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Read the Privacy Policy to learn how this information is used. One of the most challenging areas in technical analysis is the automatic detection of technical patterns that would be similarly detected by the eyes of experts. In this study, cognitive uncertainty was incorporated in technical analysis by using a fuzzy logic—based approach. The results show that the algorithm can detect subtle differences in a clearly defined pattern.

Significant postpattern abnormal returns were found that varied directly with the fuzziness of a pattern. This approach can be valuable for investors as a way to incorporate human cognition into historical trading statistics so as to form future winning strategies. Privacy Settings Functional cookies , which are necessary for basic site functionality like keeping you logged in, are always enabled.

Save Settings. Xu-Shen Zhou. The subjective judgement of experts who have used fuzzy logic techniques produces better results than the objective manipulation of inexact data. The concept of a fuzzy set is a reflection of reality reflection which serves as a point of departure for the development of theories which have the capability to model the pervasive imprecision and uncertainty of the real world.

As applied to stock analysis e. By reason of vagueness of boundaries of stock data in future and the attendant imprecision, uncertainty, and preference of decision makers, therefore, fuzzy logic and AHP seem suitable for this problem. This paper proposes an approach to stock analysis based on calculated weights from fuzzy quantitative analysis and fuzzy multicriteria decision making.

The idea of using fuzzy quantitative analysis and fuzzy multicriteria decision making to imply final investment weights for the stock selection into portfolio is different from the previous works. The practicality of the approach was demonstrated by an application to a test set of data.

Fuzzy logic was introduced by Zadeh [ 16 ] and has been widely applied to problems in various fields of study. Many researchers used fuzzy logic in stock market analysis e. In this study we use fuzzy logic in both, stock market analysis and decision making. In this subsection, definitions of the fuzzy logic terms and concepts used in this study are described below. Definition 1. Given a crisp set of a universe , a fuzzy set on is defined as and is a membership function.

Definition 2. Given a fuzzy set , an -cut set, denoted by , for all , is defined as. Definition 3. Let be a fuzzy set under the membership , and is a fuzzy number if it satisfies the following conditions: 1 is a normal fuzzy set; that is, ,. Given an fuzzy number space, condition 3 of Definition 3 ensures that every can be represented by a closed interval , where are functions that satisfy the following conditions: 1 is a bounded, left continuous, and nondecreasing function on.

Definition 4. Definition 5. Given , a trapezoidal fuzzy number is a fuzzy number whose membership function is defined by and represented by the expression. Definition 6. A trapezoidal fuzzy number is called a triangular fuzzy number and expressed as. For any real number ,. Definition 7. Given any two positive fuzzy numbers and and a real positive number , operations , , , and between and and an operation between and are defined as follows:. Definition 8. Given two trapezoidal fuzzy numbers and , the distance between and represented by the symbol is defined as For convenience, is defined for further use in this paper.

Definition 9. Definition The aggregation of , represented by , is defined as. In this subsection, we introduce the definition of consistency fuzzy matrix and consistency index which was developed by Ramik [ 3 , 4 ]. Let be an matrix where for all and is a reciprocal matrix if for all.

Let be an matrix where for all and is a consistency matrix if there exist weight vectors , , for all , where and for all. Let be an fuzzy matrix where are fuzzy numbers for all and is a reciprocal fuzzy matrix if for all. In particular, if every member of is a triangular fuzzy number , is a reciprocal fuzzy matrix if for all. Let be an fuzzy matrix, where for all and is a consistency fuzzy matrix if there exist for all and some with which is a consistency matrix; that is, there exist , , for all , where and for all.

According to Definition 14 , since for all , there exist fuzzy vectors , where for all. 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.

Investment using technical analysis and fuzzy logic software | Step 2. The aggregation ofrepresented byis defined as 2. Ming Dong. 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. Definition 5. |

Investment using technical analysis and fuzzy logic software | 483 |

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Learn more in our Privacy Policy. Privacy Settings. Xu-Shen Zhou Ming Dong. Functional cookies , which are necessary for basic site functionality like keeping you logged in, are always enabled. Allow analytics tracking. Analytics help us understand how the site is used, and which pages are the most popular. Read the Privacy Policy to learn how this information is used. One of the most challenging areas in technical analysis is the automatic detection of technical patterns that would be similarly detected by the eyes of experts.

In this study, cognitive uncertainty was incorporated in technical analysis by using a fuzzy logic—based approach. 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. This step involves the 3 decision makers, , , evaluating 6 industrial groups, , according to the decision criteria , , , utilizing linguistic terms represented by trapezoidal fuzzy numbers as in Table 9.

Journal overview. Special Issues. Academic Editor: Igor L. Received 28 Nov Revised 26 Feb Accepted 07 Apr Published 05 Jul Abstract This paper presents a stock selection approach assisted by fuzzy procedures. Introduction Presently, investors are more interested in investing in stocks and bonds than keeping their money in the bank because it yields a higher return.

Preliminaries 2. Fuzzy Logic: Application and Definitions Fuzzy logic was introduced by Zadeh [ 16 ] and has been widely applied to problems in various fields of study. Given a fuzzy set , an -cut set, denoted by , for all , is defined as Definition 3. Given any two positive fuzzy numbers and and a real positive number , operations , , , and between and and an operation between and are defined as follows: Definition 8.

The aggregation of , represented by , is defined as 2. Consistency Fuzzy Matrix In this subsection, we introduce the definition of consistency fuzzy matrix and consistency index which was developed by Ramik [ 3 , 4 ]. Financial Ratios A sustainable investment and mission requires effective planning and financial management. Let be the reference interest rate, the year-end dividend per share, , and the -quarter historical price; the current target price is defined as Definition

Finance 40- Lam. Neurocomputing 55 1-2. Control 8 3. Lecture Notes in Computer Science. PARAGRAPHLaunch Research Feed. McGraw Hill, New York Mamdani. An intelligent stock trading decision rough sets theory and genetic algorithms for stock price forecasting network and artificial neural network. Omega 29 4. Financ 37 2- Precup, R. michael real estate investments juq investment group big day of.

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.