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

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, the objective of this study is to determine the behaviour return data distribution and to improve the extended mean-variance model by considering the actual return distribution and fuzzy covariance. It is found the return distribution of the stock market is skewed that is not normal. Thus, the appropriate central tendency to measure the skewed return distribution in the fuzzy situation is represented by the mode. Then, the centroid was used to determine the covariance in the extended mean-variance model since the covariance has a relationship with the asset return. The data corresponds to monthly price from February 1998 until December 2011 were considered and analyzed it to the non-financial sector portfolio in Bursa Malaysia. Then, the effectiveness of our proposed model was compared with other fuzzy portfolio model that is VBS fuzzy model and extended mean-variance model. This study will give the true picture of the relationship between fuzzy return and covariance. Besides that, the application of fuzzy approach in selecting the portfolio is expected to provide useful model to the investor

The impact of the fuzzy front end on new product development success in Japanese NPD projects

In a study of Japanese New Product Development (NPD) projects, the fuzzy front end of innovation is explored. Our conceptual model is based on the information-processing perspective. A structual equation model was fitted to data from 497 NPD projects from Japanese mechanical and electrical engineering firms to test the proposed model. The empirical analysis found support for all hypotheses except for one. Our study suggests that an early reduction of market and technical uncertainty and a draft initial planning prior to development have a positive impact on NPD project success. The model accounts for 17% of the variance of the efficiency and 24% of the variance of the effectiveness dependent variable. Thus, the front end phase is an important driver of NPD project success. Implications of the model are discussed. --

A Note on Monitoring Fuzzy Financial Returns

This paper presents change point analysis for stock market time series where it is assumed the rate of return on securities is approximated as LR-fuzzy numbers. We consider the change point detection in the mean and variance of returns. The methods are proposed and their theoretical aspects are studied. A real data set is also considered. Finally, a conclusion sectionis given

Empirical comparison of the performance of location estimates of fuzzy number-valued data

© Springer Nature Switzerland AG 2019. Several location measures have already been proposed in the literature in order to summarize the central tendency of a random fuzzy number in a robust way. Among them, fuzzy trimmed means and fuzzy M-estimators of location extend two successful approaches from the real-valued settings. The aim of this work is to present an empirical comparison of different location estimators, including both fuzzy trimmed means and fuzzy M-estimators, to study their differences in finite sample behaviour.status: publishe

PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles

There exists an increasing demand for a flexible and computationally efficient controller for micro aerial vehicles (MAVs) due to a high degree of environmental perturbations. In this work, an evolving neuro-fuzzy controller, namely Parsimonious Controller (PAC) is proposed. It features fewer network parameters than conventional approaches due to the absence of rule premise parameters. PAC is built upon a recently developed evolving neuro-fuzzy system known as parsimonious learning machine (PALM) and adopts new rule growing and pruning modules derived from the approximation of bias and variance. These rule adaptation methods have no reliance on user-defined thresholds, thereby increasing the PAC's autonomy for real-time deployment. PAC adapts the consequent parameters with the sliding mode control (SMC) theory in the single-pass fashion. The boundedness and convergence of the closed-loop control system's tracking error and the controller's consequent parameters are confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's efficacy is evaluated by observing various trajectory tracking performance from a bio-inspired flapping-wing micro aerial vehicle (BI-FWMAV) and a rotary wing micro aerial vehicle called hexacopter. Furthermore, it is compared to three distinctive controllers. Our PAC outperforms the linear PID controller and feed-forward neural network (FFNN) based nonlinear adaptive controller. Compared to its predecessor, G-controller, the tracking accuracy is comparable, but the PAC incurs significantly fewer parameters to attain similar or better performance than the G-controller.Comment: This paper has been accepted for publication in Information Science Journal 201