Prediction of Banks Financial Distress

In this research we conduct a comprehensive review on the existing literature of prediction techniques that have been used to assist on prediction of the bank distress. We categorized the review results on the groups depending on the prediction techniques method, our categorization started by firstly using time factors of the founded literature, so we mark the literature founded in the period (1990-2010) as history of prediction techniques, and after this period until 2013 as recent prediction techniques and then presented the strengths and weaknesses of both. We came out by the fact that there was no specific type fit with all bank distress issue although we found that intelligent hybrid techniques considered the most candidates methods in term of accuracy and reputatio

The Impacts of Machine Learning in Financial Crisis Prediction

The most complicated and expected issue to be handled in corporate firms, small-scale businesses, and investors’ even governments are financial crisis prediction. To this effect, it was of interest to us to investigate the current impact of the newly employed technique that is machine learning (ML) to handle this menace in all spheres of business both private and public. The study uses systematic literature assessment to study the impact of ML in financial crisis prediction. From the selected works of literature, we have been able to establish the important role play by this method in the prediction of bankruptcy and creditworthiness that was not handled appropriately by others method. Also, machine learning helps in data handling, data privacy, and confidentiality. This study presents a leading approach to achieving financial growth and plasticity in corporate organizations. We, therefore, recommend a real-time study to investigate the impact of ML in FCP. &nbsp

Impact of sectors and political influence on financial distress across Pakistani public listed firms

Identifying financial distress provides information on ways to control and direct firms in achieving their goals. The common approach is to study the relationship between set of explanatory variables and financial distress. However, in order to improve the firms‘ financial structure, there is a need to understand the impact of sectors and different political conditions that affect financial distress. This study investigated the industry effects on financial distress as it is identified that financial distress might differ for firms due to the unique nature of each industry. The study dealt with projected key ideas to evaluate and compare diverse financial distress models to show the robustness of Pakistani listed firms across industries, and study how good financial distress can be predicted. Finally, to alleviate the severe consequences of political instability, the current study underlined the differences in financial distress determinants during different political regimes (Dictatorship and Democratic). The study analysed 153 non-financial firms listed on Karachi Stock Exchange (KSE) during a ten-year period (2004-2013) featuring two political periods; 2004 to 2008 as dictatorship period and 2009 to 2013 as democratic period. Four models were employed, namely logit analysis, decision tree, neural network and paired t-test. A diversity of models was employed to check the strength and prediction correctness of the models and t-test was employed to compare two different political regimes. From the findings, the indirect impact is clearly noticeable due to changes in the signs and magnitude of determinants across sectors. Logit analysis shows better results as compared to the other models as it was based on different industry-level variables and two different political regimes. The mechanism among the variables and financial distress is dependent on different political conditions of the country. The result shows that the impact of different political conditions varies across sectors. In addition, the results of this study are valuable for financial institutions to forecast financial distress and estimate minimum capital requirements to reduce the cost of risk management

Corporate Bankruptcy Prediction

Bankruptcy prediction is one of the most important research areas in corporate finance. Bankruptcies are an indispensable element of the functioning of the market economy, and at the same time generate significant losses for stakeholders. Hence, this book was established to collect the results of research on the latest trends in predicting the bankruptcy of enterprises. It suggests models developed for different countries using both traditional and more advanced methods. Problems connected with predicting bankruptcy during periods of prosperity and recession, the selection of appropriate explanatory variables, as well as the dynamization of models are presented. The reliability of financial data and the validity of the audit are also referenced. Thus, I hope that this book will inspire you to undertake new research in the field of forecasting the risk of bankruptcy

In-Vitro Biological Tissue State Monitoring based on Impedance Spectroscopy

The relationship between post-mortem state and changes of biological tissue impedance has been investigated to serve as a basis for developing an in-vitro measurement method for monitoring the freshness of meat. The main challenges thereby are the reproducible measurement of the impedance of biological tissues and the classification method of their type and state. In order to realize reproducible tissue bio-impedance measurements, a suitable sensor taking into account the anisotropy of the biological tissue has been developed. It consists of cylindrical penetrating multi electrodes realizing good contacts between electrodes and the tissue. Experimental measurements have been carried out with different tissues and for a long period of time in order to monitor the state degradation with time. Measured results have been evaluated by means of the modified Fricke-Cole-Cole model. Results are reproducible and correspond to the expected behavior due to aging. An appropriate method for feature extraction and classification has been proposed using model parameters as features as input for classification using neural networks and fuzzy logic. A Multilayer Perceptron neural network (MLP) has been proposed for muscle type computing and the age computing and respectively freshness state of the meat. The designed neural network is able to generalize and to correctly classify new testing data with a high performance index of recognition. It reaches successful results of test equal to 100% for 972 created inputs for each muscle. An investigation of the influence of noise on the classification algorithm shows, that the MLP neural network has the ability to correctly classify the noisy testing inputs especially when the parameter noise is less than 0.6%. The success of classification is 100% for the muscles Longissimus Dorsi (LD) of beef, Semi-Membraneous (SM) of beef and Longissimus Dorsi (LD) of veal and 92.3% for the muscle Rectus Abdominis (RA) of veal. Fuzzy logic provides a successful alternative for easy classification. Using the Gaussian membership functions for the muscle type detection and trapezoidal member function for the classifiers related to the freshness detection, fuzzy logic realized an easy method of classification and generalizes correctly the inputs to the corresponding classes with a high level of recognition equal to 100% for meat type detection and with high accuracy for freshness computing equal to 84.62% for the muscle LD beef, 92.31 % for the muscle RA beef, 100 % for the muscle SM veal and 61.54% for the muscle LD veal.  Auf der Basis von Impedanzspektroskopie wurde ein neuartiges in-vitro-Messverfahren zur Überwachung der Frische von biologischem Gewebe entwickelt. Die wichtigsten Herausforderungen stellen dabei die Reproduzierbarkeit der Impedanzmessung und die Klassifizierung der Gewebeart sowie dessen Zustands dar. Für die Reproduzierbarkeit von Impedanzmessungen an biologischen Geweben, wurde ein zylindrischer Multielektrodensensor realisiert, der die 2D-Anisotropie des Gewebes berücksichtigt und einen guten Kontakt zum Gewebe realisiert. Experimentelle Untersuchungen wurden an verschiedenen Geweben über einen längeren Zeitraum durchgeführt und mittels eines modifizierten Fricke-Cole-Cole-Modells analysiert. Die Ergebnisse sind reproduzierbar und entsprechen dem physikalisch-basierten erwarteten Verhalten. Als Merkmale für die Klassifikation wurden die Modellparameter genutzt

Fuzzy Logic

The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems

Rethinking construction cost overruns: an artificial neural network approach to construction cost estimation

The main concern of a construction client is to procure a facility that is able to meet its functional requirements, of the required quality, and delivered within an acceptable budget and timeframe. The cost aspect of these key performance indicators usually ranks highest. In spite of the importance of cost estimation, it is undeniably neither simple nor straightforward because of the lack of information in the early stages of the project. Construction projects therefore have routinely overrun their estimates. Cost overrun has been attributed to a number of sources including technical error in design, managerial incompetence, risk and uncertainty, suspicions of foul play and even corruption. Furthermore, even though it is accepted that factors such as tendering method, location of project, procurement method or size of project have an effect on likely final cost of a project, it is difficult to establish their measured financial impact. Estimators thus have to rely largely on experience and intuition when preparing initial estimates, often neglecting most of these factors in the final cost build-up. The decision-to-build for most projects is therefore largely based on unrealistic estimates that would inevitably be exceeded. The main aim of this research is to re-examine the sources of cost overrun on construction projects and to develop final cost estimation models that could help in reaching more reliable final cost estimates at the tendering stage of the project. The research identified two predominant schools of thought on the sources of overruns – referred to here as the PsychoStrategists and Evolution Theorists. Another finding was that there is no unanimity on the reference point from which cost performance could be assessed, leading to a large disparity in the size of overruns reported. Another misunderstanding relates to the term “cost overrun” itself. The experimental part of the research, conducted in collaboration with two industry partners, used a combination of non-parametric bootstrapping and ensemble modelling with artificial neural networks to develop final project cost models based on about 1,600 water infrastructure projects. 92% of the validation predictions were within ±10% of the actual final cost of the project. The models will be particularly useful at the pre-contract stage as they will provide a benchmark for evaluating submitted tenders and also allow the quick generation of various alternative solutions for a construction project using what-if scenarios. The original contribution of the study is a fresh thinking of construction “cost overruns”, now proposed to be more appropriately known as “cost growth” based on a synthesises of the two schools of thought into a conceptual model. The second contribution is the development of novel models of construction cost estimation utilising artificial neural networks coupled with bootstrapping and ensemble modelling

Using Neural Networks to Develop a New Model to Screen Applicants for Apartment Rentals

Credit scoring is a mathematical means of summarizing a consumer\u27s credit and financial history into a three-digit number. This number provides an easy means of identifying and sorting consumer behavior into categories based on their financial history. To select applicants for loans and to set interest rates on loans, banks and financial institutions routinely use credit scoring. Auto insurance companies also use scoring to decide which consumers will be offered auto insurance and to set the price for auto insurance. Despite success in these two industries, scoring does not appear to be effective in the apartment rental industry in picking desirable applicants for apartment rental. The first phase of this research analyzed the results of using six commercially available credit scores applied in one apartment complex to the task of selecting applicants. This part of the analysis answered the research question: How effective are commercially available credit scores in predicting applicant financial behavior when renting an apartment? This research determined that these six scores are not predictive and possible explanations are given. Phase two of this research used neural networks to develop a new model using both credit data and other lifestyle data about the applicant. The hypothesis was that the addition of this lifestyle data would improve accuracy in selecting apartment rental applicants over currently available models based only on credit data. This part of the analysis answered the research question: How is the prediction accuracy of a new neural network based credit scoring model improved by adding lifestyle data to the credit report data? This research indicates that accuracy is greatly improved. Three variables were found to be most predictive for the apartment rental decision and these were a) percentage of satisfactory accounts in the applicant\u27s credit file, b) total applicant income, and c) driving record of the applicant. Four areas were suggested for future study and these are a) understanding the underlying human behavior differences that influence apartment financial decisions, b) addition of fuzzy logic techniques to the neural network, c) expanding the number of commercial credit models tested and size of the data set and d) effect of geography on model prediction accuracy. This dissertation also examined U.S. information policy and addressed consumer privacy considerations when using non-credit data to select applicants