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Portfolio analysis of big US banks’ performance: the fee business lines factor

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Abstract

Fee business lines are often considered more volatile than loan business and characterized by an unfavourable risk–return trade-off. However, using FDIC samples from 2001 to 2016, we find that non-interest income still contributes positively to the performance of US banks—without impairing their solvability. On the one hand, our portfolio analysis suggests that the Big 4 seem to benefit from non-interest income, although their rescue of investment banks might not yet be profitable. We also find that trading, loan sales and securitization add flexibility in bank risk management. On the other hand, our results also suggest that the post-crisis regulation appears detrimental to bank performance.

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Notes

  1. Explicit leverage is on-balance-sheet leverage and may be defined as the ratio of total assets to equity. Implicit leverage may result from (1) regulatory capital arbitrage—i.e. a migration of bank activities from on-balance-sheet to off-balance sheet, regulatory capital being usually lower for off-balance-sheet operations; (2) transactions involving derivatives which embed an implicit leverage [17, 45].

  2. This situation is similar to the diversification discount on the stocks issued by firms which are too diversified [28, 29].

  3. i.e. the sum of the direct and indirect effects of non-interest income.

  4. As measured by the elasticity of ROA or risk-adjusted ROA to the total impact.

  5. i.e. J.P. Morgan Chase, Bank of America, Citigroup, and Wells Fargo.

  6. We thus adopt the classification of the US big banks proposed by Baily et al. [5] and Baily and Holmes [6].

  7. For instance, Merrill Lynch by the Bank of America.

  8. For a recent discussion of bank regulation, see: Cecchetti and Schoenholtz [23].

  9. Note that these new regulatory constraints may have fostered the growth of the unregulated financial sector since 2010—i.e. shadow banks [13, 33].

  10. On that matter, see: FDIC, An update on emerging issues in banking, 18 April 2002.

  11. For instance, the Big 16’s return on net interest income surged at the start of the subprime crisis.

  12. The former measure of other non-interest income which was effective until 2001 included insurance fees, investment banking fees, loan sales, securitization fees and servicing fees. These components now appear separately in the FDIC statistics.

  13. Obviously, other factors are at play to explain the decrease in ROA. First, the low level of interest rates contributes to compress bank margins [4,5,6]. Second, the surge of the bank liquidity ratio after the subprime crisis due mainly to the new bank regulation tends to decrease ROA.

  14. The risk-adjusted return is equal to the ratio between ROA and its conditional standard deviation.

  15. The minimum and maximum of the diversification index being 0 and 0.5, respectively.

  16. The non-interest expenses ratio temporarily jumped during the crisis, which explains the spikes in the plot of the efficiency ratio. Note that the downward trend in the expenses ratio coincides with the development of fees business lines in the US banking system which was fostered by the passage of the Glass-Steagall Act at the end of the 1990s.

  17. i.e. the Big 16.

  18. In this respect, securitization is often done by specialized banks rather than big ones [5].

  19. The efficiency ratio is defined as non-interest expenses as a percentage of net operating income—i.e. net interest income and non-interest income. See FDIC, ratios-by-asset-size-group statistics.

  20. Note that Stiroh [59] and Stiroh and Rumble [61] do not include the efficiency ratio in their model. We are grateful to an anonymous referee for his suggestion to incorporate this variable in our model.

  21. cf. the Cochrane and Orcutt’s [25] procedure for autocorrelation of error terms.

  22. For instance, the decision by a bank to diversify its activities may be endogenous, as suggested by Demsetz and Strahan [29], Campa and Kedia [21], Calmès and Théoret [16], and Brei et al. [11]. Indeed, RA_ROA depends on SH_NONINT but SH_NONINT may also be related to RA_ROA—e.g. the decision to diversify in fee-based activities might be due to a decrease in the return on traditional operations. Another avenue to tackle this endogeneity issue is to rely on instrumental variables (IV), but given the number of variables in our equations—especially when considering the components of non-interest income—the IV technique would consume too much degrees of freedom.

  23. Note that Stiroh and Rumble [61] do not rely on the conditional volatility of ROA to compute risk-adjusted ROA but rather on the observed quarterly standard deviation of ROA computed at an annual frequency. However, this latter measure of volatility is usually more unstable than the former, which introduces noise in the estimation process.

  24. Brunnermeier and Sannikov [12] find that diversification may increase risk because it raises the implicit leverage of a financial institution.

  25. These results are reported in the “Appendix”.

  26. i.e. the positive impact of asset growth tends to taper off.

  27. In this respect, Gambacorta and Shin [37] find that a one percentage point increase in leverage adds a four basis points to the cost of a bank’ borrowed funds.

  28. According to Adrian et al. [2], the cost advantage also falls as banks become better capitalized.

  29. For instance, commercial loans are usually riskier than personal loans.

  30. By definition, elasticity is equal to: \(\frac{{\frac{\partial y}{y}}}{{\frac{\partial x}{x}}} = \frac{\partial y}{\partial x} \times \frac{x}{y}\), and from (1), \(\frac{{\partial y_{i} }}{{\partial x_{i} }} = \beta_{i} \Rightarrow \frac{{\partial y_{i} }}{{\partial x_{i} }} \times \frac{{x_{i} }}{{y_{i} }} = \beta_{i} \times \frac{{x_{i} }}{{y_{i} }}\). For more detail on the empirical version of this formula, see Pindyck and Rubinfeld [55, p. 99].

  31. Once more, the explanatory variables are lagged to control for the reverse causality issue.

  32. See the “Appendix”.

  33. See the “Appendix”.

  34. According to Saunders et al. [56], the economies of scope are concerned with the interrelationships among products and the “jointness” in the costs of producing financial products. For instance, economies of scope are at play when the cost of joint production (via cost synergies) of different services is less than the cost of separate production of these services. Economies of scope are also related to the asymmetries of information which foster the joint production of credit products and deposits, among others. See Freixas and Rochet [35].

  35. There are other sources of concern about US big banks’ data on non-interest income which are related to frauds. Indeed, between 2002 and 2016, one of the Big 4 was involved in fraudulent operations associated with fee income. This bank was charging fees and commissions amounting to many millions of dollars on which it had no right. It was also fraudulently debiting the credit cards of its customers. In other respects, between 2007 and 2013, many US big banks colluded in order to manipulate exchange rates. All these fraudulent operations tend to inflate non-interest income data—especially the other non-interest income component.

  36. Moreira and Savov [53] call these claims “shadow money”. See also Farhi and Tirole [33] for more detail. Hicks [42] was among the first to introduce this kind of monetary claim in a toy economy strictly based on credits and debits (compensation system).

  37. Basing his arguments on a survey of the history of economic thought regarding bank credit supply and money creation, Werner [66,67,68] even argues that the imposition of higher capital requirements to banks will not necessarily prevent banking crises. Indeed, despite higher capital requirements, banks could continue to expand the money supply and even increase their capital with this newly created money in order to satisfy the regulatory capital ratios.

  38. Despite great efforts made by big banks to reduce their non-interest expenses ratio after the subprime crisis comparatively to smaller banks. See FDIC, ratios-by-asset-size-group statistics.

  39. i.e. all US banks.

  40. Note that, among the bank categories, they are the most concentrated in non-traditional fees (Table 1).

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Acknowledgements

To write this article, we have benefited from a subsidy provided by the Social Sciences and Humanities Research Council of Canada (SSHRC), grant number 435-2015-0106. We also thank Professor Alain Guay for his helpful comments and Vincent Beauséjour and François Turcotte for their very valuable assistance.

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  1. Département des sciences administratives, Université du Québec (Outaouais), Campus St. Jérôme, 5 rue St Joseph, St Jérôme, QC, J7Z 0B7, Canada

    Christian Calmès & Raymond Théoret

  2. Chaire d’information financière et organisationnelle, Université du Québec (Montréal), Montréal, Canada

    Christian Calmès & Raymond Théoret

  3. École des sciences de la gestion, Université du Québec (Montréal), 315 Ste.-Catherine est, R-2915, Montréal, QC, H2X 3X2, Canada

    Raymond Théoret

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Appendix

Appendix

The impact of traditional and non-traditional activities on US banks’ ROA

The impact of total non-interest income on bank ROA and its conditional volatility

In line with our results for risk-adjusted ROA, an increase in the level of assets leads to a significant decrease in ROA, signalling diseconomies of scale. This impact is less evident for the biggest banks than for the aggregate, their coefficients for this variable being insignificantFootnote 39 (Table 7). However, there is clearly an aggregation effect when we combine our two groups of biggest banks, since the coefficient of the assets level is negative and highly significant—particularly with OLS. Aggregation allows to reduce noise in the estimation process and to better capture the fundamental link between ROA and the level of assets. Our results indicate that the capital ratio—i.e. equity on assets—also positively impacts the Big 4’s ROA. As in the case of risk-adjusted ROA, well-capitalized banks are rewarded by a higher return on their assets and less volatile returns [62]. Analogously to the results obtained for risk-adjusted ROA, we note a negative relationship between ROA and the efficiency ratio, which is more pronounced for the Big 16. Indeed, this group has particularly suffered from a decline in the return on their net operating income (in terms of assets) which may explain the greater sensitivity of the Big 16 to the efficiency ratio. Excluding regional banks, the total impact of fee business lines is positive on bank ROA. The elasticity of ROA to the share of non-interest income exceeds 2 for the Big 4 regardless of the method of estimation used, which suggests a diversification effect (Table 7). However, similarly to risk-adjusted ROA, this elasticity is negative for the Big 16.

Table 7 Impact of the share of non-interest income on US banks’ ROA
Full size table

According to Table 8, an increase in assets results in a rise in ROA volatility. Bigger banks tend to take more risk and the corresponding increase in leverage leads to a greater return volatility. Asset growth reduces the volatility of the ROA of the Big 16 and, to a lesser degree, of the aggregate of all US banks. This partly explains why the risk-adjusted ROA of the Big 16 benefits from an increase in asset growth. However, the relationship between asset growth and ROA volatility is convex—the coefficient of the square of asset growth being positive and highly significant—which suggests that the curve linking these two variables eventually tapers off.

Table 8 Impact of the share of non-interest income on the conditional standard deviation of US banks’ ROA
Full size table

Not surprisingly, the volatility of ROA responds inversely to the capital ratio for the Big 4 and for the aggregate of all banks, a lower leverage leading to less volatile returns (Table 8). However, the effect of this ratio is not significant for the Big 16, which may partly explain its negative effect on their risk-adjusted ROA. A rise in the loans-to-assets ratio also leads to a decrease in volatility of the Big 4’s ROA, which may explain why their risk-adjusted ROA benefits from a rise in this ratio. The efficiency ratio works in the opposite direction on the ROA volatility of the Big 4 and the Big 16: it decreases the volatility of the former and rises the volatility of the latter and of the aggregate of all US banks. We expect a positive sign for this relationship. Perhaps the Big 4 deliberately reduce their financial leverage when the efficiency ratio trends upwards, which leads to a decrease in the volatility of ROA.

Interestingly, the total impact of fee business lines on the volatility of returns of all US banks is negative and quite high, the elasticity coefficient exceeding 3 in absolute value. This result is due to the direct effect of the share of non-interest income on ROA since the indirect effect is positive. This finding suggests that fee-based activities decrease bank risk. Our elasticity measure is also negative for the Big 4, albeit moderate. However, the elasticity measure is positive and substantial for the Big 16, exceeding 1 with OLS and being close to 4 with GARCH. This result partly explains why their risk-adjusted ROA does not benefit from diversification in fee business lines. The profitability of these lines has been substantially reduced for the Big 16 since the subprime crisis. As argued before, the tightening of regulatory rules regarding fee-based income is one factor which seems to have contributed to the counter-performance of the Big 16 in this field after the crisis.

The impact of the components of traditional fees and the aggregate of non-traditional fees on bank ROA and its conditional volatility

All components of non-interest income have a negative impact on the ROA of the US banks aggregate (Table 9). For example, the effect on charges on deposits is negative for all bank categories, and particularly the Big 16. Importantly, note that fiduciary fees benefit more the Big 4, the OLS coefficient being 0.183, compared to 0.041 for regional banks. Analogously, trading benefits the Big 4. By contrast, the response of regional banks’ ROA to an increase in the trading share is quite negative. Finally, note that the volatility of ROA responds in an inverse way to the share of non-traditional fees, but this response is almost due to the Big 4, which hold the most diversified portfolio of non-traditional activities in the US banking industry (Table 10).

Table 9 Impact of the detailed shares of traditional fees and of the total share of non-traditional fees on US banks’ ROA
Full size table
Table 10 Impact of the detailed shares of traditional fees and of the total share of non-traditional fees on the conditional standard deviation of US banks’ ROA
Full size table

The impact of the components of non-traditional fees on bank ROA

According to Table 11, the Big 4 is the group which benefits the most from non-traditional fees.Footnote 40 The sensitivity of their ROA to the components of non-traditional fees, by decreasing order, is: insurance (0.152), loan sales (0.061), other non-interest income (0.041), and securitization (0.034). However, investment banking has a negative impact on the Big 4’s ROA. Hence, the rescue of investment banks they performed during the subprime crisis does not seem rewarding. In other respects, regional banks’ ROA benefits from the shares of net servicing fees and other non-interest income.

Table 11 Impact of the detailed components of the share of non-traditional fees on US banks’ ROA
Full size table

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Calmès, C., Théoret, R. Portfolio analysis of big US banks’ performance: the fee business lines factor. J Bank Regul 22, 112–132 (2021). https://doi.org/10.1057/s41261-020-00131-3

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