UPPSALA UNIVERSITET

The effect of corporate liquidity and investor protection

on the behaviour of distressed equity in Europe

DD MSc International Financial Management

Department of Business Studies

ABSTRACT

This study examines the effect of corporate liquidity and investor protection on the relation

between financial distress and equity returns using a European sample over the 2002-2016

period. The results show that returns are hump-shaped and decreasing for increasing default

risk. This can be rationalized by corporate liquidity indicating that higher cash holdings

decrease liquidity risk. Moreover, firms in countries with high investor protection exhibit a

more severe decrease of returns when default risk increases relative to firms in countries with

low investor protection. This is because of the legal system that allows investors to renegotiate

upon distress and to more accurately price equities.

JEL classification: G12, G32, G33

Key words: financial distress, equity returns, cash holdings, investor protection

Name: Anneke Damhuis

Student number: 930810-T220

Supervisor: Dr. V. Purice

Second supervisor: Dr. M.M. Kramer

Date: 12 January 2018

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1. Introduction

Each year, 200,000 firms go bankrupt in the European Union (European Commission, 2016).

Hence, the new proposal of the European Commission aims to restructure business and investor

rights favouring prevention rather than failure. However, besides new procedures and rules

resulting in economic gains through governmental intervention, firms and investors should

suitably assess financial distress risk. Managers need to allow for financial distress risk when

establishing business policies. Moreover, the market aims to effectively price (distressed)

equities.

To this extent, literature has extensively reviewed the debate about default risk and equity

returns (see, e.g., Dichev, 1998; Griffin and Lemmon, 2002; Garlappi, Shu, and Yan, 2008).

However, existing empirical evidence provides a complicated view eluding a unifying and

coherent explanation (Garlappi and Yan, 2011). Contrary to the general intuition, that firms

facing higher default risk earn higher returns, distressed equities are commonly found to earn

lower returns, widely known as the distress anomaly. While some explanations have been

considered, evidence on the distress anomaly in the U.S. market is currently characterized by

disagreement. Moreover, surprisingly few studies examine the distress puzzle by using non-

U.S. data. Therefore, Gao, Parsons, and Shen (2015) argue that it is essential to shift the focus

to new non-U.S. evidence. This study goes beyond research on U.S. data and examines the

performance of distressed equity returns in Europe.

An important research direction for non-U.S. firms is to further examine the drivers of the

distress anomaly, and whether this enables us to understand the behaviour of distressed equities

outside the U.S. (Aretz, Florackis, and Kostakis, 2017). A recent study by Medhat (2014) sheds

new light on prior findings by showing that corporate liquidity is of importance to explain the

relation between equity returns and financial distress for a U.S. sample. His research is among

the first to consider corporate liquidity influencing this relation. The author finds that returns

are hump-shaped and decreasing for higher default risk. The rationale behind this finding is that

firms with default risk can decrease liquidity risk by holding precautionary cash. This study is

inspired by the research performed by Medhat (2014), but focuses on a European sample with

a deepened understanding of the theory involved in this area. As such, this study analyses the

effect of cash holdings on the equity risk of financially distressed European firms. Thereby

testing whether this has a direct impact and accounts for the observed variation in the relation

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between default risk and equity returns, over and above the well-documented effects of size and

value premiums.1

The diverse European sample allows this study to relate equity returns and default risk to

country-level characteristics that might influence the distress puzzle. Within the international

dimension, several papers elaborate upon the cross-country effect of investor protection, which

is identified as the quality of the regulatory and legal protection of a firm’s shareholders (La

Porta, Lopez-de-Silanes, Shleifer, and Vishny, 1997). Significant differences exist between the

U.S. and European countries in terms of financial and legal systems, and hence the protection

of investors and their rights (Doupnik and Perera, 2011). These differences might lead to a

deviation in pricing distressed equity and cash holding policies between European and U.S.

firms. As the analysis of Garlappi et al. (2008) highlights, the role of investor advantage,

defined as the combination of investors’ bargaining power and the efficiency gained through

bargaining, is essential in the determination of the relation between default risk and equity

returns. Hence, this study examines whether country-level characteristics drive the default

premium and if it follows that this is differently from U.S. results.

The main findings are as follows. The distress anomaly is revisited for the European

sample consisting of 714 unique firms resulting in a hump-shaped and decreasing relation

between equity returns and default risk. Moreover, corporate liquidity and investor protection

significantly affect this relation. The findings supporting the propositions are robust. These

results extend the literature on financial distress by illustrating the economic and statistical

significance of corporate liquidity and country-level institutional differences in explaining the

distress anomaly for European firms. Moreover, the results have several practical implications

including business credit evaluations, investment guidelines, and accurately pricing financial

assets when assessing default risk. Further, managers must consider the optimal level of cash

to minimize liquidity risk and the strength and quality of investor rights in corporate (risk-

taking) decisions.

The remainder of the paper proceeds as follows. The next section provides the main

theoretical background and the opposing views and results. Thereafter, the data and

methodology are described. In the fourth section, the results of both multivariate regressions

and portfolio analysis are presented, followed by an extensive elaboration upon the findings.

Finally, the conclusions and limitations are given.

1 Aretz, et al. (2017) find that the magnitude of the default risk premium declines when adjusting for size and value

premiums, suggesting that these features are related to default risk. This is in line with the conjectures of previous

studies (see, e.g., Chan and Chen, 1991; Fama and French, 1996; and Vassalou and Xing, 2004).

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2. Literature review

2.1 Financial distress and equity returns

A firm is financially distressed2 when its value of assets is not sufficient to meet contractual

interest or principal on debt obligations. These firms are assigned to have higher default risk.

The effect of default risk on equity returns is not clear because equity holders are the residual

claimants on the cash flows of a firm (Vassalou and Xing, 2004). While some papers argue that

higher default risk results in higher equity risk premiums, others find evidence for the distress

puzzle revealing that financial distress is associated with anomalously low equity returns. Since

this is one of the most ambiguous asset pricing irregularities, the subject has gained

considerable attention in both theoretical and empirical research (Eisdorfer, Goval, and

Zhdanov, 2014).

Chan and Chen (1991) and Fama and French (1992, 1996) were among the first to analyse

the effect of financial distress on equity returns. They suggest that financial distress is a possible

explanation for some of the anomalies in the cross-section of equity returns. The rationale

behind these studies rests on the conjecture that investors require a higher premium for holding

equities that are exposed to a higher probability of default. Chan and Chen (1991) argue that

the size premium is mainly driven by firms with low market value and high leverage to justify

distress risk, as these firms are more sensitive to adverse economic fluctuations. Fama and

French (1992) relate the book-to-market effect to default risk as well. In a later study, Fama and

French (1996) suggest that, if bankruptcy events are correlated across firms, the relative default

risk of a firm might be a state variable which eventually affects asset prices in the cross-section.

More recently, scholars argue that default risk is positively priced in the market and is

profoundly associated with size and value effects (see, e.g., Vassalou and Xing, 2004; Aretz et

al., 2017). In this regard, distress risk is found to explain size and value effects that are

anomalies in the standard capital asset pricing model. Though these papers find that a firm’s

default risk is positively priced in the market, existing empirical literature, by using various

measures of default risk, has not grounded consistent evidence to confirm above conjecture.

2 In this study, the terms default and financial distress are used interchangeably.

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Moreover, several studies show that distressed firms have actually lower, not higher, equity

returns.3

A common interpretation of the distress anomaly is market mispricing, that is, investors

are not able to fully assess the perspective of distressed firms and hence do not demand a default

risk premium (Garlappi and Yan, 2008). Another clarification proposed by Campbell et al.

(2008) identifies that investors might prefer positively skewed equities, and hence are willing

to hold equities with high default probabilities regardless their low returns. Furthermore, by

decomposing default risk in systematic and idiosyncratic risk, scholars indicate that the

systematic component drives the significant default risk premium.4

An explanation that helps reconcile the conflicting interpretations of the effect of default

risk on equity returns, which does not rely on capital structure, is the economic mechanism of

investors’ bargaining power. Garlappi et al. (2008) recognize that equity returns are humped

and decreasing in default probability due to the possibility of investor recovery upon distress.

Extending these findings, Garlappi and Yan (2011) theoretically show that investor recovery

upon distress implies the non-monotonic and decreasing relation between financial distress and

conditional betas, and empirically show this by using time varying betas. Moreover, Favara,

Schroth, and Valta (2012) find evidence that a firm’s equity risk is reduced when the perspective

of debt renegotiations is favourable for investors. Thus, several scholars identify that returns

are hump-shaped and decreasing when default risk increases. These papers rest on the

conjecture that investors do not require a higher return for holding distressed equities. To test

the existence of the distress anomaly for the constituted European sample, this thesis revisits

the empirical relation between financial distress and equity returns, resulting in the following

hypothesis:

Hypothesis 1: Equity returns are hump-shaped and decreasing as default probability increases.

3 Dichev (1998) finds a negative relation between returns and default probability using both Altman’s (1968) Z-

score and Ohlson’s (1980) O-score. Griffin and Lemmon (2002) document that this finding is stronger for firms

with low book-to-market ratios by using Ohlson’s (1980) model. This finding is more recently confirmed by

Campbell, Hilscher and Szilagyi (2008). Friewald, Wagner and Zechner (2014) show that firms with the highest

default probability earn the lowest returns using Merton’s (1974) model. Gao et al. (2015) find a significant

negative relation between financial distress and equity returns using Moody’s KMV, more specifically they find

that the distress anomaly is more prevalent in North America and Europe, in conformity with Eisdorfer et al. (2014)

who use Merton’s (1974) model. 4 Aretz et al. (2017) find conclusive evidence for the distress risk premium, consistent with earlier studies (see,

e.g., Bonfim, 2009; Qu, 2008; Friewald et al., 2014; Hilscher and Wilson, 2016).

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2.2 Corporate liquidity

Medhat (2014) complements the distress debate by separating default risk in solvency and

liquidity risk. The relation between default risk and equity returns is explained by the effect of

corporate liquidity for a U.S. sample. As U.S. firms are to a greater extent holding levels of

cash reserves, the field of studies related to corporate cash holdings has increased considerably.

Ample of empirical evidence suggests that the precautionary motive is of most importance for

this appearance and for their considerable growth since the 1980s.5 At the end of 2016, firms in

the S&P 500 index (excluding financial firms) held cash and cash equivalents of $1.54 trillion,

just about 13% of total assets.6 Such sizeable cash holdings are not specific for U.S. firms;

substantial liquidity seems to be a global phenomenon (Seifert and Gonenc, 2016). The sample

in this study, consisting of 18 European countries, has average cash holdings amounting to

10.4% of total assets.

The extensive literature on cash holdings distinguishes between three main theories. The

first theory is widely known as the trade-off theory arguing the existence of an optimal level of

cash and cash equivalents. Therefore, managers need to make a trade-off between the marginal

benefits and marginal costs of holding an extra dollar of cash (Myers, 1984). Fundamental

thoughts behind this model are the transaction cost theories of Keynes (1936) and Baumol

(1952). Besides the transaction motive, cash holdings can be rationalized by the precautionary

motive (Opler et al., 1999). The rationale behind the precautionary motive argues that serious

amounts of cash are hold by firms to prevent prejudicial shocks (Bates et al., 2009). The second

theory, known as the pecking order theory, of Myers and Maljuf (1984) argues that cash

holdings are established by the investment and capital expenditure policies of firms. For

individual financing decisions managers prefer internal financing, such as retained earnings, to

outside funding and debt is preferred over new equity when outside financing is required.

Consequently, firms will hold a certain level of cash to preserve their policies (Dittmar, Mahrt-

Smith, and Servaes, 2003; Ferreira and Vilela, 2004). Third, the free cash flow theory of Jensen

(1986) is influential in the debate concerning cash flows, touching upon the agency theory.

Agency issues are naturally a determinant of cash holdings and cash valuation. Managers who

are not incentivized by shareholder maximization are inclined to act in their own interest. These

managers are likely to maintain above average levels of cash (Pinkowitz, Stulz and Williamson,

2006). Another possibility prevails upon the irrelevance of financing policies. The seminal work

5 See, e.g., Opler, Pinkowitz, Stulz, and Williamson (1999); Ferreira and Vilela (2004); Bates, Kahle, and Stulz

(2009); Acharya, Davydenko, and Strebulaev (2012); Davydenko (2013), and references therein. 6 www.yardeni.com/pub/spxratios.pdf, accessed on 14 October 2017.

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of Modigliani and Miller (1958) argues that the choice of financing has no effect on firm value

or on the availability or cost of capital. Modigliani and Miller (1958) state that cash holdings

and capital structure are irrelevant based on the perfect market argument. Although this theory

is widely accepted, financing clearly matters. Perfect markets do not exist and hence the theory

is highly hypothetical. Therefore, there are several reasons, enclosing aforementioned main

theories, why it is beneficial for firms to hold substantial amounts of cash.

More recently, empirical studies have enhanced the literature by analysing cash holdings

in light of default risk. Campbell et al. (2008) argue that firms with higher cash holdings have

more liquid assets available to meet interest payments, and thus are able to delay or even prevent

default. They empirically show that, on average, firms filing for bankruptcy do not hold

considerably less cash. Davydenko (2013) elaborates on default by distinguishing between

insolvency and illiquidity – as well as economic versus financial distress. He finds that the

market value of assets is of most importance in explaining the timing of default rather than

illiquidity. Conversely, Acharya et al. (2012) show that firms with higher cash levels are less

likely to default in the short term. The results suggest that firms holding higher cash balances

in their asset and investment portfolio should face less risk. Hence, the authors suggest that

precautionary savings are central to understanding the effects of cash holdings on credit risk.

Medhat (2014) confirms the theory that the precautionary motive is of most importance for

substantial cash holdings, touching upon the trade-off theory. When a firm is in default,

managers have to make a trade-off between holding precautionary cash to cover a coupon (not

defaulting on liquidity) or acting in the interest of shareholders and voluntarily defaulting (due

to a lack of insolvency) when not holding enough assets to outweigh future coupons.

Subsequently, a firm’s optimal policy regarding cash holdings depends on the following two

approaches: an extra dollar invested in dividends increases shareholders’ equity while an extra

dollar invested in internal cash holdings increases the probability of survival. He shows that a

hump-shaped relation exists between expected returns and financial distress risk when

precautionary cash is used to offset liquidity risk, by arguing that firms want to manage their

cash to minimize liquidity risk by maintaining some of their earnings as precautionary cash.

More particular, an insolvent but liquid firm is empirically found to have relatively large cash

holdings. By eliminating liquidity risk, a firm will not default on illiquidity because it has

enough means to cover a coupon. So, even though a firm in financial distress is more likely to

default due to a lack of solvency, investors face lower risk when liquidity risk is eliminated

resulting in lower, not higher, equity returns. Moreover, a solvent firm is expected to have

higher and upward sloping returns as its asset value is mostly based on expected earnings.

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Therefore, equity returns are relatively sensitive to earnings risk. A financially distressed firm,

or a firm close to default, is expected to have lower and downward sloping returns because the

firms’ asset value is mostly based on its level of cash holdings. This is because cash holdings

are especially valuable when a firm is in financial distress since they are able to absorb shortfalls

in operating profits (Dittmar, 2008). Hence, the equity value of a firm is relatively insensitive

to earnings risk (Medhat, 2014). Based on this reasoning, the following hypothesis is

formulated:

Hypothesis 2: Cash holdings affect the relation between financial distress and equity returns.

2.3 Investor protection

Another factor that might explain the behaviour of financially distressed equities is the quality

of investor protection. A country’s legal origin is of considerable importance concerning a

country’s strategy for protecting investors (Shleifer and Vishny, 1997). Investor rights are

founded because shareholders are above all last claimants on a firm’s assets, and therefore have

different rights than debtors. In this light of debtor rights, debt contracts need to be enforced.

Hence, legal systems are originated to protect lenders from borrowers’ default. As a result,

many countries rely on courts to enforce debt contracts, generally by way of financial distress

and default procedures (Djankov, Hart, McLiesh, and Shleifer, 2008). Countries in the

European Union follow the ‘Regulation on Insolvency Proceedings’, 2002. In 2014, these

procedures are updated by the European Commission to ‘A New Approach to Business Failure

and Insolvency’ (Hillier, Ross, Westerfield, Jaffe, and Jordan, 2016). Notwithstanding these

procedures, differences in investor protection exist across countries with different legal origin.

Among the first to elaborate on investor rights were la Porta et al. (1997), by focusing on

country specific legal origin accompanying the protection of investors and elaborating on the

prevalence of these rules in a considerable amount of countries. Naturally, investors are best

protected by common law countries that have large developed equity markets, like the United

States and the United Kingdom. Contrary, code law countries tend to rely more extensively on

their banking systems. These differences in financial systems can be explained by differences

in investor protection coming from legal origins and associated quality of enforcement (La

Porta et al., 1997). For instance, the German and French system require collective procedures

and court supervision, while the U.K. system requires neither. Further, the U.K. law provides

contracting parties with a relatively free implementation of the stipulated procedure

(Davydenko and Franks, 2008). Moreover, bankruptcy filings occur more frequently in

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countries with bankruptcy regimes favouring managers and creditors rather than investors. If a

country’s bankruptcy system prevents renegotiations, investors gain less from strategic default.

Conversely, when bankruptcy codes favour renegotiations, investors have incentives to

renegotiate debt terms upon default (Favara et al., 2012).

Hence, some scholars conjecture that equities of distressed firms might be safer because

of the possibility of deviations or renegotiations by the priority rule. Literature provides

significant evidence for cross-country variations with regard to investor protection rights

affecting the pricing of default risk. Garlappi et al. (2008) show that the appraisal of default risk

should allow for the potential recovery for investors. They empirically find that the prospects

of debt renegotiation favouring investors decreases a firm’s equity risk, and hence lowers

expected returns. More specifically, the trade-off between the risk of default and the likelihood

of bargaining gains in renegotiation results in a hump-shaped relation between equity returns

and default risk. Extending these findings, Garlappi and Yan (2011) empirically explain the

relation between default risk and conditional betas by considering shareholder recovery upon

distress. Furthermore, as highlighted by Davydenko and Franks (2008), Favara et al. (2012),

and Aretz et al. (2017), the default risk premium is expected to be lower in countries with the

legal system allowing debt renegotiations and favouring investors’ bargaining power. However,

Eisdorfer et al. (2014) identify only weak support for this theory. Additionally, Gao et al. (2015)

fail to find a relation between investor protection and the default risk premium. Nevertheless,

Eisdorfer et al. (2014) find evidence for the stock market development hypothesis. Common

law countries have considerably more valuable stock markets, and successful stock markets

mandate that investors obtain the information they need and the power to act (Davydenko and

Franks, 2008). A weaker stock market, indicated by lower investor protection, is characterized

by higher information asymmetry. Therefore, it is harder for investors to properly assess the

true probability of default. Hence, the misevaluation of distressed equity is more pronounced

in less developed equity markets.

All in all, despite the pronounced differences in investor protection among countries, the

literature finds inconclusive results when analysing the default probability. Notwithstanding,

the studies finding weak or even no evidence rely on an international sample including

emerging markets, whereas this study rests on a considerable smaller European sample. As both

the U.S. and Europe can be seen as developed markets, this thesis follows the reasoning of

Garlappi et al. (2008) to indicate that strong investor rights explain reduced equity risk

premiums. So, it can be expected that the default risk premium is lower in countries with the

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legal system allowing debt renegotiations and favouring investors’ bargaining power.

Therefore, based on the theoretical founding, the following hypothesis is developed:

Hypothesis 3: Firms in countries with high investor protection earn the lowest returns for

distressed equity.

Although some characteristics in investor rights might be an endogenous response of the legal

system to variations in the economy, for the empirical analysis it is assumed that investor rights

are predetermined, in line with Acharya et al. (2011).

3. Data and methodology

3.1 Data

To empirically test the formulated hypotheses a data sample is extracted from Thomson Reuters

DataStream. Financial as well as accounting data is acquired through DataStream for the

companies constituting the ASSET4 Europe index at an annual frequency.7 When necessary,

the accounting items in local currency are converted into euros using the Thomson Reuters

DataStream conversion factors. The accounting variables are computed from DataStream as of

the fiscal year-end of a given year t. Utilities and financial firms are excluded from the sample

(SIC codes 4900-4999 and 6000-6999). Moreover, firms that went bankrupt during the sample

period are also excluded. The final sample consists of 10,710 firm-year observations for 714

unique firms in 18 European countries over the period January 2002 to December 2016. All

financial variables are winsorized at the 1st and 99th percentile to alleviate the influence of

possible reporting errors and statistical outliers.

3.2 Sample distribution

Table 1 exhibits the sample distribution of all observations for Altman’s Z-score by year and

by country. Altman’s Z-score denotes the distance-to-default, so a higher Z indicates a healthier

firm.8 This table indicates that most observations are available for the United Kingdom,

revealing that most firms in the sample are established in the U.K. (33%). Examining Altman’s

Z-score by year, the sample size increases during the period 2002-2016 as a result of more

7 Thomson Reuters Database has only yearly accounting variables. Compustat presents accounting variables at a

quarterly frequency. Notwithstanding, less information, and hence less quarterly accounting data, is available for

non-U.S. firms in Compustat. Furthermore, the income statement variable Earnings Before Interest and Taxes is

required to define Altman’s Z-score as a measure of financial distress. In both Compustat and DataStream this

variable is only available at a yearly frequency. 8 See section 3.4 for the precise measurement of Altman’s Z-score.

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available observations. The number of firms in financial distress is moderately highest in 2009

(2.772), possibly reflecting the impact of the financial crisis. The findings of Aretz et al. (2017),

who look at the number of bankruptcy filings for an international sample, also exhibit a peak in

failures in the aftermath of the global financial crisis. In 2003, firms are also found to have high

values of financial distress (2.947). This may be due to the dot-com bubble where many

internet-based companies defaulted from 2000 to 2002. Moreover, firms are found most healthy

in 2006 and 2007 with a Z-score of 3.736 and 3.891 respectively. With reference to the summary

statistics of Z by country, it can be immediately observed that the distance-to-default is

reasonably highest for firms located in Denmark (5.223) and Switzerland (4.488) suggesting

that firms in these countries are the healthiest. A possible reason is the high stock market

capitalization and bank development in Switzerland, and high bond market development in

Denmark (Oxelheim, 2006). By contrast, the most distressed firms are located in Austria

(2.361), Portugal (2.222), and France (2.372).

Table 1 Observations by year and country for distance-to-default. This table presents the observations by year and

country based on 10,710 firm-year observations for Z as a measure of financial distress. Z refers to the distance-to-default.

Data for the sample countries is collected for 714 unique European firms over the 2002-2016 period.

Year Obs. Mean Std. Dev. Country Obs. Mean Std. Dev.

2002 487 3.374 2.965 Austria 150 2.361 1.306

2003 501 2.947 2.230 Belgium 182 3.351 3.579

2004 517 3.444 2.647 Denmark 288 5.223 3.951

2005 553 3.426 2.648 Finland 323 3.186 1.606

2006 577 3.736 2.850 France 1,008 2.372 2.058

2007 605 3.891 2.946 Germany 954 3.054 2.442

2008 625 3.456 2.808 Greece 154 2.949 3.207

2009 626 2.722 2.265 Ireland 201 3.209 2.868

2010 629 3.199 2.632 Italy 271 2.526 1.935

2011 640 3.442 3.007 Luxembourg 46 4.026 3.552

2012 650 3.223 2.737 Norway 203 2.960 2.021

2013 657 3.376 2.886 Poland 158 3.409 2.287

2014 668 3.495 3.009 Portugal 99 2.222 2.548

2015 694 3.418 3.084 Spain 356 3.291 3.480

2016 703 3.338 2.965 Sweden 709 4.054 3.710

Switzerland 628 4.488 3.207

the Netherlands 378 2.623 1.701

United Kingdom 3,024 3.559 2.673

Total 9,132 3.367 2.812 Total 9,132 3.367 2.812

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3.3 Descriptive statistics

Descriptive statistics of the full sample and subsamples are presented in table 3. The subsamples

are based on countries with high investor protection against countries where investor rights are

not well protected. Deviations between firms in high and low investor protection countries can

be compared by this disjunction.9 To measure investor rights at the country level, the updated

data and rankings from the survey by Djankov et al. (2008) is used. Table 2 depicts the

classification of investor protection for each country included in the sample. The mean (median)

in the sample is 3.22 (3.25) regarding the anti-director rights index, with higher values

indicating higher investor protection. The mean (median) in the sample is 0.41 (0.38) for the

anti-self-dealing index, with higher values indicating stronger investor protection. A country is

estimated to have high investor protection when both the anti-director rights index and the anti-

self-dealing index are classified as high. Else, countries are indicated as countries with low

investor protection.

9 Scholars illustrate that quality of investor protection significantly affects financial distress (see, e.g., Garlappi et

al., 2008; Garlappi and Yan, 2011; Favara et al., 2012; Aretz et al., 2017) and cash holdings (see, e.g., Dittmar et

al., 2003; McLean, Zhang, and Zhao, 2012; Seifert and Gonenc, 2016).

Table 2 Investor protection per country. This table reports the quality of investor protection per country based on the

anti-director-rights index and anti-self-dealing index obtained from Djankov et al. (2008). Total investor protection of a

country is defined as high when both indices are classified as high. Otherwise, countries are classified as having low investor

protection.

Anti-director rights index Anti-self-dealing index Total investor protection

Country Value Classification Value Classification Classification

Austria 2.5 Low 0.21 Low Low

Belgium 3 Low 0.54 High Low

Denmark 4 High 0.46 High High

Finland 3.5 High 0.46 High High

France 3.5 High 0.38 Low Low

Germany 3.5 High 0.28 Low Low

Greece 2 Low 0.22 Low Low

Ireland 5 High 0.79 High High

Italy 2 Low 0.42 High Low

Luxembourg 2 Low 0.28 Low Low

Norway 3.5 High 0.42 High High

Poland 2 Low 0.29 Low Low

Portugal 2.5 Low 0.44 High Low

Spain 5 Low 0.37 Low Low

Sweden 3.5 High 0.33 Low Low

Switzerland 3 Low 0.27 Low Low

The Netherlands 2.5 Low 0.20 Low Low

United Kingdom 5 High 0.95 High High

Mean 3.22 0.41

Median 3.25 0.38

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Descriptive statistics for the test variables are shown in Table 3. A two-sample t-test is

conducted to test whether there are statistically significant differences in the means for the two

subsamples, see Appendix A2. The total sample encompasses 714 firms (Panel A), the

subsamples deviated by high versus low investor protection include 460 (Panel B) and 254

(Panel C) firms respectively. Returns are higher for firms in countries where investors are well

protected with a mean of 0.088 against a mean of 0.067 in low investor protection countries.

Moreover, Z is higher for firms in countries with high investor protection (3.601), suggesting

less distressed firms in the latter. This implies that firms in countries where investors are well

protected on average face less default risk. Cash is found to be higher in countries with low

investor protection, in line with literature that states that cash holdings tend to be higher in

countries with lower investor protection (Seifert and Gonenc, 2016). Book-to-market and

market value are higher for firms in low investor protection countries.

Table 3 Descriptive statistics. This table reports the descriptive statistics of the firm-level variables used in this study.

The subsamples are based on quality of investor protection. The full sample consists of 10,710 firm-year observations for

714 unique European firms over the 2002-2016 period. Variable definitions are presented in Appendix A1. N denotes the

number of observations. Financial variables are winsorized at the 1st and 99th percentiles.

Panel A: Full sample (N=714 firms)

Variables Observations Mean Std. Dev. Median Min Max

Returns 9,427 0.076 0.415 0.127 -1.355 1.077

Z 9,132 3.367 2.812 2.635 -0.397 17.775

Cash holdings 10,074 0.125 0.124 0.088 0.000 0.999

Market value 9,214 6.317 0.680 6.293 4.618 7.987

Book-to-market 9,217 -0.749 0.794 -0.721 -3.116 1.296

Panel B: High investor protection (N=460 firms)

Variables Observations Mean Std. Dev. Median Min Max

Returns 4,145 0.088 0.431 0.137 -1.355 1.077

Z 4,039 3.601 2.740 2.925 -0.397 17.775

Cash holdings 4,418 0.115 0.121 0.074 0.000 0.957

Market value 4,057 6.163 0.663 6.107 4.618 7.987

Book-to-market 4,025 -0.814 0.840 -0.785 -3.116 1.296

Panel C: Low investor protection (N=254 firms)

Variables Observations Mean Std. Dev. Median Min Max

Returns 5,282 0.067 0.401 0.118 -1.355 1.077

Z 5,093 3.182 2.854 2.436 -0.397 17.775

Cash holdings 5,656 0.132 0.127 0.098 0.000 1.000

Market value 5,157 6.438 0.669 6.444 4.618 7.987

Book-to-market 5,192 -0.699 0.753 -0.677 -3.116 1.297

3.4 Construction of variables

The following explains the measures of the variables. The Appendix to this thesis provides an

overview of (the construction of) the variables in detail (Appendix A1). The dependent variable,

14

equity returns (RET), is a common measure in existing literature when examining financial

distress (see, e.g., Dichev, 1998; Campbell et al., 2008; Favara et al. 2013; Aretz et al. 2017).

The returns are accumulated into yearly log-returns to match the yearly available accounting

data.

To study the relation between financial distress and equity returns an accounting-based

measure of default probability is used based on the seminal contribution of Altman (1968).

Bankruptcy risk appears to be a natural measure of a firm’s financial distress (Dichev, 1998).

Garlappi et al. (2008) and Medhat (2014) rely on Moody’s KMV EDF as a measure of financial

distress. However, Hilscher and Wilson (2016) document that credit ratings are relatively

inaccurate measures of bankruptcy probability. Hence the measure for financial distress is

derived from Altman’s (1968) Z-model, which is probably the most popular model of

bankruptcy prediction. As a result, the model is extensively used in practice and empirical

research (see, e.g., Dichev, 1998; Ferguson and Shockley, 2003; Hillegeist, Keating, Cram, and

Lundstedt, 2004; Acharya et al. 2012). The independent variable Z is a measure of financial

strength (higher Z means higher distance-to-default) and is calculated as follows:

Z = 1.2(working capital/total assets) + 1.4(retained earnings/total assets) +

3.3(earnings before interest and taxes/total assets) + (1)

0.6(market value of equity/book value of total liabilities) + (sales/total assets)

Corporate liquidity (LIQ) is used to explain that precautionary cash is used to offset liquidity

risk by firms with higher probability of default. The independent variable liquidity is based

upon a widely used measure, and is estimated by a firm’s cash and short-term investments

relative to its total assets (see, e.g., Acharya et al., 2012; Seifert and Gonenc, 2016). The

independent variable investor rights is measured by both the anti-director rights and anti-self-

dealing index, based on the values of Djankov et al. (2008). Total investor protection of a

country is defined as a qualitative variable with a value of zero or one. Total investor protection

is classified as high (value of 1) when a country scores high on both the anti-director rights

index and the anti-self-dealing index. Else, countries are indicated as countries with low

investor protection (value of 0).

The regressions are adjusted for two firm-level control variables, including size (MV) and

book-equity to market-equity (BE/ME). Size is measured as the market value of equity. The

book-to-market ratio is defined as common equity divided by the market value of equity. Both

size and book-to-market equity are log-transformed (when non-negative), since these variables

15

do not have a naturally bounded distribution. These firm-level variables are included because

of their well-documented association with equity returns (Dichev, 1998).

3.5 Methodology

Fama-MacBeth (1973) regressions are performed to statistically determine whether the relation

between equity returns and financial distress is hump-shaped. Moreover, multiple regressions

are run to determine a possible effect of cash holdings and country-level investor protection on

the returns of distressed equities. Furthermore, portfolio results are presented where firms are

assigned into equally weighted decile portfolios according to their default risk. An investigation

of the means of the variables will illustrate whether the results are economically significant.

Besides, an examination could reveal the expected nonlinear relation between equity returns

and financial distress, over and beyond the quadratic regressions.

Since this study uses panel data, correlation between the error terms and independent

variables could be expected, which will cause ordinary least squares estimators to fail.

Therefore, the Hausman test is performed to examine whether there are endogenous regressors.

The Hausman test shows that fixed effects is the preferred model for the regression analysis,

since fixed effects regressions control for unobserved, but constant variation cross-sectional.

Further, the assumption of homoscedasticity is tested using a modified Wald test for group

wise heteroscedasticity. It reveals that there is heteroscedasticity in the sample, which leads to

biased standard errors and significance levels. Moreover, a Woolridge test for autocorrelation

shows that there is also autocorrelation present in the sample. To account for these two issues

robust standard errors, as in Newey-West (1987), are used to correct the error terms and obtain

more trustworthy results.

In the following, both linear and quadratic regressions are considered. The linear

specification tests whether the correlation between equity returns and financial distress is as

predicted by the model. The quadratic specification is a direct and simple test of a hump-shaped

relation by assessing whether the quadratic term has a negative coefficient (Medhat, 2014).

Specifically, the following linear and quadratic regression specifications are assessed:

L: RETi,t = α0i + 1Zi,t + 2MVi,t + 3BE/MEi,t + i,t (2)

Q: RETi,t = α0i + 1Zi,t + 2Z2

i,t + 3MVi,t + 4BE/MEi,t + i,t (3)

Here, RETi,t is firm i’s accumulated return at year t, Zi,t is a measure of the firm’s distance-to-

default at year t measured by Altman’s Z-score, MVi,t and BE/MEi,t, are control variables and

16

i,t are error terms. In the linear regression, 1 estimates the marginal effect of financial distress

on returns. In the quadratic specification, 1 estimates the effect of financial distress, while 2

estimates the shape of the relation between default risk and equity returns. Hence, the expected

hump-shaped relation is examined by testing whether 2<0.

Hereafter, multiple regressions are performed to test the effect of corporate liquidity and

investor protection on the main relation. Therefore, the interaction terms of corporate liquidity

and investor protection are added to the regression to examine whether they significantly affect

the relation between financial distress and equity returns. This results in the following:

Q: RETi,t = α0i + 1Zi,t + 2Z2i,t + 3LIQi,t + 4Zi,t*LIQi,t + 5Z2i,t*LIQi,t + 6MVi,t +

7BE/MEi,t + i,t (4)

Q: RETi,t = α0i + 1Zi,t + 2Z2i,t + 3LIQi,t + 4IPi + 5Zi,t*LIQi,t + 6Z2i,t*LIQi,t +

7 Zi,t*IPi + 8 Z2i,t*IPi + 9MVi,t + 10BE/MEi,t +i,t (5)

Here, LIQi,t, a firm i’s cash holdings at time t, and IPi, a measure of a country i’s investor

protection estimated by anti-director rights and anti-self-dealing, are added to the model.

3.6 Pearson correlation

Table 4 shows the pair-wise correlation matrix. It can be observed that almost all variables are

significant on a 1% level. The correlation between Z and equity returns is positive, which agrees

with the distress hypothesis that financially distressed firms earn lower returns. Corporate

liquidity is positively related to both Z and returns. Furthermore, cash holdings are negatively

related with market value. This is explained by the fact that larger firms hold relatively less

cash because large firms have better access to capital markets relative to small firms and are

more likely to attain funds more easily (Dittmar et al., 2003). Moreover, a significant negative

correlation between cash holdings and both measures of investor protection is found, consistent

with Dittmar et al. (2003). They find that firms in countries where shareholder rights are not

well protected hold up to twice as much cash as firms in countries with high investor protection,

justified by the importance of agency costs and capital market development. Substantial

correlation is exhibited when looking at the variables anti-director-rights and anti-self-dealing,

which is justified for the reason that both are measures for country-level investor protection. A

notably negative correlation between Z and book-to-market equity is found in line with Dichev

17

(1998). Even though considerable correlation exists between the variables, this does not

necessarily mean there is causality.

*, **, *** denote significance at the 10%, 5%, and 1 % level respectively.

4. Results

This section presents the results of the multivariate regression models, the accomplished

portfolio analysis, and finally the results of the subsamples. All specifications from the

performed regressions include controls for firm size and book-to-market-equity. The tables

report yearly slope-coefficients with standard errors that are adjusted for heteroscedasticity and

autocorrelation as in Newey-West (1987). The second analysis of this paper includes portfolios

of firms sorted by their default risk. Firms are assigned into equally weighted decile portfolios

according to their probability of default, based on the Z estimates. The portfolios contain firm-

observations in percentiles 10, 10 to 20, 20 to 40, 40 to 60, 60 to 80, 80 to 90 and 90 to 100 of

the default risk distribution. The highest default risk equities are exhibited in Q1 and the lowest

default risk equities in Q10. The analysis indicates whether firms with high default risk have

uncommonly low or high equity returns relative to the predictions of standard cross-sectional

asset pricing models, in line with Campbell et al. (2008). Besides this subsample of firms, a

distinction is made between countries with high and low investor protection respectively.

4.1 Equity returns and financial distress

First, the effect of financial distress on equity returns is examined. Table 5 shows the results

from multivariate regressions for the main relation and the interaction effects of cash holdings

and investor protection. Column (1) shows that a higher Z is associated with significantly higher

returns (0.010), contradicting the theory that financial distress risk carries a premium. Hence,

this finding is consistent with the empirical studies finding evidence for the distress anomaly

Table 4 Pair-wise correlation matrix. This table reports the Pair-wise correlation statistics of the firm-level variables used

in this study. The full sample consists of 10,710 firm-year observations for 714 unique European firms over the 2002-2016

period. Variable definitions are presented in Appendix A1. Financial variables are winsorized at the 1st and 99th percentiles.

Variables (1) (2) (3) (4) (5) (6) (7)

Returns 1.000

Z 0.006 1.000

Cash holdings 0.054*** 0.374*** 1.000

Anti-director-rights 0.027** 0.064*** -0.063*** 1.000

Anti-self-dealing 0.017* 0.046** -0.082*** 0.845*** 1.000

Market value -0.107*** -0.054*** -0.088*** -0.147*** -0.199*** 1.000

Book-to-market 0.171*** -0.458*** -0.161*** -0.109*** -0.087*** -0.146*** 1.000

18

(see, e.g., Dichev, 1998; Griffin and Lemmon, 2002; Campbell et al., 2008; Friewald et al.,

2014). After including the quadratic term (Column 2), both Z as well as the quadratic term are

significant at the 1% level. The quadratic term of Z is negative (-0.002), implying that the

quadratic specification fits the model. So, the quadratic function has a negative second derivate

significant at the 1% level, as was already expected by the model. Consistently, the visual

relation between equity returns and financial distress reveals a hump-shaped and decreasing

graph, see Appendix A3. The control variables are significant at the 1% level for all models.

Market value is significantly negative, in line with Aretz et al. (2017). Book-to-market

positively affects equity returns at a 1% significance level, consistent with Medhat (2014). In

the six specifications, the adjusted R-squared exhibits that between 13.9% and 16.3% of the

dependent variable, equity returns, can be explained by the independent and control variables.

Table 6 presents portfolio results for the researched relations. An examination of the

findings for the Z-sorted portfolios illustrates that firms with the highest and lowest default risk

(Q1 and Q10) earn the lowest returns. Even a negative average return for the most distressed

firms is exhibited (-0.021), in conformity with Campbell et al. (2008). Hence, this result

indicates that the return premium is economically large for avoiding financially distressed

firms. The portfolios provide economic evidence for the non-monotonic relation between equity

returns and financial distress, consistent with Garlappi and Yan (2011). Moreover, there is little

evidence of a size effect. Market value is lowest for the healthiest and most distressed firms. In

regard to book-to-market equity, a moderately positive relation with financial distress is

observed along with a difference of 1.134 between Q1 and Q10. So, the relation between returns

and financial distress is more pronounced for firms with higher book-to-market equity. The

most distressed firms (Q1) exhibit the highest book-to-market equity, suggesting that investors

might undervalue these firms. Conversely, this finding is able to confirm the conjecture that

investors do not demand a higher premium for holding financially distressed equity.

All in all, the positive coefficient for Z in the first model (column 1) suggests that default

matters, consistent with empirical evidence presented by for instance Garlappi et al. (2008).

The quadratic coefficient further provides statistical support for the hump-shaped relation

between financial distress and equity returns, in compliance with Medhat (2014). These results

represent a conditional dependence of equity returns on financial distress. When analysing the

portfolios, returns are humped and decreasing when financial distress increases. The finding

that equities with high default risk tend to deliver anomalously low returns is consistent with

the results of Dichev (1998), who uses the same accounting-based measure of bankruptcy,

Griffin and Lemmon (2002) using Ohlson’s (1980) model, Garlappi et al. (2008), who obtain

19

default risk measures from Moody’s KMV, and Campbell et al. (2008) who use the reduced-

form econometric model to predict corporate bankruptcies and failures.

Table 5 Regression models. This table reports coefficient estimates of the multivariate regressions with firm-level returns

as dependent variable. The full sample consists of 10,710 firm-year observations for 714 unique European firms over the

2002-2016 period. Variable definitions are presented in Appendix A1. Standard errors are in parentheses, and are adjusted

for heteroscedasticity and autocorrelation as in Newey-West (1987). For all models, the standard errors are adjusted for 691

clusters (company). Financial variables are winsorized at the 1st and 99th percentiles.

Independent variables (1) (2) (3) (4) (5) (6)

Intercept 1.616***

(0.132)

1.580***

(0.132)

1.220***

(0.133)

1.122***

(0.352)

1.181***

(0.276)

1.230***

(0.134)

Z 0.010**

(0.004)

0.044***

(0.009)

0.082***

(0.011)

0.106***

(0.033)

0.061***

(0.017)

0.072***

(0.012)

Z2 -0.002***

(0.000)

-0.004***

(0.001)

-0.006***

(0.002)

-0.003***

(0.001)

-0.004***

(0.001)

Cash holdings 1.248***

(0.119)

1.257***

(0.120)

1.231***

(0.119)

1.244***

(0.119)

Cash holdings*Z -0.192***

(0.027)

-0.200***

(0.028)

-0.183***

(0.027)

-0.190***

(0.027)

Cash holdings*Z2 0.008***

(0.002)

0.009***

(0.002)

0.008***

(0.002)

0.008***

(0.002)

Anti-director-rights 0.033

(0.107)

Anti-director-rights*Z -0.005

(0.007)

Anti-director-rights*Z2 0.000

(0.000)

Anti-self-dealing 0.271

(0.889)

Anti-self-dealing*Z 0.034

(0.022)

Anti-self-dealing*Z2 -0.002

(0.001)

Investor protection

(dummy)

0.318***

(0.067)

Investor protection*Z 0.022

(0.014)

Investor protection*Z2 -0.000

(0.001)

Market value (ln) -0.247***

(0.018)

-0.251***

(0.018)

-0.235***

(0.018)

-0.236***

(0.018)

-0.235***

(0.018)

-0.234***

(0.018)

Book-to-market (ln) 0.268***

(0.013)

0.277***

(0.013)

0.290***

(0.013)

0.290***

(0.013)

0.290***

(0.013)

0.291***

(0.013)

Adjusted R2 0.139 0.144 0.163 0.163 0.163 0.163

Observations 8,761 8,761 8,761 8,761 8,761 8,761 *, ** and *** denote significance at the 10%, 5% and 1% level (2 tailed), respectively.

20

So, this study finds in conformity with Eisdorfer et al. (2014) that the distress anomaly is

not U.S. specific. According to both multivariate regressions and portfolio analysis, hypothesis

1, stating that the relation between returns and financial distress is hump-shaped and decreasing,

is statistically and economically supported. Moreover, the results show that financial distress is

significantly priced in the presence of the commonly used firm control variables, and therefore

Z is priced over and above some of its constituent variables. This is because Z is a

transformation of firm characteristics, and hence the inclusion of these controls in the regression

model cannot absorb the explanatory power of the distance-to-default measure.

4.2 Cash holdings and financial distress

The moderating effect of cash holdings on the revisited main relation is examined in this

section. The second hypothesis, whether corporate liquidity affects the relation between equity

returns and default risk is tested in column (3) by including cash holdings and the interaction

terms between financial distress and cash holdings. Higher cash holdings are associated with

significantly higher equity returns (1.248). The presence of the significant interaction terms

indicates that the effect of financial distress on equity returns is different for different values of

cash holdings. Subsequently, the unique effect of financial distress to equity returns not only

depends on the significant coefficients of Z and Z2, but depends on the coefficient of corporate

liquidity and the interaction terms with cash holdings as well. Moreover, portfolio results for

the effect of cash holdings can reveal whether cash holdings help justifying the distress

anomaly. An examination suggests that cash holdings are highest for most healthy firms (Q10).

More financially distressed firms exhibit lower cash holdings, however the most distressed

firms (Q1) show an increase from 0.092 (Q2) to 0.103 (Q1); suggesting that cash holdings

rationalize the distress anomaly.

Thus, cash holdings are found to significantly affect returns, and moreover have a

moderating effect on the relation of financial distress and returns. This finding is emphasized

by the economically significant results of the portfolios, showing that cash holdings increase

for the most distressed equities. The empirical findings are consistent with the model’s

conjecture regarding the influence of cash holdings in determining the relation between equity

returns and default risk. In particular, returns decrease and cash levels increase for firms with a

higher probability of default. Consequently, hypothesis 2, which suggests that cash holdings

affect the relation between equity returns and default risk, cannot be rejected.

21

The results are in line with Medhat (2014), who finds the same empirical evidence for U.S.

firms. So, no deviations in corporate liquidity policies between U.S. and European firms are

found in this study. The findings are consistent with the prevailing insights suggesting that firms

with high cash holdings should have a lower probability of default (Bolton, Chen, and Wang,

2011 and Acharya et al., 2012). The conclusive results convey that cash holdings allow

managers to offset liquidity risk, thereby reducing risk exposure by investors. Particularly, firms

holding higher cash balances in their asset and investment portfolio should face less risk. The

findings suggest that the level of cash holdings constitute an important extent of distressed

equity. Therefore, managers need to consider default risk in corporate liquidity decisions, and

vice versa. More specifically, managers need to make a trade-off between paying out dividends

or holding precautionary cash to outweigh future coupons.

To elucidate the distress anomaly with a firm-level variable that is more well-known to

affect financial distress, leverage is enclosed in the portfolio analysis. The explicit inclusion of

financial leverage allows this research to show how leverage amplifies the default risk effect.

The evidence indicates that financial distress is positively related with leverage, as is shown by

the linear decrease in leverage when Z increases. Thus, the negative relation between equity

returns and financial distress is more pronounced for firms with higher leverage. As presented

by Griffin and Lemmon (2002), firms in the high financial distress quantile exhibit

characteristics traditionally associated with bankruptcy probability, such as high leverage.

Moreover, market leverage increases significantly as probability of default increases. Although

Table 6 Portfolio results. This table reports portfolio results where firms are assigned into equally weighted decile

portfolios according to their probability of default. The highest default risk equities are exhibited in Q1 and the lowest

default risk equities in Q10 respectively. The full sample consists of 10,710 firm-year observations for 714 unique European

firms over the 2002-2016 period. Variable definitions are presented in Appendix A1. Standard errors are in parentheses.

Financial variables are winsorized at the 1st and 99th percentiles.

Deciles

P10-P1 1 2 3-4 5-6 7-8 9 10

Returns -0.021

(0.019)

0.067

(0.015)

0.098

(0.009)

0.099

(0.009)

0.109

(0.009)

0.084

(0.013)

0.062

(0.012)

0.083

Cash holdings 0.103

(0.004)

0.092

(0.002)

0.097

(0.002)

0.102

(0.002)

0.115

(0.003)

0.156

(0.004)

0.223

(0.005)

0.120

Market value (ln) 6.301

(0.026)

6.480

(0.022)

6.358

(0.015)

6.362

(0.016)

6.277

(0.016)

6.220

(0.022)

6.254

(0.021)

-0.047

Book-to-market (ln) -0.207

(0.031)

-0.337

(0.026)

-0.495

(0.016)

-0.707

(0.015)

-0.883

(0.015)

-1.174

(0.021)

-1.341

(0.025)

-1.134

Leverage 0.689

(0.006)

0.628

(0.005)

0.531

(0.003)

0.441

(0.003)

0.341

(0.003)

0.213

(0.003)

0.175

(0.006)

-0.514

Observations 812 879 1,764 1,770 1,763 891 1,138

22

higher market leverage will not automatically result in default, a financially distressed firm will

more likely be forced by its debtholders to anticipate on its declined solvency (Medhat, 2014).

4.3 Investor protection and financial distress

Investor protection is added to the main regression model to test the effect of this cross-country

variable on the relation between equity returns and financial distress. In column (4), anti-

director rights as a measure of investor protection is included (Table 5). The signs and

significance levels of the distance-to-default and cash holding variables remain the same.

Nevertheless, the influence of anti-director rights on the regression appears to be insignificant.

Next, the anti-self-dealing index is incorporated, resulting in the same findings as when

examining anti-director rights. When adding the dummy variable for investor protection, the

non-interacted term gives a significantly positive coefficient of 0.318. So, it can be said at the

1% significance level that firms located in high investor protection countries earn higher

returns. Notwithstanding, nothing can be said about the interaction terms of financial distress

and investor protection as their coefficients are insignificant.

In table 7 the results of a more formal test on the impact of investor protection are presented

by re-running the main estimations for each subsample. Table 7 depicts regression models for

the split sample of relatively high and low investor protection. The subsets of the data are used

to examine how differences in investor protection affect the relation of returns and financial

distress. The following countries are found to have high investor protection; Denmark, Finland,

Ireland, Norway and the United Kingdom. The regression models for the low investor

protection sample consists of subsequent countries; Austria, Belgium, France, Germany,

Greece, Italy, Luxembourg, Poland, Portugal, Spain, Sweden, Switzerland, and the

Netherlands. Column 7 and 8 exhibit that the coefficient of Z is higher for firms in high investor

protection countries, indicating that in those countries default risk decreases equity returns

more. However, the quadratic term is qualitatively the same with coefficients of -0.003 and

-0.002 and significance levels of 1% for both subsamples (column 7 and 9). When adding cash

holdings and the interaction terms of Z and cash holdings, the quadratic term is -0.005 for firms

in high investor protection countries and -0.003 for firms in low investor protection countries

respectively. Hence, equity returns decrease more severely when default risk increases for firms

in high investor protection countries relative to firms in countries where investors are less

protected.

23

Moreover, a portfolio analysis for the subsamples can reveal whether investor protection

helps justifying the distress anomaly. The results are shown in table 8. An examination suggests

that, in line with the performed regressions, returns are moderately highest for firms in countries

with high investor protection. However, for Q5-6 returns are found to be lower compared to

firm-year observations in the low investor protection countries. For the portfolios with the most

distressed firms, equity returns decrease substantially more for the high investor protection

sample (0.092 to -0.019, against 0.056 to -0.022 for firms in the low investor protection sample).

This finding suggests that investors in countries with a high quality of investor protection

require a lower default risk premium for distressed equities. The results are in compliance with

existing literature asserting that the default risk premium is lower in countries in which investors

have higher bargaining power and the judicial process favours debt renegotiation.

Table 7 Regression models for subsamples based on investor protection. This table reports coefficient estimates of the

subsamples for the run multivariate regressions with firm-level returns as dependent variable. The full sample consists of

10,710 firm-year observations for 714 unique European firms over the 2002-2016 period. Variable definitions are presented

in Appendix A1. Standard errors are in parentheses, and are adjusted for heteroscedasticity and autocorrelation as in Newey-

West (1987). Standard errors are adjusted for 309 clusters (company) in the high investor protection sample and for 382

clusters (company) in the low investor protection sample. Financial variables are winsorized at the 1st and 99th percentiles.

High investor protection Low investor protection

Independent variables (7) (8) (9) (10)

Constant 1.738***

(0.178)

1.448***

(0.181)

1.177***

(0.165)

0.828***

(0.167)

Z 0.059***

(0.011)

0.102***

(0.017)

0.031***

(0.011)

0.069***

(0.012)

Z2 -0.003***

(0.001)

-0.005***

(0.001)

-0.002***

(0.001)

-0.003***

(0.001)

Cash holdings 1.165***

(0.242)

1.377***

(0.139)

Cash holdings*Z -0.232***

(0.070)

-0.185***

(0.027)

Cash holdings*Z2 0.012***

(0.004)

0.007***

(0.002)

Market value (ln) -0.318***

(0.027)

-0.300***

(0.027)

-0.186***

(0.024)

-0.176***

(0.024)

Book-to-market (ln) 0.251***

(0.019)

0.262***

(0.019)

0.307***

(0.017)

0.320***

(0.017)

Adjusted R2 0.137 0.147 0.154 0.180

Observations 3,858 3,858 4,903 4,903

*, ** and *** denote significance at the 10%, 5% and 1% level (2 tailed), respectively.

24

All in all, the results in table 7 and 8 exhibit support for the investor’s bargaining power

hypothesis of Garlappi et al. (2008) and Favara et al. (2012) at the country level. Higher default

risk is found to result in more severe decreasing equity returns for firms in countries with high

investor protection. Besides the explanation of investors’ bargaining power, another

explanation exists within literature for a difference between returns in low and high investor

protection countries. Higher investor protection can be interpreted as higher stock market

development. It is well-documented in literature that a more developed stock market allows

investors to better price equities. So, the countries in the high investor protection sample,

common- and Scandinavian law countries, have more pronounced valuable stock markets.10

Dichev (1998) was among the first to argue that the negative relation between equity returns

and financial distress could be signified by market interpretations of available financial distress

information. Eisdorfer et al. (2014) provide evidence that the distress anomaly is more

pronounced in countries with higher information transparency. This illustrates that several

aspects of shareholders’ risk are of considerable importance in shaping distressed returns. The

lower returns in low protection countries could therefore also be explained by misevaluation of

investors; investors might not be able to accurately interpret the information about higher cash

holdings of distressed firms, and hence require higher returns.

Moreover, the regressions in table 7 including cash measures exhibit that the coefficient

for cash is relatively high for the low protection countries (column 10) compared to the high

protection sample (column 8). Analysing cash holdings for the portfolio results, they are also

found to be substantially higher for firms in low investor protection countries. This finding is

in line with existing literature (see, e.g., La Porta et al., 1997; Dittmar et al., 2003; Ferreira and

Vilela, 2004; Seifert and Gonenc, 2016), confirming the influence of managerial discretion

agency costs in clarifying levels of cash holdings (Ferreira and Vilela, 2004). Further, La Porta

et al. (2000) explain this appearance due to the fact that firms in common law (high investor

protection) countries pay out higher dividends, resulting in lower cash holdings.

The empirical evidence for cross-country differences in investor protection have several

implications for managers. Managers in high investor protection countries are less dependent

on cash flows because they can easily raise external capital (McLean, Zhang, and Zhao, 2012).

Hence, the optimal policy concerning cash holdings and dividend pay-outs for a firm in default

depends on country-level investor protection as this, among other things, assesses capital

availability. Moreover, business information might be interpreted differently by investors in

10 The indices of Djankov and Franks (2008) are statistically significant and economically strong predictors of

different qualities of stock market development among countries.

25

low protection countries relative to high protection countries. So, managers need to contemplate

the quality of the stock market in which their firm operates when making policy decisions. The

results suggest that the strength of investor rights establish an important element of these

managerial thoughts in corporate liquidity and solvency decisions. Consequently, the quality of

investor protection has effect on a firms’ investment decisions.

4.5 Robustness tests

Multiple robustness tests are performed on the main model. The results of repeated analyses are

presented in Appendices A4-9. First, Altman’s Z-score as a measure of financial distress is

replaced by an alternative accounting-based measure for default risk to see if the main findings

are robust to different definitions of default risk. The Z-model is replaced by model 1 in

Table 8 Portfolio results for the subsamples based on quality of investor protection. This table presents portfolio

results where firms are assigned into equally weighted decile portfolios according to their probability of default. The highest

default risk equities are exhibited in Q1 and the lowest default risk equities in Q10 respectively. The full sample consists

of 10,710 firm-year observations for 714 unique European firms over the 2002-2016 period. Variable definitions are

presented in Appendix A1. Standard errors are in parentheses. Financial variables are winsorized at the 1st and 99th

percentiles.

Panel A: High investor protection (N=460 firms)

Deciles

1 2 3-4 5-6 7-8 9 10 P10-P1

Returns -0.019

(0.040)

0.092

(0.028)

0.102

(0.015)

0.098

(0.015)

0.123

(0.012)

0.092

(0.018)

0.084

(0.018)

0.103

Cash holdings 0.083

(0.008)

0.076

(0.004)

0.091

(0.003)

0.094

(0.003)

0.101

(0.003)

0.136

(0.006)

0.204

(0.007)

0.121

Market value (ln) 6.023

(0.045)

6.249

(0.040)

6.239

(0.024)

6.241

(0.025)

6.157

(0.021)

6.077

(0.028)

6.069

(0.029)

0.046

Book-to-market

(ln)

-0.030

(0.057)

-0.398

(0.059)

-0.545

(0.029)

-0.753

(0.025)

-0.845

(0.022)

-1.200

(0.031)

-1.441

(0.035)

-1.411

Leverage 0.652

(0.012)

0.595

(0.008)

0.524

(0.004)

0.440

(0.005)

0.349

(0.004)

0.214

(0.004)

0.166

(0.008)

-0.486

Observations 255 264 726 781 933 469 517

Panel B: Low investor protection (N=254 firms)

Deciles

1 2 3-4 5-6 7-8 9 10 P10-P1

Returns -0.022

(0.021)

0.056

(0.017)

0.094

(0.012)

0.099

(0.012)

0.093

(0.013)

0.075

(0.019)

0.045

(0.015)

0.067

Cash holdings 0.112

(0.005)

0.099

(0.003)

0.101

(0.003)

0.109

(0.003)

0.130

(0.004)

0.177

(0.006)

0.238

(0.008)

0.126

Market value (ln) 6.428

(0.031)

6.579

(0.026)

6.441

(0.019)

6.457

(0.020)

6.411

(0.024)

6.378

(0.034)

6.407

(0.028)

-0.021

Book-to-market

(ln)

-0.288

(0.036)

-0.310

(0.027)

-0.460

(0.019)

-0.671

(0.017)

-0.925

(0.021)

-1.145

(0.028)

-1.258

(0.035)

-0.970

Leverage 0.706

(0.007)

0.642

(0.005)

0.536

(0.004)

0.442

(0.004)

0.333

(0.004)

0.212

(0.005)

0.182

(0.008)

-0.524

Observations 557 615 1,038 989 830 422 621

26

Ohlson11 (1980) that is widely used in other research (see, e.g., Dichev, 1998; Griffin and

Lemmon, 2002; Hillegeist et al., 2004). Altman’s Z-score and Ohlson’s O-score are likely to

supplement each other well for robustness analysis since the models differ regarding the time

period in which they are derived, the samples and independent variables employed, and the

predictive methodologies; multiple discriminant analysis in Altman’s model against multiple

choice analysis, more particular, conditional logit in Ohlson’s model (Dichev, 1998). A first

glance on the visual relation between Ohlson’s O-score and equity returns exhibits a hump-

shaped and decreasing graph, consistent with Altman’s Z-score (Appendix A3). Examination

of the regression results in Appendix A5 demonstrates a reliable negative association between

financial distress and returns. In line with the findings of the linear model based on Z, no

evidence is provided that financial distress risk carries a premium. Furthermore, the model

testing the first hypothesis implies that the relation between returns and insolvency is hump-

shaped and decreasing as the coefficient of the quadratic term is significantly negative.

Consistent with the models based on Altman’s Z-score, the coefficients of the cash variables

are positive and significant at the 1% level. Moreover, little significant evidence for the

interaction terms of O with cash holdings is found. The coefficient of the dummy variable for

investor protection is significant and positive in line with the Z-model. Appendix A6 shows the

results for the portfolio analysis. These results indicate a rather hump-shaped relation for returns

and default risk when neglecting the most solvent firms (Q1). The most distressed firms earn

even negative returns (-0.100). Moreover, cash holdings are found to be highest for the

healthiest and most distressed firms. This indicates, consistent with the Z-model, that cash

holdings help explain the distress puzzle. All in all, it follows that the main findings are robust

and do not change when an alternative definition of financial distress is employed.

Second, the variable cash holdings estimating corporate liquidity is replaced by other

measures of corporate liquidity. In line with Medhat (2014), corporate liquidity is measured by

liquidity ratios obtained from the balance sheet including current ratio, quick ratio and working

11 Ohlson’s O-score is an accounting-based measure of financial distress (higher O means higher probability of

default), and is calculated as follows:

O = 1.32 – 0.407 log(total assets/GNP price-level index) + 6.03(total liabilities/total assets) – 1.43(working

capital/total assets) + 0.076(current liabilities/current assets) – 1.72(1 if total liabilities > total assets, otherwise

0) – 2.37(net income/total assets) – 1.83(funds from other operations/total liabilities) + 0.285(1 if net loss for

last two years, otherwise 0) – 0.521(net incomet – net incomet-1 )/(|net incomet| + |net incomet- 1|) (6)

27

capital ratio respectively.12 The visual relation between the returns and the liquidity ratios

is humped and decreasing, in line with Medhat (2014), see Appendix A4. With reference to the

regressions (Appendix A7), the main findings of the specifications remain the same regarding

signs and significance when employing alternative definitions of corporate liquidity. The

coefficients of the new liquidity measures are for all models (including interaction terms) lower

than for the original liquidity measure, cash holdings. This may be due to the fact that cash

ratios use the market value of assets in the denominator. The coefficients of the investor

protection dummy and its interaction terms remain substantially the same. Based on this

evidence, the models remain unaffected by replacing the measure of corporate liquidity.

Moreover, the model is controlled for two country-level macroeconomic variables that

potentially drive the relation between equity returns and financial distress.13 The results

convincingly show that signs and significance levels remain unaffected by the addition of

country-level controls (Appendix A8). The adjusted R-squared rises considerably. So, more of

the variation in equity returns is explained by the model controlling for country-level variables.

Nevertheless, the firm characteristic variables remain unaffected concluding that the hump-

shaped relation between equity returns and financial distress and the interaction effects of cash

holdings and investor protection are robust and not driven by a specific country effect.

Finally, the model is controlled for the United Kingdom as 33% of the full sample is

established in the U.K. The results compellingly show that signs and significance levels of the

independent variables are qualitatively the same for the U.K. subsample and the non-U.K.

subsample (Appendix A9). With reference to the non-U.K. sample, the dummy for investor

protection has a coefficient of 0.370 with a significance level of 1%. Hence, the country-level

variable comparably affects equity returns for the non-U.K. sample relative to the full sample.

An economic deviation between the subsamples can be observed for the control variables;

market value is found to affect equity returns more and book-to-market equity is found to affect

equity returns less for the U.K. subsample. In conclusion, the model remains largely unaffected

and is not driven by U.K. specific appearances.

12 The current ratio (CR) is estimated as current assets divided by current liabilities. Generally, a current ratio

lower than 1 indicates liquidity distress as the firm’s liquid assets are insufficient to meet its short-term liabilities.

The quick ratio (QR) is determined by dividing current assets minus inventories by current liabilities. Like the

current ratio, a value below 1 is revealing liquidity distress. Lastly, the working capital ratio (WCR) which is

current assets minus current liabilities divided by total assets, and measures the net liquid assets of a firm relative

to total assets. A negative working capital ratio implies liquidity distress. 13 As the majority of defaults are more likely to occur in recessions (Campbell, Hilscher, and Szilagyi, 2011), the

value of financial distress depends on risk premiums (Almeida and Philippon, 2007). Brandt and Wang (2003)

present support that the risk premium relies to a certain extent on shocks to inflation and to aggregate consumption.

Moreover, GDP is of considerable importance in many financing decisions (Dittmar et al., 2003). Therefore, the

European countries in the sample are distinguished by real GDP growth (GDP) and inflation (INF).

28

5. Conclusion

Motivated by the lack of consensus on the pricing of distressed equities, this research examines

the influence of corporate liquidity and investor protection on the relation of financial distress

and equity returns. An accounting-based definition of default risk is used to examine whether

financial distress is a risk priced in equity returns. The hypotheses are tested using both

multivariate regressions and portfolio analysis. This study has statistically as well as

economically found evidence for the hump-shaped relation between returns and financial

distress for the European sample over the period 2002-2016. Moreover, novel evidence is

provided for the rationalizing effects of corporate liquidity and investor protection on the

distress anomaly. The results are robust to alternative definitions of default risk, corporate

liquidity and country effects.

As surprisingly few studies have researched the distress anomaly for non-U.S. data, this

research contributes to the literature by conducting a cross-country analysis of European firms.

Within this analysis, three important insights are derived. First, by revisiting the relation

between equity returns and financial distress, this study shows that higher default risk not

necessarily results in a default risk premium. In fact, distressed equities earn lower returns.

Consequently, the distress anomaly is found to be not specific for U.S. firms. Second, the results

suggest that reducing liquidity risk, a feature that is largely ignored in previous asset pricing

literature, can considerably alter the riskiness of equities when default risk rises. Hence,

managers need to make a trade-off between holding precautionary cash to offset liquidity risk

or paying out dividends. Third, the effects are found to vary for firms in different countries with

a difference in quality of investor protection. Firms in countries where investors are well

protected have lower cash holdings and less distressed equities, but equity returns are found to

decrease more severe when default probability increases. The findings illustrate the importance

of investor protection in explaining the distress anomaly. The ability of investors to accurately

price equity returns and to renegotiate upon distress depends on the quality of the stock market

and a country’s legal origin. Hence, managers need to consider the stock market and legal

environment they are operating in when making corporate policies. All in all, this study

contributes to recent discussions by exhibiting that firms respond to macro-level institutional

factors in their solvency and liquidity decisions. Thus, corporate liquidity and investor rights

are of considerable importance for managers of distressed firms when making both financial

and real corporate decisions.

29

This paper acknowledges two main limitations. First, there are several other measures to

estimate default risk, which may generate different results. Both default risk measures

employed in this study depend on accounting data. Future research can possibly use credit

ratings to assess default risk. Moody’s KMV expected default frequency (EDF) is commonly

used in literature and incorporates not only a firm’s financial and economic characteristics, but

also soft information and industry conditions. Due to unavailable access to the EDF database,

this particular research was not able to control for credit ratings. Another limitation is the

measurement of the dependent variable, equity returns. Since accounting data is only yearly

available in Thomson Reuters Database, the returns are accumulated to yearly-stock returns to

match the accounting data. However, it is likely that matching daily or monthly stock returns

to quarterly accounting data will obtain more accurate and trustworthy results. Moreover,

conditional betas could be used as dependent variable to assess the effect of default probability

on equity risk. This is also done by some other studies. In regard to future research, an important

direction is to examine other drivers of the distress anomaly in the European market. For

instance, whether lottery-like payoffs of distressed equities (Conrad, Kapadia, and Xing, 2014)

can help rationalize the distress anomaly for equities in non-U.S. markets.

30

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Appendices

Appendix A.1. Definitions of variables

Variable Definition

Dependent variable

Returns The natural logarithm of yearly accumulated equity returns in local currency.

Independent

variables

Z-score

(Altman, 1968)

Z = 1.2(working capital/total assets) + 1.4(retained earnings/total assets) + 3.3(earnings before

interest and taxes/total assets) + 0.6(market value of equity/book value of total liabilities) +

(sales/total assets)

Working capital is defined as the difference between current assets and current liabilities. Retained

earnings represent the accumulated after-tax earnings of a firm which have not been distributed as

dividends to shareholders or allocated to a reserve account. The retained earnings to total assets ratio

implicitly considers the age of a firm, as bankruptcy incidences is much higher in a firm’s earlier

years. All measures are in local currency.

O-score

(Ohlson, 1980)

O = 1.32 – 0.407 log(total assets/GNP price-level index) + 6.03(total liabilities/total assets) –

1.43(working capital/total assets) + 0.076(current liabilities/current assets) – 1.72(1 if total liabilities

> total assets, otherwise 0) – 2.37(net income/total assets) – 1.83(funds from other operations/total

liabilities) + 0.285(1 if net loss for last two years, otherwise 0) – 0.521(net incomet – net incomet-1

)/(|net incomet| + |net incomet-1|)

The GNP price-level index is the ratio of purchasing power parity to market exchange rates. This

indicator is measured as an index (OECD, 2017). Net income represents a firm’s net income used to

calculate its earnings per share. Funds from other operations is the net change in working capital

excluding the in(de)crease in short term borrowings and in(de)crease in cash and cash equivalents.

All measures are in local currency.

Cash holdings Ratio of cash and short-term investments to total assets (in local currency).

Alternative measures

of liquidity: CR, QR

and WCR

The current ratio (CR) is the logarithm of current assets to current liabilities. The quick ratio (QR) is

determined by the logarithm of current assets minus inventories to current liabilities. The working

capital ratio (WCR) is current assets minus current liabilities divided by total assets. All measures in

local currency.

Investor protection

(Djankov et al., 2008)

Based on the anti-director rights index and the anti-self-dealing index from the survey by Djankov et

al. (2008).

The original index from La Porta et al. (1998) has been subject to criticism, in a response Djankov et

al. (2008) have established a revised anti-director-rights index based on similar variables for 72

countries. “The index is formed by summing: (1) vote by mail; (2) shares not blocked or deposited;

(3) cumulative voting; (4) oppressed minority; (5) pre-emptive rights; and (6) capital.” The anti-self-

dealing index is defined as “the average of ex-ante and ex-post private control of self-dealing.” Both

the anti-director rights index and anti-self-dealing index are static, as they exhibit very little

differences over time. The dummy variable to generate subsamples is set to 1 when a country scores

above the median relative to the sample on both anti-director rights and anti-self-dealing. The other

countries are assigned to the group of weak investor protection.

Control variables

Market value The natural logarithm of market value of equity in euros.

Book-to-market The natural logarithm of the book value of equity to market value of equity in local currency.

Leverage Ratio of total liabilities to market value of equity and total liabilities in local currency.

Real GDP growth This indicator is measured in growth rates compared to previous year (OECD, 2017).

Inflation This indicator is measured by the consumer price index (OECD, 2017).

35

Appendix A.2. Two-sample t-tests. This table reports the means and t-statistics of the two-sample t-tests for all firm-

variables. The subsamples are based on quality of investor protection. The full sample consists of 10,710 firm-year

observations for 714 unique European firms over the 2002-2016 period. Variable definitions are presented in Appendix A1.

Financial variables are winsorized at the 1st and 99th percentiles. N denotes the number of observations.

Variable Full Sample

(N=714)

High investor protection

(N=460)

Low investor protection

(N=254)

t-statistic

Returns 0.076 0.088 0.067 -2.388**

Z 3.367 3.601 3.182 -7.081***

Cash holdings 0.125 0.115 0.132 7.004***

Market value 6.317 6.163 6.438 19.682***

Book-to-market -0.749 -0.814 -0.699 6.901*** *, ** and *** denote significance at the 10%, 5% and 1% level (2 tailed), respectively.

Appendix A.3. Equity returns on financial distress and liquidity measures. This figure shows the quadratic relation of

returns and financial distress and returns and cash holdings. Left panel: returns and Z-score (the x-axis for the graph is reversed

as a lower Z indicates financial distress). Middle panel: returns and O-score. Right panel: returns and corporate liquidity

measured by cash holdings. In all panels, the range of the horizontal-and vertical axis is the observed range for the variables.

The full sample consists of 10,710 firm-year observations for 714 unique European firms over the 2002-2016 period. Variable

definitions are presented in Appendix A1. Financial variables are winsorized at the 1st and 99th percentiles.

Appendix A.4. Equity returns and liquidity measures. This figure shows the quadratic relation of returns and corporate

liquidity. Left panel: returns and current ratio (ln). Middle panel: returns and quick ratio (ln). Right panel: returns and working

capital ratio. In all panels, the x-axis is reversed, and the range of the horizontal-and vertical axis is the observed range for the

variables. The full sample consists of 10,710 firm-year observations for 714 unique European firms over the 2002-2016 period.

Variable definitions are presented in Appendix A1. Financial variables are winsorized at the 1st and 99th percentiles.

36

Appendix A.5. Regression models. This table reports coefficient estimates of the multivariate regressions with firm-level

returns as dependent variable. The full sample consists of 10,710 firm-year observations for 714 unique European firms over

the 2002-2016 period. Variable definitions are presented in Appendix A1. Standard errors are in parentheses, and are

adjusted for heteroscedasticity and autocorrelation as in Newey-West (1987). For all models, the standard errors are adjusted

for 690 clusters (company). Financial variables are winsorized at the 1st and 99th percentiles.

Independent variables (1) (2) (3) (4) (5) (6)

Intercept 1.903***

(0.132)

1.916***

(0.131)

1.705***

(0.136)

2.283***

(0.353)

2.111***

(0.277)

1.754***

(0.135)

O -0.067***

(0.006)

-0.052***

(0.007)

-0.050***

(0.010)

-0.053*

(0.030)

-0.066***

(0.015)

-0.050***

(0.012)

O2 -0.008***

(0.002)

-0.011***

(0.003)

-0.044***

(0.008)

-0.024***

(0.004)

-0.018***

(0.003)

Cash holdings 0.434***

(0.087)

0.412***

(0.086)

0.409***

(0.086)

0.427***

(0.086)

Cash holdings*O 0.049

(0.034)

0.052

(0.034)

0.058*

(0.035)

0.059*

(0.034)

Cash holdings*O 2 0.018*

(0.01)

0.018*

(0.010)

0.019*

(0.010)

0.016*

(0.010)

Anti-director-rights -0.192*

(0.108)

Anti-director-rights*O 0.001

(0.007)

Anti-director-rights*O 2 0.008***

(0.002)

Anti-self-dealing -1.533*

(0.864)

Anti-self-dealing*O 0.028

(0.021)

Anti-self-dealing*O 2 0.022***

(0.006)

Investor protection

(dummy)

0.120*

(0.063)

Investor protection*O -0.000

(0.013)

Investor protection*O2 0.015***

(0.003)

Market value (ln) -0.262***

(0.018)

-0.262***

(0.017)

-0.248***

(0.018)

-0.252***

(0.018)

-0.250***

(0.018)

-0.250***

(0.018)

Book-to-market (ln) 0.244***

(0.012)

0.240***

(0.015)

0.243***

(0.012)

0.244***

(0.011)

0.245***

(0.012)

0.244***

(0.012)

Adjusted R2 0.162 0.166 0.175 0.180 0.179 0.179

Observations 8,914 8,914 8,914 8,914 8,914 8,914 *, ** and *** denote significance at the 10%, 5% and 1% level (2 tailed), respectively.

37

Appendix A.6. Portfolio results. This table reports portfolio results where firms are assigned into equally weighted decile

portfolios according to their probability of default. The lowest default risk equities are exhibited in Q1 and the highest

default risk equities in Q10 respectively. The full sample consists of 10,710 firm-year observations for 714 unique European

firms over the 2002-2016 period. Variable definitions are presented in Appendix A1. Standard errors are in parentheses.

Financial variables are winsorized at the 1st and 99th percentiles.

Deciles

1 2 3-4 5-6 7-8 9 10 P10-P1

Returns 0.141

(0.013)

0.116

(0.013)

0.126

(0.009)

0.103

(0.009)

0.066

(0.010)

0.021

(0.015)

-0.100

(0.020)

-0.241

Cash holdings 0.222

(0.005)

0.157

(0.004)

0.111

(0.002)

0.096

(0.002)

0.097

(0.002)

0.103

(0.003)

0.118

(0.004)

-0.104

Market value (ln) 6.184

(0.022)

6.210

(0.023)

6.302

(0.016)

6.398

(0.016)

6.383

(0.015)

6.389

(0.022)

6.300

(0.026)

0.116

Book-to-market (ln) -0.976

(0.026)

-0.769

(0.025)

-0.631

(0.017)

-0.690

(0.016)

-0.718

(0.018)

-0.737

(0.028)

-1.021

(0.041)

-0.045

Leverage 0.144

(0.003)

0.253

(0.004)

0.368

(0.003)

0.441

(0.004)

0.520

(0.004)

0.608

(0.006)

0.644

(0.008)

0.500

Observations 922 918 1,846 1,842 1,800 867 719

Appendix A.7. Regression models. The table presents coefficient estimates of the multivariate regressions with firm-

level returns as dependent variable. The full sample consists of 10,710 firm-year observations for 714 unique European

firms over the 2002-2016 period. Variable definitions are presented in Appendix A1. Standard errors are in parentheses, and

are adjusted for heteroscedasticity and autocorrelation as in Newey-West (1987). For all models, the standard errors are

adjusted for 690 clusters (company), except for the WCR regression (adjusted for 691 company clusters). Financial variables

are winsorized at the 1st and 99th percentiles.

CR (ln) QR (ln) WCR

Independent variables (7) (8) (9) (10) (11) (12)

Intercept 1.550***

(0.132)

1.563***

(0.133)

1.586***

(0.131)

1.598***

(0.132)

1.516***

(0.132)

1.528***

(0.132)

Z 0.070***

(0.012)

0.058***

(0.013)

0.052***

(0.009)

0.040***

(0.010)

0.073***

(0.011)

0.060***

(0.012)

Z2 -0.003***

(0.001)

-0.003***

(0.001)

-0.003***

(0.001)

-0.002***

(0.001)

-0.004***

(0.001)

-0.003***

(0.001)

RLiquidity 0.125***

(0.032)

0.127***

(0.030)

0.164***

(0.028)

0.163***

(0.027)

0.532***

(0.098)

0.534***

(0.094)

RLiquidity*Z -0.041***

(0.011)

-0.042***

(0.010)

-0.040***

(0.009)

-0.039***

(0.009)

-0.142***

(0.031)

-0.141***

(0.029)

RLiquidity*Z2 0.002**

(0.001)

0.002***

(0.001)

0.002***

(0.001)

0.002***

(0.000)

0.007***

(0.002)

0.007***

(0.002)

Investor protection (dummy) 0.232***

(0.070)

0.261***

(0.070)

0.263***

(0.070)

Investor protection*Z 0.032**

(0.015)

0.029*

(0.015)

0.031**

(0.015)

Investor protection*Z2 -0.001*

(0.001)

-0.001

(0.001)

-0.001

(0.001)

Market value (ln) -0.256***

(0.018)

-0.255***

(0.018)

-0.255***

(0.018)

-0.254***

(0.018)

-0.254***

(0.018)

-0.253***

(0.018)

Book-to-market (ln) 0.283***

(0.014)

0.285***

(0.013)

0.279***

(0.013)

0.280***

(0.013)

0.279***

(0.013)

0.281***

(0.013)

Adjusted R2 0.148 0.149 0.151 0.152 0.151 0.151

Observations 8,747 8,747 8,747 8,747 8,761 8,761 *, ** and *** denote significance at the 10%, 5% and 1% level (2 tailed), respectively.

38

Appendix A.8. Regression models. This table presents coefficient estimates of the multivariate regressions with firm-

level returns as dependent variable. The full sample consists of 10,710 firm-year observations for 714 unique European

firms over the 2002-2016 period. Variable definitions are presented in Appendix A1. Standard errors are in parentheses, and

are adjusted for heteroscedasticity and autocorrelation as in Newey-West (1987). For all models, the standard errors are

adjusted for 691 clusters (company). Financial variables are winsorized at the 1st and 99th percentiles.

Independent variables (13) (14) (15) (16) (17) (18)

Intercept 2.128***

(0.139)

2.109***

(0.138)

1.775***

(0.141)

2.101***

(0.339)

2.030***

(0.270)

1.778***

(0.142)

Z 0.011***

(0.004)

0.051***

(0.009)

0.087***

(0.010)

0.125***

(0.033)

0.076***

(0.017)

0.081***

(0.012)

Z2 -0.002***

(0.000)

-0.004***

(0.001)

-0.007***

(0.002)

-0.004***

(0.001)

-0.004***

(0.001)

Cash holdings 1.079***

(0.120)

1.092***

(0.121)

1.070***

(0.120)

1.079***

(0.120)

Cash holdings*Z -0.177***

(0.027)

-0.189***

(0.029)

-0.173***

(0.028)

-0.177***

(0.027)

Cash holdings*Z2 0.009***

(0.002)

0.010***

(0.002)

0.008***

(0.002)

0.009***

(0.002)

Anti-director-rights -0.138

(0.099)

Anti-director-rights*Z -0.008

(0.007)

Anti-director-rights*Z2 0.001

(0.000)

Anti-self-dealing -1.175

(0.822)

Anti-self-dealing*Z 0.019

(0.023)

Anti-self-dealing*Z2 -0.001

(0.001)

Investor protection

(dummy)

0.442***

(0.060)

Investor protection*Z 0.014

(0.014)

Investor protection*Z2 -0.000

(0.001)

Market value (ln) -0.304***

(0.020)

-0.311***

(0.019)

-0.295***

(0.019)

-0.297***

(0.019)

-0.295***

(0.019)

-0.294***

(0.019)

Book-to-market (ln) 0.235***

(0.013)

0.243***

(0.013)

0.257***

(0.013)

0.257***

(0.013)

0.257***

(0.013)

0.258***

(0.013)

Real GDP growth 0.001

(0.002)

-0.000

(0.002)

0.000

(0.002)

-0.000

(0.002)

0.000

(0.002)

0.000

(0.002)

Inflation -0.085***

(0.004)

-0.087***

(0.004)

-0.085***

(0.004)

-0.085***

(0.004)

-0.084***

(0.004)

-0.084***

(0.004)

Adjusted R2 0.198 0.205 0.219 0.219 0.219 0.219

Observations 8,761 8,761 8,761 8,761 8,761 8,761 *, ** and *** denote significance at the 10%, 5% and 1% level (2 tailed), respectively.

39

Appendix A.9. Regression models for subsamples based on country. This table reports coefficient estimates of the

subsamples for the run multivariate regressions with firm-level returns as dependent variable. The full sample consists of

10,710 firm-year observations for 714 unique European firms for the period 2002 to 2016. Variable definitions are presented

in Appendix A1. Standard errors are in parentheses, and are adjusted for heteroscedasticity and autocorrelation as in Newey-

West (1987). Standard errors are adjusted for 457 clusters (company) in the U.K. sample and for 234 clusters (company) in

the non-U.K. sample. Financial variables are winsorized at the 1st and 99th percentiles.

United Kingdom Rest of Europe

Independent variables (19) (20) (21) (22) (23)

Constant 1.731***

(0.197)

1.601***

(0.198)

1.291***

(0.159)

0.919***

(0.161)

0.920***

(0.161)

Z 0.063***

(0.012)

0.100***

(0.021)

0.034***

(0.011)

0.072***

(0.012)

0.070***

(0.012)

Z2 -0.003***

(0.001)

-0.005***

(0.001)

-0.002***

(0.001)

-0.004***

(0.001)

-0.004***

(0.001)

Cash holdings 1.105***

(0.317)

1.337***

(0.135)

1.360***

(0.134)

Cash holdings*Z -0.233**

(0.098)

-0.176***

(0.029)

-0.181***

(0.029)

Cash holdings*Z2 0.009

(0.006)

0.008***

(0.002)

0.008***

(0.002)

Investor protection (dummy) 0.370***

(0.076)

Investor protection*Z 0.014

(0.025)

Investor protection*Z2 0.000

(0.001)

Market value (ln) -0.326***

(0.030)

-0.315***

(0.030)

-0.205***

(0.023)

-0.191***

(0.023)

-0.191***

(0.023)

Book-to-market (ln) 0.235***

(0.020)

0.244***

(0.020)

0.305***

(0.017)

0.318***

(0.017)

0.320***

(0.017)

Adjusted R2 0.133 0.139 0.154 0.178 0.179

Observations 2,866 2,866 5,895 5,895 5,895 *, ** and *** denote significance at the 10%, 5% and 1% level (2 tailed), respectively.