Accounting conservatism and banking expertise on board of directors

Abstract

Previous studies show mixed evidence of the role of banking expertise on the board of directors on accounting conservatism. In this paper, we add to this growing literature by providing an innovative way to measure banking expertise based on life-time working history in banks of all individual directors on the board. We find that accounting conservatism is negatively affected by banking expertise on the board. Also, the results indicate that banking expertise on the board has a more pronounced impact on accounting conservatism when firms have high bankruptcy risk and when firms have high financial leverage. The evidence has some implications for boards of directors.

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Notes

  1. 1.

    Our measure of directors’ working experience in the banking industry is reasonably reliable. If a director has worked for a bank outside the UK, the name of the bank may also be included in the list, because London has been known as one of the leading financial centres in the world for many years.

  2. 2.

    Although some studies claim that Basu’s model is biased (Caskey and Peterson 2014; Dietrich et al. 2007; Givoly et al. 2007; Pae et al. 2005; Roychowdhury and Watts 2007), there is emerging and robust evidence that Basu’s coefficient is a valid measure for accounting conservatism. Ball et al. (2013b) provide formal tests in different settings and conclude that Basu’s coefficient is valid. They explain that a limitation of the work of Basu (1997) is that the author does not provide formal econometric and comprehensive analyses to support the model, which could potentially invite questions from researchers about the validity of the model. Ball et al. (2013a) provide further evidence to support the validity of Basu’s model.

  3. 3.

    García Lara et al. (2016, p. 236) provide evidence for the validity of their measure of total accounting conservatism by showing that it is strongly related to determinants of accounting conservatism, namely financial leverage, firm size, and market-to-book ratio.

  4. 4.

    Khan and Watts (2009) use SIZE, MTB, and LEV in year t to estimate GSCORE and CSCORE. In this paper, we use SIZE, MTB, and LEV in year t−1. We argue that earnings are the incomes of the whole year so that firms may rely on the conditions (characterised by LEV, SIZE, and MTB) in year t−1 to make decisions on how much accounting numbers should be conservative in year t. The idea of using firm characteristics in year t−1 is also stipulated by Ball et al. (2013a). An example of using the same approach to estimate GSCORE and CSCORE is the work of Banker et al. (2012).

  5. 5.

    We follow García Lara et al. (2016) to run regressions between CONS_RANK and determinants of accounting conservatism, which are firm size (SIZE), financial leverage (LEV), and market-to-book ratio (MTB). The findings (unreported) show that CONS_RANK is highly correlated with firm size, financial leverage, and market-to-book ratio (measured in both year t and year t−1).

  6. 6.

    A note is that coefficients reported the table are already multiplied by 100.

  7. 7.

    When we use the average number of banks directors have worked for, the results are statistically unchanged.

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Appendix: Variable definitions

Appendix: Variable definitions

Variable Definitions
Accounting conservatism measures
CONS_RANK Annual fractional rank of the three-year average of total accounting conservatism (García Lara et al. 2016), where total accounting conservatism is the sum of the timeliness of good news (GSCORE) and the asymmetric timeliness of bad news over good news (CSCORE) estimated by the model of Khan and Watts (2009), which is based on Basu (1997). We calculate the average of total accounting conservatism across years t − 2, t − 1, and t (denoted \( aCONS_{i,t} \)); then rank \( aCONS_{i,t} \) of all firms for each year; and divide the rank values by N + 1, where N is the total observations in each rank group. We refer to the new variable as the annual fractional rank of total accounting conservatism
CSCORE_RANK Annual fractional rank of the three-year average of CSCORE (Basu 1997; Khan and Watts 2009)
NOACC_RANK Annual fractional rank of the three-year average of the negative accumulation of non-operating accruals (Givoly and Hayn 2000). The calculation of negative non-operating accruals is as follows:
\( NOACC_{i,t} = - 1*\left\{ {TABD_{i,t} - OA_{i,t} } \right\} = - 1*\left\{ {\left[ {\left( {NI_{i,t} + DEP_{i,t} } \right) - CFO_{i,t} } \right] - \left[ {\Delta REC_{i,t} + \Delta INV_{i,t} + \Delta PREPAID_{i,t} - \Delta PAY_{i,t} - \Delta TAX_{i,t} } \right]} \right\} \)
where \( NOACC_{i,t} \) is negative non-operating accruals at the end of year t; \( TABD_{i,t} \) is total accruals before depreciation and amortisation at the end of year t; \( OA_{i,t} \) is operating accruals at the end of year t; \( NI_{i,t} \) is net income in year t; \( DEP_{i,t} \) is depreciation and amortisation in year t; \( CFO_{i,t} \) is cash flows from operations in year t; \( \Delta REC_{i,t} \) is change in account receivables from the end of year t − 1 to the end of year t; \( \Delta INV_{i,t} \) is change in inventories from the end of year t − 1 to the end of year t; \( \Delta PREPAID_{i,t} \) is change in prepaid expenses from the end of year t − 1 to the end of year t; \( \Delta PAY_{i,t} \) is change in account payables from the end of year t − 1 to the end of year t; \( \Delta TAX_{i,t} \) is change in tax payables from the end of year t − 1 to the end of year t. All variables are scaled by total assets at the end of year t. i represents company i and t represents fiscal year t
Banking expertise measures
yEXPERTISE Total number of years all directors on the board have worked as executives in banks
aEXPERTISE Total number of banks for which all directors on the board have worked as executives
EXPERTISE The presence of banking expertise on the board, which is equal one if a company has at least one director on board who has worked as an executive in a bank, zero otherwise
mEXPERTISE Average number of years all directors on the board have worked as executives in banks, which is equal yEXPERTISE divided by the number of board members
Other variables
\( \Delta SALE \) Sale growth, which is equal change in sales from year t − 1 to year t, scaled by total assets at the end of year t
ACC Accruals which are calculated as follows: \( ACC = \Delta INV + \Delta REC + \Delta OCA - \Delta PAY - \Delta OCL - DEP \), where \( \Delta INV \) is change in inventories from the end of year t − 1 to year the end of year t, \( \Delta REC \) is change in receivables from the end of year t − 1 to year the end of year t, \( \Delta OCA \) is change in other current assets from the end of year t − 1 to year the end of year t, \( \Delta PAY \) is change in payables from the end of year t − 1 to year the end of year t, \( \Delta OCL \) is change in other current liabilities the end of year t − 1 to year the end of year t, \( DEP \) is depreciation and amortisation in year t
AT Total assets
CASH Ratio of cash to total asset at the end of year t
CFO Cash flow from operations, which equals to net income before extraordinary items (\( IB \)) minus accruals (\( ACC \)) in year t, scaled by assets at the end of year t
CYCLE Business life cycle (Dickinson 2011), which is a dummy variable is equal one if firms are classified based on cash flows as at mature stage (positive cash flows from operating activities, negative cash flows from investing activities, and negative cash flows from financing activities), and zero if firms are classified as at young stage (negative cash flows from operating activities, negative cash flows from investing activities, and positive cash flows from financing activities), or growth stage (positive cash flows from operating activities, negative cash flows from investing activities, and positive cash flows from financing activities)
D A dummy variable that equals one if \( RET \) < 0, and zero otherwise
DCFO A dummy variable which equals to one if \( CFO \) < 0, and zero otherwise
DEBTISSUE Debt issue, which is a dummy variable with the value of one if the change in short-term and long-term debts from the end of year t − 1 to the end of year t
EARN Net income before extraordinary items in year t, scaled by market value of equity at the end of year t − 1
IB Net income before extraordinary items
LEV Financial leverage, which is the sum of long-term and short-term debts at the end of year t, scaled by the market value of equity at the end of year t
MTB Market to book ratio, which is equal to market value of equity divided by book value of equity at the end of year t
PPE Ratio of property plant and equipment (gross) to total assets at the end of year t
RET Buy-and-hold stock returns for the period from the beginning to the end of fiscal year t
SALE Sales
SEO Equity issue, which is a dummy variable with the value of one if a firm increases outstanding shares in year t at least 5% with positive proceeds from equity issuance, zero otherwise
SIZE Firm size, which is the log of the market value of equity at the end of year t
ZSCORE Financial distress at the end of year t, measured by \( ZSCORE \) following (Taffler 1983) as follows:
\( {\text{ZSCORE}} = 3.2 + 12.18 *\frac{\text{Profit before tax}}{\text{current liabilities}} + 2.50 *\frac{\text{Current assets}}{\text{Total liabilities}} - 10.68 *\frac{\text{Current liabilities}}{\text{Total assets}} + 0.029 *\frac{{\left( {{\text{Quick assets}} - {\text{Current liabilities}}} \right)}}{{\left( {{\text{Sales}} - {\text{Pretax income}} - {\text{Depreciation}}} \right)/365}} \)

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Nguyen, T.T., Duong, C.M., Nguyen, N.T.M. et al. Accounting conservatism and banking expertise on board of directors. Rev Quant Finan Acc 55, 501–539 (2020). https://doi.org/10.1007/s11156-019-00851-2

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Keywords

  • Accounting conservatism
  • Banking expertise
  • Boards of directors

JEL Classification

  • G34
  • M41