(366251723) invest.docx

Journal of Business Ethics (2009) 87:25–40 ! Springer 2008DOI 10.1007/s10551-008-9798-9

Investment Decisions, Liquidity,and Institutional Activism: An International Study

Alfredo M. BobilloJuan A. Rodriguez SanzFernando Tejerina Gaite

ABSTRACT. The activism of institutional investors tends more and more toward the supervision and control of the behavior of the managers of big companies. In this article, we present a model based on the creation of an activism index that lets us evaluate such activism’s effect on the sensitivity of the investment policies of a company in the face of financial variables (such as cash flow and

either of the nature of the assets or of the organizational mechanisms that are implied in investment decisions. Maximizing the value of the company and minimizing capital cost are the two pillars that sus- tain traditional theory. Nevertheless, neoinstitutional organizational theories, in particular Jensen’s theory

1

liquidity ratio) and market variables (ownership concen- of organizational architecture, propose a greatertration and value creation index). To test our assertions, we analyze firm-level data for United Kingdom (U.K.), Germany, France, Denmark, and Spain during the period1995–2004. Our results point to a significant reduction in the sensitivity of company investment decisions in the face of these variables, especially relative to intangible capital, as a result of the neutralizing effect of activism on the high agency costs of free cash flow and on the information asymmetries of the market.

KEY WORDS: institutional activism, firm performance, financial constraints, investment-cash flow sensitivities, corporate investment

Introduction

Traditional financial theory is a theory of valuation

complexity, from which arises a systematic andevolutionary vision of investment choices. Such theories are founded on the principle of organizational efficiency. These approaches also require the consideration of institutional and cognitive con- tracts,2 weaknesses that suppose limited rationality on the part of actors and conflicts of interest on the part of the participants within the sphere3 in which these activities occur.

In this context, the financial and strategic theories of investment decisions are brought together, based fundamentally on two elements: The first of these is limited rationality, particularly the limitations of cognitive capacity, which, together with the state of technology, can strengthen or weaken organizational efficiency, restricting the capacity to produce and use knowledge. It is also necessary to consider costs

4and as such has a normative goal in investment. This

incurred in the acquisition of knowledge. The

approach does not take into account an explanation

Alfredo M. Bobillo is a Professor of International Business at University of Valladolid (Spain). His research interests lie in the area of international business and multinationals firms.

Juan A. Rodriguez Sanz is a Professor of Finance at University of Valladolid (Spain). He is currently investigating about corporate finance and valuation of firms.

Fernando Tejerina Gaite is a Professor of Finance at University of Valladolid (Spain). His research interests include corporate governance and valuation of firms.

second element is the search for organizational effi-ciency, which may be included in investment decisions as the basis for the creation of value or may more complexly address not only the creation of value but also the method of its distribution (Charreaux and Desbrie`res, 1998).

This framework suggests a concurrence in whichnormative financial theory and neoinstitutional theory can converge for the benefit of greater clarity about the explanatory variables that shape firm’s investment decisions. For its part, the

search to optimize investments, with the objective of

26 Alfredo M. Bobillo et al.Investment Decisions, Liquidity, and Institutional Activism 26

maximizing the wealth of shareholders, supposes viewing cash flow as the total of shareholders’ investment opportunities, represented as a continu- ous curve. The relationship between investment and cash flow has had a turbulent history. The evaluation of cash flow only comprises one part of the element of investment decision-making as we present it here, as it is the normative characteristic of valuation that traditional theory addresses. With respect to this, various works since 1980, like that of Fazzari et al. (1988), demonstrate the greater effect of cash flow on the investment decisions of those firms that are more likely to face financial constraints, an effect which is interpreted as information-driven market imperfections. Similar surveys show the same results (Bond and Van Reenen, 2002; Hubbard, 1998). Some other works suggest that the significance of cash flow stems from its role in alleviating credit frictions (Carpenter and Guariglia, 2003). Thus, controversy remains over the role and significance of cash flow in investment decisions.

On the other hand, neoinstitutional theories5

place emphasis on organizational mechanisms as useful tools in making optimal investment decisions. From this perspective, it is important to consider the distortions induced by the imperfections of capital markets that create information asymmetry, which in turn translate into conflicts of interest between shareholders and bondholders, current shareholders and future shareholders, and, above all, managers and shareholders. These distortions can create a policy of underinvestment, as a result of communication problems between the company and outside investors, or overinvestment, if the managers take part in investment projects that maximize their utility over the firm’s value. The central problem that links information asymmetry to investment decisions is that the exchange of information is costly. Conse- quently, optimal investment decisions would seek to minimize such costs and ensure that corresponding benefits exceeded them. Therefore, in addition to considering cash flow, it is necessary to take into account other market or organizational variables, like institutional activism, ownership concentration, and, finally, the company’s future performance.

The objective of this paper is to analyze theinfluence of traditional variables, like cash flow, on investment decisions, and to additionally incorporate other variables derived from neoinstitutional theory

that can illuminate what happens within a firm and in financial markets during the company’s investment decision-making process. The rest of the paper proceeds as follows: In Section ‘‘Theoretical background and hypotheses’’, we discuss the alternative analytical framework in more detail, outlining test- able hypotheses. Section ‘‘Data and methodology’’ describes the data and the empirical model of investment. Section ‘‘Results and discussion’’ dis- cusses the findings, and the last section presents the conclusions.

Theoretical background and hypotheses

There is a consensus on the existence of information asymmetries between firms and markets, and the consequent agency problems motivate investments in physical capital and intangible capital, mainly R&D, to be initially financed with internal funds (Fazzari et al., 1988; Jensen and Meckling, 1976; Myers and Majluf, 1984; Stiglitz and Weiss, 1981). However, the differences between investments in tangible and intangible assets – particularly the greater risk, adjustment costs, and sunk costs of the latter (Arrow, 1962) – make it difficult to evaluate the relationship between cash flow restrictions and intangible investments.

In the group of studies analyzed here it is evident that financial restrictions – more basically, cash flow – have a significant impact on the investment policies of a firm.6 Most of the works considered show how financial variables are often cited to explain different behaviors, and only in some cases are certain institutional characteristics of the financial systems of various countries considered. Neverthe- less, none of the arguments presented take into account the different nature of certain assets or the organizational mechanisms at play in investment decisions. It eventually becomes necessary to address other variables, like institutional activism, ownership structure, and the potential growth of a company, among others, that can contribute significantly to our understanding of the investment decision- making process.

The separation between ownership and control leads to a kind of pathology that, in many cases, requires a specific treatment. The different interests of managers and funding providers create certain


attitudes and behaviors on the part of participants in the company that have repercussions on the making of financial and investment decisions. It seems evident that institutional investors display behavior that is different to that of individual investors, and they play a more active role in the supervision of investment decisions. Although the activism7 of such participants does not resolve the principal-agent problem inherent in corporate governance, it can indeed alter the repercussions of that conflict on all those involved.

Shleifer and Vishny (1989) were the first to point out how specific investments may be chosen by managers concerned about avoiding replacement, thereby contributing to those managers’ entrenchment. Later, Charreaux (1997) showed how this kind of strategy could generate inefficiency as a result of the manipulation of information8 for the purpose of increasing the dependency of investors on managerial decisions. A policy that pursues growth, based on investments that encourage entrenchment, can have negative effects for the shareholder, because such a policy likely gives up investment in intangible assets, like R&D, at a high cost in lost opportunity. On the other hand, the making of idiosyncratic investments may also not be an obstacle to strengthening a manager’s reputation.

Given this, activism leads institutional investors to distrust those managers that may make non- redeployable9 investments or who have opted for a strategy of growth. This behavior also takes on particular significance, depending on the ownership concentration and on the economic sector to which the company belongs. Accordingly, we propose the following hypothesis:

H1: Higher levels of activism will condition the sensitivity of investment policy in tangible capital in relation to market variables (the ownership concentration and the value creation index of the company) and in relation to a company’s belonging to a certain economic sector.

On the other hand, investment in intangible assets like R&D carries more risk and other particularities than ordinary investments. Managers have better information about the probability of success for their R&D projects than do outside investors or lenders.

Likewise, the value of R&D is in the future output of new products and processes, which outsiders cannot depend on as a guarantee in the present. This is because one cannot determine with precision the value of such investments. Strategic considerations, added to issues of risk and uncertainty, can be a further source of information asymmetry between suppliers and receivers of funds. In this context, managers may be reluctant to let the content and objectives of their technological activities filter outward, given the possibility of their being appro- priated by rivals.

Another essential characteristic of investments in intangible assets, as contrasted with those in tangible assets, is the existence of high fixed adjustment costs entailed by investments in R&D, as a result of indivisibility. Such costs are due to the high number of qualified personnel that must be hired, a number difficult to modify from one year to the next. This makes it difficult to evaluate the possible impact of current financial restrictions on decisions to invest in intangibles, given the rigidity of short-term cost changes in this sort of investment.

In these circumstances, supervision by institu-tional investors may be stricter, as a result of the uncertain results implied by these sorts of investments. At the same time, this supervision may help maintain a better balance of power within the company, because of the increase in resources with uncertain results controlled by the managers of the company.

Given all of the above, we put forward oursecond hypothesis:

H2: In the context of the uncertainty and risk carried by investments in intangible assets, the level of activism may cause different behavior of market variables (ownership concentration and value creation index) in some countries than in others, as a function of the development of each country’s financial markets and the relative level of legal protection of investors.

Along these same lines of argument, we propose examining the role that may be played in investment-cash flow sensitivity by greater or lesser levels of activism on the part of institutional investors. The impact of cash flow on firm investment decisions has


been analyzed in depth by Tinbergen (1939), Klein (1951), and Meyer and Kuh (1957), who show that liquidity is a significant variable. The work carried out by Fazzari et al. (1988) represented an important development in this field, because in this work they evaluate the financial restrictions of various kinds of companies, classified by their high or low rate of dividend payment. The authors show that those companies with a low rate of dividend payment present greater sensitivity of investment to cash flow than those with a high rate of dividend payment. Kaplan and Zingales (1997) took issue with this perspective, pointing out the unsuitability of classifying companies as a function of their capacity to finance themselves, and they amplify the classification criteria. Their results contradict those of Fazzari et al., showing less sensitivity in those companies with financial restrictions that in those without such restrictions. Other works, like those of Schiantarelli (1995), Hubbard (1998), and Fazzari et al. (2000), have aimed criticism at the work of Kaplan and Zingales, pointing out the subjective nature of the information used to categorize the firms, as well as the limited size of the sample of companies with financial restrictions. In the end, Kaplan and Zingales (2000) offered a conciliatory theory. Cleary (1999) introduced a new element into the formula- tion, classifying companies according to an analysis of how much the payment of dividends increases or decreases. The results show that investment-cash flow sensitivities are lower for financially constrained firms. More recent contributions can be found in Alti (2003) and Gomes (2001), whose work provided mixed conclusions about the role played by financial restrictions on the generation of high investment- cash flow sensitivities. Employing a simulation model, Moyen (2004) reconciled the results of Fazzari et al. (1988) and Kaplan and Zingales (1997) by using subsets with and without pre-existing financial constraints. Almeida and Campello (2002) found that companies with financial restrictions have greater sensitivity to investment-cash flow than those that have free access to financial markets. Using an outline of real options, Boyle and Guthrie (2003) showed that whenever firms do not face binding liquidity constraints, they are willing to invest in less favorable projects. This can be interpreted as evidence against the ‘‘classical’’ view of investment volume being decreasing in constraints.

In summary, the literature confirms the existence of positive investment-cash flow sensitivity, signaling that information asymmetry or agency costs are the main parameters in the determination of this sensitivity.

A high level of free cash flow can induce man-agers to pursue a strategy of unrestrained growth (Grossman and Hart, 1982) and cause overinvestment (Jensen, 1986). The origin of this agency problem is that the managers not only receive high remuneration from big firms for pursuing this kind of strategy (Conyon and Murphy, 2000), but they can also achieve important benefits like prestige and a good reputation (Dyck and Zingales, 2004). In this way, a high degree of corporate liquidity can encourage managers to invest in projects with returns below hurdle rate. Corporate monitoring by large outside shareholders can resolve the conflict of interests between managers and shareholders. Therefore, the cost of free cash flow can be reduced as a function of the level of activism of the outside group. A high degree of shareholder activism can moderate the behavior of the managers during a decline in performance, in the absence of managerial entrenchment (Franks et al., 2001). Because the shareholders take on the relative cost of exercising control and only receive benefits in proportion to their participation (Demsetz, 1983; Grossman and Hart, 1980), supervision will only be effective if the group of shareholders is sufficiently large and undertakes a high level of activism.

Accordingly, we present our next hypothesis:

H3: Investment-cash flow sensitivity will be less in firms with a high degree of activism, as a result of the reduction of the agency costs of free cash flow.

By contrast, if internal funds generated by the company are scarce, the managers may find themselves faced with a problem of underinvestment due to information asymmetry (Myers and Majluf, 1984). This occurs when a company has insufficient funds to finance a project and has to resort to financial lenders. Less informed financial markets demand a risk premium that reflects average project quality. This risk premium can be set at elevated levels for certain projects, and hence the managers may abandon some projects with positive NPV as a result


of information asymmetry. The positive relationship between cash flow and corporate investment induced by information asymmetry will decrease with lower levels of holdings by insiders. The problem of underinvestment described above can be mitigated if a large enough block of company shares is held by a financial institution (Kahn and Winton,1998). In this respect, Shleifer and Vishny (1986)establish that the higher the level of shareholder participation in a company, the greater their incen- tive to obtain information about it. In consequence, ownership of large blocks of shares by a financial institution can reduce information asymmetries between the company and financial markets, as a

TABLE IDistribution of companies by sector

Sectors %

1. Agriculture 0.932. Energy production, and water 6.733. Metal and non-metallic products 5.494. Construction 3.905. Food, beverage, and tobacco 4.476. Textiles 3.377. Wood 1.568. Paper and printing 3.719. Chemical products 5.0910. Rubber 1.41

result of the experience and activism of these insti-tutions and their active participation in capital markets.

Thus, we put forward the following hypothesis:

H4: A negative relationship is expected between

11. Industrial and commercial machinery

12. Electronic and electrical equipment

13. Measuring and controlling instruments

5.66

5.37

3.17

institutional blockholding and ownership concentration and investment-cash flow sen-

14. Transport equipment 2.3215. Other manufacturing industries 1.16

sitivity, as a result of the reduction of information asymmetries.

16. Transportation andcommunications

4.95

17. Wholesale trade 5.5218. Retail trade 7.19

Data and methodology19. Real State and

financial intermediation3.31

Data

The sample used here is made up of individualized figures for private corporations belonging to different industries, drawn from the databases Compu- stat and Amadeus for the years 1995–2004, from Spain, France, Denmark, Germany, and the United Kingdom. The distribution of corporations by sector and country is shown in Tables I and II. The initial sample is composed of a total of 3,535 observations for each period considered. The statistical databases of the official institutions of each country10 were consulted to obtain the series of national consumer price indices for the period under consideration, which made possible the conversion of the nominal values of the variables found into their real values for the year 1995. Table II shows that the countries that contribute the most firms to the sample are the U.K., France, and Germany, followed by Spain and Denmark, which contribute similar numbers of companies to each other.

20. Other business services 3.8821. Computer and related activities 11.1522. Other professional services 9.67Total 100.00 (3, 5 35 companies)

Variables

As to the division of the sample, we use as a selective variable the activism index (AI), obtaining two subsets on either side of the median: companies with low and high activism indices. As indicated in the theoretical section of this paper, this lets us compare and contrast companies in clusters characterized by varying degrees of shareholder activism, testing our hypotheses about activism’s effect on investment in tangible and intangible assets. For the estimation of this index it is necessary not only to quantify the ownership concentrated in the hands of the main shareholder (C1), but also to know what kind of shareholder they are (bank, pension fund, family


TABLE IIDistribution of companies by country

Countries (Cd) %

1. Germany 22.772. Denmark 4.613. Spain 4.724. France 23.115. U.K. 44.84Total 100

(3,535 companies)

manager, public administrator). We assume that the ability to exercise control is made apparent when the proportion of ownership by the main shareholder is

pension funds or investment funds. The value of the AI would be estimated between 0 and 1. Once those observations have been eliminated for which the total number of variables necessary for the calcula- tion of the two equations are not available, the median AI calculated for our sample reaches values of 0.54941 (equation for investment in tangible assets) and 0.55221 (equation for investment in intangible assets).

The empirical test is based on two equations thatwill be analyzed in more detail later in this section and whose main variables are described next.

The first of the dependent variables to be considered is investment in stock representing tangible or physical assets (ITCS), defined as:

ITAi ;t þ FADi ;tgreater than or equal to 10%. Thus, once the meanAI for a certain period has been calculated, that

ITCSi;t ¼ TCSi;t#1ð3Þ

period’s median value becomes the key to dividing the sample with the intention of estimating the equations in each subset relative to investment in tangible or intangible assets. The activism index (AI) is calculated through the following expression11:

AI ¼ I0 þ 0:1ðT # 1ÞIfC1 &10g

ð1Þ

in which T is equal to 1 if the main shareholder is a bank or multinational (low activism), T is equal to1.5 if the main shareholder is a family or domesticfirm (moderate activism), or 2 (high activism) if the shareholder is a public pension fund or an investment fund. IfC1 &10g is a dummy variable that takes the value 1 if the ownership concentration (C1) is greater than or equal to 10%. Also, I0

takes the

where ITAi,t ¼ (INTAi,t ) INTAi,t)1) is investment in fixed tangible assets by company i during period t, defined as the difference between the book value of net fixed assets for the periods t and t ) 1. FADi,t is the depreciation of fixed assets for company i during period t. TCSi,t)1 is the stock representing physical or tangible assets for company i in the instant t ) 1.

All values are expressed in USD and are deflated to 1995, the sample starting point, using the consumer price indices of each country under consideration. To calculate stock in net fixed assets we applied the method of perpetual inventory with an annual depreciation rate of 8%12 for each year after the first for which historical data for the company is available. Thus:

form: TCSi;t ¼ ð1 # dÞ ’ TCSi;t 1 þ ðITA þ FAD Þ

1 !

0 :4 "

# i;t i;t

ð4Þ

I0 ¼ 0:2

C1 IfC1<10g þ 0:5 þ 0:9

ðC1 #

0:1Þ

IfC1&10g

ð2Þwhere initial fixed assets is equal to the net value of fixed tangible assets for the first of those years forwhich data is available.with IfC1<10g again being a dummy variable that

takes the value 1 if the ownership concentration (C1)is lesser than 10%. This implies the development of alinear interpolation such that I0 takes the value 0

The second dependent variable relates to investment in stock of intangible assets (IICS):

IIAi ;t þ IADi; twhen C1 ¼ 0%; 0.5 when C1 ¼ 10%; and 0.9 when C1 ¼ 100%.

IICSi;t ¼ ICSi;t#1ð5Þ


The AI shows different control that is exercised by banks and institutional investors like public

where IIAi,t ¼ (INIAi,t ) INIAi,t)1) is investment in fixed intangible assets by company i during period

¼ i,t


t, defined as the difference between the book values of net intangible assets for the instants t and t ) 1. IADi,t is the depreciation of intangible assets for company i during period t. ICSi,t)1 is the stock in intangible assets for company i during period t ) 1.

The values of intangible assets are deflated as above. To calculate the value of stock in intangible assets, the perpetual inventory method was again used, with an annual depreciation rate of 15%13 for each year following the first for which historical data for the firm is available. Thus:

ICSi;t ¼ ð1 # dÞ ’ ICSi;t#1 þ ðIIAi;t þ IADi;t Þ ð6Þ

where initial stock in intangible assets is equal to the net value of fixed intangible assets for the first year for which data is available.

Next, we present the set of independent vari-ables that make up each of the equations to be calculated. For the first equation, concerning investment in tangible assets, the degree of liquidity is represented by cash flow over stock in physical or tangible assets:

CFTCS CFi;t ; with CF being cash flow TCSi;t#1

generated by company i during period t, calculatedas Earnings Before Interest & Taxes (EBIT) plus depreciation during the period in question. TCSi,t is stock in physical or tangible assets of company i during period t. The ownership concentration (C1) is measured as the percentage of holdings in the hands of the main shareholder. The value creation index is calculated as

Methodology

With the object of testing the hypotheses proposed in the theoretical section, and using the variables described above, two econometric models are constructed (one for each equation). The central objective is to determine whether the existence of information asymmetry in capital markets affects investment decisions (both in tangible and intangible assets) by introducing financial constraints into the different kinds of capital contribution. Following the work of Fazzari et al. (1988), we propose that investment in fixed tangible assets by businesses with credit rationing is particularly sensitive to the avail- ability of internal resources, and so cash flow over stock in physical capital will be one of the main independent variables. Likewise, the variable VCI, based on Marris’s Q ratio (Value Creation Index) which expresses the relationship between the market value and the book value of equity capital, should also be considered. Equity capital is the residual balance between assets and debts. Given that the Q ratio is a permanent indicator of the firm’s value for the investors, expectations of future performance could affect the investment policy of the firm. Finally, the ownership concentration, in this case measured as the percentage of shares held by the main shareholder (C1), stands, as we have outlined in the previous section, as one of the basic determinants of investment decisions.

Along with the independent variables, and in each of the equations, dummy variables representing each

IFPi; t Mi; t =Bi; t company’s particular economic sector (Div) orVCIi;t ¼

IPPi;t¼

ROEi;t=ki;tð7Þ particular country (Cd) were used. Nevertheless, the

high number of sectors included in the initial clas-where IFPi,t (index of future performance) is definedas the quotient of market value (M) and book value(B) of equity capital. IPPi,t (index of past performance) is the quotient of return on equity (ROE) and the cost of capital (k). The second equation applies these variables to investment in intangible assets. Here the independent variables are the liquidity ratio – CANA – (the quotient of current assets and net total assets logged over one year), the ownership concentration (C1), and the value creation index (VCI), already defined in the first equation.

sification (see Table I) makes a more simplified division of sectors recommended. We therefore use the Standard Industrial Classification Division Structure (SIC), obtained from the COMPUSTAT database, whose main clusters can be seen in Table III. The variable Scj, with j varying from 1 to9, is thus a categorical variable that takes a value of 1when the company falls in sector j and 0 if it does not. Likewise, Cdk is a categorical variable with a value of 1 when the company is from country k and0 if not.

i;t 1 TCSi;t 11k k;i TCSi;t 1

TCS

TCS

TCS


TABLE IIIDistribution of companies by sector – StandardIndustrial Classification Division Structure (SIC) ITCS ¼ a þ b

CFi ;t

þ#

4

4X

k¼1b Cd ’

C F i ; t

#

Di v isions

Division A: Agriculture, forestry, and fishingDivision B: Mining Division C: Construction Division D: ManufacturingDivision E: Transportation, communications, electric,

gas, and sanitary servicesDivision F: Wholesale tradeDivision G: Retail tradeDivision H: Finance, insurance, and real estateDi v ision I: Services

þ b2 C1i;t þ X

b2k Cdk;i ’ C1i;t

k¼14

þ b3 VCIi;t þ X

b3k Cdk;i ’ VCIi;t þ gi þ ei;t

k¼1

When the variable to be examined is the rate of investment in intangible assets, as opposed to tangible assets, the second equation is expressed as follows:

CAi; t IICSi;t ¼ d þ b1 ’ NAi;t#1

þ b2 ’ C1 þ b3 ’ VCIi;t

Keeping in mind the variables chosen as representative of investment, and the different independent variables and dummy variables described above, the first equation to be calculated is reflected in the expression that we present next. Two more calcu- lations are also made, one including dummy variables representing the industries to which the companies belong, and another including the dummy variables representing the countries under consideration.

CFi ;t

þ gi þ ei;t ð11Þ

In this equation we can see the same group of potential explanatory factors as in the first equation with one exception: the liquidity ratio. In this case, we expect investment in intangible assets to have greater sensitivity to the degree of liquidity of the firm.14

The introduction of dummy variables representing sectors and countries into this second equation follows the identical methodology for their introduction into the first equation, and so we omit their

ITCSi;t ¼ a þ b1 ’i;t#1

þ b2 ’ C1 analysis here.Important differences may exist in any of theþ b3 ’ VCIi;t þ gi þ ei;t

ð8Þ

CFi; t

models proposed as a function of shareholders activism toward the control and governance of thecompany’s managers and information asymmetry


þ b2 ’ C1

9

problems and, therefore, financial restrictions. Bearing this in mind, the two models proposed are

þ b3 ’ VCIi;t þ X

cj Divj;i þ gi þ ei;t

ð9Þj¼2

CFi; t

analyzed for two subsets of companies: those with anAI below the sample median and those with an AIabove it.

In all the equations the subscript i represents the


þ b2 ’ C1

5

company and t represents the time period. Thedisturbance is broken down in each of the two equations into two fundamental terms. The first ofþ b3 ’ VCIi;t þ

X ck Cdk;i þ gi þ ei;t ð10Þ these (eit), comprises all those factors that have some

k¼2 Country dummies are also introduced in interac- tive form in order to contrast the potential change


in the coefficient’s size for the rest of explanatory variables:

kind of influence on the rate of investment. This term constitutes a random disturbance and follows the usual conditions for a classical linear regression model. Nevertheless, fixed effect errors associated with each company (gi) frequently exist within the


term, potentially correlated with the group of independent variables, and these can introduce important biases into the estimate. The existence of this constant unobservable heterogeneity cannot only be identified but also be adequately eliminated by estimating the model in first differences. In addition, the two-step estimator, which takes into account the residual matrix from the first section, offers robust estimates on autocorrelation and heteroscedasticity (White, 1982).

In spite of all these measures, the endogeneity of the independent variables considered can become a serious problem in each of the proposed models. In this case the panel methodology can present important deficiencies that lead to inconsistent estimations. Diverse techniques are available to resolve this problem, among which we wish to highlight the Generalized Method of Moments (GMM) (Arellano and Bond, 1991; Mairesse and Hall, 1996) or the asymptotic minimum mean-squared method (Cre- pon et al., 1998). Nevertheless, the results obtained through the use of these techniques turn out to be fairly sensitive to the estimation method proposed and resolving simultaneity or endogeneity can introduce more problems than it solves (Griliches and Mairesse, 1995). In the end, the GMM is a

frequently used method that not only corrects problems of simultaneity and problems of observa- tional error, but also gives a residual structure that is robust in terms of autocorrelation and heteroscedasticity, which makes it an appropriate technique to apply to each of the models proposed.

Results and discussion

Table IV presents the summary statistics and the results of our regression can be seen in Tables V, VI, VII and VIII.

Columns 1 and 3 of Table V represent a simplemodel showing how decisions to invest in tangible assets are influenced by the financial variable of cash flow (CFTCS) and market variables (C1 and VCI). The coefficients estimated from these variables are significantly different from 0 at 1% level (positive for CFTCS and VCI, and negative for C1), although when the AI is greater than 0.5494 the coefficient for each of these variables behaves differently. The sensitivity of investment in tangible assets to CFTCS grows with the level of activism, owing to the greater idiosyncrasy and irreversibility of this type of investment. This idiosyncrasy gives rise to a greater

TABLE IVDescriptive statistics

Variables Mean Median Standard Dev. Minimum Maximum

Total assets 2416.69 206.38 10270.63 6.14 162647.00Issue market value 1846.49 179.29 7157.79 3.05 117664.93Stockholder’s equity 782.89 89.20 2592.82 0.15 35963.00Revenue 1914.29 253.78 6382.89 2.03 88963.00ITCS 0.41 0.09 13.90 )12.13 766.70IICS 2.92 0.02 37.98 )0.69 1129.62AI 0.52 0.54 0.17 0.00 0.89C1 0.22 0.12 0.23 0.00 1.00IFP 5.82 1.84 65.57 0.03 1863.13IPP 5.43 3.84 21.44 0.00 975.63VCI 1.95 0.52 24.23 0.00 785.70CFTCS 22.36 0.25 1198.38 )0.06 66107.08CANAt ) 1 0.55 0.55 0.30 0.00 2.90

The variables in Table IV are defined as follow: ITCS ¼ Investment tangible capital stock; IICS ¼ Investmentintangible capital stock; AI ¼ Activism index; C1 ¼ Ownership, IFP ¼ Index of future performance; IPP ¼ Indexof past performance; VCI ¼ Value creation index, CFTCS ¼ Cash flow over tangible capital stock;CANA ¼ Current assets over Total net assets.

34 Alfredo M. Bobillo et al.

TABLE V

Investment Decisions, Liquidity, and Institutional Activism 34

Equation 1: investment in fixed tangible assetsIndependent variables

Activism index < Median

Activism index > Median

(1) (2) (3) (4)

Dependent variable is investment in fixed tangible assets (ITCS)CFTCS C1

0.011 (0.000)***)0.029 (0.000)***

0.011 (0.000)***)0.316 (0.000)***

0.090 (0.000)***)0.024 (0.000)***

0.118 (0.000)***)0.324 (0.000)***

VCI 0.105 (0.000)*** 0.0464 (0.000)*** 0.001 (0.000)*** 0.001 (0.002)***Cons 0.064 (0.000)*** )0.186 (0.843) 0.131 (0.000)*** )2.890 (0.000)***DivB 1.269 (0.646) 1.641 (0.064)DivC )0.458 (0.626) 2.810 (0.000)***DivD 0.516 (0.597) 3.342 (0.000)***DivE 0.429 (0.651) 3.284 (0.000)***DivF )1.365 (0.248) 2.314 (0.000)***DivG 0.030 (0.975) 2.683 (0.000)***DivH 2.143 (0.090)* 2.396 (0.001)***DivI 0.488 (0.595) 3.427 (0.000)***Wald test 3.50e+09 (0.000)*** 2.44e+07 (0.000)*** 61417.11 (0.000)*** 4334.22 (0.000)***Hansen test 132.48 (0.399) 76.11 (0.056)* 142.91 (0.190) 77.10 (0.443)AR(1) )5.26 (0.000)*** )5.04 (0.000)*** )2.10 (0.036)** )2.09 (0.036)**AR(2) )1.68 (0.092)* )1.97 (0.049)** )1.19 (0.234) )1.18 (0.237)

GMM estimation.Coefficients estimated and P > |z| (between parentheses).The Hansen test is distributed following a v2 with as many degrees of freedom as coefficients estimated. The estimates in the second and fourth columns include sectorial dummy variables.*, **, ***Significant at 10%, 5%, and 1%. Median ¼ 0.54941.CFTCS ¼ Cash flow over tangible capital stock; C1 ¼ Ownership; VCI ¼ Value creation index; Div ¼ Industry dummy.

degree of managerial entrenchment, as the investments escape from shareholder control and fluctuate more with the level of liquidity. This sensitivity, nevertheless, remains at similar levels for C1, whereas for the market variable VCI higher activism indices dampen investment sensitivity. This verifies the influence of the AI on the lessened sensitivity of a company’s investment in physical capital in relation to market variables.

Columns 2 and 4 of this same table present a model that takes into account the different industry divisions as a dummy variable. In both models the Wald test reflects a high level of joint significance, although this turns out to be greater in the subset of companies with a lower AI. Nevertheless, a higher AI is apparent in industrial and service sector companies, especially in divisions D, E, and I. It is in

precisely these types of economic sectors that activism acquires its greatest importance, when one takes into account the strong ties between the industrial sector and the banking system in the form of industrial investment and the important contributions of the service sector to the output of the real economy. In summary, it becomes apparent that the introduction of economic sectors into the model increases the sensitivity of investments in tangible assets with respect to independent variables. The negative value of the ownership concentration coefficient demonstrates how large blocks of shares being held in the hands of a few, as well as the nature of the main shareholder, can reduce the ability of external shareholders to act. In addition, one notes a lesser impact of the company’s value creation index on decisions to invest in tangible capital when the AI

VCI Æ Cd3 0.455 (0.000)*** )0.012 (0.413)VCI Æ Cd4 6.851 (0.347)

Hansen test 151.44 (0.452) 53.98 (0.475)AR(1) )5.00 (0.000)*** )2.14 (0.033)**AR(2) )1.94 (0.053)* )1.27 (0.203)


TABLE V


TABLE VIEquation 1: Investment in fixed tangible assets

exclusively in Germany and Spain. This suggests, as might be expected, that the economic and financial environments of the countries considered in the

Independent variables



analysis have little influence on a company’s decisionto invest in physical capital, but rather that global- ization has favored uniformity in investment deci-

Dependent variable is investment in fixed tangible assets (ITCS)CFTCS 0.011 (0.000)*** 0.158 (0.000)*** C1 )0.035 (0.000)*** )0.160 (0.225) VCI 0.006 (0.000)*** 0.012 (0.421) Cons 0.025 (0.000)*** 0.168 (0.000)*** CFTCS Æ Cd1 2.790 (0.000)*** )1.282 (0.000)*** CFTCS Æ Cd2 5.083 (0.899) )1.158 (0.233) CFTCS Æ Cd3 )0.191 (0.574) 0.170 (0.001)*** CFTCS Æ Cd4 )6.676 (0.521)

C1 Æ Cd1 0.060 (0.016)** 0.152 (0.115) C1 Æ Cd2 )0.964 (0.990) 0.846 (0.318) C1 Æ Cd3 0.356 (0.000)*** 0.288 (0.007) C1 Æ Cd4 )0.862 (0.748) VCI Æ Cd1 )0.044 (0.000)*** )0.012 (0.418) VCI Æ Cd2 )0.600 (0.868) )0.020 (0.919)

Wald test 43513.95 (0.000)*** 3302.86 (0.000)***

GMM estimate.Interaction dummies by country.Coefficients estimated and P > |z| (between parentheses).

2

sions of this nature.Table VII displays the results of the models rela-

tive to investment in intangible assets. The coefficients of the financial and market variables increase their influence significantly with respect to investments in physical capital, which, again, vary as a function of the AI. Thus we see lower values for the estimators when the AI is greater than 0.5994. In reference to this, the results show the difficulty for external shareholders to develop policies of supervision and control over managers in matters of investment in intangibles, as a result of the uncertainty and risk that such investments tend to carry. The value creation index merits special mention here, as it gains greater relevance and significance in this type of investment for elevated activism indices, making clear the neutralization of risk and uncertainty that market valuation introduces into investment policies.

Table VIII includes the interaction dummy vari-ables for countries in order to evaluate any changes in decisions to invest in intangibles that may beproduced by different economic and institutional

The Hansen test is distributed following a v with as many environments. The coefficients present significantdegrees of freedom as coefficients estimated. (1) Subset ofcompanies with an activism index below the median. (2) Subset of companies with activism index above the median.*, **, ***Significant at 10%, 5%, and 1%. Median ¼ 0.54941.CFTCS ¼ Cash flow over tangible capital stock;C1 = Ownership; VCI ¼ Value creation index; Cd Æ(CFTCS, C1; VCI) ¼ Interaction country dummies.

is high, reflecting the lesser significance of the market value of the company as it relates to its investment policy. As hypothesis H1 suggests, the model is constructed so as to capture the different behavior of the variables for different levels of shareholder activism.

Likewise, Table VI shows how in decisions toinvest in physical capital there are no differences among countries for high levels of activism. Differ- ences for reduced activism indices are concentrated

differences in the interactions of the CANA and C1

variables with Cd2 and Cd5, and VCI with Cd5. This finding is consistent with the proposal behind hypothesis H2 and confirms what it predicts in the sense that the greater or lesser degree of development of financial markets and investor protection are variables that shape institutional activism and influence decisions to invest in intangible assets. The analysis also shows how the variable Cd5 (U.K.) is significant in the majority of interactions, revealing the particularities of the Anglo-Saxon market as far as investor protection and the institutional envi- ronment, as compared to the continental model representative of the rest of the countries in the sample.

A final consideration about the sensitivity of the liquidity variable with respect to investment decisions leads one to consider the strong influence of the AI on all investment in intangible assets, where


TABLE VII


influence on this type of investment of the differingTABLE VIII

Equation 2: investment in intangible assetsIndependent variables



(1) (2) (3) (4)

Dependent variable is investment in intangible assets (IICS)CANA C1

56.701 (0.000)***)8.187 (0.000)***

102.240 (0.000)***)4.796 (0.000)***

9.638 (0.000)***)9.514 (0.000)***

10.827 (0.000)***)2.583 (0.000)***

VCI 0.042 (0.233) 0.394 (0.005)*** 0.161 (0.000)*** 0.029 (0.000)***Cons )26.29 (0.000)*** )18.05 (0.584) )0.735 (0.000)*** 0.724 (0.219)DivA 5.302 (0.000)***DivB 65.033 (0.677) )7.410 (0.000)***DivC )103.6 (0.003)*** )18.54 (0.000)***DivD )21.38 (0.512) )2.317 (0.000)***DivE )46.71 (0.170) )10.68 (0.000)***DivF )28.07 (0.428) )7.399 (0.000)***DivG )14.39 (0.663) )4.884 (0.000)***DivI )43.39 (0.189) )6.780 (0.000)***Wald test 4124.94 (0.000)*** 1097.24 (0.000)*** 4.87e+07 (0.000)*** 1.20e+07 (0.000)***Hansen test 89.19 (0.997) 69.38 (0.332) 100.63 (0.602) 95.44 (0.958)AR(1) )1.31 (0.192) )1.4 (0.163) )1.07 (0.284) )1.07 (0.285)AR(2) )0.38 (0.700) 0.24 (0.810) )0.17 (0.868) 0.25 (0.803)

GMM estimation.Coefficients estimated and P > |z| (between parentheses).The Hansen test is distributed following a v2 with as many degrees of freedom as coefficients estimated. The estimations of the second and fourth columns include sectorial dummies.*, **, ***Significant at 10%, 5%, and 1%.Median ¼ 0.55221.CANA ¼ Current assets over Total net assets; C1 ¼ Ownership; VCI ¼ Value creation index; Div ¼ Industry dummy.

greater activism clearly brings about lesser sensitivity to liquidity in relation to investment in this type of asset. The results point to greater effectiveness of supervision and control on the part of external shareholders in intangible investment decisions, as a result of the greater risk and uncertainty that these investments produce, whether these be caused by the agency costs of free cash flow or by information asymmetries, as hypotheses H3 and H4 predicted.

Conclusions

The results obtained identify the AI as a positive discriminating factor that in some cases moderates and in others intensifies the effects of contextual variables like cash flow, ownership concentration,

and the value creation index of the company on physical or intangible asset investment decisions.

As expected, the different natures of investmentsin tangible and intangible assets, which are due to the more idiosyncratic qualities of the former and the greater risk and uncertainty that come with the latter, cause the impact of the AI to produce dis- tinct effects on each. Thus, in decisions to invest in physical capital, the sensitivity of investments to cash flow and the value creation index of the company increases along with the AI. Likewise, one can see how the results from the introduction to the model of variables representing the various countries under consideration do not change in significance with the AI. This confirms that the economic and financial environments have little influence on this type of investment decision.


TABLE VII


influence on this type of investment of the differingTABLE VIII

Equation 2: investment in intangible assets

economic and institutional environments of each country, in our case divided into two models: the

Independent variables



Anglo-Saxon and the continental.Finally, there is evidence of the moderating

effect of the AI on the sensitivity of investment policies to

Dependent variable is investment in intangible assets (IICS)CANA 23.661 (0.000)*** 13.630 (0.000)*** C1 )12.16 (0.000)*** )0.667 (0.000)*** VCI 14.525 (0.000)*** 0.048 (0.100) Cons )25.24 (0.000)*** )3.757 (0.000)*** CANA Æ Cd2 )702.2 (0.837) 27.765 (0.000)*** CANA Æ Cd3 11.173 (0.128) CANA Æ Cd5 40.935 (0.000)*** )8.303 (0.000)*** C1 Æ Cd2 8531.3 (0.851) )56.59 (0.000)*** C1 Æ Cd3 )11.00 (0.012)** C1 Æ Cd5 28.583 (0.000)*** 6.489 (0.000)*** VCI Æ Cd2 )1027.4 (0.848) )0.656 (0.000)*** VCI Æ Cd3 )0.661 (0.013)** VCI Æ Cd5 )17.68 (0.000)*** )0.181 (0.000)*** Wald test 4764.55 (0.000)*** 1.50e + 07 (0.000)*** Hansen test 77.74 (1.000) 105.78 (0.820)AR(1) )1.33 (0.185) )1.08 (0.281)

cash flow. The oversight of institutional activism attenuates investment-cash flow sensitivities, mainly in intangible assets investment, either by reducing the agency costs of free cash flow or by eliminating information asymmetries.

We can conclude that the AI gains ever moreimportance, either through its influence over the behavior of managers in relation to free cash flow and information asymmetry, or through its greater impact on sensitivity to market variables with respect to the investment policies of the company.

Notes

AR(2) )

GMM estimation.0.28 (0.778) )0.59 (0.553)

1 According to Jensen (1983), the choosing ofinvestments is not based exclusively on monetary flow, and it is also important to consider the totality of actors

Interaction dummies by country.Coefficients estimated and P > |z| (between parentheses). The Hansen test is distributed following a v2 with as many degrees of freedom as coefficients estimated. (1) Subset of companies with an activism index below the median. (2) Subset of companies with activism index above the median.*, **, ***Significant at 1%, 5%, and 10%. Median ¼ 0.55221.CANA ¼ Current assets over Total net assets;C1 ¼ Ownership; VCI ¼ Value creation index; CdÆ (CANA, C1; VCI) ¼ Interaction country dummies.

Nevertheless, one can see how, as our hypotheses predicted, the AI has a moderating effect on the influences of the financial variable CANA and the market variables C1 and VCI on decisions to invest in intangible assets. Thus, we find lower values from the estimators as the index of activism grows, which is a consequence of supervisory behavior undertaken by outside shareholders in this kind of investment, owing to the greater risk and uncertainty associated with the investment’s final out- come. The results also confirm the significant

that participate in this decision.2 In reference to this, traditional investment models

considered risk to be an exogenous variable, outside of managerial control. Nevertheless, this principle cannot be accepted if multiple risks exist and if managerial involvement and control are emphasized.

3 Such changes in context reflect not only the legalframework, but also the differing ownership structures, and imply a whole new game plan for investment decisions and their inseparability from financing decisions.

4 Jensen and Meckling (1992), basing their work onthat of Hayek (1945), postulate that the key to the performance of an economic or organizational system lies in its capacity to produce, acquire, and use the knowledge necessary for decision-making.

5 In particular, the theory of organizational architec-ture developed by Jensen (1983), as an extension of the agency theory introduced by Jensen and Meckling (1976).

6 A concise review of those studies that analyze therelationship between financial restrictions and the investment decisions of a company requires a look at: Scherer (1965), Mueller (1967), Elliot (1971), Grabowski (1968), Branch (1974), Myers and Majluf (1984), Switzer (1984), Devereux and Schiantarelli (1990), Hall (1992), Himmel- berg and Petersen (1994), Carpenter (1995), Schiantarelli (1996), Hubbard (1998), Harhoff (1998), Carpenter et al.


(1998), Mairesse et al. (1999), Bond et al. (1999), Hall et al. (1999), and Mulkay et al. (2001).

7 ‘‘Activism’’ refers here to the involvement of insti-tutional investors in the management of companies, as a consequence of the conflict of interest between shareholders and company managers.

8 According to Stigliz and Edlin (1992), any kind ofmanipulation of information can contribute to managerial entrenchment.

9 For Williamson (1994), non-redeployable invest-ments refer to assets that can not be reallocated without losing value, in case of the termination or signing of a contract.10 These official institutions were: the U.K.’s ONS,France’s INSEE, the German Federal Statistical Office, Denmark’s StatBank, and INE in Spain.11 The choice of coefficients in Eqs. (1) and (2) havebeen calculated to obtain a bounded index with a range of values between 0 and 1.12 This is the depreciation rate commonly used in theliterature. See the works of Cincera (2002), Mairesse and Hall (1996), and Pe´rez Garc´ıa et al. (2006).13 This is the depreciation rate commonly used in theliterature. Please see the works of Cincera (2002), Mairesse and Hall (1996), and Pe´rez Garc´ıa et al. (2006).14 See the works of Hall (1992) and Himmelberg andPetersen (1994).

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Alfredo M. Bobillo and Fernando Tejerina GaiteUniversity of Valladolid,

Campus Miguel Delibes, 47011 Valladolid, Spain E-mail: [email protected]

E-mail: [email protected]

Juan A. Rodriguez SanzUniversity of Valladolid,

Avda. Valle Esgueva, 47011 Valladolid, Spain E-mail: [email protected]

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