Research topic:
Financing decision of oil and gas companies and the role of
investor protection
University of Groningen Faculty of Economics and Business MSc International Financial Management Supervisor: Dr. H. Gonenc Date: 09/06/2017
Table of content
1. Introduction ...4
2. Literature review and hypothesis development ...9
2.1 Debates about internal and external investment financing. ...9
2.2 The influence of uncertainty on investments. ...11
2.3 Capital Structure Theories and Hypothesis. ...14
2.4 Investor Protection level. ...17
3. Data description and Methodology ...19
3.1 Data sample description. ...19
3.2 Estimating targeted leverage. ...20
3.3 Evidence on adjustment towards estimated target debt ratios. ...21
3.4 Developing a model for testing pecking order, tradeoff and market timing hypothesis. .... 23 3.5 Extending a model for testing of investor protection hypothesis. ...26
4. Results ...27
4.1 Results for Capital structure hypotheses. ...27
4.2 Results for investor protection hypothesis. ...31
Conclusions and Limitations ...34
Abstract
This paper analyses the financing decision related to the forming of capital structure within the oil and gas industry companies in different countries over the period of 2001-2015. In this paper are discussed the main capital structure theories: tradeoff theory, pecking order theory and market timing theory. The results are providing strong support to the tradeoff theory and partial support to the pecking order and market timing theories. Additionally this paper focuses on the country level factor – investor protection. Investor protection gives an additional support to the tradeoff theory and additionally shows that firms tend to take more debt in weakly protected countries than in strong ones. Additionally, partial support is provided for the market timing theory, in terms of that companies in the countries with high level of investment protection are willing to issue equity, comparing to the companies in the countries with a low level of investor protection.
1. Introduction
“The petroleum industry is also highly capital-intensive, so strong returns are critical to attracting low-cost debt and equity capital. In fact, while many of the integrated companies have the cash flow and financial wherewithal to fund capital spending internally, they frequently rely on external debt and new equity capital, particularly to finance larger acquisitions and mergers. “ — “Rating Methodology: Global Integrated Oil & Gas Industry,”
Moody’s Investor Services, October 2005, p. 12
The Current situation in the oil and gas industry could be described as a severe competition (Weston et al., 1999). In the past, about 20 years ago, oil and gas companies were “rewarded by the equity market for strict capital discipline ”, but developing financial markets put a lid on investments which should have improved key performance indicators of those companies (Osmundsen et al., 2006). The results orientation lead those oil and gas firms to search for new reserves, increasing the need for the capital drastically. Generally the capital structure of the oil and gas firms can be summarized as follows: 1) generally, firms that are operating in the same industry tend to have a similar capital structure (naturally, there might some differences among those firms, but the general tendency would be the same) 2) firms are not afraid to take debt if it is recognized with a necessary move 3) firms tend not to overtake debt obligations, but in the case when there is a choice between taking additional debt, or missing a vital business opportunities, firms usually decide to acquire additional debt for the financing of that opportunity (Inkpen et al., 2011).
and the comparison of those companies with other industries (Talberg et al., 2008). To test the capital structure hypothesis, they were using Debt-to-Equity definition for the capital structure as an indicator of companies’ assets financed by capital mix (Ewing and Thompson, 2016). The capital structure of the oil and gas companies is of great importance, because those companies are operating in a capital intense industry. Comparing with other industries, oil and gas companies have a large amount of fixed assets, which they need to run daily activities and which can be used as collateral for debt (Talberg et al., 2008).
The main research topics in the literature about oil and gas companies are shifted towards companies’ performance and returns. Because of this shift scholars are neglecting the potential topic about the effect that capital structure has on the financing of the investments made by oil and gas firms (Haushalter, 2000; Ewing and Thompson, 2016). Therefore in my research I want to add new findings to the existing literature in order to explain existing ways for oil and gas companies to finance their investments. In order to do that I will use of capital structure studies and studies about the effect of capital structure on the financing of investments (Elsas et al. (2014) in a study for US manufacturing companies) and will adapt their model for oil and gas companies.
Continuing with capital structure, it is essential to name and explain tree major capital theories which would be tested during my research. Flannery and Rangan (2006), Myers (2001) and Elsas et al. (2014) derive 3 major capital structure theories: dynamic tradeoff
theory, pecking order theory, and market timing theory. The dynamic tradeoff theory directly
to use internal financing, but in the case those funds are not sufficient enough, firm should issue debt first and then equity (Meyers, 2001; Frank and Goyal, 2007). The third capital structure theory is - market timing theory. Under this theory firms - “try to issue overpriced securities when they go to the market” (Elsas et al., 2014, Asquith and Mullins, 1983; Korajczyk et al., 1991; Loughran and Ritter, 1997; Baker and Wurgler, 2002; Huang and Ritter, 2009). Scholars did not have a common agreement for one of those theories over the other two.
In my sample, I collect data from 18 countries over the time period from 2001 till 2015. The limitation of countries up to 18 could be explained by my sampling criteria. My main objective was to cover as largest as possible time-frame using data from experienced oil and gas companies (at least 5 years of operations prior to the 2001), which resulted in data collection form 18 countries. Doing a cross-country study, I need to account for the country level factors that are unique for each of those 18 countries. Therefore I decided to account for Level of investor protection, factors that were developed by LaPorta et al. (2008) and Djankov et al. (2008), and was further studied and explained by McLean et al. (2012). This factors are directly related to the capital structure of the firms. Higher investor protection improve firms’ access to external finance, which is highly relevant in capital dependent oil and gas industry (LaPorta et al., 1997 and 1998). Additionally countries with a strong investor protection index, firms officials are less likely to use firms’ internal resources and therefore move eager to (compared to low investor protection countries) go for external financing as the way to benefit shareholders (Wurgler, 2000; Shleifer and Wolfenzon, 2002; Bekaert, Harvey and, Lundblad, 2011).
not add any additional for the pecking order theory, the country level interactions were found to be not significant. Surprisingly, oil and gas firms tend to use high profits as a substitution for equity, which is contradict the general idea of pecking order theory hypothesis to use debt in case of low cash flows. Market timing theory did get partial support, from both capital structure and country level hypotheses. Under the country level hypothesis I discovered that in countries with high levels of investor protection high stock returns influence the equity issuance.
2. Literature review and hypothesis development
2.1 Debates about internal and external investment financing.
Myers (2001) described that gross investments in US non-financial companies are made internally, covering somewhere around 80% of the total investment. But, also it is important that financing deeply varies among industries. For example, in the energy industry, large integrated oil companies rely more on the external financing through debt than on internal financing. Thus it is important to look thought the both sides of the investment models theory, whether the internal financing or external financing is more preferable.
new equity issued allows the firm to pay out dividends valued at one unit and has no effect on present value”. They state that using debt is irrelevant as well, even in the presence of bankruptcy risk (until there is “deadweight costs of bankruptcy”). But according to firm’s financial policy, it might find some of the sources for investment more preferable than others. That might be contradictory to previous statements, but this preferable source of investment is - internal. As they say: “a preference for retained earnings over new share issues will arise if the tax system favours capital gains over dividend income or if significant transactional charges should be paid when placing new shares. The presence of bankruptcy costs may neglect the tax advantages, and will make debt finance attractive at low levels of borrowing”. Thus, the availability of low-cost costs generated internally might be a huge boost for the investment decisions (Myers, 1984).
postponing their investment till the better market situation. She explains: “The magnitude of the changes in the cost capital of financially constrained firms is twice as large in a country with low level of financial development as it is in a country with an average level of financial development”. Additionally, Demirguc-Kunt and Maksimovic (1998) found a positive relation between the growth of the firms that are using both internal and external financing and the financial development and legal systems in those countries. Also, Rajan and Zingales (1998) showed that industries with a more need for external financing grow faster in the more developed market. The country level factors such as: legal system, uncertainty, level of corruption etc., were used in the work of Love (2003), in order to show the influence of country level factor on the formation of supply for the external financing for firms. This development adds new relatively less-costly ways of raising capital for the investment purposes. Thus this is important for my research, and in the next paragraphs I would focus on the additional factors that have an influence on the investment decisions of the companies (in particular - uncertainty level, oil price, investor protection level and etc.)
2.2 The influence of uncertainty on investments.
investments suggest that this relationship is negative (Dixit and Pindyck, 1994). But according to the contributions (like Smit and Trigeorgis, 2004), this relationship could be positive, in the case when a company decided to abandon the waiting future benefits options of their investment when they expect high levels of price volatilities (especially for gas and oil companies) (Kulatilaka and Perotti, 1998; Sarkar, 2000). Simultaneously, according to (Grenadier, 2002; Akdoğu and MacKay, 2008) that value of waiting option would be affected by the factors as imperfect competition and strategic investment. Scholars deliver two types of price uncertainty: temporal and permanent. For the temporary periods of uncertainty the oil and gas companies could consider oil price volatilities as transition phenomenon (Mohn and Bisund, 2009). This transit phenomenon with high a picks of oil price volatility considered to be followed with the period of decreasing volatility. This by any means follows the standard investment irreversibility theory. According to this theory, the relationship between investment and uncertainty (which is oil price volatility in our case) is negative; this was concluded by the studies of Favero, Pesaran, and Sharma (1992) and Mohn and Osmundsen (2006) with a different context in each study. But on the other hand there is permanent uncertainty, where the approach for strategic investments and compound options highlights the positive relation between uncertainty and investments. While scholars present their findings for the sample periods of 20 years ago (Mohn and Bisund, 2009), the current situation could have changed the relation between uncertainty and investments.
able to give certain benefits to the organization as a whole, but not only to the unit that made that decision. Usually this investment aims to achieve a competitive advantages though value creating (either from cost reduction or product differentiation) (e.g. Porter, 1980, 1998; Makadok, 2003). During period of uncertainty, firms can wait for a new information, thus declining any possible returns from early investments (strategic or not). The idea is that with new information about price changes, a company can increase the value of its waiting option thus receiving larger returns in the future and simultaneously decreasing its incentive to invest (Bernanke, 1983). But things are changing in the case of strategic investments, where you cannot postpone your investment forever for the sake of better information. Henriques and Sadorsky (2011) in their work showed, that “Increases in uncertainty increase the option value of waiting to invest which postpones investment” Dedicating attention to this topic is needed, because oil price significantly affect not only investment incentives of oil companies, but gas companies as well. According to Henriques and Sadorsky (2011): The correlation between oil and gas prices is ranging from 26% in general up to 70%, thus the changes in oil prices would have significant consequences for gas companies. In addition, the recent World crisis of 2008-2009 and oil price drops in the recent years might give new insights on the relations between uncertainty levels and investments made by oil and gas companies. Discussed internal/external financing debates and showed the influence of the uncertainty factors on the firm’s financing, it is important to look in more deep to the issue of capital structure of oil and gas firms and to develop hypotheses related to those capital structures.
2.3 Capital Structure Theories and Hypothesis.
Studying capital structure is essential to understand different theories and their findings. From one side of the capital structure debate, capital structure is considered to be stable over long periods of time, that majority of variation in the capital structure is time-invariant and that much of that variation cannot be accounted for with existing models. In order to understand leverage category in more detail it is essential to follow Lemmon et al. (2008), they showed that initial leverage ratio of the firm has a significant impact on the future ratio of the firm and should be significant in the model, despite the fact that this future ratio is time-invariant. The second important discovery was that leverage category contains in itself “unobserved firm-specific component”. This component could be differences in technologies, market power, managerial behaviour, investment and other company specific factors (Kuh, 1963; Hoch, 1962). Additionally, firms with high leverage tend to use equity to reduce their leverage (Lemmon, Roberts and, Zender, 2008), but those authors made a well-generalized conclusions about firms, thus it would be reasonable to discover whether or not that tendency is related to energy companies.
Myers (2001) tries to explain market imperfection with the next capital structure theories: tradeoff theory emphasizes on taxes and agency problems, pecking order and timing
theory emphasize on differences in information. Pecking order theory implies that companies’
shares are generally underpriced. In order to finance its projects, firms generate funds internally, then, if the internal funds are insufficient they are going for the safe debt, and the last resort option is equity issuance. Market timing theory differs from the pecking order theory in a sense that managers possess of the internal information about company and should use their abilities to sell overpriced equity shares (Baker and Wurgler, 2002). Flannery and Rangan (2006) argue that this is the main difference between those theories, that market timing theory allows managers to routinely use the provided information in order to benefit the shareholders. It is Important to note that those theories are not based on the target debt ratios, all of the decisions about debt ratios are made only according to the information the managers possess.
Tradeoff theory is completely the opposite to those theories, it is explained by Flannery and Rangan (2006) as, “tradeoff theory maintains that market imperfections generate a link between leverage and firm value, and firms take positive steps to offset deviations from their optimal debt ratios.”. Their study finds different support for all three theories mentioned above. The half of the observed changes in a capital structure they observed are because of the “targeting behaviour” and less than 10% of each change is explained by pecking order and timing theory. Their model accounts for the dynamic nature of the company’s capital structure, using targeting behaviour of firms.
Hypothesis 1: Positive deviation from the target leverage of the firms has a positive and significant impact on the amount of firm’s debt.
Pecking order Hypothesis. There are costs that are imposed on shareholders, when a firm is trying to sell its stock to the public. In order to avoid those costs, firms prefer to finance their activities internally. In the case when internal funds are not enough – firms prefer to issue debt over equity. Therefore I can show the relation between profits and levels of debt/equity/Cashflows.
Hypothesis 2: a) Profits of the firms have a positive and significant impact on the Cashflows of the firm
b) Profits of the firms have a positive and significant impact for the Debt taking over the Equity issuance
Market Timing Hypothesis. Firms are trying to issue overpriced stocks when they go to the market In order to spot this “timing”, firms are referring to their value of Q or stock returns. Therefore I can show relation between Q and Stock returns and equity issuance Hypothesis 3: Q and Stock returns have a positive and significant impact on the Equity issuance of the firm.
2.4 Investor Protection level.
finance their investments (La Porta et al., 1997, 1998, 2000, and 2002; La Porta et al. 2006). In the recent study of McLean at al. (2012) they assumed that “investor protections improve firms’ access to external finance for value-enhancing projects”, which was consistent with the work of La Porta et al. (1997, 1998, 2000 and 2002). Additionally they linked this relationship to the Q, and discovered that when country has strong investor protection laws, the correlation between Q and external finance is positive and significant. But when they were working on the cash flow issue, they found out that in countries with strong investor protection - “investment is less sensitive to the cash flow, and external finance has a negative sensitivity to cash flows”. The idea behind this is that logically firms with a limited (low) amount of cash flows would require an additional source of financing in the terms of external financing. Following the work of Fazzariet et. al. (1988, 2000) and Hubbard (1998), the situation was checked when the government officials are doing lessening of the cost for the external finance. According to scholars there would be a decrease in a demand for external financing from those firms with low amount of cash flows. The logic is as follows - if it’s easier to get external funds, what is the point to be dependent on the cash flows funding for the investments? In the countries with strong investor protection, independent variables explaining capital structure of the firm (such as Q, Stock Returns, Profit, Leverage) have a stronger relation with an investment-prediction than in the low investor protection countries. Therefore I would like to develop hypotheses that would cover country level effect in terms of investor protection.
Hypothesis 2.1: a) With the increase in the investor protection level, profits of the firms would have a positive and significant impact on the Cashflows of the firm
b) With the increase in the investor protection level, profits of the firms have a positive and significant impact on the Debt taking over the Equity issuance
Hypothesis 3.1: With the increase in the investor protection level, Q and Stock returns have a positive and significant impact on the Equity issuance of the firm.
3. Data description and Methodology
3.1 Data sample description.
In my work I would refer to the methodology scheme of Elsas et al, (2014). All of the sample firms’ data were gathered from Tomson Reuters DataStream software. I have tried to capture as much energy companies as possible, which lead to a small bias towards companies represented by certain countries. For example, as an initial sample, which were formatted further, I have got a sample of 226 company, where: USA - 60; Australia - 55; Canada - 26; UK - 21; India and Russia - 11; Hong Gong and Israel - 8; Norway and China - 5; Sweden and France - 4; Ireland and Argentina - 2; the Netherlands, Spain, Poland and Italy - 1. For the period from 2001 till 2015, I have derived an Investments made by those firms to 4 categories (according to Elsas et al, (2014) :
1) DEBT - Firms issued long term debt minus reduction in debt (DataStream items 04701 and 04701)
3) CASHFLOW - Firms value of firm’s operating cash flows, calculated as after-tax income before extraordinary items plus depreciation and amortization minus cash dividends and the increase in cash and equivalents (DataStream items 01551, 01151, 04551 and 04851) 4) OTHER - basically all of other SCF categories for the certain firms.
Combined together, those 4 categories must be equal to investment, or:
INVEST = DEBT + EQUITY + CASHFLOW + OTHER (1)
Where INVEST is the sum of the firm’s capital expenditures, acquisition of assets and investments in associated companies (DataStream items 04601, 04355, 02256). The value of mean of Debt, Equity, Cashflow and Others as the proportion to Invest should be 1. Therefore I have received the next mean values for those variables, which match the criteria: Debt – 21.9%, Equity – 21.7%, Cashflow – 49.7%, others – 6.7%.
The next step was to set criteria that would allow me to test a healthy sample of energy firms. Thus, I decided to remove firms that did not have any capital expenditures and other investment information; firms that had no debt, Cashflow expenditures and equity issuance during the time frame or did not report those categories. This resulted in a reduction from 226 to 147 firms.
3.2 Estimating targeted leverage.
The aim of this estimation is to compare the tradeoff hypothesis with the pecking order hypothesis and market timing hypothesis, because the last two do not have firm’s targeted leverage variable.
Elsas et al, (2014) and Flannery and Rangan (2006) are using market value of leverage and define it as:
Where, D - is the book value of debt (DataStream item 03255) and E is the firm’s equity value. Firm’s equity is expressed as the price per share multiplied by the number of shares outstanding (DataStream items P, 05301 and 05303).
Firms always face some costs when adjusting their capital structure, thus the partial adjustment model is used to describe firms leverage:
LEV(t) - LEV(t-1) = λ(LEV(t)* - LEV(t-1)) + ERROR. (3)
According to Elsas et al, (2014) “the typical firm annually closes a proportion λ of the deviation between its desired LEV(t)* and the leverage it actually has LEV(t-1)”. They continue, that desired/target leverage is usually being described as a combination of firm’s lagged characteristics X(t-1), which gives a rebuilt equation:
LEV(t) = (λβ)X(t-1) + (1-λ)LEV(t-1) + ERROR. (4) Elsas et al, (2014), confirms the line of previous researched, saying that vector X includes earnings, depreciation, fixed assets; assets market to book ratio, the Ln of total assets and firm’s fixed effects. They did not show constant in their model, but they include it in the estimation. The idea behind it is that they find it to be insignificant.
After defining a firm target leverage ratio as LEV(t)*, they computed each firm’s deviation from its targeted leverage as (deviation would be used in further models):
DEV(t) = LEV(t)* - LEV(t-1) = (λβ)X(t-1) - LEV(t-1). (5)
3.3 Evidence on adjustment towards estimated target debt ratios.
from (4) using the Blundell–Bond system generalized method of moments (GMM), for the data gathered from DataStream in a period from 2001 till 2015. The instruments that were used for this GMM were: second lag in leverage and additional generated lagged variable BDR (BDR is a ratio of total debt to total assets), those instruments are in line with the researches of Flannery and Rangan (2006) and Lemmon et al. (2008), which were used as a references in the background paper of Elsas et al, (2014). The results of the GMM are in Table 1. From the table we can see the value of the LEVERAGE(-1) coefficient of 0.531, which implies that annual adjustment speed is 0.469.
Having the results from Table 1 we are able to compute the targeted leverage for all firms in our sample and therefore can calculate deviation from its target leverage. In order to calculate the final DEV(t), I saved the predicted values of vector βX(t-1) multiplied by λ from the regression in (4) and then I deduct from those values the value of Lev(t-1) (equation 5).
Table 1. Adjustment speed estimation
Variable (dependent: Lev(t+1)) Coefficient (p-value)
Lev 0.531*** (0.000) Profit 0.002 (0.621) Q -0.004*** (0.00) Depreciation/TA, -0.012 (0.491) Size 0.010* (0.077)
Fixed Asset Ratio 0.031**
(0.041)
Constant -0.004
(0.977)
3.4 Developing a model for testing pecking order, tradeoff and market timing hypothesis.
In order to understand the different capital structure hypotheses, I am going to estimate a set of four SURs (Seemingly Unrelated Regressions) in order to derive an explanation about how firms pay for their investments:
F(i,t) = α + β1 * DEV(i,t-1) + β2 * Profit(i,t-1) + β3 * Stock Returns(i,t-1) + β4 * Q(i,t-1) + β5 * Investment ratio(i,t) + β6 * Fixed_Asset_Ratio(i,t-1) + β7 * Size(i,t-1) + β8 * Volatility(t-1) + β9 * OilPrice(t-1) + ERROR. (6) F - the proportion of 4 sources of financing (Debt, Equity, Cashflow or Other) of the firm to the firm’s investment value, during the t year
DEV - from the equation (5), and which shows firm’s deviation from targeted leverage at t-1 year
Profit - Elsas et al, (2014) described it as “net annual income before extraordinary items, as a proportion of book assets. Under the pecking order hypothesis, firms should issue DEBT when internal CASHFLOW cannot finance available investment projects. “
Stock Return - Stock returns of the firm. According to Korajczyk, Lucas, and McDonald, (1991), firms tend to issue stock, when they face an increase in stock returns. Data obtained from DataStream item “RI”
Q - Tobin’s Q ratio, which is calculated as - market value of equity to book value of equity. Authors suggest, that Q may include several factors that could have an influence on corporate investing and financing behaviour.
Investment ratio – The ratio is calculated as the value of investments to the book total value of assets. Some investments may require additional external financing, or firms can save their cash when they are waiting for the future investment.
Fixed_Asset_Ratio - Firms year-end book value of fixed assets divided to total assets. The need for this variable is explained, by the larger amount of fixed assets generate larger internal cash flows, which could reduce the use of external financing.
Size - Natural Log of firms book assets, used as a control variable.
Volatility - Volatility measure for the oil price. Researching oil and gas companies create a need to address attention to external factors as volatility. Its measurement is described by Mohn and Misund, (2009).
OilPrice – An additional way to control for the oil price. It Is measured as the natural logarithm of average yearly oil price (Salas-Fuma ́s et al. (2016).
My primary interest is related to first four explanatory variables, which would capture the three alternative capital structure hypotheses. The other variables (Size, Investment ratio, Fixed_Asset_Ratio), according to Elsas et al, (2014), “would control for heterogeneous investment and firms chart characteristics that might influence financing choices”. The descriptive statistics for independent variables are in the Table 2 and their correlation coefficients are presented in Table 3.
coefficients on Profit in the DEBT and CASHFLOW regressions should not be negative. Stock return and Q is referred to the market timing hypotheses, because those variables could capture the opportunistic behaviour of equity issuances. Alternatively, Elsas et al, (2014) specify “they may indicate an abundance of investment opportunities that, with the tradeoff hypothesis, implies a preference for low leverage and, thus, for equity financing ”. Q by itself should be targeting leverage and it does not have any tradeoff related effect in (6), but on the other hand Stock return reflects different set of investment opportunities so it might have an influence for the tradeoff interpretation in (6).
Table 2. Descriptive statistics, Capital structure model
Mean Median Maximum Minimum Std. Dev. Observations
Lev 0.22 0.19 0.94 0.00 0.18 1125 Profit 0.03 0.06 0.93 -1.56 0.16 1125 DEV 0.04 0.01 3.10 -2.43 0.41 1125 Stock Return 0.19 0.06 10.41 -0.94 0.68 1125 Q 1.87 1.66 5.87 -0.55 1.13 1125 Investment Ratio 0.52 0.31 12.87 -0.27 0.89 1125 Fixed assets ratio 0.61 0.64 1.91 0 0.25 1125 Size 13.92 14.13 19.61 4.96 2.83 1125 Volatility 1.32 1.26 2.52 0.53 0.49 1125 OilPrice 4.18 4.24 4.67 3.22 0.44 1125 Table 3. Correlations Profi
t DEV ReturnsStock Q Investment Ratio Fixed assets
ratio Size Volatility Oilprice
Profit 1.00 -0.06 0.30 0.16 0.14 0.01 0.40 0.03 0.01
3.5 Extending a model for testing of investor protection hypothesis.
The model developed by Elsas et al., (2014), explained above does not account for the country level factors, because their model was originally developed for the US domestic companies. That is why, in order to test investor protection hypothesis, I have to adapt their model to the international dimension of my study. By adapting, I mean adding investor protection variable in order to test investor protection hypotheses. In my extended model I am going to use the values of Anti-self index, from McLean et. al., (2012) and Djankov et al., 1
(2008)..
F(i,t) = α + β1 * DEV(i,t-1) + β2 * Profit(i,t-1) + β3 * Stock Returns(i,t-1) + β4 * Q(i,t-1) + β5 * Anti-self + β6 * DEV(i,t-1)*Anti-self + β7 * Profit(i,t-1)*Anti-self + β8 * Stock Returns(i,t-1)*Anti-self + β9 * Q(i,t-1)*Anti-self + β10 * Investment ratio(i,t) + β11 * Fixed_Asset_Ratio(i,t-1) + β12 * Size(i,t-1) + β13 * Volatility(t-1) + β14 * OilPrice(t-1) +
ERROR. (7)
Where:
Stock Returns 0.30 -0.39 1.00 0.23 0.14 -0.09 0.00 -0.01 -0.02
Q 0.16 -0.14 0.23 1.00 0.11 0.05 -0.04 -0.07 -0.08
Investment Ratio 0.14 0.06 0.14 0.11 1.00 0.16 -0.09 -0.06 -0.10 Fixed assets ratio 0.01 0.07 -0.09 0.05 0.16 1.00 0.06 -0.05 0.00
Size 0.40 0.04 0.00 -0.04 -0.09 0.06 1.00 0.13 0.22
Volatility 0.03 0.03 -0.01 -0.07 -0.06 -0.05 0.13 1.00 0.63 Oilprice 0.01 0.02 -0.02 -0.08 -0.10 0.00 0.22 0.63 1.00
The anti-self-dealing index (Anti-Self) is created by Djankov et al. (2008). Anti-Self is meant to regulate an opportunistic
1
Anti-self index values can be found in the Appendix A; Other variables are from the capital structure model (6)
4. Results
4.1 Results for Capital structure hypotheses.
Table 4 represents results of (6):
Table 4. SUR estimates
Dependent Variable Debt Equity Cashflow Others
DEV 0.359*** (0) -0.013 (0.62) -0.221*** (0) -0.097*** (0.01) Profit -0.009 (0.87) -0.471*** (0) 0.549*** (0) (0.83)0.018 Stock Return 0.033*** (0) 0.06*** (0) -0.068*** (0) (0.79)0.005 Q 0.01*** (0) -0.007** (0.03) -0.003 (0.56) -0.002 (0.56) Fixed assets ratio -0.004
The positive coefficients of DEV, in Debt regression, shows that under leveraged oil and gas firms use more debt financing, when they a relatively high deviation from their targeted level of leverage. This is consistent with tradeoff hypothesis 1 and is consistent with results of Elsas et al., (2014). Debt regression value for DEV is (0.359***) while the value of an insignificant coefficient on DEV in Equity is (-0.013). This is an indicator that at first, Debt is playing more significant role in the target adjustment toward target leverage ratio than Equity, and second that this is an indicator of a specific feature, that can be related to oil and gas companies, with a preference for debt issuance over equity. One standard deviation increase in DEV (41% in percentage points) has an impact on the entire Debt funding by increasing it by: 41%*0.359=14.79%. Thus, tradeoff hypothesis 1 receives significant support.
From the regression on Cashflow, I have discovered, that oil and gas companies which are more profitable, eager to finance their investments with internal Cashflows, the value of the coefficient for Profit is positive and highly significant (0.549***). This is a clear indicator that more profitable firms prefer internal financing for the financing of their investment than external, which is in line with pecking order hypothesis 2.a and supports it. But from here, the additional analysis is required, because according to pecking order theory, firms tend to issue Debt in case their internal funds are not enough for the investment. From my results, I can conclude that zero coefficient of Profit on Debt, and a highly significant but negative coefficient on Equity regressions, are the indicators that Cashflow substitutes for Equity issuance, leaving no affect to leverage by firm profitability. According to Myers (1984) the
R^2 0.186 0.167 0.138 0.634
substitutability between internal funds and equity is consistent with pecking order hypothesis
2.b, while Elsas et al., (2014) argued that ”substituting equity for internal funds has no effect
on leverage and is not consistent with pecking order hypothesis 2.b as it is usually presented” From the regression on Equity it is possible to derive results for market timing
hypothesis 3. Stock returns are considered to be the most important factor for the market
timing theory, because those variables could capture the opportunistic behaviour of equity issuances. Positive coefficient on Stock Return in Equity (0.06***), is an indicator that firms use higher stock returns in order to finance their investment. Positive and significant coefficient of Stock return on Debt (0.033***) represent a signal to lenders that company has growth opportunities, which is reducing their uncertainty, thus leading to more debt issuance (is not related to tradeoff hypothesis). Additionally, a negative and significant coefficient of Stock return on Cashflow, demonstrates than with a high returns, firms tend to substitute internal investment with Debt and Equity, thus giving additional indirect support to pecking order theory. The significant but negative coefficient on Q (-0.007***) adds zero additional support for the market timing hypothesis 3. Therefore having mixed results about market
timing hypothesis 3, it could be partially supported.
4.2 Results for investor protection hypothesis.
Table 5. SUR estimates with investor protection variable
Dependent Variable Debt Equity CashFlow Other
DEV 0.807*** (0) -0.004 (0.97) -0.718*** (0) -0.396** (0.02) Profit -0.004 (0.99) -0.567* (0.08) 1.024** (0.05) -1.469*** (0) Stock Return 0.307*** (0) -0.107** (0.05) -0.035 (0.67) -0.057 (0.46) Q -0.058*** (0) -0.01 (0.6) (0.46)-0.02 (0.15)0.036 Anti-self 0.006 (0.94) (0.25)0.099 -0.032 (0.8) -0.324*** (0.01) DEV* Anti-self -0.605*** (0) -0.038 (0.81) 0.745*** (0) 0.414* (0.08) Profit* Anti-self -0.016 (0.97) (0.77)0.123 -0.625 (0.35) 1.947*** (0) Stock return* Anti-self -0.321***
(0) 0.228*** (0) -0.062 (0.57) (0.51)0.066
Q*Anti-self 0.091***
(0) (0.84)0.005 0.025 (0.5) -0.051 (0.14)
Fixed assets ratio 0.018
(0.62) (0.12)0.064 0.563*** (0) -0.492*** (0) Investment Ratio -0.002 (0.27) (0.78)0.001 -0.002 (0.37) (0.13)0.004 Size 0.002 (0.57) -0.033*** (0) 0.02*** (0) 0.014*** (0.01) Volatility -0.056*** (0.01) (0.41)0.02 (0.23)0.046 (0.88)0.005 OilPrice 0.091*** (0) -0.026 (0.35) -0.096** (0.03) -0.003 (0.95) Constant -0.301*** (0.01) 0.57*** (0) (0.32)0.201 0.656*** (0) Nobs 1125 1125 1125 1125 R^2 0.218 0.175 0.146 0.073
From the results it can be seen that the value of coefficient on DEV has increased comparing to the results from Table 4, but it is partially offset by the value of interaction between DEV and Anti-self index. Having these results, it is possible to calculate the overall coefficient of DEV for the max and min values (Hong Kong and NL, the list of all values are in the Appendix A) of Anti-self index: For Hong Kong – [0.807 + 0.96*(-0.605) = 0.226]; for NL – [0.807 + 0.2*(-0.605) = 0.686]. Both of these coefficients are positive, which lead to the acceptance of the tradeoff hypothesis 1.1. The coefficient of 0.226 is lower than previously reported in the Table 4 (0.359), indicating that firms in the high Anti-self countries do not have high deviations from their targeted leverage, compared with countries with small Anti-self index (the value for DEV for NL is 3 times bigger than for Hong Kong).
= -0.449]; for NL – [-0.567 + 0.2*(0.123) = -0.542]. Showing that in the countries with high Anti-self index Cashflow substitutes for Equity issuance less than in the countries with lower value of Anti-self index. Because interaction coefficient is not significant, I have to reject
pecking order hypothesis 2.1.b.
The coefficients values on Stock returns and its interaction with Anti-self are significant. The main point that the value of the coefficient on the Stock returns are negative, comparing to the coefficient in Table 4, and this can be explained by the fact, that with the increase of the investor protection level, presented by Anti-self index, the influence of the Stock returns would increase as well. Let’s do the calculations: For Hong Kong – [-0.107 + 0.96*( 0.228) = 0.112]; for NL – [-0.107 + 0.2*(0.228) = -0.061]. As it can be seen, that with the increase in investor protection (represented by Anti-self index), there is an increase in the Stock returns, which influence the Equity issuance. The mean value of Anti-self index is 0.667, indicating the overall average value of Stock returns being 0.045. The result on Q from Table 5 are not significant for both – Q and its interaction with Anti-self. Thus this leads us to the partial support of the market timing hypothesis 3.1 (in the countries with high Anti-self index).
Conclusions and Limitations
The main goal of my paper was to discover how oil and gas companies finance their investments. My main goal was to shorten the gap between capital structure studies on oil and gas companies, which were ignored by scholars for some reasons. In order to do that, I have collected data from 18 countries, for 147 oil and gas companies having relevant data from 2001 till 2015 year. In order to analyse the data I have used the several statistical methods used by Elsas et at. (2014) in order to test capital structure hypothesis and McLean et al. (2012) in order to test for investor protection hypothesis. In order to test capital structure hypothesis, I had to calculate Deviation from target leverage ratio, for which I have used methodology from Flannery and Rangan (2006), where I have calculated adjustment speed of capital structure (using GMM) which I would later use for the calculation of Deviation form targeted leverage. Having all of the required variables, I have computed a system of Seemingly unrelated regressions in order to test capital structure hypothesis.
As the result, tradeoff theory received significant support, indicating that oil and gas companies are using a large proportion of debt in their activities, in order to meet a target leverage level. From the country level, tradeoff theory received significant support as well, indicating additionally, that firm in the countries with high investor protection level (Anti-self index), have a less deviation from their targeted leverage (and as a result eager to take less debt) than in a countries with less investor protection.
investor protection hypotheses I found out that oil and gas companies that face a low level of internal funds, tend to issue debt which is in line with pecking order theory. The country level hypothesis of the pecking order theory were found to be not significant.
Market timing theory receive partial support from capital structure hypotheses and for the country level hypotheses. I have showed that high stock returns of the oil and gas companies has a significant influence on the Equity issuance in the countries with high investor protection index. This effect reduces with the level of investor protection and can be even negative.
I believe that my study has a following limitation. It is limited to 18 countries with a great representation belonging to USA, UK, Australia and Canada. This issue could not have been avoided, because those countries have in general well-developed stock markets and they have a large number of companies functioning since 80-ties, while other companies from other countries are relatively new, they are functioning since 90-95 years. I believe that this problem could be overcome in the next research, when there will a greater amount of companies from different countries, considering the fact that a vast majority of those companies started to report their activities in the time frame between 2005-2008 years.
Appendix A.
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