DIFFERENCES BETWEEN FARMER AND AGENCY ATTITUDES REGARDING POLICIES TO REDUCE PHOSPHORUS POLLUTION IN THE MINNESOTA RIVER BASIN

Environmental Economics and Policy,

A Proposed Model For Predicting Financial Tumble And Financial Economics

The study aimed to address the concept of financial and banking defaults and to identify performance indicators and their role in predicting financial defaults in the research sample banks. Developing a standard model consisting of a set of financial ratios capable of distinguishing between troubled banks and non-performing banks. And testing the ability of the proposed model to distinguish between troubled and non-performing banks. Logistic regression model was used to test the research data. The statistical method was used logistic regression analysis to interpret the relationship between a set of variables, and then apply the stepwise selection method through which models can be generated and the best model can be selected from the sum of financial indicators that can be applied and distinguish between troubled and non-performing banks, as the classification related to banks being (in troubled, not in troubled) showed that the total non-performing Iraqi banks are (6) and the non-performing banks are (9) out of the total (15) banks within the years of the research. The study concluded that financial stumbling has a great impact on many parties and parties, as financial stumbling affects the banks themselves as well as the owners and creditors, and it can result in large losses that lead the bank to bankruptcy. This is in addition to the pressures faced by the administration, foremost of which is the relinquishment of the position to a new administration

Bankruptcy prediction models in Galician companies. Application of parametric methodologies and artificial intelligence

This paper provides empirical evidence on the prediction of non-financial companies’ failure. We develop several models to evaluate failure risk in companies from Galicia. We check the predictive ability of parametric models (multivariate discriminant, logit) compared with auditor’s report. Models are based on relevant financial variables and ratios, in financial logic and a in financial distress situations. We examine a random sample of companies in cross-sectional perspective, checking the predictive capacity at any given time, also verifying is models give reliable signals to anticipate future events of financial distress. Findings suggest that our models are extremely effective when applied in medium and long term, and that they offer higher predictive capabilities than external audit.peer-reviewe

Preface

DAMSS-2018 is the jubilee 10th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at the end of the year. The same place and the same time every year. Ten years passed from the first workshop. History of the workshop starts from 2009 with 16 presentations. The idea of such workshop came up at the Institute of Mathematics and Informatics. Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval both in the Lithuanian research community and abroad. The number of this year presentations is 81. The number of registered participants is 113 from 13 countries. In 2010, the Institute of Mathematics and Informatics became a member of Vilnius University, the largest university of Lithuania. In 2017, the institute changes its name into the Institute of Data Science and Digital Technologies. This name reflects recent activities of the institute. The renewed institute has eight research groups: Cognitive Computing, Image and Signal Analysis, Cyber-Social Systems Engineering, Statistics and Probability, Global Optimization, Intelligent Technologies, Education Systems, Blockchain Technologies. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the research community. Even 11 companies supported the workshop this year. This means that the topics of the workshop are actual for business, too. Topics of the workshop cover big data, bioinformatics, data science, blockchain technologies, deep learning, digital technologies, high-performance computing, visualization methods for multidimensional data, machine learning, medical informatics, ontological engineering, optimization in data science, business rules, and software engineering. Seeking to facilitate relations between science and business, a special session and panel discussion is organized this year about topical business problems that may be solved together with the research community. This book gives an overview of all presentations of DAMSS-2018.DAMSS-2018 is the jubilee 10th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at the end of the year. The same place and the same time every year. Ten years passed from the first workshop. History of the workshop starts from 2009 with 16 presentations. The idea of such workshop came up at the Institute of Mathematics and Informatics. Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval both in the Lithuanian research community and abroad. The number of this year presentations is 81. The number of registered participants is 113 from 13 countries. In 2010, the Institute of Mathematics and Informatics became a member of Vilnius University, the largest university of Lithuania. In 2017, the institute changes its name into the Institute of Data Science and Digital Technologies. This name reflects recent activities of the institute. The renewed institute has eight research groups: Cognitive Computing, Image and Signal Analysis, Cyber-Social Systems Engineering, Statistics and Probability, Global Optimization, Intelligent Technologies, Education Systems, Blockchain Technologies. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the research community. Even 11 companies supported the workshop this year. This means that the topics of the workshop are actual for business, too. Topics of the workshop cover big data, bioinformatics, data science, blockchain technologies, deep learning, digital technologies, high-performance computing, visualization methods for multidimensional data, machine learning, medical informatics, ontological engineering, optimization in data science, business rules, and software engineering. Seeking to facilitate relations between science and business, a special session and panel discussion is organized this year about topical business problems that may be solved together with the research community. This book gives an overview of all presentations of DAMSS-2018

Rapid Budget Analysis of the Agricultural Sector for the General Budget Support Annual Review 2010/11.

The main objective of the budget analysis chapter is to provide an overall assessment of how well the approved budget allocations in 2010-11 align with the strategic objectives and with sector strategic priorities of the Second National Strategy for Growth and Reduction of Poverty (known by its Kiswahili acronym, MKUKUTA II). It also assesses the consistency of the actual spending and approved budget in 2009-10. In evaluating the alignment of the budget and MKUKUTA's strategic objectives and sector strategic priorities, the analysis gauges the accuracy and reliability of the macro and budget framework, share of the budget allocated to MKUKUTA cluster strategies, share of the budget allocated to capital investment, and strategic prioritization within key sectors. This budget analysis chapter summarizes nine background notes that covered six key sectors and three thematic areas. The six key sectors are education, health, water, roads, energy, and agriculture; the three thematic areas are the wage bill, local government, and aggregate analysis. The six sectors were selected because they consume approximately 60 percent of the overall budget and are keys to achieving the MKUKUTA strategic objectives of growth and reduction of poverty. The three thematic areas were selected because of their crosscutting nature, as they touch each key sector but also are critical for achieving the MKUKUTA strategic objectives.

Factors affecting the conclusion of an arrangement in restructuring proceedings: evidence from Poland

The EU Restructuring Directive (2019/1023) requires Member States to provide a preventive restructuring framework for financially distressed entities that remain viable or are likely to readily restore economic viability. The first step to a successful restructuring is the approval of an arrangement between the debtor and creditors. The main research objective of the article is to identify factors affecting the conclusion of an arrangement in restructuring proceedings. In the process of filtering companies initiating a restructuring procedure, these factors are seen as increasing the probability of concluding an arrangement between debtor and creditors. Moreover, an additional research objective is to construct a turnaround prediction model aimed at assessing the probability of a conclusion of an arrangement in restructuring proceedings. The study covered the companies in Poland for which restructuring proceedings opened between 2016 and 2021 ended with the approval of an arrangement, and a similar number of companies that failed to restructure successfully. Binary logistic regression was applied to achieve the aims of this study. The results show that two financial variables affected companies in terms of their chances to conclude the arrangement: the current ratio and return on assets were among the statistically significant indicators and they are characterized by higher values for debtors reaching the arrangement with their creditors. A direct positive relationship was also identified between the company’s lifespan and the outcome of the proceedings. The probability of the conclusion of the arrangement was also affected by the type of industry. Models assessing the probability of completing restructuring proceedings with an arrangement can be useful for insolvency practitioners and financial analysts during viability assessments.The EU Restructuring Directive (2019/1023) requires Member States to provide a preventive restructuring framework for financially distressed entities that remain viable or are likely to readily restore economic viability. The first step to a successful restructuring is the approval of an arrangement between the debtor and creditors. The main research objective of the article is to identify factors affecting the conclusion of an arrangement in restructuring proceedings. In the process of filtering companies initiating a restructuring procedure, these factors are seen as increasing the probability of concluding an arrangement between debtor and creditors. Moreover, an additional research objective is to construct a turnaround prediction model aimed at assessing the probability of a conclusion of an arrangement in restructuring proceedings. The study covered the companies in Poland for which restructuring proceedings opened between 2016 and 2021 ended with the approval of an arrangement, and a similar number of companies that failed to restructure successfully. Binary logistic regression was applied to achieve the aims of this study. The results show that two financial variables affected companies in terms of their chances to conclude the arrangement: the current ratio and return on assets were among the statistically significant indicators and they are characterized by higher values for debtors reaching the arrangement with their creditors. A direct positive relationship was also identified between the company’s lifespan and the outcome of the proceedings. The probability of the conclusion of the arrangement was also affected by the type of industry. Models assessing the probability of completing restructuring proceedings with an arrangement can be useful for insolvency practitioners and financial analysts during viability assessments

Blue Economy and Resilient Development: Natural Resources, Shipping, People, and Environment

This book is a pivotal publication that seeks to address contemporary challenges to the blue economy in view of the growth in exploration and utilization of natural resources, transport connectivity, effects of climate change, sustainable fisheries management, food security, and social and economic issues of human well-being in coastal areas. Coastal territories and water areas are changing at an unprecedented pace in ways that fundamentally affect ecosystems, people, biodiversity, and sustainability. Such changes are driven primarily by rapid social and economic developments, economic disparities between countries, the internationalization of production and value chains, and industrialization. In this context, this publication supplements the existing literature by summoning political, economic, environmental, and social factors that influence various dimensions of the sustainable development of blue economy, as well as translating the findings into workable approaches and policies for the benefit of the economic actors, people, and the environment

Modelling Credit Risk for SMEs in Saudi Arabia

The Saudi Government’s 2030 Vision directs local banks to increase and improve credit for the Small and Medium Enterprises (SMEs) of the economy (Jadwa, 2017). Banks are, however, still finding it difficult to provide credit for small businesses that meet Basel’s capital requirements. Most of the current credit-risk models only apply to large corporations with little constructed for SMEs applications (Altman and Sabato, 2007). This study fills this gap by focusing on the Saudi SMEs perspective. My empirical work constructs a bankruptcy prediction model based on logistic regressions that cover 14,727 firm-year observations for an 11-year period between 2001 and 2011. I use the first eight years data (2001-2008) to build the model and use it to predict the last three years (2009-2011) of the sample, i.e. conducting an out-of-sample test. This approach yields a highly accurate model with great prediction power, though the results are partially influenced by the external economic and geopolitical volatilities that took place during the period of 2009-2010 (the world financial crisis). To avoid making predictions in such a volatile period, I rebuild the model based on 2003-2010 data, and use it to predict the default events for 2011. The new model is highly consistent and accurate. My model suggests that, from an academic perspective, some key quantitative variables, such as gross profit margin, days inventory, revenues, days payable and age of the entity, have a significant power in predicting the default probability of an entity. I further price the risks of the SMEs by using a credit-risk pricing model similar to Bauer and Agarwal (2014), which enables us to determine the risk-return tradeoffs on Saudi’s SMEs

Essays on SMEs insolvency risk

In light of the new Basel Capital Accord, Small and medium size enterprises (SMEs) play a fundamental role in the economic performance of major economies. Several lending communities proposed to treat SMEs as retail clients to optimize capital requirements and profitability. In this context, it is becoming critically important to have a detailed understanding of its risk behavior for appropriate pricing of credit risk. Thus, this thesis presents four essays on SMEs insolvency risk starting from chapter 3 through chapter 6 that investigates different dimensions of their default risk. My first essay makes distinction among SMEs that report operating cash flow and those which do not while modeling their default risk. However, I do not report any significant improvement in model’s classification performance when operating cash flow information is made available. Similarly, my second essay considers domestic and international SMEs separately while modelling their default risk and report almost identical classifications performance of the models’ developed for both the groups. The third essay compares the default risk attributes of micro, small and medium-sized firms respectively with SMEs. Test results suggest significant difference in the default risk attributes of only micro firms and SMEs. On a different line, my fourth essay deals with the methodological issues that have been witnessed recently in the bankruptcy literature that use hazard models for making bankruptcy predictions. This essay highlights the critical issues and provides appropriate guidance for the correct use of hazard models in making bankruptcy predictions. Here, I also propose a default definition for SMEs which considers both legal bankruptcy laws and firms’ financial health while defining the default event. Empirical results show that my default definition performs significantly better than its respective counterparts in identifying distressed firms with superior goodness of fit measures across all econometric specifications. Detailed abstract of respective essays are as follows.Evidence pertaining to SMEs financing strongly motivates me to believe that firms which are unable to generate sufficient operating cash flow (OCF) are more susceptible to bankruptcy. However, the role of OCF in bankruptcy of SMEs lacks empirical validation. Thus, my first essay (chapter 3) investigates the role of operating cash flow information as predictors in assessing the creditworthiness of SMEs. One-year distress prediction model developed using significant financial information of United Kingdom SMEs over a period of 2000 to 2009 confirm that the presence of operating cash flow information does not improve the prediction accuracy of the distress prediction model.My second essay (chapter 4) considers domestic and international small and medium-sized enterprises (SMEs) of the United Kingdom separately while modelling their default risk. To establish the empirical validation, separate one-year default prediction models are developed using dynamic logistic regression technique that encapsulates significant financial information over an analysis period of 2000 to 2009. Almost an identical set of explanatory variables affect the default probability of domestic and international SMEs, which contradicts the need for separate default risk models. However, the lower predictive accuracy measures of the model developed for international SMEs motivate me to compare the weights of regression coefficients of the models developed for domestic and international firms. Test results confirm that four out of the nine common predictors display significant statistical differences in their weights. However, these differences do not contribute to the discriminatory performance of the default prediction models, given that I report very little difference in each model’s classification performance.A huge diversity exists within the broad category of Small and medium size enterprises (SMEs). They differ widely in their capital structure, firm size, access to external finance, management style, numbers of employees etc. Thus, my third essay (chapter 5) contributes to the literature by acknowledging this diversity while modeling credit risk for them, using a relatively large UK database, covering the analysis period between 2000 and 2009. My analysis partially employs the definition provided by the European Union to distinguish between ‘micro’, ‘small’, and ‘medium’ sized firms. I use both financial and non-financial information to predict firms’ failure hazard. I estimate separate hazard models for each sub-category of SMEs, and compare their performance with a SMEs hazard model including all the three sub-categories. I test my hypotheses using discrete-time duration-dependent hazard rate modelling techniques, which controls for both macro-economic conditions and survival time. My test results strongly highlight the differences in the credit risk attributes of ‘micro’ firms and SMEs, while it does not support the need to consider ‘small’ and ‘medium’ firms’ category separately while modelling credit risk for them, as almost the same sets of explanatory variables affect the failure hazard of SMEs, ‘small’ and ‘medium’ firms.My fourth essay (chapter 6) considers all serious and neglected concerns while developing discrete and continuous time duration dependent hazard models for predicting failure of US SMEs. I compare theoretical and classification performance aspects of three popular hazard models, namely discrete hazard models with logit and clog-log links and the extended Cox model. I report that discrete hazard models are superior to extended Cox models in making default predictions. I also propose a default definition for SMEs which considers both legal bankruptcy laws and firms’ financial health while defining the default event. My empirical results show that my default definition performs significantly better than the default definitions which are only based on legal consequence or firms’ financial health in identifying distressed firms. In addition, my default definition also shows superior goodness of fit measures across all econometric specifications