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

2015 ◽  
Vol 20 (02) ◽  
Author(s):  
Yanina Laumann
Keyword(s):  
Linear Regression ◽  
Stock Market ◽  
Fuzzy Linear Regression

Analysis of Fuzzy Beta Coefficients. Evidence from the Mexican Stock Market

2018 ◽  
Vol 26 (Suppl. 1) ◽  
pp. 59-69
Author(s):  
M. Glòria Barberà-Mariné ◽  
Yanina Laumann ◽  
Laura Fabregat-Aibar
Keyword(s):  
Linear Regression ◽  
Stock Market ◽  
Emerging Market ◽  
Uncertain Data ◽  
Systematic Risk ◽  
Portfolio Theory ◽  
Empirical Literature ◽  
Fuzzy Linear Regression ◽  
Available Information

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.

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

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.

Fuzzy C-Regression Models

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.

scholarly journals Application of Fuzzy Linear Regression with Symmetric Parameter for Predicting Tumor Size of Colorectal Cancer

2021 ◽  
Vol 9 (1) ◽  
pp. 36-40
Author(s):  
Muhammad Ammar Shafi ◽  
Mohd Saifullah Rusiman ◽  
Siti Nabilah Syuhada Abdullah
Keyword(s):  
Colorectal Cancer ◽  
Linear Regression ◽  
Tumor Size ◽  
Fuzzy Linear Regression
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