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

Pattern Classification of Signals Using Fisher Kernels

The intention of this study is to gauge the performance of Fisher kernels for dimension simplification and classification of time-series signals. Our research work has indicated that Fisher kernels have shown substantial improvement in signal classification by enabling clearer pattern visualization in three-dimensional space. In this paper, we will exhibit the performance of Fisher kernels for two domains: financial and biomedical. The financial domain study involves identifying the possibility of collapse or survival of a company trading in the stock market. For assessing the fate of each company, we have collected financial time-series composed of weekly closing stock prices in a common time frame, using Thomson Datastream software. The biomedical domain study involves knee signals collected using the vibration arthrometry technique. This study uses the severity of cartilage degeneration for classifying normal and abnormal knee joints. In both studies, we apply Fisher Kernels incorporated with a Gaussian mixture model (GMM) for dimension transformation into feature space, which is created as a three-dimensional plot for visualization and for further classification using support vector machines. From our experiments we observe that Fisher Kernel usage fits really well for both kinds of signals, with low classification error rates

Real-time big data processing for anomaly detection : a survey

The advent of connected devices and omnipresence of Internet have paved way for intruders to attack networks, which leads to cyber-attack, financial loss, information theft in healthcare, and cyber war. Hence, network security analytics has become an important area of concern and has gained intensive attention among researchers, off late, specifically in the domain of anomaly detection in network, which is considered crucial for network security. However, preliminary investigations have revealed that the existing approaches to detect anomalies in network are not effective enough, particularly to detect them in real time. The reason for the inefficacy of current approaches is mainly due the amassment of massive volumes of data though the connected devices. Therefore, it is crucial to propose a framework that effectively handles real time big data processing and detect anomalies in networks. In this regard, this paper attempts to address the issue of detecting anomalies in real time. Respectively, this paper has surveyed the state-of-the-art real-time big data processing technologies related to anomaly detection and the vital characteristics of associated machine learning algorithms. This paper begins with the explanation of essential contexts and taxonomy of real-time big data processing, anomalous detection, and machine learning algorithms, followed by the review of big data processing technologies. Finally, the identified research challenges of real-time big data processing in anomaly detection are discussed. © 2018 Elsevier Lt

Riding the Biotechnology Wave: A Mixed-Methods Analysis of Malaysia's Emerging Biotechnology Industry

Building a sustainable bioeconomy requires strategic alliances, intellectual property,funding and talent. The research focus of this empirical study was to assess Malaysian biotechnology companies regarding their opinions on priorities and capabilities necessary to establish a thriving bioeconomy. The research questions that form the basis of this paper explore the extent to which initial factor endowments affect the trajectory of biotechnology industry development and how Malaysia should prioritise, mobilise and coordinate resources to build a bioeconomy. A mixed methods approach using qualitative interviews and case studies, as well as a quantitative survey, indicated that respondents advocated a resource-based-view in terms of resource allocation and agglomeration towards building Malaysia's bioecnomy. That is, there was strong support to leverage Malaysia's existing capabilities in agriculture and biofuels to derive value-added products towards gaining leadership positions in these respective biotechnology sectors globally. Access to funding and talent emerged as the highest priority capabilities necessary for commercialising discoveries, conducting research and development and accelerating innovation. Respondents perceived the government as having a 'very important' role in building and accelerating the Malaysian biotechnology industry. The gap between required capabilities and strategic priorities provides a framework within which the government may play a central role in coordinate, accelerating and resourcing Malaysia's nascent bioeconomy

Riding the Biotechnology Wave: A Mixed-Methods Analysis of Malaysia's Emerging Biotechnology Industry

Building a sustainable bioeconomy requires strategic alliances, intellectual property,funding and talent. The research focus of this empirical study was to assess Malaysian biotechnology companies regarding their opinions on priorities and capabilities necessary to establish a thriving bioeconomy. The research questions that form the basis of this paper explore the extent to which initial factor endowments affect the trajectory of biotechnology industry development and how Malaysia should prioritise, mobilise and coordinate resources to build a bioeconomy. A mixed methods approach using qualitative interviews and case studies, as well as a quantitative survey, indicated that respondents advocated a resource-based-view in terms of resource allocation and agglomeration towards building Malaysia's bioecnomy. That is, there was strong support to leverage Malaysia's existing capabilities in agriculture and biofuels to derive value-added products towards gaining leadership positions in these respective biotechnology sectors globally. Access to funding and talent emerged as the highest priority capabilities necessary for commercialising discoveries, conducting research and development and accelerating innovation. Respondents perceived the government as having a 'very important' role in building and accelerating the Malaysian biotechnology industry. The gap between required capabilities and strategic priorities provides a framework within which the government may play a central role in coordinate, accelerating and resourcing Malaysia's nascent bioeconomy