Government Borrowing and Crowding Out

We investigate the impact of fiscal expansions on firm investment by exploiting firms that have multiple banking relationships. Further, we conduct a localized RDD approach and compare the lending behavior of banks that barely met and missed the criteria of being a primary dealer, as well as barely winners and losers at government auctions. Our results indicate that a 1 percentage point increase in banks’ bonds-to-assets ratio decreases loans by up to 0.4%, which leads to significant declines in firm investment, profits and wages. Our findings are grounded in a quantitative model with financial and real sectors with which we undertake a welfare analysis and compute the cost of government borrowing on the overall economy.

Credit Transition and Structural Shocks

Credit risk involves not only the complexity of screening but also monitoring and estimating rating transition. The adoption of inadequate transition matrices causes a misevaluation of credit risk, a consequent misallocation of capital, with the prospect that the lending process will be affected by increasing transaction costs and limited rationality, especially after a shock. Comparing the mover–stayer and the Markov chain approaches to estimate the SME rating transition matrix, we find that the risk of a structural credit shock imposes flexible estimates not constrained by the long-run trajectory of borrowers. Improved migration estimation mitigates adverse selection in banks’ lending behavior. This conclusion is particularly true during economic downturns with the consequence of reducing the cyclicality and empowering the resilience of banks.

BANK LENDING BEHAVIOR: EVIDENCE FROM MALAYSIAN DUAL BANKING SYSTEM

This study investigates the internal and external factors that influence bank lending behaviors in Malaysian’s dual banking system. The final regression of 24 commercial and 15 Islamic banks using the pooled ordinary least square (POLS) method revealed that the size of the bank proxies by the logarithm of total assets as the most significant factor influencing bank lending behavior in Malaysia from 2010 to 2018. This suggests that larger banks are more diversified and have a larger pool of funds to be loaned out. Because banks rely on deposits to issue loans, the deposits received by the bank have a substantial impact on bank lending. The greater the number of deposits obtained; the more bank lending activities will occur. The data also demonstrated that commercial and Islamic bank lending behavior in Malaysia is strongly connected with deposit volume (DEPO), GDP, and bank size (SIZE).

The Effects of Bank-Level and Macroeconomic Variables on Commercial Bank Lending Based on Type of Use

ABSTRACTThe purpose of this study was to analyze the effect of interest rates, bank-level and macroeconomic variables on bank lending based on the type of use. The analysis method uses an autoregressive distributed lag (ARDL) model with quarterly data for the period of 2011Q1 - 2020Q1. The results show that investment lending behavior can be explained well by all bank-level and macroeconomic variables for the long run. The bank-level variable also reflects the performance and soundness of the bank, namely the capital adequacy ratio and loan to deposit ratio. Meanwhile, macroeconomic variables include inflation and real GDP. Consumer lending behavior is better explained by macroeconomic variables than bank-level variables. Meanwhile, GDP is the only variable that has a significant effect on working capital loans, which means that the behavior of working capital loans is more influenced by the business cycle as indicated by changes in real GDP. GDP is the only variable that consistently has a significant positive effect on bank loans for the three types of loans. Banks need to continue to emphasize the principle of prudence in providing credit by taking into account the term and credit risk, as well as internal and external factors.

Impact of Bank Capital and Monetary policy on Lending Behavior of USA Banking Sector Before and After Global Financial Crises

This study investigates the impact of bank capital, capital structure and monetary policy on the lending behavior of USA banks before and after global financial crises. For this purpose, sample data is collected from the annual reports of top ten banks of USA from 2001 to 2017. A panel unit root is applied to check the stationarity of variables. In order to explain the impact of bank capital, capital structure and monetary policy on lending behavior of USA banks, fixed effect and random effect model have been used. The sample data has been divided into two sets. First data set is taken from 2001 to 2008 before financial crises. Second data set is taken from 2009 to 2017 after financial crises and all above tests have been applied on these data sets. Furthermore, in order to measure the lending behavior three types of lending have been selected lending to consumers, lending to real estate and lending to commercial & industrial sector of USA banks. In order to get the better picture of lending behavior of USA banks before and after financial crises: paired sample T-test has been applied on the data of lending before and after financial crises. Results of paired sample T-test showed there is significant difference in lending to consumers, lending to commercial & industrial sector and lending to real estate before and after financial crises of USA banks because of the implementation of Basel III. So, we accept the alternative hypothesis for our second research question. Findings suggested that impact of bank capital, capital structure and monetary policy has significant impact on the lending behavior before and after the global financial crises with the positive change of sixteen percent in R-squared value. So, we accept the alternative hypothesis for our first research question. The results of coefficients shows that before financial crises (2001 to 2008) discounted interest rates have more significant impact on the lending made to consumers but after the global financial crises (2009 to 2017) discounted interest rates, capital structure and tier 1 capital ratio have more significant impact on the loan made to consumers. The results of coefficients shows that before the financial crises (2001 to 2008) discounted interest rates have more significant impact on the loan made to commercial and industrial sector but after the global financial crises (2009 to 2017) discounted interest rates, capital structure and tier 1 capital ratio have more significant impact on the loan made to commercial and industrial sector. The results of coefficients shows that before financial crises (2001 to 2008) discounted interest rates have more significant impact on the loan made to real estate but after the global financial crises (2009 to 2017) discounted interest rates and capital structure have more significant impact on the loan made to real estate. Findings of our study are aligned with Swamy (2015), who investigated the impact of bank capital on lending spreads and found that increase in capital ratio of banks would also increase their lending spreads. Our results are also matched with the findings of (Kosak et al., 2015), those concluded that capital structure significantly affect the loan growth of banks. Our results are also aligned with Chami & Cosimano (2010), they found that change in monetary policy due to Basel Accord would lead to a change in bank capital and bank loans.

„This time is different“?

Die Coronakrise ist die erste globale Pandemie seit mehr als 100 Jahren. In dieser Hinsicht ist die Coronakrise einzigartig und damit wahrhaft „different“. Aber ist sie auch „einzigartig“ wenn die Reaktion der Finanzinstitute auf die Krise in den Blick genommen wird? Vor dem Hintergrund des mehrgliedrigen Bankensystems in Deutschland untersucht der Beitrag, ob in der Coronakrise das Kreditgewerbe bei der Finanzierung der Realwirtschaft ähnlich agiert, als in vorangegangenen Krisen. Zunächst prüfen wir, wie die Kreditquote, also der Anteil der Kredite an der Bankbilanzsumme, auf die drei großen Krisen dieses Jahrhunderts, Dotcom-Krise, Finanzkrise und Coronakrise, reagiert hat. Grundlage dazu ist ein Paneldatensatz mit den Bankengruppen als Beobachtungseinheit. Im zweiten Schritt vergleichen wir die Entwicklung der Kreditbestände und Marktanteile der Bankengruppen in Coronakrise und Finanzkrise. Unsere Befunde zeigen, dass der Spruch „This time is different“ für das deutsche Bankwesen in der Coronakrise nicht falsch ist. Die empirische Schätzung ergibt, dass kapitalschwächere Bankengruppen in der Finanzkrise die Kreditquote zwar signifikant abgesenkt, diese aber in der Coronakrise signifikant erhöht haben. Der Vergleich der Kreditbestände zeigt für die Banken insgesamt und für einzelne Bankengruppen in der Coronakrise eine deutlich expansivere Kreditvergabe als in der Finanzkrise. The Corona crisis is the first global pandemic in more than 100 years. In this respect, the Corona crisis is unique and therefore truly “different”. But is it also “unique” when the response of financial institutions to the crisis is considered? Against the backdrop of the German banking system the work examines whether in the Corona crisis the banking industry has shown a similar reluctance to finance the real economy as in previous economic crises. Based on a panel data set with the banking groups as an observation unit, we explore the impact of dotcom, financial and corona crisis on the share of loans in the balance sheet of banking groups. Furthermore, we compare lending behavior and the market share trends of the individual banking groups in the financial crisis and the corona crisis. As a result, it can be said that the saying “This time is different” is an appropriate description for the German banking system in the Corona crisis. The empirical estimate shows that capital-weaker banking groups significantly reduced the share of loans in total assets during the financial crisis but significantly increased them during the corona crisis. A direct comparison of the development of loan portfolios shows an opposite trend in the Corona crisis for the banks as a whole, and also for individual banking groups than in the financial crisis.

Machine learning approach to drivers of bank lending: evidence from an emerging economy

The study analyzes the performance of bank-specific characteristics, macroeconomic indicators, and global factors to predict the bank lending in Turkey for the period 2002Q4–2019Q2. The objective of this study is first, to clarify the possible nonlinear and nonparametric relationships between outstanding bank loans and bank-specific, macroeconomic, and global factors. Second, it aims to propose various machine learning algorithms that determine drivers of bank lending and benefits from the advantages of these techniques. The empirical findings indicate favorable evidence that the drivers of bank lending exhibit some nonlinearities. Additionally, partial dependence plots depict that numerous bank-specific characteristics and macroeconomic indicators tend to be important variables that influence bank lending behavior. The study’s findings have some policy implications for bank managers, regulatory authorities, and policymakers.