The Bright Side of Bank Lobbying: Evidence from the Corporate Loan Market
Manthos D. Delis, Iftekhar Hasan, Thomas Y. To, Eliza Wu
Journal of Corporate Finance,
June
2024
Abstract
Bank lobbying has a bitter taste in most forums, ringing the bell of preferential treatment of big banks from governments and regulators. Using corporate loan facilities and hand-matched information on bank lobbying from 1999 to 2017, we show that lobbying banks increase their borrowers' overall performance. This positive effect is stronger for opaque and credit-constrained borrowers, when the lobbying lender possesses valuable information on the borrower, and for borrowers with strong corporate governance. Our findings are consistent with the theory positing that lobbying can provide access to valuable lender-borrower information, resulting in improved efficiency in large firms' corporate financing.
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Do Politicians Affect Firm Outcomes? Evidence from Connections to the German Federal Parliament
André Diegmann, Laura Pohlan, Andrea Weber
IWH Discussion Papers,
No. 15,
2024
Abstract
We study how connections to German federal parliamentarians affect firm dynamics by constructing a novel dataset linking politicians and election candidates to the universe of firms. To identify the causal effect of access to political power, we exploit (i) new appointments to the company leadership team and (ii) discontinuities around the marginal seat of party election lists. Our results reveal that connections lead to reductions in firm exits, gradual increases in employment growth without improvements in productivity. Adding information on credit ratings, subsidies and procurement contracts allows us to distinguish between mechanisms driving the effects over the politician’s career.
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Search Symbols, Trading Performance, and Investor Participation
Yin-Siang Huang, Hui-Ching Chuang, Iftekhar Hasan, Chih-Yung Lin
International Review of Economics and Finance,
April
2024
Abstract
We investigate the relationships among search symbols, trading performance, and investor participation. We use two specific datasets from Google Trends’ search volume index. The search volume by number ticker significantly predicts high returns and high investor participation when applied by active retail investors investing in large firms. This does not hold true for less active retail investors who use Chinese company name tickers as their search terms. Our results indicate that the heuristic usage of number tickers to search for company information helps active retail investors to obtain better trading performance compared with less active retail investors who use Chinese company name tickers.
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Climate Stress Tests, Bank Lending, and the Transition to the Carbon-neutral Economy
Larissa Fuchs, Huyen Nguyen, Trang Nguyen, Klaus Schaeck
IWH Discussion Papers,
No. 9,
2024
Abstract
We ask if bank supervisors’ efforts to combat climate change affect banks’ lending and their borrowers’ transition to the carbon-neutral economy. Combining information from the French supervisory agency’s climate pilot exercise with borrowers’ emission data, we first show that banks that participate in the exercise increase lending to high-carbon emitters but simultaneously charge higher interest rates. Second, participating banks collect new information about climate risks, and boost lending for green purposes. Third, receiving credit from a participating bank facilitates borrowers’ efforts to improve environmental performance. Our findings establish a hitherto undocumented link between banking supervision and the transition to net-zero.
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Homepage
A turning point for the German economy? The international political environment has fundamentally changed with looming trade wars and a deteriorating security situation in Europe.…
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Forecasting Economic Activity Using a Neural Network in Uncertain Times: Monte Carlo Evidence and Application to the
German GDP
Oliver Holtemöller, Boris Kozyrev
IWH Discussion Papers,
No. 6,
2024
Abstract
In this study, we analyzed the forecasting and nowcasting performance of a generalized regression neural network (GRNN). We provide evidence from Monte Carlo simulations for the relative forecast performance of GRNN depending on the data-generating process. We show that GRNN outperforms an autoregressive benchmark model in many practically relevant cases. Then, we applied GRNN to forecast quarterly German GDP growth by extending univariate GRNN to multivariate and mixed-frequency settings. We could distinguish between “normal” times and situations where the time-series behavior is very different from “normal” times such as during the COVID-19 recession and recovery. GRNN was superior in terms of root mean forecast errors compared to an autoregressive model and to more sophisticated approaches such as dynamic factor models if applied appropriately.
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Transformation tables for administrative borders in Germany
Transformation tables for administrative borders in Germany The state has the ability to change the original spatial structure of its administrative regions. The stated goal of…
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IWH Subsidy Database
IWH Subsidy Databse The microdatabase currently comprises nine data sets on direct business subsidy programmes in Germany. The programme statistics kept by the project sponsors…
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CompNet Database
The CompNet Competitiveness Database The Competitiveness Research Network (CompNet) is a forum for high level research and policy analysis in the areas of competitiveness and…
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Research Clusters
Three Research Clusters Research Cluster "Economic Dynamics and Stability" Research Questions This cluster focuses on empirical analyses of macroeconomic dynamics and stability.…
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