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The main objective of the paper is to build a prediction system to predict the future occurrence of an event. Fuzzy logic, among the various available Artificial Intelligence techniques, emerges as an advantageous technique in predicting future events. Subjective and Objective modeling are two types of fuzzy modeling. Objective type fuzzy modeling is used to build the prediction system. It is a combination of a clustering algorithm and fuzzy system identification which proves effective in improving the efficiency of the prediction. To train the prediction system, historical data is obtained from the web. Data specific to the desired application is obtained and is recorded. This recorded information is subjectively reasoned to develop containing only the necessary inputs to the prediction system. The subtractive clustering algorithm is used for its computational advantages and fuzzy rules are formed using system identification technique. Stock markets are excellent examples where this prediction system can be applied and the possibility of a rise or a fall in the market prices is predicted. The entire prediction system is realized using Java.
Stock trading is the process of buying and selling of stocks based on known signals which can be predicted based on past stock data. Stock price prediction involves the steps taken to determine the future value of a company’s stock traded on a financial exchange; making business more profitable as well as making reasonable recommendations as regards stock trading. The accurate predictions of stock price are important for many reasons, chief among these are the need for the investors to hedge against potential market risk and to make profit by trading indexes. Stock is the representation of the ownership in the share of profit, assets, and losses of a company; it is created when a business carves itself into pieces of units called shares and sells them to investors in exchange for cash and stock price is the cost of purchasing or selling a security on an exchange.
This paper proposes a Sugeno-type fuzzy inference system for stock price prediction using technical indicators as its input values. Knowledge Base, Fuzzification, Inference Engine and Defuzzification are the essential components of our model. We explore Sugeno-type fuzzy inference engine to optimize the estimated result. We evaluate the degree of participation of each input parameter with Trapezoidal membership function. Center of Gravity technique is employed for defuzzification. We employ object oriented design tool to model our database. JAVA and Mysql relational database are used in the implementation of our study. The development of this system is based on the selection of stock data history
which are studied and used for training the system. This system provides vital support to stock traders, researchers and other financial experts in making decisions as regards stock trading.
1.1 AIMS AND OBJECTIVES
· The objective of this work is to develop a market trading modelsugeno-type 2 inference model that can successfully trade market securities for a profit, beating buy-and-hold.
· Using a sugeno –type fuzzy inference system to make future predictions that can generate buy (low) and sell (high) signals in order to achieve maximum profit.
1.2 PROBLEM STATEMENT
The operations of the prediction of stock price are complex and risky due to fluctuation in the stock market because of the vagueness, incompleteness, and uncertainty of the information used. Predicting stock price has over time been a subject of interest for many financial investors and professional analysts because finding out the appropriate time to sell or buy stock has been a very difficult task as there are too many unknown factors of uncertainty and volatility that have direct influence on stock prices. The prediction of stock price is a highly complicated and very difficult task because there are too many factors such as inflation, short term interest rate, political events, traders’ expectations, and environmental factors amongst others.
1.3 SCOPE OF THE STUDY
This study centers on eliminating threats to loss, and low profit encountered by marketers due to unknown investment break down. This paper uses sugeno type inference system for predicting stock changes which uses a computer to imitate human intelligence.
LIMITATION OF STUDY
· Lack of prior research studies on the topic – I was unable to lay hands on any previous material concerning the topic to aid me in the research
· High cost of obtaining research data -- During the design of this project work, much finance was required and owing to the financial meltdown In Nigeria, the research was limited by finance and hence concentrated on the available materials.
· Fuzzy Logic is an approach to computing based on "degrees of truth" rather than the usual "true or false" (1 or 0) Boolean logic on which the modern computer is based. The idea of fuzzy logic was first advanced by Dr. LotfiZadeh of the University of California at Berkeley in the 1960s.
· Stock Priceis the highest amount someone is willing to pay for the stock, or the lowest amount that it can be bought for.
· Technical Indicators: look to predict the future price levels, or simply the general price direction, of a security by looking at past patterns
· Trapezoidal Membership Functionisa membership function for a fuzzy set A on the universe of discourse X is defined as µA:X → [0,1], where each element of X is mapped to a value between 0 and 1. ...
· Object-Oriented Analysis And Design (OOAD)is a popular technical approach for analyzing, designing an application or system.
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