Trade-off Model of Debt Maturity Structure

In this paper, we suggest the trade-off model to explain the choice of debt maturity. This model is based on balancing between risk and reward of using shorter-term loans. Shorter-term loans have cost advantage over, but incur higher refinancing and interest rate risk than longer-term loans. Using the Compustat data, we show that the principal components of financial attributes are financial flexibility and financial strength. Therefore, only firms with greater financial flexibility and financial strength can use proportionately more short-term loans. We also document that financially strong firms take advantage of lower interest rates of short-term debt. They use proportionately more short-term loans when the term premium is high. The results of our study also provide evidence supporting the agency cost hypothesis, which is strongly supported by current literature.     

The Original Maturity of Corporate Bonds: The Influence of Credit Rating,Asset Maturity,Security, and Macroeconomic Conditions

We examine the determinants of the new issue maturity of corporate bonds. As credit rating decreases, new bond issues have longer maturities, but substantial variation in maturity within each rating class remains. We seek to explain the variation of new issue maturity within credit classes. We find that asset maturity, security covenants, and macroeconomic conditions influence the new issue maturity of bonds within rating categories.     

The Tragedy of the Commons and Inflation Bias in the Euro Area

Central bank credit has expanded dramatically in some of the Euro Area member countries since the beginning of the financial crisis. This paper makes two contributions to understand this stylized fact. First, we discuss a simple model of monetary policy that includes (i) a credit channel and (ii) a common pool problem in a monetary union. We illustrate that the interaction of the two elements leads to an inflation bias that is independent of the standard time-inconsistency bias. Secondly, we present an institutional analysis that is consistent with the view of fragmented monetary policy and empirical evidence that illustrates the heterogeneity of central bank credit expansion.     

Performance assessment of ensemble learning systems in financial data classification

Financial data classification plays an important role in investment and banking industry with the purpose to control default risk, improve cash and select the best customers. Ensemble learning and classification systems are becoming gradually more applied to classify financial data where outputs from different classification systems are combined. The objective of this research is to assess the relative performance of existing state‐of‐the‐art ensemble learning and classification systems with applications to corporate bankruptcy prediction and credit scoring. The considered ensemble systems include AdaBoost, LogitBoost, RUSBoost, subspace, and bagging ensemble system. The experimental results from three datasets: one is composed of quantitative attributes, one encompasses qualitative data, and another one combines both quantitative and qualitative attributes. By using ten‐fold cross‐validation method, the experimental results show that AdaBoost is effective in terms of low classification error, limited complexity, and short time processing of the data. In addition, the experimental results show that ensemble classification systems outperform existing models that were recently validated on the same databases. Therefore, ensemble classification system can be employed to increase the reliability and consistency of financial data classification task.     

Firm heterogeneity, productivity and spatially bounded knowledge externalities

In this paper, we argue that conceptually disentangling the ‘context versus composition’ aspects of regional growth is a multilevel issue. By applying multilevel models (also called random-effects models), we show (1) the importance of considering firm-specific characteristics simultaneously with region-specific characteristics, as we find that a large part of what is traditionally assigned to the impact of the region should be assigned to firm-specific characteristics and (2) that existing single-level methodologies can be problematic, as they are vulnerable to the charge of estimating significance levels that are too liberally assigned and promote exaggerations. This is illustrated empirically by showing that single-level approaches would lead to the conclusion that innovation spillovers are highly significant in a setting of Dutch urban growth differentials, while multilevel analyses shows less liberally assigned significance levels. We conclude that multilevel-effect models better fit research questions that combine firm and spatial characteristics simultaneously, especially because they allow firm-specific characteristics to be differently linked to their regional contexts.