We contend that corruption must be detected as soon as possible so that corrective and preventive measures may be taken. Thus, we develop an early warning system based on a neural network approach, ...specifically self-organizing maps, to predict public corruption based on economic and political factors. Unlike previous research, which is based on the perception of corruption, we use data on actual cases of corruption. We apply the model to Spanish provinces in which actual cases of corruption were reported by the media or went to court between 2000 and 2012. We find that the taxation of real estate, economic growth, the increase in real estate prices, the growing number of deposit institutions and non-financial firms, and the same political party remaining in power for long periods seem to induce public corruption. Our model provides different profiles of corruption risk depending on the economic conditions of a region conditional on the timing of the prediction. Our model also provides different time frameworks to predict corruption up to 3 years before cases are detected.
•We combine multilayer perceptrons and self-organizing maps for bankruptcy prediction.•We calculate the probability of distress up to three years before bankruptcy occurs.•We develop a tool to assess ...bank risk in the short, medium and long term.•Our model outperforms traditional models of bankruptcy prediction.•Distressed banks are concentrated in real estate loans and have more provisions.
We develop a model of neural networks to study the bankruptcy of U.S. banks, taking into account the specific features of the recent financial crisis. We combine multilayer perceptrons and self-organizing maps to provide a tool that displays the probability of distress up to three years before bankruptcy occurs. Based on data from the Federal Deposit Insurance Corporation between 2002 and 2012, our results show that failed banks are more concentrated in real estate loans and have more provisions. Their situation is partially due to risky expansion, which results in less equity and interest income. After drawing the profile of distressed banks, we develop a model to detect failures and a tool to assess bank risk in the short, medium and long term using bankruptcies that occurred from May 2012 to December 2013 in U.S. banks. The model can detect 96.15% of the failures in this period and outperforms traditional models of bankruptcy prediction.
As research on (anti-)corruption continues to accelerate, the heterogeneity of perspectives that have emerged in the field complicates the identification of key topics and trends, limiting our ...capacity to set meaningful research priorities, risking the waste of time and funds, and potentially broadening the gap between scholarly production and policy necessities. To help elucidate this morass, we use the Latent Dirichlet Allocation (LDA) algorithm to classify a dataset of 5417 publications listed in the Global Anticorruption Blog’s (GAB)
Anticorruption Bibliography
. The results allow us to recognize eight main topics in the literature, as well as their evolution over the past 2 decades in terms of relative attention (as measured by citation count) and publication rates. The topics and trends found here invite us to reflect on the current structure of the (anti-)corruption field, and to draw attention to persistent—and emerging—gaps.
This book is ideal for people wanting to get up-and-running with the core concepts of machine learning using R 3.5. This book follows a step-by-step approach to implementing an end-to-end pipeline, ...addressing data collection and processing, various types of data analysis, and machine learning use cases.
How to select franchisees: A model proposal Calderon-Monge, Esther; Pastor-Sanz, Iván; Sendra-García, Javier
Journal of business research,
10/2021, Letnik:
135
Journal Article
Recenzirano
The selection of franchisees is a key decision for a franchisor and the success of a franchising business. The aim of this research is to design a model proposal with which franchisors may ...objectively evaluate their potential franchisees in a selection process and reduce any likelihood of error in their final choice. To do so, we apply the Analytic Hierarchy Process methodology, which yields a model proposal as its main result that prioritizes the attitude of the applicant over and above all other criteria that may be taken into account when selecting a chain franchisee. Franchisors can choose to apply the model with the proposed variables, thereby minimizing any error in their candidate selection procedures. They can also adapt the model proposal to virtually any other variables that are central to the spirit of their franchising businesses. The model will help any franchisor to refine its franchisee selection procedure.
ABSTRACT
Economic crises affect both the organizational side and the brand side of the franchise. Using self‐organizing time maps, this study examines how franchise brand behavior influences ...decisions by potential franchisees in Spain. The findings confirm that franchising offers an alternative to the business turnaround strategy, which firms apply when faced with adverse changes in the environment such as those caused by the economic crisis in Spain. Results show that all franchise brands within the same sector behaved similarly, except for brands in the catering sector, which displayed varying responses to the economic changes. The authors discuss the implications of these results for future franchisees.
Machine Learning with R Quick Start Guide takes you through the foundations of machine learning using the R programming language. Starting with the basics, this book introduces key algorithms and ...methodologies, offering hands-on examples and applicable machine learning solutions that allow you to extract insights and create predictive models.What this Book will help me doUnderstand the basics of machine learning and apply them using R 3.5.Learn to clean, prepare, and visualize data with R to ensure robust data analysis.Develop and work with predictive models using various machine learning techniques.Discover advanced topics like Natural Language Processing and neural network training.Implement end-to-end pipeline solutions, from data collection to predictive analytics, in R.Author(s)None Sanz, the author of Machine Learning with R Quick Start Guide, is an expert in data science with years of experience in the field of machine learning and R programming. Known for their accessible and detailed teaching style, the author focuses on providing practical knowledge to empower readers in the real world.Who is it for?This book is ideal for graduate students and professionals, including aspiring data scientists and data analysts, looking to start their journey in machine learning. Readers are expected to have some familiarity with the R programming language but no prior machine learning experience is necessary. With this book, the audience will gain the ability to confidently navigate machine learning concepts and practices.
This paper utilizes neural network mapping technology to assess the dynamic nature of systemic risk over time in the banking industry. We combine the nonparametric method of trait recognition with ...self-organizing maps to generate annual pictures of the 16 largest U.S. banks’ financial condition from 2003 to 2012. Results show that systemic risk was gradually rising prior to the 2008–2009 financial crisis and peaked in 2009. Thereafter, big banks were recovering but considerable systemic risk lingered. Implications to bank regulatory policy and credit risk management are discussed.
•A novel measure of systemic risk using mapping and regression methods is proposed.•Default probabilities for U.S. banks are aggregated into a single macro measure.•Our measure has predictive power ...to detect systemic volatility prior to the 2008–09 crisis.•According to our measure, systemic risk returned to normal levels by 2012.•Micro- and macro-prudential measures are useful in assessing systemic risk.
