Download Diversification, Pricing, Policy and Credit Union Risk

DIVERSIFICATION, PRICING POLICY AND
CREDIT UNION RISK
Neil Esho *
Paul Kofman **
Ian G. Sharpe ***
and
Ren Huang ***
Working Paper 2004-01
Australian Prudential Regulation Authority
January 2004
* Australian Prudential Regulation Authority (APRA)
** University of Melbourne
*** University of New South Wales.
The views and opinions in this paper are those of the author and do not necessarily reflect
those of APRA. The authors wish to thank Wayne Byres, Warren Hogan and Li- Anne
Woo for helpful comments on earlier drafts of the paper, Michael Kollo for research
assistance, and the Australian Research Council and the Australian Prudential Regulation
Authority for financial support.
DIVERSIFICATION, PRICING POLICY AND CREDIT UNION RISK
ABSTRACT
Using a sample of Australian credit unions, we examine the relationship between
diversification, pricing policy, risk, and earnings. Credit unions with more concentrated
revenues have higher earnings and volatility of asset returns. Those that use pricing
policy to reduce dependence on interest on personal loans by increasing transaction fees
have higher risk and smaller earnings while those with a higher proportion of revenues
derived as commissions and off-balance sheet facility fees have higher risk. Credit unions
deriving a higher proportion of revenue from interest on residential loans and a lower
proportion from interest on personal loans have lower risk and return.
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1. INTRODUCTION
With the deregulation of the financial system, globalisation of financial markets, a
trend towards disintermediation and funds management, and technological advances,
Australian depository institutions faced a highly competitive environment in the 1990s.
This placed pressure on interest margins and profitability of traditional lines of business
and generated pressures for institutions to cut costs, outsource back office functions,
diversify into new activities, eliminate cross-subsidies, and increase non-interest income.
Australian credit unions were particularly susceptible to these developments with
high cost structures, a focus on retail banking activities, and relatively small scale. 1 To
maintain their position in financial markets, many credit unions diversified by expanding
products and services. Initially they expanded lending beyond personal installment loans
and into residential lending. For the credit unions in this study, the proportion of total
revenue derived as interest on personal loans and advances fell from 57.7% to 38.3%
between 1993(02) and 2001(03), while the share of interest on residential loans rose from
22.7% to 31.6%. They also engaged in fee generating activities including securitisation,
off-balance sheet activities, funds management, superannuation, insurance, and financial
1
Credit unions play an important role in the Australian financial system in providing competition for the
four major nationwide banks in the provision of financial services to households. Currently, approximately
20% of Australia’s adult population are members of credit unions, while in June 2002 13% of household
deposits were held by credit unions (Sources: CUSCAL and APRA). Although Australian credit unions
differ from the banks in being mutually owned, they are subject to the same prudential regulation as banks
and are required to pay corporate taxes.
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advisory services (see CUSCAL, 1998). 2 Moreover, they gradually eliminated many of
the cross-subsidies in their transaction banking and lending activities by introducing
transaction and service fees. This resulted in transaction fees on loans and deposits
increasing from 2.0% of total credit union revenue in 1993(02) to 8.5% in 2001(03).
An important issue for credit union management and for the prudential regulator, the
Australian Prudential Regulation Authority (APRA), is the effect of the change in
product mix and the increased reliance on fee (non-interest) income on credit union risk
and earnings. Do these activities offer an efficient source of risk diversification or do they
enhance profitability at the expense of higher risk?
The conventional view is that product diversification reduces an institution’s
exposure to any particular activity and thus leads to lower risk. In addition, fee income is
often believed to be more stable than interest revenue, the latter being affected by
movements in interest rates and the business cycle. Moreover, Boot and Thakor’s (1991)
analysis of agency costs in off-balance sheet (OBS) activities demonstrates that OBS
product expansion, particularly into loan commitments, can lower bank risk.
An alternative view is that the expansion of financial institution activities beyond
traditional deposit taking and lending leads to greater risk taking. Litan (1985) notes the
moral hazard problem associated with deposit insurance that is not risk based. The
deposit insurance system distorts payoffs in such a way that risk-taking behaviour is
encouraged. 3 This may take the form of increasing risk in lending or alternatively of
2
Credit union revenues from commissions and off-balance sheet facility fees for the sample increased from
2.1% to 3.6% of total revenues between 1993(02) and 2001(03).
3
It is important to note that there is no explicit system of deposit insurance operating in Australia.
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entering new, high risk, activities. In addition, Jensen (1986) and Stulz (1990) note that
diversification of activities may be associated with high agency costs if the diversification
arises from the accumulation of excess free cash and resources. This generates an overinvestment problem and may threaten the long-run viability of the institution.
De Young and Roland (2001, pp. 56-57) question the view that fee income is more
stable than income from traditional banking activities. First, they note that high switching
and information costs make it costly for borrowers to change banks and this may lead to a
relatively stable income stream from traditional banking activities. In contrast, in some
fee-based activities such as funds management, banks face a highly competitive market,
relatively low information costs, and less stable demand for the product. Second, they
argue that fee-based financial services entail a higher ratio of fixed to variable costs
(operating leverage) than traditional banking products. Thus a given change in revenue
from fees will generate a greater change in earnings than would an equal change in
interest revenues. Finally, they note that prudential regulators do not require capital to be
held for fee-based activities. This allows banks to have greater financial leverage, and
thus higher earnings volatility, on fee-based activities.
There is an extensive empirical literature dealing with the effects of product
expansion in U.S. banking, though the results are somewhat mixed. Eisenbeis, Harris and
Lakonishok (1984) examine the announcement effects of the formation of bank holding
companies on shareholder returns and conclude that diversification enhances bank value .
However, Apilado, Gallo and Lockwood (1993) find only weak support for the
hypothesis that bank expansion into securities underwriting activities is value enhancing.
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Brewer (1989) examines the effect of non-banking activities on bank holding company
(BHC) risk and finds that they are associated with a reduced probability of bankruptcy.
However, when Demsetz and Strahan (1997) decompose the variance of bank returns into
systematic and unsystematic components they find that while large BHCs are better
diversified in their activities than smaller BHCs, this does not translate into lower risk.
Rather, diversification allows large BHCs to pursue riskier lending and to operate at
higher leverage. When Hassan, Karels and Peterson (1994) use market based risk
measures they find that off-balance sheet activities are risk-reducing. Boyd et al (1993)
examine Z-Scores from simulations of hypothetical mergers between BHCs and other
non-bank financial firms. While they find that a BHC - insurance company merger is risk
reducing, BHC -securities company and BHC-real estate company mergers are risk
increasing. Allen and Jagtiani (2000) extend the Boyd et al (1993) approach to consider
market based risk measures and find that the addition of non-banking activities increases
the merged institution’s exposure to market risk.
Whereas much of this earlier literature involved combining earnings streams from
somewhat unrelated activities with differing production and marketing functions, De
Young and Roland (2001) study the effects of changes in product mix within established,
integrated production processes. Using the degree of total leverage model they examine
the relationship between bank earnings volatility and the share of revenue generated by
fee-based activities and find that t he shift towards fee income generating activities within
traditional banking has been associated with increased bank risk. Whereas De Young and
Roland focus on the effect of the shift in product mix on risk, in this paper we extend
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their analysis by evaluating the effect of the introduction of fees and charges on
traditional product lines on the financial institution’s risk.
In contrast with the extensive U.S. empirical literature, there are few Australian
studies examining the effect of product expansion on financial institution risk. Rather, the
focus has been on macro or industry wide effects of banking deregulation in the 1980s
and early 1990s on bank risk (see Hogan and Sharpe (1984), Harper and Scheit (1992),
Gizycki and Goldsworthy (1999) and Dennis and Jeffrey (2002)). An exception,
however, is Sharpe and Tuzun’s (1998) study of the relationship between banks’ use of a
direct credit substitute, standby letters of credit, and the risk premium paid on their CDs.
They find that while riskier banks make greater use of standby letters of credit than less
risky banks, there is little evidence of a feed-back effect from standby letters of credit to
bank CD risk premiums.
This paper makes several important contributions to the literature. First, it recognizes
that the shift to fee income within financial institutions arises not only from shifts in
product mix towards new products with income derived from fees and charges but also
from a change in pricing policy on traditional banking products. Second, we use a
relatively new risk measure for financial institutions: the degree of total leverage. This
measure of risk is becoming more relevant as the business mix and production functions
of financial institutions move towards greater reliance on fee-based activities. Third, we
focus on the risk of credit unions and the effect of changes in their product mix and
pricing on that risk. The absence of prior studies of credit union risk and the observation
that many credit unions lack scale and managerial skills to implement complex risk
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management systems make a study of credit union behaviour particularly interesting.
Fourth, we use data for Australian credit unions because of the detailed disaggregation of
fee-based revenues that is available in the APRA quarterly general returns. In addition to
a comprehensive break-down of interest revenues by asset category and dividend and
trading revenues, credit unions report three categories of fee income: transaction fees on
loans and deposits, commissions received, and facility fees (off balance sheet). A shift in
pricing strategy on traditional loan and deposit products towards fee income would be
reflected in an increased share of revenues in the form of transactions fees on loans and
deposits while diversification into the non-traditional activities for credit unions would be
reflected in a greater share of commissions and facility fees in total revenue.
As credit unions are limited in their ability to tap into the public debt markets for
funding, market based measures of credit union risk are not available. Thus the focus in
this study is on earnings based risk measures that have been used in prior studies: the
coefficient of variation of earnings and return on assets, the standard deviation of the
return on assets, the probability of bankruptcy model as in Boyd et al (1993), and De
Young and Roland’s (2001) degree of total leverage model. However, we extend the
existing literature by incorporating a fifth measure. Because regulators and management
are concerned with the possibility of breaches of the regulatory risk based capital
adequacy requirement, we examine whether changes in product mix and/or pricing policy
affect the probability of credit unions exhausting their regulatory capital.
The plan of the paper is as follow s. Section II defines the five risk measures that
underlie the study. Then Section III presents the model linking diversification with credit
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union risk and describes the data. The results are then summarized in Section IV while
Section V concludes.
2. RISK MEASURES
2.1. The coefficient of variation
We define the coefficient of variation, CV, for two measures, earnings denoted p, and
return on assets, ROA :4
CV _ π =
STD _ π
Mean _ π
CV _ ROA =
(1)
STD _ ROA
Mean _ ROA
(2)
where STD is the standard deviation of the measure, p is the operating profit/(loss) before
income tax, extraordinary items, loan loss provisions and after charge -offs and
recoveries, and ROA is earnings scaled by total assets. 5 Because several credit unions
have very small or negative mean earnings, 6 we use the inverse of the coefficient of
variation multiplied by minus one to obtain a measure that is directly related to risk.
2.2. Variability of return on assets
4
Results using the coefficient of variation of return on equity were almost identical to those based on ROA
and hence are not reported in this study.
5
Return on assets is calculated using an average of the beginning and end of quarter total assets . Adjusting
the definition of profit, π, so that it is also net of the change in specific provisions, rather before loan loss
provisions, does not affect the results.
6
Only one credit union had a negative Mean_π over the sample period.
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An alternative to the coefficient of variation measures is to examine the standard
deviation of the return on assets, ROA, denoted STD_ROA.
2.3. The Z-Score
The Z-SCORE is an accounting based measure of the probability of bankruptcy, and
is defined as the likelihood of incurring a loss greater than the credit union’s total capital.
For our purposes total capital, denoted E, includes permanent share capital, share
premium account, general reserves, retained profits (accumulated losses), and outside
equity interests in controlled entities. Denoting minus the capital to total assets ratio by
k = (− E TA) , then following Boyd et al (1993, pp. 48-49) the probability of bankruptcy
is:
p(π < − E ) = p(ROA < k ) =
∫ φ( ROA )dROA
k
−∞
(3)
where φ(ROA ) is the probability density function of the return on assets. If ROA is
normally distributed then:
p (π < − E ) = ∫ N (0,1)dz
z
(4)
−∞
where z is the number of true standard deviations, σ, below the true mean return on
assets, ρ, that profit must fall to eliminate capital: 7
z=
7
k −ρ
σ
(5)
Boyd et al (1993) note that even if ROA is not normally distributed, z provides an upper bound on the
probability of bankruptcy.
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Using sample estimates for ρ and σ and the average of beginning and end of quarter data
for capital and total assets then the Z-SCORE , which is an estimate of –z, is:
Z − SCORE =
Mean _ ROA + Mean _ k
STD _ ROA
(6)
As the Z-SCORE statistic is inversely related to the probability of bankruptcy, in the
empirical analysis that follows we use minus the Z-SCORE as a direct measure of risk.
2.4. The probability of exhausting regulatory capital
From a regulatory perspective, a credit union that incurs losses greater than the
beginning of period excess regulatory capital is one that requires regulatory intervention.
Thus we define a measure of the probability that a credit union will breach the 8% risk
adjusted capital adequacy requirement, and denote the measure REG_Z-SCORE. 8 The
calculation of the REG_Z-SCORE is identical to equation (5) except that the total capital
to total assets ratio is replaced by the excess regulatory capital to total assets ratio. Excess
regulatory capital is defined as total risk adjusted capital less 8% of total risk adjusted
assets. As for the Z-SCORE measure, in the empirical analysis we use minus the REG_ZSCORE as a direct measure of risk.
2.5. The degree of total leverage model
8
For simplicity we ignore the distinction between Tier 1 and Tier 2 regulatory capital. Most regulatory
capital of credit unions is Tier 1 capital.
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The accounting treatment of the degree of total leverage, DTL, as described in De
Young and Roland (2001), is:
DTL =
REV − VC
REV − VC − FC
(7)
where REV is total revenue, VC is variable costs and FC is fixed costs. For a given total
revenue, firms with relatively high fixed costs vis-à-vis variable costs will have a high
DTL. When a firm is operating above break-even (where REV marginally exceeds the
sum of VC and FC), DTL is positive and varies in the range of +1 for a highly profitable
firm to +8 for a firm operating just above break-even. For firms operating below breakeven, DTL is negative and lies within the range of 0 for firms operating at the shut-down
point (where REV=VC) to -8 for firms operating just below break-even. Thus DTL has a
discontinuous range between 0 and 1.
De Young and Roland (2001) note that the degree of total leverage concept, while
elegantly presented in accounting texts, is difficult to apply. The distinction between
fixed and variable costs is often based on arbitrary cost accounting rules while the multiproduct nature of financial institution operations presents difficulties in allocating fixed
costs to product lines. Consequently, it is necessary to develop a proxy for the degree of
total leverage.
Noting that the variability of a firm’s earnings to a given change in revenue is
directly related to its ratio of fixed to variable costs and thus its degree of total leverage,
we then have for credit union i:
%∆ π i =
APRA
% ∆π i
× % ∆REV i = DTL i × %∆REV i
%∆ REV i
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where the degree of total leverage of credit union i is the revenue elasticity of its earnings
and π is as defined earlier. Following Mandelker and Rhee (1984), O’Brien and
Vanderheiden (1987), Dugan and Shriver (1992) and Lord (1998), De Young and Roland
use a two-stage time series regression approach to obtain an estimate of each bank’s
earnings sensitivity to changes in revenue. In the first stage of our study, each credit
union’s quarterly total revenue and earnings are de-trended and de-seasonalised by
regressing the profit and revenue series on a (cubic) time trend, T, and the first, second
and third quarter dummy variables, Q1, Q2 and Q3:
π it = πi 0 + ψ i1T + ψ i2T 2 + ψ i3T 3 + ψ i 4Q1 + ψ i5Q 2 + ψ i6Q 3 + µ(π)it
REV it = REV i 0 + γ i 1T + γ i 2 T 2 + γ i 3 T 3 + γ i 4 Q1 + γ i 5 Q 2 + γ i 6 Q 3 + µ( REV )it
(9)
(10)
The residuals from these first stage regressions, µ(π )it and µ(REV )it are then used in the
second stage regressions for each credit union:
µ(π )it = α i + β i µ(REV )it + ε it
(11)
The β i are then transformed to an elasticity measure of DTL:
 Mean _ REV
DTL i = β i 
 Mean _ π


i
(12)
3. THE MODEL AND DATA
3.1. Specification of the model
We examine the cross-sectional relationship between the six measures of credit
union risk described in the previous section and measures of product diversification and
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pricing, while controlling for merger activity and credit union size. Following De Young
and Roland (2001), we proxy product mix by revenue shares of the six primary sources of
credit union revenues: interest on personal loans and advances, INT_PERSONAL, interest
on residential loans and advances, INT_RESIDENTIAL, revenue from securities and
investments, REV_INVESTMENTS,
loan
and
deposit
transaction
fees, FEES,
commissions and off balance sheet facility fees, COMM_FEES, and other revenues,
OTHER_REVENUE.
Initially we combine the revenue shares across the six revenue sources into a single
measure of concentration (lack of diversification) of the credit unio n’s revenues across
the product categories. For this purpose we define the Herfindahl index of revenue
shares:
HERFINDAHL = ∑ (RS j × 100)
2
(13)
j
where RSj is the revenue share of product category j for j = 1 to 6.
As a proxy for merger activity associated with a credit union we include the number
of quarters within the sample period in which it was involved as an acquirer. 9 This
variable is denoted MERGER_Q. Moreover, credit union scale is proxied by the credit
union’s total revenue, REV. However, to account for possible non- linearity in the
relationship between credit union scale and risk we also include total revenue squared,
REV 2 . Thus our basic cross-sectional regression model is:
(
RISK m = f CONSTANT , HERFINDAHL , MERGER _ Q , REV , REV
9
2
)
(14)
The merger targets have been absorbed into the acquirer and hence do not feature in the database.
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for the m different risk measures defined in Section II.
The aggregate measure of credit union revenue concentration, HERFINDAHL, is
unable to distinguish the effects on risk emanating from alternative product mix and
pricing strategies. Thus as an alternative to the formulation in (14) we substitute product
revenue shares for the Herfindahl index. As the six revenue shares sum to unity, to avoid
singularity we exclude INT_PERSONAL from the regressions. As credit unions have
tended to diversify their activities away from personal loans and advances, this provides a
convenient form in which a revenue share regression coefficient indicates the effect on
risk of a shift in revenue shares from interest on personal loans and advances to the
alternative product.
3.2. The data
The data for the study is obtained from the quarterly general credit union return data
reported to APRA, and its predecessor organizations under the Financial Institutions
Scheme, expressed in real 1990 dollars using the consumer price index. The data includes
the profit and loss statement, balance sheet assets and liabilities, off-balance sheet
facilities, and risk-weighted capital adequacy. Each of the risk measures is calculated
over the 34 quarters from 1993(02) to 2001(03) for credit unions with complete data over
the sample period.10
As mergers can significantly distort measures of earnings volatility, we follow De
Young and Roland (2001) in adjusting the financial statement data for mergers. First, in
10
The use of complete data is consistent with the U.S. literature on bank risk.
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any quarter the credit union’s profit and loss or balance sheet data is the sum of its own
reported data plus the corresponding figure of the credit union(s) that it acquired during
the quarter or in subsequent quarters during the sample. Second, credit unions that were
involved in mergers in more than four quarters durin g the sample period were removed
from the sample. This reduced the sample by one, leaving a final sample of 198 credit
unions. Third, data for the merger quarter is excluded from the time series estimates of
the risk measures. Fourth, in addition to the data modifications, in the cross sectional
regressions examining the effect of diversification and pricing policy on credit union risk
we include a control for the number of quarters in which the credit union was an acquirer
in a merger or takeover of another credit union. Fifth, we perform a sensitivity analysis of
the results to merger activity by undertaking the cross-sectional regression analysis on a
smaller sample of 155 credit unions that were not involved in any mergers during the
sample period.
Summary statistics for the full sample of 198 credit unions are displayed in Table 1.
The credit unions are relatively small financial institutions with mean and median total
assets of $103m and $37m respectively in 1990 dollars. They have a mean quarterly
return on equity (ROE) of 2.84%, while the mean return on assets (ROA) of 0.27% and
return on total revenue ( ROR) of 10.71% are considerably smaller than the quarterly ROA
and ROR of 0.43% and 16.12% for U.S. banks in the De Young and Roland study. The
credit unions are well capitalized with a mean risk-adjusted capital ratio of 15.6%, well in
excess of the regulatory minimum of 8%. The typical credit union was not heavily
involved in merger activity during the sample with the mean number of quarters of
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merger activity of 0.38 suggesting that the typical credit union experiences a merger
quarter (as an acquirer) approximately once every 25 years.
The revenue mix of the credit unions over the sample period is concentrated in
interest income on personal and reside ntial loans with 46.1% and 28.1% of total credit
union revenues respectively. Of the remaining product categories, revenues from
securities and investments generated 13.5% of total revenues, while loan and deposit
transaction fees generated 5.0%, commissio ns and OBS facility fees generated 2.8%, and
all other revenue sources generated 4.5% of total revenues. The mean value of the
Herfindahl index, 3737, reflects the high level of concentration of revenues in personal
and residential lending, though it range s in value from 2132 to 8425. 11
Several features of the risk measures are noteworthy. First, the mean Z-SCORE of
73.6 (and median of 67.9) implies a default probability very close to zero. Thus the
overall risk of Australian credit unions is very low. Second, the mean DTL of 1.61 is
much higher than the mean DTL of 0.31 reported by De Young and Roland in their U.S.
bank sample. However the median credit union DTL of 1.38 is identical to the median
DTL of U.S. banks.
Table 2 examines the relationship between the six risk measures. The top panel
reports the correlation between the risk measures and, as expected, the correlations are
generally significantly positive. The exceptions are the correlations involving the degree
11
The maximum value of the Herfindahl index for a credit union with 100% of its revenues in a single
activity would be 10,000.
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of total leverage, DTL, and the remaining risk measures. While positive, they are much
weaker than the paired correlations between the other four measures. 12
As an alternative to the correlations, the bottom panel of Table 2 reports the nonparametric Wilcoxon signed-rank test of the hypothesis that the pairs of risk rankings
have the same distribution. The test statistic is based on the ranks of the absolute values
of the differences between the credit union rankings produced by the risk measures. The
test statistic for all paired risk combinations are not statistically significant indicating that
we cannot reject the hypothesis that the measures have the same distribution.
4. REGRESSION RESULTS
4.1. Credit union risk
The ordinary least squares regression results for the six risk measures for the full
sample of 198 credit unions and using the aggregate measure of product concentration,
HERFINDAHL , are reported as Regs 1 to 6 in Table 3.13 As noted previously, the
dependent variable in the coefficient of variation Regs 1 and 2 is minus the inverse of the
coefficient of variation. The explanatory power of the regressions varies considerably
across the risk measures, with an adjusted R2 of between 0.200 and .241 for the
coefficient of variation and standard deviation of the return on assets models in Regs 1 to
3, but almost zero for the Z-SCORE , REG_Z-SCORE, and DTL models in Regs 4 to 6.
12
13
This may be attributable to the discontinuity in the theoretical measure of degree of total leverage that is
ignored in the DTL proxy (see De Young and Roland, 2001, p. 71).
As in De Young and Roland (2001) the regression models were also estimated on a smaller sample of
N=155 credit unions that did not engage in merger activity. As the smaller sample results mirror those of
the full sample, for space reasons they are not reported. They are available from the authors on request.
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The results in Table 3 provide some support for the hypothesis that credit unions
with a highly concentrated product (revenue) mix have higher risk than more diversified
credit unions. For two of the six risk measures (the standard deviation of the return on
assets and degree of total leverage) the regression coefficient of the Herfindahl
concentration index is positive and statistically significant. This is cons istent with the
traditional view that more diversified credit unions have lower risk exposure. Moreover,
the coefficients of the linear and quadratic total revenue variables are generally
significantly negative and positive respectively. This is consistent with risk initially
falling with increasing scale but then increasing at large scale. Taking the derivative of
the estimated equation with respect to REV and setting the derivative equal to zero, Regs
1 and 2 suggest that, ceteris paribus, credit union risk in this sample is minimized at a
level of revenue of $14m and $16m in 1990 dollars respectively. With 193 of the 198
credit unions in our sample operating at a scale below this risk minimizing level there is
considerable scope for risk reducing scale economies in Australian credit unions. Finally,
there is evidence in the coefficient of variation regressions that the credit unions involved
in merger activity had higher levels of risk than non-acquiring credit unions.
In Table 4, the Herfindahl index is replaced by the product revenue share variables,
the revenue share on personal loans and advances being excluded to avoid singularity.
The explanatory power of the regressions with revenue shares is higher than those using
the Herfindahl index, with the adjusted R2 varying from .265 for the coefficient of
variation of the ROA model to .058 for the degree of total leverage model.
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In the introduction it was noted that Australian credit unions have diversified their
revenue streams by increasing transaction fe es on loans and deposits, by engaging in new
activities earning commissions and off-balance sheet facility fees, and by shifting their
lending away from personal loans and into residential lending. With respect to the change
in pricing policy, the results indicate that a reduction in the revenue share of interest on
personal lending offset by an equal increase in the revenue share of loan and deposit
transaction fees (FEES) is associated with higher credit union risk. For five of the six risk
measures the coefficient on the FEES variable is positive and statistically significant. The
exception is the standard deviation of the return on assets regression where the coefficient
has a positive coefficient but is insignificant. As the FEES variable captures the shift in
pricing policy away from interest income on personal loans towards a fee for service on
deposit accounts and loan facilities, the results are consistent with this change in pricing
policy being associated with increased credit union risk. Thus credit unions with a greater
share of their total revenues in loan and deposit transaction fees are associated with
higher levels of risk. Not only is the change in pricing policy effect on the risk measure
statistically significant but it is also economically significant. An increase in loan and
deposit transaction fees of 1% of total revenue matched by a fall in interest revenue on
personal loans will increase the coefficient of variation of earnings by 1.7%, the
coefficient of variation of return on assets by 1.8%, the Z-SCORE by 3.6%, and the
regulatory Z-Score by 6.3%.14
14
The respective coefficient on FEES is multiplied by 0.01 and divided by the mean value of the dependent
variable from Table 1.
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In contrast with the results for the change in pricing policy, the results in relation to
diversification into new business lines earning commission and off-balance sheet facility
fees suggest that these have had little influence on credit union risk. The coefficient on
the COMM_FEES variable is statistically significant and negative in the standard
deviation of the return on assets regression, Reg 3, but is insignificant for the five
remaining regressions. A possible explanation for the results could lie in the commissions
and off-balance sheet facility fees comprising such a small proportion of total revenues
for the average credit union (2.8%) that the effect on aggregate levels of risk is difficult
to detect.
There is strong evidence supporting the hypothesis that diversification away from
interest revenue on personal loans and advances towards residential lending reduces
credit union risk. The estimated coefficient on INT_RESIDENTIAL is significantly
negative in four of the six risk measures, including the coefficient of variation of
earnings, the standard deviation of return on assets, and the two Z-SCORE measures. In
terms of economic significance, the risk effect of an increase in interest on residential
loans of 1% of total revenue and an equal fall in interest on personal loans is considerably
less than that associated with the change in pricing policy above. Thus the coefficient of
variation of earnings falls by 0.6%, the standard deviation of the return on assets by 0.6%,
Z-SCORE by 0.6% and the regulatory Z-Score by 0.8%.
With respect to the controls for scale and merger activity, the results in Table 4
mirror those in Table 3. Risk initially falls with increasing scale to a gross revenue level
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of $13m to $15m but increases at greater scale. Moreover, credit unions that have
engaged in merger activity generally have higher risk.
4.2. Credit union returns
In Table 5 we examine the effect of changes in credit union pricing policy and
product mix on credit union earnings defined in Regs 1 and 3 as return on assets, ROA,
and in Regs 2 and 4 as the return on total revenue, ROR. Regs 1 and 2 use the Herfindahl
index as the aggregate measure of revenue concentration while Regs 3 and 4 subs titute
the revenue shares for the Herfindahl index. As the results in Table 3 suggest that greater
revenue concentration, reflected in higher levels of the Herfindahl index, is associated
with higher risk, we would expect an increase in the concentration of revenue shares to be
associated with higher earnings. This is borne out in Regs 1 and 2 of Table 5 where the
coefficient of HERFINDAHL is positive and statistically significant. Credit unions with
more concentrated revenue streams have higher returns on assets and on gross revenues.
With the disaggregated product revenue share regressions, Regs 3 and 4, a somewhat
different picture emerges. All revenue share coefficients are negative, indicating that over
our sample period, a reduction in the total revenue share of interest revenue on personal
loans was associated with a reduction in earnings. However only four of the revenue
share regression coefficients are statistically significant. Consistent with the prediction
from the asset pricing literature, an increased revenue share of residential lending is
associated with both a reduction in risk (from Table 4) and a reduced return on assets.
Moreover, the results in Table 5 are consistent with a shift from personal lending towards
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holding cash reserves and fixed income investments lowering credit union returns, though
there is no evidence from Table 4 that credit unions with greater revenue share in cash,
deposits, and liquid investments have lower risk.
Furthermore, a pricing decision that increases the share of loan and deposit
transaction fees in total revenue is associated with higher risk and lower earnings. While
this result is inconsistent with the traditional risk-return relationship, a possible
explanation may lie in the introduction of transaction fe es being a response of credit
unions experiencing severe pressures on their interest margins. When Australian banks
initially introduced transaction fees across a wide range of their business to offset
declining interest margins, their customers reacted ne gatively to the shift in pricing
policy. Credit unions then attempted to gain a competitive advantage by continuing to
offer their services without transaction fees. This placed pressure on credit union interest
margins which, by 1997, no longer covered their expenses (see CUSCAL, 1998, p.21).
Ultimately transaction fees were introduced by credit unions with those experiencing the
severest pressure on interest margins being the leaders in introducing the fees. Hence we
observe the inverse relationship between risk and return.
4.3. Sub-period results
In order to examine the stability of the results over time, the sample period of 34
quarters was split into two 17 quarter sub -periods and the risk measures and revenue
shares computed for each of the sub-perio ds. As the degree of total leverage measure
produced implausible and highly volatile estimates of risk for the 1997(03)-2001(03) sub -
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period, it is excluded from the stability results. Table 6 reports the sub-period means and
standard deviations of the risk measures and revenue shares together with a t test of the
significance of the difference in sub-period means. Three of the five measures,
STD_ROA , Z-SCORE and REG_Z-SCORE, are significantly lower in the latter period
consistent with credit union risk declining over time. Moreover, the revenue shares reveal
a significant decline in the share of personal loans in total revenues and an increase in the
proportion of revenues from fees (though from a relatively low base). The diversification
effect of these cha nges is evident in the significant fall in the mean Herfindahl index
across the sub -samples. Finally, we note the very significant fall in the return on total
assets reflecting the deteriorating competitive position of credit unions in recent years,
though this is less pronounced in the return on total revenues measure. 15
Tables 7 and 8 report the revenue share decomposition regressions for the 1993(2)1997(2) and 1997(3)-2001(2) sub -samples while the corresponding earnings regression
results are reported in Table 9.16 These results are generally consistent with the full
sample results reported earlier. The shift to residential lending is associated with lower
risk and returns in both sub-periods while the shift towards loan and deposit transaction
fees is as sociated with higher risk but lower returns. An interesting aspect of the sub sample results that is not evident in the full period results relates to the effect of the shift
towards new lines of business generating commissions and off-balance sheet facility fees.
For the earlier sub -period results in Table 7, four of the five COM_FEES regression
15
16
Consistent with the asset pricing literature, the latter period is characterized by lower credit union risk
and returns.
Sub-sample regressions were also run for the model using the Herfindahl index but were very similar to
the full sample results and hence are not reported.
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coefficients are positive and statistically significant reinforcing the pricing decision
results that the shift towards fee income is risk increasing. In the later sub-period this
variable is only significant and positive in the regulatory Z-Score regression suggesting
that the risk effect was largely associated with the early part of our sample period.
Moreover, there is little evidence in Table 9 that the shift towards new fee income
activities affected credit union returns.
With respect to the controls, the sub-period results are also consistent with the
existence of scale economies at revenues less than $15m and diseconomies at higher
levels. Thus the great majority of credit unions may increase returns and reduce risk by
increasing scale. However, unlike the full sample results, there is little evidence that
credit unions engaging in merger activity have higher levels of risk.
5. CONCLUSION
Australian credit unions have diversified their activities in the 1990s to reduce their
reliance on interest revenue from personal loans and advances. This took three primary
forms: (i) a change in pricing policy that introduced transaction fees on loans and
deposits; (ii) the introduction of new financial services including insurance, funds
management and off-balance sheet activities that generated commissions and facility
fees; and (iii) a shift in the portfolio mix of assets away from personal loans and advances
into resid ential lending.
The study uses a cross-sectional ordinary least squares regression analysis of 198
Australian credit unions and six risk measures to examine the relationship between a
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credit union’s product mix, pricing policy, risk, and earnings. The results confirm the
findings of the only other previous study to use the DTL risk measure. DeYoung and
Roland’s (2001) finding that increased reliance on fee income generating activities is
associated within increased risk, not only applies to U.S. commercial banks, but also to
Australian credit unions .
While there are differences evident in the results across the risk measures, we find
that credit unions with highly concentrated revenues have higher levels of risk and
returns. On a disaggregated level, credit unions that diversify by reducing the revenue
share of interest on personal loans and increasing the revenue share of transaction fees on
loans and deposits have higher risk and lower returns. There is also evidence in the early
part of the sample that credit unions which diversify into new activities generating
commissions and facility fees have higher levels of risk. Moreover, credit unions with a
higher proportion of total revenue in the form of interest on residential loans and a lower
proportion of revenues in interest on personal loans have significantly lower risk and
returns, consistent with modern portfolio theory. Finally, the results suggest the
possibility of scale economies in risk and return for many of the small credit unions.
An important caveat in interpreting these results is that a significant relationship
need not imply causality. Thus the finding that credit unions with a higher proportion of
fee income are associated with higher risk is consistent with either the credit unions
taking on a higher risk activity or alternatively higher risk credit unions electing to get
into this new business line. Bearing this caveat in mind, the results have several
implications for the Australian credit union industry and for its prudential regulator,
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APRA. In its 1998 review of credit union performance and future structure of the
industry, the movement’s central body, CUSCAL, identified the need to reduce credit
union costs by increasing scale as a key element of the industry’s long-run viability (see
CUSCAL, 1998, pp 60-66). While our results are consistent with this strategy, they also
suggest that the benefits of increasing scale could go beyond that of improving credit
union profitability. Enhancing the scale of smaller credit unions may also reduce their
risk. However, CUSCAL also argued that high priority needed to be given to increasing
non- interest income in order to improve returns through economies of scope and reduce
risk through diversification. Our results suggest the need for caution in imple menting this
strategy in that over our sample period the credit unions that generated a higher
proportion of gross revenues from non- interest income had higher risk and lower returns
than those that had relatively small proportions of non- interest income.
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TABLE 1
Summary Statistics for the Sample of 198 Australian Credit Unions, 1993(2) – 2001(3).
Mean
Std Dev
Median
Min
Max
Revenue in 1990$M (REV)
Profit in 1990$M (π)
$2.499
$4.230
$0.876
$0.005
$29.508
$0.295
$0.530
$0.083
-$0.001
$2.958
Total Assets in 1990$M (TA)
Return on Assets (ROA)
Return on Equity (ROE)
Return on Revenue (ROR)
$102.76
$173.41
$37.17
$0.21
$1,182.01
0.27%
0.12%
0.26%
-0.00%
0.81%
2.84%
2.75%
1.37%
-0.04%
7.44%
10.71%
4.74%
10.25%
-0.10%
28.96%
Risk Adjusted Capital Ratio
Merger Quarters (MERGER_Q)
15.63%
6.14%
13.95%
5.21%
46.44%
0.384
0.881
0.000
0.000
4.000
0.461
0.170
0.451
0.053
0.915
0.281
0.152
0.300
0.000
0.733
0.135
0.053
0.122
0.058
0.358
0.050
0.044
0.046
0.000
0.246
0.028
0.021
0.026
0.000
0.103
0.045
0.042
0.032
0.000
0.235
3736.7
1165.5
3358.4
2132.3
8425.2
-1.81
0.98
-1.77
-5.33
0.02
-1.937
1.170
-1.825
-6.540
0.018
0.17%
0.10%
0.15%
0.04%
0.73%
-73.62
35.01
-67.86
-256.62
-10.11
-37.40
26.41
-31.28
-203.94
0.68
1.61
10.06
1.38
-128.95
26.11
Revenue Shares
INT_PERSONAL
INT_RESIDENTIAL
REV_INVESTMENTS
FEES
COM_FEES
OTHER_REVENUE
Herfindahl Index
HERFINDAHL
Risk Measures
-(CV_π)-1
-(CV_ROA)-1
STD_ROA
-(Z-SCORE)
-(REG_Z-SCORE)
DTL
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TABLE 2
Correlation Matrix and Wilcoxon Rank Correlation Test for Risk Measures of Australian Credit Unions
over the 1993(2) – 2001(3) period.
Correlation Matrix
-1
-(CV_π)
-(CV_ π)
-(CV_ROA)-1
STD_ROA
-(Z-SCORE)
-(REG_Z-SCORE)
DTL
-1
1
0.77***
0.51***
0.50***
0.35***
0.02
-(CV_π)-1
-(CV_ π)-1
-(CV_ROA)-1
STD_ROA
-(Z-SCORE)
-(REG_Z-SCORE)
DTL
-0.528
-0.305
-0.430
-0.290
-1.446
-1
-(CV_ROA)
1
0.56***
0.61***
0.36***
0.02
STD_ROA
-(Z-SCORE)
-(REG_Z-SCORE)
DTL
1
0.57***
0.27***
0.14**
1
0.90***
0.14**
1
0.12*
1
Wilcoxon Rank Correlation Statistics
-(CV_ROA)-1
STD_ROA
-(Z-SCORE)
-0.109
-0.855
-0.330
-1.465
-0.622
-0.087
-0.528
-0.038
-0.649
-(REG_Z-SCORE)
-0.300
'***','**', and '*' denote significance at the 99%, 95% and 90% confidence levels respectively.
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TABLE 3
Revenue Concentration and Credit Union Risk: Ordinary Least Squares Regression Results
(Full Sample N=198)
Dependent Variable
-(CV_ π)-1
-(CV_ROA)-1
STD_ROA*100
-(Z-SCORE)
-(REG_Z-SCORE)
DTL
Reg 1
Reg 2
Reg 3
Reg 4
Reg 5
Reg 6
Independent Variables
CONSTANT
Coeff.
-1.801
HERFINDAHL x 10-3
0.084
REV
REV 2
MERGER_Q
-0.244
0.009
0.171
t-stat1
-7.32***
1.47
-6.35***
5.22***
2.18**
Coeff.
-1.618
0.036
-0.317
0.010
0.230
t-stat1
-5.67***
0.54
-7.11***
5.20***
2.53**
Coeff.
0.055
t-stat1
2.26**
Coeff.
-68.797
0.036
6.35***
0.031
-2.837
0.120
-1.929
-0.009
0.000
0.003
-2.39**
1.75*
0.43
t-stat1
-7.04***
Coeff.
-28.177
t-stat1
-3.81***
Coeff.
-3.342
t-stat1
-1.18
0.01
-2.413
-1.41
1.250
1.92*
-1.86*
1.74*
-0.62
0.287
0.008
-2.908
0.25
0.16
-1.23
0.119
-0.006
0.356
0.27
-0.32
0.40
SUMMARY STATISTICS
Adjusted R2
F-Test
1
0.200
13.3229***
0.241
16.659***
0.233
15.9231***
0.006
1.316
0.001
0.970
0.001
0.962
'***','**', and '*' denote significance at the 99%, 95% and 90% confidence levels respectively.
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TABLE 4
Revenue Composition and Credit Union Risk: Ordinary Least Squares Regression Results
(N=198)
Dependent Variable
-(CV_ π)-1
-(CV_ROA)-1
STD_ROA*100
-(Z-SCORE)
-(REG_Z-SCORE)
DTL
Reg 1
Reg 2
Reg 3
Reg 4
Reg 5
Reg 6
Independent Variable
CONSTANT
Coeff.
-1.556
t-stat1
-5.44***
Coeff.
-1.715
t-stat1
-5.12***
INT_RESIDENTIAL
REV_INVESTMENTS
FEES
COM_FEES
OTHER_REVENUE
-1.198
1.559
3.034
1.854
-0.388
-2.69***
1.20
1.95*
0.55
-0.24
-0.848
1.971
3.403
-3.127
1.593
-1.63
1.29
1.87*
-0.79
0.85
-0.265
-0.056
0.072
-0.730
-0.073
REV
REV 2
MERGER_Q
-0.238
0.009
0.160
-5.76***
5.06***
2.08**
-0.295
0.010
0.243
-6.10***
4.76***
2.69***
-0.008
0.000
0.002
Coeff.
0.288
t-stat1
9.92***
Coeff.
t-stat1
-68.083 -6.35***
Coeff.
-38.422
t-stat1
-4.87***
Coeff.
-0.397
t-stat1
-0.12
-5.86***
-0.43
0.46
-2.13**
-0.45
-44.629
-24.515
268.521
28.017
50.852
-2.67***
-0.50
4.59***
0.22
0.84
-29.211
-43.422
234.245
124.927
42.281
-2.38**
-1.21
5.45***
1.34
0.95
1.561
21.576
46.271
-28.350
-69.626
0.31
1.45
2.61***
-0.74
-3.80***
-1.87*
1.64
0.21
-3.877
0.158
-0.894
-2.50**
2.38**
-0.31
-1.013
0.052
-1.920
-0.89
1.06
-0.90
0.203
-0.011
0.094
0.43
-0.54
0.11
SUMMARY STATISTICS
Adjusted R2
F-Test
1
0.238
8.6755***
0.265
9.8914***
0.230
8.3715***
0.156
5.5481***
0.198
7.0859***
0.058
2.5212**
'***','**', and '*' denote significance at the 99%, 95% and 90% confidence levels respectively.
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TABLE 5
Revenue Concentration, Composition and Credit Union Returns: Ordinary Least Squares Regression Results
(N=198)
Dependent Variable
ROA
1
Reg 1
Independent Variables
CONSTANT
Coeff.
0.148
t-stat2
4.53***
1
ROR
ROA 1
ROR1
Reg 2
Reg 3
Reg 4
Coeff.
6.876
t-stat2
5.35***
INT_RESIDENTIAL
REV_INVESTMENTS
FEES
COM_FEES
OTHER_REVENUE
HERFINDAHL
REV
REV 2
MERGER_Q
0.025
3.29***
0.767
2.59**
0.022
-0.001
-0.015
4.32***
-3.21***
-1.46
0.711
-0.024
-0.631
3.55***
-2.62***
-1.54
Coeff.
0.411
t-stat2
10.69***
Coeff.
14.059
t-stat2
9.34***
-0.144
-0.628
-0.346
-0.412
-0.184
-2.41**
-3.58***
-1.65
-0.90
-0.85
-0.174
-14.596
-25.214
-22.988
-6.251
-0.07
-2.12**
-3.07***
-1.29
-0.74
0.018
-0.001
-0.016
3.17***
-2.44**
-1.56
0.656
-0.022
-0.702
3.02***
-2.33**
-1.73*
SUMMARY STATISTICS
Adjusted R2
F-Test
1
2
APRA
0.100
6.4644***
0.064
4.3617***
0.122
4.4281***
0.093
3.5302***
Return on assets and return on revenues are scaled by a factor of 100.
'***','**', and '*' denote significance at the 99%, 95% and 90% confidence levels respectively.
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TABLE 6
Sub-Sample Summary Statistics for the sample of 198 Australian Credit Unions.
1993(2) - 1997(2)
Sub-Sample
Mean
-2.263
-2.274
0.171
-74.699
-36.847
3.928
Std Error
1.334
1.340
0.110
35.029
24.450
10.975
Mean
-2.141
-2.258
0.145
-100.650
-51.827
22.816
Std Error
1.437
1.613
0.104
54.828
38.520
464.733
Test of Difference
in Means
t-Statistic 1
0.86
0.07
-2.79***
-5.61***
-4.62***
0.57
ROA
ROR
0.003
0.112
0.001
0.049
0.002
0.103
0.001
0.054
-5.58***
-1.68*
HERFINDAHL
3628.4
1328.3
3056.2
1263.1
-4.38***
INT_PERSONAL
INT_RESIDENTIAL
REV_INVESTMENTS
FEES
COM_FEES
OTHER_REVENUE
0.440
0.271
0.138
0.028
0.022
0.040
0.206
0.160
0.052
0.035
0.019
0.045
0.349
0.288
0.133
0.067
0.032
0.049
0.203
0.158
0.059
0.054
0.023
0.047
-4.37***
1.25
-1.78*
8.79***
4.71***
2.15**
Model Variables
-(CV_ π)-1
-(CV_ROA)-1
STD_ROA
-(Z-SCORE)
-(REG_Z-SCORE)
DTL
1
APRA
1997(3) - 2001(3)
Sub-Sample
'***','**', and '*' denote significance at the 99%, 95% and 90% confidence levels respectively.
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TABLE 7
Revenue Composition and Credit Union Risk: Ordinary Least Squares Regression Results for Sub-Sample 1993(2) – 1997(2)
(N=198)
Dependent Variable
-(CV_π)-1
-(CV_ROA)-1
STD_ROA * 100
-(Z-SCORE )
-(REG_Z-SCORE)
Reg 1
Reg 2
Reg 3
Reg 4
Reg 5
Independent Variable
CONSTANT
Coeff.
-1.885
t-stat1
-5.37***
Coeff.
-2.057
t-stat1
-6.15***
INT_RESIDENTIAL
REV_INVESTMENTS
FEES
COM_FEES
OTHER_REVENUE
-1.811
1.702
3.600
10.425
0.126
-3.19***
0.98
1.44
2.09**
0.07
-0.853
1.947
3.273
8.641
1.817
-1.58
1.18
1.38
1.82*
0.99
REV
REV 2
MERGER_Q
-0.301
0.010
0.116
-4.88***
3.11***
0.60
-0.429
0.015
0.318
-7.29***
5.13***
1.73*
t-stat1
8.62***
Coeff.
-61.599
t-stat1
-6.53***
Coeff.
-26.701
t-stat1
-4.04***
-0.224
-0.032
0.203
-0.068
-0.104
-4.63***
-0.22
0.95
-0.16
-0.63
-29.414
-67.810
326.757
273.453
2.175
-1.93*
-1.46
4.88***
2.04**
0.04
-23.611
-84.722
250.763
202.950
13.051
-2.22**
-2.61***
5.34***
2.17**
0.36
-0.014
0.000
-0.006
-2.59**
1.75*
-0.34
-6.650
0.205
-2.429
-4.01***
2.44**
-0.47
-2.004
0.050
-4.449
-1.73*
0.84
-1.22
Coeff.
0.258
SUMMARY STATISTICS
Adjusted R2
F-Test
1
23.34%
8.4992***
30.92%
12.0239***
17.72%
6.3031***
19.85%
7.0983***
19.33%
6.9008***
'***','**', and '*' denote significance at the 99%, 95% and 90% confidence levels respectively.
APRA
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TABLE 8
Revenue Composition and Credit Union Risk: Ordinary Least Squares Regression Results for Sub-Sample 1997(3) – 2001(3)
(N=198)
Dependent Variable
-(CV_π)-1
-(CV_ROA)-1
STD_ROA * 100
-(Z-SCORE)
-(REG_Z-SCORE)
Reg 1
Reg 2
Reg 3
Reg 4
Reg 5
Independent Variable
CONSTANT
Coeff.
-2.084
t-stat1
-4.83***
Coeff.
-2.008
t-stat1
-4.18***
INT_RESIDENTIAL
REV_INVESTMENTS
FEES
COM_FEES
OTHER_REVENUE
-1.156
3.531
2.665
-0.763
1.106
-1.79*
1.95*
1.40
-0.17
0.51
-1.530
3.539
1.969
-2.226
2.282
-2.13**
1.76*
0.93
-0.45
0.95
REV
REV 2
MERGER_Q
-0.248
0.008
0.214
-4.76***
4.17***
1.29
-0.273
0.008
0.280
-4.71***
3.86***
1.52
t-stat1
8.20***
Coeff.
-91.982
t-stat1
-5.46***
Coeff.
-55.590
t-stat1
-4.85***
-0.277
-0.018
0.009
-0.405
0.017
-6.04***
-0.14
0.07
-1.29
0.11
-100.510
3.995
206.176
266.057
114.484
-4.00***
0.06
2.77***
1.55
1.36
-61.109
-28.902
234.246
245.341
78.339
-3.57***
-0.60
4.63***
2.09**
1.37
-0.008
0.000
0.002
-2.29**
2.22**
0.19
-5.590
0.223
1.426
-2.75***
2.98***
0.22
-1.698
0.085
-1.344
-1.23
1.68*
-0.31
Coeff.
0.252
SUMMARY STATISTICS
Adjusted R2
F-Test
1
18.49%
6.5843***
19.88%
7.1088***
21.50%
7.7436***
14.81%
5.2795***
20.04%
7.1702***
'***','**', and '*' denote significance at the 99%, 95% and 90% confidence levels respectively.
APRA
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TABLE 9
Sub-Sample Ordinary Least Squares Regression Results for Profitability Measures of 198 Australian Credit Unions.
1993(2) - 1997(2) Sub-Sample
1997(3) - 2001(3) Sub-Sample
Dependent Variable
ROA
1
Reg 1
Independent Variables
CONSTANT
INT_RESIDENTIAL
REV_INVESTMENTS
FEES
COM_FEES
OTHER_REVENUE
Coeff.
0.425
-0.118
-0.557
-0.047
-0.546
-0.365
t-stat2
10.10***
-1.74*
-2.69***
-0.16
-0.91
-1.58
Dependent Variable
1
ROR
1
ROA
ROR1
Reg 2
Reg 3
Reg 4
t-stat
Coeff.
12.969
2
9.01***
0.197
-7.435
-15.042
-37.963
-12.470
0.08
-1.05
-1.47
-1.86*
-1.58
Coeff.
0.392
2
t-stat
t-stat
2
9.85***
Coeff.
14.855
8.77***
-0.145
-0.738
-0.381
-0.125
-0.098
-2.43**
-4.43***
-2.17**
-0.31
-0.49
-0.325
-22.618
-26.603
-10.380
-1.760
-0.13
-3.19***
-3.56***
-0.60
-0.21
0.010
2.19**
0.428
2.09**
HERFINDAHL
REV
2
REV
MERGER_Q
0.019
2.52**
0.710
-0.001
-0.018
-2.03**
-0.77
-0.029
-0.870
2.81***
-2.25**
-1.10
0.000
-0.018
-1.54
-1.15
-0.012
-0.516
-1.58
-0.79
SUMMARY STATISTICS
Adjusted R2
F-Test
1
2
APRA
0.065
2.7141***
0.045
2.1587**
0.126
4.5521***
0.103
3.815***
Note: Return on assets and return on sales are scaled by a factor of 100.
'***','**', and '*' denote significance at the 99%, 95% and 90% confidence levels respectively.
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