ACKNOWLEDGEMENTS

The completion of this dissertation could not have been possible without the help, advice and of course patience of my supervisor, Dr R.V. Sannassee. His continuous guidance was greatly appreciated and his kindness with students did not go unnoticed in smoothing the difficulties encountered throughout our studies.

I also want to express my gratitude to my lecturers and friends including the course officers for all their selfless help and support during the period of this course.

Furthermore, I would like to thank my family, my parents and my sister for their strong support and understanding over the period of this course.

Abstract

This paper provides an empirical study on the relationship between leverage and investment for firms listed on the Stock Exchange of Mauritius. It also permits the determination of other factors that may affect a particular firm's investment. By using panel data for the year 2001 to 2008, the sample was tested in 3 different circumstances; firstly the whole sample was tested, secondly low growth firms and finally high growth firms. The results obtained give different conclusions as to which factors are the most crucial when dealing with low growth and high growth firms. But the overwhelming result remains the one where all the firms were taken into consideration. However, the most important independent variable, leverage, tests positive for the whole sample and high growth firms while it tends to be insignificant for low growth firms i.e. investment is negatively related with leverage for all firms considered and high growth firms.

1. Introduction

Firms play a determining role in the growth, economic and social success of a country. However, the government must implement policies to aid such growth in any part of the world. Mauritius is no stranger to this universal truth and the Mauritian government has contributed a lot to the development of local firms since the independence of the country.

The years 1980s' onwards saw the rapid development of the various sectors of the Mauritian economy and this led to increased investment in modern technologies, infrastructure, product development and product promotion. These innovations remain an important feature of Mauritian companies as it allows them to retain their competitive edge over their competitors.

There exists several sources for financing such investment the objective of the management is to find the right mix to maximise the value of the company. Financial leverage is one of them and can be described as the amount of debt used to finance a firm's assets, projects and other aspirations. During the Great Depression, financial leverage was badly viewed as a source of financing as it was thought that it increased financial distress and led to bankruptcy of businesses. However, such thoughts are no longer widespread nowadays as firms increasingly make use of leverage to finance their activities and for expansion purposes.

Indeed, there are several reasons that justify the use of debt in a company. Issuing shares for future investment purposes is regarded as costly and dilutes the future expected earnings of existing shareholders. The issue of shares also dilutes ownership of the firm and this may send a negative signal to the market. The benefit of the tax deductibility received from interest paid increases cash flow and makes debt a valuable financing tool.

The arguments against debt is that if debt reaches a level where the costs associated with financial distress outweighs the tax benefits, then adding more debt will decrease the value of the firm and increase the probability that the firm will default on interest payments and capital repayment in the future. It must also be highlighted that the existence of risky borrowing causes a company to adopt a less profitable investment strategy. Therefore, it can be said that leverage amplifies both gains and losses.

This dissertation primarily focuses on the impact of leverage on investment and also analyses the other factors that Mauritian firms need to take into consideration when making an investment. The panel data set covers 18 companies listed on the Stock Exchange of Mauritius for the period 2001 - 2008. The rest of the project is classified as follows:

Chapter 2 provides a broad empirical review of the relations between the dependent variable investment and the independent variables leverage, sales, Tobin's Q, cash flow, profitability and liquidity.

Chapter 3 details the methodology to be used to conduct the analysis. The econometric model adopted and the dependent as well as the independent variables are defined in this chapter. In addition, information about the data sources, the sample period, the statistical package and an overview of panel data estimation is also provided.

Chapter 4 reports the estimated results and their interpretations.

Finally, in chapter 5, a brief summary is presented together with a general conclusion as well as some of the limitations of the study. In addition, some policy implications and recommendations are also provided in that chapter.

2. Literature Review

The term Investment is frequently used in jargon of economics, business management and finance. According to economic theories, investment is defined as the per-unit production of goods, which have not been consumed, but will however, be used for the purpose of future production. The decision for investment, also referred to as capital budgeting decision, is regarded as one of the key decisions of an entity.

Leverage is a method of corporate funding in which a higher proportion of funds is raised through borrowing than stock issue. It is measured as the ratio of total debt to total assets; greater the amount of debt, greater the financial leverage. Financial Leverage is the ability of a company to earn more on its assets by taking on debt that allows it to buy or invest more in order to expand.

Nowadays financial leverage is viewed as an important attribute of capital structure alongside equity and retained earnings. Financial leverage benefits common stockholders as long as the borrowed funds generate a return greater than the cost of borrowing, although the increased risk can offset the general cost of capital.

In the past years, a large body of the literature has provided robust empirical evidence that financial factors have a significant impact on the investment decisions of firms. While traditional research on investment was based on the neoclassical theory of optimal capital accumulation (where under the assumption of perfect capital markets, the cost of financing does not depend on the firm's financial position), more recent literature has increasingly incorporated frictions such as asymmetric information and agency problems as a source behind the relevance of the degree of financial pressure faced by the firm in determining the availability and the costs of external financing

This chapter will seek to enclose literature on the impact of financial leverage on investment and other factors that may affect investment in firms.

2 .1 Modigliani & Miller (M&M) - 1958 theory with no taxation

In what has been hailed as the most influential set of financial papers ever published, Franco Modigliani and Merton Miller addressed capital structure in a rigorous, scientific fashion, and their study set off a chain of research that continues to this day.

Modigliani and Miller (1958) argued that the investment policy of a firm should be based only on those factors that will increase the profitability, cash flow or net worth of a firm.

The M&M view is that companies which operate in the same type of business and which have similar operating risks must have the same total value, irrespective of their capital structures. It is based on the belief that the value of a company depends upon the future operating income generated by its assets. The way in which this income is split between returns to debt holders and returns to equity should make no difference to the total value of the firm. Thus the total value of the firm will not change with gearing, and therefore neither will its Weighted Average Cost of Capita (Pandey, 1995).

Many empirical literatures have challenged the leverage irrelevance theorem of Modigliani and Miller. The irrelevance proposition of Modigliani and Miller will be valid only if the perfect market assumptions underlying their analysis are satisfied

Under the original M&M propositions, leverage and investment were unrelated. If a firm had profitable investment projects, it could obtain funding for these projects regardless of the nature of its current balance sheet.

2 .2 Modigliani &Miller - 1963 theory with tax

M & M (1963) found that the corporation tax system carries a distortion under which returns to debt holders (interest) are tax deductible to the firm, whereas returns to equity holders are not. They therefore concluded that geared companies have an advantage over ungeared companies, i.e. they pay less tax and will have a greater market value and a lower WACC. Following this research, the consensus that emerged was that tax is positively correlated to debt (Graham 1995, Miller 1977) and is considered a major influence in the debt policy decision.

Modigliani et al (1963) argued that we should not “ waste our limited worrying capacity on second-order and largely self correcting problems like financial leveraging ”. That is firms should not be worried about growth as long as they have good projects in hand, since they will always be able to find means of financing those projects.

2.3 The Trade-Off Models

Some of the assumptions inherent in the M&M model can be relaxed without changing the basic conclusions as argued by Stiglitz (1969) and Rubenstein (1973) . However, when financial distress and agency costs are considered, the M&M models are altered significantly. The addition of financial distress and agency costs to the M&M model results in a trade-off model. In such a model, the optimal capital structure can be visualized as a trade-off between the benefit of debt (the interest tax shield) and the costs of debt (financial distress and agency costs) as presented by Myers ( 1997 )

The trade-off models have intuitive appeal because they lead to the conclusion that both no-debt and all-debt are bad, while a “moderate” debt level is good. However, the “trade-off models have very limited empirical support”, Marsh (1982) , suggesting that factors not incorporated in this model are also at work.

Jensen and Meckling (1976) invoked a moral hazard argument to explain the agency costs of debt, proposing that high levels of debt will induce firms to opt for excessively risky investment projects. The incentive for such a move is that limited liability provisions in debt contracts imply that risky projects will provide higher mean returns to the shareholders: zero in low states of nature and high in good states. However, the higher probability of default will induce investors to demand either interest rates premiums or bond covenants that restrict the firm's future use of debt.

2.4 Pecking-Order Theory

Initiated by Donaldson (1961), the Pecking-Order theory argues that firms simply use all their internally-generated funds first, move down the pecking order to debt and then lastly issue equity in an attempt to raise funds. Firms follow this line of least resistance that establishes the capital structure.

Myers noted an inconsistency between Donaldson's findings and the trade-off models, and this inconsistency led Myers to propose a new theory. Myers (1984) suggested asymmetric information as an explanation for the heavy reliance on retentions. This may be a situation where managers have access to more information about the firm and know that the value of the shares is greater than the current market value. If new shares are issued in this situation, there is a possibility that they would be issued at a too low price, thereby transferring wealth from existing shareholders to new shareholders.

2.5 Investment and Leverage

One of the main issues in Corporate Finance is whether financial leverage has any effects on investment policies. The corporate world is characterized by various market imperfections, due to transaction costs, institutional restrictions and asymmetric information. The interactions between management, shareholders and debt holders will generate frictions due to agency problems and that may result in under-investment or over-investment incentives. Whenever we refer to investment, it is essential to distinguish between over- investment and under-investment.

In his model, Myers (1977) argued that debt can create an ‘overhang' effect. His idea was that debt overhang reduces the incentives of the shareholder-management coalition in control of the firm to invest in positive net-present-value investment opportunities, since the benefits accrue, at least partially, to the bondholders rather than accruing fully to the shareholders. Hence, highly levered firms are less likely to exploit valuable growth opportunities as compared to firms with low levels of leverage.

U nderinvestment theory centers on a liquidity effect in that firms with large debt commitment invest less, no matter what their growth opportunities (Lang et al, 1996). In theory, even if debt creates potential underinvestment incentives, the effect could be attenuated by the firm taking corrective action and lowering its leverage, if future growth opportunities are recognized sufficiently early (Aivazian & Callen, 1980). Leverage is optimally reduced by management ex ante in view of projected valuable ex post growth opportunities, so that its impact on growth is attenuated. Thus, a negative empirical relation between leverage and growth may arise even in regressions that control for growth opportunities because managers reduce leverage in anticipation of future investment opportunities. Leverage simply signals management's information about investment opportunities. The possibility that leverage might substitute for growth opportunities is referred to as the endogeneity problem.

Over-investment theory is another problem that has received much attention over the years.It is described as investment expenditure beyond that required to maintain assets in place and to finance positive NPV projects. In these kind of situations, conflicts may arise between managers and shareholders (Jensen,1986 & Stulz,1990). Managers seek for opportunities to expand the business even if that implies undertaking poor projects and reducing shareholder worth in the company. Managers' abilities to carry such a policy is restrained by the availability of cash flow and further tightened by the financing of debt. Issuing debt commits the firm to pay cash as interest and principal, forcing managers to service such commitments with funds that may have otherwise been allocated to poor investment projects.

Thus, leverage is one mechanism for overcoming the overinvestment problem suggesting a negative relationship between debt and investment for firms with weak growth opportunities. Too much debt also is not considered to be good as it may lead to financial distress and agency problems.

Cantor (1990) explains that highly leveraged firms show a heightened sensitivity to fluctuations in cash flow and earnings since they face substantial debt service obligations, have limited ability to borrow additional funds and may feel extra pressure to maintain a positive cash flow cushion. Hence, the net effect would be reduced levels of investment for the firm in question.

Accordingly, Mc Connell and Servaes (1995) have examined a large sample of non financial United States firms for the years 1976, 1986 and 1988. They showed that for high growth firms the relation between corporate value and leverage is negative, whereas that for low growth firms the relation between corporate value and leverage is positively correlated. This trend tends to indicate that to maximise corporate value, it is preferable to keep down leverage to a low level and to increase investment.

Lang, Ofek and Stulz (1996) used a pooling regression to estimate the investment equation. They distinguish between the impact of leverage on growth in a firm's core business from that in its non-core business. They argue that if leverage is a proxy for growth opportunities, its contractionary impact on investment in the core segment of the firm should be much more pronounced than in the non-core segment. They found that there exists a negative relation between leverage and future growth at the firm level. Also they argued that debt financing does not reduce growth for firms known to have good investment opportunities. Lang et al document a negative relation between firm leverage and subsequent growth. However, they find that this negative relation holds only for low q firms, i.e. those with fewer profitable growth opportunities. Thus, their findings appear to be most consistent with the view that leverage curbs overinvestment in firms with poor growth opportunities.

Myers (1997) has examined possible difficulties that firms may face in raising finance to materialize positive net present value (NPV) projects, if they are highly geared. Therefore, high leverages may result in liquidity problem and can affect a firm's ability to finance growth. Under this situation, debt overhang can contribute to the under-investment problem of debt financing. That is for firms with growth opportunities, debt have a negative impact on the value of the firm.

Peyer and Shivdasani (2001 ) provide evidence that large increases in leverage affect investment policy. They report that, following leveraged recapitalizations, firms allocate more capital to business units that produce greater cash flow. If leverage constrains investment, firms with valuable growth opportunities should choose lower leverage in order to avoid the risk of being forced to bypass some of these opportunities, while firms without valuable growth opportunities should choose higher leverage to bond themselves not to waste cash flow on unprofitable investment opportunities.

Ahn et al. (2004 ) document that the negative relation between leverage and investment in diversified firms is significantly stronger for high Q segments than for low Q business segments, and is significantly stronger for non-core segments than for core segments. Among low growth firms, the positive relation between leverage and firm value is significantly weaker in diversified firms than in focused firms. Their results suggest that the disciplinary benefits of debt are partially offset by the additional managerial discretion in allocating debt service to different business segments within a diversified organizational structure.

Childs et al (2005) argued that financial flexibility encourages the choice of short-term debt, thereby dramatically reducing the agency costs of under-investment and over-investment. However the reduction in the agency costs may not encourage the firm to increase leverage, since the firm's initial debt level choice depends on the type of growth options in its investment opportunity set.

Aivazian et al (2005) analysed the impact of leverage on investment on 1035 Canadian industrial companies, covering the period 1982 to 1999. Their study examined whether financing considerations (as measured by the extent of financial leverage) affect firm investment decisions inducing underinvestment or overinvestment incentives. They found that leverage is negatively related to the level of investment, and that this negative effect is significantly stronger for firms with low growth opportunities than those with high growth opportunities. These results provide support to agency theories of corporate leverage, and especially to the theory that leverage has a disciplining role for firms with weak growth opportunities

2.6 Investment and Profitability

The idea that investment depends on the profitability of a firm is amongst the oldest of macroeconomic relationships formulated. The sharp fluctuations in profitability in the average cost of capital since the 1960s revived interest in this relationship (Glyn et al, 1990) . However the evidence for the impact of profitability on investment remains sketchy.

Bhaskar and Glyn (1992) concluded that profitability must be regarded as a significant influence on investment, though by no means the overwhelming one. Their results indicated that “enhanced profitability is not always a necessary, let alone a sufficient condition for increased investment”.

However, years later Glyn (1997) provided an empirical study that examined the impact of profitability on capital accumulation. He tested the impact of profitability in the manufacturing sector on investment for the period 1960-1993 for 15 OECD countries. His findings suggested that the classical emphasis on the role of profitability on investment wass still highly significant and had a very tight relationship.

Korajczyk and Levy (2003) investigated the role of macroeconomic conditions and financial constraints in determining capital structure choice. While estimating the relation between firms' debt ratio and firm-specific variables, they found out that there was a negative relation between profitability and target leverage, which was consistent with the pecking order theory. This indicated that if leverage of the firm is low, profitability will be high and the entity will be able to invest in positive NPV projects i.e. increase investment.

Bhattacharyya (2008) recently provided an empirical study where he examined the effect of profitability and other determinants of investment for Indian firms. He found that “Short-run profitability does not have consistent influence on investment decisions of firms”, implying that one should concentrate on the long-run profitability of a firm. This indicates that profitability is still regarded as one of the major determinants underlying investment decisions of firms. However, he suggested that liquidity is relatively more important than profitability when it comes to firms' investment decisions.

2.7 Investment and Liquidity

“Under the assumptions of illiquid capital and true uncertainty, management can never be sure that investment projects will produce sufficient liquidity to cover the cash commitments generated by their financing. Yet failure to meet these commitments may result in a crisis of managerial autonomy or even in bankruptcy. Thus, capital accumulation is a contradictory process. Investment is inherently risky, while the failure to invest will ultimately lead to the firm'smarginalization or demise.” Crotty and Goldstein (1992)

Chamberlain and Gordon (1989 ) used the annual domestic investment of all nonfinancial corporations in the United States between 1952 and 1981 in an attempt to determine the impact of liquidity on the profitable investment opportunities available to the corporation. They have put forward that in their long-run survival model, liquidity variables play an essential role as it captures the firm's desire to avoid bankruptcy. It was also noted that there was a significant improvement in the explanation of investment when liquidity variables were added to the profitability variables of their regression, thereby supporting the view that liquidity is a pre-dominant determinant of investment and that they are positively related.

Hoshi, Kashyap and Scharfstein (1991) attempted to find the relationship between investment and liquidity for Japanese firms.They found thathigh current profits increase current liquidity, thereby generating further investment from the firm to ensure future profitability and increased output to meet demand.

Myers and Rajan (1998) suggested that liquid assets are generally viewed as being easier to finance and therefore, asset liquidity is a plus for nonfinancial corporations or individual investors. However, Myers and Rajan argued that although more liquid assets increase the ability to invest in projects, they also reduce management's ability to commit credibly to an investment strategy that protects investors.

Johnson (2003) found that short debt maturity increases liquidity risk, which in turn, negatively affects leverage and the firm's investment. Jonson also suggested that firms trade off the cost of underinvestment problems against the cost of increased liquidity risk when choosing short debt maturity

2.8 Investment and Sales

Sales growth targets play a major role in the perceptions of top managers. Using surveys, Hubbard and Bromiley (1994) find sales is the most common objective mentioned by senior managers. Additional explanatory variables like current or lagged sales are very important in the investment equation as they can act as proxy for the missing information about expected future conditions in case such information has not been captured by Tobin's Q.

Kaplan and Norton (1992, 1993, 1996) argue that firms must use a wide variety of goals, including sales growth, to effectively reach their financial objectives. They suggested that “Sales growth influences factors…..all the way to the implied opportunities for investments in new equipment and technologies…..”

According to this study of 396 corporations, Kopcke and Howrey (1994) found that the capital spending of many of the companies corresponds very poorly with their sales and profits. These divergences suggest that sales and profits do not represent fully an enterprise's particular incentives for investing. Consequently, these findings do not support generalizations contending that companies with more debt are investing less than their sales and cash flows would guarantee.

Athey and Laumas (1994) using panel data over the period 1978-86, examined the relative importance of the sales accelerator and alternative internal sources of liquidity in investment activities of 256 Indian manufacturing firms. They found that when all the selected firms in the sample were considered together, current values of changes in real net sales and net profit were all significant in determining capital spending of firms.

Azzoni and Kalatzis (2006) considered the importance of sales for investment decisions of firms. They found that sales presented a positive and significant relationship with investment in all cases.

2.9 Investment, Cash Flow and Tobin's Q

It was traditionally believed that cash flow was important for firms' investment decisions because managers regarded internal funds as less expensive than external funds. In the 1950s and 1960s, this view led to numerous empirical assessments of the role of internal funds in firm investment behaviour. These studies found strong relationships between cash flow and investment.

Considerable empirical evidence indicates that internally generated funds are the primary way firms finance investment expenditures. In an in-depth study of 25 large firms, Gordon Donaldson (1961 ) concludes that: "Management strongly favoured internal generation as a source of new funds even to the exclusion of external funds except for occasional unavoidable 'bulges' in the need for new funds."

Another survey of 176 corporate managers by Pinegar and Wilbricht (1989) found that managers prefer cash flow over external sources to finance new investment; 84.3% of sample respondents indicate a preference for financing investment with cash flow.

Researchers have also discovered the impact of cash flow on investment spending in Q models of investment. Fazzari, Hubbard, and Petersen (1988) find that cash flow has a strong effect on investment spending in firms with low-dividend-payout policies. They argue that this result is consistent with the notion that low-payout firms are cash flow-constrained because of asymmetric information costs associated with external financing. One reason these firms keep dividends to a minimum is to conserve cash flow from which they can finance profitable investment expenditures.

Fazzari and Petersen (1993) find that this same group of low-payout firms smooths fluctuations in cash flow with working capital to maintain desired investment levels. This result is consistent with the Myers and Majluf (1984) finding that liquid financial assets can mitigate the underinvestment problem arising from asymmetric information.

Whited (1992) also extended the Fazzari, Hubbard, and Petersen (1988) results in a study of firms facing debt financing constraints due to financial distress. She found evidence of a strong relationship between cash flow and investment spending for firms with a high debt ratio or a high interest coverage ratio, or without rated debt.

Himmelberg and Petersen (1994) in a study of small research and development firms find that cash flow strongly influences both capital and R & D expenditures. They argue that the asymmetric information effects associated with such firms make external financing prohibitively expensive, forcing them to fund expenditures internally, that is by making use of cash flows.

An alternative explanation for the strong cash flow/investment relationship is that managers divert free cash flow to unprofitable investment spending. One study assessing the relative importance of such an agency problem was performed by Oliner and Rudebusch (1992) , who analysed several firm attributes that may influence the cash flow/investment relationship. They find that insider share holdings and ownership structure (variables that proxy for agency problems) do little to explain the influence that cash flow has on firm investment spending.

Carpenter (1993) focused on the relationships among debt financing, debt structure, and investments pending to test the free cash flow theory. He finds that firms that restructure by replacing large amounts of external equity with debt increase their investment spending compared to non-restructured firms. He sees these results as inconsistent with free cash flow behavior, because cash flow committed to debt maintenance should be associated with reductions in subsequent investment spending.

Findings by Strong and Meyer (1990) and Devereux and Schiantarelli (1990) support the free cash flow interpretation.

Strong and Meyer (1990) disaggregate the investment and cash flow of firms in the paper industry into sustaining investment (i.e., productive capacity maintaining) and discretionary investment, and total cash flow and residual cash flow (i.e., cash flow after debt service, taxes, sustaining investment, and established dividends). Residual cash flow and discretionary investment are found to be positively and strongly related. This evidence suggests that residual cash flow is often used to fund unprofitable discretionary investments pending.

Devereux and Schiantarelli (1990) find that the impact of cash flow on investment spending is greater for larger firms. One explanation they provide for this result is that large firms have more diverse ownership structures, and are more influenced by manager/shareholder agency problems.

The Q model of investment relates investment to the firm's stock market valuation, which is meant to reflect the present discounted value of expected future profits, Brainard and Tobin (1968).

In the case of perfectly competitive markets and constant returns to scale technology, Hayashi (1982) showed that average Q, the ratio of the maximised value of the firm to the replacement cost of its existing capital stock, would be a sufficient statistic for investment rates.

Tobin's Q , further assumes that the maximised value of the firm can be measured by its stock market valuation. Under these assumptions, the stock market valuation would capture all relevant information about expected future profitability, and significant coefficients on cash-flow variables after controlling for Tobin's Q could not be attributed to additional information about current expectations.

However if the Hayashi conditions are not satisfied, or if stock market valuations are influenced by ‘bubbles' or any factors other than the present discounted value of expected future profits; then Tobin's Q would not capture all relevant information about the expected future profitability of current investment. If that is the case, then additional explanatory variables like current or lagged sales or cash-flow terms could proxy for the missing information about expected future conditions.

The classification of q ratios into high and low categories is based on a cut-off of 'one' Lang, Stulz, and Walkling (1989). The latter's motivation for this cut-off is partially based on the fact that under certain circumstances firms with q ratios below one have marginal projects with negative net present values (Lang and Litzenberger, 1989). However, q is also industry specific and one may argue that managers should not be held responsible for adverse shocks to their industries. As such, the industry average may be a useful alternative cut-off point to separate high q firms from low q firms.

Hoshi, Kashyap, and Scharfstein (1991) regressed investment on Tobin's q, other controlling variables, and cash flow. They interpreted differences in the importance of cash flow between different groups of firms as evidence of financing constraints .

Results obtained by Vogt (1994) indicate that the influence of cash flow on capital spending is stronger for firms with lower Q values. This result suggests that cash flow-financed capital spending is marginally inefficient and provides initial evidence in support of the FCF hypothesis. The stronger the influence cash flow had on capital spending in this group, the larger the associated value of Tobin's Q.

After the results presented by Kaplan and Zingales (1997 and 2000), several studies have criticised the empirical test based on the cash flow sensitivity as a meaningful evidence in favour of the existence of financing constraints. The significance of the cash flow sensitivity of investment, it was argued, may then be the consequence of measurement errors in the usual proxy for investment opportunities, Tobin's Q, and may provide additional information on expected profitability rather than being a signal of financing constraints.

Gomes (2001) showed that the existence of financing constraints is not sufficient to establish cash flow as a significant regressor in a standard investment equation, while Ericson and Whited (2000) demonstrate that the investment sensitivity to cash flow in regressions including Tobin's Q is to a large extent due to a measurement error in Q. Likewise, Alti (2003) shows that investment can be sensitive to changes in cash flow in the benchmark case where financing is frictionless.

3. RESEARCH METHODOLOGY

3.1 Research Objective

For any business organisation, the right balance of leverage is of paramount importance since continuous investment will ensure that the entity retains its competitive edge over rivals whilst excessive leverage will commit the firm to more servicing of interest payments, thereby curbing down investment levels.

The primary objective of this research is to determine the impact of financial leverage on investment levels using firm level panel data in Mauritius. This paper will also attempt to shed light on the other determinants that may affect the investment decisions of firms. Two types of firms will be demarcated for the purpose of this dissertation, namely: high growth firms and low growth firms by using the firm's Price Earnings Ratio (P/E ratio) and then the impact of leverage on investment in these firms will be determined.

This study will also help to understand the rationale behind the financing - investment decisions of Mauritian firms.

3.2 Research Questions

The overriding questions that this research will purport to answer are:

  1. What is the impact of financial leverage on the investment decisions of Mauritian firms?
  2. Demarcate between high growth firms and low growth firms and investigate how investment varies with leverage in these companies
  3. Test whether the determinants of investment, identified by previous empirical studies, are applicable to the Mauritian context.

3.3 Data

Data is categorized into two type namely primary and secondary data.

Primary data are raw data obtained through observations, surveys and interviews.

Secondary data is information which has already been collected by other researchers.

For the purpose of this study, secondary data extracted from Statement of Comprehensive Income and Statement of Financial position, would be used.

From panel data collected, two dimensions of the data set are obtained, cross-sectional data and time series data. From a statistical point of view, observations for several time periods for each of the several individual firms will be noted.

3.4 Sample

Data, for the period 2001-2008, will be obtained from handbooks released by the SEM. These handbooks provide detailed reports of the Statement of Comprehensive Income and Statement of Financial position for the past 8 years along with relevant information of the companies. It is worthwhile to note that this represents a reliable source of information as the companies' accounts are subject to strict auditing, control and compliance procedures.

The sample consisting of 18 firms include elements of both time series and cross sectional data.

Number of observations per sector would be “Number of years” X “Number of companies in the sector”. The distribution of firms across the six broad industries categorised by the SEM is presented in Table 1.

Table 1: Sector wise distribution of 18 firms listed on SEM.

SECTOR

Number of companies

Number of observations

Transport, Leisure and Hotels

1

8

Commerce

3

24

Industry

5

40

Sugar

3

24

Investments

5

40

Banks, Insurance and other Financial institutions

1

8

Total

18

144

3.5 Variables

The variables to be used in this study and their measurements are adopted from existing literatures. All variables will be measured at book values instead of market values due to data limitation.

The investment equation used to examine the impact of leverage on investment (adapted from Hoshi, Kashyap and Scharfstein (1991) and Aivazian, Ge and Qiu (2005)) is as follows:

I i,t / K i,t-1 = α + β 1 (CF i,t / K i,t-1 ) + β 2 Q i,t-1 + β 3 LEVERAGE i,t-1

+ β 4 SALES i,t-1 + β 5 PROFIT i,t-1 + β 6 LIQ i , y t-1 + u it

Where

I i,t

: Represents the net investment of the firm I during the period t

CF i,t : Represents the cash flow of firm I at time t

K i,t-1 :Represents the net fixed assets

Q i,t-1 : Represents the Tobin's Q

LEV i,t-1

: Represents the leverage undertaken by firm I at time t

SALES i,t-1

: Stands for the net sales of firm i

PROFIT i,t-1

: Stands for the profitability of the firm i

LIQ i, Yt-1

: Represents the liquidity ratio as measured by current assets divided by current liabilities

Determinants

Definitions

Expected Results

Major Empirical

Results

Major

Authors

Leverage (LEV)

Book Value of long term debt divided by book value of total assets

_

_

Peyer and Shivdasani (2001)

Ahn et al. (2004)

Aivazian et al (2005)

Tobin's Q (Q)

Market Value of total assets of the firm divided by the book value of assets

+

+

Hoshi, Kashyap, and Scharfstein (1991)

Sales (SALES)

Net sales of the firm deflated by net fixed assets

+

+

Athey and Laumas (1994)

Azzoni and Kalatzis (2006)

Liquidity (LIQ)

Ratio of current assets to current liabilities

+

+

Chamberlain and Gordon (1989)

Myers and Rajan (1998)

Profitability (Profit)

Earnings after Tax add interest minus tax advantage on interest divided by total fixed assets

+

+

Glyn (1997)

Korajczyk and Levy (2003)

Bhattacharyya (2008)

Cash Flow (CF)

Total Earnings before extraordinary items and depreciation

+

+

Fazzari, Hubbard, and Petersen (1988),

Whited (1992)

Himmelberg and Petersen (1994)

Leverage

Our main variable of interest is leverage . For this study, we will use the book value definition of leverage as to the market value of leverage. Lang et al (1996) pointed out that market value weightings caused deviations in equity values. If leverage produces a significant negative effect on investment, two interpretations are possible. First, it would imply that capital structure has a determining role in a firm's investment decision making. Second, it can mean that an agency problem exists between shareholders and agents i.e. if managers have committed the company to huge interest payments, they may give up projects with positive NPV.

Tobin's Q

Tobin's Q measures growth opportunities and compares the value of a company given by financial markets with the value of a company's assets. If the market value reflected only the assets of the company, Tobin's q would be equal to 1.0. If Tobin's q is greater than 1.0, it implies that the market value is greater than the value of the company's assets. This suggests that the market value reflects some unmeasured or unrecorded assets of the company. High Tobin's q values encourage investors to devote more capital to it because they are more profitable than the price they would be paying for them. However if Tobin's q is less than 1, the market value is less than the recorded value of the assets of the company, thereby discouraging investors to invest in the entity.

Sales

Sales measures the efficiency with which net fixed assets are measured. A high ratio indicates a high degree of efficiency in asset utilization and a low ratio reflects inefficient use of assets.

Cash Flow

Cash flow of firms is an important determinant for growth opportunities. If firms have enough cash inflows in the company, it can be used in investing activities. This provides evidence that investment is related to the availability of internal funds. Cash flow may be termed as the amount of money in excess of that needed to finance all positive net present value projects. The purpose of allocating money to projects is to generate a cash inflow in the future, significantly greater than the amount invested thereby increasing shareholders' wealth. Cash flows will be measured by Total Earnings before extraordinary items and depreciation. This method was utilized by Lehn and Poulson (1989) and Lang et al (1991).

Profitability

Profitability is an important component of the investment equation since it tries to explain the extent to which the firm's assets are contributing to the total profitability. Hence, an increased investment in assets contributes to the increase of profitability.

Liquidity

Liquidity is the ability of firms to meet its current obligations. Firms should ensure that they do not suffer from lack of liquidity as this may result into a state of financial distress ultimately leading to bankruptcy. Lack of liquidity can lead to a struggle in terms of current obligations, which can affect firms' creditworthiness. Bernanke and Gertler (1990) argued that “ both the quantity of investment spending and its expected return will be sensitive to the credit worthiness of borrowers. ” That leads us to say that investment decisions of firms are sensitive to current liquidity. However, firms with high liquidity signal that funds are tied up in the current assets.

P/E Ratio

High-growth firms and low-growth firms will be differentiated by using the firm's price-to-operating-earnings (P/E) ratio. This ratio is obtained by dividing the stock price at the end of the period by the operating earnings per share for these years. Operating earnings per share will be used because it is calculated before interest payments and the earnings figure will unaffected by leverage. Firms with negative earnings will be removed from the sample. The next step will be to rank the firms for each year according to their end-of-year P.E ratio. Firms with high P.E ratio (i.e. above the median P.E ratio) will be classified as high growth opportunities firms and firms with low P.E ratio (i.e. below the median P.E ratio) are classified under low growth opportunities firms. This method was also adopted McConnell and Servaes (1994).

3.6 Panel Data treatments: Pooled Regression, Fixed effects (FE) and Random Effects (RE)

The first step when performing the econometric analysis is to differentiate between pooled estimates, fixed effect estimates and random effect estimates.

To compare the pooled estimates and the random effect estimates, the Lagrange Multiplier Test was performed. With a large chi-square test, indicative of a low p-value, the rejection of the null shows that the pooled estimate is inappropriate. However, if a low chi square test is obtained, then one should use pooled estimates without the need to compare Fixed Effect (FE) estimates and Random Effect (RE)

Regression analysis will then be carried out using the fixed and random effects model which is quite popular when using panel data. A fixed effect model is a statistical model that represents the observed quantities in terms of explanatory variables that are all treated as if those quantities were non-random. It enables to use the changes in the variables over time to estimate the effects of the independent variables on the dependent variable.

On the other hand, a random effects model is a kind of hierarchical linear model. It assumes that the dataset being analysed consists of a hierarchy of different populations whose differences relate to that hierarchy. In econometrics, random effects models are used in the analysis of hierarchical or panel data when one assumes no fixed effects (i.e. no individual effects). The fixed effects model is a special case of the random effects model.

The generally accepted way of choosing between fixed and random effects is running a Hausman test. The Hausman test checks a more efficient model against a less efficient but consistent model to make sure that the more efficient model also gives consistent results. The Hausman test tests the null hypothesis that the coefficients estimated by the efficient random effects estimator are the same as the ones estimated by the consistent fixed effects estimator. If they are (insignificant P-value, Prob>chi2 larger than .05) then it is safe to use random effects. If a significant P-value is obtained, then the fixed effects model should be used.

Statistically, fixed effects are always a reasonable thing to do with panel data (they always give consistent results) but they may not be the most efficient model to run. Random effects will give better P-values as they are a more efficient estimator, so it is advisable to run random effects if it is statistically justifiable to do so.

Therefore, it follows that a multiple regression using the above two methods, whichever is better under the Hausman tests, will be used to study the dependence of a dependent variable on other explanatory variables.

3.7 Regression model:

Multiple linear Regression

A multiple regression analysis will be used to study the dependence of a dependent variable on other explanatory variables. Investment will be regressed on the leverage and the other determinants that may affect investment in a firm. The multiple regression model is as follows:

I i,t / K i,t-1

=

α + β 1 (CF i,t / K i,t-1 ) + β 2 Q i,t-1 + β 3 LEVERAGE i,t-1

+ β 4 SALES i,t-1 + β 5 PROFIT i,t-1 + β 6 LIQ i , y t-1 + u it

Regression will be carried out using the software package Stata.

3.8 Tests Performed

3.8.1 Multicollinearity

Multicollinearity is a problem which arises due to the interconnectedness of independent variables. Multicollinearity occurs when variables are so highly correlated with each other that it is difficult to come up with reliable estimates of their individual regression coefficients. When two variables are highly correlated, they are basically measuring the same phenomenon or in other words convey essentially the same information.

Such a problem might exist in our model thereby making our p-values insignificant or presents such a high correlation that results cannot be separated to determine the true relationship. The VIF test will be used to check for multicollinearity. VIF stands for Variance inflation factor.

3.8 .2 Serial Correlation

A regression model assumes that the error terms are independent of one another. Serial correlation is a problem that arises in regression analysis when successive values of the random error term are not independent. When errors associated with observations of different time periods are related to each other, we refer to the errors as being serially correlated. If serial correlation is present in the data, the least squares estimator will still be unbiased, but no longer Best Linear Unbiased Estimate (B.L.U.E). Moreover, in the case of positive serial correlation, the estimates of the standard errors will be lower than they should be.

The Wooldridge test for serial correlation in panel data is used to test for serial correlation. If Prob > F is greater than 0.05, it means that no serial correlation is present in the model. However, if Prob > F is less than 0.05, then the Durbin-Watson Test is undertaken to eliminate any such serial correlation. According to this method, a value of 2 indicates there appears to be no autocorrelation. If the Durbin-Watson statistic is substantially less than 2, there is evidence of positive serial correlation. As a rough rule of thumb, if Durbin-Watson is less than 1.0, there may be cause for alarm.

3.8 .3 Heteroscedasticity

One of the key assumptions of the ordinary regression model is that the errors have the same variance throughout the sample. This is also called the homoscedasticity model. If the error variance is not constant, the data are said to be heteroscedastic . Since ordinary least-squares regression assumes constant error variance, heteroscedasticity causes the OLS estimates to be inefficient.

Heteroscedasticity can be tested by Modified Wald test for groupwise heteroskedasticity in fixed effect regression model. If Prob>chi2 is less than 0.05, it entails that heteroskedasticity is present. To cancel the effect of heteroskedasticity, a robustness check is then performed.

4. Findings and Analysis of Results

4.0 Introduction

The purpose of this chapter is to shed light on the impact of leverage on investment firstly for the sample of firms chosen, secondly for low growth firms and thirdly for high growth firms. The econometric analysis also highlighted the impact of other independent variables on investment.

As discussed in the methodology chapter, the first step is to differentiate between pooled estimates, fixed effect estimates and random effect estimates.

To compare the pooled estimates and the random effect estimates, the Lagrange Multiplier Test was performed. With a large chi-square test, indicative of a low p-value, the rejection of the null shows that the pooled estimate is inappropriate. However, if a low chi square test is obtained, then one should use pooled estimates without the need to compare Fixed Effect (FE) estimates and Random Effect (RE)

If the null hypothesis is rejected, the second step is then performed to differentiate between the fixed effect estimates and random effect estimates using the Hausman test. If the model is correctly specified and if the individual effects are uncorrelated with the independent variables, the fixed effect and random effect should not be different. A high chi-square value is indicative of the appropriateness of the fixed effect.

After the Hausman test, the estimates must be corrected for both heteroskedasticity and serial correlation.

The issue of multicollinearity is then addressed and a correlation table detailing the correlation between the independent variables is presented.

4.1 Whole Sample

This section details the results and analysis for the whole sample of firms chosen from the SEM. It shows the pooled estimates, random effect estimates and fixed effect estimates. Table 1 gives an overview of the various tests performed, results, significance and indicate whether any further test was needed.

Table 1: Overview of Results of Tests for all firms in the sample

Tests Performed

Result

Significance

Further Test Performed

Pooled Regression

chi2(1) = 41.54

Prob>chi2 = 0.000

Use Random Effect

Hausman Test Performed to now differentiate between FE and RE

Hausman Test

Chi2(6) = 0.18

Prob>chi2 = 0.9999

Use Fixed Effect

NO

Multicollinearity

Mean VIF = 1.54

No Multicollinearity

NO

Heteroskedasticity

Chi2(1) = 18.71

Prob>chi2 = 0.00

Heteroskedasticity present

YES

Serial Correlation

F(1,8) = 29.290

Prob>F = 0.006

No Serial Correlation present

NO

Prob > F = 0.000. Since this number is less than 0.05, it proves that the model is well specified. This is a test (F) to see whether all the coefficients in the model are different than zero.

The null hypothesis of the one-way random group effect model is that variances are zero. If the null hypothesis is not rejected, the pooled regression model is appropriate. With the large chi-square of 41.54 and Prob > Chi2 = 0.000 (i.e. less than 0.05), the null hypothesis is rejected in favour of the random group effect model as shown in table 1.

The Hausman test produces a chi-square of 51.24 and Prob > Chi2 = 0.0291 (i.e. less than 0.05). Hence, the fixed effect model is the appropriate model to be used in this case. The fixed effect variables then were tested for multicollinearity, heteroskedasticity and serial correlation.

The multicollinearity test performed showed that no multicollinearity was present in the regression. The correlation table (Table 2) plotted below shows further evidence of the absence of multicollinearity. The correlation between cash flow and profitability is high i.e. 0.5099. However, since multicollinearity is a problem of degree not kind, it does not affect the result and all p-values are significant in Table 3.

Table 2: Correlation Between Independent Variables

Correlation

Table

Leverage

Tobin's Q

Sales

Liquidity

Profitability

Cash Flow

Leverage

1

-

-

-

-

-

Tobin's Q

-0.0184

1

-

-

-

-

Sales

-0.1582

0.2498

1

-

-

-

Liquidity

-0.1666

0.1674

0.3667

1

-

-

Profitability

-0.1382

0.3214

0.3615

0.3054

1

-

Cash Flow

-0.2028

0.3295

0.2630

0.2688

0.5099

1

Wooldridge test for serial correlation in panel data was executed. The result, Prob>F= 0.8476, implied that no serial correlation was present in the regression.

Modified Wald test for groupwise heteroskedasticity in fixed effect regression model was carried out and heteroskedasticity was found to be present. Robust standard errors were obtained under the robustness test so as to eliminate heteroskedasticity from the regression.

The fixed effect coefficients and p-values are obtained after performing the above tests and are shown in Table 3.

Table 3 : Regression Results for all firms

Dependent

Variables

Pooled

Random Effect

Fixed Effect

Coefficient

p-value

Coefficient

p-value

Coefficient

p-value

Constant

-.0111583

.0212759

0.601

-.0430453

.0223127

0.054

-.0663716

.0221511

0.003

Leverage

-.250186

.0754803

0.001

-.238568

.0707505

0.001

-.2267076

. 0651518

0.001

Tobin's Q

.0671312

.0229636

0.004

.082807

.0222426

0.049

.0935957

.0206345

0.000

Sales

.0488193

.0130019

0.050

.0619066

.0131428

0.013

.0750922

.023437

0.002

Liquidity

.0415329

.0109539

0.041

.0465343

.0113045

0.000

.0487155

.0154027

0.002

Profitability

.0584478

.0572805

0.309

.1254025

.0547005

0.022

.1770694

.0502312

0.001

Cashflow

.340044

.064717

0.036

.3011494

.0615857

0.011

.2610303

.0713642

0.000

N.B: Standard errors are shown in italics.

R 2

R 2 within describes the goodness of fit for the observations that have been adjusted for their individual means while R 2 between describes the goodness of fit for the N different individual means. The value for R 2 between can often be quite unsatisfactory if it is not optimized by the FE estimator.

Finally, the R 2 overall corresponds to the usual R 2 of OLS regression. If the R 2 overall is very close to the R 2 within , then individual heterogeneity is low, and one obtains an indication that one could work with the pooled regression.

The fixed effect estimation produces a within R2 and a between R2 of 0.7602 and 0.0696 respectively. The overall R2 is 0.5780. Since the within R2 and overall R2 are very different, there is further evidence of the non-appropriateness of using the pooled regression. The overall R2 explains about 57.8% of the variation in the dependent variable i.e. investment.

Leverage

Leverage, the variable of interest, is statistically significant as its p-value is less than 0.05 and negatively related to investment. A 1 unit increase in the leverage ratio leads to a 0.22 decrease in net investment (refer to table 1). This implies that as leverage increases, firms listed on the SEM struggle to increase investment which is line with results obtained by Peyer and Shivdasani (2001 ) , Ahn et al. (2004 ) and Aivazian et al (2005) .

Tobin's Q

It is observed that Tobin's Q is statistically significant and positively related to investment of the firm. The regression estimate is 0.0936. This implies that most of the listed firms have growth opportunities in their respective fields.

Sales

Table 1 reports that firms are utilising efficiently their fixed assets and it reflects the ability in producing large volume of sales. The estimate of sales is .075 and is positively related to investment. Unlike Kopcke and Howrey's (1994) study where they found that investment from companies have a very poor relationship with sales level, this result shows a positive relationship between investment and sales when Mauritian firms are considered. This study's result corresponds to the result obtained by Athey and Laumas (1994) and Azzoni and Kalatzis (2006) where in both studies, sales presented a positive and significant relationship with investment when all the firms in the sample were considered.

Liquidity

Liquidity is positively related with investment and is statistically significant with a p-value of 0.002 at the 95% level. When a firm fails to meet its ongoing obligations due to lack of sufficient liquidity, it results in poor reputation for the business and loss of creditor's confidence. This is not the case as shown by the results from the above table. The presence of liquid assets in a company facilitates investment in fixed assets as capital is not restrained. This result is similar to the one obtained by Hoshi, Kashyap and Scharfstein (1991) and Myers and Rajan (1998) . Johnson (2003) further argued that too much of short term debt increases liquidity risks which results in the firm foregoing many profitable investments. Such liquidity risks are not present on the firms tested in the sample.

Profitability

The Regression coefficient of profitability is 0.18 and is statistically significant. Since it is positively related to investment, it indicates that the operating efficiency of the total funds over investment is positive. It is worthy to note that high profitability attracts funds from investors for expansion and growth. It also contributes towards the social overheads for the welfare of the society. This result is of the view that companies need to be profitable so as to be able to invest in positive NPV projects, in line with the analysis of Glyn (1997) , Korajczyk and Levy (2003) and Bhattacharyya (2008) .

Cash Flow

Firms that have a propensity to expand the scale of the business and management's ability to carry out such a policy is constrained by the availability of free cash flow, and this constraint can be further tightened via financial leverage. The issuance of debt engages the firm to pay cash as interest and principal, forcing managers to service such commitments with funds that may have otherwise been allocated for investment projects. As depicted by Table 1, Cash Flow has a point estimate of 0.26.This indicates that cash flow is a significant determinant of investment and that the availability of internal funds is positively related to investment.

The positive relationship obtained between investment and cash flow is consistent with the results obtained by Fazzari, Hubbard, and Petersen (1988) , Whited (1992) and Himmelberg and Petersen (1994) . These authors found evidence of a strong relationship between investment and cash flow for firms with a high debt ratio. These results highlight of the ever increasing important aspect of cash flow of a capital investment decision.

4.2 Low Growth Firms

Table 4 :Overview of Results of Tests for low growth firms in the sample

Tests Performed

Result

Significance

Further Test Performed

Hausman Test

Chi2(6) = 0.18

Prob>chi2 = 0.9999

Use Random Effect

NO

Multicollinearity

Mean VIF = 1.54

No Multicollinearity

NO

Heteroskedasticity

Chi2(1) = 18.71

Prob>chi2 = 0.00

Heteroskedasticity present

YES

Serial Correlation

F(1,8) = 29.290

Prob>F = 0.006

Serial Correlation present

YES

Prob > chi2 = 0.000. Since this number is less than 0.05, it implies that the random effect model is correctly specified and confirms that all the coefficients in the model are different than zero.

The Hausman test produces a low chi2 with a low p-value which indicates that the random effect model is the appropriate model to be used when considering low growth firms.

Since the Mean VIF of 1.54 is below the established norm of 10, it implies that no multicollinearity is present in the model. The correlation table depicts the correlation exisiting between the independent variables as shown in Table 5. A high correlation is noted between cash flow - liquidity (0.5461) and profitability - Tobin's Q (0.5370). However, these correlations are not severe enough to affect the estimates.

Table 5: Correlation between Independ ent Variables (Low Growth Firms)

Correlation

Table

Leverage

Tobin's Q

Sales

Liquidity

Profitability

Cash Flow

Leverage

1

-

-

-

-

-

Tobin's Q

-0.0226

1

-

-

-

-

Sales

-0.3046

0.2204

1

-

-

-

Liquidity

-0.4467

0.2569

0.4015

1

-

-

Profitability

-0.2267

0.5370

0.3066

0.3364

1

-

Cash Flow

-0.2180

0.4011

0.2318

0.5461

0.2815

1

Testing for Heteroskedasticity was carried out by using the Modified Wald test in the random effect regression model. Heteroskedasticity was found to be present as was the case when the whole sample of firms was considered. The model was regressed using the robust option to eliminate heteroskedasticity.

The Wooldridge test was performed and Prob > F was less than 0.05. This result indicated that serial correlation existed in the model. The Durbin-Watson Test was then undertaken to eliminate any such serial correlation. The transformed Durbin-Watson statistic was 1.991528, implying that serial correlation was no more present in the regression.

The random effect estimates were obtained after performing the above test and shown in Table 6 below.

Table 6: Regression Results for Low Growth firms

Dependent

Variables

Random Effect

Coefficient

p-value

Constant

-.0588403

.0351653

0.094

Leverage

-.1575799

.0915324

0.085

Tobin's Q

.0870648

.0361093

0.016

Sales

.0485512

.0165042

0.003

Liquidity

.1219783

.0275095

0.000

Profitability

-.0461962

.0884308

0.601

Cashflow

.1555286

.078678

0.048

N.B: Standard errors are shown in italics.

R 2

R 2 within is 0.8137, R 2 between is 0.4455 and R 2 overall is 0.7465. R 2 overall is a better estimate than its two other counterparts and accounts for 74.65% of the variation in the dependent variable.

Leverage

Referring to Table 6, it is observed that leverage is statistically insignificant at 5% significance level and therefore has no impact on investment.

Leverage has no impact on low growth firms' investment and the logic behind this insignificant relationship is that leverage has less of an effect for firms whose investment opportunity is recognised sufficiently early by the capital market. These firms can then obtain funds from the capital market and thus, they do not depend on financial leverage to boost their investment.

To fund new projects, they will move down the pecking order and resort to using internal funds which may prove to be more expensive when they are to raise equity from shareholders. This may result in shareholder - management conflicts.

Also it must be noted that not committing the firm to interest payment will increase the amount of tax to be paid since there will no longer be any tax advantage on debt and hence these cash flows will be wasted.

Such agency problems have also been addressed by Myers (1997) while the insignificance of long term borrowings and investment is similar to the study of Childs et al. (2005). This study's outcome is similar to the proposition put forward by M & M (1958) and M & M (1963) where they argued that investment and leverage were unrelated and that one should not waste time on such problems like financial leverage which are largely self-correcting.

Tobin's Q

Tobin's Q is statistically significant and positively related to investment. This shows that low growth firms have the potential to invest more and expand.

Sales

Table 6 reports a positive and significant relationship with investment. This result confirms that firms are utilising efficiently their fixed assets and it reflects the ability of the company to produce large volume of sales. This relation is in line with the result of Athey and Laumas (1994) and Azzoni and Kalatzis (2006 )

Liquidity

The liquidity coefficient is 0.122 and is statistically significant with a p-value of less than 0.05. Since liquidity is positively related to investment, this may be interpreted as low growth firms not having any liquidity crunch kind of situation which results in good credit worthiness and being in a favourable situation to repeat business. The liquidity coefficient also means that the companies are managing their inventories well whereby they do not have funds tied up inventories.

This result is in line with Chamberlain and Gordon (1989 ) who argued that liquidity plays an essential role as it captures the firm's desire to avoid bankruptcy. Myers and Rajan (1998) suggested that liquid assets are a plus for companies as they increase the ability to invest in profitable projects, thereby providing evidence of the importance of liquidity for low growth firms.

Profitability

As shown in Table 6, profitability is insignificant and has no impact on profitability. Bhaskar and Glyn (1992) suggested that although profitability can be considered as a determinant of investment, it is by no means the most overwhelming one. It can therefore be concluded that low growth firms do not focus on short term profitability when considering investment and regard investment as an attainment of its long term objectives in that it will contribute to increased market share.

Cash Flow

Cash flow is statistically significant and positively related to investment. It clearly indicates that low growth firms rely much on free cash flow to increase investment as it is the cheapest mode of financing. Issuing equity shares are therefore not at their advantage since dividend payment will plummet their cash flow and constrain them from making increased investment.

These results are in line with those obtained by Fazzari, Hubbard, and Petersen (1988) , Whited (1992) and Himmelberg and Petersen (1994) .

4.3 High Growth Firms

Table 7 :Overview of Results of Tests for high growth firms in the sample

Tests Performed

Result

Significance

Further Test Performed

Hausman Test

Chi2(6) = 131.15

Prob>chi2 = 0.000

Use Fixed Effect

NO

Multicollinearity

Mean VIF = 1.55

No Multicollinearity

NO

Heteroskedasticity

Chi2(9) = 1257.46

Prob>chi2 = 0.000

Heteroskedasticity present

YES

Serial Correlation

F(1,8) = 0.013

Prob>F = 0.9119

No Serial Correlation present

NO

Prob > chi2 = 0.000. This result implies that the fixed effect model is correctly specified and confirms that all the coefficients in the model are different than zero.

The Hausman test produces a Chi-square value of 131.15 that indicates that the fixed effect model is the appropriate model to be used.

The Mean VIF of 1.55 is a clear indication that no multicollinearity is present in the model. The correlation among the independent variables is shown in table 8 below. A high correlation of is noted between profitability and cash flow. Such a relationship is normal as a profitable company needs to have a good cash flow balance to help for the day to day running of the business. However, it is not severe enough to affect the regression estimates

Table 8 : Correlation between Independ ent Variables (High Growth Firms)

Correlation

Table

Leverage

Tobin's Q

Sales

Liquidity

Profitability

Cash Flow

Leverage

1

-

-

-

-

-

Tobin's Q

-0.0863

1

-

-

-

-

Sales

-0.0722

0.2152

1

-

-

-

Liquidity

-0.0015

0.2215

0.4421

1

-

-

Profitability

-0.0823

0.2009

0.4491

0.2893

1

-

Cash Flow

-0.2229

0.2077

0.2716

0.1531

0.6930

1

The Modified Wald test for heteroskedasticity was performed and proved to be negative. Hence, a further test was required in order to obtain robust standard errors and remove heteroskedasticity.

The Wooldridge test for serial correlation showed no presence of serial correlation.

The fixed effect estimates were obtained after performing the above test and shown in Table 9 below.

Table 9: Regression Results for High Growth firms

Dependent

Variables

Fixed Effect

Coefficient

p-value

Constant

-.0742471

.0320688

0.024

Leverage

-.2156355

.078895

0.008

Tobin's Q

.1052677

.0373286

0.007

Sales

.1475422

.0420844

0.001

Liquidity

.0183237

.0136679

0.185

Profitability

.2507209

.0756918

0.002

Cashflow

.1388021

.1537664

0.370

N.B: Standard errors are shown in italics.

R 2

R 2 within is 0.7907, R 2 between is 0.0115 and R 2 overall is 0.4827. R 2 overall is a better estimate than its two other counterparts and accounts for 48.27% of the variation in the dependent variable.

Leverage

Table 9 reports that leverage is statistically significant with a p-value of 0.008. Leverage is once again negatively related to investment with a coefficient of the order of -0.2156. The results once again reflect that when high growth firms are debt overhang, their investment opportunities decrease due to a lack of recognised investment opportunities and poor managerial performances.

These results are in line with those obtained by Mc Connell and Servaes (1995) , Peyer and Shivdasani (2001 ) , Ahn et al. (2004 ) and Aivazian et al. (2005) .

Tobin's Q

Tobin's Q is statistically significant and positively related to investment, implying that this set of high growth firms have the potential to increase investment and expand even more.

Sales

The sales coefficient is 0.1475. Hence, high growth firms have an incentive in that they well utilizing their fixed assets for sales purposes.

Liquidity and Cash Flow

Since both liquidity and cash flow variables are insignificant, it means that high growth firms make increasing use of leverage to finance new projects and they do not have resort to retained earnings, issue of shares and other liquid assets. Authors like Kaplan and Zingales (1997 and 2000) and Gomes (2001) have in fact criticised the presence the cash flow as a standard regressor as they found that the significance of the cash flow sensitivity of investment may be the consequence of measurement errors in the usual proxy for investment opportunities and may provide additional information on expected profitability rather than other factors.

Profitability

Profitability is significant with a p-value of 0.002 and positively related to investment. It is a good indicator to boost investors' confidence that the company is making profits and increasing investment and other holdings.

This result are in line with those of Glyn (1997) , Korajczyk and Levy (2003) and Bhattacharyya (2008) .

4.4 Implication of results for Mauritian Firms

A firm's leverage and investment decisionis the part of the risk management and investment process that determines what risks and how much the firm should bear as part of its normal business.

When all 18 firms were taken into consideration altogether, the econometrics results revealed that all six independent variables under investigation were significant in a firm's investment decision process. A high sales volume entails increased profitability ratio for a firm. However, high profit figures alone are not indicative of a good financial performance. The firm must commit itself to increased investment for new equipment or for expansion of its current activites such that its net current assets increase on a yearly basis. A firm is said to be in a healthy position only when its profits and net assets increase altogether.

The Accounts Receivable department, responsible for the collection of receivables, must be an efficient one or else the firm might suffer from insufficient cash flow and liquidity problems, which in turn might affect the smooth running of business operations. This may also force the firm in taking more loans to fund investment projects and to service more interest payments, thereby putting larger strain on the cash flow statement. In the event that a firm is experiencing trouble to manage its receivables, it has a wide range of methods available to it….discounting, factoring etc….

In the case of low growth firms where leverage is not a significant determinant of investment, companies should concentrate on planning the amount of retained earnings that will be needed for investment purposes. Such kinds of planning will enable the firm from exerting excessive pressure on the liquidity and cash flow of the business. These retentions will also allow the firm to avoid paying high premiums on asymmetric information.

Since low growth firms depend on their cash flow for financing of profitable projects, these firms must reduce the risk exposure of each component of the cash flow statement.

Conclusion

Financial leverage is deemed to be the crux of investment decisions in corporate finance. This study systematically diagnoses the relationship between gearing and investment from several crucial perspectives. Using a panel of Mauritian publicly traded firms between 2001 and 2008, it was extricated that, under incentives of overinvesting or underinvesting, whether firm investment decisions roots from financial considerations as measured by the extent of its financial leverage.

In this study, it has been shown that leverage has a significant negative effect on investment, suggesting that capital structure plays an important role in investment policies. While the negative relationship persists when taking into account high growth firms and all the firms, such is not the case when solely considering low growth firms. The econometric results propose that the negative relationship between investment and leverage is not statistically significant for these firms. The outcome of the investigation puts forward that low growth firms encounter hurdles to raise capital from banks and other financial institutions mainly due to their higher perceived risks. Under such strenuous circumstances, the value of the firm and wealth of the shareholders will not be maximised. It must also be stressed out that the company will be losing out on tax benefit since no tax deductibility on interest payments will be obtained. Consequently, the cash flow of the firm is reduced.

The findings also tend to indicate that leverage is not the only determinant that needs to be borne in mind for making investment decisions. Decisive factors such as sales, profitability, cash flow, liquidity and Tobin's Q need to be accounted for as they are directly linked to the investment process.

The econometric results also show that other factors need to be taken into consideration when appraising an investment decision for a low growth firm which are sales, liquidity and cash flow. These determining factors imply that internal business operations of the firm should be strong enough so as to enable them to invest even further.

On the other hand, the fact that high growth firms enjoy the reputation of fast growing companies coupled with good profitability and sales ratios and higher market share imply that it is relatively easy to obtain external financing for their investment projects and further expansion.

On a final note, the results of this investigation clearly establishes that as leverage increases, firms listed on the SEM struggle to increase investment - which is line with results obtained by Peyer and Shivdasani (2001), Ahn et al. (2004) and Aivazian et al (2005).

Source: Essay UK - http://turkiyegoz.com/free-essays/finance/impact-of-leverage.php


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