Keywords: capital expenditure, takeovers, initiator, agency theory, Tobin’s q, two-staged least squared
Studentnr: s2463709 Name: Patrick Driessen
Study Program: MSc Finance & MSc Economics
Capital expenditure of targets in pre-takeover settings
Abstract2
Introduction
In 2016 the market of mergers and acquisitions in the US totalled $1,771 billion. AT&T took over Time Warner Inc for $89.2 billion which was the largest US takeover in that year. Takeovers are key moments during the lifespan of companies. These moments are associated with important strategic decisions, for example regarding capital structure, cash flow management and capital expenditure. One of the main motives for buyers in transactions are growth opportunities. To capture these opportunities, investments are needed. Bidders prefer targets that are actively pursuing growth opportunities as bidders would benefit from these opportunities following the takeover. Therefore, bidders prefer targets that are making sufficient investments. On the other hand, targets do not reap the benefits from the investments that occur after the takeover. It may therefore forgo positive net present value projects. Consequently, these decisions are likely to affect the value of the target as well. This paper investigates the difference between target-initiated takeovers and bidder-initiated takeovers. This helps to gain a better understanding of the effects of takeovers, as takeovers have more far-reaching effects than merely a change of ownership. Furthermore, as capital expenditure decisions have an influence on the value of targets, the results are of interest to potential bidders and targets.
One important aspect during the takeover process is whether the target is aware that it will be taken over in the near future. Without this knowledge targets have no motive to limit capital expenditure related to the upcoming takeover. To determine whether targets had prior knowledge of upcoming takeovers, the initiator of the takeover process is used. Targets putting themselves up for sale are likely to have planned this sale well in advance. As a result, the targets may already alter their capital expenditure in expectation of the upcoming sale. In the case of bidder-initiated takeovers, bidders show interest in acquiring the target and at this moment the target become aware of a potential upcoming takeover. Targets are most likely maximizing their stand-alone value until that moment, which means capital expenditure is not affected by the upcoming takeover. Furthermore, the interests of the owner of the target also influences capital expenditure decisions. Certain owners, including private equity firms, are mostly interested in the return on their investment whereas other owners, such as the founding family are also interested in the continuity of the firm. These interests influence the capital expenditure of a target prior to the takeover. Another factor to consider is firm value. Tobin’s q shows how valuable firms are relative to their size. Firms that create more value are able to obtain more favourable financing conditions and are able to increase capital expenditure. Furthermore, high capital expenditure results in higher valuations of firms. This leads to possible endogeneity problems in determining the coefficient of firm value.
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suffering from these problems are good takeover candidates as bidders can restore proper management and capture the resulting increase in shareholder value. Another target characteristic is growth opportunities, which are often the reason for bidders to take over targets. Bidders want to grow their business beyond their internal growth rate and to do so bidders look for targets with good growth opportunities. As these targets have good growth opportunities, their capital expenditure is most likely relatively high in order to capture them. The research question covered in this paper is: “What determines capital expenditure of US targets prior to takeovers?”
Sub questions are used to break down the overall research question.
• How does the targets’ awareness of being taken over affect capital expenditure of US targets prior to takeovers?
• What effect does the ownership structure of targets have on capital expenditure of US targets prior to takeovers?
• What effect does firm value of targets have on capital expenditure of US targets prior to takeovers?
In order to answer these research questions an analysis of a sample of 112 publicly traded US takeover targets in 2015 and 2016 is performed. We use the ratio of capital expenditure in the year prior to the takeover and the capital expenditure norm to examine the effects of a target-initiated deal on capital expenditures of the target and we use agency theory to explain possible effects. In these takeovers the initiator of the deal is identified to determine the prior knowledge of the target regarding the takeover. Moreover, only takeovers which consider a significant transfer of control are regarded as otherwise the targets still have no incentives to decrease capital expenditure. 2015 and 2016 are the most recent years available and allows for calculating a norm of capital expenditure of four to five years while excluding the financial crisis. The analysis is performed using an OLS regression including several control variables. The third part of the research question concerns the effect of capital expenditure on firm value. Firm value is determined using Tobin’s q. As Tobin’s q also depends on capital expenditure, a two-staged least squared (2SLS) estimation is used.
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Literature overview
In answering the research questions it is valuable to discuss literature dealing with capital expenditure in pre-takeover situations. This analysis focusses on the relation between the targets knowledge of being taken over and its capital expenditure in the period prior to the takeover. Furthermore, the effects of ownership and firm value on capital expenditure of targets in the period prior to the takeover are considered. Several target characteristics may also affect the capital expenditure of targets in the period prior to the takeover. Existing literature on these relations are examined in two sections. The first section consists of the theoretical perspective and the second section considers empirical results to test these theories.
Theoretical perspective
Prior knowledge of upcoming takeover by target
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Ownership
Management is hired to act on behalf of the interests of the shareholders. Until now it was assumed shareholders only care about value. Even though return is likely one of the key interests of shareholders, certain types of shareholders may also have different interests. According to Gersick (1997) roughly one third of the S&P500 companies are family owned or family controlled. Therefore, this is a substantial share of the companies and it makes this type of ownership important to consider. The founding family may care about the continuation of the firm as well. As a result founding family owned targets may forgo certain risky projects. Other important owners are private equity firms, which are very concerned with obtaining high returns for their investors. MacArthur (2017) finds that the average holding period of private equity firms is between 5 and 5.5 years, which is roughly the period under consideration. Private equity firms are investing capital of others by investing in firms, maximizing the value and selling it again. In general, private equity owned targets are willing to undertake more risky projects than other shareholders in order to obtain high returns on their investments. Different shareholders result in different decisions by management. As a result, the capital expenditure ratio of private equity-owned targets is likely to be higher than the capital expenditure ratio of non-private equity-owned targets. This is the expected outcome based on business as usual. However, the two groups of targets may act on information of an upcoming takeover differently as well. As mentioned before certain owners care about the continuation of the firm and private equity owned companies are more focussed on value maximisation. Reducing capital expenditure compared to the norm when the target gains knowledge of the upcoming takeover increases the value for the target’s shareholders, yet it creates risk for the continuation of the target. Therefore, the effect of knowledge of the upcoming takeover is expected to decrease the capital expenditure ratio of targets owned by private equity firms more than it decreases the capital expenditure ratio of targets owned by non-private equity parties.
Firm value
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as firm value. Tobin’s q has also been used to proxy growth opportunities, such as in Lang and Litzenberger (1989), Lang et al. (1991), Denis (1994) and Jung et al. (1996). To capture growth opportunities firms need to have capital expenditure and capital expenditure will then result in increased firm value. Even though Tobin’s q will be used to capture firm value, endogeneity issues resulting from the growth opportunity approach need to be considered.
Target characteristics
The mechanisms described above directly affect targets’ deviations of capital expenditure from the norm. Alternatively, deviations from the norm found in capital expenditure of targets may be a result of these firms being more likely takeover candidates. Firstly, agency theory is used to identify potential effects on capital expenditures. Imperfect information between management teams of targets who serve as agents and shareholders of targets who are the principals may cause adverse selection and moral hazard. These issues result in lower returns for shareholders as management of the targets could be improved.
Takeovers are part of the market for corporate control as described in Manne (1965). Management can be challenged by outsiders or minority shareholders in three ways; proxy fights, direct share purchases or takeovers. Takeovers are supposed to be the most cost-efficient one. Adverse selection is the inability of shareholders to identify the most qualified management team. Management is supposed to maximize shareholder value. Under-qualified management may engage in among other things: not pursuing positive NPV projects, pursuing negative NPV projects and reaching goals cost-inefficiently. As a result of inefficient management the firm is valued below its potential in the capital market. Another related effect resulting from this situation is below average yearly results. Bidders search for firms that are currently being inefficiently managed. Bidders are able to acquire the firm at a discount compared to its potential if it was managed efficiently. Following the takeover, the bidder may establish efficient management by replacing current management or taking measures to ensure efficiency of current management. Firms subject to inefficient management have lower performance than firms that are efficiently managed. Lower performance results in less funds available for investment. Therefore, capital expenditure of targets is lower compared to the normal level of capital expenditure of the target in the period prior to takeovers.
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discipline management. The results of Jensen (1986), Shleifer and Vishny (1989) and Scharfstein and Stein (2000) imply capital expenditure of targets facing moral hazard is above the capital expenditure norm because management is pursuing negative NPV projects. Non-shareholder maximizing behaviour of management is facilitated by high levels of available cash. The pursue of own interests by management causes lower market valuations of the firm. Therefore, bidders are able to acquire the firm under its potential value. Following the takeover, the bidder establishes disciplinary measures or different management to ensure management maximizes shareholder value.
Another characteristic of targets is based on growth opportunities as a takeover motive for bidders. Information asymmetry between shareholders and management was the underlying issue that caused problems with the management of the target in the previous two target characteristics. Bidders carry out takeovers to acquire assets and grow beyond the internal growth rate. If targets are able to capture all positive NPV projects, takeovers only occur if the bidder pays a premium over the firm value included the NPV of the ongoing projects. Therefore, takeovers are more likely to occur when targets are financially constraint. In this situation the bidder is able to take over the firm and create value by investing in the positive NPV projects the target could not invest in. Fazzari et al. (1988) provide support for this statement. They model that in financially constraint firms, additional funds available for investments firms lead to higher firm values. Financial constraints are caused by financial market imperfections as firms are unable to obtain necessary financing. As a result, capital expenditure of targets is below the norm in the year prior to the takeover. To realise the growth, bidders aim to take over targets with many growth opportunities. An alternative view regarding growth opportunities is put forward by Shleifer and Vishny (2003). Their model assumes rational managers while markets are not fully rational. Financial constraints are not a necessary condition for takeovers motivated by growth opportunities in this model. Bidders believe the firm and growth opportunities of targets are undervalued in the market and aim to create value for their shareholder through takeovers of these undervalued firms. Whether the expectations for capital expenditure of targets prior to takeovers depends on the targets’ management’s valuations of the growth opportunities. There is no useful information available regarding these valuations. Therefore, Shleifer and Vishny (2003) does not lead to expectations regarding capital expenditure of targets prior to takeovers. It does provide an alternative set of assumptions to Fazzari et al. (1988) that results in different levels of capital expenditure of targets prior to takeovers.
Empirical perspective
Prior knowledge of upcoming takeover by target
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process is target-initiated. They also suggest self-selection problems increase the likelihood of target initiated deals. This implies more imperfect information that also allows targets to decrease capital expenditure prior to the takeover to create value for the target’s shareholders. Therefore, capital expenditure is expected to be below the norm in the year prior to the takeover if the takeover is initiated by the target.
Ownership
Additionally, this paper investigates to what extend ownership affects capital expenditure of targets prior to takeovers. Jensen et al. (1992) examines the effects of insider ownership using data of 565 firms in 1982 and 632 firms in 1987. Insider ownership is related to lower dividends and less debt. The argument is that firms with high insider ownership rely more on internal financing for capital expenditure. This suggests that there might be a relation between ownership and leverage. Ownership types vary widely, as found by La Porta et al. (1999) using 540 large firms in 27 countries in 1995. Insider ownership is likely to be relatively high in family-owned firms, as the founding family often occupies positions in the management teams. La Porta et al. (1999) finds that 36% of US firms is widely held, 30% is family controlled, 18% is government-controlled and 15% falls into a residual category. This shows that family-owned firms represent a substantial portion of firms. Anderson, Mansi and Reeb (2003) argue founding family owners care about the continuation of the firm which is caused by the desire to pass a legacy onto the next generations. This implies these firms are more risk averse than other owners. Moreover, Jensen and Meckling (1976) provide further supporting evidence. Diversified owners are more likely to engage in asset substitution. This means that shareholders capture bondholder wealth as the firm engages in risky high return projects. This situation is a result of the limited liability of shareholders. The risk is mostly carried by the bondholders while most of the potential up-side will go to the shareholders. Founding family owned firms would limit risk-taking to ensure continuity, which results in less capital expenditure compared to other firms. When the family owned companies become too risk-averse, bidder will take over the firm to unlock the potential value by allowing more risk. Private equity firms are examined as they are mostly interested in return on investment. Wruck (1989) examines the effects of private equity sales in 128 sales during 1979-1985. The market responds positively to these sales, suggesting there is a change in the underlying value following these sales. It is caused by an increased concentration of ownership which limits agency problems. It also suggests management is going to focus stronger on shareholder value, supporting the theoretical argument regarding private equity firms. Empirical evidence shows that the capital expenditure of targets is at least partly determined by ownership. The capital expenditure ratio of targets owned by private equity firms is higher than the capital expenditure ratio of targets owned by non-private equity parties.
Firm value
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significant relation between capital expenditure and Tobin’s q. Only given high analyst covering and high outside directorships of targets there is a positive relationship between Tobin’s q and capital expenditure. Contrarily, Chan et al. (1990) and Hall (1993) do find a significant positive relation between investment and firm value. Both papers used stock prices instead of Tobin’s q. Chan et al. (1990) considers 95 investment announcements in the US during 1979-1985. Hall (1993) observes a drop in the stock returns in 1986-1991 compared to 1979-1983, yet the relation remains positive. As noted before, using Tobin’s q may lead to endogeneity issues which will be addressed in the method section. Capital expenditure is key to capture growth opportunities. Firms that have good growth opportunities at their disposal, increase capital expenditure which in turn increases the firm value. Chan et al. (1990) indeed finds that increased capital expenditure results in higher market valuations of shares using 95 capital expenditure announcements during 1979-1985. However, effects are only positive for high-technology firms and are negative for low-technology firms. The result implies that firm value affects capital expenditure as well as the reverse. Cho (1998) also discovered endogeneity with respect to firm value and capital expenditure. The results are that for the Fortune 500 companies in 1991 capital expenditure determines firm value. Davies et al. (2005) directly examine the direction of the relationship between ownership and firm value using 802 industrial firms in 1995. The capital expenditure of targets with high firm value is above the capital expenditure norm.
Target characteristics
Agency theory led to interesting expectations regarding capital expenditure of targets in pre-takeover situations. Now empirical papers are used to examine whether these hypotheses hold. Firstly, adverse selection is dealt with. Lang et al. (1989) find evidence for takeovers to be motivated by inefficient management of targets. This is done using 87 tender offers from 1968 to 1980 in the US. Tobin’s q is used as a measurement for management quality. They find that the Tobin’s q of the target drops in the five-year period prior to the takeover. This suggests that as management of the target is becoming less efficient, bidders acquire targets to improve management and unlock the potential value. Martin and McConnell (1991) examined management turnover in 253 takeover during 1958-1980 in the US. Indeed as expected, management turnover of targets increases following takeovers. Moreover, targets that replace management had lower performance prior to the takeover than targets that do not replace management. These results suggest that firms that are not managed properly have an increased chance of becoming a takeover target. Empirical evidence is supportive of the theoretical papers discussed earlier.
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during 1979-1984. A decrease in dividend pay-outs is interpreted by the market as management increasingly undertaking negative NPV projects. The finding of Harford (1999) and Lang and Litzenberger (1989) on the value destruction of these takeovers are also related to Martin and McConnell (1991). Following value destroying takeovers by management acting in their own interests, firms are more likely to become takeover targets. Firms facing moral hazard have higher capital expenditure as management is pursuing its own interests. Bidders target these firms to unlock the value lost by the behaviour of management.
Healy, Palepu and Ruback (1992) use 50 targets during 1979-1984 in the US to track their post-takeover performance. The performance of targets increases following the takeover measured as cash flow returns and asset productivity. They argue this is caused by growth opportunities. Furthermore, the performance increases most for takeovers in overlapping sectors. However, their evidence is not consistent with the financial constraints as rationale for the existence of takeovers. Capital expenditure is in line with industry levels following the takeover. If targets are financially constraint, the capital expenditure should be above average following the takeover to capture the non-utilized positive NPV projects. This contradicts the expectations that follow from the model in Fazzari et al. (1988). On the other hand, the results may be in line with Shleifer and Vishny (2003). Bidders believe the targets growth opportunities are undervalued in the market. If this believe is also present at the target than capital expenditure of the target is above the norm prior to the takeover.
Combining the insights from theoretical and empirical literature the following hypotheses are formed. These are confronted with data on takeovers of publicly traded firms from the US in 2015 and 2016 in order to answer the research question; what determines capital expenditure of targets prior to takeovers?
Hypothesis 1: The capital expenditure ratio of targets with knowledge of being taken over in the near future is lower than the capital expenditure ratio of targets without this knowledge.
Hypothesis 2: The capital expenditure ratio of targets with a private equity firm as largest shareholder is higher than the capital expenditure ratio of targets with a non-private equity party as largest shareholder.
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Data
To explain the capital expenditure of targets prior to takeovers and the effect on the firm value, takeovers of publicly listed targets valued $50 million or more with the announcement date of the takeover in 2015 and 2016 in the US are considered. Only completed deals as of 01/10/2017 are considered. Listed firms need to comply with reporting standards which allows the collection of the required data. There needs to be a substantial acquisition of ownership, otherwise the incentives are still aligned and agency problems will not prevail. Therefore, only acquisitions of a majority interest are included in the sample. Firms that are target in two or more takeovers are excluded from the sample. Takeovers where the target is US-based are considered in the sample irrespective of the bidder’s country of origin as the target is unaware of the bidder’s country of origin at the time of capital expenditure decisions. Therefore, targets cannot considers the bidder’s country of origin in capital expenditure decisions. Moreover, by restricting the considered takeovers to US targets, similarity in legal and market conditions is ensured. An overview of all observations is available in appendix C: Dataset.
The years of consideration with respect to the takeovers are 2015 and 2016, which are the most recent years for which sufficient relevant data is available. Firms are assumed to have knowledge of becoming a target one year prior to the takeover if the target initiated the process. In the case of bidder-initiated deals, targets are unaware of the upcoming takeover one year prior to the takeover. Using 2015 and 2016 takeovers allows us to determine a normal level of capital expenditure from 2010 to 2014 and 2010 to 2013, respectively which excludes the financial crisis. The norm is the average capital expenditure of targets over 2010-2014 for takeovers in 2016 and 2010-2013 for takeovers in 2015. The capital expenditure of targets in the year prior to the takeover is compared to the norm of these targets.
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made between these types as in both types the targets’ interests differ from the bidders’ interests. Disinvestments are also part of gross capital expenditure and lower the capital expenditure. Targets may sell parts of the firm to transform parts into cash before takeovers. This increases the cash position at the time of the takeover and increases the deal value. On the other hand, potential cash inflow from this part of the firm are lost which lowers the deal value. However, cash flows are uncertain so it may result in extensive discussions between targets and bidders while cash items are easier topics in takeover discussions. As with low capital expenditure ratios of targets to maximize the value for the targets’ shareholders disinvestments are a similar possibility for management to maximize the targets’ shareholders’ value. Therefore, disinvestments are included in determining capital expenditure.
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Methodology
The main model that is used to test the hypotheses is specified in equation (1). (1) 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶15𝑖𝑖
𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖= 𝛼𝛼 + 𝛽𝛽1∗ 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖 + 𝛽𝛽2 ∗ 𝑂𝑂𝑂𝑂𝐼𝐼𝑂𝑂𝐼𝐼𝑂𝑂ℎ𝐼𝐼𝑖𝑖𝑖𝑖 + 𝛽𝛽3∗ (𝑂𝑂𝑂𝑂𝐼𝐼𝑂𝑂𝐼𝐼𝑂𝑂ℎ𝐼𝐼𝑖𝑖𝑖𝑖 ∗
𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖) + 𝛽𝛽4∗ 𝑇𝑇𝐼𝐼𝑇𝑇𝐼𝐼𝐼𝐼′𝑂𝑂 𝑞𝑞𝑖𝑖+ 𝛽𝛽5∗ 𝑆𝑆𝐼𝐼𝐼𝐼𝑆𝑆𝑆𝑆 𝑣𝑣𝐼𝐼𝑣𝑣𝐼𝐼𝐼𝐼𝐼𝐼𝑣𝑣𝐼𝐼𝐼𝐼𝑣𝑣𝑖𝑖 + 𝛽𝛽6∗ 𝐴𝐴𝐼𝐼𝐼𝐼𝑣𝑣𝑣𝑣𝑂𝑂𝐼𝐼 𝑆𝑆𝐼𝐼𝑣𝑣𝑂𝑂𝐼𝐼𝐼𝐼𝑐𝑐𝑂𝑂𝑖𝑖 + 𝛽𝛽7∗
𝑃𝑃𝑂𝑂𝐼𝐼𝑃𝑃𝐼𝐼𝐼𝐼𝑃𝑃𝐼𝐼𝐼𝐼𝑆𝑆𝑂𝑂𝑖𝑖 + 𝛽𝛽8∗ 𝐿𝐿𝐼𝐼𝑞𝑞𝐿𝐿𝐼𝐼𝐿𝐿𝐼𝐼𝐼𝐼𝑣𝑣𝑖𝑖 + 𝛽𝛽9∗ 𝐿𝐿𝑂𝑂𝑣𝑣𝑂𝑂𝐼𝐼𝐼𝐼𝑐𝑐𝑂𝑂𝑖𝑖+ 𝛽𝛽10∗ 𝐹𝐹𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑆𝑆𝐼𝐼𝐼𝐼𝑣𝑣 𝑆𝑆𝐼𝐼𝐼𝐼𝑂𝑂𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑂𝑂𝑖𝑖 +
𝛽𝛽11∗ 𝐼𝐼𝐼𝐼𝐿𝐿𝐿𝐿𝑂𝑂𝐼𝐼𝐼𝐼𝑣𝑣𝑖𝑖 + 𝛽𝛽12∗ 𝐶𝐶𝐼𝐼𝐼𝐼𝑂𝑂𝑂𝑂 − 𝑇𝑇𝐼𝐼𝐼𝐼𝐿𝐿𝑂𝑂𝐼𝐼𝑖𝑖+ 𝛽𝛽13𝑌𝑌𝑂𝑂𝐼𝐼𝐼𝐼𝑖𝑖 + 𝜀𝜀𝑖𝑖
The dependent variable is the ratio of capital expenditure of target i in the year prior to the takeover over the capital expenditure norm of target i. For 2016 takeovers the capital expenditure norm is defined as the average of capital expenditure over 2010-2014. For 2015 takeovers the capital expenditure norm is defined as the average of capital expenditure over 2010-2013. The capital expenditure ratio allows for an interpretation of the results as percentage deviations of capital expenditure instead of absolute numbers. The capital expenditure in the year prior to the takeovers is corrected for market movements based on the S&P 500 compared to the capital expenditure norm.
Hypothesis one focusses on the coefficient of 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖. This dummy variable equals one if
the takeover process is initiated by the target and equals zero if the takeover process is initiated by the bidder. Initiators of takeover processes cannot be identified for all takeovers. To avoid the loss of meaningful observations, this variable is only included in model (1) to test hypothesis one.
In order to test hypothesis two the sign of the coefficient of 𝑂𝑂𝑂𝑂𝐼𝐼𝑂𝑂𝐼𝐼𝑂𝑂ℎ𝐼𝐼𝑖𝑖𝑖𝑖is examined. This
dummy variable equals one if the largest shareholder of the target is a private equity firm and equals zero of the largest shareholder of the target is a non-private equity party for target i. To avoid the loss of meaningful observations, this variable is only included in model (3) to test hypothesis two. Roberts and Whited (2012) show that endogeneity creates many problems in corporate finance settings. As mentioned in the literature overview Cho (1998) discovered this issue in an analysis of capital expenditure, firm value and ownership. Therefore, it is key to take these issues into account. This issue is revisited in the results section. (𝑂𝑂𝑂𝑂𝐼𝐼𝑂𝑂𝐼𝐼𝑂𝑂ℎ𝐼𝐼𝑖𝑖𝑖𝑖 ∗ 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖) is an interaction term which identifies the interaction effect of the
dummy variables 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖 and 𝑂𝑂𝑂𝑂𝐼𝐼𝑂𝑂𝐼𝐼𝑂𝑂ℎ𝐼𝐼𝑖𝑖𝑖𝑖. The coefficient should be interpreted as how
much the capital expenditure ratio of target i is affected more by the knowledge of an upcoming takeover if the target is private equity firm owned compared to a target with knowledge of an upcoming takeover if the target is non-private equity owned.
To determine the effect of firm value on the capital expenditure decisions of targets, Tobin’s q is used to measure the firm value of target i on 01/01 in the year prior to the takeover. Chung and Pruitt (1994), Lewellen and Badrinath (1997), and Erickson and Whited (2006) find that rigorous algorithms for Tobin’s q hardly contribute to the accuracy of estimating Tobin’s q. Therefore, this approximation suggested by Chung and Pruitt (1994) is used as it limits the loss of useable observations.
𝑇𝑇𝐼𝐼𝑇𝑇𝐼𝐼𝐼𝐼′𝑂𝑂 𝑞𝑞
𝑖𝑖 = 𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖 + 𝑃𝑃𝑆𝑆𝑇𝑇𝐴𝐴𝑖𝑖+ 𝐷𝐷𝑀𝑀𝐷𝐷𝑇𝑇𝑖𝑖 𝑖𝑖
𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖 is the share opening price of the firm’s share multiplied by the number of outstanding
shares on the first trading day of the year in the year prior to the takeover of target i. 𝑃𝑃𝑆𝑆𝑖𝑖 is
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start of 01/01 in the year prior to the takeovers. 𝐷𝐷𝑀𝑀𝐷𝐷𝑇𝑇𝑖𝑖 is the net of short-term liabilities and
short term assets plus the book value of the firm’s long-term liabilities in the year prior to the takeover of target i on the start of 01/01 in the year prior to the takeovers. 𝑇𝑇𝐴𝐴𝑖𝑖 is the book
value of the total assets of target i on the start of 01/01 in the year prior to the takeovers. The main differences with other proxies of the true underlying q is that this measure uses book values for total asset, short-term debt and long-term debt. However, book values may not be the best measurement of actual replacement costs. On top of that, book values are sensitive to accounting standards. Other measurements are used as robustness tests. Another issue with Tobin’s q is that capital expenditure directly influences the total assets in the firm. Therefore, two-staged least squared estimation is used to eliminate endogeneity issues. Chung, Wright and Kedia (2003) showed that high analyst coverage is a necessary condition for the relationship between capital expenditure and Tobin’s q to be significantly positive. Therefore, we control for this effect using a dummy 𝐴𝐴𝐼𝐼𝐼𝐼𝑣𝑣𝑣𝑣𝑂𝑂𝐼𝐼 𝑆𝑆𝐼𝐼𝑣𝑣𝑂𝑂𝐼𝐼𝐼𝐼𝑐𝑐𝑂𝑂𝑖𝑖. 𝐴𝐴𝐼𝐼𝐼𝐼𝑣𝑣𝑣𝑣𝑂𝑂𝐼𝐼 𝑆𝑆𝐼𝐼𝑣𝑣𝑂𝑂𝐼𝐼𝐼𝐼𝑐𝑐𝑂𝑂𝑖𝑖
equals one if more than 20 analysts are following the target on 01/01 in the year of the takeover and zero if less than 20 analysts are following the target on said date. 20 analyst is the cut-off point as it is nearly the average number of analysts following the targets in the sample. The dummy variable is multiplied with Tobin’s q to ensure high analyst coverage as suggested by Chung, Wright and Kedia (2003). The relation between capital expenditure and firm value is based on the effect of Tobin’s q on financing costs and volatility of stock returns. Volatility of stock returns is 𝑆𝑆𝐼𝐼𝐼𝐼𝑆𝑆𝑆𝑆 𝑣𝑣𝐼𝐼𝑣𝑣𝐼𝐼𝐼𝐼𝐼𝐼𝑣𝑣𝐼𝐼𝐼𝐼𝑣𝑣𝑖𝑖 which is defined as the standard deviation of
annual stock returns of target i over the same period as the capital expenditure norm. This
implies 𝑆𝑆𝐼𝐼𝐼𝐼𝑆𝑆𝑆𝑆 𝑣𝑣𝐼𝐼𝑣𝑣𝐼𝐼𝐼𝐼𝐼𝐼𝑣𝑣𝐼𝐼𝐼𝐼𝑣𝑣𝑖𝑖 and capital expenditure are negatively related. These two variables
are only included in the model when hypothesis 3 is tested. In order to account for likely endogeneity the explanatory variables likely relevant based on economic grounds are regressed on Tobin’s q. Xia (2015) shows target-initiated takeovers often involve targets with a low Tobin’s q and Zha Giedt (2017) finds that bidder-initiated takeovers often involve targets with a high Tobin’s q. Therefore, initiator is included in the model. As the goal of private equity firms is to maximize the value of the targets it is likely to affect Tobin’s q as well. The relevance for stock volatility and analyst coverage has just been covered by Chung, Wright and Kedia (2003). Stock returns directly affect Tobin’s q, so performance is included in the model. Liquidity and leverage influence the operations of targets, which should be related to Tobin’s q. It is specified as shown in (2).
(2) 𝑇𝑇𝐼𝐼𝑇𝑇𝐼𝐼𝐼𝐼′𝑂𝑂 𝑞𝑞
𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽1∗ 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖 + 𝛽𝛽2∗ 𝑂𝑂𝑂𝑂𝐼𝐼𝑂𝑂𝐼𝐼𝑂𝑂ℎ𝐼𝐼𝑖𝑖𝑖𝑖 + 𝛽𝛽3∗
𝑆𝑆𝐼𝐼𝐼𝐼𝑆𝑆𝑆𝑆 𝑣𝑣𝐼𝐼𝑣𝑣𝐼𝐼𝐼𝐼𝐼𝐼𝑣𝑣𝐼𝐼𝐼𝐼𝑣𝑣𝑖𝑖 + 𝛽𝛽4∗ 𝑃𝑃𝑂𝑂𝐼𝐼𝑃𝑃𝐼𝐼𝐼𝐼𝑃𝑃𝐼𝐼𝐼𝐼𝑆𝑆𝑂𝑂𝑖𝑖 + 𝛽𝛽5∗ 𝐿𝐿𝐼𝐼𝑞𝑞𝐿𝐿𝐼𝐼𝐿𝐿𝐼𝐼𝐼𝐼𝑣𝑣𝑖𝑖 + 𝛽𝛽6∗ 𝐿𝐿𝑂𝑂𝑣𝑣𝑂𝑂𝐼𝐼𝐼𝐼𝑐𝑐𝑂𝑂𝑖𝑖 + 𝛽𝛽7∗
𝐼𝐼𝐼𝐼𝐿𝐿𝐿𝐿𝑂𝑂𝐼𝐼𝐼𝐼𝑣𝑣𝑖𝑖 + 𝜀𝜀𝑖𝑖
15
If adverse selection is the rationale for takeover, performance should be negatively related to capital expenditure. 𝑃𝑃𝑂𝑂𝐼𝐼𝑃𝑃𝐼𝐼𝐼𝐼𝑃𝑃𝐼𝐼𝐼𝐼𝑆𝑆𝑂𝑂𝑖𝑖 is measured as the annual stock return in the year prior
to the takeover of target i with reinvested dividends. This means the performance of target i taken over in 2015 is the cumulative of stock returns from the start of the first trading day of 2013 until the start of the first trading of 2014. These returns are adjusted for market movements by subtracting the annual return of the S&P500 index with reinvested dividends. As with the adjustment for the capital expenditure ratio this is done to remove the market trend. The S&P500 annual return for 2015 takeovers is 32.39% and for 2016 takeovers it is 13.69%. The free cash flow theory predicts that high liquidity increases the capital expenditure ratio of target i. 𝐿𝐿𝐼𝐼𝑞𝑞𝐿𝐿𝐼𝐼𝐿𝐿𝐼𝐼𝐼𝐼𝑣𝑣𝑖𝑖 is measured as the quick ratio in the year prior to the
takeover of target i. The quick ratio is defined as (current assets including cash-inventory)/current liabilities. Finally, growth opportunities are related to 𝐿𝐿𝑂𝑂𝑣𝑣𝑂𝑂𝐼𝐼𝐼𝐼𝑐𝑐𝑂𝑂𝑖𝑖. Goyal,
Lehn and Racic (2002) investigate the relation between growth opportunities and leverage using 61 defence firms in the US in 1980-1995. They find a negative relation between leverage and growth opportunities. Leverage is expected to have the same effect on growth opportunities in this sample. 𝐿𝐿𝑂𝑂𝑣𝑣𝑂𝑂𝐼𝐼𝐼𝐼𝑐𝑐𝑂𝑂𝑖𝑖 is defined as the book value of debt over the book
value of debt and equity in the year prior to the takeover of target i on the start of 01/01 in the year prior to the takeover. 𝐹𝐹𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑆𝑆𝐼𝐼𝐼𝐼𝑣𝑣 𝑆𝑆𝐼𝐼𝐼𝐼𝑂𝑂𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖 is included as well to investigate
whether this has an effect in the growth opportunities argument. Fazzari et al. (1988) argue to use dividends over net income to measure financial constraints. Targets are divided into the same three classes as in Fazzari et al. (1988) as the sample is roughly equally split among the classes. Class one contains targets with dividends over net income lower than 0.1. Class two contains firms with dividends over net income between 0.1-0.2. Class three contains firms with dividends over net income of 0.2 or higher. This is measured in the year prior to the takeover. Low pay-out rates suggest binding financial constraints. Most takeover processes start in the year prior to the takeover which is why the variables are measured in the year prior to the takeover. To verify whether the characteristics and behaviour of targets in takeovers are based on the year prior to the takeover values other measurements are used as robustness tests.
The following remaining control variables are included in the OLS regression model 𝐶𝐶𝐼𝐼𝐼𝐼𝑂𝑂𝑂𝑂 − 𝑇𝑇𝐼𝐼𝐼𝐼𝐿𝐿𝑂𝑂𝐼𝐼𝑖𝑖 is a dummy variable which equals one if the takeover is cross-border and
equals zero if the takeover is domestic for takeover i. In dealing with foreign parties, linguistic and cultural difference may increase information asymmetry. This may create distortions in the targets’ capital expenditure. 𝐼𝐼𝐼𝐼𝐿𝐿𝐿𝐿𝑂𝑂𝐼𝐼𝐼𝐼𝑣𝑣𝑖𝑖 is the one-digit SIC section code (0-9) of the main
16 Table 1: Categorical variables
Variable
Bidder Target Unknown
Initiator 86 59 34 Non-PE PE PE owner 126 53 Other PE&Target Initiator*PE owner 160 19 0-10 10-20 >20 Financial constraints 131 21 27 Agriculture, Forestry and Fishing Mining and
construction Manufacturing Transportation, Communications, Electric, Gas and Sanitary service Wholesale and Retail trade Finance, Insurance and Real Estate Services Industry 0 9 94 25 9 3 39
Domestic International Unknown
Cross-border 130 42 7
Table 2: Numerical variables
Variable Mean Median Standard deviation Observations
Capex ratio 1.023 0.979 0.649 154 Tobin’s q 0.738 0.000 1.432 149 Stock volatility 45.283 34.013 37.965 154 Analyst coverage 20.804 17 15.499 146 Performance -15.530 -12.130 36.924 154 Quick ratio 3.229 1.658 8.781 154 Debt/capital 0.588 0.550 0.510 152
17
Results
Table 3: Output regressions
Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
(1) (2)
VARIABLES Capital expenditure ratio
(actual Tobin’s q) Capital expenditure ratio (fitted Tobin’s q)
Target-initiated -0.284** -0.193
(0.136) (0.149)
Private equity owned 0.375** 0.264
(0.145) (0.160) Target-initiated* Private equity owned -0.449** -0.449**
(0.205) (0.206)
Tobin’s q 0.130** 0.503
(0.0616) (0.324)
Stock volatility 1.32e-05 0.00206
(0.00108) (0.00218) Performance 0.00159 -0.000914 (0.00155) (0.00290) Liquidity 0.0154 0.00921 (0.0378) (0.0346) Leverage -0.172 -0.125 (0.276) (0.280) Financial constraints class 1 0.179 0.166
(0.118) (0.126) Financial constraints class 2 -0.0917 -0.0674
(0.165) (0.149)
Manufacturing -0.219 -0.303**
(0.155) (0.129) Transportation, Communications, Electric, Gas
and Sanitary service -0.0223 -0.0198 (0.157) (0.161) Wholesale and Retail trade 0.162 -0.0195
(0.341) (0.306) Finance, Insurance and Real Estate -0.223 -0.300
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In this section the results are presented. The outcomes of the various specifications are shown in table 3: Output regressions. Firstly, hypothesis 1 and 2 are considered using specification (1) in table 3: Output regressions. The main variables of interest are 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖, 𝑂𝑂𝑂𝑂𝐼𝐼𝑂𝑂𝐼𝐼𝑂𝑂ℎ𝐼𝐼𝑖𝑖𝑖𝑖
and (𝑂𝑂𝑂𝑂𝐼𝐼𝑂𝑂𝐼𝐼𝑂𝑂ℎ𝐼𝐼𝑖𝑖𝑖𝑖 ∗ 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖). 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖 is a dummy variable where bidder-initiated deals
are zero and target-initiated deals are one. The coefficient of this variable is negative and significant at the 5% level at -0.314. This suggests that the capital expenditure ratio of targets in target-initiated takeovers is 0.314 lower than the capital expenditure of targets in bidder-initiated takeovers. 𝑂𝑂𝑂𝑂𝐼𝐼𝑂𝑂𝐼𝐼𝑂𝑂ℎ𝐼𝐼𝑖𝑖𝑖𝑖 is also a dummy variable which equals zero if the target’s
owner is a private equity party and one if the owner is a non-private equity party. The coefficient of ownership in this specification is positive and significant at the 1% level at 0.414. Therefore, the capital expenditure ratio of targets owned by private equity parties is 0.414 higher than the capital ratio of targets owned by non-private equity parties. The interaction term of these variables equals 1 if both the takeover is target-initiated and the target is private equity owned and zero otherwise. The coefficient of this interaction term is negative and significant at the 5% level at -0.449. This implies that the capital ratio of private equity owned targets in target-initiated deals is 0.449 lower than the combination of the individual effects of private equity ownership of targets and the target initiation of takeovers. Combined these effects result in 0.349 lower capital expenditure ratio for private equity owned targets in target-initiated takeovers compared to non-private equity owned target in bidder-initiated deals. Table 4: Coefficient matrix ownership initiator shows the overall effect for the four combinations of initiator and ownership.
Table 4: Coefficient matrix ownership initiator
Ownership\initiator Bidder Target
Non-PE owned 0 -0.314
PE owned 0.414 -0.349
The coefficient of Tobin’s q is significant at the 5% level with a value of 0.130. This is given high analyst coverage. Without this condition the coefficient becomes insignificant. Therefore, the capital expenditure ratio is expected to be 0.013 higher if Tobin’s q is 0.1 higher. However, as found in the literature overview, endogeneity issues need to be addressed. The control variables based on target characteristics are 𝑃𝑃𝑂𝑂𝐼𝐼𝑃𝑃𝐼𝐼𝐼𝐼𝑃𝑃𝐼𝐼𝐼𝐼𝑆𝑆𝑂𝑂𝑖𝑖,
𝐿𝐿𝐼𝐼𝑞𝑞𝐿𝐿𝐼𝐼𝐿𝐿𝐼𝐼𝐼𝐼𝑣𝑣𝑖𝑖, 𝐿𝐿𝑂𝑂𝑣𝑣𝑂𝑂𝐼𝐼𝐼𝐼𝑐𝑐𝑂𝑂𝑖𝑖 and 𝐹𝐹𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑆𝑆𝐼𝐼𝐼𝐼𝑣𝑣 𝑆𝑆𝐼𝐼𝐼𝐼𝑂𝑂𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑂𝑂𝑖𝑖. Performance, liquidity, leverage and
financial constraints all result in insignificant coefficients. Target characteristics are not causing variations in the capital expenditure ratio in this setting. The other control variables are 𝐼𝐼𝐼𝐼𝐿𝐿𝐿𝐿𝑂𝑂𝐼𝐼𝐼𝐼𝑣𝑣𝑖𝑖 𝐼𝐼𝐼𝐼𝐿𝐿 𝐶𝐶𝐼𝐼𝐼𝐼𝑂𝑂𝑂𝑂 − 𝑇𝑇𝐼𝐼𝐼𝐼𝐿𝐿𝑂𝑂𝐼𝐼𝑖𝑖. No industry differs significantly from the baseline
19
20
Discussion
In this last section the results are used to conclude whether there is support for the hypothesizes. Furthermore, the implications of these results are discussed. Hypothesis one stated that the capital expenditure ratio of targets with knowledge of being taken over in the near future is lower than the capital expenditure ratio of targets without this knowledge. As the coefficient for initiator is significantly negative, this seems to be the case. Targets that are aware of being taken over in the near future reduce their capital expenditure compared to the norm. As a result, the capital expenditure ratio of target in target-initiated takeovers is 0.314 lower. The average capital expenditure norm of targets is roughly $100 million, so the capital expenditure in the year prior to the takeover of targets in target-initiated takeovers is about $314,000 lower. Therefore, this effect is also economically significant. This is may be to increase the reduce the net debt position of the target which results in a higher deal value. The costs of the forgone opportunities are mostly passed onto the shareholders of the bidder that miss out on potential cash inflows. Previous empirical papers find target-initiated deals often involve low performance targets with financial constraints. This could be related to the low level of capital expenditure of targets in target-initiated takeovers. However, as word of caution is needed as hypothesis one is tested using the initiator of the takeover process based on SEC filings as a variable for the targets knowledge of the upcoming takeover. This variable does not perfectly capture the actual knowledge of the upcoming takeover which is a limitation of this analysis. The SEC filings provide evidence of this issue as some bidders have been regularly probing the interest of the target for a takeover. Therefore, the target is well aware of the bidder’s interest and even though the eventual takeover is initiated by the bidder, the target may have anticipated this and adjusted its capital expenditure accordingly. Moreover, at the time of the takeover process both parties where uncertain about the outcome of the process, thus the target may not have altered its capital expenditure in the year prior to the takeover. In order to provide stronger evidence for the hypothesizes one might consider a better variable to capture the knowledge of upcoming takeovers. A potential approach would be to contact the target firms in inquire at what point in time they strongly felt a takeover would happen in the near future.
21
non-private equity owned targets in the case targets become aware of being taken over in the near future. Private equity owned targets are more willing to forgo positive net present value projects if the cash inflow from these projects cannot be captured by the private equity firm. The dollar effect of the interaction term is $449,000 less capital expenditure in the year prior to the takeover. Combining all effects, the capital expenditure of private equity owned targets in the year prior to a target-initiated takeover is $349,000 lower than the capital expenditure of non-private equity owned target in the year prior to a bidder-initiated takeover. The analysis here was limited to whether the target’s owner was a private equity firm. However, one might also consider various other ownership structures as well. Family, state and insider ownership may be good candidates for doing so.
Hypothesis three states that the capital expenditure ratio of targets is positively related to the firm value of these targets. In the specification (1) in table 3: Output regressions Tobin’s q has a positive effect on the capital expenditure ratio as expected. Just as in Chung, Wright and Kedia (2003) the effect of Tobin’s q is only significant given high analyst coverage. The effect of 0.1 higher Tobin’s q is $103,000 more capital expenditure in the year prior to the takeover. These results are in line with literature as high Tobin’s q is related to higher investments. Next, the two-staged least squared method is used using the fitted values of Tobin’s q. Now the coefficient is not signed as expected and insignificant. This suggest that firm value measured as Tobin’s q does not impact the capital expenditure ratio of targets in the US in 2015 and 2016. Moreover, stock volatility which was also insignificant. Under hypothesis three high firm value would result in lower stock volatility and lower financing costs. Therefore, stock volatility should be negatively related to the capital expenditure ratio of targets. The effect of Tobin’s q is ambiguous as the OLS found the expected result, while the two-stage least squared estimation finds no effect.
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Appendix A: Examples of initiation Target-initiated takeovers
Constant Contact, Inc.
Beginning in early February 2015, Ms. Goodman and Messrs. Grewal, Leiman, and Nault, with the assistance of Morgan Stanley, finalized a process for contacting the companies identified by senior management as potential acquirers, including Endurance and Bidder X. During the first two weeks of February, Morgan Stanley, together with Ms. Goodman, or Mr. Grewal in some cases, contacted eight potential strategic acquirers, including Endurance and Bidder X, to inform them that the Board had authorized a formal review of strategic alternatives, including a potential sale of Constant Contact.
Alliance Fiber Optic Products, Inc
On March 20, 2015, at the request of AFOP's management, representatives of Cowen and Company, LLC ("Cowen"), AFOP's financial advisor, contacted a representative of Corning to discuss Corning's interest in a combination with AFOP.
On March 23, 2015, representatives of Cowen held a telephonic discussion on behalf of AFOP with representative of Corning to discuss Corning's interest in a transaction with AFOP. Corning indicated that it was interested in an in person meeting and a meeting was subsequently scheduled for April 9, 2015.
On April 9, 2015, representatives of Corning and members of the senior management of AFOP held an in person meeting to discuss a potential transaction.
On May 7, 2015, Corning delivered a written non-binding indication of interest to acquire AFOP at a preliminary valuation of $375 million to $400 million in equity value.
Bidder-initiated takeovers
MedAssets, Inc.
On July 20, 2015, Mr. Wise received a joint indication of interest from a private equity fund, Pamplona Capital Management LLC (“Pamplona”), and a potential strategic buyer, VHA-UHC Alliance NewCo, Inc. (“VHA”), to acquire the Company for $26.50 to $27.50 per share in cash, subject to confirmatory diligence and certain other conditions.
Clarcor Inc.
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On September 16, 2016 Mr. Banks contacted Mr. Conway and indicated that he and Tom Williams, Parker’s Chairman and Chief Executive Officer, desired to meet with Mr. Conway so that Mr. Williams could introduce himself to Mr. Conway and Messrs. Banks and Williams could share Parker’s current view of the potential acquisition. Mr. Conway informed Robert Burgstahler, the independent lead director of the Clarcor board of directors, as well as Richard Wolfson, Clarcor’s General Counsel, of this proposed meeting. Mr. Wolfson also contacted representatives of Bass, Berry & Sims PLC (“Bass Berry”), Clarcor’s outside legal counsel, regarding the proposed meeting.
Keywords: capital expenditure, takeovers, initiator, agency theory, Tobin’s q, two-staged least squared
Appendix C: Dataset
Ticker Target Capex ratio
Initiator (0=bidder, 1=target, 2=unclear) PE owne r Ini*Own. Tobin's q (high An. Cov.) Total q (high An.
Cov. Quick Ratio Current Ratio D/V Fin. Constrai nt Industry CB (0=domestic , 1=cross-border, 2=mixed) Volatilit y returns Yearly total return
ACW ACCURIDE CORP 0.434 1 1 1 0.000 0.550 0.854 1.246 0.883 0 2 0 77.874 11.968
ADT ADT CORP 1.281 0 0 0 0.000 0.000 0.491 0.606 0.728 0 7 0
AEGR AEGERION PHARMACEUTICALS INC 1.189 2 0 0 0.000 0.000 1.349 2.025 0.734 0 2 1 AEPI AEP INDUSTRIES INC 0.324 1 0 0 0.000 0.000 1.183 2.090 0.796 0 2 0 54.113 7.307
AFFX AFFYMETRIX INC 1.177 1 0 0 1.199 0.000 2.992 3.781 0.380 0 2 0 70.271 5.419
AGL AGL Resources Inc. 1.016 0 0 0 0.000 0.000 0.675 0.898 0.743 1 4 0
AFOP ALLIANCE FIBER OPTIC PRODUCT 1.157 0 1 0 0.000 0.000 2.730 3.431 0.220 0 2 0 73.139 1.434
ALTR Altera Corporation 0.979 0 0 0 1.625 0.000 5.450 5.716 0.421 2 2 0
ANAC ANACOR PHARMACEUTICALS INC 1.406 0 1 0 0.000 2.189 5.665 5.764 0.703 0 2 0 APOL APOLLO EDUCATION GROUP INC 0.569 1 0 0 1.475 0.000 1.552 1.552 0.475 0 7 0 48.697 15.721 ABUS Arbutus Biopharma Corporation 1.272 2 0 0 0.000 0.000 5.762 5.762 0.255 0 2 1 121.587 88.635 ARUN Aruba Networks, Inc. 1.408 0 1 0 2.715 0.000 1.852 2.019 0.472 0 2 0 34.997 -26.656
SIDE ASSOCIATED MATERIALS LLC 1.054 2 0 0 0.000 0.875 1.577 1.198 0 2 2
ATML ATMEL CORP 0.368 1 1 1 2.437 0.000 2.088 3.307 0.304 2 2 0 53.910 -2.855
AXLL AXIALL CORP 1.270 0 0 0 0.000 0.000 1.662 2.379 0.636 1 2 0 41.412 -18.593
BALT Ballast Point Brewing & Spirits, Inc. 0.304 2 1 0 0.862 1.307 1.503 0.358 2 4 0 78.916 110.540 -BRLI BioReference Laboratories, Inc. 0.748 0 0 0 0.000 0.000 2.290 2.434 0.334 0 7 0 36.431 -5.732 BLT Blount International, Inc. 0.906 0 0 0 0.000 0.000 1.374 2.501 0.801 0 2 0 24.607 -7.302
NILE BLUE NILE INC 0.762 0 0 0 2.684 4.251 0.689 1.034 0.883 0 5 0 53.047 -33.583
BTH Blyth, Inc. 0.214 1 0 0 0.000 0.000 1.138 1.492 0.533 0 2 0 48.412 -37.720
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AVGO Broadcom Limited 2.180 0 1 0 1.507 0.000 3.264 3.775 0.691 1 2 1 24.961 37.420 CVC Cablevision Systems Corporation 0.767 1 0 0 0.000 1.103 1.103 1.744 0 4 1 40.153 -12.499 CAM Cameron International Corporation 0.837 0 0 0 1.062 0.000 1.083 1.769 0.578 0 2 1 10.057 -46.833 CKEC CARMIKE CINEMAS INC 1.243 0 0 0 0.000 0.000 1.195 1.239 0.682 0 7 0 64.371 -15.514 CSCD CASCADE MICROTECH INC 1.842 0 0 0 0.000 0.422 3.701 4.927 0.173 0 2 0 47.834 41.601 CTRX Catamaran Corporation 1.533 0 0 0 1.086 0.000 1.429 1.429 0.434 0 7 0 24.228 -19.174 CKP CHECKPOINT SYSTEMS INC 0.807 1 0 0 0.000 0.000 1.476 1.945 0.582 2 2 1 47.073 -21.901
CHMT CHEMTURA CORP 0.436 1 0 0 0.000 0.000 1.837 2.709 0.577 0 2 1 186.021 -22.532
CLC CLARCOR INC 1.269 0 0 0 0.000 0.000 1.914 3.264 0.389 1 2 0 15.876 -7.173
CTCT Constant Contact, Inc. 1.015 1 1 1 2.582 0.000 3.170 3.170 0.181 0 7 0 62.229 -6.854
CNW Con-way Inc. 1.001 0 0 0 0.740 0.000 1.668 1.700 0.641 0 4 0 20.803 -7.004
CRRC Courier Corporation 0.498 0 0 0 0.000 0.000 1.911 3.094 0.333 2 2 0 27.828 -41.565 CTIG CTI Group (Holdings) Inc. 0.866 1 1 1 0.000 0.000 1.308 1.308 0.556 0 7 1 67.940 -28.882 CYT Cytec Industries Inc. 0.979 0 1 0 0.000 0.000 1.437 2.275 0.562 0 2 1 25.479 -31.337 TRAK Dealertrack Technologies, Inc. 2.480 0 0 0 0.000 0.000 2.862 2.862 0.535 0 7 0 23.722 -32.950
DWRE DEMANDWARE INC 2.266 1 1 1 5.020 0.000 3.719 3.719 0.237 0 7 0
DMN
D Diamond Foods, Inc. 0.736 1 1 1 0.000 0.000 0.896 1.826 0.762 0 2 0 62.894 -18.891 HILL Dot Hill Systems Corp 0.958 0 0 0 0.000 0.000 1.769 1.976 0.480 0 2 0 84.444 11.802
DOVR Dover Saddlery 1.737 1 0 0 0.000 0.000 0.404 2.473 0.582 0 5 0 33.950 -41.341
DTSI DTS INC 1.117 0 1 0 0.000 1.348 1.498 1.498 0.497 0 6 0 55.811 19.026
KODK EASTMAN KODAK CO 0.389 2 0 0 0.000 0.000 2.100 2.784 0.952 0 2 0 59.059 -55.275
EMC EMC Corporation 1.017 0 0 0 1.160 0.000 1.235 1.344 0.487 1 2 0 14.819 -12.130
EDE EMPIRE DISTRICT ELECTRIC CO 1.001 1 1 1 0.000 0.000 0.726 1.118 0.673 2 4 1 10.696 19.994 ELX Emulex Corporation 0.703 2 0 0 0.711 0.000 3.763 4.143 0.367 0 2 1 29.552 -49.647 ENTR Entropic Communications Inc 1.699 1 0 0 0.000 0.000 4.981 5.374 0.164 0 2 0 EPIQ EPIQ SYSTEMS INC 0.746 1 1 1 0.000 0.000 2.321 2.321 0.626 1 7 0 18.041 -0.422
EXAM EXAMWORKS GROUP INC 1.358 0 0 0 0.000 0.000 2.224 2.224 0.658 0 7 0
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FRP FAIRPOINT COMMUNICATIONS INC 0.586 0 0 0 0.000 0.000 0.966 0.966 1.001 0 4 0 182.014 11.972
FEIC FEI CO 0.420 0 0 0 0.000 0.000 2.077 2.690 0.267 2 2 0 14.993 -4.466
FSYS Fuel Systems Solutions, Inc. 0.695 1 1 1 0.000 0.000 2.174 3.170 0.270 0 2 1 24.054 -50.790 FRM Furmanite Corporation 0.652 1 0 0 0.000 0.000 2.948 3.615 0.499 0 1 0 44.041 -49.560 FXEN FX Energy, Inc. 0.848 0 0 0 0.000 0.000 3.695 3.711 0.739 0 1 1 43.628 -78.414 GK G&K SERVICES INC -CL A 1.546 0 0 0 0.000 0.000 0.951 2.052 0.575 1 7 0 23.680 17.681 GKNT Geeknet, Inc. 0.165 0 0 0 0.000 0.432 2.708 3.445 0.285 0 5 0 45.689 104.878 -HNSN HANSEN MEDICAL INC 0.450 0 1 0 0.000 1.269 0.644 0.842 0.968 0 2 0 60.721 102.730 -HAR HARMAN INTERNATIONAL INDS 1.379 0 0 0 1.276 0.000 1.064 1.391 0.596 1 2 1 39.296 18.994 HPY Heartland Payment Systems, Inc. 1.123 0 0 0 1.690 0.000 1.006 1.029 0.819 0 6 0 17.999 -20.931
ONE HIGHER ONE HOLDINGS INC 0.128 1 0 0 0.000 0.000 1.234 1.234 0.437 0 6 0
AWAY HomeAway, Inc 1.678 1 1 1 2.231 3.636 3.754 3.754 0.385 0 7 0
HSP Hospira Inc 1.164 0 1 0 1.044 0.802 1.401 2.272 0.498 0 2 0 37.089 8.875
IGTE iGate Corporation 2.941 0 1 0 0.000 0.000 1.774 1.774 0.631 2 7 1 57.609 -30.673
SAAS INCONTACT INC 1.577 0 0 0 0.000 0.000 3.713 3.713 0.507 0 7 1 38.096 3.091
BLOX INFOBLOX INC 1.202 1 0 0 0.000 0.000 2.822 2.885 0.401 0 7 0
INFA Informatica Corporation 0.425 1 0 0 2.167 0.000 2.093 2.093 0.295 0 7 1 33.957 -38.909 IQNT INTELIQUENT INC 1.151 0 0 0 0.000 3.422 8.687 8.687 0.092 2 4 0 114.816 56.579 ISIL INTERSIL CORP -CL A 0.694 0 0 0 1.476 0.000 1.814 2.203 0.161 2 2 1 37.444 18.642
IL INTRALINKS HOLDINGS INC 1.335 1 1 1 0.000 1.076 1.306 1.306 0.389 0 7 0
IPCM IPC Healthcare, Inc. 1.464 1 1 1 1.648 1.943 1.733 1.733 0.402 0 7 0 17.835 -54.040 JAH Jarden Corporation 1.059 0 1 0 0.937 0.000 1.354 2.079 0.758 0 2 0 33.313 -15.261 LXK LEXMARK INTL INC -CL A 0.556 1 0 0 0.000 0.000 0.681 0.879 0.714 1 2 1 28.153 8.157
LTM Life Time Fitness, Inc. 1.643 1 0 0 1.241 0.000 0.363 0.542 0.588 0 7 0
LOCK LIFELOCK INC 1.521 1 1 1 0.000 0.000 1.021 1.021 0.463 0 7 0
LLTC LINEAR TECHNOLOGY CORP 0.969 0 0 0 5.029 0.000 8.118 8.664 0.162 2 2 0 18.278 -9.845
LNKD LINKEDIN CORP 1.800 1 0 0 3.318 0.000 3.312 3.312 0.359 0 7 0
LIOX LIONBRIDGE TECHNOLOGIES INC 0.799 0 0 0 0.000 0.000 1.687 1.687 0.688 0 7 0 53.011 -8.487
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LRE LRR Energy, L.P. 0.958 1 1 1 1.005 0.000 3.659 3.659 0.609 2 1 0
MAG Magnetek Inc 0.910 1 0 0 0.000 0.000 1.624 2.422 0.700 0 2 0 72.083 32.606
MWE MarkWest Energy Partners, L.P. 1.410 0 1 0 1.274 1.333 0.791 0.840 0.436 2 1 0 20.118 -22.259 MDAS MedAssets, Inc. 0.952 0 0 0 1.269 0.000 0.655 0.655 0.740 0 7 2 58.749 -29.443 MDV
N MEDIVATION INC 2.313 0 1 0 5.156 0.000 2.383 2.383 0.390 0 2 0 81.511 41.307
MRGE Merge Healthcare Inc 0.792 1 1 1 0.000 0.000 1.074 1.130 0.865 0 7 0
MCRL Micrel, Incorporated 0.996 1 0 0 0.000 0.000 2.759 3.589 0.208 2 2 0 32.999 12.193 MW
W MONSTER WORLDWIDE INC 0.445 1 1 1 0.470 0.000 1.125 1.125 0.585 0 7 1 65.790 -41.406 MFLX MULTI-FINELINE ELECTRON INC 0.201 0 0 0 0.000 0.000 2.257 2.682 0.280 0 2 1 57.318 -30.170 MWIV MWI Veterinary Supply, Inc. 1.520 0 0 0 0.000 0.000 0.787 1.608 0.518 0 5 0 21.192 -29.183
N NETSUITE INC 2.626 0 0 0 7.470 0.000 1.262 1.262 0.732 0 7 0 12.143 -1.129
NSR NEUSTAR INC 0.497 0 0 0 0.000 0.000 0.797 0.797 0.671 0 4 0 5.865 -59.664
NEWP NEWPORT CORP 1.026 0 0 0 0.000 0.000 1.706 2.869 0.363 0 2 0 38.832 -5.525
NTK NORTEK INC 1.173 0 0 0 0.000 0.000 0.821 1.571 0.994 0 2 1 53.814 -1.757
NPSP NPS Pharmaceuticals, Inc. 1.746 0 1 0 10.320 0.000 3.948 4.610 0.538 0 2 1 64.360 -6.169 NTLS NTELOS Holdings Corp. 1.010 1 1 1 0.000 0.000 2.252 2.506 1.049 1 4 0 36.182 155.907 -OCAT Ocata Therapeutics Inc. 0.997 0 1 0 0.000 0.000 0.837 0.837 1.477 0 2 1 100.417 -21.856
OCR Omnicare, Inc. 1.021 1 1 1 1.146 0.000 1.244 1.843 0.551 0 5 0 21.902 -10.902
OWW Orbitz Worldwide Inc. 0.926 1 1 1 1.239 0.594 0.575 0.575 0.940 0 4 0 63.671 -8.906
OUTR OUTERWALL INC 0.369 1 0 0 0.000 0.000 0.644 1.094 1.016 0 2 0 36.262 1.849
PSEM Pericom Semiconductor Corporation 0.287 1 0 0 0.000 0.000 7.912 8.563 0.136 0 2 0 21.419 13.569 PNY Piedmont Natural Gas Company Inc 1.036 0 1 0 0.000 0.000 0.422 0.559 0.726 2 4 0 11.714 -9.231 PLNR Planar Systems, Inc. 0.138 1 1 1 0.000 0.000 1.366 2.173 0.455 0 2 1 37.431 151.064
PMCS PMC-Sierra, Inc. 0.688 0 0 0 1.333 0.000 1.875 2.175 0.278 0 2 0 27.903 6.714
PMFG PMFG Inc. 1.545 0 0 0 0.000 0.000 1.491 2.108 0.417 0 2 0 32.953 -78.217
PLCM POLYCOM INC 0.595 1 0 0 0.842 0.000 2.276 2.512 0.409 0 2 0 42.273 6.858
33
PKT Procera Networks, Inc. 1.203 1 0 0 0.000 0.000 5.310 5.922 0.179 0 2 0 51.219 -98.231 PWX PROVIDENCE AND WORCESTER RR 1.110 1 0 0 0.000 0.000 1.903 2.049 0.294 1 4 0 35.169 -19.871
QLIK QLIK TECHNOLOGIES INC 1.346 0 0 0 3.751 0.000 2.014 2.014 0.457 0 7 0
QLTY Quality Distribution Inc 1.334 0 0 0 0.000 1.424 2.495 2.495 1.074 0 4 1 74.700 -47.419 RPTP RAPTOR PHARMACEUTICAL CORP 1.566 0 0 0 0.000 0.000 4.369 4.529 0.723 0 2 2 78.560 -7.249
RLOC REACHLOCAL INC 0.638 1 1 1 0.000 0.770 0.358 0.358 1.016 0 7 0
RGP Regency Energy Partners LP 1.834 0 1 0 0.717 0.000 0.841 0.930 0.438 2 1 0 20.131 -30.692 REMY Remy International, Inc. 0.882 0 0 0 0.000 0.000 1.153 1.670 0.556 0 2 0 243.386 -36.418 RNF Rentech Nitrogen Partners, L.P. 1.474 0 0 0 0.000 0.000 0.791 1.229 0.979 1 2 0
ROSE Rosetta Resources Inc. 1.492 1 0 0 1.155 0.819 1.096 1.096 0.607 0 1 0
RNDY Roundy's, Inc. 1.059 0 1 0 0.000 0.000 0.420 1.218 1.077 0 5 0
RTI RTI International Metals, Inc. 0.484 0 0 0 0.000 0.000 1.782 3.496 0.492 0 2 0 13.676 -57.898
RKUS RUCKUS WIRELESS INC 1.652 2 0 0 1.812 0.000 3.526 3.823 0.256 0 2 0
SLXP Salix Pharmaceuticals, Ltd. 1.464 1 0 0 1.960 0.000 0.563 0.682 0.942 0 2 1 46.921 1.857 SNDK SanDisk Corporation 0.847 0 0 0 1.470 0.000 1.598 1.917 0.353 1 2 0 35.230 5.275 SQN
M SEQUENOM INC 0.156 1 0 0 0.000 0.883 4.026 4.138 1.323 0 7 0 62.095 42.105
SGI SILICON GRAPHICS INTL CORP 0.620 1 1 1 0.000 0.000 1.090 1.506 0.862 0 2 0 19.605 -22.620 SIMG Silicon Image, Inc. 0.957 1 0 0 0.000 0.000 3.952 4.296 0.209 0 2 0 66.602 -38.964
SKUL SKULLCANDY INC 1.129 0 1 0 0.675 0.408 3.358 4.538 0.190 0 2 0
SLI SL INDUSTRIES INC 1.236 2 0 0 0.000 0.000 1.057 1.525 0.425 0 2 0 28.838 30.996
SCTY SOLARCITY CORP 2.522 0 0 0 1.119 0.000 0.468 0.756 0.762 0 2 0
SWI SolarWinds, Inc. 4.358 0 0 0 3.304 0.000 1.586 1.586 0.286 0 7 0 46.795 -1.492
SLH Solera Holdings, Inc. 1.067 1 0 0 0.000 0.000 3.741 3.741 0.682 1 7 0 21.792 -62.121 SEP SPECTRA ENERGY CORP 1.189 0 1 0 1.184 0.000 0.395 0.486 0.700 2 4 1 22.223 15.549 STJ ST JUDE MEDICAL INC 0.552 0 0 0 1.763 0.000 0.953 1.320 0.691 1 2 0 26.203 -5.290 HOT Starwood Hotels & Resorts Worldwide, Inc. 0.824 1 0 0 2.069 0.000 0.851 0.947 0.824 2 7 0 31.654 -23.530
STRZA STARZ 1.020 0 0 0 0.000 0.000 2.297 2.297 0.861 0 4 0 65.678 -9.242
SWC STILLWATER MINING CO 0.810 1 1 1 0.000 0.000 7.181 8.680 0.291 0 1 1 63.130 9.046
