372 research outputs found
Gauge theory approach to glass transitions
This theory combines a thermodynamic approach with a dynamic one in order to
describe glass transition. Glass transition is regarded as an inaccessible
second order phase transition, which is interrupted because of premature
critical slowing down, caused by the system's frustration. The
frustration-induced vortices are present in the structure besides
thermoactivated vortices, and prevent the development of the order parameter
fluctuations, that leads to the critical slowing down the system kinetics at
some temperature above the phase transition point
Reconstruction of smooth and discontinuous components of solutions to linear ill-posed problems
Description of paramagnetic--spin glass transition in Edwards-Anderson model in terms of critical dynamics
Possibility of description of the glass transition in terms of critical
dynamics considering a hierarchy of the intermodal relaxation time is shown.
The generalized Vogel-Fulcher law for the system relaxation time is derived in
terms of this approach. It is shown that the system satisfies the
fluctuating--dissipative theorem in case of the absence of the intermodal
relaxation time hierarchy.Comment: 10 pages, 6 figure
The need for disruption in the credit ratings landscape : a model for machine learning computed credit ratings.
I present the results from the research on the topics of (1) credit ratings, which are usually
provided by credit rating agencies, and (2) Artificial Intelligence and Machine Learning as a
form of solving classification tasks, such as credit ratings, without the involvement of human
experts. My research problem is stated as follows: to improve the solutions for the credit
rating problem introduced by other credit rating agencies, I propose a rating system in the
form of an expert system. Then I show that this system is more efficient than traditional rating
systems on different hold-out samples of large-scale, multi-period data for public nonfinancial
corporate entities worldwide, and with respect to different forecasting horizons. I
show that my rating system, which is based on an ensemble machine learning method,
specifically Gradient Boosted Decision Trees, when applied to the rating process, outperforms
incumbent rating systems on the accuracy-stability scale measured by a compound metric
Index of the Quality of Ratings, which I develop and introduce.
In the course of the research in addition to the topic of rating performance evaluation,
I have included the comparison of market-implied ratings with fundamental ratings, ratings
forecasting and replication, mapping of ratings of different providers to the universal scale,
financial effects of qualitative ratings for the investors, the stability of ratings, and the cyclical
effects of ratings. The novelty is in the amount of data that I used, including the number and
diversification of the rated entities, also in the number of other rating providers involved in
performance comparison tests and the number of optional models built and tested. I have
shown performance results for different forecasting horizons. The complexity of the proposed
model, its iterative revisions throughout the estimation periods, as well as mapping of ratings
directly through the default ratios, also mark out my research.
The significance of the research is in showing a more reliable, hi-tech, cost- and timeeffective
solution for the problem of credit risk assessment for financial markets participants,
who now rely upon the opinion of credit rating agencies. The key output of the research is
therefore to re-imagine the credit ratings according to modern advances in finance, datascience,
information technology and software. The results of my analysis can be used as a
starting point or proxy for choosing the optimal rating agency for investor’s needs, as a stepby-
step manual to develop a rating system, as a benchmark for the regulation of rating
agencies, or when discussing the quality of ratings in academic and financial papers
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