RLBO PERFORMANCE
Name: Andrew Leek
Student number: 10561897
Specialization: Finance and Organization – BSc ECB
Field: Finance
Supervisor: dr. J.E. Ligterink
Abstract
This thesis investigates whether or not reverse leveraged buyouts (RLBOs) outperform the stock market. To research this, 100 RLBOs that are listed on a large United States stock exchange between 1996 and 2013 are analyzed. One and three year time periods are examined to find stock outperformance. Raw buy-and-hold returns and alphas are used to measure performance. Raw buy-and-hold returns suggest underperformance of the market by RLBOs, while alphas suggest outperformance of the stock market.
Statement of Originality
This document is written by Student Andrew Leek who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is 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.
Table of Contents
1. Introduction ... 2 2. Literature review ... 4 2.1. LBOs in general ... 4 2.2. Reverse-LBO performance ... 6 2.3. Reverse-LBO characteristics ... 10 3. Data ... 11 3.1. Data sources ... 11 3.2. Descriptive statistics... 12 4. Methodology ... 13 5. Results ... 16 6. Discussion ... 19 6.1. Literature comparison ... 19 6.2. Limitations ... 21 7. Conclusion ... 22 7.1. Concluding remarks ... 22 7.2. Further research ... 23 8. Reference list ... 24 9. Glossary ... 25 10. Appendix ... 26 11. Introduction
Private equity (PE) firms are controversial. PE firms use debt to acquire companies by way of a leveraged buyout (LBO) (Palepu, 1990). After an acquisition a PE firm restructures a
company in such a way that the firm can earn significant returns on their investment. According to Lerner et al. (2011), many critics of PE firms argue that these firms try to cash out without creating any value for stakeholders. One way a PE firm can cash in on their investment is by going public with a company they acquired. This is known as a reverse LBO (RLBO), also called an LBO-backed public offering (Holthausen & Larcker, 1996) (Cao, 2011). PE firms can, as seen with Refco (Cao & Lerner, 2009), take a company to the stock exchange at more than double the value. After the public offering, Refco collapsed and a dozen
lawsuits were filed. The New York Times (2005) argued that overleveraged companies are listed publicly too easily, as it is unclear who will invest in them. The New York Times (2005) also mention that PE firms have to sell $500 billion of assets, while the industry has never sold more than $153.2 billion. More recently, the LBO-practices of PE firms in the
Netherlands were heavily criticized. The PVDA, a political party, has proposed initiatives against these practices (FD, 2015).
As seen above LBOs are under public scrutiny. It is uncertain if they add value for stakeholders. PE firms don’t share much data on transactions they enter into (Degeorge & Zeckhauser, 1993). Holthausen and Larcker (1996) mention that high leverage and
concentrated equity ownership could contribute to LBO firms operating more efficiently. Holthausen and Larcker (1996) imply that a decline of these two factors would result in a decline of overall performance. Measuring the performance of RLBOs can provide more data on private equity activities and whether or not these activities are sustainable. By looking at companies that have undergone an LBO and then subsequently have undergone an IPO, the effect of an LBO on the stock performance of a company can be measured. Therefore, the research question is:
Do buyout-backed IPO companies outperform the stock market?
With this research question there can be argued that RLBOs are profitable investment
opportunities. A company that goes public after an LBO should not automatically imply a bad investment opportunity. This thesis will analyze 100 buyout-backed IPOs from 1996 to 2013 in the United States (U.S.). Raw buy-and-hold returns and alphas will provide information on stock performance. Jensen’s alpha methodology will give any under- or outperformance of a stock adjusted for market factors.
Firstly, current literature on this research topic will be reviewed. Literature on RLBOs, regular IPOs and abnormal returns will be discussed. Secondly, the main hypothesis is
explained and its relevance proved. Thereafter the data sources and criteria are shown. Thirdly, the methodology is discussed and research methods will be explained. Also, the regression methods will be further detailed. Fourthly, the results will be interpreted and these results will be compared in the discussion to other results found in existing literature. Lastly, a conclusion will be drawn from these results and findings. Also, possible future research topics will be suggested.
2. Literature
This thesis will mainly be focusing on buyout-backed IPO long-term performance using raw returns and alphas. First of all, LBOs in general will be looked at. Secondly, research
investigating RLBO characteristics and performance is discussed. Lastly, literature regarding RLBO timing and exits are analyzed.
2.1. LBOs
Consequences of LBOs in general has been researched by Palepu (1990) and more recently by Kaplan and Strömberg (2009). LBOs have several characteristics according to Palepu (1990). A characteristic of LBOs is a significant increase in leverage. Management’s ownership also rises substantially. Corporate governance changes because of large block-holder investors join the board and actively monitor performance and strategy. Investors cannot access public equity markets after a buyout. These factors distinguish LBOs from a typical public firm according to the author. Palepu (1990) also observes, that over time LBOs have increased in size and frequency. Organizational changes caused by LBOs have improved incentives for managers to maximize firm value. This leads to better operating and
investment decisions (Palepu, 1990). Kaplan & Strömberg (2009) make clear that LBOs lead to changes in corporate governance, capital structures and management incentives. They also find that market timing is an important factor in economic value creation. Opponents of private equity argue that increased leverage leads to short-term decision making and
vulnerability to financial distress. Palepu describes three reasons why an LBO might fail: a structure prone to failure, poor management performance and bad general economic conditions. Furthermore, the author argues that mostly large successful LBOs go public. A reason according to Palepu for going public are that investors gain liquidity and
diversification opportunities. Also, Palepu (1990) mentions that buyouts increase a firm’s depreciation and interest tax shields. Kaplan and Strömberg (2009) argue that increased leverage from LBOs creates pressure on management of the target firm to efficiently allocate money. The authors refer to this as ‘’free cash flow’’ problems as first mentioned in Jensen (1986). The free cash flow should be returned to investors, instead of being dissipated Kaplan (1991) recognizes two forms of post-buyout behavior. Firstly, an LBO brings more efficient management to a firm under which the LBO firm remains private for an
uncertain period of time. Secondly, an LBO can bring one-time improvement to a company. After these improvements the firm can be brought back to the market in a reverse LBO. If a firm is brought back to the market quickly, then the LBO is called a ‘’revolving door’’ LBO. The author also finds that post-buyout leverage is lower than when the firm was private (Kaplan, 1991). RLBOs maintain higher levels of leverage than other IPOs.
Guo et al. (2011) also look at LBOs in general and compare 1980s LBO performance to more current performance. They use their own sample to find explanations for increased post-buyout operating performance. The explanations exactly match the factors described by Palepu (1990). Concludingly, Guo et al. (2011) find that buyouts completed between 1990 and 2006 are more conservatively priced and contain less debt than buyouts from the 1980s. Holthausen and Larcker (1996) mention that high leverage and concentrated equity
ownership could contribute to LBO firms operating more efficiently. They imply that a decline of these two factors would result in a decline of overall performance.
Boucly et al. (2011) analyze 839 French LBOs. They find that in the three years following an LBO, targets increase profitability, grow more than non-LBO firms, increase capital expenditures and increase leverage. Boucly et al. show that PE firms create value by alleviating credit constraints (more debt). This allows LBO firms to explore more growth opportunities. Firms that operate in external finance dependent industries gain more from an LBO according to the authors. Post buyout growth is biggest in private-to-private LBOs (as described in Datta et al. (2015)).
2.2. Reverse-LBO performance
In this section RLBOs are defined, and their performance is explained. PE-backed IPOs will be referenced to as a RLBO or buyout-backed IPOs.
Table 2-1 RLBO performance
Study Sample size Cou ntry Method Results Degeorge & Zeckhauser (1993)
62 U.S. CAR No statistical evidence of underperformance compared to a control portfolio.
Mian & Rosenfeld (1993)
85 U.S. CAR RLBOs outperform the market over periods between 14 and 36 months.
Holthausen and Larcker (1996)
90 U.S. BHAR CAPM
RLBOs outperform the market over two years. Significant positive alphas and BHRs for 24 and 36 months.
Chou et al. (2006) 247 U.S. CAR BHAR Fama-French
RLBOs do not underperform the market up to four years, conservative firms perform better than aggressive firms.
Katz (2009) 147 U.S.
Fama-French
Significant positive alphas for companies controlled by large managing PE firms over one year periods.
Cao & Lerner (2009) 496 U.S. BHAR BHR CAPM Fama-French
Buyout-backed IPOs outperform over one to five years considering alpha (CAPM and Fama-French), BHRs suggest
outperformance over one year. CAPM alpha of -0.32% and Fama-French alpha of -0.38% are found.
Levis (2011) 1595 U.K. BHAR Fama-French
PE-backed IPOs outperform VC-backed and market indexes over up to 36 months. Significant three year alphas of 0.8% (value-weighted) and 0.7% (equal-(value-weighted). Datta et al. (2015) 207 U.S. BHR
Carhart
Public-to-private RLBOs outperform other RLBOs and first IPOs at 12, 36, 60 month periods. Significant BHRs for one to five year holding periods.
Degeorge & Zeckhauser (1993) and Mian & Rosenfeld (1993) are the first to investigate stock performance of RLBOs. The former has a sample size of 56 and the latter a sample of 85 firms. The research method used in these two papers are CARs. Degeorge & Zeckhauser find that in the year before RLBOs go public, they outperform continuing LBOs. This could suggest that better performing LBOs are only brought to the market. Another finding is that RLBOs outperform comparable firms over two years after IPO. They prove this by finding a CAR of 15.22%. However, this is not found to be statistically significant. Mian & Rosenfeld use similar methods to measure performance compared to Degeorge & Zeckhauser, but their findings are statistically stronger. Mian & Rosenfeld find that RLBOs outperform over periods between 14 and 36 months. They mention that this performance is driven by takeovers of RLBOs, as 39% of the sample is acquired within three years.
Holthausen and Larcker (1996) look at accounting and market performance of RLBOs. They analyze a sample of 90 RLBO firms between 1983 and 1988. The authors discuss that it is typical of RLBO firms to retain high equity ownership by managers and insiders. Also, leverage is relative high compared to other public firms. These two characteristics are important for the relevance of this thesis, because these characteristics can separate RLBO firm performance from other IPOs. The authors find that RLBO firms outperform median firms in their industry significantly when looking at accounting performance. Information asymmetry could also contribute to early returns. Managers can take advantage of their information by timing the IPO, so that they gain from an inflated offering price. Because of this asymmetry, Holthausen and Larcker and other researchers look at long-term stock performance (3 years) most of the time. By using the CAPM, the paper finds similar results to Degeorge & Zeckhauser and Mian & Rosenfeld. Also, the pricing of RLBO is found to be rational. Most alphas found are found to be positive, but not significant. However, the two-year alpha of 0.164 is found to be significant. The authors find that the outperformance is due to that 51% of the sampled firms are acquired or delisted after going public. Firms still listed after 48 months give non-significant alphas, while firms delisted before 48 months give significant long-term alphas. The conclusion of the paper is that there is no statistical
evidence of either under- or outperformance.
According to Chou et al. (2006) ‘’revolving door’’ LBOs, as described by Kaplan (1991), can result in earnings management by insiders. A reverse LBO offering can be manipulated to improve the offering price. Chou et al. (2006) research stock performance of RLBOs by
looking at abnormal returns, Fama-French regressions and cross-sectional regressions. They use a sample of 247 firms. When using the Fama-French three factor model, the authors find that conservative accounting by management leads to significant positive alphas over holding periods of three to twelve months, while aggressive accounting leads to low insignificant alphas. Over periods longer than one year, there is no statistical evidence of outperformance.
Cao and Lerner (2009) look at 526 RLBOs between 1981 and 2003. Identifying the amount of RLBOs contains two main barriers according to them. Firstly, buyout firms are secretive of nature. This means new investments are rarely disclosed and should thus be less covered in major databases (Cao & Lerner, 2009). Secondly, the differences between private equity buyouts and venture capital are increasingly blurred. Cao & Lerner use several
methods to analyze RLBO performance, including: buy-and-hold abnormal returns (BHAR), the CAPM and the Fama-French three factor model. They find that mean BHARs of buyout-backed IPOs outperform the market index over periods from one to five years. Median-BHARs on the other hand suggest underperformance. Out- and underperformance are based on value-weighted stock market index returns. Alphas obtained from CAPM and Fama-French regressions suggest that there is outperformance of the market by 0.3% to 0.5%. There is also a portfolio calendar-time series CAPM and Fama-French regression done. Alpha is negative, but not significant. According to the paper, performance is dragged down by one poorly performing year (1982), because this year has only 1 negative observation.
Katz (2009) researches how ownership structure of PE sponsors in private companies affects reporting, earnings management and post-IPO performance. The author finds that PE sponsors avoid upward earnings management and stimulate accounting conservatism. Majority ownership of PE firms is also linked to better stock price performance. This finding matches the conclusion of Chou et al. (2006). Katz (2009) also finds a significant positive Fama-French alpha of 1.2% for large managing PE firms over one year periods.
Levis (2011) defines a PE-backed IPO as a company where a PE sponsor has a controlling interest attained at the time of a buyout. In the paper venture capital (VC) backed IPOs are defined as companies that receive start-up aid or expansion money. VC firms usually have a minority interest and are usually active for longer periods than PE-backed IPOs. When using a Fama-French regression, Levis (2011) finds significant positive alphas for PE sponsors for value and equal weighted market returns. Consistent with Datta
et al. (2015) and Cao and Lerner (2009) the author finds that buyout-backed IPOs are on average larger in terms of market capitalization compared to comparable IPOs. This implies that PE sponsors target larger than average firms to invest in.
Datta et al. (2015) differentiate three kinds of RLBOs: private-to-private, division-to-private, and public-to-private RLBOs. In previous literature RLBOs are seen as a homogenous group, while this study looks at further characteristics determining performance. Public-to-private RLBOs (re-IPOs) are the most common type. The authors find that information asymmetry is the least in this order: public-to-private, division-to-private and then private-to-private. Univariate (raw buy-and-hold returns) and multivariate analysis are used. The results find that post-IPO performance is positively connected to available information. The authors mention that when more information is available to investors, there is less chance of investor over-optimism. This means there is less chance that shares will be mispriced. Public-to-private RLBOs consequently outperform the other kinds of RLBOs over 36 to 60 month periods after IPO. Datta et al. (2015) find two potential explanations for their findings. Firstly, more complete information leads to private equity investors to make better decisions. Good targets are better identified. Secondly, buyout firms put more effort into restructuring their targets. The authors conclude that re-IPOs outperform first IPOs over 12, 36 and 60 months.
2.3. Reverse-LBO characteristics
When looking at regular IPOs, stocks underperform the market by 25% over three years after listing, according to Ritter (1991). In contrast, RLBO firms have actually outperformed the market according to Degeorge and Zeckhauser (1993), Holthausen and Larcker (1996) and Cao and Lerner (2009).
High ranked IPO underwriters experience higher performance than lower ranked underwriters (Datta et al., 2015) (Katz, 2007). They both find that underwriter type signals RLBO quality. Usually underwriters signal future performance of a firm according to Datta et al. (2015). Brav and Gompers (1997) and Carter et al. (1998) note that underwriter quality is related to long-run performance of regular IPOs. Cao and Lerner (2006) also find that RLBOs are backed by better quality underwriters than other IPOs. They mention that a cause could be that RLBOs are larger in size than regular IPOs. Levis (2011) also tests underwriter
reputation on performance, but does not find significant results.
Cao (2011) researches IPO timing of RLBO firms. He finds that LBO duration is negatively correlated with booming IPO market conditions. LBO duration is the amount of time a buyout sponsor remains in a company before exit. It is also seen as a proxy for LBO restructuring efforts (Cao, 2011). RLBOs with short LBO duration deteriorate in performance significantly. Also the probability of bankruptcy increases with shorter LBO duration. Cao (2011) names a quick IPO by a buyout-sponsor a ‘quick flip’. However, the shorter LBO duration does not affect PE sponsor exit timing. Cao (2011) credits this to lockup provisions and reputational concerns. High cash flow RLBOs have a longer buyout-sponsor presence according to the author. Cao (2011) concludes that premature listings of LBOs destroys value and leads to increased bankruptcy chance. Sponsor reputation helps mitigate moral hazard behavior.
Fürth and Rauch (2015) look at 222 buyout-backed IPOs in the U.S. and analyzes the exit strategy of the PE sponsor. They find three major results. First, PE sponsors exit a firm over an average period of 2.8 years after IPO. Ownership stakes and board seats are gradually exited. Secondly, the period from initial LBO to going public (LBO duration) is driven by company specific factors, like profitability and restructuring intensity. Finally, investor reactions to PE sponsor exits are driven by the success of the RLBO from the sponsor’s perspective.
3. Data
3.1. Data sources
Previous research has used several databases to find data of buyout-backed IPOs. Initially, papers in the 1990s used different methods to identify RLBOs, including: published IPO prospectus summaries, Wall Street Journal Index and Dow Jones News Retrieval Service. More recent research has used computer databases to identify companies that underwent a RLBO. Boucly et al. (2011), Cao and Lerner (2009) use databases, including: Securities Data Company (SDC) Platinum, Capital IQ and VentureXpert. In this thesis the Thomson One database is used to identify buyout-backed IPOs. Thomson One is a newer database that combines SDC Platinum and VentureXpert. After identifying the sample, stock returns are calculated based on Center for Research in Security Pricing (CSRP) and Datastream data. The dataset is based on U.S. data. The U.S. has the most private equity and buyout activity (appendix: Table B). Information asymmetry is also lower on the U.S. market
(Holthausen and Larcker, 1996). Only firms listed on the NYSE and NASDAQ are considered, because this are the biggest U.S. stock exchanges. The companies listed on these exchanges have a relatively high market capitalization. Following literature, firms that are listed under $1 per stock will also be removed from the dataset. Data from 1996 and onwards is used. The Thomson One database contains data from up to 20 years back, so for this thesis only RLBOs after 1996 will be considered. All buyout-backed IPOs after 2013 will not be
considered, because there isn’t enough stock return data to measure long-term stock performance yet. Missing ticker codes have been manually added by consulting the NYSE and NASDAQ websites. An overview of the distribution of RLBOs over the years for the dataset is given (appendix: Table C).
Single company stock returns will be based on stock prices. The return will be calculated by looking at the change over time in price: 𝑅𝑅𝑖𝑖,𝑡𝑡= 𝑃𝑃𝑡𝑡+1𝑃𝑃 −𝑃𝑃𝑡𝑡 𝑡𝑡.
𝑅𝑅𝑓𝑓, SMB and HML are given by the Ken French factor database. 𝑅𝑅𝑚𝑚𝑚𝑚𝑡𝑡 is obtained from CRSP.
Existing research uses equal- and value-weighted index including distributions of the
NASDAQ and NYSE index returns as a benchmark. As suggested by Canina et al. (1998) equal-weighted returns will not be compounded. The S&P500 composite index is also compared to the other indexes (correlations are provided in the appendix: Table A).
Thomson One provided 1344 buyout-backed IPOs with tickers available. These tickers have then been used in Datastream to provide stock prices. In Datastream a monthly time series lookup is used from 1/1/1996 to 31/12/2015. Datastream does not have information on all tickers used. Because of this, tickers without data are removed from the dataset. Also, companies with less than 12 return observations are removed. Companies that are listed for long periods under $1 are deleted. Tickers that contain corrupted database data are also removed. After these adjustments the dataset is reduced from n=1344 to n=675. To further narrow down the dataset for statistical analysis, 100 firms are randomly picked. Unavailable information is random and will not cause a bias in the dataset.
3.2. Descriptive statistics
Table A (appendix) shows the correlations between the different market indexes. All the indexes are highly positively correlated with each other. North America has the most buyout-backed IPOs with the highest market capitalization of all geographic regions (appendix: Table B). In Table C (appendix) the yearly frequency of the dataset is given. The most common IPO years are 2004 and 2012 and the years with the lowest amount of IPOs are 2001, 2009 and 2013. The median year is 2005.
In STATA ‘’Summary statistics’’ has been selected to give descriptive output regarding the sample (appendix: Table D). Each company has been given 36 observations after IPO. Average monthly return over the whole sample is +0.6%, thus implying a monthly portfolio gain. The statistics also show that outliers are limited, so that any disproportionate values in the analysis are minimal.
4. Methodology
To answer the research question, the following hypotheses will be discussed: Hypothesis 1: H0: BHRRLBO = BHRmkt H1: BHRRLBO ≠ BHRmkt Hypothesis 2: H0: αRLBO = 0 H1: αRLBO ≠ 0
The first hypothesis looks at buy-and-hold returns (BHRs). These BHRs are measured and then compared with market returns. Barber and Lyon (1997) describe a method to calculate BHAR. Kothari and Warner (2006) argue that BHARs better resemble investor’s investment decisions than CARs. In the same way, the raw BHR of a stock can be calculated. This can be represented in formula form:
𝐵𝐵𝐵𝐵𝑅𝑅𝑖𝑖𝑡𝑡 = �[1 + 𝑅𝑅𝑖𝑖𝑡𝑡] 𝑖𝑖
𝑡𝑡=1
Barber and Lyon (1997) and Kothari and Warner (1997, 2006) find that there is skewness bias in long-run abnormal returns. Cumulative abnormal returns (CARs) are found to have an upward bias, while BHRs produce downward biased test statistics (Barber & Lyon, 1997). BHRs have been chosen as a univariate performance measure. If the null hypothesis holds, the market does not statistically perform better than RLBO firms. A t-test for unequal
heteroskedastic samples is used to compare the BHR of the companies with the market BHR. The second hypothesis looks at alpha, which according to Jensen (1967) looks at abnormal returns of a stock or portfolio. This means alpha is the excess return not predicted by variables in the CAPM (Jensen, 1967) or the Fama-French model (Fama & French, 1993). The performance measure alpha can be positive for two reasons according to Jensen (1967), namely: excess returns earned on a portfolio due to a manager’s ability and a positive bias in 13
the alpha estimate resulting from a negative bias in the beta estimate. Alpha can also be used to measure historic stock performance, as seen in RLBO performance literature discussed in the sections above. In this context, alpha gives the historical performance of a firm.
If the null hypothesis holds, there is not enough evidence to say that the firm
generates abnormal returns compared to the stock market. A two-sided test is used to check for over- and underperformance of a stock as usual in relevant literature (Barber & Lyon, 1997) (Kothari & Warner, 1997). Alpha is chosen as a performance measure, because every public traded company can be compared to others with this measure. Excess returns are mostly independent from industry differences (Ambrose & Winters, 1992). Alphas are less influenced by biases and skewness compared to CARs or BHARs (Kothari & Warner, 1997). Holthausen and Larcker (1996), Chou et al. (2006), Cao and Lerner (2009) and Katz (2009) also use alpha to measure outperformance of the market.
Katz (2009) calculates the buy-and-hold daily returns for multiple years after the IPO. The author also estimates the value-weighted monthly abnormal returns using the Fama-French model to avoid overlapping periods of buy-and-hold returns. Up to five years is researched. Alphas found are not significant in this research.
Holthausen and Larcker (1996) use BHR, BHAR and the CAPM to measure long-term stock performance of RLBOs. Periods of one to four years are measured. When a firm delists from a stock exchange, subsequent returns and alphas are zero, as will be the case in this thesis. Market performance is found to be either positive or insignificant.
Cao et al. (2006) use the same methods as above, but both value- and
equal-weighted market returns are used. Cao and Lerner (2009) again use the same methods, but adds the S&P500 composite return.
Canina et al. (1998) find that compounded daily returns of equal-weighted index can contain a large upward long-term return bias. The difference the authors find between daily returns compounded to months amounts to an upward bias of 6% per year. A solution provided by the authors is to use value-weighted indexes or the uncompounded equal-weighted index, as will be done in this thesis.
For individual regressions the value-weighted index is used as a benchmark, while for the portfolio regression the equal-weighted index is also used. In the portfolio all companies have the same weight.
These researches have in common that they all use the one-month U.S. Treasury bill rate as the risk-free rate proxy. Also, outperformance is statistically supported, and
underperformance is not significant. Survivorship bias is avoided by also considering firms that are delisted or acquired.
Two models will be applied to the dataset to answer the second hypothesis: Sharpe-Lintner-Black model (CAPM) and the Fama-French three factor model:
CAPM:
𝑅𝑅𝑖𝑖𝑡𝑡− 𝑅𝑅𝑓𝑓 = 𝛼𝛼 + 𝛽𝛽1�𝑅𝑅𝑚𝑚𝑚𝑚𝑡𝑡− 𝑅𝑅𝑓𝑓� + 𝜀𝜀𝑖𝑖𝑡𝑡
Fama-French model:
𝑅𝑅𝑖𝑖𝑡𝑡− 𝑅𝑅𝑓𝑓 = 𝛼𝛼 + 𝛽𝛽1�𝑅𝑅𝑚𝑚𝑚𝑚𝑡𝑡− 𝑅𝑅𝑓𝑓� + 𝛽𝛽2𝑆𝑆𝑆𝑆𝐵𝐵 + 𝛽𝛽3𝐵𝐵𝑆𝑆𝐻𝐻 + 𝜀𝜀𝑖𝑖𝑡𝑡
The CAPM (Jensen, 1967) looks at relative risk compared to the market, defined by 𝛽𝛽1. The
Fama-French model (Fama & French, 1993) adds SMB and HML to the CAPM. Fama and French (1993) add these factors because they provide more variance explanation. SMB corrects for the returns between small and large market capitalizations. Smaller firms tend to outperform larger firms. HML corrects for the returns between value and growth stocks. High book-to-market stocks usually outperform low book-to-market stocks, HML corrects for this. According to Barber and Lyon (1997) the Fama-French model has as advantage that SMB and HML of the sample firms are not needed, while this is needed when comparing the dataset with a control sample.
To estimate the alphas, OLS regression is used. Data is imported from Excel to STATA. The date variable is then converted to a time-series variable. For each firm OLS regressions are done over a three year time period for both models. Alpha is expected to be higher in the CAPM compared to the Fama-French model, following literature from Fama and French (1993) and Carhart (1997). More unexplained performance is moved to added variables, making the more extensive models more complete. The R squared is expected to be higher in the Fama-French model than in the CAPM, because more variables are added.
5. Results
When holding the RLBO stock portfolio for one year, the holding period return is 1.81% (Table 5-1). The market gives a return of 7.42% in the same period. The market outperforms RLBOs by 5.61% over a one year holding period. After three years of holding the stocks, the return is 14.82%. RLBOs underperform the market index over a three year holding period by 5.93%. 52 of the 100 gave a negative return over a one year holding period. The three year holding period has 59 negative results after IPO. The company stocks that did gain value from one year to three years, rose by 86.85%. 54 companies performed worse over three years than one year, leading to a loss in value of 48.28%. To test significance of the
difference in means between the market and the dataset, an unpaired heteroskedastic t-test is used. Differences are not statistically significant. The sign of the t-values indicates that the RLBO sample performs worse than the market index.
Table 5-1 Raw buy-and-hold returns
Values are given in percentages, except t-values.
BHR1year BHR3year
Mean 1.81 14.82
Market 7.42 20.75
Difference -5.61 -5.93
t-value -0.90 -0.49
Considering the results above, some companies cause the total return to increase by a large margin. 16 companies double their IPO stock value over three years. For the three year period delisting is also considered. In this period of time only one company delists. Performance is thus not driven by takeovers for this sample.
Individual regressions for each company are included in the appendix in Table F. When looking at three year periods with CAPM, 13 alphas are significant at the 10% significance level. 4 of the 13 are negative. The Fama-French model gives 10 significant alphas with 3 negative alphas. All coefficients are tested with robust standard errors. Robust standard errors (robustness) controls for econometric issues like heteroscedasticity and
multicollinearity (White, 1980). As expected, the CAPM gives more significant alphas than 16
the Fama-French model. More variance is explained by the Fama-French model, which leads to SMB and HML explaining more performance (Fama & French, 1993). When combining all individual regressions the CAPM has a mean alpha of +0.7%, while the Fama-French model has a mean alpha of +1.6%. The F-statistic is mostly not significant. The CAPM provides 10 significant F-values and the Fama-French gives 13 significant F-values. Of these 13 significant F-values, SMB is significant four times and HML only once. The highest R squared for the CAPM is 0.3767 and for the same company in the Fama-French model it is 0.3777. By adding variables with the Fama-French model, the R squared should increase, but this effect is small.
The dataset is also combined into an unweighted portfolio (Table 5-2). Both CAPM and the Fama-French model give positive alphas. The market coefficient RmktRf is significant for both models. SMB and HML are not significant. The equal panel is a better explanatory model than the value panel, because the R squared is higher. Against the equal-weighted benchmark, alpha is 0.9% for the CAPM and alpha is 1.1% for the Fama-French model. Both are statistically insignificant. Against the value-weighted benchmark, the CAPM gives a 1% alpha and Fama-French a 1.1% alpha. The 1% CAPM alpha is significant at a 10% level. The RmktRf coefficients are low, as values of RmktRf < 1 imply that if the market experiences a 1% value increase the RLBO firms increase by less than 1%. RLBOs in this sample are less risky than the market. F-values for the portfolio regression are highly significant (p-value < 0.000).
Table 5-2 Three year portfolio regressions
In the first panel the equal-weighted index is used as a benchmark. In the second panel the value-weighted index is used as a benchmark. For both benchmarks the CAPM and the Fama-French model are estimated. t-values are given in parentheses. * indicates significance: *=10% significant, **=5% significant, ***=1% significant.
Equal Value
CAPM Fama-French CAPM Fama-French
Alpha 0.009 (1.61) 0.011 (1.18) 0.010* (1.83) 0.011 (1.13) RmktRf 0.511*** (4.82) 0.547*** (5.16) 0.391*** (3.03) 0.423*** (3.39) SMB -0.411 (-1.63) -0.275 (-1.03) HML 0.338 (1.09) 0.278 (0.85) The one year estimates of the models are not accurate, because they use only 12 monthly observations for each company. For this reason, only the three year period is reported as there are more observations to calculate statistically relevant results.
H0 of Hypothesis 1 (defined in the methodology section) is not rejected, as there is
not enough statistical evidence. H0 of Hypothesis 2 is only rejected for the value-weighted
CAPM model. For the other models there is not enough statistical evidence to reject the null hypothesis.
6. Discussion
6.1. Literature comparison
In this section previous research results on long term buyout-backed IPO stock performance will be compared to results found in this thesis. Subsequently, explanations for differences in results will be explained. Results of previous studies referred to in this section are available in Table 2-1.
In this thesis, BHRs and alphas are used to analyze performance of RLBOs. The one and three year BHRs of 1.81% and 14.82% suggest underperformance, but this is not statistically significant. The equal-weighted benchmark provides a 0.9% CAPM alpha and a 1.1% Fama-French alpha, which are both insignificant. For the value-weighted benchmark, CAPM gives a significant 1% alpha, while the Fama-French model provides an insignificant 1.1% alpha.
Degeorge and Zeckhauser (1993), Mian and Rosenfeld (1993) and Chou et al. (2006) use CAR to investigate performance. Degeorge and Zeckhauser (1993) find a two year CAR of 15.22% which outperforms comparison firms, but this is not statistically significant. Mian and Rosenfeld (1993) find significant CARs in periods from 14 to 36 months. Both papers find that takeovers of RLBOs increase CARs significantly. Only one firm is acquired in the thesis sample, while in Mian and Rosenfeld (1993) 33 of the 85 firms are acquired. They find that buyout-backed IPOs that are not taken over, record CARs close to 0%.
Holthausen and Larcker (1996) find a BHR of 45.98% for a three year period. Only the one year holding period does not suggest outperformance. Firms that are delisted before four years of listing achieve even higher BHRs. Alphas are also found to be significant. The two year alpha is 16.4% and the three year alpha is 14.76%. RLBOs delisted before four years perform even better (24.16% and 19%).
Alphas found in this thesis are significantly lower than research from the 1990s, but similar to more recent studies. Samples in earlier studies contain RLBOs from the 1980s. In this period RLBOs were a new phenomenon and perhaps mispriced (Degeorge & Zeckhauser, 1993), leading to large significant alphas.
Chou et al. (2006) find negative CAR and BHAR values when using value- and equal-weighted market indexes, but these are not statistically significant. Alphas for firms with aggressive accounting are negative and insignificant. Conservative firms have significant
positive alphas for periods between three and twelve months. The six month alpha is 1.8% and the one year alpha is 1.6%.
Cao and Lerner (2009) distinguish two sample periods, 1981-1995 and 1996-2003. The one year BHR is 18.81%, while the market has a 10.25% return. The three year BHR for the whole sample is 42.15%, and the market a 34.82% return in this period. The sample between 1981 and 1995 has a one year BHR of 22.37% and a three year BHR of 53.61%. The RLBOs between 1996 and 2003 give an 8.53% and a 22.95% return. When estimating the Fama-French model and CAPM, significant positive alphas are found. For the whole sample alpha is significant at a 5% level for 12, 24, 48 and 60 month period. The one year alpha is 0.59% and the two year alpha is 0.37%. The RLBO sample between 1996 and 2003 give insignificant alphas. Insignificance for this sample may explain the insignificant alphas found in this thesis, as 33 of the 100 firms of the dataset are from this period.
Datta et al. (2015) compare BHRs for three different types of RLBOs (public-to-private, private-to-private and division-to-private). Respectively, one year BHR periods give 15.83%, 23.23% and 12.85% and three year BHR periods give 45.25%, 23.44% and 24.19%. All BHRs are statistically significant. The differentiation between multiple types of RLBOs may explain the BHRs found in thesis. Alphas found using the Fama-French model give values of 0.7% for value-weighted and 0.8% for equal-weighted, which are both significant. Alphas found in Chou et al. (2006), Cao and Lerner (2009) and Datta et al. (2015) are similar to values found in this thesis. The samples used in these studies are more comparable than the samples used in earlier studies.
BHRs found for the sample companies in this thesis are lower than in previous research. There a few possible causes for these low values. Firstly, the market returns in the same period are lower than in previous research. This could indicate that the sample is taken from a bad performing period. Secondly, the sample only contains 100 randomly sampled companies over 18 years. This may have led to a random selection of relatively bad
performing companies. Thirdly, the sample could contain more underperforming RLBO types than outperforming types leading to lower performance, described by Datta et al. (2015). Lastly, the lack takeovers of RLBO firms may lead to lower BHRs.
6.2. Limitations
In this thesis a few limitations and simplifications are present that may explain conflicting results. Firstly, the dataset is not very specific. Companies that have been listed over a time span of 18 years are researched, while this could have been narrowed down significantly. Also, because of technical limitations a smaller dataset than intended has been analysed. Secondly, more firm-specific observations could have been used to look at long-term performance. Three years only gives 36 monthly observations per firm. A problem with looking at even longer periods is that the sample firms are possibly not influenced by PE sponsors anymore. Finally, there are different types of buyout-backed IPOs that have not been distinguished in the dataset.
7. Conclusion
7.1. Concluding remarks
In this thesis, 100 buyout-backed IPOs in the U.S. between 1996 and 2013 have been analyzed using BHRs and alphas. This has been done to answer the question if buyout-backed IPOs outperform the stock market. Following from the results several concluding remarks can be made.
Following from the results, RLBOs perform worse over one and three years than the value-weighted market index when looking at BHRs. This means that holding a portfolio of the sample RLBOs has lower returns than the market portfolio. By using the CAPM and the Fama-French model, several alphas have been found. When investigating individual alphas, the CAPM gives 13 significant alphas, while the Fama-French model gives 10 positive significant alphas. Individual alphas suggest that a few companies generate positive
significant alphas, while most companies do not. For the combined RLBO portfolio sample, only the value-weighted CAPM alpha is significant. All portfolio alphas suggest
outperformance of the market, although only one is statistically significant. Both null hypotheses are thus not rejected. Only for the value-weighted CAPM alpha the null hypothesis is rejected.
In conclusion, BHRs suggest that buyout-backed IPOs underperform the stock market. Alphas obtained from the CAPM and the Fama-French model suggest that buyout-backed IPOs experience abnormal returns compared to the stock market.
7.2. Further research
Further research can improve on the issues in this thesis, while also adding new analysis to long term buyout-backed IPO performance. The effect of delisting and acquisitions of PE-backed firms is unclear. In some cases performance increases significantly because of a takeover (not in this thesis), while delisting could also be caused by bad performance. Current literature does not look at the relative size of buyout-backed IPOs. Only a distinction between regular IPO size and buyout-backed IPO size is made. Literature on LBO
performance is mostly focused on buyout-backed IPOs and RLBOs. LBO performance is not fully researched this way, because many firms with PE-backing exist that remain private. By only looking at the firms that go public, overall LBO performance is not investigated. PE sponsors mostly go public with firms that are expected to perform, because the sponsor has a reputation to uphold (Cao, 2011). Researching economic performance of PE-backed private companies may be difficult as data is not readily available (Kaplan & Strömberg, 2009).
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9. Glossary
LBO Leveraged buyout
RLBO Reverse leveraged buyout PE Private equity
IPO Initial public offering BHR Buy-and-hold return raw BHAR Buy-and-hold abnormal return CAR Cumulative abnormal return SDC Securities Data Company U.S. United States
NYSE New York Stock Exchange
CSRP Center for Research in Security Prices
RLBO, buyout-backed IPO and PE-backed IPO are used synonymously. First IPO and regular IPO are used synonymously.
10. Appendix
Table A Correlation market indexes
Value is the value-weighted CSRP index minus Rf. Equal is the equal-weighted CSRP index minus Rf. SP500 is the S&P500 composite index minus Rf. RmRf is the Ken French database excess return.
Correlation Value Equal SP500 RmRf
Value 1
Equal 0.873 1
SP500 0.9821 0.8026 1
RmRf 0.9978 0.8608 0.9860 1
Table B PE-backed IPO county distribution
Date Issued : 01/01/1996 to 05/09/2016 Exit type: IPO Sum (USD Mil) Exit Type by Company World Sub
Location No. of Deals Avg. Time to Exit (Years) Post Offer Value
North America 2,682 4.9 1,637,101.80 East Asia 1,216 3.5 1,488,079.20 Western Europe 372 5.2 186,140.34 Southern Asia 144 4.1 56,947.67 Pacific 78 3.9 26,284.42 Northern Europe 67 4.9 27,060.19 Middle East 44 5.8 15,290.87 Southern Europe 42 5.0 9,068.74 SouthEast Asia 40 4.5 15,845.90 South America 31 4.2 38,524.13 Eastern Europe 26 4.6 41,421.33 Carribean 11 4.7 15,861.89 Southern Africa 7 3.5 2,560.49 Northern Africa 5 4.7 148.27 Central Asia 1 0.7 440.57 Western Africa 1 3.4 18.08 TOTAL 4,767 4.5 3,560,793.91 27
Table C Sample frequency distribution Date Frequency Cumulative
1996 10 10 1997 2 12 1998 2 14 1999 8 22 2000 5 27 2001 1 28 2002 2 30 2003 3 33 2004 12 45 2005 10 55 2006 10 65 2007 4 69 2008 5 74 2009 1 75 2010 6 81 2011 6 87 2012 12 99 2013 1 100 Total 100 100 28
Table D Variable descriptives
Variable Obs Mean Std. Dev. Min Max Variable Obs Mean Std. Dev. Min Max
Date 240 16786 2113 13149 20423 HOS 36 0.0271 0.1272 -0.2321 0.3021 Rf 240 0.0020 0.0018 0.0000 0.0056 HRZN 36 -0.0017 0.0476 -0.1587 0.0927 RmktRf 240 0.0057 0.0456 -0.1723 0.1135 HSCL 36 -0.0482 0.2092 -0.6720 0.5555 SMB 240 0.0251 0.0248 0.0002 0.2232 HTZ 36 0.0197 0.2580 -0.6306 0.8571 HML 240 0.0233 0.0235 0.0001 0.1391 HUN 36 -0.0029 0.1033 -0.1764 0.3450 UMD 240 0.0352 0.0409 0.0001 0.3458 INVA 36 0.0124 0.1047 -0.2080 0.2724 Value 240 0.0056 0.0459 -0.1854 0.1140 INWK 36 0.0063 0.2604 -0.4673 1.1197 Equal 240 0.0078 0.0558 -0.2060 0.2196 ITC 36 0.0155 0.0647 -0.0864 0.1569 SP500 240 0.0040 0.0442 -0.1702 0.1077 JCDA 36 -0.0584 0.2339 -0.4179 0.5271 ACAD 36 0.0388 0.2378 -0.2791 1.1341 JDAS 36 0.0117 0.2355 -0.5983 0.7217 ACME 36 -0.0007 0.2875 -0.5195 0.7262 KNL 36 -0.0032 0.0646 -0.1206 0.1914 ACUS 36 -0.0238 0.1180 -0.3757 0.2495 LOGM 36 0.0186 0.1213 -0.2003 0.3215 ADSI 36 0.0412 0.1309 -0.2421 0.3858 LOOK 36 -0.0119 0.3872 -0.4675 0.9273 ROLL 36 0.0268 0.0972 -0.2694 0.2274 LPLA 36 0.0092 0.0790 -0.1848 0.1911 AL 36 0.0109 0.0883 -0.1971 0.2556 MDRX 36 -0.0088 0.3469 -0.5335 1.4120 APEI 36 0.0037 0.1427 -0.4289 0.3017 MDTH 36 0.0076 0.1843 -0.3600 0.4160 ARDM 36 0.0134 0.2229 -0.3393 0.6624 MOTV 36 -0.0056 0.0702 -0.1817 0.1214 BCOV 36 0.0020 0.2057 -0.4356 0.4073 MRLN 36 0.0050 0.0578 -0.1131 0.1537 BGFV 36 0.0273 0.1300 -0.2545 0.2582 NKA 36 -0.0017 0.1089 -0.2256 0.3770 BKD 36 -0.0239 0.1627 -0.4095 0.2943 NTI 36 0.0172 0.0852 -0.1498 0.2102 BLDR 36 -0.0267 0.1246 -0.3531 0.2187 OCIS 36 -0.0324 0.2017 -0.3713 0.6213 BLKB 36 0.0224 0.0833 -0.1725 0.1655 OPXT 36 -0.0340 0.2102 -0.5251 0.4285 BLOX 36 0.0144 0.1516 -0.3593 0.3014 ORCH 36 0.0248 0.5202 -0.5147 1.9310 BXC 36 -0.0292 0.1362 -0.3075 0.4002 ORN 36 0.0097 0.1963 -0.4033 0.5507 CATM 36 0.0531 0.2479 -0.7674 0.7017 OSIR 36 0.0241 0.2006 -0.3919 0.5962 CBDR 36 0.0128 0.2541 -0.3804 1.1658 PACB 36 -0.0045 0.2647 -0.5326 0.6393 CBEY 36 0.0183 0.1790 -0.4902 0.5648 PFCB 36 0.0221 0.1065 -0.1955 0.2180 CNSL 36 0.0001 0.0952 -0.2174 0.2503 PRSS 36 -0.0254 0.1733 -0.4440 0.4720 COSI 36 0.0473 0.2892 -0.4433 0.8388 RAX 36 0.0440 0.1577 -0.3424 0.3775 CYTC 36 0.0284 0.2909 -0.4619 0.9813 RFMD 36 0.1121 0.2786 -0.2795 0.8435 CYTK 36 -0.0135 0.1663 -0.4485 0.3072 RSNT 36 -0.0021 0.1946 -0.4493 0.4193 DEIX 36 -0.0684 0.2117 -0.7378 0.4815 RXN 36 0.0101 0.0963 -0.2187 0.2119 DEX 36 -0.0243 0.1707 -0.5125 0.3198 SAFT 36 0.0337 0.0775 -0.2036 0.2463 DIVX 36 -0.0263 0.1714 -0.3514 0.4178 SGLP 36 0.0115 0.2195 -0.3759 0.5396 DMD 36 -0.0270 0.1322 -0.3727 0.2671 SHOP 36 -0.0053 0.0940 -0.2572 0.2867 29
DOX 36 0.0559 0.2219 -0.3539 0.5972 SLAB 36 0.0077 0.2968 -0.4906 1.1118 DPRC 36 -0.0118 0.1501 -0.3529 0.3260 SLGN 36 -0.0143 0.1226 -0.2353 0.2948 DRAD 36 0.0500 0.0765 -0.1116 0.1999 SMA 36 -0.0038 0.0873 -0.2007 0.2720 DSH 36 -0.0188 0.1624 -0.3783 0.5558 SONS 36 0.0781 0.5422 -0.8011 1.7767 DVOX 36 -0.0810 0.2244 -0.5111 0.4118 SSTK 36 0.0182 0.1729 -0.3567 0.4907 EAC 36 -0.0053 0.1748 -0.3177 0.4187 TNGO 36 0.0128 0.1338 -0.2035 0.2645 ESPR 29 0.0542 0.2491 -0.4925 0.7137 TOWR 36 0.0221 0.1369 -0.2523 0.2785 ETM 36 0.0219 0.1665 -0.2094 0.4228 TPX 36 0.0121 0.1185 -0.2829 0.2507 EVEP 36 0.0130 0.1459 -0.2934 0.3375 TRGT 36 0.0091 0.2715 -0.7125 1.1840 EXEL 36 -0.0012 0.2604 -0.3324 0.7161 TSLA 36 0.0624 0.1930 -0.2251 0.7378 FET 36 0.0044 0.0982 -0.2176 0.1954 TUMI 36 0.0044 0.1099 -0.3561 0.2557 FIVE 36 0.0126 0.1134 -0.1814 0.2248 UCOR 36 -0.0220 0.1412 -0.2821 0.5397 FLXI 36 -0.0840 0.3635 -0.4245 1.2457 UDRL 36 -0.0339 0.1835 -0.6253 0.3967 FSL 36 0.0514 0.1014 -0.1792 0.3137 VNTV 36 0.0202 0.0677 -0.1111 0.2005 GHDX 36 0.0261 0.1562 -0.3171 0.5004 WCAA 36 0.0383 0.1731 -0.2210 0.4065 GOLF 36 -0.0272 0.2166 -0.6474 0.5685 WCC 36 -0.0169 0.1705 -0.4232 0.3593 GRPN 36 -0.0115 0.1673 -0.3597 0.3446 WTW 36 0.0084 0.0749 -0.1327 0.1931 HCA 36 0.0184 0.1150 -0.2457 0.2668 XNPT 36 0.0439 0.1591 -0.1904 0.5011 HCCI 36 0.0089 0.1477 -0.2412 0.3972 ZINC 36 0.0036 0.2496 -0.3947 0.7279 HMTT 36 0.0032 0.2949 -0.5169 1.1272 Company return Total 0.6229 Mean 0.00623 30
Table E BHR over 1 and 3 year periods
Company BHR1year BHR3year Company BHR1year BHR3year
ACAD 1.3402 1.9538 HUN 0.7270 0.8532 ACME 0.2951 0.2541 INVA 1.3112 1.4646 ACUS 0.6890 0.3499 INWK 1.1857 0.4756 ADSI 2.8780 3.7620 ITC 1.1244 1.8228 ROLL 1.3588 2.4578 JCDA 0.2757 0.0497 AL 0.8457 1.2942 JDAS 1.7158 0.6263 APEI 0.8676 0.7864 KNL 0.9749 0.9362 ARDM 0.5488 0.8659 LOGM 1.3267 1.5235 BCOV 0.3836 0.4885 LOOK 0.7203 0.0599 BGFV 0.9511 2.0548 LPLA 0.8566 1.2448 BKD 0.9357 0.2775 MDRX 1.3398 0.1469 BLDR 1.2531 0.3153 MDTH 0.8325 0.7509 BLKB 1.4672 2.2086 MOTV 0.7992 0.8371 BLOX 1.0189 1.0796 MRLN 1.0040 1.2342 BXC 0.7973 0.2785 NKA 1.0366 0.7699 CATM 0.1276 1.7418 NTI 1.6204 1.6369 CBDR 0.2057 0.7203 OCIS 0.1939 0.1735 CBEY 2.8619 1.2103 OPXT 0.2718 0.1223 CNSL 1.1076 0.9641 ORCH 0.5084 0.1001 COSI 0.2768 1.5915 ORN 0.6440 0.7840 CYTC 1.1194 0.8284 OSIR 1.1825 1.3157 CYTK 0.2922 0.3957 PACB 0.2140 0.2489 DEIX 0.7957 0.0278 PFCB 1.0738 2.0782 DEX 2.3082 0.2541 PRSS 0.3335 0.2209 DIVX 0.6803 0.2372 RAX 1.0899 3.1295 DMD 0.3014 0.2642 RFMD 0.5726 1.9025 DOX 1.5783 3.8017 RSNT 0.6181 0.5045 DPRC 0.6818 0.4977 RXN 0.9434 1.2241 DRAD 2.2581 5.8790 SAFT 1.3406 3.1344 DSH 0.3544 0.3459 SGLP 0.6873 0.6651 DVOX 0.3956 0.0150 SHOP 0.7169 0.7169 EAC 2.4587 0.4871 SLAB 0.2280 0.3531 ESPR 1.1320 2.0807 SLGN 1.3702 0.5288 ETM 1.6851 1.5794 SMA 0.9599 0.8629 EVEP 1.7774 1.1681 SONS 1.2251 0.2166 EXEL 0.5318 0.3514 SSTK 2.8311 1.1733 FET 1.1780 0.9896 TNGO 1.6811 1.1624 FIVE 1.3449 1.2574 TOWR 0.9146 1.5771 FLXI 0.1532 0.0071 TPX 1.3813 1.3200 31
FSL 1.6281 5.1639 TRGT 1.2859 0.4150 GHDX 1.2250 1.9110 TSLA 1.3215 5.3360 GOLF 0.7040 0.1500 TUMI 0.9002 0.9321 GRPN 0.2169 0.3897 UCOR 0.6579 0.3772 HCA 0.7700 1.5382 UDRL 1.7009 0.1419 HCCI 0.4499 0.9662 VNTV 1.1777 1.9023 HMTT 1.1076 0.3333 WCAA 1.2177 2.6993 HOS 1.9547 2.2235 WCC 0.4904 0.3590 HRZN 0.9967 0.9030 WTW 1.3580 1.2746 HSCL 0.1698 0.0674 XNPT 1.8088 3.5858 HTZ 1.1596 0.6430 ZINC 0.4409 0.4399 32
Table F CAPM and Fama-French 3 year alphas
Where rStd.Err. is the robust standard error. * indicates significance: *=10% significant, **=5% significant, ***=1% significant.
CAPM Fama-French
n=36 Alpha rStd. Err t-value n=36 Alpha rStd. Err t-value
ACAD 0.022702 0.032192 0.71 ACAD 0.055052 0.058851 0.94 ACME 0.001015 0.04896 0.02 ACME 0.013077 0.092029 0.14 ACUS -0.02554 0.021136 -1.21 ACUS 0.00844 0.044377 0.19 ADSI 0.035802 0.022546 1.59 ADSI 0.010218 0.043976 0.23 ROLL 0.026135 0.015824 1.65 ROLL 0.020084 0.034386 0.58 AL 0.013073 0.01499 0.87 AL 0.03713 0.031266 1.19 APEI 0.003762 0.023986 0.16 APEI 0.008008 0.044697 0.18 ARDM -0.01738 0.028913 -0.6 ARDM 0.138471 0.084606 1.64 BCOV 0.021668 0.036001 0.6 BCOV -0.03412 0.081202 -0.42 BGFV 0.025985 0.023029 1.13 BGFV 0.055275 0.048354 1.14 BKD -0.01928 0.02592 -0.74 BKD 0.037928 0.047328 0.8 BLDR -0.02675 0.021204 -1.26 BLDR 0.036582 0.032539 1.12 BLKB 0.021579 0.016193 1.33 BLKB -0.00338 0.02663 -0.13 BLOX 0.03143 0.024825 1.27 BLOX 0.017389 0.06316 0.28 BXC -0.02717 0.024788 -1.1 BXC -0.00728 0.057957 -0.13 CATM 0.049132 0.038243 1.28 CATM -0.01146 0.061188 -0.19 CBDR 0.020165 0.048527 0.42 CBDR 0.092154 0.103244 0.89 CBEY 0.029305 0.030112 0.97 CBEY -0.00468 0.052238 -0.09 CNSL 0.000369 0.016384 0.02 CNSL -0.00988 0.031074 -0.32 COSI 0.046185 0.050721 0.91 COSI 0.0039 0.076013 0.05 CYTC -0.02235 0.035962 -0.62 CYTC -0.13384 0.105227 -1.27 CYTK -0.00928 0.029859 -0.31 CYTK 0.069115 0.043716 1.58 DEIX -0.05254 0.029443 -1.78* DEIX -0.07868 0.064364 -1.22 DEX -0.02032 0.033008 -0.62 DEX 0.023023 0.051906 0.44 DIVX -0.02497 0.028617 -0.87 DIVX -0.05191 0.062903 -0.83 DMD -0.0225 0.025602 -0.88 DMD 0.020822 0.032625 0.64 DOX 0.055679 0.037144 1.5 DOX -0.06353 0.053608 -1.19 DPRC -0.03261 0.021441 -1.52 DPRC 0.021213 0.060462 0.35 DRAD 0.049006 0.013244 3.7*** DRAD 0.051588 0.028638 1.8* DSH -0.0156 0.030651 -0.51 DSH 0.058204 0.109246 0.53 DVOX -0.06847 0.035505 -1.93* DVOX 0.047754 0.072876 0.66 EAC 0.017808 0.037551 0.47 EAC 0.101958 0.079992 1.27 ESPR 0.062495 0.047337 1.32 ESPR -0.0641 0.130939 -0.49 ETM 0.019635 0.027442 0.72 ETM 0.007523 0.045739 0.16 EVEP 0.013874 0.024956 0.56 EVEP 0.000643 0.033413 0.02 EXEL 0.003315 0.043172 0.08 EXEL 0.068133 0.071704 0.95 33
FET 0.023205 0.018628 1.25 FET 0.023583 0.034356 0.69 FIVE 0.019255 0.020847 0.92 FIVE -0.05197 0.045285 -1.15 FLXI -0.0894 0.058455 -1.53 FLXI -0.21218 0.081364 -2.61** FSL 0.053458 0.018557 2.88*** FSL 0.084911 0.03515 2.42** GHDX 0.025561 0.026852 0.95 GHDX 0.018447 0.051726 0.36 GOLF -0.02683 0.034931 -0.77 GOLF -0.01348 0.058314 -0.23 GRPN -0.01112 0.037829 -0.29 GRPN 0.09111 0.062309 -1.46 HCA 0.014749 0.022102 0.67 HCA 0.053049 0.03354 1.58 HCCI 0.006994 0.023647 0.3 HCCI 0.029394 0.06517 0.45 HMTT -0.00214 0.047551 -0.04 HMTT 0.049444 0.099241 0.5 HOS 0.034657 0.022944 1.51 HOS 0.025693 0.044701 0.57 HRZN -0.0011 0.007374 -0.15 HRZN 0.01441 0.014007 1.03 HSCL -0.03505 0.035672 -0.98 HSCL -0.04474 0.118205 -0.38 HTZ 0.020941 0.043633 0.48 HTZ -0.09896 0.056744 -1.74* HUN -0.0013 0.017786 -0.07 HUN 0.022646 0.032931 0.69 INVA 0.010137 0.018035 0.56 INVA 0.041955 0.038644 1.09 INWK 0.011774 0.043583 0.27 INWK -0.10554 0.085377 -1.24 ITC 0.015559 0.010862 1.43 ITC 0.015404 0.01751 0.88 JCDA -0.06657 0.03824 -1.74* JCDA -0.02629 0.070485 -0.37 KNL -0.00452 0.010354 -0.44 KNL 0.013983 0.01617 0.86 LOGM 0.017855 0.020737 0.86 LOGM 0.021409 0.036468 0.59 LOOK -0.00541 0.070677 -0.08 LOOK 0.026587 0.136493 0.19 LPLA 0.008768 0.01452 0.6 LPLA 0.051128 0.03147 1.62 MDRX 0.006368 0.065856 0.1 MDRX -0.06996 0.085492 -0.82 MDTH 0.007417 0.030712 0.24 MDTH 0.097556 0.056832 1.72* MOTV -0.00463 0.011546 -0.4 MOTV -0.02724 0.024901 -1.09 MRLN 0.005684 0.00944 0.6 MRLN 0.012113 0.021895 0.55 NKA 0.001773 0.025699 0.07 NKA 0.045465 0.044764 1.02 NTI 0.017954 0.013921 1.29 NTI 0.034004 0.027856 1.22 OCIS -0.03724 0.034955 -1.07 OCIS -0.02411 0.103547 -0.23 OPXT -0.03384 0.035531 -0.95 OPXT -0.06597 0.060134 -1.1 ORCH 0.037003 0.092002 0.4 ORCH 0.093179 0.190643 0.49 ORN 0.008505 0.033104 0.26 ORN -0.07158 0.063284 -1.13 OSIR 0.022848 0.034248 0.67 OSIR 0.003585 0.073464 0.05 PACB -0.01154 0.042268 -0.27 PACB -0.00621 0.093538 -0.07 PFCB 0.021238 0.018097 1.17 PFCB -0.00914 0.027208 -0.34 PRSS 0.015005 0.033576 -0.45 PRSS 0.011354 0.056449 0.2 RAX 0.039026 0.021048 1.85* RAX 0.029988 0.045256 0.66 RFMD 0.115379 0.049914 2.31** RFMD 0.092828 0.071603 1.3 RSNT 0.004873 0.033386 0.15 RSNT -0.01323 0.074204 -0.18 RXN 0.031229 0.016174 1.93* RXN 0.014235 0.026794 -0.53 SAFT 0.029387 0.015306 1.92* SAFT 0.051026 0.02733 1.87* 34
SGLP 0.013957 0.036514 0.38 SGLP -0.02529 0.069589 -0.36 SHOP -0.00604 0.015115 -0.4 SHOP -0.00987 0.020533 -0.48 SLAB 0.01501 0.057884 0.26 SLAB 0.185192 0.09847 1.88* SMA -0.00696 0.014508 -0.48 SMA -0.00956 0.031543 -0.3 SONS 0.082194 0.09413 0.87 SONS 0.339018 0.195678 1.73* SSTK 0.011262 0.030685 0.37 SSTK 0.013599 0.057939 0.23 TNGO 0.026058 0.023934 1.09 TNGO 0.03917 0.04222 0.93 TOWR 0.013781 0.023731 0.58 TOWR 0.053172 0.052084 1.02 TPX 0.003564 0.021762 0.16 TPX -0.03292 0.039479 -0.83 TRGT 0.010316 0.042152 0.24 TRGT -0.03609 0.04706 -0.77 TSLA 0.057098 0.033192 1.72* TSLA 0.093017 0.060768 1.53 TUMI 0.032422 0.014362 2.26** TUMI 0.033658 0.043138 0.78 UCOR -0.03488 0.019614 -1.78* UCOR -0.01117 0.058095 -0.19 UDRL -0.03276 0.029388 -1.11 UDRL 0.068273 0.044368 1.54 VNTV 0.029644 0.012785 2.32** VNTV 0.047395 0.025918 1.83* WCAA 0.043393 0.031033 1.4 WCAA 0.086333 0.065659 1.31 WCC -0.02417 0.028993 -0.83 WCC 0.00568 0.049965 0.11 WTW 0.010256 0.012127 0.85 WTW -0.00505 0.030456 -0.17 XNPT 0.043463 0.026328 1.65 XNPT 0.061376 0.053485 1.15 ZINC 0.007217 0.040852 0.18 ZINC -0.13768 0.071751 -1.92* JDAS -0.01875 0.034619 -0.54 JDAS 0.083106 0.09811 -0.85 SLGN -0.02534 0.019007 -1.33 SLGN -0.01833 0.033523 -0.55 (13significant 4negative) (10significant 3negative)
Total 0.731998
Total 1.591714
Mean 0.00732
Mean 0.015917
