Econometrics

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Market to Book

Market to Book Ratio reflects the market's expectation of future cash flows of the company relative to its assets. Shareholders would prefer to take advantage of the growth opportunities within the company rather than low risk projects. But to eliminate this risk, management will have to take on short-term debt. For this reason, we predict a negative relationship between growth opportunities and debt maturity as managers take on short-term debt to eliminate this conflict. This prediction relies on the assumption proposed by Johnson (2003) that debt matures after opportunities to invest have expired.

Leverage

Johnson (2003) also illustrates that, in general, whenever a firm has risky debt in its capital structure, managers also tend to over or under invest in growth opportunities such that it aims to maximize only shareholder value rather than total value of firm. He argues that in the absence of mechanisms to control these conflicts (e.g Restrictive Covenants), rational bondholders will require higher levels of return being aware of possibilities of these conflicts. It will therefore be wise for companies to use these mechanisms to reduce conflict. But the best way to avoid all the conflicts and corresponding agency costs overall will be to take on lower level of leverage. However according to liquidity risk hypothesis firms are more likely to face liquidity risk by taking on short-term debt and for that reason will also reduce leverage to eliminate such risks leading to our prediction that leverage and short-term debt maturity will have a negative relationship.

3.Summary statistics of variables

This table reports the mean, standard deviation, minimum, maximum, and numbers of observations for debt maturity structure variables and firm characteristics. The sample consists of observations of US firms from the WRDS Compustat database over the period from 2000 to 2014. Financial industries (SIC codes 6000-6999) and utilities (SIC codes 4000-4999) are dropped, and the final sample has a total of 48522 observations. [pic 1]

Notes:

DD2 is the debt due in 2nd year. DD3 is the debt due in 3rd year. DD4 is the debt due in 4th year. DD5 is the debt due in 5th year. We include both firm characteristics and macroeconomic variables. Market to book ratio (mb) is the ratio of the market value of assets to the book value of total assets. Abnormal earnings (abnearn) is the ratio of the difference between the income before extraordinary items, adjusted for common or ordinary stocks (capital) equivalents (ibadj) at time t and t–1, to the market value of equity. R&D expenses (rd) is the ratio of research and development expenditure to the total assets. Leverage (lev) is the ratio of total debt to the market value of assets ((dlc+dltt)/ (csho*prcc+at– ceq)). Lnsize is the market value of assets, defined as the market value of equity (csho*prcc) plus the book value of total assets (at) minus the book value of equity (ceq), the variable is measured in natural logarithm. Lnsizesq is the square of the lnsize. Asset maturity (atmat) is the property, plant and equipment over depreciation (ppegt/dp) times the proportion of property, plant and equipment in total assets (ppegt/at), plus the ratio of current assets (act/cogs) to the cost of goods sold times the proportion of current assets in total assets (act/at). Asset volatility (atvol) is the standard deviation of the stock return (during the fiscal year) times the market value of equity, all divided by the market value of assets. Terms structure (termstr) as the macroeconomic variable, is the difference between the yield on 10-year government bonds and the yield on 1-year government bonds. Additionally, we choose short-term debt maturity less than 3 years (st3) as our dependent variable, which is the ratio of debt in the current liabilities (dlc) plus the debt maturing in two or three years (dd2+dd3) to the total debt (the sum of debt in current liabilities plus long- term debt, i.e., dlc+dltt).

4.Empirical results and discussion

4.1determinants of debt maturity

We start our analysis by building the following model:

The dependent variable st3 is measured as the short-term debt maturity less than three years. According to the previous research, we use market-to-book ration, abnormal earnings, research and development (R&D) expenses, leverage, natural logarithm of size, the natural logarithm of size squared (which is used to capture the non-linear relationship with dependent variable), asset maturity, asset volatility and term structure. Before we run the regression, we winsorize all the control variables at 1 % and 99% level to eliminate the impact of the extreme elements in the sample, in order to get a more accurate result. We drop the data in financials (industrial codes 6000-6999) and utilities (industrial codes 4000-4999) sector, because the capital structures are substantially different from the other sectors because of the regulation (Custodio et al., 2012).

Firstly, we regress the short-term debt on the control variables with clustered standard errors—the pooled OLS (POLS) model (per table1 column1). It is found that the market-to-book ratio, abnormal earnings, firm size squared, asset volatility and term structure are positively related to the short-term debt, and R&D expenses, leverage, firm size and asset maturity are negatively related with the short-term debt. All the control variables are significant at 5 % level. In addition, the R-squared illustrates that 28.1% of the variance is explained by the POLS. Compared to the previous research, majority of the coefficient estimates of the independent variables are consistent. However, the results from R&D expenses and term structure are different from the prior studies per the literature review, this is possibly because that we use different sample. As for term structure, the 2008 financial crisis may distort the result. According to Wheelock and Wohar (2009), a sharp declining in the long-term government bond yield compared to the short-term treasury bill always signals recession afterwards, so that short-term debts are preferred, which indicates a positive relationship between term structure and short-term debt.

Next, we regress the short-term debt on the determined variables taking account of the industry dummies (omitting financials and utilities sectors), in order to control the impact that the industry characteristics may exert on the choice of debt maturity for the firms (Dang & Phan, 2015). We can see from