## wp17111 - Section II discusses literature related to our study. In Section III we describe the event study

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### Literature Review
- Study relates to three literatures: empirical corporate finance (announcement effects), theoretical banking (balance-sheet determinants of monitoring), and bank risk taking (monetary policy and lending risk).
- Key empirical benchmark: bank loan announcements are followed by positive and significant abnormal equity returns, while equity issues, bond issues and private placements are not (Mikkelson & Partch 1986, James 1987, Dahiya et al. 2003).
- Cross-firm heterogeneity in announcement effect:
  - Stronger announcement effects for smaller firms, firms with negative profit trends, or low investment opportunities (Slovin et al. 1992, Wansley et al. 1992, Best & Zhang 1993).
  - Lender identity matters: abnormal returns significant only when the lead bank has a high credit rating (Billett et al. 1995).
- Theoretical predictions:
  - Balance sheet size and diversification affect monitoring incentives (Diamond 1984; Besanko & Kanatas 1993; Holmstrom & Tirole 1997).
  - Higher bank exposure (loan value relative to bank assets or capital) increases monitoring intensity.
- Bank risk taking literature:
  - Monetary policy affects quantity and riskiness of lending (Kashyap & Stein 2000; Bernanke & Blinder 1992; Bernanke et al. 1994).
  - Riskiness captured by changes in lending standards, loan decisions, syndicated loan pricing, or internal risk ratings (Maddaloni & Peydró 2011; Jiménez et al. 2014; Paligorova & Santos 2013; Dell’Ariccia et al. 2016).
  - Market-based announcement effect interpreted as an ex-ante measure of risk taking; complements surveys and internal ratings.

### Loan Announcements and Bank Balance Sheets — Event Study and LAE
- Sample: 1,431 UK syndicated loans.
- Loan announcement effect (LAE) definition and estimation:
  - LAE = change in market value of the borrower in response to syndicated loan announcement.
  - Computed as cumulative abnormal return over a two-day event window (credit date plus following day).
  - Abnormal returns derived from a 3-factor model regressing daily equity returns on UK market return and Fama-French “size” and “value” indices.
  - Regression sample: union of 120 days preceding the pre-event window and 80 days following the post-event window.
- Event window choices:
  - Main: two-day window; robustness: one- and three-day windows do not significantly alter results.
  - Pre-event window: 10 days before the event. Post-event window: 20 days after.
- Identification assumptions:
  - (i) market learns about the loan during the event window;
  - (ii) other information during event window captured by model;
  - (iii) loans independently distributed over time and across firms;
  - (iv) equity price changes do not affect likelihood of a deal.
- Cross-sectional regressions link LAE to loan, borrower, and bank characteristics.

### Key statistics and findings on LAE
- Full sample average LAE: 0.41 percent (statistically significant at the 1 percent level).
- Subsample patterns (high vs low = above/below median across loans):
  - LAE large and significant for above median loan values (i.e. above US$ 335 million); small and insignificant for below median values.
  - LAE larger/more significant when borrower is relatively small, has low profitability, spends relatively more on R & D, or has low Tobin’s Q.
  - LAE nearly identical for low- and high-leverage firms.
  - LAE only significant for firms with highly liquid assets.
  - LAE significant only when the borrowing firm does not have a credit rating: No = 0.48*** (Yes = 0.06, not significant).
- Numeric sample counts: N = 1,431 (varied across subsamples).

### Announcement effect and lender (lead bank) characteristics
- Syndicate context:
  - Average 8 banks per syndicate; 37 lead banks in sample; average each lead bank manages 39 loans.
- Dispersion of LAE:
  - Between-bank standard deviation: 2.4 percent.
  - Within-bank standard deviation: 1.5 percent.
  - These are about 6 and 4 times the average LAE, respectively.
- Lead bank variables used as proxies for monitoring incentives and ability:
  - Balance sheet size (proxy for diversification), profitability (RoA), share of non-performing assets, capitalization (Tier-1), balance sheet growth rate, price-to-earning (P/E) ratio.
- Quartile regressions reveal a non-monotonic (bell-shaped) relationship:
  - Banks in intermediate quartiles (2nd and 3rd) for size, growth, profitability and capitalization are associated with significant abnormal returns.
  - Banks in extreme quartiles (1st and 4th) are generally associated with insignificant abnormal returns.
  - Examples: average LAE in intermediate regions is 0.56, 0.46 and 0.61 percent for bank size, profit and capitalization, respectively; average LAE in extreme regions is 0.26, 0.37 and 0.28 percent, respectively.
- Interpretation:
  - Higher lead bank profits and lower non-performing assets → higher LAE (more resources → more monitoring).
  - Above a size threshold, LAE decreases with size → consistent with negative effect of diversification on monitoring intensity.
  - Fastest-growing, most capitalized, and highest market valuation banks do not yield the highest LAE.

### Robustness tests (firm- and loan-level controls)
- Controls included: historical volatility of firm profits, Tobin’s Q, number of participants in syndicate, dummy for first relationship between firm and lead bank.
- Main robustness findings:
  - Tobin’s Q is the only significant firm-level control in most cases: lower Tobin’s Q → more positive market reaction.
  - The bell-shaped relationship between bank characteristics and LAE remains robust after adding firm-level controls.
  - Introducing firm-level controls flattens some differences, but results that the strongest banks do not display highest LAE persist.

### Structural break / threshold analysis (Chow tests)
- Procedure: rank loans by bank variable and test structural break at successive cutoffs to find thresholds where LAE relationship changes.
- Table 4 results (Chow test p-values and breakpoints):
  - Balance sheet size: Break Point $900 bn — Percentile 80 — Chow test p-value 0.059.
  - Asset growth: Break Point 10.5% — Percentile 49 — Chow test p-value 0.051.
  - Tier-1 capital: Break Point 4.8% — Percentile 90 — Chow test p-value 0.040.
  - RoA: Break Point 1.5% — Percentile 82 — Chow test p-value 0.035.
  - P/E ratio: Break Point 18.5 — Percentile 92 — Chow test p-value 0.044.
  - Non-performing loans: Break Point 1.9% — Percentile 63 — Chow test p-value 0.063.
- General pattern: LAE tends to increase below identified threshold and decrease above it; thresholds are typically at the upper end of distributions.

### Borrower Performance — LAE as predictor of ex-post outcomes
- Objective: validate LAE as ex-ante market-based measure by studying correlation with borrower ex-post performance between origination and maturity.
- Four performance measures:
  - Indicator: interest coverage ratio falls below 1 between origination and maturity (distress).
  - Indicator: firm pays dividends (vs no dividends) during same period.
  - Relative profitability: return on assets (RoA) quartile (1–4).
  - Financial liquidity: Kaplan & Zingales 1997 measure of financial constraints quartile (1–4).
- Estimation framework:
  - Equation (3): P_{l,t,t+m} = α + β x_{l,t} + γ LAE_{l} + δ (LAE_{l} z_{l,t}) + ε_{l}
  - P as 0/1 for distress/dividend indicators (probit); 1–4 for quartiles (OLS).
  - Controls include past average and standard deviation of RoA, shares of liquid and tangible assets, z-score (distance to default), recession dummy.
- Main results (Table 5):
  - LAE coefficients:
    - (1) Interest coverage ratio < 1: Announcement effect = -0.06** (reduces probability of distress).
    - (2) No dividend payment: Announcement effect = -0.06** (reduces probability of missing dividends).
    - (3) Average RoA (quartiles): Announcement effect = -0.05 (not always significant alone); interaction with z-score significant.
    - (4) Average Kaplan-Zingales score: Announcement effect = -0.06* (positive association with better liquidity quartile).
  - Z-score main effect: -0.53*** to -0.55*** across specifications (higher z-score lowers distress).
  - Announcement effect × Z-score has opposite sign to LAE alone and is significant: marginal contribution of LAE larger for riskier firms.
  - Recession dummy: positive and significant for absolute performance (worse in downturn).
  - Economic magnitude: one standard deviation increase in LAE (4.1 percentage points) lowers probability of financial distress by around 30 percent for an average firm, ceteris paribus.
- Interpretation:
  - Higher LAE predicts lower probability of distress, higher probability of dividends, higher relative profitability and liquidity.
  - LAE appears to capture lead bank screening and monitoring that translate into better borrower outcomes, especially for riskier borrowers.

### Discussion and Conclusions — Main implications
- Empirical regularity: non-monotonic, bell-shaped relationship between lead bank balance sheet strength and value of lending (LAE):
  - Banks of intermediate balance sheet strength produce the most positive and significant announcement effects.
  - Loans by the largest, most capitalized and profitable banks — and by the weakest banks — tend to produce insignificant announcement effects.
  - Thresholds where relationship turns negative are at high percentiles (e.g., size and RoA near 80th–90th percentile).
- Potential explanations:
  - Measurement / matching issues:
    - Strong banks may pair with safe borrowers or repeated relationships, reducing signaling value and thus LAE; robustness checks control for borrower riskiness and repeated relationships but bell-shape persists.
  - Behavioral / strategic explanations:
    - Very strong banks may intentionally reduce screening and monitoring (competitive loosening of standards) → higher risk taking.
    - Lower monitoring by strong banks could increase short-term profits but raise future default risk.
- Theoretical model (Appendix B) consistent with empirical pattern:
  - Bank chooses lending L and monitoring μ to maximize profit Π = { r[1 − δ(L, μ)] − c exp(μ) } L with δ(L, μ) = (L / q) exp[ − μ (1 − L) ].
  - Optimal μ* shows bell-shaped relation with L: low μ at both low and high L; profits and monitoring intensity can decline beyond a lending threshold.
  - Calibrations (baseline r = 5 percent; monitoring cost chosen so average monitoring cost = 0.5 percent; q = 2) illustrate non-monotonic monitoring vs lending volume and sensitivity to parameters.
- Policy implications:
  - “Too-important-to-fail” and excessive credit growth: restraining excessive credit growth could limit bank risk taking and support financial stability.
  - Capital requirements: support for minimum capital since poorly capitalized banks may under-monitor; but excessively high capital requirements could, beyond a threshold, induce less diligent screening and monitoring.

### Appendix — Data Description (key dataset facts)
- Data sources: Dealogic (syndicated loans), Thomson Reuters Worldscope (firm and bank balance sheets), Thomson Reuters Datastream (equity returns).
- Initial Dealogic pull: ~6,800 loans issued by ~2,900 UK PNFCs since 1990.
- Final restricted sample (public equity issuers): ~1,430 loans and 380 companies; merged loan, balance sheet, and abnormal return data produce a panel of ~23,300 loan-years.
- Table A1 selected summary statistics:
  - Syndicated loan characteristics:
    - Loan value ($millions): N 1,390 — Mean 895 — S.D. 2,972 — 25th 311 — 50th 1533 — 75th 5,800.
    - Maturity (years): N 1,280 — Mean 2.8 — S.D. 4 — 25th 3 — 50th 5 — 75th 5.
    - Interest rate margin (over LIBOR, basis points): N 698 — Mean 126 — S.D. 152 — 25th 40 — 50th 75 — 75th 150.
    - Number of participant banks: N 1,431 — Mean 9 — S.D. 8 — 25th 3 — 50th 6 — 75th 12.
    - Credit rating indicator (rated = 1): N 1,431 — Mean 0.19 — S.D. 0.39.
  - Firm characteristics (examples):
    - Total assets ($billions): N 1,424 — Mean 5.8 — S.D. 17.8 — 25th 0.3 — 50th 1.0 — 75th 3.3.
    - Leverage ratio (book debt/book assets): N 1,423 — Mean 0.30 — S.D. 0.28 — 25th 0.16 — 50th 0.24 — 75th 0.36.
    - Liquid asset ratio: N 1,387 — Mean 0.07 — S.D. 0.09 — 25th 0.02 — 50th 0.04 — 75th 0.09.
  - Lead bank characteristics (examples):
    - Total assets ($billions): N 1,433 — Mean 1,210 — S.D. 6,316 — 25th 2 — 50th 18 — 75th 4,398.
    - Annual asset growth: N 1,372 — Mean 0.20 — S.D. 0.61 — 25th 0.05 — 50th 0.11 — 75th 0.21.
    - Tier-1 capital ratio: N 1,248 — Mean 0.04 — S.D. 0.12 — 25th 0.03 — 50th 0.04 — 75th 0.05.
    - Annual RoA (%): N 1,372 — Mean 0.73 — S.D. 0.55 — 25th 0.41 — 50th 0.75 — 75th 0.97.
    - Non-performing loans (%): N 1,189 — Mean 1.2 — S.D. 0.9 — 25th 0.6 — 50th 1.1 — 75th 1.5.

*Source: https://www.imf.org/-/media/files/publications/wp/2017/wp17111.pdf*

### Section II discusses literature related to our study. In Section III we describe the event study

### wp17111 - Section II discusses literature related to our study. In Section III we describe the event study

### Literature Review
- Study relates to three literatures: empirical corporate finance (announcement effects), theoretical banking (balance-sheet determinants of monitoring), and bank risk taking (monetary policy and lending risk).
- Key empirical benchmark: bank loan announcements are followed by positive and significant abnormal equity returns, while equity issues, bond issues and private placements are not (Mikkelson & Partch 1986, James 1987, Dahiya et al. 2003).
- Cross-firm heterogeneity in announcement effect:
  - Stronger announcement effects for smaller firms, firms with negative profit trends, or low investment opportunities (Slovin et al. 1992, Wansley et al. 1992, Best & Zhang 1993).
  - Lender identity matters: abnormal returns significant only when the lead bank has a high credit rating (Billett et al. 1995).
- Theoretical predictions:
  - Balance sheet size and diversification affect monitoring incentives (Diamond 1984; Besanko & Kanatas 1993; Holmstrom & Tirole 1997).
  - Higher bank exposure (loan value relative to bank assets or capital) increases monitoring intensity.
- Bank risk taking literature:
  - Monetary policy affects quantity and riskiness of lending (Kashyap & Stein 2000; Bernanke & Blinder 1992; Bernanke et al. 1994).
  - Riskiness captured by changes in lending standards, loan decisions, syndicated loan pricing, or internal risk ratings (Maddaloni & Peydró 2011; Jiménez et al. 2014; Paligorova & Santos 2013; Dell’Ariccia et al. 2016).
  - Market-based announcement effect interpreted as an ex-ante measure of risk taking; complements surveys and internal ratings.

### Loan Announcements and Bank Balance Sheets — Event Study and LAE
- Sample: 1,431 UK syndicated loans.
- Loan announcement effect (LAE) definition:
  - Change in market value of the borrower in response to syndicated loan announcement.
  - Computed as cumulative abnormal return over a two-day event window (credit date plus following day).
  - Abnormal returns derived from a 3-factor model regressing daily equity returns on UK market return and Fama-French “size” and “value” indices.
  - Regression sample: union of 120 days preceding the pre-event window and 80 days following the post-event window.
- Event window choices:
  - Main: two-day window; robustness: one- and three-day windows do not significantly alter results.
  - Pre-event window: 10 days before the event. Post-event window: 20 days after.
- Assumptions for identification:
  (i) market learns about the loan during the event window;
  (ii) other information during event window captured by model;
  (iii) loans independently distributed over time and across firms;
  (iv) equity price changes do not affect likelihood of a deal.
- Cross-sectional regressions used to link LAE to loan, borrower, and bank characteristics.

Key statistics and findings on LAE (Table 1 summary)
- Full sample average LAE: 0.41 percent (statistically significant at the 1 percent level).
- Subsample patterns (high vs low = above/below median across loans):
  - LAE large and significant for above median loan values (i.e. above US$ 335 million); small and insignificant for below median values.
  - LAE larger/more significant when borrower is relatively small, has low profitability, spends relatively more on R & D, or has low Tobin’s Q.
  - LAE nearly identical for low- and high-leverage firms.
  - LAE only significant for firms with highly liquid assets.
  - LAE significant only when the borrowing firm does not have a credit rating: No = 0.48*** (Yes = 0.06, not significant).
- Numeric sample counts shown in Table 1: N = 1,431 (varied across subsamples).

Announcement effect and lender (lead bank) characteristics
- Syndicate context: average 8 banks per syndicate; 37 lead banks in sample; average each lead bank manages 39 loans.
- Dispersion of LAE:
  - Between-bank standard deviation: 2.4 percent.
  - Within-bank standard deviation: 1.5 percent.
  - These are about 6 and 4 times the average LAE, respectively.
- Lead bank variables used as proxies for monitoring incentives and ability:
  - Balance sheet size (proxy for diversification), profitability (RoA), share of non-performing assets, capitalization (Tier-1), balance sheet growth rate, price-to-earning (P/E) ratio.
- Quartile regressions (equation 1) reveal a non-monotonic (bell-shaped) relationship:
  - Banks in intermediate quartiles (2nd and 3rd) for size, growth, profitability and capitalization are associated with significant abnormal returns.
  - Banks in extreme quartiles (1st and 4th) are generally associated with insignificant abnormal returns.
  - Examples: average LAE in intermediate regions is 0.56, 0.46 and 0.61 percent for bank size, profit and capitalization, respectively; average LAE in extreme regions is 0.26, 0.37 and 0.28 percent, respectively.
- Interpretation:
  - Higher lead bank profits and lower non-performing assets → higher LAE (more resources → more monitoring).
  - Above a size threshold, LAE decreases with size → consistent with negative effect of diversification on monitoring intensity.
  - Surprisingly, fastest-growing, most capitalized, and highest market valuation banks do not yield the highest LAE.

Robustness tests (firm- and loan-level controls)
- Controls included: historical volatility of firm profits, Tobin’s Q, number of participants in syndicate, dummy for first relationship between firm and lead bank.
- Main robustness findings (Table 3):
  - Tobin’s Q is the only significant firm-level control in most cases: lower Tobin’s Q → more positive market reaction.
  - The bell-shaped relationship between bank characteristics and LAE remains robust after adding firm-level controls.
  - Introducing firm-level controls flattens some differences, but results that the strongest banks do not display highest LAE persist.

Structural break / threshold analysis (Chow tests)
- Procedure: rank loans by bank variable and test structural break at successive cutoffs to find thresholds where LAE relationship changes.
- Table 4 results (Chow test p-values and breakpoints):
  - Balance sheet size: Break Point $900 bn — Percentile 80 — Chow test p-value 0.059.
  - Asset growth: Break Point 10.5% — Percentile 49 — Chow test p-value 0.051.
  - Tier-1 capital: Break Point 4.8% — Percentile 90 — Chow test p-value 0.040.
  - RoA: Break Point 1.5% — Percentile 82 — Chow test p-value 0.035.
  - P/E ratio: Break Point 18.5 — Percentile 92 — Chow test p-value 0.044.
  - Non-performing loans: Break Point 1.9% — Percentile 63 — Chow test p-value 0.063.
- General pattern: LAE tends to increase below identified threshold and decrease above it; thresholds are typically at the upper end of distributions (e.g., $900 bn ≈ 80th percentile).

### Borrower Performance — LAE as predictor of ex-post outcomes
- Objective: validate LAE as ex-ante market-based measure of value of lending by studying correlation with borrower ex-post performance between origination and maturity.
- Four performance measures:
  - Indicator: interest coverage ratio falls below 1 between origination and maturity (distress).
  - Indicator: firm pays dividends (vs no dividends) during same period.
  - Relative profitability: return on assets (RoA) quartile (1–4).
  - Financial liquidity: Kaplan & Zingales 1997 measure of financial constraints quartile (1–4).
- Estimation:
  - Equation (3): P_{l,t,t+m} = α + β x_{l,t} + γ LAE_{l} + δ (LAE_{l} z_{l,t}) + ε_{l}
  - P as 0/1 for distress/dividend indicators (probit); 1–4 for quartiles (OLS).
  - Controls include past average and standard deviation of RoA, shares of liquid and tangible assets, z-score (distance to default), recession dummy.
- Main results (Table 5):
  - LAE coefficients:
    - Specification (1) Interest coverage ratio < 1: Announcement effect = -0.06** (reduces probability of distress).
    - (2) No dividend payment: Announcement effect = -0.06** (reduces probability of missing dividends).
    - (3) Average RoA (quartiles): Announcement effect = -0.05 (not always significant alone); interaction with z-score significant.
    - (4) Average Kaplan-Zingales score: Announcement effect = -0.06* (positive association with better liquidity quartile).
  - Z-score main effect: -0.53*** to -0.55*** across specifications (higher z-score lowers distress).
  - Announcement effect × Z-score has opposite sign to LAE alone and is significant: marginal contribution of LAE larger for riskier firms.
  - Recession dummy: positive and significant for absolute performance (worse in downturn).
  - Economic magnitude: one standard deviation increase in LAE (4.1 percentage points) lowers probability of financial distress by around 30 percent for an average firm, ceteris paribus.
- Interpretation:
  - Higher LAE predicts lower probability of distress, higher probability of dividends, higher relative profitability and liquidity.
  - LAE appears to capture lead bank screening and monitoring that translate into better borrower outcomes, especially for riskier borrowers.

### Discussion and Conclusions — Main implications
- Empirical regularity: non-monotonic, bell-shaped relationship between lead bank balance sheet strength and value of lending (LAE):
  - Banks of intermediate balance sheet strength produce the most positive and significant announcement effects.
  - Loans by the largest, most capitalized and profitable banks — and by the weakest banks — tend to produce insignificant announcement effects.
  - Thresholds where relationship turns negative are at high percentiles (e.g., size and RoA near 80th–90th percentile).
- Potential explanations:
  - Measurement / matching issues:
    - Strong banks may pair with safe borrowers or repeated relationships, reducing signaling value and thus LAE; robustness checks control for borrower riskiness and repeated relationships but bell-shape persists.
  - Behavioral / strategic explanations:
    - Very strong banks may intentionally reduce screening and monitoring (competitive loosening of standards) → higher risk taking.
    - Lower monitoring by strong banks could increase short-term profits but raise future default risk.
- Simple theoretical model (Appendix B) consistent with empirical pattern:
  - Bank chooses lending L and monitoring μ to maximize profit Π = { r[1 − δ(L, μ)] − c exp(μ) } L with δ(L, μ) = (L / q) exp[ − μ (1 − L) ].
  - Optimal μ* shows bell-shaped relation with L: low μ at both low and high L; profits and monitoring intensity can decline beyond a lending threshold.
  - Calibrations (baseline r = 5 percent; monitoring cost chosen so average monitoring cost = 0.5 percent; q = 2) illustrate non-monotonic monitoring vs lending volume and sensitivity to parameters.
- Policy implications:
  - “Too-important-to-fail” and excessive credit growth: restraining excessive credit growth could limit bank risk taking and support financial stability.
  - Capital requirements: support for minimum capital since poorly capitalized banks may under-monitor; but excessively high capital requirements could, beyond a threshold, induce less diligent screening and monitoring.

### Appendix — Data Description (key dataset facts)
- Data sources: Dealogic (syndicated loans), Thomson Reuters Worldscope (firm and bank balance sheets), Thomson Reuters Datastream (equity returns).
- Initial Dealogic pull: ~6,800 loans issued by ~2,900 UK PNFCs since 1990.
- Final restricted sample (public equity issuers): ~1,430 loans and 380 companies; merged loan, balance sheet, and abnormal return data produce a panel of ~23,300 loan-years.
- Table A1 selected summary statistics (sample size notes):
  - Syndicated loan characteristics (N shown where reported):
    - Loan value ($millions): N 1,390 — Mean 895 — S.D. 2,972 — 25th 311 — 50th 1533 — 75th 5,800.
    - Maturity (years): N 1,280 — Mean 2.8 — S.D. 4 — 25th 3 — 50th 5 — 75th 5.
    - Interest rate margin (over LIBOR, basis points): N 698 — Mean 126 — S.D. 152 — 25th 40 — 50th 75 — 75th 150.
    - Number of participant banks: N 1,431 — Mean 9 — S.D. 8 — 25th 3 — 50th 6 — 75th 12.
    - Credit rating indicator (rated = 1): N 1,431 — Mean 0.19 — S.D. 0.39.
  - Firm characteristics (examples; N reported):
    - Total assets ($billions): N 1,424 — Mean 5.8 — S.D. 17.8 — 25th 0.3 — 50th 1.0 — 75th 3.3.
    - Leverage ratio (book debt/book assets): N 1,423 — Mean 0.30 — S.D. 0.28 — 25th 0.16 — 50th 0.24 — 75th 0.36.
    - Liquid asset ratio: N 1,387 — Mean 0.07 — S.D. 0.09 — 25th 0.02 — 50th 0.04 — 75th 0.09.
  - Lead bank characteristics (examples; N reported):
    - Total assets ($billions): N 1,433 — Mean 1,210 — S.D. 6,316 — 25th 2 — 50th 18 — 75th 4,398.
    - Annual asset growth: N 1,372 — Mean 0.20 — S.D. 0.61 — 25th 0.05 — 50th 0.11 — 75th 0.21.
    - Tier-1 capital ratio: N 1,248 — Mean 0.04 — S.D. 0.12 — 25th 0.03 — 50th 0.04 — 75th 0.05.
    - Annual RoA (%): N 1,372 — Mean 0.73 — S.D. 0.55 — 25th 0.41 — 50th 0.75 — 75th 0.97.
    - Non-performing loans (%): N 1,189 — Mean 1.2 — S.D. 0.9 — 25th 0.6 — 50th 1.1 — 75th 1.5.

*Source: https://www.imf.org/-/media/files/publications/wp/2017/wp17111.pdf*

### References

### wp17111 - References

### Full reference list
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- Bridges, J., Gregory, D., Nielsen, M., Pezzini, S., Radia, A. & Spaltro, M.2014. ‘The Impact of Capital Requirements on Bank Lending’.
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- Dell’Ariccia,  G.,  Laeven,  L.  &  Suarez,  G.  A.2016.  ‘Bank  Leverage  and  Monetary  Policy’s Risk-Taking Channel: Evidence from the United States’,The Journal of Finance.
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- Demsetz, R. S. & Strahan, P. E.1997. ‘Diversification, Size, and Risk at Bank Holding Companies’,Journal of money, credit, and bankingpp. 300–313.
- Diamond,  W. D.1984. ‘Financial Intermediation and Delegated Monitoring’,The Review of Economic Studies51(3), 393 – 414.
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*Source: wp17111 - References (pdf).*

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_Source: https://www.imf.org/-/media/files/publications/wp/2017/wp17111.pdf_