Bank Failures in the Major Trading Countries of the World: Causes and Remedies
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Most research papers on bank failures are based on financial market models, that originate from the seminal paper of [ 3 ], in which the market value of bank assets, typically modelled as a diffusion process, is matched against bank liabilities. The literature on systemic risk is very recent and follows closely the developments of the financial crisis, started in A comprehensive review is provided in [ 5 ]. Specific measures of systemic risk have been proposed, in particular, by [ 5 ] and [ 6 ]. All of these approaches are built on financial market data, on the basis of which they lead to the estimation of appropriate quantiles of the estimated loss probability distribution of a financial institution, conditional on a crash event on the financial market.
A different approach, explicitly geared towards estimation of the interrelationships among all institutions, is based on network models, and has been proposed in [ 7 ].
Here we shall follow this latter approach, and add a stochastic framework, based on graphical Gaussian models. We will thus be able to derive, on the basis of market price data on a number of financial institutions, the network model that best describes their interrelationships and, therefore, explains how systemic risk is transmitted among them. All models described so far, both in failure estimation as well in systemic risk modelling, are based on financial market data. Market data are relatively easy to collect, are public, and are quite objective.
On the other hand, they may not reflect the true fundamentals of the underlying financial institutions, and may lead to a biased estimation of the probability of failure. This bias may be stronger when the probability of multiple failures are to be estimated, as it occurs in systemic risk. Indeed, the recent paper by Hirsch [ 8 ] shows that market models are not much reliable in predictive terms.
More generally, it is well known that market prices are formed in complex interaction mechanisms that, often, reflect speculative behaviours rather than the fundamentals of the companies to which they refer. This weakness of the market suggests to enrich financial market data with data coming from other, complementary, sources. Indeed, market prices are only one of the evaluations that are carried out on financial institutions: other relevant ones include ratings issued by rating agencies, reports of qualified financial analysts, and opinions of influential media.
Most of the previous sources are private, not available for data analysis.
Big data analysis for financial risk management
However, summary reports from them are now typically reported, almost in real time, in social networks and, in particular, in financial tweets. To extract from tweets data that can be assimilated to market prices, their text has to be preprocessed using semantic analysis techniques. In this paper we propose how to select and model semantic based tweet data, so to compare and integrate them with market data, within the framework of graphical network models.
We also introduce a criteria, the T-index, aimed at selecting in advance the most relevant twitter sources, to avoid using non-informative data that may distort the results. The novelty of this paper is twofold. From a methodological viewpoint, we propose a framework that can estimate systemic risks with models based on two different sources: financial markets and financial tweets, and suggest a way to combine them, using a Bayesian approach.
In this section we introduce our proposal. First we describe a methodology able to select in advance tweets, based on the H-index proposed by Hirsch [ 8 ], employed to measure research impact, for which a stochastic version has been proposed by Cerchiello [ 9 ]. The h-index is employed in the bibliometric literature as a merely descriptive measure, that can be used to rank scientists or institutions where scientists work. A similar ranking can be achieved for tweeterers; however the stochastic variability surrounding tweet citations retweets is greater than that of paper citations.
Here we briefly recall such methodology that we will use in the following. In the context of research impact measurement, the n random variables are the citations of the n papers of a given scientist.
Beirlant [ 10 ] and Pratelli [ 11 ], among other contributions, assume that F is continuous, at least asymptotically, even if retweet counts have support on the integer set. Having introduced a method aimed at selecting financial tweets, we now introduce the graphical network models that will be used to estimate relationships between N banks, both with market and tweet data.
Relationships between banks can be measured by their partial correlation, that expresses the direct influence of a bank on another. Stochastic inference in graphical models may lead to two different types of learning: structural learning, which leads to the estimation of the graphical structure G that best describes the data and quantitative learning, that aims at estimating the parameters of a graphical model, for a given graph. In the systemic risk framework, we are mainly interested in structural learning.
Structural learning can be achieved by choosing the graphical structure with maximal likelihood, or its penalised versions, such as AIC and BIC. Here we follow the backward selection procedure implemented in the software R and, specifically, in the function glasso from package glasso.
For the aim of strcutural learning, we now recall the expression of the likelihood of a graphical Gaussian model. For a complete fully connected graph such an estimator is simply the observed variance-covariance matrix. For a general decomposable incomplete graph, an iterative procedure, based on the clique and separators of a graph, must be undertaken see e.
Through model selection, we obtain a graphical model that can be used to describe relationships between banks and, specifically, to understand how risks propagate in a systemic risk perspective. More precisely, in our context, we select one graphical model for each given data source: one from market data and one from tweet data. Besides comparing the two models, it is quite natural to aim at integrating them into a single model.
This task can be achieved within a Bayesian framework, as follows. When the graph G is not complete, a similar result holds locally, at the level of each clique and separator. In this section we consider the application of our proposed methodology. For reasons of information homogeneity we concentrate on a single banking market: the Italian banking system, a very interesting context, characterised by a large number of important banks, dominating the economy of the country, in a rapidly changing environment. We focus on large banks that are listed, for which there exist daily financial market data, that we would like to compare and integrate with tweet data.
For the same period, we have crawled Twitter, using the software TwitteR, available open source within the R project environment, and chosen all tweets that contain, besides one of the banks in Table 1 , a keyword belonging to a financial taxonomy, that we have built, based on our knowledge of which balance sheet information may affect systemic risk. Each obtained tweet has then been elaborated by a commercial partner of ours, Expert System, that has transformed each tweet into a sentiment class, with categories ranging from 1 to 5. Such categories are associated to tweets on the basis of a semantic analysis that allows a text to be automatically processed on the basis of codified rules based the experience of our partner company in business textual analysis.
The higher the category, the more positive the sentiment or value that the tweet assigns to the bank under analysis. From Table 4 note that correlations are low, especially for smaller banks, that have less tweet information, and this was expected. A detailed inspection of each bank tweet data reveals that banks of similar size show a higher correlation when more information is disclosed.
The graph in Fig.
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In terms of a systemic risk framework, the first group is more central than the second and, in particular, BPE is the most central, followed by UCG and MB. By one-fifth of the banks in existence in had failed, leading the new Franklin D. The natural consequence of widespread bank failures was to decrease consumer spending and business investment, because there were fewer banks to lend money. There was also less money to lend, partly because people were hoarding it in the form of cash.
According to some scholars, that problem was exacerbated by the Federal Reserve , which raised interest rates further depressing lending and deliberately reduced the money supply in the belief that doing so was necessary to maintain the gold standard see below , by which the U. The reduced money supply in turn reduced prices, which further discouraged lending and investment because people feared that future wages and profits would not be sufficient to cover loan payments.
Bank failures in the major trading countries of the world : causes and remedies - Semantic Scholar
The gold standard. Whatever its effects on the money supply in the United States, the gold standard unquestionably played a role in the spread of the Great Depression from the United States to other countries. As the United States experienced declining output and deflation, it tended to run a trade surplus with other countries because Americans were buying fewer imported goods, while American exports were relatively cheap.
Such imbalances gave rise to significant foreign gold outflows to the United States, which in turn threatened to devalue the currencies of the countries whose gold reserves had been depleted. Accordingly, foreign central banks attempted to counteract the trade imbalance by raising their interest rates, which had the effect of reducing output and prices and increasing unemployment in their countries.
The resulting international economic decline, especially in Europe, was nearly as bad as that in the United States. Reinhart, Carmen, and Kenneth Rogoff.
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Financial market failures
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