Abstract
Investment banks’ core functions expose them to a wide array of risks. This paper analyses cost and profit efficiency for a sample of investment banks for the G7 countries (Canada, France, Germany, Italy, Japan, UK and US) and Switzerland prior to the recent financial crisis. We follow Coelli et al. (J Prod Anal 11:251–273, 1999)’s methodology to adjust the estimated cost and profit efficiency scores for environmental influences including key banks’ risks, bank- and industry- specific factors and macroeconomic conditions. Our evidence suggests that failing to account for environmental factors can considerably bias the efficiency scores for investment banks. Specifically, bank risk-taking factors (including liquidity and capital risk exposures) are found particularly important to accurately assess profit efficiency: i.e. profit efficiency estimates are consistently underestimated without accounting for bank risk-taking. Interestingly, our evidence suggests that size matters for both cost and profit efficiency, however this does not imply that more concentrated markets are more efficient.
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Notes
Other operating expenses are generally lower than compensation expenses, and concern communication and technology, occupancy and depreciation, brokerage, clearing and exchanges fees, marketing and advertisements, office supplies, etc. For further details see e.g. Liaw (2006).
As usual, symmetry and linear homogeneity restrictions are imposed standardising total cost TC and input prices P i by the last input price.
For further readings see Coelli et al. (1999, p. 255).
Using a translog specification we have to solve the problem of sample banks with negative values of profit, for which we cannot take the logarithm. Therefore, the constant term θ=|πmin| + 1 is added to every firm’s dependent variable in the profit function so that natural log is taken for a positive number (Berger and Mester 1997). Thus, for the firm with the lowest value for that year, the dependent variable will be ln(1) = 0. However our analysis has shown that by using this measure, for dealing with losses, we obtain biased data. Based on that, and since the number of banks exhibiting a loss is small relative to the sample size (less than 10% of the sample), we choose to drop relative observations as in Humphrey and Pulley (1997). Similar adjustments have been used by e.g. Vander Vennet (2002); Casu and Girardone (2004); Fiordelisi (2007); Fitzpatrick and McQuinn (2008).
The investigation of the financial crisis’ effects on investment banks’ efficiency would require a different research methodology and a tailored dataset (therefore we do not include 2008 data in the sample). In addition, the number of banks available in Bankscope for 2008 would be drastically reduced making our sample heterogeneous.
We are aware that Bankscope data on investment banks is not as detailed as for commercial banks. One of the main limitations is that the input and output data cannot be disaggregated by investment banking function or activity (e.g. merger and acquisition advisory).
As a robustness test, we also re-estimate all models with the total interest income added in the definition of investment banks’ total earning assets. The new efficiency estimates are very similar to the original estimates thereby suggesting that our main findings are robust across different output specifications. We thank the referee for suggesting this test.
Following the Bankscope ‘global’ specification, the item ‘other earning assets’ comprises deposits with banks, due from central banks, due from other banks, due from other credit institutions, total securities, treasury bills, other bills, bonds, CDs, equity investments and other investments.
However, in this study we have carried out various robustness tests by using alternative models. First, the number of input and output specifications for our model has been tested. We have estimated alternative models with total funds and interest income as additional outputs (we thank an anonymous referee for suggesting these robustness checks). The chosen specification seems to best fit the available data.
As usual one dummy (Canada) is dropped from the model to avoid multicollinearity. So, we have a total of seven country dummies.
Only the combination of structural characteristics and environmental ones allows us to capture the industry efficiency and explain national differences.
Statistically testing which model would best fit the data is not straightforward. As suggested by the relevant literature (see for example Coelli et al. 1999), one would need to create an artificial nested model. Due to data constraints and to different set of environmental variables used in the two models, we can only decide on the basis of theoretical motivations.
These results are similar with Casu and Girardone (2004).
On the relationship between efficiency and stock performance see e.g. Beccalli et al. (2006).
To further confirm these results, we carried out several robustness tests. Specifically, we carried out a t-test to the two subsamples of the largest and smallest banks in the sample (i.e. the fourth and first quartile of the size distribution). Results are robust only for the cost case where smaller investment banks are more efficient then larger banks at 10% significance level.
For an extensive review see DeYoung et al. (2009).
Actually these findings could even be signaling some evidence of the validity of Hick’s quiet life hypothesis in investment banking and thus should be explored in more detail (Hicks 1935).
FSIBs offer clients a range of services including underwriting, merger and acquisition advisory services, trading, merchant banking and prime brokerage. BIBs specialize in particular segments of the market. Typically they do not offer a broad range of services and are not part of larger financial institutions. For more details see Davis (2003), and Gardener and Molyneux (1995).
We assess the similarity between the two set of efficiency estimates using both the Spearman’s rho correlation and Pearson correlation coefficients (for all models). All estimated coefficients are constantly greater than 90%.
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The authors wish to express thanks to HalukÜnal (the Editor) and an anonymous referee for their useful comments and suggestions. We also thank Meryem D. Fethi, Iftekhar Hasan, Fotios Pasiouras, and conference participants at the International Conference on “Global trends in the Efficiency and Risk Management of Financial Services and the Financial Crisis” held 2009, in Leicester. All errors, of course, rest with the authors.
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Radić, N., Fiordelisi, F. & Girardone, C. Efficiency and Risk-Taking in Pre-Crisis Investment Banks. J Financ Serv Res 41, 81–101 (2012). https://doi.org/10.1007/s10693-011-0111-1
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DOI: https://doi.org/10.1007/s10693-011-0111-1