سرمایه گذاری با استفاده از تجزیه و تحلیل فنی و منطق فازی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|28348||2002||20 صفحه PDF||سفارش دهید||7511 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Fuzzy Sets and Systems, Volume 127, Issue 2, 16 April 2002, Pages 221–240
Deploy fuzzy logic engineering tools in the finance arena, specifically in the technical analysis field. Since technical analysis theory consists of indicators used by experts to evaluate stock prices, the new proposed method maps these indicators into new inputs that can be fed into a fuzzy logic system. The only required inputs to these indicators are past sequence of stock prices. This method relies on fuzzy logic to formulate a decision making when certain price movements or certain price formations occur. The success of the system is measured by comparing system output versus stock price movement. The new stock evaluation method is proven to exceed market performance and it can be an excellent tool in the technical analysis field. The flexibility of the system is also demonstrated.
Technical analysis is an attempt to predict future stock price movements by analyzingthe past sequence of stock prices. Technical analysis dismisses such factors as the 0scal policy of the government, economic environment, industry trends and political events as beingirrelevant in attemptingto predict future stock prices. The concern in technical analysis is the historical movement of prices and forces of supply and demand that a:ect those prices.Technical analysis relies on charts and look for particular con0gurations that are supposed to have predictive value. Analysts focus on the investor psychology and investor response to certain price formation and price movements. The price at which an investor is willingto buy or sell depends on his or her expectation. If he or she expects the security price to rise, he or she will buy it; if the investor expects the security price to fall, he or she will sell it. These simple statements are the cause for a major challenge in setting security prices, because they refer to human expectations and attitudes . As some people say securities never sell for what they are worth but for what people think they are worth. It is very important to understand that market participants anticipate future development and take action now and their action drive the price movement. Since stock market processes are highly nonlinear, many researchers have been focusingon technical analysis to improve the investment return [3,10,17,21,4,18]. This paper describes a methodology using fuzzy logic and technical analysis, which can be used to build an investment model system.
نتیجه گیری انگلیسی
This paper examined fuzzy logic systems in the 0- nance arena. It simulated human behavior in reacting to stock price movement and formation. A number of inputs were created to recommend buy=sell of a speci 0c stock when certain price formation exists. We simpli0ed some details on investingprocedures (cost per transaction is constant, taxation e:ects, etc.) in order to highlight the properties of the methodology presented. This simulation system used Matlab program to generate the results. In this study, we applied fuzzy informational technologies to investments through technical analysis. Most investment models are of a proprietary nature and their characteristics are not reported in the scholarly literature. Pruitt and White  is a published assessment of the potential of technical analysis. They used three technical indicators (cumulative volume, relative strength, and moving average) to build their tradingmodel. Our system examined various companies and proved to be e:ective. The investment returns were excellent. Most companies consider the performance of S&P 500 as average performance. Our results surpassed the S&P 500 by a substantial amount. The buy and sell trigger level can produce di:erent results. Accordingto our analysis, the decision to choose the trigger levels for sell and buy depends on the investor and the stock’s long-term trend. Because of this, di:erent strategies can be implemented using the fuzzy indicator to match the investor preferences and the industry conditions. One strategy was described and implemented. The results were excellent as shown in Section 8.