Pendekatan Rasio Fibonacci Dan Fuzzy Logic Sebagai Analisis Teknikal Dalam Menentukan Keputusan Investasi Saham

Along with the rapid development of the times there was a shift in the investment perspec- tive for the community. At present investment has become a necessity for most people. One type of investment that has high profit potential and a not too long period of time is stock. Shares are a sign of ownership or ownership of a person or entity in a company. Stock prices continue to experience movement following supply and demand. Efforts to anticipate the movement of stock prices every day in the capital market is by using stock price analysis. One form of approach used to analyze stock prices is by applying Fibonacci ratios and Fuzzy Logic analysis.The purpose of this study is (1) to apply the Fibonacci ratio approach to determine stock support and resistance levels in food and beverage sub-sector companies on the IDX, (2) to apply fuzzy logic analysis based on support and resistance levels with the Fibonacci ratio approach to sub company the food and beverage sector on the IDX, (3) to provide investment decision recommendations in the form of linguistic information on stock price movements in the food and beverage sub-sector companies on the IDX.The research design used in this study is a case study of the price movements of ten shares in the food and beverage sub-sector companies on the Stock Exchange. Researchers used a research design with secondary data. Furthermore, stock price movement data were analyzed using a merging analysis approach with Fibonacci ratios and fuzzy logic analysis with fuzzy systems so that an investment decision recommen- dation was obtained in the form of linguistic information.Based on the results of the study showed that technical analysis of stock price move- ments using Fibonacci ratios and fuzzy logic analysis can be used properly. Furthermore, the results of this linguistic information can be utilized by investors who do not own shares and who already have shares, but the results of this data analysis are not absolute and must be adjusted to the conditions in their application

Artificial Counselor System for Stock Investment

This paper proposes a novel trading system which plays the role of an artificial counselor for stock investment. In this paper, the stock future prices (technical features) are predicted using Support Vector Regression. Thereafter, the predicted prices are used to recommend which portions of the budget an investor should invest in different existing stocks to have an optimum expected profit considering their level of risk tolerance. Two different methods are used for suggesting best portions, which are Markowitz portfolio theory and fuzzy investment counselor. The first approach is an optimization-based method which considers merely technical features, while the second approach is based on Fuzzy Logic taking into account both technical and fundamental features of the stock market. The experimental results on New York Stock Exchange (NYSE) show the effectiveness of the proposed system.Comment: 7 pages, 8 figures, 1 tabl

Applying concepts of fuzzy cognitive mapping to model IT/IS investment evaluation factors

The justification process is a major concern for many organisations that are considering the adoption of Information Technology (IT) and Information Systems (IS), and is a barrier to its implementation. As a result, the competitive advantage of many companies is being put at risk because of management's inability to evaluate the holistic implication of adopting new technology, both in terms of on the benefit and cost portfolios. This paper identifies a number of well-known project appraisal techniques used in IT/IS investment justification. Furthermore, the concept of multivalent, or fuzzy logic, is used to demonstrate how inter-relationships can be modeled between key dimensions identified in the proposed conceptual evaluation model. This is highlighted using fuzzy cognitive mapping (FCM) as a technique to model each IT/IS evaluation factor (integrating strategic, tactical, operational and investment considerations). The use of an FCM is then shown to be as a complementary tool which can serve to highlight interdependencies between contributory justification factors

Sustainability ranking of desalination plants using Mamdani Fuzzy Logic Inference Systems

As water desalination continues to expand globally, desalination plants are continually under pressure to meet the requirements of sustainable development. However, the majority of desalination sustainability research has focused on new desalination projects, with limited research on sustainability performance of existing desalination plants. This is particularly important while considering countries with limited resources for freshwater such as the United Arab Emirates (UAE) as it is heavily reliant on existing desalination infrastructure. In this regard, the current research deals with the sustainability analysis of desalination processes using a generic sustainability ranking framework based on Mamdani Fuzzy Logic Inference Systems. The fuzzy-based models were validated using data from two typical desalination plants in the UAE. The promising results obtained from the fuzzy ranking framework suggest this more in-depth sustainability analysis should be beneficial due to its flexibility and adaptability in meeting the requirements of desalination sustainability

Exploring fuzzy cognitive mapping for IS evaluation: A research note

Existing IS Evaluation (ISE) techniques tend to focus on modeling individuals, teams, organization, or systems, in relation to process and environmental boundaries. Whilst such approaches are noteworthy and of merit, they do not necessarily provide insights into those causal interdependencies that are inherent within decision-making task. As has been noted by the extant literature in the field, the ISE task is dependent upon many factors – the resulting outputs of which may be tangible or intangible. The implicit level of uncertainty associated with modeling such decision-making tasks and behaviors, are therefore difficult to comprehend and impart via wholly Quantitative and / or Qualitative analyses. The authors therefore present and propose supporting and on-going research into the application of Fuzzy Logic, in the guise of Fuzzy Cognitive Mapping (FCM) simulations, as a means to model tangible/intangible aspects of the ISE decision-making task. Such a Fuzzy Information Systems Evaluation (F-ISE) is shown via the application of the FCM technique, in terms of three models of investment appraisal that are aligned to an ISE task within a UK manufacturing organization. In doing so, it is anticipated that such a technique may be a useful addition to the plethora of ISE techniques available to both researcher and practitioner alike

Digital Modeling of Economic Processes and Supply Chain Management in the Formation of Cooperative Relations in the Petrochemical Cluster of the Region

Abstract- A study of the state of the theory of fuzzy sets shows that until recently in Russia there were almost no studies in the chemical sector of the economy and finance using fuzzy analysis and forecasting, although by that time all necessary prerequisites for modeling financial systems had been created. The current situation in Russia is characterized by a high degree of science lagging behind the requests of state and commercial supply chain management.Fuzzy sets have not been used to date for financial analysis and planning of chemical corporations, evaluating the investment attractiveness of securities, optimizing the stock portfolio, forecasting stock indices and macroeconomic indices. You can also talk about the lack of software based on fuzzy models, although abroad, such software products and information technologies that solve economic problems using fuzzy-plural and related descriptions already exist.In the 80's, software solutions and information technologies began to emerge, solving economic problems using fuzzy-plural and related descriptions. Thus, under the leadership of C. Zopounidis at the Technical University on the island of Crete, an expert system was developed for detailed financial analysis of corporations. A little earlier in Germany, a group led by H. Zimmerman developed a strategic planning system, in which the positioning of the corporation's business is based on fuzzy descriptions of the competitiveness and attractiveness of the business.As an example of such software, you can use expensive complex systems, which include fuzzy logic, which bankers and financiers use to solve the most complicated problems of forecasting financial indicators. The beginning of this process was laid by the Japanese financial corporation. Having set out to automate the game on the securities market, this company attracted about 30 specialists in artificial intelligence

A methodology for the selection of new technologies in the aviation industry

The purpose of this report is to present a technology selection methodology to quantify both tangible and intangible benefits of certain technology alternatives within a fuzzy environment. Specifically, it describes an application of the theory of fuzzy sets to hierarchical structural analysis and economic evaluations for utilisation in the industry. The report proposes a complete methodology to accurately select new technologies. A computer based prototype model has been developed to handle the more complex fuzzy calculations. Decision-makers are only required to express their opinions on comparative importance of various factors in linguistic terms rather than exact numerical values. These linguistic variable scales, such as ‘very high’, ‘high’, ‘medium’, ‘low’ and ‘very low’, are then converted into fuzzy numbers, since it becomes more meaningful to quantify a subjective measurement into a range rather than in an exact value. By aggregating the hierarchy, the preferential weight of each alternative technology is found, which is called fuzzy appropriate index. The fuzzy appropriate indices of different technologies are then ranked and preferential ranking orders of technologies are found. From the economic evaluation perspective, a fuzzy cash flow analysis is employed. This deals quantitatively with imprecision or uncertainties, as the cash flows are modelled as triangular fuzzy numbers which represent ‘the most likely possible value’, ‘the most pessimistic value’ and ‘the most optimistic value’. By using this methodology, the ambiguities involved in the assessment data can be effectively represented and processed to assure a more convincing and effective decision- making process when selecting new technologies in which to invest. The prototype model was validated with a case study within the aviation industry that ensured it was properly configured to meet the

Fuzzy investment decision support for brownfield redevelopment

Tato disertační práce se zaměřuje na problematiku investování a podporu rozhodování pomocí moderních metod. Zejména pokud jde o analýzu, hodnocení a výběr tzv. brownfieldů pro jejich redevelopment (revitalizaci). Cílem této práce je navrhnout univerzální metodu, která usnadní rozhodovací proces. Proces rozhodování je v praxi komplikován též velkým počet relevantních parametrů ovlivňujících konečné rozhodnutí. Navržená metoda je založena na využití fuzzy logiky, modelování, statistické analýzy, shlukové analýzy, teorie grafů a na sofistikovaných metodách sběru a zpracování informací. Nová metoda umožňuje zefektivnit proces analýzy a porovnávání alternativních investic a přesněji zpracovat velký objem informací. Ve výsledku tak bude zmenšen počet prvků množiny nejvhodnějších alternativních investic na základě hierarchie parametrů stanovených investorem.This dissertation focuses on decision making, investing and brownfield redevelopment. Especially on the analysis, evaluation and selection of previously used real estates suitable for commercial use. The objective of this dissertation is to design a method that facilitates the decision making process with many possible alternatives and large number of relevant parameters influencing the decision. The proposed method is based on the use of fuzzy logic, modeling, statistic analysis, cluster analysis, graph theory and sophisticated methods of information collection and processing. New method allows decision makers to process much larger amount of information and evaluate possible investment alternatives efficiently.