Abstract:-
This paper surveys recent literature in the area of Neural Network, Data Mining, Hidden Markov Model and Neuro-Fuzzy system used to predict the stock market fluctuation. Neural Networks and Neuro-Fuzzy systems are identified to be the leading machine learning techniques in stock market index prediction area. The Traditional techniques are not cover all the possible relation of the stock price fluctuations. There are new approaches to known in-depth of an analysis of stock price variations. NN and Markov Model can be used exclusively in the finance markets and forecasting of the stock price. In this paper, we propose a forecasting method to provide better an accuracy rather traditional method. Forecasting stock return is an important financial subject that has attracted researchers’ attention for many years. It involves an assumption that fundamental information publicly available in the past has some predictive relationships to the future stock returns.
INTRODUCTION
The stock market is essentially a non-linear, nonparametric system that is extremely hard to model with any reasonable accuracy [1]. Investors have been trying to find a way to predict stock prices and to find the right stocks and right timing to buy or sell. To achieve those objectives, and according to [2], [3-4] some research used the techniques of fundamental analysis, where trading rules are developed based on the information associated with macroeconomics, industry, and company. The authors of [5] and [6] said that fundamental analysis assumes that the price of a stock depends on its intrinsic value and expected return on investment. Analyzing the company’s operations and the market in which the company is operating can do this. Consequently, the stock price can be predicted reasonably well. Most people believe that fundamental analysis is a good method only on a long-term basis. However, for short- and medium-term speculations, fundamental analysis is generally not suitable. Some other research used the techniques of technical analysis [2], in which trading rules were developed based on the historical data of stock trading price and volume. Technical analysis as illustrated in [5] and [7] refers to the various methods that aim to predict future price movements using past stock prices and volume information. It is based on the assumption that history repeats itself and that future market directions can be determined by examining historical price data. Thus, it is assumed that price trends and patterns exist that can be identified and utilized for profit. Most of the techniques used in technical analysis are highly subjective in nature and have been shown not to be statistically valid. Recently, data mining techniques and artificial intelligence techniques like decision trees, rough set approach, and artificial neural networks have been applied to this area [8]. Data mining [9] refers to extracting or mining knowledge from large data stores or sets. Some of its functionalities are the discovery of concept or class descriptions, associations and correlations, classification, prediction, clustering, trend analysis, outlier and deviation analysis, and similarity analysis. Data classification can be done in many different methods; one of those methods is the classification by using Decision Tree. It is a graphical representation of all possible outcomes and the paths by which they may be reached.
This paper surveys recent literature in the area of Neural Network, Data Mining, Hidden Markov Model and Neuro-Fuzzy system used to predict the stock market fluctuation. Neural Networks and Neuro-Fuzzy systems are identified to be the leading machine learning techniques in stock market index prediction area. The Traditional techniques are not cover all the possible relation of the stock price fluctuations. There are new approaches to known in-depth of an analysis of stock price variations. NN and Markov Model can be used exclusively in the finance markets and forecasting of the stock price. In this paper, we propose a forecasting method to provide better an accuracy rather traditional method. Forecasting stock return is an important financial subject that has attracted researchers’ attention for many years. It involves an assumption that fundamental information publicly available in the past has some predictive relationships to the future stock returns.
INTRODUCTION
The stock market is essentially a non-linear, nonparametric system that is extremely hard to model with any reasonable accuracy [1]. Investors have been trying to find a way to predict stock prices and to find the right stocks and right timing to buy or sell. To achieve those objectives, and according to [2], [3-4] some research used the techniques of fundamental analysis, where trading rules are developed based on the information associated with macroeconomics, industry, and company. The authors of [5] and [6] said that fundamental analysis assumes that the price of a stock depends on its intrinsic value and expected return on investment. Analyzing the company’s operations and the market in which the company is operating can do this. Consequently, the stock price can be predicted reasonably well. Most people believe that fundamental analysis is a good method only on a long-term basis. However, for short- and medium-term speculations, fundamental analysis is generally not suitable. Some other research used the techniques of technical analysis [2], in which trading rules were developed based on the historical data of stock trading price and volume. Technical analysis as illustrated in [5] and [7] refers to the various methods that aim to predict future price movements using past stock prices and volume information. It is based on the assumption that history repeats itself and that future market directions can be determined by examining historical price data. Thus, it is assumed that price trends and patterns exist that can be identified and utilized for profit. Most of the techniques used in technical analysis are highly subjective in nature and have been shown not to be statistically valid. Recently, data mining techniques and artificial intelligence techniques like decision trees, rough set approach, and artificial neural networks have been applied to this area [8]. Data mining [9] refers to extracting or mining knowledge from large data stores or sets. Some of its functionalities are the discovery of concept or class descriptions, associations and correlations, classification, prediction, clustering, trend analysis, outlier and deviation analysis, and similarity analysis. Data classification can be done in many different methods; one of those methods is the classification by using Decision Tree. It is a graphical representation of all possible outcomes and the paths by which they may be reached.
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