The Experts below are selected from a list of 363633 Experts worldwide ranked by ideXlab platform
Miguel J Bagajewicz - One of the best experts on this subject based on the ideXlab platform.
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managing Financial Risk in planning under uncertainty
Aiche Journal, 2004Co-Authors: Andres F Barbaro, Miguel J BagajewiczAbstract:A methodology is presented to include Financial Risk management in the framework of two-stage stochastic programming for planning under uncertainty. A known probabilistic definition of Financial Risk is adapted to be used in this framework and its relation to downside Risk is analyzed. Using these definitions, new two-stage stochastic programming models that manage Financial Risk are presented. Computational issues related to these models are also discussed. © 2004 American Institute of Chemical Engineers AIChE J, 50: 963–989, 2004
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Financial Risk Management in Offshore Oil Infrastructure Planning and Scheduling
Industrial & Engineering Chemistry Research, 2004Co-Authors: Ahmed Aseeri, Patrick Gorman, Miguel J BagajewiczAbstract:This paper discusses the Financial Risk management in the planning and scheduling of offshore oil infrastructure. The problem consists of determining the sequence of platforms to build and the wells to drill as well as how to produce these wells over a period of time. The problem has shown numerical difficulties, and several decomposition methods have been attempted to alleviate these difficulties. We added budgeting constraints to this model, following thus the cash flow of the project, taking care of the distribution of proceeds, and even considering the possibility of taking loans against some built equity. The model was made stochastic, the Financial Risk, an important aspect of these ventures, is analyzed, and the ability to manage it is discussed. The numerical difficulties resulting from the addition of uncertainty are overcome by using the sampling average algorithm (SAA; Verweij, B.; Ahmed, S.; Kleywegt, A. J.; Nemhauser, G.; Shapiro, A. Comput. Appl. Optim. 2001, 24, 289−333). We show that the S...
Vanessa Ratten - One of the best experts on this subject based on the ideXlab platform.
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The Perception And Knowledge Of Financial Risk Of The Portuguese
Sustainability, 2020Co-Authors: Fernando Oliveira Tavares, Eulália Santos, Vasco Tavares, Vanessa RattenAbstract:This study will help academics, researchers, and professionals to better understand how the Portuguese population perceives Financial Risk. Thus, the main objective of this study is to analyse and compare the perception and knowledge of Financial Risk by the Portuguese. The methodology used is quantitative, and the measurement instrument consists of three parts: Financial Risk perception, Financial Risk knowledge and sociodemographic characterization of the participants. The sample is composed of 830 Portuguese individuals, over 18 years old. The results demonstrate that Financial Risk perception is a one-dimensional measurement and that there are low levels of both perception and knowledge of Financial Risk. It can also be concluded that the Portuguese individuals have a higher level of Financial Risk perception, when compared to Financial Risk knowledge, and it is men who have higher levels of perception and knowledge of Financial Risk. Thus, this study contributes to the literature on Financial Risk by presenting empirical evidence and relevant conclusions, and it is therefore expected that it will help to improve the perception and knowledge of the Financial Risk of the Portuguese and, consequently, their Financial decisions and Financial well-being. Therefore, the study fills a gap, since there are no studies in Portugal that assess the perception and knowledge of Financial Risk of the Portuguese.
Guoxun Wang - One of the best experts on this subject based on the ideXlab platform.
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evaluation of clustering algorithms for Financial Risk analysis using mcdm methods
Information Sciences, 2014Co-Authors: Gang Kou, Yi Peng, Guoxun WangAbstract:The evaluation of clustering algorithms is intrinsically difficult because of the lack of objective measures. Since the evaluation of clustering algorithms normally involves multiple criteria, it can be modeled as a multiple criteria decision making (MCDM) problem. This paper presents an MCDM-based approach to rank a selection of popular clustering algorithms in the domain of Financial Risk analysis. An experimental study is designed to validate the proposed approach using three MCDM methods, six clustering algorithms, and eleven cluster validity indices over three real-life credit Risk and bankruptcy Risk data sets. The results demonstrate the effectiveness of MCDM methods in evaluating clustering algorithms and indicate that the repeated-bisection method leads to good 2-way clustering solutions on the selected Financial Risk data sets.
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An empirical study of classification algorithm evaluation for Financial Risk prediction
Applied Soft Computing, 2011Co-Authors: Yi Peng, Guoxun Wang, Gang Kou, Yong ShiAbstract:A wide range of classification methods have been used for the early detection of Financial Risks in recent years. How to select an adequate classifier (or set of classifiers) for a given dataset is an important task in Financial Risk prediction. Previous studies indicate that classifiers’ performances in Financial Risk prediction may vary using different performance measures and under different circumstances. The main goal of this paper is to develop a two-step approach to evaluate classification algorithms for Financial Risk prediction. It constructs a performance score to measure the performance of classification algorithms and introduces three multiple criteria decision making (MCDM) methods (i.e., TOPSIS, PROMETHEE, and VIKOR) to provide a final ranking of classifiers. An empirical study is designed to assess various classification algorithms over seven real-life credit Risk and fraud Risk datasets from six countries. The results show that linear logistic, Bayesian Network, and ensemble methods are ranked as the top-three classifiers by TOPSIS, PROMETHEE, and VIKOR. In addition, this work discusses the construction of a knowledge-rich Financial Risk management process to increase the usefulness of classification results in Financial Risk detection.
Piotr Winkielman - One of the best experts on this subject based on the ideXlab platform.
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nucleus accumbens activation mediates the influence of reward cues on Financial Risk taking
Neuroreport, 2008Co-Authors: Brian Knutson, Elliott G Wimmer, Camelia M Kuhnen, Piotr WinkielmanAbstract:In functional magnetic resonance imaging research, nucleus accumbens (NAcc) activation spontaneously increases before Financial Risk taking. As anticipation of diverse rewards can increase NAcc activation, even incidental reward cues may influence Financial Risk taking. Using event-related functional magnetic resonance imaging, we predicted and found that anticipation of viewing rewarding stimuli (erotic pictures for 15 heterosexual men) increased Financial Risk taking, and that this effect was partially mediated by increases in NAcc activation. These results are consistent with the notion that incidental reward cues influence Financial Risk taking by altering anticipatory affect, and so identify a neuropsychological mechanism that may underlie effective emotional appeals in Financial, marketing, and political domains.
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nucleus accumbens activation mediates the influence of reward cues on Financial Risk taking
MPRA Paper, 2008Co-Authors: Brian Knutson, Elliott G Wimmer, Camelia M Kuhnen, Piotr WinkielmanAbstract:In functional magnetic resonance imaging (FMRI) research, nucleus accumbens (NAcc) activation spontaneously increases prior to Financial Risk taking. Since anticipation of diverse rewards can increase NAcc activation, even incidental reward cues may influence Financial Risk-taking. Using event-related FMRI, we predicted and found that anticipation of viewing rewarding stimuli (erotic pictures for 15 heterosexual males) increased Financial Risk taking, and that this effect was partially mediated by increases in NAcc activation. These results are consistent with the notion that incidental reward cues influence Financial Risk taking by altering anticipatory affect, and so identify a neuropsychological mechanism that may underlie effective emotional appeals in Financial, marketing, and political domains.
Gang Kou - One of the best experts on this subject based on the ideXlab platform.
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evaluation of clustering algorithms for Financial Risk analysis using mcdm methods
Information Sciences, 2014Co-Authors: Gang Kou, Yi Peng, Guoxun WangAbstract:The evaluation of clustering algorithms is intrinsically difficult because of the lack of objective measures. Since the evaluation of clustering algorithms normally involves multiple criteria, it can be modeled as a multiple criteria decision making (MCDM) problem. This paper presents an MCDM-based approach to rank a selection of popular clustering algorithms in the domain of Financial Risk analysis. An experimental study is designed to validate the proposed approach using three MCDM methods, six clustering algorithms, and eleven cluster validity indices over three real-life credit Risk and bankruptcy Risk data sets. The results demonstrate the effectiveness of MCDM methods in evaluating clustering algorithms and indicate that the repeated-bisection method leads to good 2-way clustering solutions on the selected Financial Risk data sets.
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An empirical study of classification algorithm evaluation for Financial Risk prediction
Applied Soft Computing, 2011Co-Authors: Yi Peng, Guoxun Wang, Gang Kou, Yong ShiAbstract:A wide range of classification methods have been used for the early detection of Financial Risks in recent years. How to select an adequate classifier (or set of classifiers) for a given dataset is an important task in Financial Risk prediction. Previous studies indicate that classifiers’ performances in Financial Risk prediction may vary using different performance measures and under different circumstances. The main goal of this paper is to develop a two-step approach to evaluate classification algorithms for Financial Risk prediction. It constructs a performance score to measure the performance of classification algorithms and introduces three multiple criteria decision making (MCDM) methods (i.e., TOPSIS, PROMETHEE, and VIKOR) to provide a final ranking of classifiers. An empirical study is designed to assess various classification algorithms over seven real-life credit Risk and fraud Risk datasets from six countries. The results show that linear logistic, Bayesian Network, and ensemble methods are ranked as the top-three classifiers by TOPSIS, PROMETHEE, and VIKOR. In addition, this work discusses the construction of a knowledge-rich Financial Risk management process to increase the usefulness of classification results in Financial Risk detection.