corporate investment in nepal-kapil deb subedi

8/14/2019 Corporate Investment in Nepal-kapil Deb Subedi

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Corporate Investment and Financing Constraints; A Case of Nepalese

Manufacturing SectorKapil Deb Subedi

Head- Department of Management

Saptagandaki Multiple Campus

1.1General BackgroundAn efficient selection of investment projects is essential for sustained economic

growth of any country. In market economy, the decision process about investment can be

characterized as bottom to top process. In other words, investment is carried out by individual

firm and the firm itself decides whether to invest or not. The variation of firm investment

behavior in a perfect capital market is fully explained by the market opportunity and expected

profitability of the proposed project. Therefore, adjusting capital expenditure in response to

changes in expected future demand represents rational economic behavior at the firm level

that reduces inefficient investment outlays and lead to optimal investment at the aggregate

level.

In case of perfect capital market, the firm's financial structure is irrelevant since the market

value of the firm depends only on the expected profit stream from the investment project andnot on the financial structure. Firms are thus indifferent between the various (internal or

external) means to finance their investment. Investment project will be carried out if their

expected return exceeds the (given) cost of capital which is thought to be the same for all

firms. In this neo-classical view of financial markets internal and external funds are perfect

substitutes and investment can never be constrained by a lack of internal finance.

However, it is widely accepted that there does not exist perfect capital markets in real world.

Therefore, the other factors affecting corporate investment patterns has been identified as the

existence of capital market imperfections that restrict access to or increases the cost of funds

necessary to maintain, otherwise desirable investment level. As a result, capital expenditure

reduction will be accelerated during tough times and opposite results hold during period of

strong economic growth. Therefore, the corporate investment changes due to the existence offinancial constraint has been the subject of much attention by researchers and policy makers

for the study of investment pattern and behavior at corporate level capital market

imperfections lead to firms into different financing hierarchies facing different financing

constraints. When firms face financing constraints, investment spending will vary with the

availability of internal funds, rather than just with the availability of positive Net Present

Value (NPV) projects.

Existence of incomplete asymmetric information between the borrowers and lenders

of external funds leads to problem of adverse selection and moral hazard. These problems of

asymmetric information lead to a difference between the cost of internal and external funds.

The providers of external finance will require a (firm specific) premium because they are

unable to monitor or screen all the aspects of investment projects. The size of external finance

premium depends on firm characteristics, like firm size or net worth which provide an

imperfect indication for the lender of the creditworthiness of the borrowing firms.1 Due to

external finance premium, firms will albeit to a different degree; prefer to finance their

investment by internal funds. The upshot is that internal or external finance are no longer

perfect substitute

Considerable empirical evidence Myers and Majluf (1984) indicates that internal

funds play an important role in financing investment projects under asymmetric information.

What matters to the present purpose is that these problems of asymmetric information lead to

a difference between the cost of internal and external and external funds. As a consequence

investment by firms facing high information cost is not only determined by expected profitsbut also potentially by the availability of internal funds. Investments by those firms expected

to face higher information cost are thought to be more constrained by the availability of

internal finance and vice versa. Therefore, the present study derives a theoretical investment-


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liquidity constraint model to test the hypothesis that the investment decisions of more

financially constrained firm will be more sensitive to their internal funds as compared to the

less financially constrained firms. The basic idea to this notion underlies various empirical

studies on the severity of liquidity constraints for investment (e.g. Fazzari et al., 1988; Kaplan

& Zingales, 1997).

1. It is off course also possible that information problems lead to quantity rationing of externalfunds for firms, see Stiglitz and Weiss (1981)


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This study is directed to resolve the following issues in the context of Nepal

Whether the internal funds are the dominant source of financing for all enterprises or

there are any significant differences on financing decisions of the firms under

different liquidity constraints?

Whether the firm investment is sensitive to the investment opportunities of Nepalese

enterprises? Whether the managers choose to rely primarily upon internal cash flow for investment

despite the availability of additional low cost external funds or they ignore the cost of

internal or external funds?

Whether the investment decisions of the smaller firms (according to assets size group)

are more sensitive to their liquidity than the investment decision of larger firms?

Are there any differences in investment coefficient of Nepalese enterprises across

different groups of firms formed according to their financial status?

1.3 Objectives of the Study:

The major objective of this study is to measure the relationship of the liquidity

sensitivity to firm's investment behavior and to make comparison of investment-liquiditysensitivities across different groups of enterprises in Nepal. Another facet of the study is

to examine the investment opportunity as proxies by difference in sales scaled by net

fixed investment of the Nepalese enterprises and establishes their relationship to the firm

investment behavior. Specifically, the study objectives can be broken down into

following parts;

1. To measure the relationship of investment-liquidity sensitivities of Nepalese

enterprises.

2. To make comparison of investment-liquidity sensitivities across different groups of

enterprises in Nepal.

3. To re-examine the firm investment decision in the presence of financial constraints.

4. To examine the firm sensitivity to investment opportunity as proxied by difference in

sales scaled by net fixed investment of the Nepalese enterprises.

1.4 Organization of the Study:

The remainder of this study will be organized as follows. The next section reviews the

literature and global findings on relationship between firm investment and firm financial

status. The section that follows describes the methodology utilized for the study. The

empirical analysis and results will be considered in next section followed by summary and

conclusions in the final section.

Research Methodology:Research Design:

The research methodology to this study more or less follows the approach of the study

by Kaplan and Zingales (1997). The 14 listed enterprises in NEPSE Ltd. from manufacturing;

hotel and trading sectors are chosen by using non-random judgmental sampling. The study is

based on secondary data. The firms are classified into financially constrained and not

financially constrained groups using first the subjective criterion but later they are objectively

supported and classified according to discriminant analysis using equation (1). The regression

equation for this study has been estimated (Eq.-2) to test the hypothesis that whether the

financial constraints have significant impact on the investment decisions of the firms. The

basic idea underlies various empirical studies on the severity of liquidity constraints for

investment (e.g., Fazzari, Hubhard and Peterson, 1988; Kaplan and Zingales, 1997; etc.).

3.2 Nature and Source of Data:


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This study is based on secondary data. A combination of quantitative and qualitative

information extracted from company annual reports have been used to rank firms in terms of

their apparent degree of financial constraints. The quantitative data required for the study

have been extracted from the secondary sources. The company annual financial statements

and the "Financial Statements of Listed Companies", Vol. VII compiled and published by

stock exchange limited served for the secondary data required to capture the liquidity

constraints of the firm.

3.3Selection of Companies and Sample Characteristics:

There are 106 enterprises listed in Nepal Stock Exchange Limited (NEPSE Ltd.) by

the end of FY 2004/05. The present study does not cover the enterprises in banking, finance

and insurance sectors that are listed in NEPSE Ltd. Therefore, the enterprises in

manufacturing hotel and trading sectors that are listed in NEPSE Ltd. can be regarded as size

of population for this study. The study covers a sample of 14 enterprises listed in NEPSE Ltd.

for the 1995/96 to 2004/05 periods by using judgmental non-random sampling method.

Considering the study period of 1995/96 to 2004/05, usable data could be obtained as

indicated in the Table 3.1

Table 3.1Name and sectors of companies selected as sample

S.N. Name of the Enterprises Years Observations

A. Trading Sectors

1. Salt Trading Corporation 1999 to 2004 6

2. Bishal Bahar Company Ltd. 1998 to 2001 4

Total: 10

B. Hotel

1. Soaltee Hotel Ltd. 1998 to 2000 3

Total: 3

C. Manufacturing & ProcessiSectors

1. Botlers Nepal Ltd. 1998 to 2001 4

2. Unilever Nepal Ltd. 1998 to 2005 8

3. Botlers Nepal (Terai) Ltd. 19996 to 2004 9

4. Bhrikuti Pulp and Paper Ltd. 2000 to 2003 4

5. Shree Ram Sugar Mills Ltd. 2000 to 2001 2

6. Necon Air Ltd. 1998 to 2001 4

7. Butwal Power Company Ltd. 2000 to 2004 5

8. Jyoti Spinning Mills Ltd. 1998 to 2001 4

9. Nepal Lube Oil Ltd. 1998 to 2001 4

`10 Nepal battery company 1995 to 2001 6

11 Arun Vanaspati Udduog Limited 2001 1

Total. 51

Table - 1 shows that there are 64 observations selected for study out of 140 population

observations (14 enterprises x 10 years). Therefore, the percentage of selected observation is

n/N (64/140 )=46 percent.

3.6 Classification Methodology:

Following the approach of Cleary (2005), in this study, firms are classified into

groups according to a beginning of period financial status index (Zfs), with classificationupdated every period to reflect the fact that financial status changes continuously. The index

is determined using multiple discriminant analysis, which considers an entire profile of


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characteristics shared by a particular firm and transforms them into a univariate statistic. The

sample is divided into two groups:

1. Group - 1 firms, which increase dividends or keep constant payout during the period

and are likely not financial constrained

2. Group - 2 firms, which cut dividends or do not pay dividends at all during the period

and are likely financial constrained; .

The present study uses the following beginning of period variables for the purpose of

discriminant analysis: current ratio (current), debt ratio (debt), interest coverage (int.cov.), net

income margin (nl %), and Sales growth(salgr.)

Zfs = B1current+ B2int.cov + B3nI % + B4sales growth + B5Debt (1)

The hypothesis is that these variables will enable us to predict if firms will increase or

decrease dividend payments in the subsequent period. Coefficient values are estimated that

best distinguish each independent variable between the two groups according to the Zfs value.

3.7 Regression Equation:

The present study estimates the following regression equation to measure therelationship between firm investment and their financial status.

(I/K)it= bo + b1 (SAL/K)it+ b2 (CF/K)i + it ... (2)

where, i denotes the ith firm, Iit is investment in plant and equipment during period t, k is the

beginning of period book value for net property, plant and equipment, CF represents current

period cash flow to the firm as measured by net income plus depreciation; SAL/K denotes

the change in sales scaled by fixed assets and proxies for investment opportunities of the

firms and uit is the error term. The liquidity variables are assumed to be uncorrelated with the

investment opportunity. A positive and significant coefficient of the liquidity variable, b2(CF/K), is thought to indicate that liquidity constraints matter to the extent that investment is

sensitive to fluctuations in internal finance (in case of perfect capital markets, our bench markthe liquidity co-efficient would be insignificantly different from zero). This is the basic

equation estimated by Kaplan and Zingales (1997) and Cleary (1999) among other, with

market to book ratio used in place of marginal Q as a proxy for growth opportunities, and

current period cashflow (CF) scaled by K, used to measure the availability of internally

generated funds. But in this study, the first difference of sales scaled by fixed assets as a

proxy for the investment opportunities of the firm has been used. This proxy is also used in

other studies on transition and developing economies (see e.g.; Lensink and Sterken, 1998)

where tradition of share market trading is not regular and only limited number companies are

listed. Similar to previous evidence, the reported regression results are estimated using fixed

effects to control for firm and time specific influences.

The equation mentioned above is estimated for different categories (for example;

predicted group 1, predicted group 2, FC, PFC, NFC group.. The basic test is to see whether

the liquidity coefficient is significantly higher/lower for the given more or less financially

constrained group of firms.

Presentation and Analysis of Secondary Data:4.1 Introduction:

. This paper forms different portfolios of Nepalese enterprises under different

financial status groups as characterized by assets size, discriminant score etc. and it

estimates the investment liquidity constraint model for different portfolios using least square

regression estimate.

4.2. Firms Classification Methodology and Classification Result:In present paper, firms are classified into groups according to a beginning of period

financial status index (Zfs), with classification model updated every period to reflect the fact

that financial status changes continuously. The index is determined using multiple


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discriminant analysis, which considers an entire profile of characteristics shared by a

particular firm and transforms them into a univariate statistic. The multiple discriminant

analysis requires establishing two or more mutually exclusive groups according to some

explicit group classification. It is difficult to categorize explicitly which firms are financially

constrained without considering to a number of firm characteristics simultaneously. However,

it is possible to establish two mutually exclusive groups by making use of the past knowledge

that firms paid dividend or not. This basis suggests us dividing the sample into two

categories; group 1 firms which increase or do not change dividend per share in period t and

are likely not financially constrained; and group 2 firms which cut dividend or do not pay

dividends in period t and are likely financially constrained.

Discriminant analysis uses a number of variables that are likely to influence

characterization of a firm in one of the two mutually exclusive groups of interest. The present

study applies the following variables that are taken as proxy for firm liquidity, leverage,

profitability and growth. The independent variables chosen for the Discriminant analysis are

current ratio, debt ratio, interest coverage ratio, net profit margin and sales growth as selected

by the of study of Sean Cleary (1999). The hypothesis is that these variables will enable us to

predict, if firms will increase or decrease dividends payments in the subsequent period.Coefficient values are estimated that best distinguish each independent variable between the

two groups according to the equation-1 presented in section three. More importantly, firms

are classified very reasonably according to their financial status as measured by traditional

financial ratio.

Table 4.2.1 reports summary statistics of mean and median and various financial variables for

the sample period which confirm that firms likely to reducing dividends or no dividends

(Predicted group 2 or financially constrained group) exhibit lower current ratios, higher debt

ratios, lower net profit margin and lower sales growth, than the firms that are likely to

increase or no change in dividend in period t (Predicted group 1or not financially constrained

group). Univariate significance level indicates that the net profit margin and debt ratio are

significant at 1 percent level of significance where as current ratio; sales growth and interestcoverage ratio are significant at five, seventeen, and thirty three percent levels of significance

respectively.

[Insert table-4.2.1 here]

Table 4.2.2 presents the correlations among the financial variables, as well as those used in

the subsequent regression analysis. The largest positive correlation between discriminant

score (Zfs) and the independent variable are 0.843 with net profit margin and 0.473 with

current ratio. These both coefficients are significant at 5% level of significance. These

observations suggest that firms tend to increase or make constant the dividend payout during

periods of increasing profits and liquidity. At the same time, the largest negative correlation

between discriminant scores (Zfs) and debt ratio is 0.71 and the coefficient is significant at

five percent level of significance. This result suggests that the firms tend to decrease or payno dividends at the period when they have higher debt ratio. The correlation coefficient

between fixed assets purchase and discriminant score (Zfs) is the lowest one (i.e.0.029) among

all these variables. This observation suggests that the firms are likely to be indifferent with

dividend payout and fixed assets purchase decision.

[Insert Table 4.2.2 here]

For classification purpose, 64 firms-year observations were taken into account for the

discriminant analysis using the independent variables as mentioned above. The discriminant

function classified the 33 observation as predicted group one (likely to increase or no change

in dividend) and 31 firms-year observation were classified as predicted group two (likely to

decrease or no dividends) firms. While in the original grouped cases, the 29 firms year

observations were classified into first group (increase or no change in dividend payout) and

35 firms-year observations were classified into second group (decrease or no dividend


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payout) of firms. The table 4.2.3 presents the classification result. Overall, the independent

variables do a good job of successfully predicting the firms in group one if they will increase

or do not change in dividend payout in period t and predicting the firms in group two which

cut or do not pay their dividends in period t. In aggregate, the firms are being properly

classified at 66 percent of the time. The discriminant function result suggests that firms are

classified very reasonably according to their financial status as measured by traditional

financial ratios for the purpose of this study

[Insert table-4.2.3 here]

In addition to the predicted group classification, firms are also classified into three separate

groups according to their discriminant score (Zfs) value. The firms with discriminant scores in

the top third over the entire period are categorized as not financially constrained (NFC), the

next third as partially financially constrained (PFC), and the bottom third as financially

constrained (FC).

[Insert Table-4.2.4 here]

Summary statistics for these groups presented in table 4.2.4 indicate the classificationstrategy has successfully captured the desired cross-sectional properties. The financial ratios

are superior for the NFC groups, inferior for the FC groups and for the PFC groups lying

somewhere between these two groups of firms.

Firms are also classified into groups according to another additional criteria designed

to measure financial constraint by reference to their susceptibility to market imperfection, as

articulated by Fazzari et al. (2000). This criterion classifies firms according to size, similar to

the approach of Gilchrist and Himmelberg (1995), based on the notion that smaller firms will

be more financially constrained because they face higher informational asymmetry problems

and agency costs. In particular, firms are classified into three groups each year based on the

size of their reported total assets, with smallest third being classified as TA Group one, the

largest as TA Group three, and the middle groups as TA Group two.The Table 4.2.5 reports the mean and median of various financial ratios according to different

group in firm size .The reported financial variables mean and median values indicates that the

smaller the firms have the healthier financial ratios. The financial ratios are superior for the

smaller firms, inferior for the larger firms and for the middle assets size firms lying

somewhere between these two groups of firms . (Insert Table 4.2.5 here)

4.3. Regression Estimation:

Firms can finance their investment by external funds by equity or debt. Alternatively

the firm can also use internal funds to finance its investment projects. In the case of perfect

capital markets, the firms financial structure is irrelevant since the market value of the firm

depends on the expected profit stream from the investment project and not on the financialstructure. But in incomplete capital market, asymmetric information leads to problems of

adverse selection and moral hazard. What matters to our present purpose is that these

problems of asymmetric information lead to a difference between the cost of internal and

external funds. As a consequence, investments by firms facing high information costs are not

only determined by expected profits but also potentially by the availability of internal funds.

Investments by those firms expected to face higher (lower) information costs are thought to

be more (less) constrained by the availability of internal finance. This basic idea underlies

various empirical studies on the studies on the severity of liquidity constraints for investment

(e.g.; Fazzari et al., 1988; Kaplan & Zingales, 1997). The reduced form investment equation

in this type of study has the following general form (Hubbard, 1998: 202):

(I/k)it = Co + C1f (X/Kit) + C2f (L/K)it + it ... (1)

Where I is the net investment in fixed assets and the dependent variable in the

regression, it is obtained as the first difference of tangible fixed assets plus depreciation in


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our present study. Investment opportunity (X) in equation (1) is an important explanatory

variable. Theoretically marginal Q could be used for the approximation of present and

expected future investment opportunities. Since marginal Q is unobservable, many

investment/liquidity studies for industrialized countries use average Q as a proxy. However in

order to be able to calculate average Q, the country concerned should have a well developed

stock market. In Nepal this is still not the case, and only a limited number of companies are

listed and have their market trading regularly. We therefore use the first difference of sales as

a proxy for the investment opportunities of the firm. This proxy is also used in other studies

on transition economies (see e.g.; Lensink and Sterken, 1998) and more often than not it out

performs Tobin's Q (Fazzari et al.). Cash flow is used as a proxy for the liquidity variable in

equation (1). So in our empirical study, equation (1) be comes:

(I/K)it = Co + C1 (SAL/K)it + C2 (CF/K)i + it ... (1')

Where it denotes the ith firm in period t, I is net investment in fixed assets, SAL

denotes the change in sales and proxies for investment opportunities, and CF represents the

cash flow variable; is the error term and the scalar, K is the net fixed assets at the

beginning of each year. Similar to previous evidence, the reported regression results are

estimated using fixed effects to control for firm and time specific influences.

4.4. Regression Results:

We estimated the regression equation in total sample and the results are presented in

Table 4.4.1. Our hypothesis in liquidity constraint model is that a positive and significant

coefficient of the liquidity variable C2 is thought to indicate that liquidity constraints matter

to the extent that investment is sensitive to fluctuation in internal finance. The first column in

Table 4.4.1 represent the full sample estimation results, where cash flow coefficient is

positive and significant. The regression results for investment-opportunity sensitivity of

Nepalese enterprises indicate that the coefficient is positive as per prior expectation but it is

very small and not significant. This result suggests that the liquidity constraints are relevant

for the Nepalese enterprises whereas, the investment sensitivity to market opportunity is notso significant for investment decision in Nepalese enterprise in our sample.

In order to identify the regression coefficient of constrained and unconstrained firms, the

equation mentioned above is estimated for sub samples where the sub- sampling is based on

above mentioned discriminant analysis. The predicted group one (firms likely to increase

dividends or no change in dividends) firms regression estimation (as shown in Table 4.4.1

column two) shows the positive and significant cash flow coefficient. But at the same

estimation, these firms showed a negative co-efficient for market opportunity as proxied by

first difference of sales to fixed assets. It also estimated the regression equation for predicted

group two, the regression result for these firms shows the small cash flow co-efficient (Table

4.4.1 Column - III). However it is insignificant, the co-efficient value is positive. The main

conclusion to arise from Table 4.4.1 is that liquidity constraints are relevant for the

Nepalese enterprises whereas the firms that are financially unhealthy are relatively insensitive

to the availability of internal funds, while the opposite result holds for firms with reasonably

solid financial positions. This result supports the main findings of Kaplan and Zingales

(1997) and Cleary (1999) regarding the impact of financial health the investment decisions of

firms with stronger financial positions are much more sensitive to the availability of internal

funds

[.Insert table 4.4.1 here]

4.4.1 Regression Result for Sample Split Based on Discriminant Score

The present study also estimated the regression equation of different financial status group;(for example FC, PFC and NFC groups) as mentioned above according to their discriminant

score and presented the result in Table 4.4.2. They indicate that internal cash flow is the

significant determinants of investment for all three groups of firms. For all the groups, cash


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flow co-efficient are positive. For PFC group, however the co-efficient being positive, it is

not significant but for rest of the group, the co-efficient are significant at the 1 percent level

of significance. The result also suggests that the market opportunity as peroxied by first

difference of sales to fixed assets is insensitive to investment decisions of FC and NFC

groups of firms. The co-efficient, as opposite to prior expectation, are negative and also

insignificant to FC and NFC groups. As regard to the PFC group, the market opportunity co-

efficient is positive as per prior expectation but it is insignificant. The adjusted R value

ranges from 11.4 percent to 44.5 percent, which is higher than the previous studies.

The positive and significant co-efficient for liquidity variable suggests that firms investment

decisions are sensitive to the availability of internal funds. More importantly, the investment

outlays of the FC firms are significantly more sensitive to liquidity than that of NFC firms.

The estimated cashflow co-efficients for the NFC, PFC and FC groups are 0.404, 0.392 and

1.404 respectively. This result contradicts the main findings of Kaplan and Zingales (1997)

and Cleary (1999) regarding the impact of financial health on investment decisions of firms

with stronger financial positions are much more sensitive to the availability of internal

funds.But at the same time, it supported the main findings of Fazzari et. al. (1988) regarding

the impact of financial health on investment decisions of firms with least financiallyconstrained firms are much less sensitive to the availability of internal funds

[Insert table 4.4.2 here]

4.4.2. The Impact of Leverage on Firm Investment:

A number of prior studies (for example Lang, Ofek and Stulz, 1996) find that future

growth and investment are negatively related to leverage; particularly for firms with low

Tobins q value and high debt ratio. It is very important to control the firm leverage effect on

investment liquidity constraint model. Therefore, an additional test is performed to examine

the robustness of results to the influence of firm leverage. This implies the significance of

examining whether the pattern of investment-liquidity sensitivities defected in the presentstudy could be attributed to a systematic tendency of the classification scheme to assign firms

to a group whose investment decisions are more sensitive to firm leverage than those of other

groups. This hypothesis is tested by running regression that include debt to total assets as an

independent variable in the regression specification, in addition to SAL/K and CF/K. The

results are reported in Table 4.4.3 The co-efficient for debt to total assets is found to be

negative and insignificant for FC and NFC groups, hence the cashflow co-efficient virtually

remain identical for these two groups.

About the PFC groups the Debt/Total assets co-efficient is positive and virtually the cashflow

co-efficient has increased from 0.392 to 0.501 and is significant at 1 percent level of

significance. This evidence suggests that the observed pattern of investment-liquidity

sensitivity is attributable to a leverage effect for PFC groups of firms.[Insert table- 4.4.3 here]

4.4.3 Liquidity Constraints and the Relevance of Firm's Size:

In this section; the present study used firm characteristics to classify firms that a priori

can be considered to differ in their access to external funds or, in other words, in their

liquidity constraints. There are various measures to determine firm size. This study presented

estimations based upon the total assets of each firm for each period. Table 4.4.4. reports

result for the groups formed according to firm size. This result suggests that smaller firms are

more liquidity sensitive than larger ones. The cashflow co-efficients for all three groups of

firm are positive. The TA Group one has highest cashflow co-efficient than other two groups.

The cashflow co-efficient for largest group is positive as per prior expectation but it is not

significant.. This observation confirms the result of Gilchrist and Himmelberg (1995) but


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contradicts with recent international evidence provided by Kadapakkam et al. (1988), and

Cleary (2005) who find that smaller firms are less sensitive to cashflow than the larger ones.

This result supports with prior results presented in this study and suggested the notion that

the smaller firms will be more financially constrained because they face higher informational

asymmetry problems and agency cost so they are more sensitive to internal funds than the

larger firms. This findings is incompliance with the conclusion of FHP(1988)

[Insert table- 4.4.4]

6. Summaries and ConclusionsFirst, the literature in this filed has tended to proffer different causal analysis for clarifying

the relationship between firm investment and financial status. Many studies supports the

existence of a strong relationship between financial factors like leverage, liquidity,

profitability and investment decision which contradicts the basic irrelevancy proposition

established by Modigliani and Miller (1958). This suggests that firms operate in imperfect

market, which leaves researchers and policy makers to deal with critical issues of how and

why these imperfections affect firm investment decisions.Second, market imperfection is the predominant phenomena over the all countries in the

world whether they are well developed one or the country striving for development only

the difference lies in its characteristics like legal, social, geographical and economic

development stage and its degree to imperfection. A number of empirical studies (e.g.

Myers and Majluf (1984), Bernanake and Gertler (1989) provide the foundation for

market imperfection which is mainly resulted by asymmetric information problems in

capital market and the agency cost causing a premium on external finance.

Third, the theoretical argument asserted the existence of a financing hierarchy, in which

firms finance investment primarily through internal funds, and issue equity as a last resort

(as in the pecking order theory postulated by Myers, 1984). Given the existence of

imperfect financial market, this relationship should hold for all firms, even the large well-known ones. There is ample empirical evidence to support this notion, and in fact, there

does not appear to be very much debate on this issue.

Fourth, on the observed condition of financial hierarchy the investment outlays of

constrained firms would be the most sensitive to internal funds availability. This claim

was substantiated by an overwhelming amount of empirical evidence (e.g. Fazzari et. al.:

1988, Hoshi et. al. 1991; Oliner and Rudebush: 1992; Whited; 1992, etc) based on the

studies that categorized firms according to different characteristics.

Fifth, however the existence of such a financing hierarchy also suggests the importance of

maintaining adequate financial slack as argued by Myers and Majluf (1984). In such

situation, it is reasonable to argue that firms with weaker financial positions will display

relatively low investment cash flow sensitivity, due to the necessity of using internal funds

to pay down debt and improve firm liquidity. Many empirical studies (e.g. Kaplan and

Zingales: 1997, Cleary: 1999; Kaplan and Zingales: 2000, Cleary et. al.: 2005 and Cleary:

2005) support this notion that the investment decision of firms with stronger financial

positions are much more sensitive to the availability of internal funds than those that are

less creditworthy.

Sixth, Debate over this matter as explained above has been widespread by the recent work

of Kaplan and Zingales (2000) and Fazzari et.al.(2000) who articulated two conflicting

views of this issue. This debut poses a conceptual argument questioning the

appropriateness of the measure and validity of financial constraints as explained by

Kaplan and Zingles (1997). Hence, this theoretical debate has challenged the generality ofconclusions of the either study as emanating from the seminal study conducted by Fazzari

et. al.(1988) or the study result of Kaplan and Zingales (1997).


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Seventh, the conclusions of the above-explained studies relate to the investment behavior of

firms operating in well-developed economies, which may not necessarily hold for firms

operating in distinctly different environments specially in developing economies. In the

context of Nepal, only a few researches (e.g. Pradhan and Kurmi, 2004) have been conducted

in this very issue; therefore it is useful conduct the research in this issue to generalize the

behavior of investment pattern of Nepalese firms that are operating in very small, imperfect

and volatile capital markets.

In the context of Nepal, there are only a few listed companies operating in imperfect capital

market. These companies ownership and control is strongly concentrated. Most firms have a

controlling owner, family controlled, many large firms are members of business groups and a

numbers of firms have not yet been listed. In the context of these country specific

characteristics, the present study is primarily concerned with the testing of the conclusions of

the above-explained studies e.g, studies of Fazzari et. al. (1988) or Kaplan and Zingales

(1997) (they relate to the investment behavior of firms operating in well-developed

economies) in context of Nepal.

The estimated regression results in total sample suggested that the liquidity constraints are

relevant for the Nepalese enterprises whereas, the investment sensitivity to marketopportunity is not so significant for investment decision in Nepalese enterprise in our sample.

In order to identify the regression coefficient of constrained and unconstrained firms, the

regression was estimated for sub samples where the sub- sampling based on above mentioned

discriminant analysis. The predicted group one (firms likely to increase dividends or no

change in dividends) firms regression estimation showed the positive and significant cash

flow coefficient. But at the same estimation, these firms showed a negative co-efficient for

market opportunity as proxied by first difference of sales to fixed assets. The estimated the

regression cofficient for predicted group two,also gave the same results however the

coefficient was smaller than those of first group.

The present study estimated the regression equation of different financial status group; (for

example FC, PFC and NFC groups) as mentioned above according to their discriminant Theresult indicated that internal cashflow is the significant determinants of investment for all

three groups. The result also suggests that the market opportunity as peroxied by first

difference of sales to fixed assets is insensitive to investment decisions of FC and NFC

groups of firms. The co-efficient, as opposite to prior expectation, were negative and also

insignificant to FC and NFC groups. As regards to the PFC group, the market opportunity co-

efficient was positive as per prior expectation but it was insignificant.

The positive and significant co-efficient for liquidity variable suggested that firms

investment decisions are sensitive to the availability of internal funds. More importantly, the

investment outlays of the FC firms are significantly more sensitive to liquidity than that of

NFC firms.

This result supported the main findings of Fazzari et. al. (1988) regarding the impact offinancial health on investment decisions of firms with stronger financial positions are much

less sensitive to the availability of internal funds and contradicted the findings of Kaplan and

Zingales (1997) and Cleary (1999).

The present study also estimated the regression equation of different portfolio of firms based

upon the total assets of each firm for each period. This result suggested that smaller firms are

more liquidity sensitive than larger ones. The cashflow co-efficients for all three groups of

firm were positive. The Total assets Group one firms (smaller Firms) had highest cashflow

co-efficient than other two groups. The cashflow co-efficient for largest assets group is

positive as per prior expectation but it is not significant.. This observation confirms the result

of Gilchrist and Himmelberg (1995) but contradicts with recent international evidence

provided by Kadapakkam et al. (1988), and Cleary (2005) who find that smaller firms are less

sensitive to cashflow than the larger ones. This result supports with prior results presented in

this study and suggested the notion that the smaller firms will be more financially constrained


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because they face higher informational asymmetry problems and agency cost so they are

more sensitive to internal funds than the larger firms. This findings is incompliance with the

conclusion of FHP(1988)

Table 4.2.1

Sample Summary Statistics:

The followings are the reports of financial variable means for the sample of firms-year

observations of Nepalese non-financial sectors of enterprises. All financial variables are for

the beginning of period of the fiscal year except for cash flow, investment and change insales(dsales) which represents firm cash flow , capital expenditure and difference in sales

during period t. k is the firm's beginning of period net fixed assets value. The discriminant score (zfs ) is calculated using discriminant analysis according to equation 1. A full-

description of the variables is included in the Appendix.

Group Statistic

Debt

ratio

NP

ratio

Salesgrowt

h C/R CF/K FA/K

sales/

k

Interestcoverag

e

NFC

Group

n=33

Mean .0402 .1736 .1612 2.100 .5871 .1874 .7363 11.63

Median.0000 .1039 .2052 1.788 .4663 .0637 .2269 10.94

FC

Group

n=31

Mean

.3999 -.0178 .0216 1.476 .2278 .1524 .0277 5.334

Median .4817 .0061 .0346 1.130 .0928 .0534 .0423 1.140

Total

n=64

Mean.2144 .0809 .0936 1.798 .4131 .1704 .3931 8.584

Median .0488 .0723 .0809 1.616 .2962 .0548 .0773 5.541

Table 4.2.2

Correlation among Variables

The followings are the reports of financial variable means and their correlation for the sample of firms-year observations of Nepalese non-financial sectors of enterprises. All

financial variables are for the beginning of period of the fiscal year except for cash flow,

investment and change in sales(dsales) which represents firm cash flow , capital expenditureand difference in sales during period t. k is the firm's beginning of period net fixed assets

value A full-description of the variables is included in the Appendix.


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* Correlation is significant at the 0.05 level (2-tailed).** Correlation is significant at the 0.01 level (2-tailed).

Table 4.2.3 Classification Results

The following are classification result ofthe sample of firms-year observations of Nepalese

non-financial sectors of enterprises . Firms are classified into groups according to abeginning of period financial status index (Zfs ), The index is determined using multiple

discriminant analysis considering an entire profile of characteristics shared by a particularfirm and transforming them into a univariate statistic.

Predicted groups

membership

Total

Group 1 Group 2

Original

Count Group 1 20 9 29

Group 2 13 22 35

% Group 1 69.0 31.0 100

Group 2 37.1 62.9 100

Table-4.2.4

Firm's financial status group-wise sample summary statistics

The followings are the reports of financial variablestatistics for the sample of firms-yearobservations of Nepalese non-financial sectors of enterprises. The FC, PFC and NFC groups

are formed by sorting all firms according to their discriminant scores. Every year, the firmswith the lowest discriminant scores (the bottom one-third) are categorized as financially

constrained (FC); the next one third are categorized as partially financially constrained

(PFC); and the top one-third are categorized as not financially constrained (NFC).

firms

Debt

ratio NP ratio CR

Interest

Covera

ge CF/K FA/K

Sales/

k

Sales

Growt

h

NFC

N=21

Median .0000 .1056 1.8043 10.94 .4663 .0340 .7121 .2459

Mean .0318 .2145 2.2323 10.64 .6044 .1386 1.1187 .2246

PFC Median .0120 .0923 1.7100 20.00 .4435 .1081 .0081 .0075

C/rDebtratio

NPratio Int. cov CF/K FA/K sales/k Sal.grt Dis. Scr

C/r 1

Debt ratio -.188 1

NP ratio .270* -.503** 1

Int.tcov..016 -.661** .416** 1

CF/k .031 -.560** .390** .623** 1

FA/K .092 -.001 .056 .156 .413** 1

sale/k .075 -.051 .062 .047 .379** .259* 1

Sal.grt -.095 -.026 .281* .068 .296* .136 .612** 1

Dis. Scr .473** -.719** .843** .259* .419** .029 .209 .362** 1


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N=22 Mean.0647 .1010 1.7374 13.92 .5975 .2190 -.0607 .0233

FC

N=21

Median .5501 -.0201 .9600 .79 .0111 .0319 .0519 .0490

Mean .5317 -.0660 1.4413 1.51 .0544 .1545 .1335 .0356

Table-4.2.5

Firms assets size wise financial summary statistics

The followings are the reports of financial variablestatistics for the sample of firms-yearobservations of Nepalese non-financial sectors of enterprises. All financial variables are for

the beginning of period of the fiscal year except for cash flow, investment and change insales(dsales) which represents firm cash flow , capital expenditure and difference in sales

during period t. k is the firm's beginning of period net fixed assets value A full-description of

the variables is included in the Appendix. Firms are sorted into size groups according tototal assets. TA Group 1 includes the smallest third of firms according to total assets every

year, while TA Group 3 includes the largest third and TA Group 2 the middle third.

firms

Debtratio

NPratio CR

InterestCov. CF/K FA/K Sales/k

SalesGrowth

TAGrp

Three

n=21

Median .5222 .0101 1.400 .9714 .167 .04 .1241 .1121

Mean .3873 .0516 2.087 3.725 .239 .119 .4894 .1187

TAGrp

Two

.n=22

Median .0595 .0820 1.5700 8.130 .286 .113 .0216 .0080

Mean.1783 .0468 1.6184 10.69 .343 .209 .0757 .0218

TA.Grp

One

n=21

Median .0104 .0929 1.6310 7.417 .510 .063 .1087 .1272

Mean.1194 .1155 1.6317 10.47 .682 .178 .6237 .1419

Table 4.4.1Regression Results for the Total Sample:

Reported coefficients are the within fixed firm and year estimates over the sample

period (t-statistics and p-value are in parenthesis). Investment in fixed assets divided by thenet fixed assets is the dependent variable. The first difference in sales divided by net fixed

assets and the cash flow/net fixed assets are the independent variable. Predicted group 1

includes firm that are classified as likely to increase dividends or keep payout at constantrate in year t according to discriminant analysis, predicted group 2 includes firms that are

classified as likely to decrease or no dividend per share in year t.

Investment-Liquidity Constraint Model Estimated for the Total SampleTotal

Sample

Predicted

Group 1

Predicted

Group 2

C. Constant term 0.025 -0.168 0.092

T-statistic (0.395) (-1.657) (1.134)

Probability (0.69) (0.10) (0.26)

SAL/K, First dif. Sales to Fixed Assets 0.119 -0.058 0.074

T-statistic (0.962) (-1.212) (1.654)

Probability (0.34) (0.23) (0.10)

CF/K, Cash flow to Fixed Assets 0.367 0.679 0.254T-statistic (2.962) (3.983) (1.538)

Probability (0.00) (.00) (0.13)


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Adjusted R 0.156 0.323 0.117

F-statistic 6.922 8.633 2.98

Number of Observation 64 33 31

Table 4.4.2

Regression Result for Sample Split based on Discriminant Score:Reported coefficients are the within fixed firm and year estimates over the sample period (t-statistics are in parenthesis). Investment in fixed assets divided by the net fixed assets is the

dependent variable the first difference in sales divided by net fixed assets and the cash

flow/net fixed assets are the independent variable. The FC, PFC and NFC groups are formedby sorting all firms according to their discriminant scores. Every year, the firms with the

lowest discriminant scores (the bottom one-third) are categorized as financially constrained(FC); the next one third are categorized as partially financially constrained (PFC); and the

top one-third are categorized as not financially constrained (NFC).Investment-Liquidity Constrained Model Estimated for FC, PFC, NFC Group

FC Group PFC Group NFC Group

Constant 0.138 -0.029 -0.103T-statistic (1.566) (-0.204) (-1.482)

Probability (0.13) (0.84) (0.15)

SAL/K, First dif. Sales to

Fixed Assets

-0.025 0.091 -0.025

T-statistic (-0.431) (1.043) (-0.763)

Probability (0.67) (0.30) (0.45)

CF/K, Cash flow to Fixed

Assets

1.404 0.392 0.446

T-statistic (3.574) (1.926) (3.882)

Probability (0.00) (0.06) 0.00

Adjusted R 0.443 0.114 0.445

F-statistic 8.55 2.41 9.004

Prob. (F-Statistic) 0.00 0.11 0.00

Number of Observation 21 22 21

Table 4.4.3

Regression result for effect of firm leverage on investment-liquidity constraint model

estimated for FC, PFC and NFC group

Reported coefficients are the within fixed firm and year estimates over the sample period (t-

statistics are in parenthesis). Investment in fixed assets divided by the net fixed assets is the

dependent variable the first difference in sales divided by net fixed assets and the cashflow/net fixed assets and long term debt/total assets are the independent variable. The FC,PFC and NFC groups are formed by sorting all firms according to their discriminant scores.

Every year, the firms with the lowest discriminant scores (the bottom one-third) are

categorized as financially constrained (FC); the next one third are categorized as partially financially constrained (PFC); and the top one-third are categorized as not financially

constrained (NFC).Effect of firm leverage on investment-liquidity constraint model estimated for FC, PFC

and NFC group

Group SAL/K CF/K LTD/FA R n

FC -0.022 (-.376) 0.1.388

(3.359)

-0.116

(0.229)

0.410 21

PFC 0.046 (.496) 0.501 (2.29) 1.112 (1.26) 0.25 22

NFC -0.021 (-.586) 0.440 (3.68) -0.218(0.34) 0.504 21


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Table 4.4.4

Regression Estimate for Size group:

Reported coefficients are the within fixed firm and year estimates over the sample period (t-statistics are in parenthesis). Investment in fixed assets divided by the net fixed assets is the

dependent variable the first difference in sales divided by net fixed assets and the cash

flow/net fixed assets are the independent variable. Firms are sorted into size groupsaccording to total assets. TA Group 1 includes the smallest third of firms according to total

assets every year, while TA Group 3 includes the largest third and TA Group 2 the middlethird.

Group CF/K SAL/K R n

TA Group 1 0.656(3.205) 0.008 (0.332) 0.412 22

TA Group 2 0.526 (2.56) 0.025 (0.192) 0.299 21

TA Group 3 0.166 (0.651) 0.054 (0.247) 0.111 21


17/20

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Hubbard, R.G. 1998, Capital market imperfections and investment, Journal of

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Appendix A

WINSORIZEA number of observations are "winsorized" (if the value of the variable exceeded

cutoff values) according to the following rules:

(i) assign a value of 50 percent (-50 percent) if growth in sales is greater (less) than 50

Percent (-50 percent)

(ii) assign a value of 2 (-2) if investment/fixed assets is greater (less) than 2 (-2).(iii) assign a value of 20 if interest coverage ratio is greater than 20.

Appendix B

Description of Financial Ratio CalculationCurrent assets

1. Current ratio =

Current liabilities

Long-term debt

2. Debt ratio =

Total assets

Earning before interest and taxes

3. Net profit Margin =

Interest expenses

Net profit

4. Net profit margin =

Net sales

5. Cash flow = Net profit + Taxes + Depreciation

6. Investment = Net fixed assets t - 1 - Net fixed assets

7. Difference in sales ( sales) = Net sales t - Net sales t 1

Difference in sales8. Sales growth =

Net sales t - 19. Net fixed assets (K) = Net property, plant and machinery

Appendix D

Financial Variables for Regression Analysis (Firms and Year wise)

SN Company Year SALES/K CASHFLOW/K FA/K

1 NLL 1998 1.77008 0.52877 -0.00609

2 NLL 1999 1.50656 0.71944 0.03940

3 NLL 2000 1.17101 0.86543 -0.01234

4 NLL 2001 -1.11511 0.69127 0.28157

5 NLL 2002 -1.58131 0.40510 0.00451


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6 NLL 2003 0.05041 0.84007 -0.03153

7 ULL 2004 1.91687 1.46100 0.06370

8 ULL 2005 -0.31936 2.01260 0.07000

9 BPPL 2000 0.03449 -0.08677 0.00529

10 BPPL 2001 0.14881 0.00404 0.00027

11 BPPL 2002 -0.07405 0.18075 0.04493

12 BPPL 2003 0.12406 0.00050 -0.03272

13 BNL 1998 1.01273 1.46500 1.64741

14 BNL 1999 0.22692 0.31444 0.14895

15 BNL 2000 0.01201 0.29585 0.14917

16 BNL 2001 0.11591 0.24852 0.09062

17 NLOL 1998 1.17516 0.52547 0.05466

18 NLOL 1999 1.36206 0.86495 0.0321519 NLOL 2000 -0.00954 0.52997 0.33719

20 NLOL 2001 -1.92143 -0.02582 0.00385

21 JSP 1998 -0.14849 -0.07183 0.04041

22 JSP 1999 -0.00849 -0.00028 0.03250

23 JSP 2000 0.25548 0.09284 0.07314

24 JSP 2001 0.03283 0.00691 0.04869

25 AVUL 2001 0.90092 0.07861 0.02224

26 BNTL 1996 4.36455 1.46432 0.65751

27 BNTL 1997 -0.81181 1.26434 0.37790

28 BNTL 1998 1.65428 1.60724 0.57300

29 BNTL 1999 0.60191 1.05062 0.24845

30 BNTL 2000 0.16633 0.90149 0.31198

31 BNTL 2001 0.73591 0.72074 0.13018

Cont

SN Company Year SALES/K CASHFLOW/K FA/K

32 BNTL 2002 -0.58638 0.57103 0.20172

33 BNTL 2003 0.03123 0.44122 0.35252

34 BNTL 2004 -0.22474 0.32049 0.02055

35 SSM 2000 0.08899 0.01175 0.14120

36 SSM 2001 0.22610 0.05719 0.02404

37 STC 1999 2.60516 0.24217 0.11431

38 STC 2000 -6.22410 -0.30561 0.05490

39 STC 2001 3.87149 0.38101 0.06830


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40 STC 2002 3.10332 0.90697 9.45582

41 STC 2003 1.31454 0.15491 0.03336

42 STC 2004 3.14295 0.22230 0.01923

43 BBCL 1998 0.06505 0.37304 -0.02297

44 BBCL 1999 0.02817 0.42210 0.24868

45 BBCL 2000 0.13765 0.45384 0.03404

46 BBCL 2001 0.07498 0.55478 0.01894

47 SHL 1998 0.05499 0.27469 0.15381

48 SHL 1999 0.00806 0.27077 0.11947

49 SHL 2000 -0.05781 0.24597 0.10811

50 NIL 1998 0.75874 0.41087 1.32183

51 NIL 1999 0.05339 0.01208 -0.08513

52 NIL 2000 -0.22304 -0.07817 -0.1670153 NIL 2001 -0.53356 -0.80322 -0.19723

54 BPC 2001 -0.21390 0.20190 0.03003

55 BPC 2002 0.06728 0.21681 0.01097

56 BPC 2003 -0.19408 -0.00373 0.05342

57 BPC 2004 0.24469 0.39086 -0.37987

58 NBCL 1995 0.71208 1.33496 0.56934

59 NBCL 1996 1.62891 0.62242 0.13392

60 NBCL 1997 0.07966 0.52145 0.02696

61 NBCL 1998 -1.32487 0.56998 0.02393

62 NBCL 1999 4.96630 1.56445 0.13142

63 NBCL 2000 -2.19756 1.35596 0.07311

64 NBCL 2001 -0.17989 1.12362 0.27860

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