd
• Online since: February 2023
• Authors: M.Uma Devi*, P. Akilandeswari, M. Eliazer
• Keywords: Artificial Neural Network, Kernel Regression, Ontology, Portfolio, Regression, Stock Market

References

[1] Aarish Pathare, Siddhartha Gade and Ted Hwangm, Exploring Trading Strategies Using Machine Learning,, Georgia Institute of Technology, (2016).

[2] Aditya Gupta and Bhuwan Dhingra, Stock Market Prediction Using Hidden Markov Models,, IEEE.

[3] Aditya Bhardwaj, Yogendra Narayan, Vanraj, Pawan and Maitreyee Dutta , Sentiment Analysis for Indian Stock Market Prediction Using Sensex and Nifty ,, Proceedings of the 4thInternational Conference on Eco-friendly Computing and Communication Systems , Procedia Computer Science ,70 , 85 – 91, (2015).

DOI: 10.1016/j.procs.2015.10.043

[4] Ibrahim M. Hamed, Ashraf S. Hussein and Mohamed F. Tolba , Intelligent Model for Stock Market Prediction,,. Computer Engineering and Systems. doi : 10.1109/ICCES.2011.6141021, (2011).

DOI: 10.1109/icces.2011.6141021

[5] Ko Ichinose and Kazutaka Shimada , Stock Market Prediction from News on the Web and a New Evaluation Approach in Trading,, Advanced Applied Informatics. doi :  10.1109/IIAI-AAI.2016. 157, 77 – 81, (2016).

DOI: 10.1109/iiai-aai.2016.157

[6] Mehak Usmani and Syed Hasan Adil , Stock Market Prediction Using Machine Learning Techniques,, Proceedings of the 3rd International Conference on Computer and Information Sciences (ICCOINS), (2016).

DOI: 10.1109/iccoins.2016.7783235

[7] Ruchi Desai and Prof. Snehal Gandhi , Stock Market Prediction Using Data Mining ,, International Journal of Engineering Development and Research. Vol. 2, Issue 2, pp.2780-2784(2014).

[8] Tao Xing, Yuan Sun, Qian Wang and Guo Yu, The analysis and prediction of stock price,, Proceedings of the IEEE International Conference on Granular Computing (GrC) , doi : 10.1109/GrC.2013.6740438, (2013).

DOI: 10.1109/grc.2013.6740438

[9] Xiaodong Li, Haoran Xie, Yangqiu Song, Shanfeng Zhu, Quing Li and Fu Lee Wang , Does Summarization help stock prediction? News impact analysis via Summarization,, IEEE Intelligent Systems, vol. 30, pp.26-34,.

DOI: 10.1109/mis.2015.1

[10] Protégé and Protégé OWL extensions. http://protege.stanford.edu/overview/protege-owl.html.