1. Searat Ali*, Benjamin Liu, Jen Je Su
Does corporate governance quality reduce default risk? New panel evidence from Australia
Searat Ali*, Benjamin Liu, Jen Je Su
Griffith University, Australia
Accounting and Finance Association of Australia and New Zealand (AFAANZ) Conference
4-5th July 2016
Good afternoon everyone.
I am Searat Ali a PhD student at Griffith University
I am honoured to have you all here in this session to listen to my talk on the paper titled “does ”
This paper belongs to one of the empirical chapters of my PhD thesis.
I have written this paper with my supervisors Dr Benjamin Liu, Dr Jen Je Su
*Presenter

2. Research questions (RQs)
Does corporate governance affect default risk?
RQ2
Does the impact of corporate governance on default risk depend on growth opportunities?
RQ3
Does corporate governance affect default risk through the channel of stock liquidity/or information asymmetry?

3. Importance of default risk
Solution
Consequences
Reasons
Reason
Firm’s future cash flow is not sufficient to cover interest payments on its debts and the principal amount.
When a firm experiences either an expected decrease in its future cash flow or unexpected increases in the volatility of cash flow.
Consequences
bankruptcy-filing & legal costs
Interrupts supply chain
causes disruptions in productivity
customers dissatisfaction
strict credit terms
Employees demotivation
Mental stress
Victims commit suicide
Solution
Better corporate governance mechanisms (e.g., independence of a board and its sub- committees) can be used by a firm to help avoid such a situation.
Theoretically, these governance mechanisms reduce information asymmetries between management and shareholders, leading to lower agency cost and thus lower a level of default risk.

4. Gap in the literature (1/3)
Some studies link governance mechanisms to default risk (e.g., Daily and Dalton, 1994; Fich and Slezak, 2008; Platt and Platt, 2012; Switzer and Wang, 2013; Chiang et al., 2015; Miglani et al., 2015; Schultz et al., 2015).
Most of these studies find a significant inverse relation between governance mechanisms and default risk.
However, this conclusion in the literature is subject to several limitations.
These studies are tieher conducted in an ‘ex-post’ setting, which is subject to criticism in terms of sampling design (e.g., Balcaen and Ooghe 2006), or limited to considering individual governance mechanisms.
Moreover, these studies are limited to the period prior to the global financial crisis (GFC) i.e., 2008–2009; thus, the finding might not be directly applicable to the post-GFC (2010 to onwards) conditions.
To the best of our knowledge, only two studies investigate the role of individual governance mechanisms in the default risk for Australian firms and find mixed results.
Given these contradictory findings and methodological shortcomings in the literature, our study is timely and sheds new light on the ongoing literature debate: Does corporate governance reduce default risk?

5. Gap in the literature (2/3)
Prior literature on corporate governance and default risk does not recognize that such a relationship may vary across firm types such as growth firms (e.g., Coles, Daniel, and Naveen 2008).
We argue that the effect of corporate governance on default risk may not be uniform across high growth and low growth firms and that one size-fits-all governance practices can be counterproductive.
The high growth firms are in great need of governance control because of the higher incidence of information asymmetry in these firms (Hutchinson and Gul 2004).
The optimal default strategy of a firm depends on its mix of growth options and assets in place. Shareholders of a firm with valuable investment opportunities would be willing to wait longer before defaulting on their contractual debt obligations (Lyandres and Zhdanov, 2013).
We hypothesize that the inverse relation between corporate governance and default risk should be stronger for firms with more growth opportunities than for firms with fewer growth opportunities.

6. Gap in the literature (3/3)
Another important limitation of the prior literature is that it does not examine the channel through which corporate governance may affect default risk.
We examine information asymmetry (as measured through stock liquidity) as a channel between corporate governance and default risk.
Better corporate governance mitigates information asymmetry between insiders (e.g., managers) and outsiders (e.g., investors), as well as among outsiders by improving informational transparency of a firm.
Defaulted firms are found to have greater information asymmetries few months prior to default (Frino, Jones, and Wong, 2007).
We posit that corporate governance reduces information asymmetries, and reduced information asymmetries in turns reduces default risk.

7. Motivation: Why Australia?
Australian Context
Series of corporate collapses
HIH in 2001
Ansett Australia in 2002
OneTel in 2001
Corporate environment
less stringent corporate governance
weak market for control
high ownership concentration
low litigation risk
Contradictory findings
Schultz et al. (2015) no relation
individual governance variables
Large firms
Pre GFC
Miglani et al. (2015) inverse relation

8. Hypothesis development
H1a. All else being equal, better CGQ reduces the default risk.
H1b. All else being equal, the quality of board and its sub-committees reduce the default risk.
H2. All else being equal, the inverse relation between CGQ and default risk is stronger for firms with more growth opportunities than for firms with fewer growth opportunities.
H3. All else being equal, the CGQ’s reduction in default risk is strengthened via the channel of information asymmetry (stock liquidity).

9. Data and methodology Sample and data
The initial sample of 13,500 firm-years consists of all the Australian listed firms whose corporate governance data are available in the SIRCA during the period from 2001 to 2013.
Exclusion: financial firms and observations with missing data.
The final sample comprises 8,950 observations on 1086 non-financial firms from all size groups (small, medium and large).
We obtain default risk data from the Risk Management Institute at the National University of Singapore, data for the calculation of stock liquidity from SIRCA, and firm characteristics data from Morningstar DatAnalysis Premium databases.

10. Data and methodology Measures of stock liquidity
Measuring default risk (DEFAULT)
To measure default risk, we use two market-based proxies; namely, the Merton (1974) distance to default (DD) and credit default swap spread (CDS).
The higher (lower) the DD (CDS), the lower (higher) the default risk
Measures of corporate governance (CGQ)
We construct corporate governance index by following Horwath report. Our governance index is based on 17 criteria relating to board structure and its subcommittees.
The higher the governance index, the better is the governance quality.
Measures of growth opportunities (MTB)
We measure growth opportunities through market to book ratio.
Measures of information asymmetry/Stock liquidity (SLIQ)
We use three proxies for stock liquidity that capture trading cost, price impact and immediacy dimensions.
Time-weighted quoted spread (trading cost); Amihud illiquidity estimate (price impact); Turnover- adjusted zero daily volumes (Immediacy)
Higher value represent lower stock liquidity.
A number of accounting and market-based default risk models have been developed in the literature.
The validity of accounting-based models has been questioned due to the backward-looking nature of the financial statement through which these models are derived (Agarwal and Taffler 2008).
On the other hand, market-based models using the option pricing approach developed by Black and Scholes (1973) and Merton (1974) provide an appealing alternative to the prediction of default risk conditions of listed firms and have been used in extant empirical studies (e.g., Hillegeist et al. 2004; Bharath and Shumway 2008; Charitou, Dionysiou, Lambertides, and Trigeorgis 2013).
Empirical studies such as Gharghori et al. (2006) find the, Merton (1974) market-based model to be superior to their accounting counterparts in predicting default in the Australian context. Similarly, Hillegeist et al. (2004) recommend researchers to use market-based models of default prediction since these models contain more information about default than accounting-based models.
We therefore use the market-based Merton (1974) distance to default (DD) in gauging default risk.
We also check the robustness of our results by using the market-based credit default swap spread (CDS) to proxy pricing of default risk.
CDS are credit derivatives that allow the transfer of the firm’s default risk between two agents for a predetermined time period.
In a typical CDS contract, the protection seller offers the protection buyer insurance against the default of an underlying bond issued by a certain company (the reference entity). In the event of default by the reference entity, the seller commits to buy the bond for a price equal to its face value from the protection buyer. In exchange for the insurance, the buyer pays a quarterly premium, called the CDS spread, quoted as an annualized percentage of the notional value insured.
The higher the default risk of the reference entity, the higher is the CDS spread.
Horwath report
Unlike the well-renowned US-based Gompers, Ishii, and Metrick (2003) governance index (i.e., G-index), which focuses on the resistance of firms to external control mechanisms, the Horwath report places emphasis on the quality of a firm’s internal structures and processes.
1) board structure 2) audit committee 3) nomination committee 4) remuneration committee
5) external auditor independence 6) code of conducts and other policy disclosures
Limitations
Only top 250 firms, only from
Methodology is confidential
Subjective categories
No category scores
Construction of CG index
We address these issues by collecting an extended CG dataset across both cross section (large, mid-cap and small firms) and time series ( ) on the objective Horwath categories.
We assign the value ‘1’ if a firm meets the particular criteria and ‘0’ otherwise. For instance, if the majority of directors in a firm are independent we assign 1 and otherwise 0. These individual values are then aggregated to construct a composite CG index which ranges from 0 to 17 where 0 indicates the ‘worst’ governance and 17 indicates the ‘best’ governance.
Each governance category is the aggregate of the respective individual criteria.
Measures of stock liquidity
Stock liquidity is considered a “slippery and elusive concept” (Kyle 1985: p ) for a number of transactional properties of the market, including tightness (trading cost), depth (price impact) and resiliency.
Prior stock liquidity research normally does not rely on one single measure of stock liquidity because each measure proxies different dimensions and has its own limitations (Goyenko, Holden, and Trzcinka 2009); therefore, we use three proxies for stock liquidity (as a measure of information asymmetry) that capture trading cost, price impact and immediacy dimensions.
Dimension 1: Trading cost
Time-weighted quoted spread (TWQS)
Dimension 2: Price impact of trade
Amihud illiquidity estimate (ILLIQ)
Dimension 3: Immediacy
Turnover-adjusted zero daily volumes (LM)

11. Data and methodology Control variables Return on assets (ROA)
Liquidity Ratio (LIQUID)
Leverage (TLTA)
Firm size (LNTA)
Firm age (LNAGE)
Growth opportunities (MTB)
Year effect
Industry effect

12. Data and methodology Empirical models and estimation methods
To test the corporate governance quality and default risk hypothesis (H1a and H1b), we specify regression Eq. (1) as follows:
To test our corporate governance quality, growth opportunities and default risk hypothesis (H2), we formulate regression Eq. (2) as follows:
To test our corporate governance quality, stock liquidity and default risk hypothesis (H3), we specify regression Eq. (4) as follows:

13. Potential endogeneity
Data and methodology
Estimation methods
Baseline
Pooled OLS
Fixed effect
Between Estimator
Potential endogeneity
Lagged independent variables
Instrumental variable approach
Dynamic panel estimation

14. Correlation Analysis

15. Does CGQ affect default risk (H1a)? Baseline results

16. Does CGQ affect default risk (H1a)
Does CGQ affect default risk (H1a)? Potential endogeneity: Lagged Independent variables

17. Does CGQ affect default risk (H1a)? Potential endogeneity: 2SLS and GMM

18. Do governance categories affect default risk (H1b)?

19. Growth opportunities and the impact of CGQ on default risk (H2)

20. Stock liquidity and the impact of CGQ on default risk (H3)

21. Additional analysis: Does CGQ affect default risk (H1a)? GFC effect

22. Additional analysis: Does CGQ affect default risk (H1a)
Additional analysis: Does CGQ affect default risk (H1a)? Alternative default risk proxies

23. Additional analysis: Does CGQ affect default risk (H1a)
Additional analysis: Does CGQ affect default risk (H1a)? Alternative control variables

24. Conclusion
Our study contributes to the ongoing literature debate: Does corporate governance quality reduce default risk in Australia?
Our study shows that CGQ is inversely related to default risk. From a wider regulatory perspective, our index-based findings are supportive of the development of a comprehensive code of governance practice and provide guideline for investors to use composite governance as a benchmark in the selection of stocks that are less likely to face default.
Our study shows that CGQ’s significant reduction of default risk is mostly for firms with more growth opportunities. These findings suggest that improvement in corporate governance is more effective in reducing default risk for the firms with high growth opportunities.
Our study shows that CGQ reduces default risk through the channel of information asymmetry, as captured by various dimensions of stock liquidity. These findings imply that firms with poor stock liquidity should attempt to have high standards of corporate governance so that they can prevent future default conditions.
Given these implications, investors and firms may wish to monitor the governance quality more closely so as to devise sound investment and corporate strategies, respectively.
We recommend researchers to employ our governance index as a key input to predict actual default events.

25. Contributions Our contribution to the literature is fivefold.
1-Index based evidence
2-One size does not fit all
3-Corporate finance, market microstructure, asset pricing
4-Large panel dataset
5-Usefulness of Horwath report
First, we contribute to the literature on corporate governance and default risk. As far as could be ascertained, we are the first to show that composite internal corporate governance is relevant to default risk in Australia even when a potential endogeneity bias is considered.
Second, we contribute to the growing governance literature that argues “one size does not fit all” (e.g., Coles et al. 2008) by providing a new insight that the relationship between corporate governance and default risk depends on growth opportunities.
Third, we bring together three streams of literature; namely, corporate finance (i.e., corporate governance), market microstructure (i.e., stock liquidity), asset pricing (i.e., default risk) by showing that corporate governance interacts with information asymmetry to reduce default risk.
Fourth, we contribute to the literature by constructing a large panel dataset, i.e., large cross-section (1,089 unique firms) and long-time series (2001 to 2013).
Fifth, our study provides additional evidence on the usefulness of the Horwath report by linking it to the default risk. Prior studies measuring corporate governance through the Horwath report either are cross-sectional or have linked it to corporate activities other than default risk, such as firm performance (Linden and Matolcsy 2004), information disclosure (W. Beekes and Brown 2006; Wendy Beekes, Brown, and Zhang 2015), and stock liquidity (Ali, Liu, and Su 2016)

26. Thank you