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  • Mukminov, Rinat (Svenska handelshögskolan, 2015-01-28)
    In the period from 2007 to 2009 the world experienced the deepest financial crisis since the Great Depression. The world economy was in the most severe recession since the Second World War. The financial crisis was followed by a debt crisis in the euro area, which is still far from being resolved. The world economy is yet to recover from the crisis. The financial crises are recurring phenomena. The financial crisis of 2007-2009 is in many ways similar to the previous crises. It has been argued that banks’ poor screening incentives at the peak of the business cycle are one of the main causes of the recurring crises. Bank screening literature argues that in boom times, when the majority of loan applications are good, the price competition between the banks intensifies, leading to lower returns from screening loan applicants. As a consequence, screening standards decline and many bad loans end up on the bank balance sheets. Defaults of the bad loans lead to a deterioration of the banks’ loan portfolios, which causes credit crunches and bank crises. There is also an emerging finance literature arguing that a lower cost of funds, such as a lower cost of deposits, cheaper credit in the interbank market, a lower discount rate, encourages the banks to take excessive risks. Excessive risk-taking by the banks can also lead to a bank crisis. These two approaches explain excessive bank risk-taking from two different points of view: the former one from the point of view of bank revenues, while the latter approach explains excessive risk-taking from the point of view of bank costs. The aim of this dissertation is to build a bridge between these two approaches. This dissertation contributes to the screening literature by explicitly introducing the cost of funds into a bank screening model. This is novel, as most previous bank screening literature has assumed the deposit market to be fully competitive with zero interest rate, thus ignoring the impact of the deposit interest rate on bank screening incentives. This dissertation also extends the literature, which explores the effects of costs of funds on the bank risk-taking, by explicitly modelling the banks’ investment in screening of potential loan applicants.
  • Löthner, Emelie (Svenska handelshögskolan, 2011-08-09)
  • Solitander, Nikodemus (Svenska handelshögskolan, 2011-05-17)
    The thesis focuses on one of the most dominant articulations of the relation between geographical place and development, clusters - internationally competing place-bound economic system of production in related industries. The dominant articulation of cluster discourse represents the subnational region as a system of production, and as a means for competitiveness for Western countries. Its reproduction in theories has become one of the most prolific exports of economic geography to other disciplines and for policymaking. By analysing cluster discourse the thesis traces how the languages and processes of globalization have over time altered the understandings of the relation between geographical place and the economy. It shows how in its latest incarnation of the cluster discourse, the language of mainstream economics is combined with ‘softer’ elements (e.g. community, learning, creativity) in the economic geographic discourse. This is typical for the idea of soft capitalism, wherein it is assumed that economic success emanates from soft characteristics, such as knowledge, learning and creativity, rather than straightforward technological or cost advantages. A reoccurring critique against the dominant understanding of the relationship between competitiveness and regions, as articulated in cluster discourse, has pinpointed the perspective’s inability to reconcile the respective and reciprocal roles of local standard of living with firm competitiveness. The thesis traces how such critique is increasingly appropriated through the fusion of the economic, social and cultural landscape into the language of capitalism. It shows how cluster discourse has appropriated its critique, by focusing on creativity, with its strong associations to arts, individual artists and the cultural sphere in general, while predominantly creating its meaning in relation to competitiveness. The thesis consists of six essays that each outlines the development of the cluster discourse. The essays show how meaning systems and strategies are created, accepted and naturalized in cluster discourse, how this affects individuals, the economic landscape and society at large, as well as showing which understandings are marginalized in the process. The thesis argues that clusters are a) inseparable from ideology and politics and b) they are the result of purposeful social practice. It calls for increased reflexivity within corporate and economic geographic research on clusters, and underlines the importance of placing issues of power at the centre of analysis.
  • Kovacs, Gyöngyi; Matopoulos, Aristides; Hayes, Odran (IGI Global, 2012)
  • Ehrnrooth, Charlotte (Hanken School of Economics, 2010-10-06T13:00:06Z)
  • Vuorenmaa, Anna-Kaisa (Svenska handelshögskolan, 2015-08-03)
  • Viitala, Matias (Hanken School of Economics, 2010-05-04T06:02:50Z)
  • Höglund, Henrik (Svenska handelshögskolan, 2010-11-29)
    Detecting Earnings Management Using Neural Networks. Trying to balance between relevant and reliable accounting data, generally accepted accounting principles (GAAP) allow, to some extent, the company management to use their judgment and to make subjective assessments when preparing financial statements. The opportunistic use of the discretion in financial reporting is called earnings management. There have been a considerable number of suggestions of methods for detecting accrual based earnings management. A majority of these methods are based on linear regression. The problem with using linear regression is that a linear relationship between the dependent variable and the independent variables must be assumed. However, previous research has shown that the relationship between accruals and some of the explanatory variables, such as company performance, is non-linear. An alternative to linear regression, which can handle non-linear relationships, is neural networks. The type of neural network used in this study is the feed-forward back-propagation neural network. Three neural network-based models are compared with four commonly used linear regression-based earnings management detection models. All seven models are based on the earnings management detection model presented by Jones (1991). The performance of the models is assessed in three steps. First, a random data set of companies is used. Second, the discretionary accruals from the random data set are ranked according to six different variables. The discretionary accruals in the highest and lowest quartiles for these six variables are then compared. Third, a data set containing simulated earnings management is used. Both expense and revenue manipulation ranging between -5% and 5% of lagged total assets is simulated. Furthermore, two neural network-based models and two linear regression-based models are used with a data set containing financial statement data from 110 failed companies. Overall, the results show that the linear regression-based models, except for the model using a piecewise linear approach, produce biased estimates of discretionary accruals. The neural network-based model with the original Jones model variables and the neural network-based model augmented with ROA as an independent variable, however, perform well in all three steps. Especially in the second step, where the highest and lowest quartiles of ranked discretionary accruals are examined, the neural network-based model augmented with ROA as an independent variable outperforms the other models.
  • Huopalainen, Lasse Wilhelm (Svenska handelshögskolan, 2013-08-05)
  • Eriksson, Fanny (Hanken School of Economics, 2010-04-29T08:39:28Z)