Comparative Analysis of Credit Risk Models in Relation to SME Segment

The importance of credit risk management is well known and was deeply investigated by the banking industry. There is a pressure on financial institutions to still improve their credit risk management systems, so the credit risk of a bank is an unflagging object of discussion. The aim of this article to compare the predicting abilities of several bankruptcy models to the SME segment in the Czech Republic and its subsegments - medium sized, small and micro enterprises. We have focused on small and medium sized enterprises (SMEs) considering their fundamental role played in the Czech economy and the considerable attention placed on SMEs. We have chosen popular bankruptcy models that are often applied, namely the Altman Z-score, Altman model developed especially for SMEs in 2007, the Ohlson O-score, the Zmijewski’s model, the Taffler’s model, and the IN05 model. The basic form of the models was used as proposed by their authors. The results were compared using the contingency table and ROC curve. We have found that the best prediction models are Zmijewski´s and Ohlson´s models which use probit and logit methodologies and according to our analysis, their prediction ability is better than that of models based on discriminant analysis. Surprisingly, model IN05 designed for Czech companies provides average results only. One of the worst performing models is Altman 2007, which was created specifically for SMEs, but according to our analysis it only provides subordinates results

Determinants of knowledge transfer in Turkish textile and apparel industry

The knowledge transfer activities in Turkish Textile and Apparel Industries have been explored in this study. The knowledge transfer is undisputedly important subjects as knowledge provides competitive advantage to firms. Only few percentages of the Turkish textile and apparel industries are engaged in knowledge transfer activity although it is recorded as the largest industry in Turkish economy. Turkish textile and apparel industries are mostly run by family and most of them are either unaware or reluctant to involve in the knowledge transfer activities. This study examines the knowledge transfer activities in Turkish SMEs through qualitative research and quantitative analysis by undertaking extensive li terature reviews and present situationin Turkey and proposes hypotheses to test the knowledge transfer activities in Turkish SMEs.T he proposed hypotheses consider various related factors (determinants) such as knowledge sharing, organisational culture, communication channel, knowledge acquisition and IT resource to analyse the overall scenario of knowledge transfer behaviour in Turkish textile and apparel industries. The analysis results indicate that in case of Turkish textile and apparel industries, the pattern of knowledge transfer activities are different from the available literature and mostly affected by local environments. This report points out severalt hought provoking findings and concludes with recommendation for researchers and practitioners. The work presented in this thesis suggests a novel way forward in the development of knowledge transfer activities in Turkish textile and apparel industries and, therefore it is considered that the work constitutes a valuable contribution to knowledge in this area of study. Also, there are a number of ways in which the work presented in this thesis can be extended to many other challenging domains

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

Proceedings of the Salford Postgraduate Annual Research Conference (SPARC) 2011

These proceedings bring together a selection of papers from the 2011 Salford Postgraduate Annual Research Conference(SPARC). It includes papers from PhD students in the arts and social sciences, business, computing, science and engineering, education, environment, built environment and health sciences. Contributions from Salford researchers are published here alongside papers from students at the Universities of Anglia Ruskin, Birmingham City, Chester,De Montfort, Exeter, Leeds, Liverpool, Liverpool John Moores and Manchester

Collected Papers: Entrepreneurship

These collected papers serve as a student exercise in critical thinking. The aim is to explore and discover knowledge relating to differing aspects of entrepreneurship. Critical thinking skills, academic writing and the ability to build arguments are all skills we consider an essential part of our student progression. Our students understand critical thinking as an intellectually disciplined, cognitive process which involves the reflective, active analysis and evaluation of knowledge and arguments in order to develop their own defensible knowledge and arguments. Reading and writing are enquiries that require an action rather than just repeating what has been previously stated or done, it is an act of discovery. It is for this reason we are not offering definitions of entrepreneurship or explanations of any aspects of the challenges in entrepreneurship education and practice, we will leave this to our students. Whether our approach to entrepreneurship education on this particular module serves to empower and emancipate or to just challenge and explore, might be open for debate. It can be argued that entrepreneurship education should be a way of action rather than a specific subject area . We don’t disagree, but in this instance embrace the subject area as a means to building knowledge, skills and exploring the subject area with our students

DEVELOPMENT OF AN INTERNATIONALIZATION STRATEGY TO EFFECTIVELY MARKET PAKISTAN SMEs PRODUCTS TO EUROPEAN COUNTRIES

In many countries, small-medium enterprises (SMEs) play a vital role in creating jobs and economic development, especially in developing countries like Pakistan. However, despite possible internationalisation benefits and strategic importance, numbers of Pakistani SMEs are not actively serving the international market because of their weak business strategy and market barriers faced in the global marketplace. This study focuses on the strategy and methods used by the SMEs of Pakistan to enter the international market. The upstream and downstream internationalisation model is used for the technological knowledge and acquisition of the market and the foreign customers' insight and foreign market insight. The dimensions, SMEs are required to focus for internationalisation are investigated in this research, such as market, product, and time and operation mode. This is the main focus of the study. To solicit the opinion of sampled Pakistan SMEs managers from the list provided by the Small and Medium Enterprises Authority (SMEDA), a qualitative research design was employed and interviews were used for data collection. Findings reveal that SMEs' current strategy is the major challenge and obstacle for them entering the internationalisation process. Results show that market choice and region is not a part of the deliberate strategy but an unexpected opportunity outcome. Furthermore, the findings revealed that a lack of market knowledge makes it difficult for SMEs to gain access and assistance, as the government institutions' policies and regulations are not supportive. In addition, financial constraints make it challenging to manage this internationalisation process efficiently. The current research contributes to identifying the challenges required to be solved internationalisation process. Government institutions, skilled workforce, R&D, logistics, market access, and financial support are more likely to play a role in SMEs' journey to achieve internationalisation effectively

Essays in financial technology: banking efficiency and application of machine learning models in Supply Chain Finance and credit risk assessment

The financial landscape is undergoing a significant transformation, driven by technological innovations that are reshaping traditional banking practices. This thesis examines the evolving relationship between financial technology (FinTech) and banking, specifically addressing the credit risk aspects within the domains of Supply Chain Finance (SCF) and peer-to-peer (P2P) lending. FinTech has experienced rapid growth and innovation over the past decade. It encompasses a wide range of technologies and services that aim to enhance and streamline financial processes, disrupt traditional banking models, and offer new solutions to consumers and businesses. The status of FinTech and banking is assessed through an extensive review of the current literature and empirical data. Accordingly, FinTech development has significantly impacted the financial landscape, driving innovation, competition, and customer expectations while it has exposed inefficiencies within traditional banking, it has also compelled banks to evolve and embrace technological advancements. The impact of FinTech on traditional banking models, customer behaviours, and market competition is aimed to be explored. This investigation highlights the challenges and opportunities that arise as FinTech disrupts and reshapes the banking sector, emphasizing its potential to enhance efficiency, accessibility, and customer experiences. As Chapter 3 focuses on an empirical analysis of the impact of FinTech on the operating efficiency of commercial banks in China. Further, in the context of credit risk, the thesis focuses on SCF and P2P lending, two prominent areas influenced by FinTech innovation. SCF has witnessed substantial transformation with the infusion of FinTech solutions. Digital platforms have streamlined the flow of funds within complex supply networks, enhancing the liquidity of suppliers and optimizing working capital for buyers. However, this transformation introduces new credit risk challenges. As suppliers' financial data becomes more accessible, the need for accurate risk assessment and predictive modelling becomes paramount. The integration of big data analytics, machine learning, and artificial intelligence (AI) holds the promise of refining credit risk evaluation by offering real-time insights into supplier financial health, thereby improving lending decisions and reducing defaults. Similarly, P2P lending has redefined the borrowing and lending landscape, enabling direct connections between individual borrowers and lenders. While P2P lending platforms offer speed, convenience, and access to credit for previously underserved segments, they also grapple with credit risk concerns. Evaluating the creditworthiness of individual borrowers without sufficient credit history demands innovative risk assessment methodologies. The emergence of data issues, such as imbalanced data issues, feature selection, and data processing, presents challenges in building accurate credit risk profiles for P2P lending participants. FinTech solutions play a pivotal role in creating and implementing these alternative risk assessment models. Note that, few studies in the literature investigate the benchmark of the advanced method of solving the credit risk assessment in emerging financial services. This thesis aims to address this research gap by evaluating the effectiveness of credit risk assessment models in these FinTech-driven contexts, considering both traditional methodologies and novel data-driven approaches. Chapter 4 investigates the credit risk assessment issue in Digital Supply Chain Finance (DSCF) with the Machine Learning approach and Chapter 5 emphasises the issue of data imbalance of credit risk assessment in P2P Lending. By addressing these gaps and issues, this thesis aims to contribute to the broader discourse on FinTech's role in shaping the future of banking. The findings have implications for financial institutions, policymakers, and regulators seeking to harness the benefits of FinTech while mitigating associated risks. Ultimately, this study offers insights into navigating the evolving landscape of credit risk in SCF and P2P lending within the context of an increasingly technology-driven financial ecosystem