ANALYSIS OF BETA COEFICIENTS IN THE BRAZILIAN STOCK MARKET USING FUZZY LINEAR REGRESSION METHODOLOGY

This paper represents a contribution to the empirical literature on systematic risk at a sectoral level in an emerging market, the Mexican Stock Market, incorporating all the available information of the different asset quoted prices for the beta calculation. We estimate the fuzzy beta coefficients for individual stocks as well as for sectoral indices comparing the results with OLS beta coefficients. Then, we contrast if the fuzzy estimations verify two hypothesis of the traditional portfolio theory, specifically those related to the influence of the number of stocks and the length of estimation period over beta stability. The methodology used to calculate the fuzzy beta coefficients is the fuzzy linear regression. The results suggest that, in the Mexican Stock Market, hypotheses of the portfolio theory are also verified when the return of the portfolio is considered as an uncertain data.

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The fuzzy linear regression model for finding sales forcastion from financial leasing in China’s equipment manufacturing industries

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189667 ◽

2021 ◽

Vol 40 (4) ◽

pp. 8477-8484

Author(s):

Chengke Zhu ◽

Junshan Wang

Keyword(s):

Linear Regression ◽

Regression Model ◽

Linear Regression Model ◽

Substantial Effect ◽

Manufacturing Industries ◽

Fuzzy Linear Regression ◽

Sales Revenue ◽

Financial Leasing ◽

Equipment Manufacturing ◽

Fuzzy Linear Regression Model

With the development of technology and artificial intelligence(AI), financial leasing is used frequently in China’s equipment manufacturing industries, especially after 2008, which attracts our attention. This paper focuses on the problem. A theoretical model is provided in the paper to explain the motivation that why financial leasing is used in equipment manufacturing industries. In this paper, we employ multi-level fuzzy linear regression model to analyze data of manufacturing equipment industry of China in order to discover impact between sales revenue and financial leasing. We also discover credit sale forecasting using this model. We discover that economic leasing has a constructive important effect on sales revenue in total samples, which is consistent with the theoretical framework. However, the results are different in sub-industries, which shows that economic leasing has a constructive and important effect on sales profits in some sub-industries, but some of them are not. Furthermore, we also find that asset characteristics has a substantial effect on financial leasing decision. The outcome demonstrates that the more precise the asset, the easier it can be leased.

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Fuzzy linear regression in models of micro and macroeconomics

2015 XVIII International Conference on Soft Computing and Measurements (SCM) ◽

10.1109/scm.2015.7190475 ◽

2015 ◽

Author(s):

V. B. Gisin ◽

E. S. Volkova

Keyword(s):

Linear Regression ◽

Fuzzy Linear Regression

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Some Applications of Fuzzy Linear Regression Analysis to Geotechnical Analysis

GEOINFORMATICS ◽

10.6010/geoinformatics1990.2.3_265 ◽

1991 ◽

Vol 2 (3) ◽

pp. 265-274

Author(s):

Katsuaki KOIKE ◽

Michito OHMI

Keyword(s):

Regression Analysis ◽

Linear Regression ◽

Linear Regression Analysis ◽

Fuzzy Linear Regression

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Fuzzy C-Regression Models

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.278-280.1323 ◽

2013 ◽

Vol 278-280 ◽

pp. 1323-1326

Author(s):

Yan Hua Yu ◽

Li Xia Song ◽

Kun Lun Zhang

Keyword(s):

Linear Regression ◽

Least Squares ◽

Linear Models ◽

Least Squares Method ◽

Least Square Method ◽

Least Square ◽

Estimation Methods ◽

Statistical Regression ◽

Fuzzy Linear Regression ◽

Fuzzy Degree

Fuzzy linear regression has been extensively studied since its inception symbolized by the work of Tanaka et al. in 1982. As one of the main estimation methods, fuzzy least squares approach is appealing because it corresponds, to some extent, to the well known statistical regression analysis. In this article, a restricted least squares method is proposed to fit fuzzy linear models with crisp inputs and symmetric fuzzy output. The paper puts forward a kind of fuzzy linear regression model based on structured element, This model has precise input data and fuzzy output data, Gives the regression coefficient and the fuzzy degree function determination method by using the least square method, studies the imitation degree question between the observed value and the forecast value.

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