Why using a general model in Solvency II is not a good idea : an explanation from a Bayesian point of view

The passing of Directive 2009/138/CE (Solvency II) has opened a new era in the European insurance market. According to this new regulatory environment, the volume of own resources will be determined depending on the risks that any insurer would be holding. So, nowadays, the model to estimate the amount of economic capital is one of the most important elements. The Directive establishes that the European entities can use a general model to perform these tasks. However, this situation is far from being optimal because the calibration of the general model has been made using figures that reflects and average behaviour. This paper shows that not all the companies operating in a specific market has the same risk profile. For this reason, it is unsatisfactory to use a general model for all of them. We use the PAM clustering method and afterwards some Bayesian tools to check the results previously obtained. Analysed data (public information belonging to Spanish insurance companies about balance sheets and income statements from 1998 to 2007) comes from the DGSFP (Spanish insurance regulator).Solvency II, PAM, Longitudinal multinomial model

Why using a general model in Solvency II is not a good idea : an explanation from a Bayesian point of view

The passing of Directive 2009/138/CE (Solvency II) has opened a new era in the European insurance market. According to this new regulatory environment, the volume of own resources will be determined depending on the risks that any insurer would be holding. So, nowadays, the model to estimate the amount of economic capital is one of the most important elements. The Directive establishes that the European entities can use a general model to perform these tasks. However, this situation is far from being optimal because the calibration of the general model has been made using figures that reflects and average behaviour. This paper shows that not all the companies operating in a specific market has the same risk profile. For this reason, it is unsatisfactory to use a general model for all of them. We use the PAM clustering method and afterwards some Bayesian tools to check the results previously obtained. Analysed data (public information belonging to Spanish insurance companies about balance sheets and income statements from 1998 to 2007) comes from the DGSFP (Spanish insurance regulator)

Tree Induction vs. Logistic Regression: A Learning-Curve Analysis

Tree induction and logistic regression are two standard, off-the-shelf methods for building models for classification. We present a large-scale experimental comparison of logistic regression and tree induction, assessing classification accuracy and the quality of rankings based on class-membership probabilities. We use a learning-curve analysis to examine the relationship of these measures to the size of the training set. The results of the study show several remarkable things. (I) Contrary to prior observations, logistic regression does not generally outperform tree induction. (2) More specifically, and not surprisingly, logistic regression is better for smaller training sets and tree induction for larger data sets. Importantly, this often holds for training sets drawn from the same domain (i.e., the learning curves cross), so conclusions about induction-algorithm superiority on a given domain must be based on an analysis of the learning curves. (3) Contrary to conventional wisdom, tree induction is effective at producing probability-based rankings, although apparently comparatively less so for a given training--set size than at making classifications. Finally, (4) the domains on which tree induction and logistic regression are ultimately preferable can be characterized surprisingly well by a simple measure of signal-to-noise ratio.Information Systems Working Papers Serie

Analytical customer relationship management in retailing supported by data mining techniques

Tese de doutoramento. Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 201

Tree Induction vs. Logistic Regression: A Learning-Curve Analysis

Tree induction and logistic regression are two standard, off-the-shelf methods for building models for classification. We present a large-scale experimental comparison of logistic regression and tree induction, assessing classification accuracy and the quality of rankings based on classmembership probabilities. We use a learning-curve analysis to examine the relationship of these measures to the size of the training set. The results of the study show several things. (1) Contrary to some prior observations, logistic regression does not generally outperform tree induction. (2) More specifically, and not surprisingly, logistic regression is better for smaller training sets and tree induction for larger data sets. Importantly, this often holds for training sets drawn from the same domain (that is, the learning curves cross), so conclusions about induction-algorithmsuperiority on a given domain must be based on an analysis of the learning curves. (3) Contrary to conventional wisdom, tree induction is effective at producing probability-based rankings, although apparently comparatively less so for a given training-set size than at making classifications. Finally, (4) the domains on which tree induction and logistic regression are ultimately preferable can be characterized surprisingly well by a simple measure of the separability of signal from noise.NYU, Stern School of Business, IOMS department, Center for Digital Economy Researc

A workplace design that reduces employee stress and increases employee productivity using environmentally responsible materials

In today’s competitive global environment, employee productivity is an essential element of a company’s success. Employee productivity can be significantly hindered by high levels of stress experienced in the work environment. In addition, poor indoor air quality contributes to deterioration of employee health and well-being, which further reduces productivity. The object of this study was to explore interior design techniques that may reduce employee stress and enhance productivity while using environmentally responsible materials and furnishings. The design paradigm was qualitative, and the research method used was a case study. Specifically, this was an action-research project consisting of a design proposal for an advertising firm in Michigan. The design solution includes elements that increase collaboration and enable teamwork among employees, combined with flexible and ergonomic furniture as a means to enhance productivity. Environmentally responsible material and furnishings were selected to protect the health and well-being of both employees and global ecosystems. The study discusses limitations as to the wider applicability of the approach described herein and proposes recommendations for future works in this area

The Role of Self-congruity in Consumer Preferences: Perspectives from Transaction Records

Personalised marketing is more persuasive than traditional techniques aimed at the masses, however marketers do not always have access to consumers’ private attributes in order to apply these insights. The effect of personalisation is based on an established theory in consumer psychology – self-congruity theory – which posits that individuals prefer products, brands and advertisements that embody characteristics that match with their self-concepts. Self-congruence not only enhances marketing effectiveness, it can also be used to improve consumer well-being. While it has been established that consumers who spend in a way that is more congruent with their personality are happier, clarifications around the types of individuals who are more or less likely to engage in self-congruent spending, as well as the moderating effects on the benefit in happiness from such consumption could inform policy for improving happiness at a collective level. This thesis contributes to a growing body of research which attempts to understand how consumption patterns are related to consumers’ characteristics, its applications in advertising, as well as consumer well-being. By using a dataset containing more than 1 million transactions recorded over a period of 12-months, the thesis demonstrates the value of the digital footprint in the form of bank transactions for enriching our understanding of key questions in consumer research, underpinned by the theory of self-congruity. This thesis combines methods from computational social science with personality psychology to test research questions on consumer preferences. Two components of the thesis focused on the predictive utility of transaction records in inferring consumer attributes with which to personalise advertising, as well as the use of transaction records in examining self-congruence in overall consumption patterns and its relationship with happiness. Through five empirical studies, this work suggests that consumer attributes such as age and financial distress can be reliably inferred from consumption patterns reflected in transaction records (Chapter 3 and 5). The inferred age can be used to personalise advertisements in order to increase their appeal (Chapter 4). Using an objective measure of self-congruence in overall consumption pattern computed from transaction records and panel ratings, the thesis shows that individuals differ in their tendency to spend in a way that is congruent with their personality based on their levels of materialism and financial distress (Chapter 6). As the most important predictor of self-congruent spending, financial distress moderates the relationship between self-congruent spending and happiness (Chapter 7). These findings contribute insights into how consumption patterns are related to consumer attributes and usefulness for personalisation in marketing, as well as policy recommendations for improving well-being by targeting consumption patterns in financially distressed individuals. In addition, this thesis also showcases the value of machine learning and large-scale behavioural field data in the study of consumer psychology. Privacy and ethical concerns surrounding automated profiling and microtargeting are also cautioned

The NEBLINE, December 2001

Contents: Bioterrorism and the Biosecurity Measures We Can Use to Reduce our Risks Biosecurity Related NebGuides and NebFacts Deicing Salts Harmful to Plants Houseplant Problems Horticulture information center Growing Cacti and Succulents How Do Honey Bees Survive the Winter? Nothing Brightens up a Winter Day Like the Song of a Bird Looking for a New Hobby? Learn to Manage Bees and Produce Honey! Extension Resources on Feeding Birds and Wildlife Habitat Fungus Gnats Are Nuisance Often Found in Soil of Houseplants Producer Question — Should I Aerate my Pasture? Management Pays Better than Labor Biosolids Improves Soil and Cuts Production Costs The Legends and Traditions of Holiday Plants Protecting Water from Freezing Winter Burn Recycling Christmas Trees Healthy Eating: Enjoying Nebraska Foods! Cooking Ahead for Holiday Meals Monthly Meetings Go Global FREE Brochure on Preparing Roast Beef Clarice’s Column Family Community Education (FCE) Leader Training Lessons for 2002 Storing Holiday Decorations Depression During the Holidays Stain removal tips Make a Family New Year’s Resolution Model the Behavior You Expect from Your Teen CHARACTER COUNTS! Corner: Caring 4-H CAN Fight Hunger Fall Rabbit Clinic a Success Getting a New Pet for the Holidays? 4-H Volunteer Forum 4-H Horse VIPS Committee Update Area Youth Represent Nebraska at National Contest 4-H Achievement Night Feb. 5, 2002 Join the 4-H Speech VIPS 5th and 6th Grade 4-H Lock-In Centennial Celebration Help Generate Next Century Ideas for 4-H What Will Lincoln, Lancaster County Be Like in 25 Years? Soni Cochran Receives State “Distinguished Extension Associate” Award Dr. Boshra Rida Joins Staff Planning Acreages Extension Calendar Crop Protection Clinic Scheduled for January 3 Parents Forever and Kids Talk About Divorce Faces of Middle East and the Survival English Annual Report Special Pull-Out Sectio

Putting Purpose Into Practice

The book provides a detailed and practical description of how companies can put purpose into practice in their organizations. Based on a ground-breaking research project on the Economics of Mutuality undertaken jointly by the Saïd Business School at the University of Oxford and Mars Catalyst, the think tank of Mars Inc., the food and beverages company, over a period of five years, the book describes how purpose promotes business growth and performance. In particular, it gives a highly accessible and readable account of how companies can determine and implement their corporate purposes, and how, by so doing, they address critical issues in their ecosystems, such as rising inequality and environmental degradation, while delivering superior performance and resilience. The book will equip executives, managers, investors, policymakers, academics, and students with tools to understand the way in which companies can build purpose-centric businesses, map and orchestrate stakeholder ecosystems, identify untapped resources, create unconventional partnerships, measure and manage performance beyond financial reporting, and adopt a new definition of profit to promote corporate purposes. The book includes fourteen case studies of companies of varying sizes, sectors, and geographies that sought to put purpose into practice. They provide deep insights into the way in which companies have delivered corporate purpose and the challenges they faced in doing this. The book stresses both the opportunity and obligation on business to reposition itself to address the changing needs of society and the planet in the twenty-first century

The BG News May 4, 2009

The BGSU campus student newspaper May 4, 2009. Volume 99 - Issue 148https://scholarworks.bgsu.edu/bg-news/9088/thumbnail.jp