When does the company go bankrupt?4 answersA company typically goes bankrupt when it faces financial instability, insufficient funds to pay debts, and operational inefficiencies, leading to an inability to generate profits and meet financial obligations. Factors contributing to bankruptcy include low current assets, ineffective cost management, inappropriate business strategies, and poor human resource management. Moreover, the risk of bankruptcy can be exacerbated by market uncertainty, financial statement manipulation, and creative accounting practices. To prevent bankruptcy, it is crucial to conduct a comprehensive analysis of a company's financial state, activity results, and cash flows early on, utilizing methodologies like forecasting models and operational measures. Ultimately, bankruptcy can have significant repercussions not only for the company itself but also for the state and society at large.
What are the main factors contributing to the increasing number of bankruptcies in the EU?5 answersThe increasing number of bankruptcies in the EU can be attributed to various factors. Research indicates that financial development, bank assets, insurance corporation assets, interest rate spreads, and regulatory quality significantly influence the growth of shadow banking. Moreover, the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to significant disruptions in personal freedoms, public health, and socioeconomics, potentially impacting the financial stability of businesses and individuals. Additionally, the analysis of threatened preterm labor cases revealed that clinical decision-making regarding antenatal corticosteroid administration is influenced by factors such as cervical dilation, effacement, and BMI. These combined factors from different studies highlight the complex interplay of economic, health-related, and regulatory aspects contributing to the rise in bankruptcies across the EU.
What are the current trends in finance research?4 answersCurrent trends in finance research include the analysis of global scientific research within the banking industry during the COVID-19 pandemic. There is also a focus on bibliometric analysis of papers published in top finance-related journals. Investment management and performance research are areas of interest, with the adaptation of classic CAPM and factor models playing a crucial role. Financial mathematics is being used to solve financial problems, with a growing trend in research on financial transactions. Additionally, there is a focus on the analysis of new technologies in the field of financial services, including territorial development, legal regulation, and methodology for studying financial technologies.
What are the future trends in a resume analyzer?3 answersThe future trends in resume analyzers include the use of machine learning and natural language processing (NLP) techniques to analyze resumes and provide automatic feedback and rating reports to help students enhance their resumes. These tools aim to assist college students, especially those in Information Technology (IT) and cybersecurity fields, in finding their first full-time, major-related job by strengthening their portfolio and highlighting the skills they need to work on. Additionally, resume analyzers can streamline the job recruitment process by automating the initial screening of job applications, saving time for both employers and candidates. They can also incorporate NLP techniques to assist human resources departments in analyzing the information provided in resumes, improving the recruitment process and identifying the best candidates for the job. The future direction includes evaluating the effectiveness of these tools and diving deeper into specific types of cybersecurity positions.
HOW predicting default HAS EVOLVED?5 answersDefault prediction has evolved with the advancement of technology and the availability of big data. Traditional models like linear models and machine learning models have been used for predicting credit defaults. However, recent studies have shown that big data risk control models outperform traditional models in terms of prediction accuracy. Different approaches have been explored, such as support vector domain description (SVDD) and case-based reasoning (CBR), to improve default prediction models. The combination of different methods has been found to enhance the performance of default prediction models. Additionally, the use of more sophisticated methods like random forest has led to remarkable increases in prediction accuracy. Overall, the evolution of default prediction has involved the integration of big data, machine learning, and different modeling approaches to improve prediction accuracy and better serve financial institutions.
What are the determinants of bankruptcy prediction?5 answersBankruptcy prediction is influenced by various determinants. Financial ratios such as debt ratio, sales growth, cash flow, return on assets, total assets turnover, current ratio, debt-to-equity ratio, and board size are significant factors in predicting bankruptcy. Other determinants include firm age, ownership concentration, and gender diversity. The Covid-19 pandemic has also been found to affect the ability of financial ratios to predict bankruptcy, with sales growth being the most important factor during the pandemic due to restrictions on consumers' activities. Additionally, the use of predictive models and statistical approaches, such as logistic regression, extreme gradient boosting, decision trees, random forests, quadratic discriminant analysis, neural networks, adaptive boosting, gaussian naïve bayes, balanced bagging, and ensemble SVM classification, can improve the accuracy of bankruptcy prediction.