This section seeks a more detailed understanding of the underlying relationship between risk appetite statements and performance. In order to accomplish this, further performance and risk outcome data are identified, collected and tested in a similar empirical framework. The purpose of this section is to test key underlying sources of income to determine whether a thread of underlying performance drivers can be detected beneath the headline ROA and NIM measures, in order to gain an improved understanding of channels of impact relating to Risk Appetite.
Roman (2015) employs an analysis of the channels of impact to determine the underlying drivers of her empirical results for US BHCs. This analysis of risk governance seeks further evidence of the potential underlying channels of impact to explain the headline results already observed.
Copeland (2012) helpfully identifies the key components of BHC operating revenue attribution as interest income, other operating income (non-interest income), and loan losses provisions and actual loan losses.51 These elements of
underlying income are defined further below:
− Interest income (less interest expense), which may be thought of as the
traditional banking activities of borrowing and lending, at different interest rates in order to create a positive lending margin.
− Other operating income is more volatile, and includes service charges,
trading revenues, fees, investment banking and advisory services (Duane, Schuermann and Reynolds, 2013).52
− Provisions for loan losses53 and Actual Loan Losses, resulting in the so-
called bad risks identified by Stulz (2015) in the conceptual literature if not otherwise recovered.54
In order to probe the headline performance measures more deeply, further data is collected from Bloomberg for various underlying BHC variables, including Operating Income, Other Operating Income, Net Interest Income (as a percentage of average assets), and Total Interest Income and Total Interest Expenses.
These variables, defined in Appendix 2, may serve as more precise underlying BHC performance variables, potentially driving the headline source of BHC performance gains noted earlier in this study.55
51 See Copeland 2012, Chart 1, p 86. DeYoung and Rice (2004) and Li and Zou (2014) who use similar descriptions of
this bank revenue-modeling frameworks.
52 In Chapter 1, it was noted that some firms began to increase this source of revenue and alter their risk governance
structures just as the credit cycle was beginning to turn, as described in Blundell-Wingnall, Atkinson and Lee, 2008.
53 This study has focused on Actual Loan Losses consistent with Hines and Peters (2015) as a proxy for credit risks that
generate a negative net present value. Loan loss provisions (LLPs) would have been an alternative variable of choice, however recent evidence argues that LLPs are positively, as opposed to negatively, related to good corporate governance as boards embrace income smoothing and compliance to reserving standards (Zagorchev and Gao, 2015).
54 Many thanks to Tom Millar, Partner of Deloitte LLP, for guidance related to basic BHC accounting practices.
55 The first two of these three variables rest within the conceptual diagram framework illustrated earlier as BHC
performance variables. As such, they are consistent with hypotheses H1a and H2a. Actual Loan Losses, on the other hand, rest within BHC risk variables as noted earlier in this paper in Table 5. It rests with hypotheses H1b and H2b, which have already been tested in Chapter 5.
Table 17 presents the results of this set of new estimations. Starting with the first two models, the coefficient values observed for Risk Appetite are significant and positive in Models 8a and 8b, indicating improved BHC performance demonstrated by greater Operating Income and greater Net Interest Income (NII). This suggests that adopters of Risk Appetite arrangements report greater Net Interest Income that may find its way up to Operating Income and ROA. It may be that improvements in ROA through this NII channel are a result of either better (i.e., higher) asset or improved (i.e., lower) liability-pricing discipline on the part of BHCs that have adopted Risk Appetite arrangements. However, it is not possible to ascertain, at this stage, whether improved performance relates to higher-priced assets or lower-cost BHC liabilities and debt structure. In order to probe this question, it is necessary to delve deeper and examine BHC interest income and expense profiles separately and in greater detail.
Interest income and interest expense can now be isolated in this regard. Table 17 considers the relationship of Risk Appetite arrangements to Total Interest Income (TII) and Total Interest Expense (TIE) in Models 8c and 8d. Model 8c reports that the coefficient for Risk Appetite is not significant, suggesting the performance gains do not originate with more disciplined asset pricing which might otherwise come with the greater focus associated with Risk Appetite setting.
However, Risk Appetite in Model 8d does report a significant and negative coefficient at the one per cent level, indicating that TIE (i.e., funding costs) are lower on average for Risk Appetite adopters. This indicates that adopters of Risk Appetite enjoy a lower cost of funding relative to other BHCs, which appears to also contribute to improved headline performance measures.56
This finding provides preliminary evidence that BHCs that adopt Risk Appetite arrangements appear to enjoy higher levels of ROA, NIM and NII levels, apparently originating with lower funding costs (typically from depositors and other debt capital providers). This analysis unearths a plausible and precise source of the value created by the adoption of Risk Appetite arrangements, which originates with TIE and makes its way up the NII channel to Operating Income and eventually contributes to broad BHC headline measures such as ROA.57
56
This observation calls out for a plausible explanation. Why would adopters of Risk Appetite arrangements enjoy a lower cost of funds relative to non-adopting BHCs? Effective liquidity is a first order requirement for BHCs. BHCs face liquidity risks given their asset and liability mismatch but may also expose firms to a bank run (Elyasiani and Zhang, 2015, p. 240). Certain banks may have impaired liquidity and engage in excessive risk-taking, especially in the presence of a public safety net (Claessens, 2013, p. 756). It may be that BHCs that are perceived in the funding markets as better managed and also use Risk Appetite arrangements may attract funding at superior (i.e., lower) rates relative to BHCs that are perceived to be less well managed, thus needing to “pay up” for money market and deposit funding, especially in difficult market conditions. Thanks to Dr Gabriele Sabato for insight into this plausible explanation.
Table 17. Model 8: Examining the channels of impact upon performance
Variable Model 8a
(Operating Income)
Model 8b
(NII) (Total Interest Model 8c
Income) Model 8d (Total Interest Expense)58 Model 8e (Other Op. Income) Risk Appetite 1183.5 *** 0.243*** -658.7 -752.3*** 226.7** (2.83) (2.66) (-0.52) (-2.94) (2.18) RC Exists -39.29 0.0407 -26.42 207.3 160.7 (-0.07) (0.34) (-0.02) (0.61) (1.19) CRO Age 50.30 -0.0259* 98.66 20.73 -13.93 (0.80) (-1.90) (0.52) (0.54) (-0.90) BHC Assets .0121*** -.000001 -0.00881 -.00757*** -.00233** (2.74) (-1.02) (-0.66) (-2.79) (-2.12) BOD Meeting -59.33 0.00888 38.88 5.007 11.04 Number (-1.47) (1.01) (0.32) (0.20) (1.10) BOD Meeting -1.409 -0.00487 -16.58 0.0363 -5.928 Attendance (-0.05) (-0.85) (-0.21) (0.00) (-0.91) BOD Non-insider 446.1 -0.482 -317.5 26.85 146.1 (0.23) (-1.16) (-0.05) (0.02) (0.31) Board Size 11.06 0.0325* 27.72 -67.27 11.45 (0.13) (1.79) (0.11) (-1.28) (0.55) TLTA 16.70 0.00639 18.37 -1.714 -3.739 (0.53) (0.94) (0.19) (-0.09) (-0.48) Deposits / Assets 67.00* 0.0285*** 49.38 -31.23 2.792 (1.97) (3.85) (0.48) (-1.49) (0.33)
CEO Shares .000003 8.46e-11 -.000006 -.000004 -.000001
(0.50) (0.07) (-0.38) (-1.23) (-1.08) Observations 314 314 306 306 314 R2 0.1782 0.2012 0.0322 0.1848 0.0896 AIC 5241 -54 5781 4802 4365 BIC 5297 2 5837 4858 4421
Yearly Dummies Yes Yes Yes Yes Yes
Fixed Effects Yes Yes Yes Yes Yes
Notes: Table 18 and Model 9 probes further into what may be driving BHC performance observed thus far (ROA and NIM) across a set of underlying BHC performance metrics in order to better appreciate potential channels of impact of Risk Appetite. It consists of fixed effects estimations with Risk Appetite as the predictor variable. The dependent variable is named underneath the Model number, such as Operating Income, NII, Interest Income and Expense, and Other Operating Income. All dependent variables defined in Appendix 13.2. Significant findings are denoted with *, ** and *** for 10%, 5% and 1% levels respectively with coefficient values reported (and t values reported in parentheses).
58 Total Interest Expense is an expense line item and part of net interest income, a performance measure. Thus, Risk
Appetite arrangements are related to improved BHC performance levels when its coefficient sign is negative in model 8d.