11,912 research outputs found

    The prevalence of credit risk in Greek Banking: Risk management & methodology for evaluating corporate credit risk and Greek Business Environment

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    Our article consists of the following 4 parts: - In the first part: documented the importance of credit risk with the presentation - analysis of the growth of 6 Greek major financial institutions loan portfolio, for the period 2001 to 2008 and comparison of the loan amount with their own funds and total assets them (based on published accounts). - In the second part: we refer to the approval and monitoring procedures that should be followed by banks using the internal ratings (IRB) methods for corporate loans. Our interest is focused on linking credit ratings to the terms of financing (collateral costs) and on the importance of evaluation / assessment and collateral for the balance of exposures. For typesetting the above is estimated the Risk Weight Assets for PD rating scale (National Bank of Greece published data) and relevant Figure/diagram. - In the third part: we analyse the methodology of key criteria for evaluating the creditworthiness of companies. At the same time for a short description of Greek Business environment we used the -List Easy of Doing Business index 2010- of the World Bank and the results of the assessment of business sectors in Greece according to the model of Credit Risk Tracker Greece's Standard & Poor's, as published by the Hellastat. (The key criteria for evaluating creditworthiness of companies mainly come from research on the websites of the companies Fitch, S & P, Moody's KMV, Hellastat, Easy of Doing Business index). - In fourth part: presented, properly treated, the results of empirical research conducted through distribution of questionnaire to 25 experienced in Risk Management, executives, which was called to assess 20 companies on the basis of their specific quantitative and qualitative characteristics. Finally it is noted that in this final part are also presented all the findings and related conclusions, resulting from the scientific research throughout this paper.

    Highly accurate model for prediction of lung nodule malignancy with CT scans

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    Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://bioinformatics.astate.edu/NoduleX

    Credit risk management in banks: Hard information, soft Information and manipulation

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    The role of informationā€™s processing in bank intermediation is a crucial input. The bank has access to different types of information in order to manage risk through capital allocation for Value at Risk coverage. Hard information, contained in balance sheet data and produced with credit scoring, is quantitative and verifiable. Soft information, produced within a bank relationship, is qualitative and non verifiable, therefore manipulable, but produces more precise estimation of the debtorā€™s quality. In this article, we investigate the impact of the informationā€™s type on credit risk management in a principalagent framework with moral hazard with hidden information. The results show that access to soft information allows the banker to decrease the capital allocation for VaR coverage. We also show the existence of an incentive of the credit officer to manipulate the signal based on soft information that he produces. Therefore, we propose to implement an adequate incentive salary package which unables this manipulation. The comparison of the results from the two frameworks (information hard versus combination of hard and soft information) using simulations confirms that soft information gives an advantage to the banker but requires particular organizational modifications within the bank, as it allows to reduce capital allocation for VaR coverage.Hard information; Soft information; risk management; Value at Risk; moral hazard; hidden information; manipulation

    New Trends regarding the Operational Risks in Financial Sector

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    Risks, especially "operational risks" are part of corporate life, they are the essence of financial institutions' activities. Operational risks are complex and often interlinked and have to be managed properly. Today, there is more pressure to avoid operational risks while continuing to improve corporate performance in the new environment. The operational risk management of the future has to be seen in the wider context of globalization and Internet-related technologies. The two major future drivers - globalization and Internet-related technologies - will challenge the firms from financial sector to take on additional and partly new operational risk.operational risk, financial sector, models, trends

    Deep Extreme Multi-label Learning

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    Extreme multi-label learning (XML) or classification has been a practical and important problem since the boom of big data. The main challenge lies in the exponential label space which involves 2L2^L possible label sets especially when the label dimension LL is huge, e.g., in millions for Wikipedia labels. This paper is motivated to better explore the label space by originally establishing an explicit label graph. In the meanwhile, deep learning has been widely studied and used in various classification problems including multi-label classification, however it has not been properly introduced to XML, where the label space can be as large as in millions. In this paper, we propose a practical deep embedding method for extreme multi-label classification, which harvests the ideas of non-linear embedding and graph priors-based label space modeling simultaneously. Extensive experiments on public datasets for XML show that our method performs competitive against state-of-the-art result

    Cognitive finance: Behavioural strategies of spending, saving, and investing.

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    Research in economics is increasingly open to empirical results. The advances in behavioural approaches are expanded here by applying cognitive methods to financial questions. The field of "cognitive finance" is approached by the exploration of decision strategies in the financial settings of spending, saving, and investing. Individual strategies in these different domains are searched for and elaborated to derive explanations for observed irregularities in financial decision making. Strong context-dependency and adaptive learning form the basis for this cognition-based approach to finance. Experiments, ratings, and real world data analysis are carried out in specific financial settings, combining different research methods to improve the understanding of natural financial behaviour. People use various strategies in the domains of spending, saving, and investing. Specific spending profiles can be elaborated for a better understanding of individual spending differences. It was found that people differ along four dimensions of spending, which can be labelled: General Leisure, Regular Maintenance, Risk Orientation, and Future Orientation. Saving behaviour is strongly dependent on how people mentally structure their finance and on their self-control attitude towards decision space restrictions, environmental cues, and contingency structures. Investment strategies depend on how companies, in which investments are placed, are evaluated on factors such as Honesty, Prestige, Innovation, and Power. Further on, different information integration strategies can be learned in decision situations with direct feedback. The mapping of cognitive processes in financial decision making is discussed and adaptive learning mechanisms are proposed for the observed behavioural differences. The construal of a "financial personality" is proposed in accordance with other dimensions of personality measures, to better acknowledge and predict variations in financial behaviour. This perspective enriches economic theories and provides a useful ground for improving individual financial services

    Capital markets, CDFIs, and organizational credit risk

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    Can Community Development Financial Institutions (CDFIs) get unlimited amounts of low cost, unsecured, short- and long-term funding from the capital markets based on their organizational credit risk? Can they get pricing, flexibility, and procedural parity with for-profit corporations of equivalent credit risk? One of the key objectives of this book is to explain the reasons why the answer to the two questions above remains ā€œno.ā€ The other two key objectives are to show the inner workings of what has been done to date to overcome the obstacles so that we donā€™t have to retrace the same steps and recommend additional disciplines that position CDFIs to take advantage of the mechanisms of the capital markets once the markets stabilize
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