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This study analyzes information production and trading behavior of banks with lending relationships. We combine trade-by-trade supervisory data and credit-registry data to examine banks' proprietary trading in borrower stocks around a large number of corporate events. We find that relationship banks build up positive (negative) trading positions in the two weeks before events with positive (negative) news, even when these events are unscheduled, and unwind positions shortly after the event. This trading pattern is more pronounced in situations when banks are likely to possess private information about their borrowers, and cannot be explained by specialized expertise in certain industries or certain firms. The results suggest that banks' lending relationships inform their trading and underscore the potential for conflicts of interest in universal banking, which have been a prominent concern in the regulatory debate for a long time. Our analysis illustrates how combining large data sets can uncover unusual trading patterns and enhance the supervision of financial institutions.
In this study, we analyze the trading behavior of banks with lending relationships. We combine detailed German data on banks’ proprietary trading and market making with lending information from the credit register and then examine how banks trade stocks of their borrowers around important corporate events. We find that banks trade more frequently and also profitably ahead of events when they are the main lender (or relationship bank) for the borrower. Specifically, we show that relationship banks are more likely to build up positive (negative) trading positions in the two weeks before positive (negative) news events, and also that they unwind these positions shortly after the event. This trading pattern is more pronounced for unscheduled earnings events, M&A transactions, and after borrower obtain new bank loans. Our results suggest that lending relationships endow banks with important information, highlighting the potential for conflicts of interest in banking, which has been a prominent concern in the regulatory debate.
Am 6. Februar 2013 hat die Bundesregierung den "Entwurf eines Gesetzes zur Abschirmung von Risiken und zur Planung der Sanierung und Abwicklung von Kreditinstituten und Finanzgruppen” veröffentlicht. Artikel 2 des Gesetzesentwurfs sieht vor bei systemrelevanten Finanzinstitutionen das Einlagen- und Kreditgeschäft vom Handelsgeschäft abzutrennen. Die Zielsetzung des Gesetzentwurfs, Kapitalkosten wieder in direkte Abhängigkeit des Risikos von Geschäftsfeldern zu setzen und eine Abwicklung zu erleichtern, die ohne den Einsatz von Steuermitteln gelingen kann, ist begrüßenswert. In seiner derzeitigen Ausgestaltung läuft der Gesetzesentwurf jedoch Gefahr, zwar symbolträchtig zu sein, aber in der Zielerreichung hinsichtlich Stabilität des Finanzmarktes und Schutz von Einlegern und Steuerzahlern hinter den Erwartungen zurückzubleiben.
Das Ergebnis des Volksentscheids im Vereinigten Königreich ist ein Weckruf. Alle Entscheidungsträger der Europäischen Union und ihrer Mitgliedstaaten sind aufgerufen, grundlegende Reformen der Verfassung einer Europäischen Union, möglicherweise nur noch einer europäischen „Kontinentalunion“ unverzüglich in Angriff zu nehmen. Unverzüglich bedeutet, einen Reformprozess nicht erst dann zu beginnen, wenn die Verhandlungen über ein Austrittsabkommen beendet worden sind. Eine Rückentwicklung der Europäischen Union zu einer bloßen Wirtschaftsgemeinschaft dürfte dabei keine Lösung sein. Es ist jetzt angezeigt, offen und – notfalls kontrovers – zu diskutieren, wie ein künftiger Bundesstaat auf europäischer Ebene aussehen könnte.
We theoretically and empirically study large-scale portfolio allocation problems when transaction costs are taken into account in the optimization problem. We show that transaction costs act on the one hand as a turnover penalization and on the other hand as a regularization, which shrinks the covariance matrix. As an empirical framework, we propose a flexible econometric setting for portfolio optimization under transaction costs, which incorporates parameter uncertainty and combines predictive distributions of individual models using optimal prediction pooling. We consider predictive distributions resulting from highfrequency based covariance matrix estimates, daily stochastic volatility factor models and regularized rolling window covariance estimates, among others. Using data capturing several hundred Nasdaq stocks over more than 10 years, we illustrate that transaction cost regularization (even to small extent) is crucial in order to produce allocations with positive Sharpe ratios. We moreover show that performance differences between individual models decline when transaction costs are considered. Nevertheless, it turns out that adaptive mixtures based on high-frequency and low-frequency information yield the highest performance. Portfolio bootstrap reveals that naive 1=N-allocations and global minimum variance allocations (with and without short sales constraints) are significantly outperformed in terms of Sharpe ratios and utility gains.
We analytically characterize optimal monetary policy for an augmented New Keynesian model with a housing sector. In a setting where the private sector has rational expectations about future housing prices and inflation, optimal monetary policy can be characterized without making reference to housing price developments: commitment to a 'target criterion' that refers to inflation and the output gap only is optimal, as in the standard model without a housing sector. When the policymaker is concerned with potential departures of private sector expectations from rational ones and seeks to choose a policy that is robust against such possible departures, then the optimal target criterion must also depend on housing prices. In the empirically realistic case where housing is subsidized and where monopoly power causes output to fall short of its optimal level, the robustly optimal target criterion requires the central bank to 'lean against' housing prices: following unexpected housing price increases, policy should adopt a stance that is projected to undershoot its normal targets for inflation and the output gap, and similarly aim to overshoot those targets in the case of unexpected declines in housing prices. The robustly optimal target criterion does not require that policy distinguish between 'fundamental' and 'non-fundamental' movements in housing prices.
We offer evidence of a new stylized feature of corporate financing decisions: the tendency of managers to rely more on debt financing when earnings prospects are poor. We term this 'leaning against the wind' and consider three possible explanations: market timing, precautionary financing, and 'making the numbers'. We find no evidence in favor of the first two hypotheses, and provisionally accept the 'making the numbers' hypothesis that managers who are under pressure because of unrealistically optimistic earnings expectations by analysts and deteriorating real opportunities, will rely more heavily on debt financing to boost earnings per share and return on equity.
Learning and equilibrium selection in a monetary overlapping generations model with sticky prices
(2003)
We study adaptive learning in a monetary overlapping generations model with sticky prices and monopolistic competition for the case where learning agents observe current endogenous variables. Observability of current variables is essential for informational consistency of the learning setup with the model set up but generates multiple temporary equilibria when prices are flexible and prevents a straightforward construction of the learning dynamics. Sticky prices overcome this problem by avoiding simultaneity between prices and price expectations. Adaptive learning then robustly selects the determinate (monetary) steady state independent from the degree of imperfect competition. The indeterminate (non-monetary) steady state and non-stationary equilibria are never stable. Stability in a deterministic version of the model may differ because perfect foresight equilibria can be the limit of restricted perceptions equilibria of the stochastic economy with vanishing noise and thereby inherit different stability properties. This discontinuity at the zero variance of shocks suggests to analyze learning in stochastic models.
Measuring and reducing energy consumption constitutes a crucial concern in public policies aimed at mitigating global warming. The real estate sector faces the challenge of enhancing building efficiency, where insights from experts play a pivotal role in the evaluation process. This research employs a machine learning approach to analyze expert opinions, seeking to extract the key determinants influencing potential residential building efficiency and establishing an efficient prediction framework. The study leverages open Energy Performance Certificate databases from two countries with distinct latitudes, namely the UK and Italy, to investigate whether enhancing energy efficiency necessitates different intervention approaches. The findings reveal the existence of non-linear relationships between efficiency and building characteristics, which cannot be captured by conventional linear modeling frameworks. By offering insights into the determinants of residential building efficiency, this study provides guidance to policymakers and stakeholders in formulating effective and sustainable strategies for energy efficiency improvement.