A study of Taiwan's issuer credit rating systems using support vector machines

Chen, Wun-Hwa; Shih, Jen-Ying

doi:10.1016/j.eswa.2005.10.003

Cited by 93 publications

(38 citation statements)

References 24 publications

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“…The evolution of the neural network yields information on banking failures through decaying processes due to loss of information. Secondly, support vector machines (SVM) are used to model risk in financial markets [30,103]. SVM is a type of learning machine based on statistical learning theory.…”

Section: Econobiology Modeling Of the Financial Systemmentioning

confidence: 99%

Complexity Analysis and Systemic Risk in Finance: Some Methodological Issues

Mertzanis

2014

Network Models in Economics and Finance

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The standard financial analysis has proven unable to provide an adequate understanding and therefore a timely warning of the financial crisis. In order to strengthen financial stability, policy makers are looking for new analytical tools to identify and address sources of systemic risk. Complexity theory and network analysis can make a useful contribution. The financial crisis has highlighted the need to look at the links and interconnections in the financial system. Complexity and network theory which can help identify the extent to which the financial system is resilient to contagion as well as the nature of major triggers and channels of contagion. However, the methodological suitability of the premises of complexity theory for financial systems is still debatable. The use of complexity analysis in finance draws on two distinct but related strands of theory: econophysics and econobiology. Each strand is associated with advantages and drawbacks in explaining the dynamics of financial systems. Properly combined, these theories could form a coherent body of theoretical premises that are capable of approximating reality in financial systems, i.e. explain the "stylized facts", better than the traditional financial analysis model, which is crucially based on the false conception of a Gaussian distribution of financial returns.

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Section: Econobiology Modeling Of the Financial Systemmentioning

confidence: 99%

Complexity Analysis and Systemic Risk in Finance: Some Methodological Issues

Mertzanis

2014

Network Models in Economics and Finance

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“…Thus, this study prefers a grid-search on (C, k) using fivefold cross-validation. More detailed descriptions on cross-validation could be obtained from Chen & Shih (2006), Ding et al (2008), Li & Sun (2009a). During the optimization process, the value of (C, k) is increasing with the index grade by reference the relevant empirical research (Lee, 2007), let C = 2 À5 , 2 À3 , .…”

Section: Parameter Optimizationmentioning

confidence: 99%

Integration of similarity measurement and dynamic SVM for electrically evoked potentials prediction in visual prostheses research

Peng

et al. 2011

Expert Systems with Applications

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“…ecological modeling [30], evaluation of consumer loans [31], studying credit rating systems [32], bank performance prediction [33], bankruptcy predictions [34], financial forecasting [35,36]. Some such applications in the construction engineering and management domains include slope reliability analysis [37], studying settlement of shallow foundations [38], supply chain demand forecasting [39], model induction [40], document classification for information systems [41] information integration and situation assessment [42] and conceptual cost estimates in construction projects [43].…”

Section: Introductionmentioning

confidence: 99%