The week's pick
Random Articles
Reseach Article
Decision Support System for the Stock Market using Data Analytics and Artificial Intelligence
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 117 - Number 8 |
| Year of Publication: 2015 |
| Authors: Ajinkya M. Vaidya, Nikunjkumar H. Waghela, Sneha S. Yewale |
10.5120/20574-2977
|
Ajinkya M. Vaidya, Nikunjkumar H. Waghela, Sneha S. Yewale . Decision Support System for the Stock Market using Data Analytics and Artificial Intelligence. International Journal of Computer Applications. 117, 8 ( May 2015), 21-28. DOI=10.5120/20574-2977
Abstract
The stock market is a complex, non-stationary and chaotic dynamic system. It is a popular investment platform that appeals to a wide variety of masses. While the stock market remains a significant way to earn profit, it is often considered one of the most risky forms of investment due to the underlying nature of the financial domain and a host of various factors that often elude the attention of naïve investors. The stock market is a hostile environment that demands undivided attention to the events that transpire throughout the day along with a certain consideration to the effects of the past and the implications on the future. Hence, many investors, face (or stand a risk) of failure on a daily basis. Therefore, the need of the hour is a Decision Support System (DSS) that takes into account market trends, financial analysis and strategies to identify the best time to purchase stocks and the actual stocks to purchase. This paper highlights the above concerns regarding the volatile stock market and discusses the implementation of a DSS taking into account the modern and sophisticated techniques of Data Analytics like Clustering and forecasting models like Holt-Winters. Also, the DSS uses popular supervised learning algorithm used extensively in machine learning and Artificial Intelligence, the Perceptron. While the data analytics form the initial stage of the DSS, the decision-making will be aided by the Perceptron, which would consider the results of the aforementioned analysis and various local stock market parameters and a host of statistical concepts. This will culminate in a comprehensive DSS that will assist the potential investors in the most important aspect of success in the stock market i. e. decision-making.
References
- Cutler, D. Poterba, J. & Summers, L. (1991). "Speculative dynamics". Review of Economic Studies 58 (3): 520–546.
- Mandelbrot, Benoit & Hudson, Richard L. (2006). The Misbehavior of Markets: A Fractal View of Financial Turbulence, annoted. Basic Books. ISBN 0-465-04357-7.
- Taleb, Nassim Nicholas (2008). Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets, 2nd ed. Random House. ISBN 1-4000-6793-6.
- Tversky, A. & Kahneman, D. (1974). "Judgment under uncertainty: heuristics and biases". Science 185 (4157): 1124–1131.
- Lewis-Beck, Michael S. (1995). Data Analysis: an Introduction, Sage Publications Inc, ISBN 0-8039-5772-6
- Vance (September 8, 2011). "Data Analytics: Crunching the Future". Bloomberg Businessweek. Retrieved 26 September 2011.
- B. Uma Devi, D. Sundar, Dr. P. Alli. (2011). A Study on Stock Market Analysis for Stock Selection – Naïve Investor's Perspective using Data Mining Technique. International Journal of Computer Applications (0975 – 8887) Volume 34– No. 3,
- R B. Parihar, R V. Argiddi. (2011). An Optimized Approach to Analyze Stock market using Data Mining Technique- Proceedings published by International Journal of Computer Applications (IJCA) International Conference on Emerging Technology Trends (ICETT).
- Aurangzeb Khan, Khairullah Khan. Frequent Patterns Mining of Stock data using Hybrid Clustering Association Algorithm, University Technology PETRONAS.
- Keen, Peter; (1980),"Decision support systems: a research perspective. "Cambridge, Mass: Center for Information Systems Research, Alfred P. Sloan School of Management.
- Sprague, R; (1980). "A Framework for the Development of Decision Support Systems. " MIS Quarterly. Vol. 4, No. 4, pp. 1-25.
- Power, D. J. (2002). Decision support systems: concepts and resources for managers. Westport, Conn. , Quorum Books.
- Bernhard Warner (April 25, 2013). "'Big Data' Researchers Turn to Google to Beat the Markets". Bloomberg Businessweek. Retrieved August 28, 2013.
- Jenks, George F. 1967. "The Data Model Concept in Statistical Mapping", International Yearbook of Cartography 7: 186–190.
- ESRI FAQ, What is the Jenks Optimization method?
- Lynwood A. Johnson Douglas C. Montgomery and John S. Gardiner (1990). Forecasting and Time Series Analysis. McGraw-Hill, Inc, 2nd Edition
- Average and Exponential Smoothing Models | Duke University.
- Prajakta S. Kalekar. Time series Forecasting using Holt-Winters Exponential Smoothing | IIT Bombay
- Chatfiel Yar. The Statistician (1988). "Holt-Winters Forecasting: Some Practical Issues"
- Rosenblatt, Frank (1957), The Perceptron--a perceiving and recognizing automaton. Report 85-460-1, Cornell Aeronautical Laboratory.
- Liou, D. -R. ; Liou, J. -W. ; Liou, C. -Y. (2013). "Learning Behaviors of Perceptron". ISBN 978-1-477554-73-9. iConcept Press.
- Shiller, Robert (2005). Irrational Exuberance (2d ed. ). Princeton University Press. ISBN 0-691-12335-7.
Index Terms
Keywords