R&D Investment timing,default and capital structure

Review of Quantitative Finance and Accounting - This paper investigates the interaction between R&D investment timing, probability of default, and capital structure. In particular we are...     

Breaking ties in collective decision-making

Decisions in Economics and Finance - Many classic social preference (multiwinner social choice) correspondences are resolute only when two alternatives and an odd number of individuals are...     

Digital Machines,Space, and Time: Towards a Behavioral Perspective of Flexible Manufacturing

Recently, the diffusion of digital machines has further enhanced firms’ manufacturing flexibility, but also opened questions on potential challenges and implications in the production process. To respond to these timely issues, this study adopts a behavioral perspective and comparatively explores how four different types of digital machines—characterized by increasing degrees of manufacturing flexibility—affect the perception and use of space and time for routines within the production plant. To this end, 45 digital manufacturing machines, sampled across 14 firms in the British and Italian motorsport industry, were qualitatively observed and compared. A model emerges where four key mechanisms reshape (1) the interactive space around the machine, (2) the innovation activities performed in the machine space, (3) the time within activities involving the machine, and (4) the time perception. Such mechanisms mediate the relationship between manufacturing flexibility and firm performance. Further, data show how increasing digitalization in the manufacturing process enhances the establishment of new routines as flexible machines get introduced in the production. Finally, theoretical and practical implications related to fostering a behavioral perspective in innovation and operations management studies are discussed.     

Bioenergie – Beitrag zum heutigen und zukünftigen Energiesystem

The introduction and establishment of renewable energies is still discussed intensively with the focus on climate-neutral coverage of the global energy demand. In this regard it is a valid option to use biomass as a feedstock. Already today biomass contributes significantly to cover the energy demand in the global energy system. Therefore, the present and future global bioenergy potentials are investigated in the following and compared to the respective biomass use. The results show, amongst others, that the share of biomass for heat, electricity and fuel production will increase worldwide until 2030, although the availability of biomass for energy production is likely to decline in the future.     

Systemic liquidity contagion in the European interbank market

Systemic liquidity risk, defined by the International Monetary Fund as “the risk of simultaneous liquidity difficulties at multiple financial institutions,” is a key topic in financial stability studies and macroprudential policy-making. In this context, the complex web of interconnections of the interbank market plays the crucial role of allowing funding liquidity shortages to propagate between financial institutions. Here, we introduce a simple yet effective model of the interbank market in which liquidity shortages propagate through an epidemic-like contagion mechanism on the network of interbank loans. The model is defined by using aggregate balance sheet information of European banks, and it exploits country and bank-specific risk features to account for the heterogeneity of financial institutions. Moreover, in order to obtain the European-wide topology of the interbank network, we define a block reconstruction method based on the exchange flows between the various countries. We show that the proposed contagion model is able to estimate systemic liquidity risk across different years and countries. Results suggest that our effective contagion approach can be successfully used as a viable alternative to more realistic but complicated models, which not only require more specific balance sheet variables with high time resolution but also need assumptions on how banks respond to liquidity shocks.

     

CEO performance-based grants’ vesting provisions and debt contracts: Evidence from GAAP,Non-GAAP and KPI metrics

We examine the influence of CEOs’ equity and cash grants’ vesting provisions that are based on (i) accounting performance metrics prepared under US generally accepted principles (GAAP), (ii) non-GAAP performance metrics and (iii) key performance indicators (KPIs) on debt contracts. We find that grants with vesting provisions based on GAAP metrics and KPIs lead to a lower cost of debt, a lower likelihood of collateral requirements and less restrictive covenant terms. In contrast, performance-based grants with non-GAAP vesting provisions lead to a higher cost of debt, a higher likelihood of collateral requirements and more restrictive covenant terms. Supplementary analyses reveal that our results are incremental to other debtholder-friendly features in the CEO contracts, such as grants with debt-related performance measures and CEOs’ inside debt holdings, and robust to alternative variable definitions and specifications. Overall, our results suggest that debtholders understand the differing incentives associated with GAAP, non-GAAP and KPI-based performance measures, and incorporate these differences into debt contracts.     

Nowcasting Indian GDP

Nowcasting has become a useful tool for making timely predictions of gross domestic product (GDP) in a data‐rich environment. However, in developing economies this is more challenging due to substantial revisions in GDP data and the limited availability of predictor variables. Taking India as a leading case, we use a dynamic factor model nowcasting method to analyse these two issues. Firstly, we propose to compare nowcasts of the first release of GDP to those of the final release to assess differences in their predictability. Secondly, we expand a standard set of predictors typically used for nowcasting GDP with nominal and international series, in order to proxy the variation in missing employment and service sector variables in India. We find that the factor model improves over several benchmarks, including bridge equations, but only for the final GDP release and not for the first release. Also, the nominal and international series improve predictions over and above real series. This suggests that future studies of nowcasting in developing economies which have similar issues of data revisions and availability as India should be careful in analysing first‐ vs. final‐release GDP data, and may find that predictions are improved when additional variables from more timely international data sources are included.