Using clustering ensemble to identify banking business models

The business models of banks are often seen as the result of a variety of simultaneously determined managerial choices, such as those regarding the types of activities, funding sources, level of diversification, and size. Moreover, owing to the fuzziness of data and the possibility that some banks may combine features of different business models, the use of hard clustering methods has often led to poorly identified business models. In this paper we propose a framework to deal with these challenges based on an ensemble of three unsupervised clustering methods to identify banking business models: fuzzy c‐means (which allows us to handle fuzzy clustering), self‐organizing maps (which yield intuitive visual representations of the clusters), and partitioning around medoids (which circumvents the presence of data outliers). We set up our analysis in the context of the European banking sector, which has seen its regulators increasingly focused on examining the business models of supervised entities in the aftermath of the twin financial crises. In our empirical application, we find evidence of four distinct banking business models and further distinguish between banks with a clearly defined business model (core banks) and others (non‐core banks), as well as banks with a stable business model over time (persistent banks) and others (non‐persistent banks). Our proposed framework performs well under several robustness checks related with the sample, clustering methods, and variables used.     

Density estimation in the uniform deconvolution model

We consider the problem of estimating a probability density function based on data that are corrupted by noise from a uniform distribution. The (nonparametric) maximum likelihood estimator for the corresponding distribution function is well defined. For the density function this is not the case. We study two nonparametric estimators for this density. The first is a type of kernel density estimate based on the empirical distribution function of the observable data. The second is a kernel density estimate based on the MLE of the distribution function of the unobservable (uncorrupted) data.     

An examination of the effects of management earnings forecast form and explanations on financial analyst forecast revisions

Using a sample of 978 quarterly management earnings-per-share forecasts made during the period 1993 to 1999, we document that financial analyst revisions to management earnings forecasts are a function of management forecast form. More precise forecasts (measured three different ways) lead to greater revision of financial analyst consensus EPS forecasts for a given level of unexpected earnings as predicted by Kim and Verrecchia (1991) and Bayesian adjustment models. Also, consistent with our arguments, maximum forecasts are interpreted as bad news by analysts. Our results, while consistent with theory, are inconsistent with recent experimental studies which do not reject the null hypothesis of no effect of management earnings forecast form on the association between unexpected earnings and financial analyst forecast revisions. We also re-examine Baginski, Hassell, and Kimbrough's (2004) finding that attributions used to explain management forecasts affect the reaction to the forecast using analyst data. Consistent with their findings using stock prices, the attribution presence (especially external attributions) increases financial analyst revisions pursuant to management forecasts.     

The dynamics of economic systems,or how to transform a failed socialist economy

How to transform a failed socialist economy raises questions about the processes of structural and institutional changes, which standard economics is inadequately equipped to study. An economic theory that can study both these processes is suggested and applied to the actual debate of how the transformation should be conducted. Standard notions of institutional roles (R) and organizational structures (S) are seen in a non-standard way as outcomes of two interrelated but distinct evolutions. Comparing different given Rs for their influence on S-evolution throws new light on which R should be the target of the transformation. Compared to standard analysis, the case against socialism on the supply side is strengthened, whereas the case against a redistributive welfare society on the demand side is weakened. S-evolution is shown to require more time and be more difficult to be helped by government than R-evolution. This clarifies several controversial points about the speed and the sequencing of the transformation policies. The study of R-evolution discloses an important cognitive constraint on these policies, which teaching of economics can help to soften. Standard economics, shown to be a special case of the suggested theory, is rehabilitated as the best subject by which such teaching should start.     

Trade and Industry Policy

This article consists of a review of changes in trade and industry policy, along with discussions of the determinants of change and the major problem areas. Policy between 1965 and 1985 is summarised, and the growth of unemployment in the 1970s is suggested as a key determinant of change. Higher protection in certain industries, and moves to more quantitative methods ot protection can be related to unemployment, in Australia as in other countries. Theory and empirical evidence suggest that protection is likely to prove ineffective in reducing unemployment. The reliance on protection in particular cases is ascribed to a wish by governments to preserve specific jobs, not to increase overall employment. Policy can have a role in facilitating or restricting structural change which can arise from several sources, such as changing trade patterns and preferences. The devices of policy are considered briefly to determine whether they are likely to facilitate change or not. It is concluded that they often provide incentives to avoid change. The scale problem is considered, and it is suggested that the cost of protection could be high in industries subject to significant scale economies. Scale and adjustments are considered in the light of two industries, motor vehicles and whitegoods, and the role of industry plans is examined. The actuality of trade and industry policy often differs from announced intentions, and this ambiguity can give rise to uncertainty which itself has a cost.     

The Short‐term Determinants of Capital Flows for a Small Open Economy: The Case of Greece

This paper presents the factors driving capital flows inwards and outwards of a small open economy such as Greece during the period 1983–2009. Apart from the determinants of capital flows the possibility of speculative attacks is also considered. Applying the ordinary least squares (OLS) method it is found that variables linked to external factors such as the differential of domestic and foreign interest rates and financial crises, such as the Asian and Russian financial turmoil of 1997 and 1998 respectively, are the main factors influencing capital inflows and outflows in Greece during the period 1983–2009. Additionally the results of probit analysis support that the same factors are significant in determining the probability of speculative attacks to the Greek economy.     

Rustem,B., and Howe,M.: Algorithms for Worst-case Design and Applications to Risk Management. XV, 389 pp. Princeton University Press,Princeton, New Jersey. 2002. Hardcover £ 52.00.

Journal of Economics -     

Foreign direct investment and technical progress in Spanish manufacturing

This article analyses the effects of Foreign Direct Investment (FDI) on technical progress in Spanish manufacturing. Particularly, we study how FDI's contributions vary depending on the economic structure of the industry. The results show that most FDI goes to capital-intensive sectors, especially when those sectors are also Research and Development (R&D)-intensive. Our estimates of the Solow residual show that the positive effect of contemporaneous and lagged FDI on manufacturing productivity is only attributable to capital and R&D intensive industries in what seems to be related to a dynamic capabilities explanation or to complementarities with R&D expenditures.