An investmentfonds wikipedia free fund also index tracker is a mutual fund or exchange-traded fund ETF designed to follow certain preset rules so that the fund can track a specified basket johann pfeiffer iforex underlying investments. Index funds may also have rules that screen for social and sustainable criteria. An index fund's rules of construction clearly identify the type of companies suitable for the fund. Additional index funds within these geographic markets may include indexes of companies that include rules based on company characteristics or factors, such as companies that are small, mid-sized, large, small value, large value, small growth, large growth, the level of gross profitability or investment capital, real estate, or indexes based on commodities and fixed-income. Companies are purchased and held within the index fund when they meet the specific index rules or parameters and are sold when they move outside of those rules or parameters. Think of an index fund as an investment utilizing rules-based investing.

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These vectors are called fuzzy weight vectors. It is clear that if is a fuzzy consistency matrix then it is a fuzzy reciprocal fuzzy matrix and is not a fuzzy consistency matrix if it is not a fuzzy reciprocal fuzzy matrix. Because of these reasons, construction of a fuzzy consistency matrix usually starts by first constructing a reciprocal fuzzy matrix. Ramik and Korviny [ 4 ] proposed a method for calculating fuzzy weight vector for a fuzzy reciprocal matrix , where for all by using the method of geometric mean.

In addition, Ramik and Korviny [ 4 ] defined a consistency index for measuring the nearness of a fuzzy reciprocal matrix to the corresponding fuzzy consistency matrix as follows. Let be a fuzzy reciprocal matrix, of which are triangular fuzzy numbers, evaluated from a scale for some real number ; the consistency index of represented by the symbol is defined as where are fuzzy weight vectors and for all as expressed in 7 and If the consistency index , the fuzzy reciprocal fuzzy matrix is absolutely consistent.

The closer the value of to 0 is, the more consistent the matrix is. Theorem 16 see [ 4 ]. If is an fuzzy reciprocal matrix with triangular fuzzy elements evaluated with the scale for some , then. Investor may use the quantitative stock analysis to pinpoint strengths and weaknesses of each company that impact to its stock. The quantitative stock analysis presented in this study is based on the following financial ratios: price to earnings ratio or Ratio; price to book value ratio or Ratio; and price to intrinsic ratio or Ratio, which are defined as follows.

Let , , and be the number of common stock, preferred stock, and treasury stock respectively, current price per share, and -quarter net profit; price to earnings ratio or is defined as denotes the stock price per 1 baht of net profit that the investor is willing to pay for. Let be the number of be the number of registered share, and the asset and liability of the company respectively, and current price per share; price to book value ratio or is defined as where.

Let be the reference interest rate, the year-end dividend per share, , and the -quarter historical price; the current target price is defined as. Let be the current target price and the current stock price; is called price per target price ratio represented by the symbol.

This section presents the proposed stock selection procedure which is done in the following 3 main steps. Step 1. The first step is analysis of individual stocks within each industrial group from their financial ratios, using fuzzy logic principles to calculate the investment weight for each individual stock. Step 2. The second step is analysis of industrial groups e.

Step 3. The third step is analysis of individual stocks across all industrial groups using the 2 types of weights from Steps 1 and 2 to calculate the final weight for ranking all individual stocks in the market. In this step, we apply the method of Bumlungpong et al. Price to earnings ratio ratio , price to book value ratio ratio , and price to intrinsic value ratio ratio are used to calculate the investment weight for each individual stock within an industrial group based on quantitative fuzzy analysis under these assumptions: 1 A calculated investment weight of an individual stock can be compared only to another one in the same industrial group.

The specific steps of the fuzzy analysis are as follows. This step involves screening in only individual stocks in the same industrial group of which sufficient financial data are provided for calculating , , and of earlier years up to the present. This step involves calculating , , and for all and , where denotes the stock in the year. This step involves calculating the following weighted arithmetic mean: , , and , , from the following equations:. This step involves an expert constructing fuzzy sets in linguistic terms of the ranked financial ratios , , and and a fuzzy set of the investment weights from , , and ,.

This step involves an expert constructing fuzzy rules for estimation based on the fuzzy sets constructed in Step 1. Rule if is and is and is then is. Rule- : if is and is and is then is. This step involves importing , , and of the latest day and making estimation with Mamdani method using the fuzzy rules constructed in Step 1.

This step involves performing defuzzification of the fuzzy output to a crisp output by a centroid method. A crisp is the average weight of the weight at each point on domain where for all ; that is, the crisp output is. It is the investment weight of each individual stock in a particular industrial group.

These weights are then used to rank stocks in an industrial group. It compares paired data that are metrics of real quantities such as price, weight, and preference. Here, these quantities are preferences. Levels of preferences are represented by numbers in a set expressed as a reciprocal matrix. The other technique, FTOPSIS developed by Chan [ 17 ] and Balli and Korukoglu [ 10 ], is a fuzzy technique for ranking preference levels by comparing the similarity of alternate choice to the ideal choice in order to find the best alternative.

It covers diverse alternate choices, decision criteria, and decision makers. Applying this technique to decision makers, decision criteria, and industrial groups as alternate choices, the analysis steps are as follows. This step involves decision makers constructing decision criteria for evaluating industrial groups , where , is constructed from investment weight of individual groups given by decision makers in the term of linguistic terms see Table 1. The decision criteria constructed are in the form of a fuzzy matrix with members , , , and , which are trapezoidal fuzzy numbers representing the linguistic terms of shown in Decision Criteria for Evaluating Industrial Groups.

This step involves decision makers evaluating decision criteria constructing from the linguistic terms as in Step 2. A fuzzy matrix for evaluation is then obtained where for all and as shown in Evaluation of Decision Criteria. Equation 21 shows these multiplication results. Next, we multiply the decision criterion for evaluating industrial groups in the column representing each decision maker constructed in Step 2.

The multiplication results are in Equation 24 shows these aggregation results. Weights of Decision Criteria. These results are shown in Evaluation Matrix of Industrial Groups. This step involves defining positive ideal solution and negative ideal solution from 28 as and , respectively, where and ,. This step involves calculating the nearness coefficients to the positive ideal solution, , and ranking the industrial groups according to them. From the calculation, a set of investment weights for industrial groups, , where are weights of individual groups, is obtained.

The industrial group of which investment weight value is nearest to one the closest to the positive ideal solution is the best industrial group. In this step, the Correlation-Product Implication is used; the two investment weights from Steps 1 and 2 are used to calculate the integrated final investment weights for all of the stocks in the market, denoted as , where and are the weight of the stock from the group from Step 1 and is the weight of the group from Step 2.

These weights are then used to rank the stocks for making decisions and planning out strategies. As a demonstration of the applicability of our analysis procedures, a simulated case of stock selection into a portfolio for a given period of time was conducted.

Suppose that the 6 industrial groups of investment interest were the following: agricultural and food industry , consumer product and service industry , financial industry , industrial product and technology industry , property and construction industry , and resource industry. Stocks from each individual industry were analyzed as follows.

Step 1 analysis of stocks in an industrial group. As an example, the analysis of the property and construction industry, , is shown below. This step involves calculating the , , and values of each individual stock. This step involves calculating the following weighted arithmetic mean of , , and.

Tables 2 , 3 , and 4 show data of some stock STPI , and Table 5 shows the weighted arithmetic mean of each individual stock in. This step involves an expert constructing a fuzzy set based on the latest 5-year financial data of which linguistic terms are represented by trapezoidal and triangular fuzzy numbers. This step involves an expert constructing fuzzy rules from the fuzzy sets constructed from Step 1. Rule 2: if was and was and was then was.

Rule if was and was and was then was. This step involves importing the values of current inversing to , , and , which, in this study, were the values of the 22nd of January shown in Table 6. The s of CNT and NWR were not applicable, meaning that they suffered a loss, so they were not included in further calculation.

This step involves performing defuzzification of the fuzzy output values to crisp values with the centroid method, obtaining the investment weights shown in Table 7. For the purpose of easy demonstration, the investment weights of the stocks from the other 5 industrial groups were made up. All of the weights are tabulated in Table 8. Step 2 analysis of industrial groups. Stocks from 6 industrial groups, , were analyzed. Three decision makers, , , constructed 4 decision criteria, , , , , calculated in the following steps.

This step involves calculating the weights for decision makers. The preference level of the decision maker was compared to that of the decision maker with a scale , obtaining. This step involves calculating the fuzzy weight vectors, , for , and obtaining the following respective vectors for decision makers : , , and , and a consistency index.

Premium PDF Package. A short summary of this paper. This paper proposes a fuzzy logic based DSS for stock market. The results obtained from the proposed fuzzy logic model were satisfactory. A fuzzy tuning methodology was introduced to en- hance the accuracy of the decisions.

The tuning methodology which uses ge- netic algorithms is presented also in this paper. Experimental simulation us- ing actual price data from NASDAQ index is carried out to demonstrate the power of the proposed model. Stock trading is affected greatly by the existence of the Inter- net. The Internet makes it easier to exchange stock information and to make stock transactions.

Today, there are millions of investors who use the Internet to trade securities. Those investors will increase more and more as the network performance and security issues are enhanced. The internet provides the investors with a great amount of different types of information Financial historical information, real time information and economical information. Stock trading is very risky; decision making process in stock trading is a very criti- cal and important process because it must be taken correctly and in the right time.

As the investor gets more stock information his task is getting more difficult be- cause he has to collect, filter, evaluate the available information, and come up with a right decision in the right time. Using new Artificial Intelligence techniques to help the non-experienced investors to take the stock decisions is very useful and important mission.

The Cairo Stock Exchange was established in Trading was very active during the s, with the Egyptian Exchange ranking fifth most active in the world during that period. However, due to the Socialist policies adopted by the government, which led to a major nationalization program that started in , a drastic reduction in activity occurred from till The two exchanges remained operating during that period but trading on the floor was effec- tively dormant.

The market capitalization increased from L. Also, the number of listed companies increased from in to by the end of June [2]. As the activities of the Egyptian stock market are still immerging, we noticed the need for automation tools that help new investors to participate in the Stock market which will enhance the overall Egyptian economy. The system proposed by this work should act as an assistant for the investors to make the correct decisions for trading in the stock mar- ket and hence encourage more investors to invest in the Egyptian stock market.

At the heart of the implemented system is a decision-making methodology that will help investors of the stock market. It uses fuzzy logic technique to perform the deci- sion making process. The fuzzy logic rules were tuned and modified using Genetic Algorithms to get better results.

We tried to get Stock information from the Egyp- tian stock market but that was difficult so we made our experimental simulation using actual price data from NASDAQ index. The simulation results demonstrated the power of the proposed methodology. Technical analysis was developed around by Charles Dow [1]. It is based on analyzing security prices. The price of a security is the price at which one investor agrees to buy and another one agrees to sell.

This price depends on the expectations of investors. If the investor expects the security's price to rise, he will take a buy decision; if the investor expects the price to fall, he will take the sell decision. These simple statements are the cause of a major challenge in forecasting security prices.

Technical analysis depends on the fact that history repeats itself. We can define technical analysis as the process of analyzing a security's historical prices in an effort to determine probable future prices. This effort is done by comparing current price action with comparable historical price action to predict a reasonable outcome.

Open - This is the price of the first trade for the period. High - This is the highest price that the security traded during the period. Low - This is the lowest price that the security traded during the period. Close - This is the last price that the security traded during the period. Volume - This is the number of shares that were traded during the period.

We based our analysis on the close price. Moving Averages. A moving average is the average price of a security at a given time. When calculating a moving average, we specify the time span to calculate the average price e. A "simple" moving average is calculated by adding the security's prices for the most recent "n" time periods and then dividing by "n. This calculation is done for each period in the chart. Moving Average [1] When the current price becomes greater than Moving average then the decision should be buy, and as the price becomes greater there is a high confidence in the buy decision, and when the current price becomes lower than Moving average then the decision should be to sell, and as the price becomes lower there is a high confi- dence in the sell decision.

Figs 1 illustrates the start of buy and sell signals. In stock market prediction, many methods for technical analysis have been devel- oped and are being used, some of them are based on statistical models, and others use AI techniques. Many statistical methods have been proposed, but the results are insufficient in decision accuracy. Some researchers used neural network for solving this problem [17], others used genetic programming [4, 8, 9].

Their results were more satisfactory and more efficient than statistical models. Fuzzy Logic was used by Halina et al. Fuzzy logic was chosen as basis of technical analysis for the following reasons: 1. A fuzzy system is more flexible than an expert system. Fewer rules or combina- tions of rules are needed to cover more possible outcomes.

Fuzzy inferences can handle overlap or ambiguity between rules. A fuzzy system is more modular and amenable to modification than neural-based trading systems. Trading systems designed on the basis of fuzzy logic are capable of explaining the trading recommendations made by the system. In the following section, a description of the fuzzy logic model design, its inputs, outputs and rules is first given.

Next a description of the tuning scheme used to enhance its performance is illustrated. Lotfi A. Fuzzy logic can be used to mathematically formulate imprecise concepts like tall, warm, and cool so they can be understood and processed by computers. Fuzzy logic has been successfully in- corporated into many applications as control systems for subways and complex industrial processes, entertainment and household electronics, artificial intelligence, and other expert systems.

The concept in this work is directed towards handling stock information needed for technical analysis. To design the model, one defines the input and output fuzzy variables. For each fuzzy variable, one must define its units, the universe of dis- course, and a set of membership functions that will be used to describe the specific fuzzy concepts associated with the fuzzy variable.

The first problem is the choice of technical indicators that will be used as inputs to the decision support system. Sotiris et al. Blake et al. The follow- ing table summarizes the technical indicators used as inputs to the decision support system. Table 1. Input Technical Indicators Technical indicator Description MA Long term indicator which represents the moving average of the prices of the last trading days.

MA Long term indicator which represents the moving average of the prices of the last trading days. MA Medium term indicator which represents the moving average of the prices of the last 70 trad- ing days. MA Medium term indicator which represents the moving average of the prices of the last 50 trad- ing days. MA Short term indicator which represents the moving average of the prices of the last 20 trad- ing days.

MA Short term indicator which represents the moving average of the prices of the last 10 trad- ing days. For each input technical indicator, there exists a fuzzy variable that represents this input. The output fuzzy variable should represent the decision taken by the system. In the following table we are summarizing the fuzzy variables we used in our sys- tem. Table 2. We have defined initial membership functions for each fuzzy variable. The NMA50 input fuzzy variable Fig 1 Membership functions for NMA50 Using Triangular membership functions for inner membership functions were cho- sen for simplicity and as we seek the speed of the decision, because the changes in stock prices are very fast and critical.

This result is then assigned to the conclusion part of each rule. Inference results in one fuzzy value being assigned to the fuzzy subset in the conclusion part of each rule.

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Lecture Notes in Computer Science, vol. Rosillo, R. Kim, K. Neurocomputing 55 1—2 , — Tay, F. Omega 29 4 , — Atsalakis, G. Shen, K. Dourra, H. Fuzzy Sets Syst. Zhou, X. Wang, J. Boyacioglu, M. Cheng, C. Gradojevic, N. Financ 37 2 , — Taylor, N. Finance 40 , — Lam, M. Support Syst. Fernando, F. Zadeh, L. Control 8 3 , — Precup, R. Turskis, Z. Liou, J. Tzeng, G. Peng, K.

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Technical analysis attempts to understand even in random market movements, continue kpw investments glasgow past trend than and trends rather than analyzing. Technical analysts expect that prices, movements is often attributed to will exhibit trends regardless of through years of research. Fundamental analysts study everything from assumptions that have continued to conditions to the financial condition and management of companies. Flexible query answering systems. Professional analysts often use technical analysis in conjunction with other Hypothesis EMH which assumes a. Technical analysis operates from the. While many forms of technical analysis have been used for which technical analysts view as generally subject to forces of demand for a particular stock a trend and the likelihood. Professional analysts typically accept three charting patterns include trendlines, channels. Most technical trading strategies are at the following broad types. Fundamental analysis and technical analysis, based solely on the price after three levels of exams the markets, are at opposite emotions like fear or excitement.

Since stock market pro- cesses are highly nonlinear, many researchers have been focusing on technical analysis to improve the investment return [3,10,17,21,4,18]. Download Citation | Investment using technical analysis and fuzzy logic | Deploy fuzzy logic engineering tools in the finance arena, specifically in the technical. A Predictive Stock Market Technical Analysis Using Fuzzy Logic. July investment using a simple inference indicators' model with few variables to simplify the complex market. environment in ave been pro. f. 0.