The dependent variables

This section discusses the dependent variables used in this study: risk-taking, credit rating and cost of capital. These variables were chosen so as to hopefully throw light on the performance of the companies. These dependent variables are operationalised using a variety of measures. Risk-taking is measured using expenditure on research and development (R&D) by dividing R&D on Total Assets, R&D on Sales and R&D Expenditure, as well as volatility in Return on Assets (ROA), which are estimated in equation (1). Similarly, following prior studies and assuming that all relations are linear. In measuring risk using R&D/Assets, Jiraporn, Chatjuthamard, Tong and Kim provided evidence that corporate governance influence corporate risk-taking. Other studies that examined risk using R&D/ Assets are Han, Bose, Hu, Qi and Tian (2015) which looked at the impact of director impact on corporate R&D investment, while Honore, Munari, and de La Potterie (2015) examined corporate governance practices and the impact this had on the companies’ R&D intensity. In measuring risk using R&D/Sales, Honore et al. (2015) noted how sales could affect the companies’ intensity in investing in R&D, and also how risk can have an impact on decision-making around R&D expenditures.

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The four chosen risk measures are intended to be alternate ways of measuring the same type of risk. Thereafter, separate regressions are run to determine the effect of CG on risk- taking. There are countless ways of measuring risk in all types of firms, it includes concepts such as alpha, beta, R-square, standard deviation and Sharpe ratio. Bromiley and Miller (1990) suggested several measures with basis in the financial statements, such as volatilit y in ROA and R&D divided by total Sales. Also operational measures such as cash holdings and volatility in turnover are common. Furthermore, Johansson (2005), claims total risk to be made up of operative and financial risk as measured by ROA and to be able to make assumptions about risk, it is required to study the changes in the key ratios over time. A higher volatility in ROA corresponds to a lower operative risk.

In measuring risk using ROA volatility, Faccio, Marchica and Mora (2016) identify efficiency in the allocation of capital as influenced by the gender of the CEO, as evident in corporate risk-taking. Also, the degree to which bank governance has an effect on risk- taking is revealed in company performance. In short, ROA is a good measure for risk and demonstrates the role of good corporate governance in financial performance of companies.

Data on credit rating is taken from the long-term issuer credit ratings by Moody, Standard and Poor’s, and Fitch. Standard and Poor’s, Fitch and Moody have distinctly differe nt measures, as mentioned above. Going from highest to lowest, the three agencies agree on the following broad ratings: premier, high grade, upper medium grade, lower medium, non- investment grade, speculative, highly speculative, substantial risks, extremely speculative, default imminent, and lastly, in default. They all agree that a premier credit rating is reserved for companies with long-term Aaa for Moody’s and AAA for Standard and Poor’s and Fitch. However, Standard and Poor’s credit rating system uses the same notation as Fitch for practically all levels of credit ratings, and the notations are similar to Moody’s. Basically, the three credit agencies agree with respect to companies that are rated in the As, Bs and Cs classifications.

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Ashbaugh-Skaife and LaFond (2006) showed that corporate governance has a positive effect on a firm’s credit rating, while Attig, El Ghoul, Guedhami and Suh (2013) revealed that corporate social responsibility can lead to better credit ratings for firms that commit to this practice. Bo, Lensink, and Murinde (2009) showed a positive link between corporate investment and credit ratings, while Dasilas and Papasyriopoulos (2015) found that credit ratings in both small and large listed companies revealed a link between corporate governance, capital structure and credit ratings. Elbannan (2009) showed that corporate governance and the level of internal control within companies had an impact on credit ratings.

Some researchers point to the difference between Japanese and American rating agencies. One of the criticism is that there are split ratings between these agencies, with Japanese managers believing that the reason for the differences is the fact that American rating agencies do not take the uniqueness of Japanese companies into consideration (Shin and Moore, 2003). There is also the belief that Japanese agencies give higher ratings to Japanese firms than do Moody’s and Standard and Poor’s, and that Japanese agencies, namely, the Japan Credit Rating Agency (JCR) and Rating and Investment Informat io n (R&I) seldom rate Japanese companies lower than the American agencies (Shin and Moore, 2003). But these researchers point out that in their study, American agencies use tougher ratings measures, and it is often argued that because of Japanese keiretsu affilia t io n may be seen as contributing to this (Shin and Moore, 2003). But these researchers point out that despite the differences between Japanese and American companies, it was found that there was not much difference in the ratings between Japanese and American raters in terms of financial risk, although there was a difference between ratings with respect to business risk (Shin and Moore, 2003). However, the rating process and the use of the letter grade system is similar among Japanese and American agencies, and the two Japanese agencies use a rating scheme that is similar to Standard and Poor’s (Shin and Moore, 2003).

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In order to analyse the findings, this study has collapsed the various ratings into 21 credit ratings that show the assessment of ordinal risk. The 21 credit ratings are: D, C, CC, CCC, CCC+, B-, B, B+, BB-, BB, BB+, BBB-, BBB, BBB+, A-, A, A+, AA-, AA, AA+ and AAA. D has an ordinal risk of zero, while C has an ordinal risk of 1, B an ordinal risk of 6 and AAA an ordinal risk of 21, which are estimated in equation (2). Similarly, follow ing prior studies and assuming that all relations are linear.

In the cost of capital, companies have to develop measures to do so. It is important to point out that the cost of capital to a company includes not only the cost of borrowing new capital funds, but also the cost of equity. The cost of borrowing funds is based on what lenders demand from companies. The cost of equity is the percentage that a company’s owners would require to invest their money in the company. Therefore, cost of capital must be seen as involving the costs that both lenders and owners demand. This can be appreciated by looking at how publicly owned companies raise their capital. They either borrow money directly from a lender or they sell shares in the company. Therefore, the cost of capital would have to be based on the cost of debt as well as the cost of equity.

Therefore, when companies set out to determine the cost of capital, they must develop a measure that would allow them to capture cost of equity as well as the cost of debt. The cost of equity is sometimes inferred by using the discount rate to determine the present value of the dividends expected (Gode and Mohanram, 2003). One way of measuring the value of equity is by using the Capital Asset Pricing Model (CAPM), which is really the rate of return based on risk (Gode and Mohanram, 2003). But as these authors point out, using the CAPM as a measure based on risk premium is weak, as expected returns often differ markedly from actual returns (Gode and Mohanram, 2003).

Another approach is what is referred to as the ex ante approach, where one infers risk from looking at the expected dividends in terms of the current price. As these authors contend, future dividends are not easily observable, as analysts estimate earnings based on periods, and do not have the whole earnings stream on which to base their analysis (Gode and

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Mohanram, 2003). Easton (2004) puts forward a method of estimating the expected rate of return on equity capital, which was shown to be important in determining the cost of capital. According to this author, two methods that have been used to evaluate rate of return on equity capital, namely, the price-earnings (PE) ratio and the price-earnings ratio divided by the short-term earnings rate (PEG ratio), are not accurate because they fail to capture the long-term picture. Easton (2004) therefore promotes the Ohlson-Juettner model.

Gode and Mohanram (2003) explain why the PE and PEG methods do not work well. They note that it is difficult to use either of these approaches because certain assumptions have to be made about a pattern of payout ratios and the value at the end of the forecast period to a perpetual growth rate (Gode and Mohanram, 2003). The model that these authors see as taking these assumptions into consideration in evaluating cost of equity is the Ohlson- Juettner model. This model is based on taking the current price, relating it to earnings per share and assuming a perpetual growth rate (Gode and Mohanram, 2003).

Li and Mohanram (2014) believe that in computing the implied cost of capital, analysts encounter difficulty because only about half of the companies have earnings forecasts. These researchers explain that research has shown that the relations between measures of risk and realised returns are often weak, and in some cases non-existent (Li and Mohanram, 2013).

Li and Mohanram (2013) examine work by Gode and Mohanram (2003) and Easton (2004), and note that while these researchers have attempted to deal with assumptions using the Ohlson and later the Juettner-Nauroth (2005) models, they are still found to be lacking because they only work for about half the companies, and because forecasts by analysts are often unreliable. They recommend the use of a cross-sectional forecasting approach put forward by Hou, van Dijk and Zhang (2012), based on using current information from companies and making forecasts based on this information (Li and Mohanram, 2013). Consideration of these shortcomings is given adequate attention in this study, which are

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estimated in equation (3). Similarly, following prior studies and assuming that all relations are linear.