Electronic copy available at: http://ssrn.com/abstract=1292598

1

Financial Constraints and Stock Returns

- Evidence from Australia *

Howard Chan#

Department of Finance

Faculty of Economics and Commerce,

University of Melbourne

Xin Chang

Division of Banking and Finance

Nanyang Business School

Nanyang Technological University

Robert Faff

Department of Accounting and Finance

Faculty of Business and Economics

Monash University

George Wong

School of Accounting and Finance

The Hong Kong Polytechnic University

Keywords: Financial Constraints, Stock Returns, Australian Firms

JEL classification: G12, G32

_____________________ *The financial assistance provided by an ARC Linkage grant (LP0560381) and the help and encouragement

of our industry partner, Acorn Capital, are gratefully acknowledged.

#Corresponding author: Howard Chan, Department of Finance, The University of Melbourne, Parkville

VIC 3010, Australia. Tel. +61 3 83447166, Fax. +61 3 83446914. Email: chanhw@unimelb.edu.au

Electronic copy available at: http://ssrn.com/abstract=1292598

2

Financial Constraints and Stock Returns

- Evidence from Australia

Abstract

Using multiple discriminant analysis, we construct an index that measures firms’ external

financial constraints in an Australian setting. We form portfolios of firms based on our

financial constraints index and find that financially constrained firms earn lower return

than their unconstrained counterparts. Moreover, stock returns of financially constrained

firms are found to move together, indicating the potential existence of a financial

constraints factor. Neither the variation nor the mean return of the constraints factor are

well explained by existing asset pricing models, suggesting an independent role for our

financial constraints factor in affecting stock returns.

Keywords: Financial Constraints, Stock Returns, Australian Firms

JEL classification: G12 G32

1

1. Introduction

The literature on the relation between financial constraints and stock returns has

developed quite impressively over recent years (see, for example, Fazzari, Hubbard and

Peterson, 1988; Kaplan and Zingales, 1997; Cleary, 1999; Lamont, Polk, and Saá-

Requejo, 2001; Whited and Wu, 2006). Two notable features are evident from this work:

first, the focus has been on the US market and, second, quite a degree of mixed evidence

has been produced. With the goal of helping on both fronts, our paper explores the

possible existence of a financial constraints factor in the Australian market.

Focusing on a non-US market like Australia allows us to respond to Leamer’s

(1983) general critique of data snooping, reinforced in the asset pricing context by Lo and

MacKinlay (1990). Investigating alternative data samples either across time or across

markets can alleviate the threat of data snooping. While a range of major developed

markets could do a similar job, selecting Australia as our experimental setting presents

several empirical advantages. Australia has much in common with the US, including

language, legal system and other institutional characteristics. However, there are some

fundamental differences between the Australian and the US markets. Australia is one of

the major exporters of raw materials while the US is the major global consumer of raw

materials and natural resources. The difference is also reflected in the composition of

listed firms in the two stock markets. In contrast to the US stock market, our set of

Australian firms consists of a larger proportion of firms in the energy and materials

sectors and fewer firms in the information technology and telecommunications sectors.

As such, this contrasting industrial structure affords us a meaningful opportunity to assess

the robustness of existing US evidence.

2

While the previous argument presents a broad-based thrust for choosing Australia,

the differential taxation regimes operating between the two countries provides a much

more specific motivation. Most notably, Australian firms have a much higher propensity

to pay dividends than their US counterparts since the former operate under an imputation

tax system.1 To the extent that tax incentives influence the payment of dividends out of

internally generated cash flows, the differences in American and Australian tax

frameworks should result in disparities in the effects of financial constraints on corporate

policies between the two countries.2 Specifically, consistent with much of the relevant

literature, we use changes in cash dividends as a measure that is indicative of a firm's

financial constraints status. Since firms seldom cut dividends, a decrease in dividends is

most likely to occur for firms subject to financial constraints (or even in financial

distress). This will be particularly so since the Australian tax system favors payment of

dividends. As such, the Australian setting presents a more powerful context in which to

capture the elusive concept of financial constraints. Moreover, it should provide a

persuasive source of evidence that will help resolve conflicting views in the existing US

tests.

Lamont, Polk, and Saá-Requejo (2001) test whether there is a financial constraints

(FC) factor in US stock returns using a sample of growing manufacturing firms for the

period from 1968-1997. Lamont, Polk, and Saá-Requejo apply the methodology

1 In a recent paper, Pattenden and Twite (2008) examine the changes in corporate dividend policy around

introduction of the imputation tax system in Australia. Their results indicate that all dividend payout

measures and dividend reinvestment plans increased with the introduction of the imputation taxation

system. 2 Chang et al. (2007a) document that the investment decisions of Australian firms that are financially

constrained are much less sensitive to the availability of internal funds than those of unconstrained firms.

This Australian evidence provides strong support for the generality of the results of Kaplan and Zingales

(1997) and Cleary (1999), but contradicts the conclusions of several prior studies, including Fazzari,

Hubbard, and Petersen (1988).

3

developed by Kaplan and Zingales (1997), to identify firms subject to financial

constraints. By restricting interest to firms with growing sales, Lamont, Polk, and Saá-

Requejo (2001) distinguish financial constraints from financial distress. They find that

financially constrained firms earn lower average stock returns.

Fama and French (1993, 1992) find that the HML mimicking portfolio achieves a

positive mean return – that is, high book-to-market (BM) firms tend to earn higher

returns than low BM firms. If the Fama-French HML factor is a proxy for financially

distressed firms and if there is a positive correlation between financially constrained and

financially distressed firms, then the findings of Lamont, Polk, and Saá-Requejo (2001)

are problematic. At one level, their findings contradict the well established paradigm that

investors facing a higher systematic risk need to be compensated by a positive risk

premium. Alternatively, their findings might suggest that their sample selection criteria is

biased and is not truly capturing the broad spectrum of financially constrained firms.

A recent paper by Whited and Wu (2006) utilize an alternative methodology to

that employed by Kaplan and Zingales (1997) to classify firms that are financially

constrained. They find that constrained firms move together and that their financial

constraint factor mimicking portfolio earns higher average stock returns, though it is not

statistically significant. This is in contrast to the evidence documented by Lamont, Polk,

and Saá-Requejo (2001).

To explore the impact of financial constraints on stock returns in Australia, we

construct an Australian financial status index (ZFC). The index is determined using

multiple discriminant analysis on a set of variables that are expected to influence the

magnitude of financial constraints. We then use this index to study whether there is a

4

financial constraints factor in stock returns. We study this issue from both a time series

and cross-sectional perspective. After constructing portfolios with different size and

financial constraint rankings, we compute the average monthly excess returns for the

different portfolios. The analysis reveals that average returns on constrained firms are 42

basis points lower than average returns on unconstrained firms.

We also perform time-series tests and find that stock returns on constrained firms

positively co-vary with the returns of other constrained firms after controlling for other

sources of common variation, such as the market factor and the size factor. This evidence

of common variation in stock returns associated with financial constraints points to a

financial constraints factor in stock returns.

Accordingly, we then examine whether the financial constraints factor reflects

known empirical factors related to the market, size, book-to-market and momentum.

Specifically, we regress the financial constraints factor on the market, SMB, HML and

momentum mimicking factors. If these known factors correctly price the financial

constraints factor, then the intercept from these regressions should be zero. Further, the

R2 in these regressions should be high. We document that neither the variation nor the

mean return of the constraints factor are well explained by existing asset pricing models,

suggesting an independent role for our financial constraints factor.

Finally, we further examine whether financially constrained firms earn a positive

risk premium on a cross-sectional basis using individual stock returns. For our sample of

firms with an estimated financial constraints index, we regress returns in excess of the

one-month Treasury Bill yield on characteristics such as firm size, the book-to-market

ratio, momentum, and the financial constraints index. The negative and statistically

5

significant coefficient on our financial constraints index confirms that financially

constrained firms earn a lower return than their unconstrained counterparts.

Taken together, our findings are suggestive of a financial constraints factor in

Australian stock markets. This factor represents an identifiable independent common

source of economic shocks to firm value. However, our findings that financially

constrained firms earn significantly lower returns than unconstrained firms can not be

explained by existing asset-pricing models. Our evidence is consistent with that found by

Lamont, Polk, and Saá-Requejo (2001). The source of the common economic shocks to

financially constrained firms remains an open question.

The remainder of the paper is structured as follows. Section 2 presents a literature

review and empirical methodology on financial constraints and asset pricing. In Section 3,

the data are presented. Section 4 outlines and discusses our results and Section 5 presents

our conclusion.

2. Empirical Framework

2.1. Primary Financial Constraints Proxy

A challenging part of examining the impact of financial constraints on corporate

policies is to categorize firms according to a priori measures of financial constraints.

The existence of financial constraints requires some financing frictions that make

external financing more costly than internal funds. Standard corporate finance

considerations suggest that financially constrained firms tend to be small or unprofitable,

have high growth potential, or have high leverage and, hence, low debt capacity.

Accordingly, various metrics of financial constraints, such as the Kaplan and Zingales

6

(1997) and Whited and Wu (2006) indices, have been constructed based on these firm’s

characteristics.

The Kaplan and Zingales (1997) and Whited and Wu (2006) indices, however,

cannot be (directly) employed for our analysis because they are constructed using US

data. Following Cleary (1999), we measure financial constraints using a financial status

index (ZFC). The index is determined using multiple discriminant analysis on a set of

variables that are expected to influence the magnitude of financial constraints. Firms

with high discriminant scores (high ZFC) are categorized as more financially constrained;

firms with low ZFC are deemed as financially unconstrained.

The basic idea of multiple discriminant analysis is to utilize a set of firm-specific

variables to establish a function which best distinguishes between companies in two

mutually exclusive groups. In our setting, we need to establish two mutually exclusive

groups that are significantly different from each other in terms of financial constraints.

Cleary (1999) argues that changes in cash dividends are indicative of a firm’s status of

financial constraint. Therefore, grouping our firms according to changes in their dividend

payout ratios should provide us with groups of firms with distinctive magnitudes of

financial constraints. We divide our sample into three categories: dividend-increasing

firms, dividend-decreasing firms and firms that do not change their dividend payments.3

Our application of discriminant analysis estimates coefficients that best discriminate

between dividend-increasing firms and dividend-decreasing firms. ZFC scores are

computed using the estimated coefficients, and are assigned to all firms (including those

that experience no change in dividend payments) for the subsequent regression analysis.

3 We scaled dividend by net income. One-off (special) dividends are not included in the calculation.

Adding stock repurchases, measured by the decrease in outside equity, does not affect our main results.

7

Specifically, we rely on the same set of beginning-of-period variables as those

used by Cleary (1999) - current ratio (Current) and financial slack (Slack) are included to

control for liquidity; debt ratio (Leverage) and fixed charge coverage (FCCov) capture

the effect of leverage; net income margin (NI) measures the profitability and sales growth

(Growth) proxies for growth potentials.4 Based on Australian data, our discriminant

analysis generates the following financial constraint index (ZFC), which is expressed as a

function of the set of firm-specific variables mentioned above.

0.0218 0.0095 0.5581 0.2149

0.8034 1.6908

FCZ Current FCCov NI Slack

Growth Leverage

(1)

where the coefficient values are estimated for each independent variable which best

discriminates between dividend-increasing and dividend-decreasing firms.5

The

coefficients of firm-specific variables suggest that ZFC does a good job of capturing

corporate financial status. Firms with high leverage, high growth, low profitability, and

low financial slack are more likely to decrease dividends.6 Our financial constraints index

(ZFC) is thus higher for more constrained firms and lower for less constrained ones.

2.2. Alternative Financial Constraints Proxies

4 Current is equal to current assets divided by current liabilities. NI equals net income deflated by sales.

Growth is the change in net sales divided by sales lagged one period. Slack equals cash plus inventory and

account receivables minus short-term debt scaled by net PPE. FCCov equals EBIT plus fixed charge

expenses divided by the sum of fix charge expenses and interest expenses. Leverage equals total debt

divided by total assets. 5 Untabulated summary statistics confirm that firms reducing dividends appear to be more financially

constrained. Specifically, dividend-decreasing firms exhibit lower current ratios, higher debt ratios, and

lower interest coverage and lower net income margins, and lower sales growth than firms increasing

dividends. 6 In our sample, 69% of dividend-decreasing firms are classified as constrained firms (High ZFC), while 63%

of dividend-increasing firms are classified as unconstrained firms (Low ZFC).

8

It is important to subject our primary analysis to a robustness check to help rebut

any skepticism that the findings are a product of the particular proxy chosen for FC.

While the Kaplan and Zingales (1997) and Whited and Wu (2006) indices seem the ideal

candidates for performing such robustness analysis, they are not directly applicable to our

setting since they are constructed using (narrow) US data. For example, Kaplan and

Zingales (1997) focus on a small sample of 49 American low-dividend manufacturing

firms. As such, the KZ index is not an appropriate measure of equity dependence for a

broader set of firms in Australia. Accordingly, we create “adjusted” measures of both

indices, as described below.

As suggested by Baker, Stein, and Wurgler (2003), the original KZ index does not

have to be a perfect measure of financial constraints because of missing variables or

incorrect weights. But it could still be of practical use because the component variables

are indicative of financial constraints and the signs of their coefficients are economically

meaningful. Specifically, to ensure the KZ index reflects the characteristics of our own

sample (Chang et al., 2007b), we reassign the weights of the original index so that each

of the five variables accounts for one-fifth of the variation in the index, with the sign of

the weights for each variable unchanged. The resulting “adjusted” KZ index (AKZ) is:

1 7.03 1.59 1.14 0.16 ,AKZ CashFlow Dividend Cash Leverage MB (2)

where CashFlow is cash flow over lagged assets; Dividend is cash dividends over assets;

Cash is cash balances over assets; Leverage is total debt to asset ratio and MB is the

market-to-book assets ratio. This adjusted KZ index possesses intuitive features which

portray a financially constrained firm (i.e. high KZ value); namely, a low operating cash

9

flow, pays low dividends, little cash in hand, highly levered and good investment

opportunities.

Similarly, we define an adjusted WW index (AWW) as follows:

1 0.62 1.06 0.14 ( )

2.41 0.06 ,

AWW CashFlow Div Leverage Ln Assets

ISG Growth

(3)

where CashFlow is cash flow over lagged assets; Div is the paying-dividend-or-not

dummy variable; Leverage is total debt to asset ratio; Ln(Assets) is the natural log of total

assets; ISG is the 2-digit GICS industry sales growth (ISG); and Growth is the firm sales

growth defined as the change in net sales divided by sales lagged one period.

2.3. Portfolio Formation

Each December of year t, portfolios are formed according to independent sorts of

size and the financial constraints index. The financial constraint index (ZFC) is measured

using accounting data from the firm’s fiscal year end in calendar year t and size is

measured using market capitalization of equity in December of year t. Then, we classify

all firms into one of nine portfolios based upon independent sorts of size and the financial

constraints index with partitions into the top, middle and bottom thirds: low index/small

size (LS), middle index/small size (MS), high index/small size (HS), low index/medium

size (LM), middle index/medium size (MM), high index/medium size (HM), low

index/large size (LB), middle index/large size (MB), high index/large size (HB). We

then calculate value-weighted average monthly portfolio returns across stocks in the same

portfolio with Australian Graduate School of Management (AGSM) monthly data from

January to December of year t+1 and reform the portfolios in December of t+1.

10

We then form 3 portfolios that are linear combinations of the nine portfolios.

The 1st portfolio (HFC), is the equal-weighted average of the three size-sorted portfolios

in the top one third of the financial constraints: HFC = (HS + HM + HB)/3. The 2nd

portfolio (LFC) is similarly the equal weighted average of the three size-sorted portfolios

in the bottom third of the financial constraints: LFC = (LS + LM + LB)/3. The 3rd

portfolio, FC, is the difference between these two portfolios: FC = HFC – LFC.

Specifically, FC is a monthly time series of returns on a zero-cost factor mimicking

portfolio for financial constraints. FC is the hypothetical return that one would get by

buying (highly) constrained firms and shorting less constrained firms – as such, it

represents our basic measure of the constraints factor.

2.4. Regression Analysis

After constructing portfolios with different size and financial constraint rankings,

we first investigate whether financially constrained firms earn higher returns. Next, we

test formally whether financially constrained firms have returns that move together,

controlling for other sources of common variation, such as the market factor and the size

factor. We regress returns of each of the nine size and financial-constraints cross-sorted

value-weighted portfolios on reference portfolio returns, which proxy for the market

factor, the size factor, the HML factor, and the value-weighted financial constraints factor.

Then, we examine whether the financial constraints factor reflects known

empirical factors such as the market, size and book-to-market. We regress the financial

constraints factor on the market, SMB and HML mimicking factors. If these known

factors correctly price the financial constraints factor, then the intercept from these

11

regressions should be zero. Further, the R2 in these regressions should be high.

Otherwise, the financial constraints factor measures sources of variation independent of

the known factors. As a robustness check and similar to Lamont, Polk, and Saá-Requejo

(2001), we also perform the above with the Fama-French model augmented by a

momentum factor.

Finally, we further examine whether financially constrained firms earn a positive

risk premium on a cross-sectional basis using individual stock returns. For our sample of

firms with an estimated financial constraints index, we regress returns in excess of one-

month Treasury Bill yield on characteristics such as size, the book-to-market ratio,

momentum, and the financial constraints index.

3. Data and Summary Statistics

Our sample is the most comprehensive ever used in the Australian setting, both in

its time series and cross sectional dimensions. Specifically, we examine monthly data

covering the 30-year period from 1975 to 2004. We drop firms that do not have the

available information on market capitalization and accounting information on the

variables used for the construction of the financial constraints index. Moreover, we have

carefully created a hand collected dataset of individual stocks for the initial (final) sample

after all the selection criteria which encompass an average of 84.4 % (29.8%) of listed

stocks by number, over the 30-year period.7 The summary statistics in Table 1 suggests

that companies in our final sample are on average larger than the average firm listed on

the ASX.

7 For example, the sample used by Chan and Faff (2005) only covered 33% (66%) of listed Australian

stocks in the year 1990 (1998) when using accounting data and monthly share price data to conduct asset

pricing tests in the context of the Fama-French 3-factor model.

12

[TABLE 1 ABOUT HERE]

The market return and stock price data are provided from the AGSM Share Price

and Price Relative file. Company details, market returns, market capitalization, share

returns and risk-free rate at a monthly frequency were collected from this database.

Firms’ monthly returns are expressed as percentages in excess of the monthly risk-free

rate. Information for the accounting data was collected from two main sources. For the

period 1974 to 1992, the accounting items were hand collected from the Australian Stock

Exchange Research Service which provides the Balance Sheet and Profit and Loss

statement of publicly listed companies during that period.8 In the period 1993 to 2004,

the accounting items were taken from the Aspect Financial data files.9

[TABLE 2 ABOUT HERE]

We partition the entire sample into three groups according to the financial

constraints index (ZFC). Table 2 reports the mean values of key financial ratios for the

whole sample and three financial constraints groups. The summary statistics indicate that

our financial constraints index (ZFC) is successful in capturing the desired cross-sectional

properties of financial status. In general, firms with low ZFC appear more solid and

financially healthy than those with high ZFC scores. More specifically, we see that (when

compared with their more financially constrained counterparts) less constrained firms

8 Examples of items collected include cash, current investment, inventory, receivables, total current assets,

total non-current assets, net PPE, book value of assets, short-term debt, total current liabilities, total non-

current liabilities, long-term debt, share capital, reserves, share premium reserves, retained profits, net

sales, depreciation and amortization, fixed charge expenses, EBIT, interest expenses, net income, ordinary

dividends, preference dividends, cash paid for PPE, sales of PPE, net profit before and after tax, trading

revenue and interest expense. 9 Total assets and market capitalization are expressed in millions of 1990 dollars using an Australian CPI

deflator. Though accounting information was collected for 1974, we lose one year of data in our analysis

due to the requirement of possessing accounting information before our portfolio formation period.

13

have higher cash flows, higher dividends, lower leverage, are more profitable, higher

current ratio and higher interest coverage. These are exactly the patterns we would expect

to observe for an index that reliably discriminates between different degrees of financial

constraints.

As briefly alluded in the introduction, the interplay between the concepts of

“financial constraints” and “financial distress” is an important consideration in this

empirical literature. Indeed, researchers aiming to examine FC need to be wary of the

most plausible alternative interpretation of their results that relates to the more extreme

condition of distress. For example, it could be argued that a firm which decreases

dividends is more an indicator of financial distress than it is a reflection of financial

constraints.

To address this concern we assess our sample for signs of a meaningful

distinction between “constraints” versus “distress”. Specifically, in Table 2 we report

average financial ratios for dividend decreasing firms versus dividend increasing firms.10

As shown in the table, dividend-decreasing firms appear to have reasonably large cash

balances, cash flow/assets, current ratio, and dividend to assets ratio. Thus, on average,

they are not likely to be financially distressed. However, it is acknowledged that

dividend decreasing firms are indeed less profitable, hold less cash, have lower cash

flows and have a lower interest coverage ratio compared to their dividend increasing

counterparts.

10

In our sample there are 3,877 (4,470) firm-year observations which are found to decrease (increase)

dividends.

14

The SMB and HML mimicking factors are created in a similar manner to Fama

and French (1993). 11

The average number of firms in each of the size/financial

constraints index portfolios are reported in Table 3. Also reported are the mean values of

firm characteristics for groups of firms sorted into portfolios. By construction,

financially constrained (high ZFC) firms have high leverage and high book-to-market

ratios. On average, financially constrained firms are smaller than their unconstrained

counterparts (low ZFC). More importantly, Table 3 shows the average monthly excess

returns for different portfolios. The average returns on constrained firms are 42 basis

points lower than that of unconstrained firms. The (untabulated) t-statistic of the mean

return of FC portfolio is -2.95, suggesting that financially constrained firms induce a

negative and statistically significant risk premium. This finding is not driven by any

particular size class: each of the 3 size-sorted constrained portfolios underperforms their

less constrained counterparts of the same size. In general, our results are consistent with

those documented by Lamont, Polk, and Saá-Requejo (2001) but contradict the findings

of Whited and Wu (2006).

[TABLE 3 ABOUT HERE]

Figure 1 reports the time series of the cumulative returns on FC portfolio from

1975 to 2004. The return on this factor-mimicking portfolio represents the return one

would experience from a self-financing strategy of buying a portfolio of financially

11

Size rankings are based on market capitalisation of equity and book-to-market rankings are based on the

ratio of book equity (net tangible assets) to market equity. For the SMB mimicking portfolio, the firms are

ranked according to market capitalisation in December of year t and are allocated to two portfolios (based

on these rankings) from January to December of year t+1. For the HML mimicking portfolio, the firms are

ranked according to book to market (based upon the book value in June of year t divided by the market

value in December of year t due to Australia having a June financial year end) and they are allocated to

three portfolios (based on these rankings) from January to December of year t+1.

15

constrained firms and shorting a portfolio of less constrained firms. The cumulative

returns are simply the sum of these monthly returns and show the percent total return on

the long portfolio minus the total return on the short portfolio. The figure suggests that

the underperformance of financially constrained firms has been persistent. If one started

buying financially constrained firms and short-selling financially unconstrained firms in

1975, the total losses are as high as -153% by the end of 2004.12

[FIGURE 1 ABOUT HERE]

4. Empirical results

We first test formally whether financially constrained firms have returns that

move together, controlling for other sources of common variation, such as the market

factor and the size factor. To this end, we regress returns of each of the nine-size and

financial-constraints cross-sorted value-weighted portfolios listed in Table 3 on 4

reference portfolio returns. The first reference portfolio proxies for the market factor, the

second is a proxy for the size (SMB) factor, the third is HML, and the fourth is the value-

weighted financial constraints factor (FC). Table 4 reports the results of these 9

regressions (reading down the columns of the table). Not surprisingly, we find that big

firms have negative loadings on the SMB and HML factors. More importantly, we

document that the coefficients on FC are higher for financially constrained firms (High

ZFC), and lower, or even negative, for unconstrained firms. Within each size class, FC

loadings increase as ZFC increases. Thus, our findings in Table 4 suggest that constrained

firms have stock returns that positively covary with the returns of other constrained firms,

suggesting the potential existence of a constraints factor in Australian stock markets.

12

This is similar to the pattern observed and documented by Lamont, Polk, and Saá-Requejo (2001, p. 544)

over a similar time period.

16

[TABLE 4 ABOUT HERE]

Next we examine whether the financial constraints factor reflects known

empirical factors such as the market, size, book-to-market and momentum factors.13

More specifically, we regress the financial constraints factor on these factors. If these

conventional factors subsume the financial constraints factor, then the intercept for these

regressions should be zero. Further, the R2 in these regressions should be high. Since the

R2

in these regressions measures how much of the variation in the constraints factor can

be explained using other systematic factors, if the R2 is low, then the constraints factor is

more likely to be capturing an independent source of return variance.

[TABLE 5 ABOUT HERE]

The first column of Table 5 shows to what extent the constraints factor can be

explained by the Capital Asset Pricing Model (CAPM). The constraints factor has an

estimated market β of 0.126, and significant at the 1% level, suggesting that financial

constrained firms tend to have higher β than unconstrained firms. The constraints factor

is mispriced by the CAPM, with an intercept of -60 basis points per month. Column 2

reports counterpart results obtained using the Fama and French (1993) three-factor model.

The extent of estimated mispricing decreases slightly, going from the -60 basis points

(CAPM) to -50 basis points (three-factor model). A very similar result is found for the

three-factor Fama-French model augmented by a momentum factor. The R2 values are all

very small, regardless of which model is used – varying between 6% and 8%. Taken

13

We calculate a momentum mimicking factor similar to that used in Lamont, Polk, and Saá-Requejo

(2001) and Carhart (1997). The exception is that this paper used a lagged 5-month formation period while

the US papers used a lagged 11-month formation period to determine whether stocks are in high, medium

or low momentum groups. The reason is that Demir et al (2004) found that the momentum effect is shorter

in Australia.

17

together, we document that neither the variation nor the mean return of the constraints

factor are well explained by existing asset pricing models, further suggesting an

independent role for our financial constraints factor.

Finally, we examine the role/importance of financially constrained firms on a

cross-sectional basis using individual stock returns. For our sample of firms with an

estimated financial constraints index, we regress returns in excess of Treasury notes

returns on firm characteristics: size (measured as the market capitalization in millions of

dollars), the book-to-market ratio, and momentum (measured as the prior 5-month mean

return excluding the latest month). The key independent variable of interest is our

financial constraints index, ZFC. Note that we regress firm returns on firm characteristics

rather than the betas estimated from asset pricing models. The benefit of using firm

characteristics is that they are much more precise than the betas from the pricing

equations. The purpose of this regression is to assess whether more financially

constrained firms earn lower returns, and whether the difference is statistically significant.

We run regressions month by month and report in Table 6 the sample mean and the time

series t-statistics of the estimated coefficients.

[TABLE 6 ABOUT HERE]

Column (1) presents our main results in which the estimated coefficient on our

variable of interest, ZFC, is negative and statistically significant at the 1% level,

confirming our previous finding that financially constrained firms earn lower return than

their unconstrained counterparts. In columns (2) and (3) of Table 6, we show the outcome

of some robustness checking in which adjusted versions of the KZ and WW (AKZ and

AWW), are separately substituted as the measure of financial constraints. Importantly, we

18

see that our core finding is upheld – the estimated coefficient on each of the alternative

FC variables are both negative and statistically significant at the 5% and 10% levels,

respectively. This reinforces our claim above that inferior returns associate with

financially constrained firms.14

As expected, the coefficient of the book-to-market ratio is positive and significant

at the 1% level, indicating that value stocks earn higher returns. Price momentum,

measured as the past 5-month return, has a positive coefficient, which is significant at the

5% level. These findings are robust across all three regressions reported in Table 6.

5. Conclusion

In this paper, we construct an index of financial constraints for companies in

Australia. We demonstrate that firms classified as “financially constrained” by this index

indeed exhibit characteristics typically associated with exposure to external financial

constraints. We then construct portfolios with different size and financial constraints

rankings and find that financially constrained firms earn lower return than their

unconstrained counterparts. We also conduct time-series tests and find that stock returns

of constrained firms covary with the return of other constrained firms. These findings

suggest the existence of a financial constraints factor in stock returns. A significant

component of the variation in the financial constraints factor cannot be explained by the

Fama-French factors - whether augmented by a the momentum factor or not. This result

is similar to that found by Lamont, Polk, and Saá-Requejo (2001). Cross-sectional

regressions of individual stock returns on the financial constraints index and firm

14

In untabulated analysis, with regard to Tables 4 and 5 we confirm that substituting the adjusted FC

measures (in place of ZFC) produces similar results, although weaker for AWW than for AKZ.

19

characteristics confirm the negative relation between financial constraints and stock

return.

Our core finding that financially constrained firms earn low returns are generally

consistent with similar evidence documented by Lamont, Polk, and Saá-Requejo (2001).

They suggest that firm-level financial constraints do not represent a source of

undiversifiable risk that is priced in financial markets. The source of the common

economic shocks to financially constrained firms merits further investigation.

20

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Investments”, Brookings Papers on Economic Activity, 1, pp. 141-195.

21

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19, pp. 531-559.

22

Figure 1: Cumulative returns for the financial constraints factor mimicking

portfolio

Data are collected from companies’ annual reports, Aspect, and AGSM. Firms are sorted

independently based on size and our financial constraints index (ZFC). Firms are

classified into nine portfolios based upon independent sorts of size and the financial

constraints index with partitions into the top third, middle third and bottom third: low

index/small size (LS), middle index/small size (MS), high index/small size (HS), low

index/medium size (LM), middle index/medium size (MM), high index/medium size

(HM), low index/large size (LB), middle index/large size (MB), high index/large size

(HB). HFC is the equal-weighted average of the three size-sorted portfolios in the top

third of the financial constraint sort. HFC = (HS + HM + HB)/3. LFC is the equal

weighted average of the 3 size-sorted portfolios in the bottom third of the financial

constraint sort: LFC = (LS + LM + LB)/3. FC is the difference between these two

portfolios: FC = HFC – LFC. The cumulative returns of FC portfolio are plotted against

time. The cumulative return is the sum of monthly returns.

-150

-100

-50

0

Pe

rcen

t

1975 1980 1985 1990 1995 2000 2005year

Cumulative returns of the ZFC portfolio 1975:7-2004:12

23

Table 1: Yearly sample coverage This table shows the average number of stocks and average market capitalisation per year as at

the 30th June (end of financial year) for the ASX population, our initial sample (after

excluding financial firms, REITs, negative book-to-market firms, extreme positive book-to-

market firms and firms with extreme monthly returns) and our final sample contains firms

with the available information on market capitalization and on ZFC. ZFC is a linear combination

of six accounting ratios and is defined in the text.

Average number of stocks Average market capitalization

Pop Initial

sample

Final

sample

Population

(million$)

Initial

sample

Final

sample

1975 1,072 790 243 14.27 16.17 41.83

1976 1,084 855 321 18.04 19.74 37.29

1977 1,107 920 423 17.50 18.96 31.34

1978 1,050 915 439 20.59 21.50 37.05

1979 985 857 417 26.66 27.92 56.05

1980 966 844 396 46.96 48.96 86.66

1981 949 815 387 57.18 59.52 78.96

1982 907 797 348 41.80 43.39 77.12

1983 885 757 332 61.43 57.37 112.95

1984 893 746 321 67.04 64.49 103.98

1985 981 786 329 94.11 81.44 149.98

1986 1,163 852 377 102.78 111.45 223.51

1987 1,546 1058 458 143.22 167.97 172.79

1988 1,775 1274 553 118.19 135.72 182.39

1989 1,706 1300 468 119.82 134.89 267.47

1990 1,535 1165 376 140.88 161.95 260.99

1991 1,321 1016 302 168.01 189.88 426.31

1992 1,150 860 290 235.07 266.27 599.95

1993 1,058 971 264 384.04 390.68 793.08

1994 1,151 959 279 389.64 406.95 614.37

1995 1,177 1079 321 402.61 371.66 621.93

1996 1,171 1089 332 463.73 439.69 639.05

1997 1,191 1089 325 817.10 845.76 719.08

1998 1,213 1141 333 813.24 832.78 761.65

1999 1,214 1138 320 855.69 893.88 958.27

2000 1,336 1262 326 858.92 551.76 880.16

2001 1,421 1337 372 868.10 903.20 826.65

2002 1,423 1337 389 858.06 889.29 882.37

2003 1,411 1327 399 838.26 865.15 982.28

2004 1,514 1331 407 872.76 957.67 1224.22

Average 1,212 1022 362 320.52 332.54 428.32

24

Table 2: Mean values of financial ratios for financial constraint groups, 1975 - 2004

Data are collected from companies’ annual reports, Aspect, and AGSM. Firm-years are partitioned into three groups according to the financial

constraint index (ZFC), which is calculated using discriminant analysis according to equation (1) in the main text. Every year, the firm with the

lowest (highest) value of ZFC are categorized as the least (most) financially constrained. All financial variables are measured at the end of the

fiscal year, except cash flow which is measured as firm internal cash flows during the fiscal year. Total assets (in millions) are adjusted to the

2000 dollar value using a GDP deflator. Current is equal to current assets divided by current liabilities. NI equals net income deflated by sales.

Growth is the change in net sales divided by sales lagged one period. Slack equals cash plus inventory and accounts receivables minus short-term

debt scaled by net PPE. FCCov equals EBIT plus fixed charge expenses divided by the sum of fix charge expenses and interest expenses.

Leverage equals total debt divided by total assets. The differences in firm characteristics between dividend decreasing and dividend increasing

firms and those between the least financially constrained (Low ZFC) and the most financially constrained firms (High ZFC) that are significant at

10%, 5%, and 1% levels are marked with *, **

, ***

, respectively.

Entire

Sample

Dividend

decreasing firms

Dividend

increasing firms

Low ZFC

(least constrained)

Middle ZFC High ZFC

(most constrained)

Financial constraints index 1.07 0.91 0.84***

0.157 0.892 2.168***

Total assets (A) 655.4 1221.1 1405.9 349.7 876.9 746.4***

Cash/A 0.059 0.058 0.064**

0.072 0.042 0.064**

Cash Flow/A 0.091 0.114 0.124***

0.148 0.107 0.019***

Market-to-book ratio 1.240 1.215 1.240 1.257 1.139 1.324***

Dividend/A 0.026 0.029 0.040***

0.037 0.024 0.016***

Debt ratio (Leverage) 0.491 0.469 0.466 0.349 0.518 0.608***

Net income margin (NI) -0.207 0.050 0.094**

0.158 0.035 -0.817***

Sales growth (Growth) 0.358 0.285 0.261 0.029 0.139 0.906***

Current ratio (Current) 1.768 2.221 2.119 2.056 1.525 1.722***

Fixed charge coverage (FCCov) 8.281 14.67 17.29**

40.71 0.296 -18.96***

Financial slack (Slack) 0.354 0.373 0.375 0.364 0.381 0.316***

25

Table 3: Financial constraints and firm characteristics, 1975 - 2004

Data are collected from companies’ annual reports, Aspect, and AGSM. The summary statistics, from July 1975 to December 2004, are reported

for nine value-weighed portfolios by ranking in each December of year t all ASX firms that have information on market capitalization and

accounting information on ZFC. Excess return is the difference between monthly stock return and the monthly return of 13 week Treasury notes.

Firms are sorted independently based on size and our financial constraints index (ZFC). Firms are classified into nine portfolios based upon

independent sorts of size and the financial constraints index with partitions into the top third, middle third and bottom third: low index/small size

(LS), middle index/small size (MS), high index/small size (HS), low index/medium size (LM), middle index/medium size (MM), high

index/medium size (HM), low index/large size (LB), middle index/large size (MB), high index/large size (HB). HFC is the equal-weighted average

of the three size-sorted portfolios in the top third of the financial constraints sort. HFC = (HS + HM + HB)/3. LFC is the equal weighted average

of the 3 size-sorted portfolios in the bottom third of the financial constraints sort: LFC = (LS + LM + LB)/3. FC is the difference between these

two portfolios: FC = HFC – LFC. The average annual number of firms in each of these portfolios is also reported

Number

of firms

ZFC Book-to

-market ratio

Leverage

ratio

Market

capitalization

Excess

return

Small firms

Low ZFC LS 34 0.20 3.01 0.33 7.13 1.26

Middle ZFC MS 46 0.91 4.95 0.53 6.10 1.13

High ZFC HS 47 2.38 5.95 0.62 5.82 0.65

Mid-cap firms

Low ZFC LM 34 0.12 1.99 0.34 47.7 0.95

Middle ZFC MM 40 0.89 3.24 0.51 44.5 0.95

High ZFC HM 53 2.08 4.39 0.59 38.0 0.43

Large firms

Low ZFC LB 60 0.18 1.52 0.35 814.2 0.70

Middle ZFC MB 40 0.87 2.03 0.51 2107.4 0.61

High ZFC HB 27 1.72 3.12 0.58 1023.3 0.51

LFC 0.17 2.08 0.35 321.2 0.98

HFC 2.14 4.85 0.60 233.9 0.56

FC 1.97 2.77 0.25 -87.3 -0.42

26

Table 4: Covariance tests, 1975 - 2004

Data are collected from companies’ annual reports, Aspect, and AGSM. Firms are sorted independently based on size and our financial constraints

index (ZFC). Firms are classified into nine portfolios based upon independent sorts of size and the financial constraints index with partitions into

the top third, middle third and bottom third: low index/small size (LS), middle index/small size (MS), high index/small size (HS), low

index/medium size (LM), middle index/medium size (MM), high index/medium size (HM), low index/large size (LB), middle index/large size

(MB), high index/large size (HB). HFC is the equal-weighted average of the three size-sorted portfolios in the top third of the financial

constraints sort. HFC = (HS + HM + HB)/3. LFC is the equal weighted average of the 3 size-sorted portfolios in the bottom third of the financial

constraints sort: LFC = (LS + LM + LB)/3. FC is the difference between these two portfolios: FC = HFC – LFC. The table reports regression

results of 9 value-weighted portfolios described in Table 3. The excess returns on each portfolio are regressed upon 4 reference portfolios: a

market proxy, an SMB factor proxy, a HML factor proxy, and a financial constraints factor (FC). t-statistics are reported in parentheses.

Coefficients that are significant at 10%, 5%, and 1% are marked with *, **

, ***

, respectively.

Small Mid-Cap Big

LS MS HS LM MM HM LB MB HB

Market 0.736*** 0.563*** 0.758*** 0.614*** 0.572*** 0.739*** 0.854*** 0.680*** 0.707***

(15.7) (11.2) (18.3) (18.0) (14.4) (17.2) (14.9) (11.5) (10.6)

SMB 0.019 0.001 0.090*** 0.021 -0.091*** -0.101*** -0.337*** -0.293*** -0.286***

(0.4) (0.0) (2.7) (0.6) (-3.6) (-3.0) (-8.1) (-5.4) (-5.2)

HML -0.009 0.032 0.091** 0.095* -0.013 -0.000 -0.170*** -0.199*** -0.175***

(-0.2) (0.8) (2.3) (1.9) (-0.3) (-0.0) (-4.5) (-4.6) (-3.3)

FC -0.852*** -0.075 0.666*** -0.256*** 0.105 0.467*** -0.381*** -0.138** 0.378***

(-7.2) (-0.7) (9.3) (-3.8) (1.5) (5.5) (-5.0) (-2.1) (4.2)

Constant -0.003 0.002 -0.006*** -0.002 0.005*** -0.001 0.005** 0.007*** 0.008***

(-1.0) (0.6) (-3.0) (-1.3) (3.0) (-0.5) (2.4) (3.0) (2.9)

# of months 360 360 360 360 360 360 360 360 360

R-squared 0.52 0.36 0.72 0.58 0.58 0.67 0.63 0.56 0.54

27

Table 5: Regression of financial constraints factor on other known factors

Data are collected from companies’ annual reports, Aspect, and AGSM. The table reports

regression results of asst pricing tests of the financial constraint factor (FC). The asset pricing

models considered are the CAPM, the Fama and French (1993) three-factor model and the

momentum augmented Fama and French (1993) three-factor model. The FC portfolio is

constructed as follows. Firms are sorted independently based on size and our financial

constraints index (ZFC). Firms are classified into nine portfolios based upon independent sorts of

size and the financial constraints index with partitions into the top third, middle third and bottom

third: low index/small size (LS), middle index/small size (MS), high index/small size (HS), low

index/medium size (LM), middle index/medium size (MM), high index/medium size (HM), low

index/large size (LB), middle index/large size (MB), high index/large size (HB). HFC is the

equal-weighted average of the three size-sorted portfolios in the top third of the financial

constraints sort. HFC = (HS + HM + HB)/3. LFC is the equal weighted average of the 3 size-

sorted portfolios in the bottom third of the financial constraints sort: LFC = (LS + LM + LB)/3.

FC is the difference between these two portfolios: FC = HFC – LFC. t-statistics are reported in

parentheses. Coefficients that are significant at 10%, 5%, and 1% are marked with *, **

, ***

,

respectively.

(1)

CAPM

(2)

FF 3- Factor

(3)

FF 3-Factor + Momentum

Market 0.126*** 0.149*** 0.151***

(4.9) (5.7) (5.8)

SMB -0.040* -0.042*

(-1.7) (-1.7)

HML -0.000 -0.001

(-0.1) (-0.1)

Momentum 0.015

(0.6)

Constant -0.006*** -0.005*** -0.005***

(-4.5) (-3.2) (-3.3)

R-squared 0.06 0.08 0.08

28

Table 6: Cross-sectional regression of returns on firm characteristics

Data are collected from companies’ annual reports, Aspect, and AGSM. The table reports the

results for the month-by-month cross-sectional regressions of firm excess returns on firm

characteristics including size measured as the market capitalisation in millions of dollars, the

book-to-market ratio, momentum measured as the prior 5-month mean return excluding the latest

month. The key independent variable of interest is our financial constraints index ZFC defined as

0.0218 0.0095 0.5581 -0.2149

0.8034 1.6908

FCZ Current FCCov NI Slack

Growth Leverage

Two alternative measures of financial constraints are also analyzed, AKZ and AWW:

1 7.03 1.59 1.14 0.16 ,AKZ CashFlow Dividend Cash Leverage MB

1 0.62 1.06 0.14 ( ) 2.41 0.06 ,AWW CashFlow Div Leverage Ln Assets ISG Growth

We run regressions month by month and report in the sample mean and the time series t-statistics

of the estimated coefficients. t-statistics are reported in parentheses. Coefficients that are

significant at 10%, 5%, and 1% are marked with *, **

, ***

, respectively.

Dependent Variable: Excess return

(1) (2) (3)

Size -0.001 -0.001 -0.001

(-0.2) (-0.2) (-0.3)

B/M 0.001*** 0.001*** 0.001***

(3.6) (3.2) (3.3)

Momentum 0.005** 0.009*** 0.006***

(2.1) (3.4) (2.7)

ZFC -0.002***

(-3.8)

AKZ -0.003**

(-2.3)

AWW -0.002*

(-1.8)

Constant 0.006*** 0.005*** 0.004**

(3.0) (2.7) (1.9)

R-squared 0.01 0.01 0.01