Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks
We develop a model of neural networks to study the bankruptcy of U.S. banks, taking into account the specific features of the recent financial crisis. We combine multilayer perceptrons and self-organizing maps to provide a tool that displays the probability of distress up to three years before bankruptcy occurs. Based on data from the Federal Deposit Insurance Corporation between 2002 and 2012, our results show that failed banks are more concentrated in real estate loans and have more provisions. Their situation is partially due to risky expansion, which results in less equity and interest income. After drawing the profile of distressed banks, we develop a model to detect failures and a tool to assess bank risk in the short, medium and long term using bankruptcies that occurred from May 2012 to December 2013 in U.S. banks. The model can detect 96.15% of the failures in this period and outperforms traditional models of bankruptcy prediction.
The paper analyses how the individuals' deposits influences the resources of Russian banks. We show that the depositors panic in the crisis has a serious effect on stability of both bank and national bank system. We show the tendencies how the volume and structure of individuals' deposits change; how to avoid the rash of withdrawals by individual depositors; and how the resources of Russian banks shrank because of such withdrawals happened in the period of the crisis. We also present our assessment of how the resources of Russian banks reduced because of the rash of withdrawals in the crisis.
We proposed the nonlinear dynamic model of the formation of the market prices of precious metals based on the econophysic considerations. This model is a system of three ordinary differential equations relating the time dependence of elasticity, variations of bid and ask prices; it is similar to the Lorenz system. The areas of the dynamic stochasticity in experimental data were found with the comparing of the experimental and the theoretical ask and bid prices. These areas are the precursors of the crisis mode in the form of dynamic chaos.
This book constitutes the refereed proceedings of the 6th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2014, held in Montreal, QC, Canada, in October 2014. The 24 revised full papers presented were carefully reviewed and selected from 37 submissions for inclusion in this volume. They cover a large range of topics in the field of learning algorithms and architectures and discussing the latest research, results, and ideas in these areas.
The Group of Eight (G8) has had extensive and even existential experience with financial crises (Kirton 2007). The groups creation was driven by financial crises created by and in the US, in the form of the Nixon Administration’s unilateral destruction of the Bretton Woods system of fixed exchange rates on August 15, 1971 and the imminent bankruptcy of New York City at the time of the first summit at Rambouillet in November 1975. Then came a succession of real and potential crises, notably Britain’s need for support from the International Monetary Fund (IMF) in the mid 1970s and Italy’s need in 1976, the developing countries debt crisis of the early 1980s, the American stock market plunge of October 1987, the attack on the European Monetary System (EMS), the Mexican peso crisis starting on December 20, 1994, the Asian-turned-global financial crisis of 1997–1999, the 9/11 terrorist attacks on America, the Enron–dot.com bust and the America-turned-global financial crisis from 2008 to now. Since the G8’s 1975 start, such crises have been created by others to afflict a vulnerable America, and been created by America to attack the rest of the world. In both cases such crisis have been conscious, calculated controlled and targeted, as on August 15, 1971 and September 11, 2001, and unco.nscious, uncalculated, uncontrolled and untargeted events characterized by contagion, complexity and uncertainty that no one can fully comprehend, as in the global crisis from 2008 until now.
The paper theorizes on the general architectonics of idealized cognitive models (ICMs) and their involvement in metonymy and metaphor. The article posits that an ICM's structure should reflect the architecture of the neural network/s engaged in processing of a given concept. The ICM nodes, or cogs, construct a complex, hierarchically organized neural connections, with the superior nodes being highly selective, invariant and prototypical. Signals travelling from one cog to another within one ICM are essentially metonymical, while a cog shared by two or more ICMs marks a metaphoric shift.
This article provides the results of development of bankruptcy prediction static model and its testing on the sample of more than thousand companies of manufacturing industry. The main scenarios of bankruptcy are identified and it is shown that depending on the bankruptcy scenario possible insolvency can be predicted one or four years before.
This book constitutes the refereed proceedings of the 12th Industrial Conference on Data Mining, ICDM 2012, held in Berlin, Germany in July 2012. The 22 revised full papers presented were carefully reviewed and selected from 97 submissions. The papers are organized in topical sections on data mining in medicine and biology; data mining for energy industry; data mining in traffic and logistic; data mining in telecommunication; data mining in engineering; theory in data mining; theory in data mining: clustering; theory in data mining: association rule mining and decision rule mining.
The paper examines the structure, governance, and balance sheets of state-controlled banks in Russia, which accounted for over 55 percent of the total assets in the country's banking system in early 2012. The author offers a credible estimate of the size of the country's state banking sector by including banks that are indirectly owned by public organizations. Contrary to some predictions based on the theoretical literature on economic transition, he explains the relatively high profitability and efficiency of Russian state-controlled banks by pointing to their competitive position in such functions as acquisition and disposal of assets on behalf of the government. Also suggested in the paper is a different way of looking at market concentration in Russia (by consolidating the market shares of core state-controlled banks), which produces a picture of a more concentrated market than officially reported. Lastly, one of the author's interesting conclusions is that China provides a better benchmark than the formerly centrally planned economies of Central and Eastern Europe by which to assess the viability of state ownership of banks in Russia and to evaluate the country's banking sector.
The paper examines the principles for the supervision of financial conglomerates proposed by BCBS in the consultative document published in December 2011. Moreover, the article proposes a number of suggestions worked out by the authors within the HSE research team.