Clustering of Financial Ratios of the Quoted Companies Through Fuzzy Logic Method (original) (raw)

A fuzzy set approach to financial ratio analysis

European Journal of Operational Research, 1988

Ratio analysis is a useful tool of financial analysis. Nevertheless, the traditional ratio analysis is under several constraints: over-empiricist, certainty, standard of reference not useful in all circumstances, etc. Recent researches have pointed out that to overcome those constraints formal decision models can be applied. In this article, fuzzy set theory is applied to ratio analysis with respect to one of the major management problems: liquidity. This approach enables the decision maker to include his own experience and any other type of information to that obtained by the ratio. If all the possible decisions are uniform in time, it is possible to adopt them by the decision maker in each period of analysis in a programmed form through a simple model inputs combination. The approach provided in this article can be extended to other ratio or ratio sets.

Clustering Stock Performance Considering Investor Preferences Using a Fuzzy Inference System

Symmetry

The fact that many stocks are traded in the marketplace makes the selection process of choosing the right stocks for investment crucial and challenging. In the literature on stock selection, cluster analysis-based methods have usually been used to group and to determine the best stock for investment. Many established cluster analysis-based methods often cluster stocks under consideration using the average of the variables, where stocks with similar scores are concluded as having the same performances. Nevertheless, the performance results obtained do not reflect the actual performance of the stocks. Depending only on the average score of each variable is inefficient, as market situations usually involve uncertain extreme values. Moreover, when grouping stock performance, the established clustering methods assume that investors’ selection preferences are single and unclear, when actually, in reality, investors’ selection preferences vary; some investors are pessimistic, while others ...

Using fuzzy computing for making decisions on the formation of an investment portfolio

Economics of development, 2015

The diversification of the investment portfolio may be regarded as one of the ways to manage investment risk. One of the solutions to this problem is the approach of Markowitz. However, it uses a number of assumptions which are poorly consistent with the realities of investment processes. Thus, the requirement of statistical homogeneity cannot be achieved in real conditions. The use of to the subjective probabilities almost does not improve the situation. It is assumed that there are some projects (investment projects, food programs, securities) from which an investment portfolio is to be formed and investments in these projects should be appropriately distributed. The information about the projects is vague and its possible refinement is associated with unacceptable time and material costs. Besides, the necessary level of certainty is not guaranteed. The resulting estimates are expert ones and they do not always have a quantitative representation, often being approximate. A mathematical substantiation, an algorithm and practical implementation of the solution to the problem are given, this problem being regarded as a fuzzy analogue of a statistical game. This problem is formulated in a fuzzy statement and several ways to solve it are presented. An algorithm and computational and analytical methods of making a rational decision on the formation of the investment portfolio have been described. These methods are free from defects of other known approaches, making it possible to take into account the multiplicity of identical estimates of yield components of the investment portfolio which ultimately enhances the validity of the distribution of investment resources. The presented approach has been successfully applied to practice in the assessment of the options and management and economic decision-making in the economic analysis and portfolio management in a number of commercial banks.

Optimization of Portfolio Using Fuzzy Selection

BAREKENG: Jurnal Ilmu Matematika dan Terapan

The problem of portfolio optimization concerns the allocation of the investor’s wealth between several security alternatives so that the maximum profit can be obtained. One of the methods used is Fuzzy Portfolio Selection to understand it better. This method separates the objective function of return and the objective function of risk to determine the limit of the membership function that will be used. The goal of this study is to understand the application of the Fuzzy Portfolio Selection method over shares that have been chosen on a portfolio optimization problem, understand return and risk, and understand the budget proportion of each claim. The subject of this study is the shares of 20 companies included in Bursa Efek Indonesia from 1 January 2021 until 1 January 2022. The result of this study shows that from 20 shares, there are 10 shares that is suitable in the forming of optimal portfolio, those are ADRO (0%), ANTM (43.3%), ASII (0%), BBCA (0%), BBRI (0%), BBTN (0%), BRPT (0%...

A fuzzy approach to portfolio selection at bursa Malaysia / Wan Rosanisah Wan Mohd

2014

Selecting the right portfolio is one of the problems to fund managers, investors, individual or institutional investors. Some authors introduced portfolio models to solve the portfolio problem such as Markowitz, Fishburn, Konno and Yamazaki, Jorion and Young. The model introduced by the authors did not consider fuzzy number in their model. The portfolio problem arises due to the uncertainty in stock market investment. Therefore, some scholars are seeking a new way to solve uncertainty of stock market investment. The fuzzy approach is the suitable approach to solve the portfolio problem. The scholars that considered the fuzzy approach are Katagiri and Ishii, Inuiguichi and Tanino, Tanaka et aI., Vercher et al. and Mohamed. In this study, we refer the extended mean-variance as a controller for our analysis purpose. The problem of the extended mean-variance model is the model assumed that the return distribution is normally distributed and the covariance is not in fuzzy numbers. Hence,...

On portfolio analysis using oriented fuzzy numbers for the trade-related sector of the Warsaw Stock Exchange

Operations Research and Decisions, 2022

Oriented fuzzy numbers are useful in portfolio management since they convey information regarding uncertainty and imprecision when considering financial markets. One may apply a fuzzy discount factor and an imprecise present value in the form of a trapezoidal oriented fuzzy number. An investor can obtain recommendations on individual stocks (buy, sell, accumulate, reduce). Analogous recommendations are also issued by experts. In such cases, recommendations are mostly based on available data and expert's knowledge and experience. The purpose of the paper is to present a procedure for comparing the accuracy of both types of recommendations. Also, the real impact the recommendations have on potential changes in portfolio composition in trading-related industries is considered. The research uses quotations from companies from the trading sector of the Warsaw Stock Exchange (WSE). Theoretical considerations are presented in the form of an empirical case study.

Investment Portfolio Evaluation by the Fuzzy Approach

This paper presents a new fuzzy approach for the evaluation of investment portfolio, where the approach is viewed by the authors as a sub-phase of the management process of these port-folios. The approach defines the mutual and delayed effects among the significant variables of the investment portfolio. The evaluation of the effects is described as fuzzy trapezoidal num-bers and they are aggregated by mathematical operations with incidence matrices and fuzzy functions " experton " . Key words: management process of investment portfolio, fuzz y evaluation; fuzz y expertons and incidence ma-trices; delayed effects

Clustering Problem with Fuzzy Data: Empirical Study for Financial Distress Firms

American Journal of Applied Mathematics, 2015

In many real applications, the data of classification problems cannot be precisely measured. However, in an increasingly complex environment, these variables can be imprecise, qualitative or linguistic. In such a case, fuzzy set theory seems to be the convenient tool to fill this insufficiency. Thus, we proposed a new approach, based on the ranking function, which consists in solving the classification problems via fuzzy linear programming model. This approach has been applied for the financial distress firms. The obtained results are satisfactory in terms of correctly classified rates

A Fuzzy Approach to Portfolio Selection

Stock market investing is undoubtedly challenging. Investors have to deal with random, vague and ambiguity stock price volatility before embarking on investment decision. Due to these weaknesses, the conventional model has several limitations; as a result investors are demanding for a new robust model which is able to represent their real situation to solve the uncertainty issues. In this study we developed a new fuzzy portfolio selection model using semi-variance as a risk measure integrates with investor's judgment on assets' future performance. Linear programming approach was used to optimize the portfolio risk and return. Empirical data showed that the model were able to derive a resourceful portfolio compared to the naïve portfolio selection. ABSTRAK Tidak dapat dinafikan bahawa melabur di pasaran saham adalah mencabar. Pelabur terpaksa berhadapan dengan kerawakan, kekaburan dan ketaktentuan turun naik harga saham sebelum mengambil keputusan pada pelaburan. Disebabkan oleh kekurangan tersebut, model konvensional mempunyai beberapa pembatasan dan akibatnya pelabur menuntut model baru yang berkesan yang mampu mewakili keadaan sebenar untuk menyelesaikan isu ketakpastian. Dalam kajian ini, kami membangunkan model pemilihan portfolio kabur yang baru menggunakan semivarian sebagai pengukur risiko digabungkan dengan pertimbangan pelabur pada pencapaian hadapan aset. Pendekatan pengaturcaraan linear digunakan untuk mengoptimum risiko dan pulangan portfolio. Data empirik menunjukkan bahawa model ini dapat menerbitkan portfolio bermaklumat berbanding pemilihan portfolio naif. Kata kunci: Nombor kabur; pemilihan portfolio; pengaturcaraan linear; penilaian pelabur; semivarian

Investment Portfolio Evaluation by Fuzzy Approach

Boston University Questrom School of Business Research Paper Series, 2011

This paper presents a new fuzzy approach for the evaluation of investment portfolio, where the approach is viewed by the authors as a sub-phase of the management process of these portfolios. The approach defines the mutual and delayed effects among the significant variables of the investment portfolio. The evaluation of the effects is described as fuzzy trapezoidal numbers and they are aggregated by mathematical operations with incidence matrices and fuzzy functions “experton”.