Stock Prices Prediction Using Artificial Neural Networks
Stock Prices prediction using Artificial Neural Networks
Ajay Kamat
Flat 2, Jaysagar 2, Navy Colony
Liberty Garden, Malad west, Mumbai – 400064
+919833796261
ajay1185@gmail.com
ABSTRACT
The aim of this research paper is to facilitate prediction of the closing price of a particular stock for a given day. A thorough analysis of the existing models for stock market behavior and different techniques to predict stock prices was carried out. These included the renowned Efficient Market Hypothesis and its rival, the Chaos Theory. It was found that the Chaos Theory is the best model for modeling the behavior of a stock market. Chaos is a nonlinear process which appears to be random, i.e. there is an order-disorder relation between the various parameters affecting the process. Chaos theory is an attempt to show that order does exist in apparent randomness, and can be expressed mathematically.
The problem domain required a model which could deal with uncertain, fuzzy, or insufficient data which fluctuate rapidly in very short periods of time. Hence an Artificial Intelligence approach was selected which could adapt to dynamic systems like the stock market. The model had to make systematic use of hints in the learning-from-examples approach. Artificial Neural Networks represent a general class of non-linear models that has been successfully applied to a variety of problems, with special emphasis on prediction of a time series. The ability of Neural Networks to effectively map non-linear relationships in input data proved to be a useful characteristic.
With this in mind, there was an attempt to study similar systems that have been practically and successfully implemented elsewhere, albeit on a much larger scale. These included the models developed for the Tokyo Stock Exchange and the Johannesburg Stock Exchange. The former adopted a clustering approach using Self-Organizing Maps based on the Kohonen Model, while the latter implemented the system using a
Ajay Kamat
Flat 2, Jaysagar 2, Navy Colony
Liberty Garden, Malad west, Mumbai – 400064
+919833796261
ajay1185@gmail.com
ABSTRACT
The aim of this research paper is to facilitate prediction of the closing price of a particular stock for a given day. A thorough analysis of the existing models for stock market behavior and different techniques to predict stock prices was carried out. These included the renowned Efficient Market Hypothesis and its rival, the Chaos Theory. It was found that the Chaos Theory is the best model for modeling the behavior of a stock market. Chaos is a nonlinear process which appears to be random, i.e. there is an order-disorder relation between the various parameters affecting the process. Chaos theory is an attempt to show that order does exist in apparent randomness, and can be expressed mathematically.
The problem domain required a model which could deal with uncertain, fuzzy, or insufficient data which fluctuate rapidly in very short periods of time. Hence an Artificial Intelligence approach was selected which could adapt to dynamic systems like the stock market. The model had to make systematic use of hints in the learning-from-examples approach. Artificial Neural Networks represent a general class of non-linear models that has been successfully applied to a variety of problems, with special emphasis on prediction of a time series. The ability of Neural Networks to effectively map non-linear relationships in input data proved to be a useful characteristic.
With this in mind, there was an attempt to study similar systems that have been practically and successfully implemented elsewhere, albeit on a much larger scale. These included the models developed for the Tokyo Stock Exchange and the Johannesburg Stock Exchange. The former adopted a clustering approach using Self-Organizing Maps based on the Kohonen Model, while the latter implemented the system using a
References: [3] Jacek M. Zurada, Introduction to Artificial Neural Systems, 7th Ed., India: Jaico Publishing House, 2004, pp. 185-220 [4] T [5] C. Klimasauskas. Applying neural networks. In Neural Networks in Finance and Investing, chapter 3,pages 47–72. Probus Publishing Company, 1993. [8] Stefan Zemke. On Developing a Financial Prediction System: Pitfalls and Possibilities, Stockholm University and Royal Institute of Technology, Department of Computer and System Sciences, 2000. [9] R. Timothy Edwards. An Overview of Temporal Backpropagation, Stanford University, 1991. [10] Ramon Lawrence on Using Neural Networks to Forecast Stock Market Prices, University of Manitoba, Department of Computer Science, 1997 Dec 12. [11] Master Thesis on Stock Price Prediction using Neural Networks, Leiden University, 1997 Aug 4.