Introduction

Zadanie pt. „Digitalizacja i udostępnienie w Cyfrowym Repozytorium Uniwersytetu Łódzkiego kolekcji czasopism naukowych wydawanych przez Uniwersytet Łódzki” nr 885/P-DUN/2014 zostało dofinansowane ze środków MNiSW w ramach działalności upowszechniającej naukę

Presenting a fuzzy model for fuzzy portfolio optimization with the mean absolute deviation risk function

The main purpose of this paper is portfolio optimization with the use of fuzzy method based on the mean absolute deviation risk function in firms listed in Tehran Stock Market. In the present research, for the purpose of fuzzy portfolio optimization the stock portfolio Value at Risk criterion and for calculation of this value the parametric method and for fuzzy optimization also the Hybrid intelligent algorithms (genetic algorithms and neural networks) have been used. For selecting the portfolio with 15 during the research time span (2005-2011) fuzzy optimization based on the following six criteria were used including Asymmetric Value at Risk, Symmetric Value at Risk , Interval Value at Risk (interval of 5%-95%), Interval Value at Risk (interval of 10%-90%), and Normal Value at Risk. Since the calculated probability ratio statistic Kupiec based on fuzzy optimization for the 6 above mentioned models is larger than the obtained critical value from chi-square distribution at the confidence level of 95%, the research hypothesis stating that the application of fuzzy optimization method improves the efficiency of portfolio in the actual world problems with lack of certainty was confirmed. Also, the results of the Kupiec probability ratio statistic indicate that the model of value at risk based on the mean absolute deviation risk function (MVAR) is more successful and have less failure comparing to other models, hence; the research hypothesis stating that fuzzy variables have a higher ability in modeling asymmetric uncertainties in financial domains is also confirmed

Presenting a fuzzy model for fuzzy portfolio optimization with the mean absolute deviation risk function

The main purpose of this paper is portfolio optimization with the use of fuzzy method based on the mean absolute deviation risk function in firms listed in Tehran Stock Market. In the present research, for the purpose of fuzzy portfolio optimization the stock portfolio Value at Risk criterion and for calculation of this value the parametric method and for fuzzy optimization also the Hybrid intelligent algorithms (genetic algorithms and neural networks) have been used. For selecting the portfolio with 15 during the research time span (2005-2011) fuzzy optimization based on the following six criteria were used including Asymmetric Value at Risk, Symmetric Value at Risk , Interval Value at Risk (interval of 5%-95%), Interval Value at Risk (interval of 10%-90%), and Normal Value at Risk. Since the calculated probability ratio statistic Kupiec based on fuzzy optimization for the 6 above mentioned models is larger than the obtained critical value from chi-square distribution at the confidence level of 95%, the research hypothesis stating that the application of fuzzy optimization method improves the efficiency of portfolio in the actual world problems with lack of certainty was confirmed. Also, the results of the Kupiec probability ratio statistic indicate that the model of value at risk based on the mean absolute deviation risk function (MVAR) is more successful and have less failure comparing to other models, hence; the research hypothesis stating that fuzzy variables have a higher ability in modeling asymmetric uncertainties in financial domains is also confirmed

Presenting a fuzzy model for fuzzy portfolio optimization with the mean absolute deviation risk function

The main purpose of this paper is portfolio optimization with the use of fuzzy method based on the mean absolute deviation risk function in firms listed in Tehran Stock Market. In the present research, for the purpose of fuzzy portfolio optimization the stock portfolio Value at Risk criterion and for calculation of this value the parametric method and for fuzzy optimization also the Hybrid intelligent algorithms (genetic algorithms and neural networks) have been used. For selecting the portfolio with 15 during the research time span (2005-2011) fuzzy optimization based on the following six criteria were used including Asymmetric Value at Risk, Symmetric Value at Risk , Interval Value at Risk (interval of 5%-95%), Interval Value at Risk (interval of 10%-90%), and Normal Value at Risk. Since the calculated probability ratio statistic Kupiec based on fuzzy optimization for the 6 above mentioned models is larger than the obtained critical value from chi-square distribution at the confidence level of 95%, the research hypothesis stating that the application of fuzzy optimization method improves the efficiency of portfolio in the actual world problems with lack of certainty was confirmed. Also, the results of the Kupiec probability ratio statistic indicate that the model of value at risk based on the mean absolute deviation risk function (MVAR) is more successful and have less failure comparing to other models, hence; the research hypothesis stating that fuzzy variables have a higher ability in modeling asymmetric uncertainties in financial domains is also confirmed

Sustainable and traditional product innovation without scale and experience, but only for KIBS!

This study analyzes the ideal strategic trajectory for sustainable and traditional product innovation. Using a sample of 74 Costa Rican high-performance businesses for 2016, we employ fuzzy set analysis (qualitative comparative analysis) to evaluate how the development of sustainable and traditional product innovation strategies is conditioned by the business’ learning capabilities and entrepreneurial orientation in knowledge-intensive (KIBS) and non-knowledge-intensive businesses. The results indicate two ideal strategic configurations of product innovation. The first strategic configuration to reach maximum product innovation requires the presence of KIBS firms that have both an entrepreneurial and learning orientation, while the second configuration is specific to non-KIBS firms with greater firm size and age along with entrepreneurial and learning orientation. KIBS firms are found to leverage the knowledge-based and customer orientations that characterize their business model in order to compensate for the shortage of important organizational characteristics—which we link to liabilities or smallness and newness—required to achieve optimal sustainable and traditional product innovation.Peer ReviewedPostprint (published version

Assessing the Number of Components in Mixture Models: a Review.

Despite the widespread application of finite mixture models, the decision of how many classes are required to adequately represent the data is, according to many authors, an important, but unsolved issue. This work aims to review, describe and organize the available approaches designed to help the selection of the adequate number of mixture components (including Monte Carlo test procedures, information criteria and classification-based criteria); we also provide some published simulation results about their relative performance, with the purpose of identifying the scenarios where each criterion is more effective (adequate).Finite mixture; number of mixture components; information criteria; simulation studies.

Multivariate Profile Monitoring Method: An Application in Product Portfolio Management

Several authors refer to product portfolio management as an essential process because it may be used as a corporate management tool. However, the product portfolio management methods which are often adopted have limitations that prevent its use in practice, mainly due to the high dimensionality of selecting an optimal portfolio. Moreover, the large amount of available data is a relevant issue for practical applications. Thus, the contribution of this article is to propose a method for the product life cycle to monitor time-series behaviour patterns. The goal is to identify changes that may indicate that the product portfolio needs to be revised. The proposed method uses a multivariate regression model to relate financial variables associated with the products portfolio, the performance of products against competition, and even macroeconomic data. The objective is, through profile monitoring, to identify the specific time for the product portfolio review decision-making. We adopted three tools to develop a method – principal component analysis, multivariate regression model, and profile monitoring with Hotelling T 2 Control chart. A Monte Carlo simulation validated the approach. The results showed false alarm rate and average time to signal to be similar to previous studies. Finally, the application of the model is illustrated in a real case, using data provided by a company’s portfolio of agricultural equipment

Forecasting Startup Return using Artificial Intelligence Methods and Econometric Models and Portfolio Optimization Using VaR and C-VaR

In this paper, we have tried to study the main role of startups in economy, their characteristics, main goals and etc. The main goal of article is prediction of startup's return using artificial intelligence methods such as genetic algorithm (GA) and artificial neural network (ANN). Some global indices such as S&P500, DJAI, and economic indicators such as 10 years Treasury yield, Wilshire 5000 Total Market Full Cap Index along with some other special indicators in startups like team, idea, timing and etc. are used as input variables. GA is used as feature selection and finding the most important variables. ANN is used as an optimization model and prediction of startup's returns. We used econometric models such as regression analysis. We have estimated Value at risk (VaR) and Conditional Value at risk (C-VAR) for considered portfolios including three startups (public company) such as Dropbox, Inc. (DBX), Scout24 SE (G24.DE) and TIE.AS and optimal portfolio formation. The results show that AI based methods are more powerful in prediction of startup's return. On the other hand, VaR and C-VaR models are very beneficial approach in minimizing risk and maximizing return