These tables provide an overview of the distribution of risk in syndicated loan portfolios of banks and other financial institutions. A syndicated loan is a loan extended by a group of financial institutions a loan syndicate to a single borrower.
Syndicates often include both banks and non-bank financial institutions, such as collateralized loan obligation structures CLOs , insurance companies, pension funds, or mutual funds. After origination, shares of syndicated loans can be traded in the secondary market, changing the composition of the loan syndicate.
Syndicated loans are included in the financial accounts of the individual lenders, but are not identified specifically as syndicated loans.
The information provided in these tables provides an overview of the exposure of banks and other financial institutions to credit risk from syndicated loans. The tables summarize total exposures to syndicated loans, then break the data down by drawn credit lines, undrawn credit lines, and term loans. This figure is related to the syndicated loan market in the United States from to A describes market size and the number of loans extended by lead arrangers to borrowers every quarter.
Market size is defined as the sum of the loan amounts extended by each bank. The number of loans is defined as the total number of loans extended during each quarter. B represents the average loan size, which is the market size divided by the number of loans during each quarter. Gray shadows represent recessions as measured as the subprime morgage crisis periods during We then examine the ways in which network topology and investment characteristics impact bank performance.
We investigate the effect of bank network centrality on bank performance because of the importance of the bank-firm lending structure in terms of information asymmetry. The structure of an interbank network should affect bank performance. Interbank networks, which are created by the degree of information asymmetry during the bank-firm lending process, should affect the performance of lending banks.
A bank with a higher level of information asymmetry might mimic the loan portfolio structure of a bank with a lower level of information asymmetry to reduce this asymmetry and generate profits.
The systemic risk research has identified network connectivity and centrality as channels that transmit contagions related to negative events [ 1 , 2 , 5 , 29 ]. This implies that a highly interconnected structure can increase systemic risk.
Ultimately, increased connectivity and rapid propagation in bank-to-bank networks can allow high-centrality banks to address market instability.
In summary, we expect that well-connected banks should experience lower levels of information asymmetry than do poorly connected banks and that they should also experience higher levels of market performance. Since the amount of syndicated loans is related to exposure to assets, a decline in asset prices should affect the stability of the banking system. We analyze syndicated loans issued during each quarter from to A visual inspection of the amount of syndicated loans over time suggests that this figure reflects the state of the financial market.
Figure 1A shows the amount of syndicated loans as a measure of overall banking loans and the number of syndicated loans. We measure the total amount of syndicated loans in each quarter. First, we find that both the overall amount and the number of syndicated loans follow a similar pattern.
The total amount of syndicated loans started to increase in and continued to rise until Q4 of , finally decreasing in After the subprime crisis, these loans rapidly increased until Figure 1A shows a pattern similar to that of the results in Figure 1A.
The nodes represent each bank, and the node size is determined by the corresponding bank's degree centrality. A node with a higher degree centrality is colored pink and one with a lower degree centrality is colored light green. The main goal of this paper is to conduct more rigorous tests on the relationship between the interconnectivity of banks and bank performance. To test the validity of our hypothesis, we construct an interbank network using the PMFG method developed by [ 17 ] based on loan portfolio data in Figure 2.
In January , this interbank network for the normal market status consisted of connections and 88 nodes. The interbank network during and after the financial market crisis consisted of connections and 87 68 nodes in January If the loan portfolio of each bank tended to have a distinct and unique investment strategy, then the interbank network would be disconnected, and each bank would correspond to a random network.
The obtained interbank network, shown in Figure 2 A—D , displays the banks with higher connections between banks, regardless of market status, suggesting that the syndicated loan portfolios of banks are shared with other banks. The CDF for the degree of the interbank network is plotted with a double logarithmic scale. The cumulative distribution function for the degree of network during four years A from to and B from to , the Gaussian distribution, and the fitted line are denoted using dotted blue lines, a black line, and dashed red lines, respectively.
The degree k distribution of the interbank network indicates that most of the banks are linked to a few other banks, whereas a few banks with a large amount of capital are connected to many individual banks. As shown in Figure 3 , the degree distribution in follows the power-law distribution with an exponent of 4. Consistent with [ 30 , 31 ]; Table 3 compiles the results of the likelihood ratio test and includes judgments supported by statistical methods for the power-law hypothesis for each distribution over four years.
We find that the degree distributions follow a power-law when comparing to exponential, stretched exponential, power law with cutoff, and log normal distributions. The power-law exponents of degree distributions of PMFG network are in the range 3. As a result, we think that there are the influential banks with a lot of connections in the interbank network.
This figure shows the correlation between diversification DIV and the degree of the PMFG network during the sample period of six months. Gray shadows represent recessions as measured as the subprime mortgage crisis periods during Is this loan portfolio strategy, i. We estimate the correlation between the diversification of portfolios and network structure to test whether the investment strategy of a bank is related to the other banks in the network.
Figure 4 shows the correlation between diversification and the degree of network centrality for each year.
Overall, there is a positive correlation between diversification and degree of centrality, regardless of the subperiod observed. In particular, the correlation value starts to increase in and continues to rise until before the subprime crisis; after this, it decreases in , suggesting that the correlation between the loan portfolio strategies of banks and the centrality of the network connectivity among banks should be understood as indicators of the financial crisis.
To observe the relationship between the degree of network centrality and portfolio strategies, we divided the whole sample into three groups according to centrality: G high , G middle , and G low. Figure 5 displays the distribution function of these three groups using box plots and calculates the similarity of each distribution function using the Kolmogorov-Smirnov test K-S test [ 32 ].
The results are reported in Table 3. In addition, we calculate the average diversification of the three groups over time. Figure 6 shows the time evolution of the average diversification of these three groups defined according to their degrees of network centrality from January to December The diversification of the three groups is calculated based on the loan portfolios using the entropy method. The red circles, blue diamonds, and black triangles indicate the high-, middle-, and low-centrality groups, respectively.
As shown in Figure 6 , we find that since , the diversification levels of low-centrality groups have moved more volatile than high-centrality groups. We divide banks into three groups: high, middle, and low-centrality. The core banks have higher levels of diversification than middle and low-centrality groups.
Time series of diversification of three groups according to their degree centrality. This figure shows the time series of the monthly diversification of syndicated loan portfolios from January to December The diversification of the three groups is computed by using the entropy method based on their loan portfolios.
We divided sample into three groups. The red circles, blue diamonds, and black triangles indicate the high, middle, and low-centrality groups, respectively. To the extent that interbank networks in the United States have heterogeneous characteristics, we suggest that the strategic behaviors of banks and the central characteristics of banks have impacts on performance.
We focus on two properties of banks: structural properties and strategic properties. We use the four measures of centrality as structural properties in the PMFG network.
The relationships between lenders and borrowers are likely to mitigate the problem of information asymmetry because lending banks collect a considerable amount of information about the corporate management of their borrowers and have stable and long-term relationships with the managers of these organizations [ 33 ]. We found that capitalized banks tend to centralize their networks.
Therefore, we assume that banks with a high level of centrality in their networks have the unique abilities of quickly obtaining resources through the members of their network and of reducing the level of information asymmetry between lenders and borrowers.
Based on our assumption, centralized banks would feel more secure when expanding their business. In this context, we would expect to see that these banks hold portfolios that are more diverse.
Diversification in the syndicated loan market creates the potential advantage of reducing credit risk exposure [ 5 ]. Banks become more resilient to common shocks such as exposure to risk when holding diversified portfolios. We estimate the following regression with pooled data:. As a proxy for structural importance in the PMFG network, c e n t r a l i t y i , t is replaced by four representative types of centrality: degree centrality, eigenvector centrality, closeness centrality, and betweennes centrality.
By including the variables market size, market share, and bank size in this regression, we control for the systematic and idiosyncratic effects that we cannot directly observe. Market share is measured by the natural logarithm of the amount of outstanding loans held by each bank [ 34 ].
Market size is calculated as the natural logarithm of the sum of the loan amounts of newly originated syndicated loans in billions of United States. Controlling high performance of bank with higher asset, bank size is estimated by the natural logarithm of total assets of each bank. In all regressions, we include market size and year fixed effects to remove the time characteristics. We report the results related to diversification and four centrality measures of the interbank networks.
In all models, the regression coefficients of the measures of diversification are statistically highly significant, and they indicate a positive relationship 0.
These findings are in line with the results of the descriptive studies by [ 35 ]; which report that product-diversified firms have high levels of performance and innovation. There are simply too many results and perspectives about the agency theory of diversification to include them in this paper.
These findings allow us to determine whether each type of centrality is able to represent a factor of composite centrality index CCI in Table 1. Overall, our results suggest that higher levels of the individual dimensions of centrality based on loan portfolio similarities are related to increases in the profitability of banks.
Next, we estabilish dummy variable with centrality indices to exclude the financial crisis effect in — As shown in columns 1—4 of Table 4 , although the dummy variable has a negative sign, the main effect for the dimension of centrality and diversification is positive and significant. It means that the impact of network centrality on performance is negative during — financial crisis and positive during the normal period.
We then show the results of the regression using our composite centrality index CCI through principal component analysis, including degree centrality, eigenvector centrality, closeness centrality, and betweenness centrality based on the results shown in Table 4. The results of the regression including CCI are reported in Table 1 using equation model 3. Consistent with the preceding regressions, we use the dummy variable with CCI to remove the recession trends.
Several papers have highlighted the likelihood that board interlocking between banks has more power and information in the market when they reduce financial risk [ 22 , 34 , 36 , 37 ]. Because the importance of each bank in the network is not homogeneous, we group the banks by their degrees centrality into groups consisting of core banks and of peripheral banks.
Table 5 represents the Pearson correlation of diversification between each subset of banks. The high- and middle-centrality groups have positive correlations 0. Additionally, we investigate a two-sample Kolmogorov-Smirnov test to assess the distribution of the two samples in brackets.
This test implies a heterogeneous distribution of diversification among the three groups of banks. As a result, we conclude that the three groups classified by degree centrality could have investment strategies with differing characteristics.
Our interpretation is consistent with the results in Figure 5 and Figure 6. Specifically, we run the following regression on two sets of banks; core and peripheral.
TABLE 5. The relation of the diversification of the subsets of banks to degree centrality. Table 6 shows the results of the linear regressions regarding bank diversification using the same explanatory variables we used for the subset of banks. These results indicate that core banks could obtain better private information than peripheral banks. This result is consistent with the study of [ 14 ]; who insist that concentrated lenders had higher profits than diversified lenders during the financial crisis.
The primary lender conducts most of this due diligence , but lax oversight can increase corporate costs. A company's legal counsel may also be engaged to enforce loan covenants and lender obligations. The Loan Syndications and Trading Association LSTA is an established organization within the corporate loan market that seeks to provide resources on loan syndications.
It helps to bring together loan market participants, provides market research, and is active in influencing compliance procedures and industry regulations. For most loan syndications, a lead financial institution is used to coordinate the transaction. The lead financial institution is often known as the syndicate agent. This agent is also often responsible for the initial transaction, fees, compliance reports, repayments throughout the duration of the loan, loan monitoring, and overall reporting for all lending parties.
A third party or additional specialists may be used throughout various points of the loan syndication or repayment process to assist with various aspects of reporting and monitoring. Loan syndications often require high fees because of the vast reporting and coordination required to complete and maintain the loan processing. Company ABC is interested in purchasing an abandoned airport and converting it into a large development, consisting of a sports stadium, multiple apartment complexes, and a mall.
JPMorgan acts as the lead agent on the syndicated loan, bringing together other banks to participate. As the lead bank on the loan syndicate, JPMorgan also organizes the terms, covenants , and other details needed for the loan. Home Equity. Investing Essentials. Your Privacy Rights. To change or withdraw your consent choices for Investopedia. At any time, you can update your settings through the "EU Privacy" link at the bottom of any page.
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