University of Amsterdam Amsterdam Business School BSc Economics & Business
Bachelor Specialisation Finance and Organisation
Mergers and Acquisitions in the High Tech Industry
Author: Alina Taran
Student number: 10969845
Thesis supervisor: Dr. Jan Lemmen
Statement of Originality
This document is written by Alina Taran who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
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Abstract
This paper examines the effect of mergers and acquisitions on the firm value of top ten technology companies in the US in the long run. In this study, the firm value is measured by the buy-and-hold abnormal returns. Also, the impact of several variables, such as profitability, leverage, firm size, number of deals and border mergers on the firm value is analysed through the use of the pooled cross-sectional OLS-regression. The time span, which is used in this study, is the period from 2002 to 2017. The primary results of this study are as follows: the buy-and-hold abnormal returns are positive for four out of ten biggest tech companies in the US, specifically Apple, Alphabet, Amazon and Facebook. Other six companies, namely Cisco, IBM, Oracle, Intel, HP and Microsoft have shown negative BHARs. Furthermore, the effect of profitability, leverage, cross-border mergers and payment method on the firm value appeared to be insignificant, whereas the firm size and the number of deals undertaken by the acquirer have a significant negative relationship with the firm value.
Keywords: firm value, M&A, high-technology, shareholder value, BHAR
Table of Contents
LIST OF TABLES ... 5
CHAPTER 1 Introduction ... 6
1.1
Mergers in General ... 6
1.2 Mergers in High Tech ... 6
1.3 Research Question ... 7
1.5 Main results ... 8
1.6 Structure of the paper ... 9
CHAPTER 2 Literature Review ... 10
2.1 Definitions ... 10
2.2 Theories on Motives of M&A Activity ... 10
2.3 Acquisitions and Innovation ... 12
2.4 Current literature overview of post-M&A effects and performance ... 13
2.5 Firm Valuation Measures ... 14
2.6 Factors Affecting Firm Value ... 15
CHAPTER 3 Methodology and Data ... 19
3.1 Methodology ... 19
3.1.1. Event Study ... 19
3.1.2. Buy-and-hold Abnormal Returns ... 19
3.1.3. OLS regression ... 20
3.2 Data ... 20
3.2.1 Sample Selection ... 20
3.2.2 Descriptive Statistics ... 21
CHAPTER 4 Preliminary Empirical Results and Analysis ... 24
4.1 BHARs ... 24
4.2 Cross-sectional Regression Analysis ... 24
4.2.1 Multicollinearity test ... 24
4.2.2 Regression Analysis ... 25
4.2.3 Correlogram for Residuals (White Test) ... 27
CHAPTER 5 Conclusions ... 28
5.1 Conclusions ... 28
5.2. Limitations ... 28
REFERENCES ... 30
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LIST OF TABLES
Table 1 Regression Summary (page 8)
Table 2 Descriptive Statistics of Top 10 Tech Companies (page 21)
Table 3. Deal Values Statistics (page 22)
Table 4. BHARs over the period 2002-2017 (page 24) Table 5. Correlation Coefficients Matrix (page 25)
Table 6. VIF Test (page 25)
CHAPTER 1 Introduction
1.1 Mergers in General
M&As have been used as an essential business and strategic tool of corporate restructuring for a long time dating back to 1987 (Ghosh & Dutta, 2014). They also provide company opportunities for rapid growth. Starting from 2000, companies were spending around three trillion US dollars every year on more than 30000 M&A transactions (Schoenberg, 2006).
There are various reasons for M&A activities. Firstly, M&As may be a strategic move, which is undertaken by companies to enter a new business or geographical market in order to diversify their business (Schoenberg, 2006). Secondly, M&As may be a part of a financial strategy of the firm since M&A may contribute to instantaneous earnings per share improvement to the acquiring firm. Finally, M&A deals may have managerial motives. Schoenberg (2006) suggests that M&A may help a manager to improve his position in the company by increasing the firm size through a merger.
According to Schoenberg (2006), 43% of international acquisitions fail to demonstrate equal or higher financial return than the acquirer’s cost of capital. 50% of the US companies failed to gain positive after-merger cumulative abnormal returns, whereas 43% of merged firms worldwide reported lower profits than other benchmark non-merged firms (Banal-Estañol & Seldeslachts, 2011). Furthermore, Bradt (2015) stated that 83% of mergers fail to increase shareholders’ value. Even though the large percentage of all merger deals fails to be successful, there is a consistent M&A activity with an increasing number of M&A deals every year.
1.2 Mergers in High Tech
It is undoubtful that with the growing development of technology, the worldwide standards of living have increased. The high technology sector plays a vital role in enhancing the technological developments and scientific discoveries. This makes technology firms essential and innovative participants of the modern economy (Kohers and Kohers, 2001). The technology has profoundly affected the global economy, increased job opportunities and bolstered the economic growth. More than 50% of the developed
countries GDP is based on technology industries, including telecommunications, computers and software (Kohers and Kohers, 2000).
M&A activity in the technology sector has been growing every year with its boom in 2015. The 35% annual stock return of high-technology firms outperformed all the other sectors from 1993 through 1996 (Kohers and Kohers, 2000). According to Boston Consulting Group 2017 M&A report, the aggregate deal value between technology companies was 2.5 trillion US dollars. Baker and McKenzie (2018) predicted that M&A in the technology sector will reach its highest level since 2000, specifically M&A activity is
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predicted to increase to $415 billion in the US only. 2015-2016 were the boom years for mergers and acquisitions in the technology sector with values of $350 billion in 2015 rising to $371 billion in 2016. This can be seen as evidence for increasing importance of technology and innovations.
Business Insider (2011) provided a list of the biggest merger and acquisitions deals ever in the technology industry. For example, the most significant M&A deal in the technology industry was made between AOL and Time Warner in 2000. The transaction value of this acquisition was 181.6 billion US dollars. Dell’s acquisition of EMC in 2015 for 67 billion dollars was the second largest deal value and the biggest pure-technology merger. The recent 2016 acquisition of LinkedIn by Microsoft had a deal value of 26 billion.
Despite many existing kinds of literature which study the general effects of mergers and acquisitions on the firm performance, measuring the value of tech companies may be more problematic than doing it for the companies from other industries. High levels of both growth and uncertainty characterize high technology industry (Rossi et al., 2013). Therefore, it leads to difficulties in measuring performance in this sector. However, it predominantly concerns small start-ups which are acquired by big firms. The uncertainty arises from the start-ups', which express the need for new technical knowledge and skills, while bidding firm does not have enough of in-house resources to develop the target company.
Among the other obstacles in defining the high tech company value, is that it may be difficult due to the uncertainty about whether a new tech product will be successful. Also, high tech companies own a lot of intangible assets. Intangible assets are nonphysical assets that are the primary source of benefits for high tech firms. Among examples of intangible assets owned by a high tech firm are know-how, knowledge, patents and copyrights. The difficulty comes from the fact that there is still no general way of measuring intangible assets and each firm uses different methods.
1.3 Research Question
Despite the various studies and researches conducted on the M&A deals, there is still no clear answer whether M&As increase firm value and performance. This paper will contribute to the modern literature on M&As by investigating how long-term firm value is affected by the completion of M&A. Therefore, the research question, which will be investigated in this paper, is: “Do Mergers and Acquisitions increase firm value of the top 10 largest high technology companies? ”. This paper will analyze whether mergers and acquisitions deals in the technology sector, which happened between 2002 and 2017, were indeed successful.
The focus of this study will be the top 10 most prominent tech companies in the US: Alphabet Inc., Amazon.com Inc., Apple Computer Inc., Cisco Systems Inc., Facebook Inc., Hewlett-Packard Company, Intel Corporation, International Business Machine Corporation, Microsoft Corporation, Oracle
Corporation. According to CNBC (2018), the combined revenue of these tech giants is increasing by 15.7%, more specifically from $146 billion to $1.078 trillion in 2018, based on the estimates of Thompson Reuters. The returns of these ten firms outperformed the returns of both NASDAQ and S&P500 indexes. Furthermore, 10 American tech companies are also listed in the top 25 world’s largest companies in the world, according to Forbes Global 2000 list.
Although there exist numerous studies done on M&As, their effect on firms’ long-term performance is still not apparent. Furthermore, there are not many studies, which were conducted to test the long-term post-merger performance specifically in the tech industry. Since this industry is fast growing and fast developing, it is crucial to investigate it more. Therefore, this study will contribute to the existing literature by adding some novelty to the M&A studies.
Taking into consideration all the information mentioned above, the hypotheses of this paper are stated as follows:
H0: Mergers and Acquisitions do not increase firm value in the technology industry for the top 10 firms.
H1: Mergers and Acquisitions do increase firm value in the technology industry for the top 10 firms.
1.5 Main results
The calculations of the buy-and-hold abnormal returns as a measure of firm value have shown that over the period from 2002 till 2017 only four companies, more specifically Apple, Alphabet, Amazon and Facebook have a positive return. On the other hand, Cisco, Intel, IBM, Oracle, HP and Microsoft have shown negative buy-and-hold returns over this time span.
The summary of the regression analysis can be found in the table below: Table 1. Regression Summary
Independent Variable Significance/
Relationship with the dependent variable
ROA No/negative
D/E Ratio No/negative
Firm Size Yes/negative
# of Deals Yes/negative
Cross-Border Dummy No/negative
Payment Method Dummy No/positive
The results of this study state that the only independent variables, which have an impact on the dependent variable (BHAR) are the firm size and the number of deals. The regression analysis showed that there
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exists a negative relationship between the buy-and-hold abnormal returns and the size of a firm. There may be various reasons for this negative result: either the tech firms became too big, or the organizational structure became too complicated. It might also be that the cultural differences are too strong, which lead to the problems of integration of corporate culture. The number of deals undertaken by the bidding firm is also negatively related to the BHAR. All the other variables, such as ROA and D/E and two dummy variables (cross-border mergers and payment method) have appeared to have an insignificant effect.
1.6 Structure of the paper
This paper is structured as follows. Section 2 will provide a literature review on mergers and acquisitions. Section 3 provides methodology and data, which were used in order to answer the research question. Section 4 represents a summary of the results and their discussion. Finally, section 5 provides conclusions and limitations to further research.
CHAPTER 2 Literature Review
In this section, general definitions and theories regarding mergers and acquisitions and their motives are provided. Factors affecting the firm value are also discussed. Lastly, a summary of academic researches and studies on whether M&A improve the firm value will be reviewed.
2.1 Definitions
According to Gaughan (2010) merger is a combination of two firms in which one company survives, and the merged one ceases to exist, whereas acquisition is defined as an entire purchase of the firm.
According to Berk & DeMarzo (2011), there are various types of mergers, such as horizontal, vertical and conglomerate. Duyster & Hagedoorn (2002) defined horizontal mergers as those, which involve M&As between firms, which are closely related in the sense of the product or services that they provide. On the other hand, vertical mergers are created between the firms, which had buyer-seller relationships before the merger deal. Lastly, conglomerate mergers involve a deal between the companies, which are unrelated in terms of their operating activities.
2.2 Theories on Motives of M&A Activity
The existing academic literature does not have definite conclusions on the reasons why firms merge since it is difficult to distinguish between various motives (Kiymaz & Baker, 2008). Therefore, authors suggest dividing M&A motives into different categories such as synergies, agency and hubris. In the current literature, there are various theories on why do mergers occur despite considerable evidence of mergers failure. Thus, this paper will focus on synergy, managerialism and hubris theories of mergers and acquisitions motives.
Sirower (1997) defined synergy as the enhancement in company competitiveness and improved cash flows, which are beyond those that would be earned independently. Synergies are also used as a synonym for cost reduction. Therefore, post-merger financial performance may be improved by removing
intersecting expenditures, such as administration costs (Ali-Yrkkö, 2002). The author claims that in case of vertical integration, cost reductions may also be achieved through lower production costs as well as eliminated communication and bargaining costs. Among other synergies, which may be created after the merger, are financial synergies, such as tax advantages.
According to Chondrakis (2016), the unique synergy from the merger between the high technology companies may be created in two ways: through the informational benefits or the patents ownership. Firstly, the info advantages come from the diminished info asymmetry, which means that the bidding firm has a greater insight into the target’s technological resources. Secondly, innovation and patents create special synergies due to the failure of the other firms to imitate the technology resources of the bidding company.
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However, Sirower (1997) claimed that predicting the post-merger synergy values is difficult. Furthermore, the author stated that it is challenging to accomplish the potential synergies due to difficulties in attaining integration between firms. There might be several problems with integrations, which may arise. For instance, in the process of M&A, companies are likely to face the problem of coordinating between two different systems. It is required to have a clear plan of integration to make the whole process easier. Also, the process of integration may be costly, since implementing unification of two companies and merging technologies may come at a high cost. Finally, corporate cultures might be different in two companies, which makes integration more complicated. Both companies may have a different vision and values, decision-making or leadership style or different beliefs in general.
The principal-agent problem may be used as a background for managerial motives of M&As. According to Kiymaz & Baker (2008), agency motives claim that M&A activity occurs because they improve the bidding company management at the expense of the target one. It means that the self-interest of the acquirer management drives the motivation to engage into M&A. Therefore, instead of maximizing the wealth of shareholders, managers seek to expand their wealth (Ali-Yrkkö, 2002). There are various ways in which manager may be engaged in the opportunistic behaviour. For instance, with the use of M&A strategy, a manager may be involved in the empire-building activities, such as the use of the free cash flows to increase the size of the firm (Kiymaz & Baker, 2008).
There is also evidence for the existence of the principal-agent problem and manager’s opportunistic behavior in the high-technology industry. In 2015 Samsung was engaged in a scandal, which was connected to a merger of two Samsung units (Reuters, 2017). This merger was controversial and was accused of paying bribes to the government to get the support of the officials since this merger would give Samsung’s leader, Lee Jae-yong, more power. Also, in February 2017 the same Samsung leader was engaged in a big scandal, which could be seen as an example of the principal-agent problem. He got accused of bribery, corruption and hiding assets abroad (CNN, 2018).
Hubris hypotheses state that managers usually make mistakes when evaluating targets and engage in acquisition activities even when no synergies are created (Kiymaz & Baker, 2008). It means that the bidding firm management may overestimate the synergies, which may be formed after the merger with the target firm and overpay for the acquired company. Finally, the main conclusion of a hubris hypothesis is that the management of the acquiring company makes mistakes when valuing the target firm (Ali-Yrkkö, 2002). An example of hubris hypothesis within the technology industry may be the merger deal between AOL and Time Warner. The deal value was $111 billion, and these two companies merged to unify the Internet and the old media. The managers’ overestimation of the merger outcomes led to the most significant post-merger annual loss ever made.
Finally, in the research done by Mueller & Sirower (2003), there is reasonable support for agency and hubris hypotheses, while very few evidence exists that would state that the mergers do create synergies.
The research done by Kiymaz & Baker (2008) concluded that the evidence supports the synergy motives for M&As, while there is also some support for hubris hypotheses. However, there is still no clear evidence and conclusion regarding these theories of merger motives and their relevance in the high technology sector.
2.3 Acquisitions and Innovation
Innovation in the high tech industry is an essential factor, which determines the success of the firm. In order to survive and grow, tech companies need to come up with new and innovative products and systems. Innovation is crucial since it stimulates growth, creates new jobs and even boosts the overall economy of the country by increasing the competitiveness of the products (Mandel & Carew, 2011). The technology is one the most important sources of innovation and growth (Mandel & Carew, 2011). The company growth may come in two ways: organic or inorganic. Organic is the one, which is created through an active and effective strategic planning, management and control. With an organic growth, the firm management has full control over the actions and decisions. However, with organic growth, there might be a lack of resources needed to achieve the desired path of the development of the firm. On the other hand, inorganic growth is generated by mergers and acquisitions with other businesses. M&As may help to enlarge not only firm’s assets and market share but also knowledge and expertise. Furthermore, through inorganic growth, a firm can get an opportunity to enter new markets and sectors with the help of the successful M&A. Also, it is believed that inorganic growth is the faster technique for companies to achieve a growth than the organic one. However, some challenges may appear with the inorganic growth, such as the complicated process of integration between two merging firms.
There is evidence from the real-life acquisitions, that technology acquisitions do facilitate innovation and growth. For instance, Apple’s successful iPhone and iPad was launched with the help of acquisition of small Fingerworks, which provided Apple with the gesture recognition system (Mandel & Carew, 2011). Another example is Google’s purchase of Android, which swung into one of the widely used operating systems for mobile phones in the world. There are more noteworthy acquisitions, which happened from 2007 through 2018, such as IBM’s purchase of SPSS, Google’s acquisitions of Youtube or Facebook and Linked-In deal.
However, it is still not clear whether acquisitions lead to innovation and growth since there were many unsuccessful deals. For instance, in 2007 Cisco purchased Pure Digital, a company, which developed the technology of the flip video cameras. The deal value of this acquisition was 600 million US dollars. However, this acquisition failed because right after the deal, cell phones started to include flip cameras into mobile phones (Mandel & Carew, 2011). Another example is New Corp’s purchase of MySpace in
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2005 for 580 million dollars. Few years after the deal, MySpace’s went into a steep decline. Besides that, the number of users of Facebook was increasing at a fast speed, which led to the selling of MySpace. With the rapid growth of innovation, in order to keep growing, large tech companies pay vast sums of cash for small startups (Business Insider, 2018). One of the reasons for these numerous start-up acquisitions might be that the tech companies fail to produce new and innovative products. Therefore, they end up buying small start-ups to get the ready products straightaway, which reduces the acquiring company’s R&D expenses (Colombo & Rabiosi, 2014). Another reason for start-ups acquisitions may be connected to a dot-com crisis of 1999. Before this crisis, technology companies used to pay large sums of money to any companies, which demonstrated any innovative perspective. This led to many failed acquisitions and mergers. As a result, companies were losing billions of dollars. After the 2000s, many companies revised their M&A strategies. Therefore, large tech firms started buying small start-ups instead of large companies to avoid significant losses in case of an unsuccessful acquisition.
Furthermore, start-ups acquisitions are also facilitated by the fact that nowadays small companies prefer to be bought, rather than launching an initial public offering (IPOs) (Mandel & Carew, 2011). The reason for this trend is that IPO may be too costly for a small firm. Also, when going public, the company has to follow many regulations and sets of rules, such as the Sarbanes-Oxley Act. Therefore, it might be easier for small start-ups’ owners to agree to the acquisition deal.
2.4 Current literature overview of post-M&A effects and performance
There are plentiful of researches done which examine the effects of mergers and acquisitions on firm performance. However, for decades, there is an ongoing debate about the post-merger effects since there is no definite conclusion whether M&As end up in positive or negative outcomes. For example, Agrawal et al. (1992) found significantly negative cumulative average abnormal returns for the holding periods of 1 to 5 years. More recent researchers also conclude that on average, merger and acquisitions do not lead to better firm performance.
On the other hand, Healy et al. (1992) claimed that there are considerable improvements in the merged firms operating cash flows, which resulted from asset productivity enhancements. These authors also found some evidence on the positive effect between the increase in the post-merger cash flow and the abnormal stock returns at merger announcements. However, this study considers mergers in general, and not specifically in the technology industry.
There is also literature, which focuses explicitly on the high-technology industry. For example, Loughran and Vrijh (1997) found that over the period of 5 years after the merger, the acquiring company have buy-and-hold negative abnormal returns of -15.9%. Kohers & Kohers (2000) results are also consistent with the previous findings, stating that in comparison to target companies, the bidding firms show poor
performance in 3 years after the high technology merger announcement. According to Boston Consulting Group Report (2017), only 51% of the tech acquisitions deals resulted in positive cumulative abnormal returns at the announcement.
In contrast, Kohers & Kohers (2000) also found that the positive returns and general success of targets in technological M&As are due to the positive results of the economies of scale and R&D. A study, which was done by Hagedoorn & Duysters (2002), also found an improved technological performance in the high-technology firms. Research conducted by Cloodt et al. (2006) concluded that the mergers in high technology industry lead to positive results and improved innovative performance of the firm due to the transfer of knowledge between the firms. Furthermore, Odagiri & Hase (1989) proposed that the long-term positive outcomes in the technology sector are due to the diversification across related product lines. To sum up, in general, current academic literature does not have a concrete conclusion about post-merger value creation.
2.5 Firm Valuation Measures
There is always a concern about the right choice of a long-run performance measure in any long-run event study. Existing literature offers various measures of the firm performance, such as measures of the periodic performance: cumulative abnormal returns (CARs), averages of monthly abnormal returns (AARs) or compounded buy-and-hold abnormal returns (BHARs); and calendar time methods, such a Calendar Time Abnormal Returns (CTAR). Ibbotson build-up method is also used as a tool of business valuation.
An event study conducted by Barber and Lyon (1997) tried to discover the long-run abnormal stock returns. In their study, authors use buy-and-hold abnormal stock returns instead of cumulative ones. There are few reasons for that. It is argued, that CARs are biased predictors of BHARs, which may lead to the wrong conclusions. Therefore, a failure to reject the null hypothesis of no abnormal return as measured by CAR does not indicate the absence of abnormal returns as measured by BHAR. According to Barber & Lyon (1997), BHAR seems to be a better measure of the long-term returns.
Barber & Lyon (1997) and Barber et al. (1999) recognized three potential drawbacks with the use of BHAR as a measure of the firm performance: new listing, skewness and rebalancing. Firstly, the same as is valid for cumulative returns, BHARs are subject to a listing bias. According to Ritter (1991), newly listed companies underperform market averages. Thus, it is expected that the new listing bias would lead to a positive bias in the mean of the long-run buy-and-hold abnormal returns. Furthermore, BHARs are tending to be sharply positively skewed. This positive skewness leads to a negative bias in test statistics (Barber & Lyon, 1997). It means that this negative bias emerges from the positive correlation between the standard deviations in positively skewed distributions and the sample means. Also, positive skewness may lead to an asymmetric power of the test (Kothari and Warner, 1997). Lastly, when computing
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BHARs, equally weighted market index is compounded assuming monthly rebalancing, while the sample firms’ returns are calculated without rebalancing. Rebalancing and skewness generate a negative bias in a population mean, whereas new listing bias leads to a positive bias. (Barber et al., 1999).
Despite the disadvantage mentioned above, buy-and-hold abnormal returns are considered to be a better measure of the long-term firm performance. Therefore, in this study BHAR will be used as a firm performance measure.
2.6 Factors Affecting Firm Value
The buy-and-hold abnormal returns will be used to investigate the firm value. Numerous academic studies have been tried to analyze the determinants of the firm value. In this paper, the following five factors will be introduced in order to investigate whether they have an impact on determining the firm value: stock prices, profitability, leverage ratio, firm size and payment method. Also, two additional variables will be added into the regression analysis, namely the number of deals undertaken by the acquirer and the cross-border mergers.
Stock Prices
Stock prices are another crucial determinant of the firm value. According to Khanna & Sonti (2004), higher prices play a role of a signal about improved prospectus to firm managers and market. Therefore, it also has an impact on the firm's investment decisions and the firm value. There is a numerous evidence that the higher stock prices attract more external funds to the firm (Khanna & Sonti, 2004). Hence, it is believed that higher the stock prices of the firm, more flexible is its budget constraints, i.e. the firm may boost its investments. Additionally, Khanna & Sonti (2004) stated that the higher stock prices might also drive firms to make more acquisitions. In this paper, the firm value is determined by buy-and-hold abnormal returns, which include stock prices in the formula of BHARs calculation. Therefore, stock prices automatically are related to the BHAR and thus will not be included as an independent variable in the regression analysis to prevent biased results.
Profitability
Profitability can be interpreted as the ability of a company to generate profit (Tayeh et al., 2015). It is another crucial factor, which is vital for the firm valuation. Numerous studies have been done in order to determine the relationship between the profitability and the firm value. For instance, Haugen & Baker (1996) claimed that anticipated value of a firm would be higher when the profitability is high. Chen & Chen (2011) in their research confirmed that there is a positive impact of profitability on the firm value.
There are various indicators, which may be used to calculate profitability, such as accounting ratios. For example, Return on Assets, Return on Investments, Return on Equity or EBITDA (Tayeh et al., 2015). Several studies, such as Chen & Chen (2011) use ROA as an indicator of the profitability since ROA indicates the efficiency of the firm’s assets. Authors concluded that the return on assets has a statistically significant effect on the firm profitability. ROA measures the relationship between the number of total assets and the profit before the interest and tax. Researchers and analysts use ROA in order to evaluate operating performance of the firm (Tayeh et al., 2015). Therefore, similar to researches, which were mentioned above, in this paper, ROA will be a measure of the firm profitability. Thus, profitability will be calculated with the use of Return on Assets (ROA).
H0: Profitability does not have an impact on firm value in the technology industry for the top 10 firms.
H1: Profitability does have an impact on firm value in the technology industry for the top 10 firms.
Leverage Ratio
The famous theorem, proposed by Modigliani Miller, states that the firm value is not affected by the capital structure, i.e. whether the company is financed by equity or debt (Berk & DeMarzo, 2011). However, the assumptions, which are imposed by this theory, such as the absence of taxes or bankruptcy costs, do not indeed hold in real life. Therefore, the level of debt is an essential factor, which may affect the firm performance.
Various investigations have been done on the relationship between leverage and the firm performance; however, the results of the previous researches are ambiguous. The existing results are mixed. For example, De Jong (2002) investigated the role of leverage on Dutch firm values. After conducting the analysis, the author concluded that the leverage does not influence the firm performance. Fama & French (1998) found that there is an adverse effect of the leverage on the firm value. Furthermore, these authors stated that this negative relationship between the leverage and the value occurs because of the agency costs, which are generated by the leverage.
On the other hand, various researches, such as Azeez (2015) and Brick and Ravid (1985) found a positive relationship. For instance, Modigliani & Miller (1958) stated that there is a positive effect of leverage on the firm value since profitable firms signal about their excellent performance in the market by taking more leverage. Another study, such as Robb and Robinson (2009), claimed that there are considerable gains from the leverage. Specifically, authors argue that the firm value is improved through the use of leverage because the returns earned are more significant than the average interest expense earned on the leverage. Therefore, it would be interesting to investigate whether the leverage of the acquiring company has effects on the firm value in case of mergers. In this paper, the leverage ratio will be calculated through the Debt-to-Equity ratio of the bidding firm.
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H0: Leverage does not have an impact on firm value in the technology industry for the top 10 firms.
H1: Leverage does have an impact on firm value in the technology industry for the top 10 firms.
Firm Size
The current academic literature states that the firm size may be a determinant of the firm value. For instance, Agrawal & Knoeber (1996) said that the large size of a firm might lead to considerable growth opportunities, which in turn lead to better firm performance and value. Research done by Klapper & Love (2004) came to a similar conclusion. Therefore, firm size will be measured by the ln(net sales) of the bidding firm.
The relationship between the firm size and the firm performance is not widely discussed. However, the literature, which does exist regarding the firm size, does not have a definite conclusion. For instance, Azeez (2015) found that the relationship between the firm size and the firm value is positive. The author stated that larger the firm size, better the firm is at the exploitation of economies of scale and scope, which in turn leads to better performance. In contrary to this study, Klapper & Love (2004) stated that the large firm size might lead to the inefficiencies, which may further result in the poor firm value. Therefore, this paper will investigate whether the firm size has an impact on the firm value. Lastly, the impact of firm size on the firm value will be tested:
H0: Firm Size does not have an impact on firm value in the technology industry for the top 10 firms.
H1: Firm Size does have an impact on firm value in the technology industry for the top 10 firms.
Payment Method
Payment Methods in M&A are financial resources through which the bidding company acquires the ownership of the target one. The most important methods of payment are the cash payments and the stock payments. Travlos (1987) found evidence for negative excess returns for the acquirer after the
announcement of the stock-paid acquisition. On the other hand, the cash-financed deals appeared to have average returns. Therefore, these results may be referred to a signalling theory, which states that the stock-financed acquisitions give a negative signal to the market and the investors. It is believed that the stock financing means that the acquirer firm is overvalued. Loughran & Vrij (1997) also found that cash tenders earned positive returns, whereas the stock-financed deals showed significantly negative returns. Therefore, the payment method is another important determinant of the firm value after the M&A. Also, high-technology firms tend to hold a vast amount of cash. For example, Apple, Microsoft, Alphabet, Cisco and Oracle are the top five US cash holders with the joint cash holdings of more than $590 billion (Egan, 2018). Therefore, increase in M&A deals within these firms could be explained by the excessive amount of cash they hold, which has to be spent. Thus, the following hypotheses will be tested:
H0: Deals paid with cash do not have an impact on firm value H1: Deals paid with cash do have an impact on firm value Two additional hypothesis will be added here, such as:
H0: The number of deals the acquirer firm undertakes does not affect firm value H1: The number of deals the acquirer firm undertakes does affect firm value H0: The Cross-Border deals do not affect firm value
H1: The Cross-Border deals do affect firm value
Therefore, the number of deals will be included as an independent variable, while the cross-border deals will be included as a dummy variable into the cross-sectional regression analysis in order to test its effect on BHAR.
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CHAPTER 3 Methodology and Data
3.1 Methodology
The fundamental aim of this study is to find whether mergers and acquisitions in technology industry lead to an increased firm value. Thus, this section will explain the model of buy-and-hold abnormal returns. The data description and the regression model will also be introduced.
3.1.1. Event Study
Based on the literature review on post-merger firm valuation, there is no apparent effect of the merger on the firm performance. Therefore, in line with the other researches, in this study, the long-run event study approach will be used to evaluate the firm performance. An event study approach is an analysis of the effects of a specific event on a company and its stock returns. An event study catches any abnormal returns around the event and compares to the return in a regular period, without the event taking place. There are few steps, which will be done in this study in order to conduct an event study:
1) Collect stock and benchmark data 2) Calculate stock returns
3) Calculate benchmark returns 4) Find buy-and-hold abnormal returns 3.1.2. Buy-and-hold Abnormal Returns
As it was discussed above in chapter 2, that the general conclusion between various studies about the firm value measure is that the value should be measured in terms of the buy-and-hold abnormal returns. The buy-and-hold abnormal returns are defined as the difference between the long-run return of a benchmark and that of a sample asset. Ritter (1991) states that the BHAR measures the long-run investor experience. Therefore, in this paper buy-and-hold abnormal returns will be used as a firm value measure.
BHAR is calculated by firstly compounding stock returns and benchmark returns separately, and taking the difference afterwards. The formula used to calculate buy-and-hold abnormal returns is taken from the research by Barber et al. (1999) and is stated as follows:
BHARit = !!!!(1 + 𝑅!" ) – !!!!(1 +Rbenchmark,t ),
where BHARit is a t period buy-and-hold abnormal return of security i; Rit is a t period buy-and-hold return
and Rbenchmark, t is the t period expected return for a benchmark.
As a benchmark, the S&P 500 technology market index will be used. The benchmark returns play a role of a proxy for the expected return of the firm. In order to find BHARs, firstly prices were
collected from the Datastream for each company and the S&P technology market index in a given period. After the returns were computed using the following formula: ((Pricenew-Price old)/Priceold). Next, 1+return and the products of it were found. Lastly, the difference between the product of company return and the S&P technology index was calculated.
3.1.3. OLS regression
In order to see how the factors, mentioned above in chapter 2, affect the firm value, the following regression model is used:
𝐵𝐻𝐴𝑅 = 𝛼 + 𝛽!∗ 𝑃 + 𝛽!∗ 𝐿 + 𝛽!∗ 𝐹𝑆 + 𝛽!∗ 𝐴𝐷 + 𝛽!𝐶𝐵 + 𝛽!𝑃𝑀 + 𝜀
where,
𝛼 = Constant
P = Profitability, measured by Return on Assets (ROA) L = Leverage, measured by Debt/Equity Ratio (D/E) FS = Firm Size, measured by ln(net sales)
AD= Amount of Deals completed by the acquiring company over 2002-2017 CB = dummy variable, which equals to 1 if the acquisition was cross-border, 0 if domestic
PM = dummy variable, which equals to 1 if the payment method was cash, 0 otherwise In this paper, the cross-sectional regression will be conducted. Cross-sectional regression is the one in which the dependent and the independent variables are associated with one period of time. Therefore, instead of computing ten different regressions for each firm, the pooled OLS cross-sectional regression will be done. Also, the cross-cross-sectional regression analysis will be used to investigate whether the independent factors had an impact on BHARs after the acquisitions took place.
3.2 Data
3.2.1 Sample Selection
In order to measure the impact of mergers and acquisitions on firm value, the data of the merging US technology firms were collected over the period from 2002 to 2017. This period is chosen for several reasons. The starting year is 2002 because it is better to exclude the crisis period,
specifically the dot-com bubble of 1997-2001. This crisis is excluded from the sample because this period was characterised by the excessive speculation. Thus, in order to avoid the impact of dot-com bubble on results of this analysis, the period of 1997-2001 is not included in the analysis. The period goes up to the year 2017 because the boom years (2016-2017) of M&A activity in the tech industry
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are to be investigated. Current year (2018) is excluded from the data sample due to the lack of new information about firms since this year has not passed yet.
Data is collected from Zephyr. Zephyr contains information about M&As, IPOs and venture capital deals. The following search criteria are set on the sample population:
1. Industry description: Technology, High Technology
2. Company Name: Alphabet Inc., Amazon.com Inc., Apple Computer Inc., Cisco Systems Inc., Facebook Inc., Hewlett-Packard Company, Intel Corporation, International Business Machine Corporation, Microsoft Corporation, Oracle Corporation
3. Deal Type: Acquisition, Merger
4. Company Type: Listed acquirer, listed target, unlisted target 5. Current Deal Status: Completed
6. Time Period: 2002-2017 (completed-confirmed) 7. Country:
a) Acquirer nation country: United States of America b) Target nation country: the US, non-US
8. Deal Value (mil Euro): min = 0.5
These criteria provide a sample of 200 acquisitions.
All independent variables, specifically return on assets, debt-to-equity ratios and the firm size were taken from the Datastream. Also, the S&P 500 technology market index was obtained from the Datastream. The number of deals of the acquirer firm, the cross-border mergers information and the payment methods were obtained from Zephyr. The tables in the appendix provide descriptive statistics of all independent variables, as well as the dependent one.
3.2.2 Descriptive Statistics
The table below provides the summary of the descriptive statistics of rounded mean and standard deviation of independent variables for top 10 tech companies. A more extensive Stata output table can be found in Appendix.
Table 2. Descriptive Statistics of Top 10 Tech Companies
ROA D/E Firm Size
Mean (St. Dev.) Mean (St. Dev.) Mean (St. Dev.) Apple 15.94 (7.67) 0.17 (0.27) 17.74 (1.29)
Amazon 5.97 (6.61) 0.38 (2.82) 17.15 (1.18) Alphabet 17.03 (10.36) 0.03 (0.03) 16.73 (1.53) Microsoft 16.24 (5.58) 0.23 (0.32) 17.9 (0.35) Ibm 10.59 (3.18) 1.67 (0.82) 18.35 (0.09) Cisco 11.22 (3.08) 0.26 (0.16) 17.4 (0.33) Intel 12.77 (3.75) 0.15 (0.13) 17.58 (0.26) Oracle 14.46 (3.93) 0.46 (0.28) 16.96 (0.52) Facebook 11.75 (6.37) 0.04 (0.07) 15.47 (1.51) HP 4.74 (4.64) 0.09 (0.79) 18.33 (0.31)
From the table of descriptive statistics, it can be noticed that the mean firm size figures are in the same range for each company, showing that they are indeed ten largest firms. Also, IBM has the highest debt-to-equity value which means that the company has the highest debt levels among all ten firms. The ROA numbers vary from a minimum of 4.74 for HP to a maximum of 17.03 for
Alphabet.
Given the search criteria, Zephyr provided 200 deals for the top ten tech companies. The Table 3 below provides the details for the number of deals and the deal values for the top ten tech companies. As it can be seen from the table, between 2002 and 2017, Cisco and Alphabet (ex-Google) were the most active acquirers with 59 and 36 acquisitions respectively. Even though Microsoft conducted only 13 deals, its total deal value is the highest one and equals to
13,038,044.98 million euro, whereas Oracle spent only 72,925.72 euro, which is the smallest spending among the top ten companies. The total number of deals of these ten companies is 200 deals, and the total deal value of ten tech companies amounts to 49,463,146.48 million euro. It should also be noticed that the acquisition deals were mainly domestic ones with the target company coming from the same country as the acquirer.
Table 3. Deal Values Statistics
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deals vs public targets vs cross-border deals (in €) Apple 19 19/0 13/6 2,955,103.81 Amazon 22 22/0 10/12 5,352,861.81 Alphabet 36 36/0 26/10 9,466,489.54 Microsoft 13 13/0 7/6 13,038,044.98 Ibm 19 19/0 12/7 12,065,093.05 Cisco 59 59/0 51/8 3,936,180.17 Intel 20 20/0 8/12 1,962,413.33 Oracle 4 3/1 0/4 72,925.72 Facebook 1 1/0 1/0 365,498.91 HP 7 7/0 4/3 248,535.16 Total 200 199/1 132/68 49,463,146.48CHAPTER 4 Preliminary Empirical Results and Analysis
4.1 BHARs
Buy-and-hold abnormal returns for each company were calculated in Excel. The results below show the overall BHARs over the period of 2002 to 2017:
Table 4. BHARs over the period 2002-2017
Apple 115.6802 Alphabet 17.0314 Amazon 142.0845 Cisco -1.0350 Facebook 2.0443 Intel -1.7198 IBM -2.2298 Oracle -0.0668 HP -1.0467 Microsoft -0.5111
As it can be seen from the table above, the results vary from highly positive returns to negative from firm to firm. For example, Apple's buy-and-hold abnormal return equals 115.6802%, which is quite high. In the period from 2002 to 2017, Apple made around 19 acquisition deals. Therefore, this result shows that these deals were indeed successful. On the other hand, IBM had even more acquisitions than Apple in this time span. However, the results suggest that not all of them turned out to be a good deal. This can be seen from the negative buy-and-hold abnormal return
of -2.2298%. The detailed daily buy-and-hold abnormal returns and the summary statistics can be found in the appendix.
To conclude, the buy-and-hold abnormal returns of Apple, Alphabet, Amazon and Facebook appeared to have positive values from 2002 through 2017. However, the rest of the companies showed a negative BHARs. So, four out of ten biggest US tech firms obtained positive buy-and-hold abnormal returns. Since each of these ten firms had more than 15 acquisitions in this time-period, it can be concluded that the acquisition deals were not always successful, which led to overall
negative BHARs.
4.2 Cross-sectional Regression Analysis
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One of the most crucial assumptions which are used to obtain the OLS estimators is the absence of the multicollinearity. In order to check multicollinearity two tests will be examined. Firstly, correlation coefficients will be investigated. If correlation coefficients between the independent variables are higher than 90%, it can be assumed that multicollinearity between the variables exists. From the table below, it could be seen that the independent variables are not highly correlated with each other. Therefore, there is no sign of multicollinearity.
Table 5. Correlation Coefficients Matrix
roa d/e size # of deals cb cash
roa 1.000 d/e -0.1948 1.000 size -0.0421 0.2563 1.000 # of deals -0.0639 0.0921 0.2966 1.000 cb 0.0083 0.1410 0.1563 -0.0635 1.000 cash -0.0480 0.1315 0.0711 0.1882 0.3282 1.0000
Another test is the Variance Inflation Factors (VIF) analysis, which suggests that if the VIF values are higher than 10, then it can be concluded that there is multicollinearity. Tale 6 below shows that each variable has a VIF value lower than 10. Thus, there is no multicollinearity. The Stata Output of the VIF test can be found in Appendix.
Table 6. VIF Test
Variable VIF roa 1.04 d/e 1.13 size 1.20 # of deals 1.17 cb 1.19 cash 1.19 Mean VIF 1.16 4.2.2 Regression Analysis
In this sub-section, the robust regression analysis and the White Test will be conducted. The table below shows the results of the robust pooled cross-sectional regression analysis for the top 10 tech firms which was computed through Stata. The robust regression is used to identify significant observations. The more comprehensive Stata output table can be found in Appendix. In this paper, a 5% significance level is chosen in order to test the hypotheses.
Table 7. Regression Results
BHAR Coeff. t P-value
roa -2.30e-06 (0.0000153) -0.15 0.880 d/e -0.0000838 (0.0000966) -0.87 0.386 size -0.0002558 (0.0001161) -2.20 0.028 #of deals -0.0000149 (5.76e-06) -2.58 0.010 cb -0.0000667 (0.0001531) -0.44 0.663 cash 0.0001022 (0.0001637) 0.62 0.532
Based on the existing literature, it was expected that the profitability and the leverage levels would have a statistically significant impact on the buy-and-hold abnormal returns. However, the general expectation of return on assets (ROA) and the debt-to-equity ratio (D/E) to have a significant impact on BHAR is rejected. The coefficients of return on assets (-2.30e-06), the debt-to-equity ratio (-0.0000838) and the dummy variables of the cross-border mergers (-0.0000667) from the table suggest that these variables do have a negative relationship with BHAR. However, they do not significantly affect the dependent variable. Finally, the last dummy variable of cash is not significant at 5% significance level, but it does have a positive relationship with BHAR.
Lastly, the only two variables which appeared to be significant at 5% level of significance and have a negative impact on the buy-and-hold abnormal return are the firm size and the number of deals undertaken by each company. The firm size coefficient states that if the company size increases by 1%, then the buy-and-hold abnormal return will decrease by 0.02558%. This negative result may be explained by the number of acquisitions which are done by the top 10 tech companies. Since these firms have huge cash piles, they have to spend it by buying start-ups or small companies. This results in a significant number of acquisitions done by the tech companies each year. Since not all of the acquisition deals are successful, this may explain the negative relationship between the firm size and the buy-and-hold abnormal returns.
The number of deals also has a negative relationship with the buy-and-hold abnormal returns. This could be explained by the fact that the company undertakes new M&A deal when the previous one has not been fully integrated yet. Also, as it has been mentioned in Chapter 2, Cisco appeared to be
27
the most active acquirer with the largest amount of deals among the top ten companies. However, from the table 4 (Chapter 3) it can be seen that Cisco's BHARs are negative, which also supports the hypothesis that the number of M&A deals negatively affects firm value. Therefore, the null
hypothesis, which stated that the amount of deals does not affect firm value, is rejected. 4.2.3 Correlogram for Residuals (White Test)
The White Test examines whether the variance of the errors is constant. The test measures whether the heteroskedasticity occurs in the regression analysis. The existence of heteroskedasticity is problematic since it leads to biased standard errors. Therefore, the following additional hypothesis will be tested:
H0: homoskedasticity H1: heteroskedasticity
The table in Appendix provides the results of the White Test which was conducted in Stata. Following the results of the test, there is not enough statistical evidence to infer that the null-hypothesis is rejected. Therefore, it can be concluded that the errors do have constant variance. Furthermore, the results also state that there is no serial autocorrelation.
CHAPTER 5 Conclusions
5.1 Conclusions
Based on the results obtained, it can be concluded that acquisitions do increase the firm value, which is measured by the buy-and-hold abnormal returns, for four top tech companies in the US, specifically Apple, Alphabet, Amazon and Facebook. However, the negative BHARs of Cisco, Intel, IBM, Oracle, HP and Microsoft lead to a conclusion that the null-hypothesis should not be rejected for these six firms. Also, the independent variables such as the profitability, the leverage ratio and the two dummy variables appeared to affect BHARs, but all of them have an insignificant impact; whereas the firm size and the number of deals undertaken by each firm have a significant negative relationship with the BHAR. Therefore, there is no statistically significant evidence to infer that profitability, leverage and dummies of cross-border mergers and payment method do affect the firm value. Therefore, the null-hypotheses are not being rejected.
On the other hand, the null hypothesis that the firm size and the number of deals do not have an impact on the firm value is rejected, since the results have shown that there is a statistically significant effect of these variables on the firm performance. Finally, the White test was conducted to test the presence of homoskedasticity. The null hypothesis was not rejected, which leads to a conclusion that the standard errors of the regression are constant.
5.2. Limitations
Despite the numerous studies done on M&As, further research should still be conducted since there are no apparent conclusions which state whether M&As do or do not improve firm value. There are also few limitations of this study. Firstly, this paper only investigates the top ten tech firms in the US. Therefore, the buy-and-hold abnormal returns and the cross-sectional regression analysis could show different results if more firms and more countries would be taken into the sample size. Additionally, the more improved model could be used in order to obtain more clear results. For example, more diversification and variation could be discussed, such as private targets vs public, small targets vs large; or whether it was diversifying or focused merger. This paper also does not make a distinction between horizontal and vertical mergers, which could also be investigated to find more accurate results.
Furthermore, other measures of the firm performance should also be taken into account. Using various measures would help to test the differences in the results and lead to the improved analysis.
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This paper used buy-and-hold abnormal returns, as a measure of the firm value. BHAR is a periodic performance measure. However, there exist numerous other methods which can be used in order to find the firm value. For instance, the value may be examined with the use of accounting measures, such as Return on Equity or Return on Assets. There are also other calendar methods for the long-term performance measurement, which could also be investigated. For instance, Calendar Time Abnormal Returns (CTAR) or recently introduced Standardized Calendar Time Approach (SCTA), which deals good with heteroskedasticity problem or Mean Monthly Abnormal Returns (MMAR). Also, given the data search criteria in this paper, the Zephyr provided 200 deals for the ten tech companies. However, among these deals, there was only one acquisition of a public firm I-Flex Solutions Ltd by Oracle in 2006. Therefore, this paper focuses on private targets and does not examine the differences between the private and the public targets. Therefore, there could be more variety to examine the long-term effects of M&As of firm value if more public targets would be taken into account.
Moreover, the data in the sample could be improved because there are missing values for some companies. For example, Facebook was established in 2004; however, it only went public in 2012. Therefore, there are not enough observations for Facebook from 2002 until 2012. Secondly, in 2015 there was a split in the Hewlett-Packard company, which divided its printers business from goods & services business into the two separate public companies, namely, HP Inc and Hewlett-Packard Enterprise. Therefore, the data after 2015 may be affected by this split and could lead to the different results.
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APPENDIX
Descriptive Statistics of Daily Buy-and-hold abnormal retrns (BHARs)
Descriptive statistics of returns on assets (ROAs)
Descriptive statistics of Debt-to-Equity Ratio (D/E)
. microsoft 4,273 1.74e-06 .010532 -.0944392 .0945339 hp 4,273 .0000322 .016009 -.1727869 .1718291 oracle 4,273 .0000854 .0134587 -.1808512 .0951278 ibm 4,273 -.0002483 .0105361 -.0976567 .0856467 intel 4,273 -.0000662 .0119508 -.1582317 .0890888 facebook 1,565 .0005655 .0207258 -.1388589 .2956627 cisco 4,273 .0000137 .012607 -.1444352 .1345255 amazon 4,273 .0011201 .0222697 -.2184232 .2802256 alphabet 3,586 .0005432 .0144989 -.0941838 .1765072 apple 4,273 .0009598 .0162623 -.1389548 .1403631 Variable Obs Mean Std. Dev. Min Max
. hp 4,131 4.737444 4.643506 -10.27 9.05 ibm 4,174 10.59408 3.177291 4.13 14.82 oracle 4,022 14.45922 3.93023 8.68 22.9 cisco 4,064 11.21852 3.080991 5.4 17.1 intel 4,174 12.77493 3.745062 7.16 19.81 alphabet 4,174 17.03454 10.35948 7 53.95 facebook 1,825 11.74875 6.366911 .61 21.31 microsoft 4,044 16.24308 5.582763 7.29 26.46 amazon 4,174 5.96702 6.605594 -3.1 25.66 apple 4,109 15.94119 7.668884 1.14 28.54 Variable Obs Mean Std. Dev. Min Max
. hp 4,131 .0891758 .7858594 -1.979753 .9533785 ibm 4,174 1.666671 .816871 .6840594 3.437863 oracle 4,022 .4595034 .2783277 .0215134 1.056981 cisco 4,064 .2558282 .1615851 0 .5098054 intel 4,174 .1533975 .1340019 .0234324 .3884872 alphabet 4,174 .0315181 .0307458 0 .0772084 facebook 4,174 .0375939 .0731253 0 .2187789 microsoft 4,044 .2253491 .3213164 0 1.190624 amazon 4,174 .3751916 2.820883 -8.141234 6.174797 apple 4,109 .169676 .273846 0 .8629809 Variable Obs Mean Std. Dev. Min Max
Descriptive statistics of Firm Size
Robust Pooled Cross-sectional Regression Analysis
hp 4,131 18.3341 .3083844 17.69166 18.66162 ibm 4,174 18.34629 .095864 18.18672 18.48755 oracle 4,022 16.95616 .5193757 16.06417 17.46031 cisco 4,064 17.39723 .3250463 16.75351 17.71236 intel 4,174 17.5817 .2552253 17.10257 17.95484 alphabet 4,174 16.73159 1.531521 12.99341 18.52373 facebook 2,609 15.46984 1.513078 12.51356 17.52058 microsoft 4,044 17.90002 .3545458 17.16067 18.35433 amazon 4,174 17.15296 1.184796 15.1849 18.99654 apple 4,109 17.74089 1.293871 15.56332 19.26961 Variable Obs Mean Std. Dev. Min Max
_cons .0049614 .0020865 2.38 0.017 .0008719 .0090509 cash .0001022 .0001637 0.62 0.532 -.0002186 .000423 cb -.0000667 .0001531 -0.44 0.663 -.0003667 .0002334 ofdeals -.0000149 5.76e-06 -2.58 0.010 -.0000262 -3.59e-06 size -.0002558 .0001161 -2.20 0.028 -.0004833 -.0000284 de -.0000838 .0000966 -0.87 0.386 -.0002733 .0001056 roa -2.30e-06 .0000153 -0.15 0.880 -.0000322 .0000276 bhar Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust Root MSE = .01507 R-squared = 0.0007 Prob > F = 0.0043 F(6, 37838) = 3.16 Linear regression Number of obs = 37,845
