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Private Equity Investor Sentiment around the World
Minjie Zhang
Odette School of Business - University of Windsor
401 Sunset Avenue
Windsor, Ontario N9B 3P4
Canada
http://ssrn.com/author=2010278
This Draft: September 2017
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Private Equity Investor Sentiment around the World
ABSTRACT
I propose a new investor sentiment measurement for the private equity market
based on over 12,000 private equity deals from 68 countries over 1992 to 2012. The data
indicate that institutional environments and firm-specific characteristics are both strong
determinants of the private equity investor sentiment. This investor sentiment will be
relatively higher for smaller entrepreneurial firms in countries with better legal
environments and with cultures characterized by higher levels of risk-taking. Such
behaviors are robust when accounting for the prior investor sentiment. I also document the
differences of this investor sentiment across different markets and investor types as well as
the impacts from the recent financial crisis on it. In addition, I find that this investor
sentiment can help identify and predict the investee firm-level profitability and earnings
potential both in the short and long time horizons. It also can reflect the market timing
abilities of PE investors when executing their divestment strategies successfully by using
initial public offerings or acquisitions.
Keywords: Investor Sentiment, Private Equity, Alternative Investments, IPO, Acquisition, Law
and Finance, Culture, Financial Crisis
JEL Codes: G01, G02, G11, G23, G24, G34, K22
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1. Introduction
In November 11th, 2014, investors witnessed a record-breaking event in Sotheby’s
Switzerland when the super-complication pocket watch made by Patek Philippe was hammered at
an auction price of $24 million, almost $10 million above its estimated value. Benjamin Franklin’s
famous “Time is money” maxim is so pragmatic in the financial world. Unlike the traditional
financial investments such as the stocks and bonds, alternative investments such as the collectable
Patek Philippe watch or other alternative asset classes are mostly illiquid assets, hard to determine
the current market values, lack of historical risks and returns data and relatively of higher costs to
buy and sell. Due to such natures, there is very little literature discussing the investor sentiment in
the alternative investments market. Private equity (PE), as one of the major alternative investments
asset classes, expands its market at an unprecedented pace in the past few decades. It is important
to provide more evidence on the investor sentiment in the PE market around the world and the role
of international differences in legal and cultural institutions in the determinants of and outcomes
from this investor sentiment in the booming PE market.
In this paper, I seek to add to the behavioral finance and private equity literature by
examining the theory and empirical evidence on the investor sentiment in the PE market across the
world. I aim to shed light on how to quantify the PE investor sentiment by proposing a new
measurement, how the PE investor sentiment looks around the world, what legal and cultural
environments affect this investor sentiment, how the PE investor sentiment is different across
markets as well as between different investor types. I also consider whether this investor sentiment
will affect the investee firm-level profitability both in the short and long terms and the success of
divestment strategies. In addition, I explore the possible links between the public equity and PE
markets by studying the investor sentiment in both markets.
My analysis exploits the comprehensive data collected at the deal level of investee firms
from PitchBook, which comprise 12,457 completed PE deals from 68 countries over 1992 to 2012.
The dataset allows me to investigate the PE investor sentiment at both the investee firm and country
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level. The data indicate that, institutional environments and firm-specific characteristics are both
strong determinants of the PE investor sentiment. This investor sentiment will be relatively higher
for smaller investee firms in countries with better legal environments and with cultures
characterized by higher levels of risk-taking. Such behaviors are robust when accounting for the
prior investor sentiment. I also document the differences of this PE investor sentiment across
different markets and investor types as well as the impacts from the recent financial crisis on it. I
find that the PE investor sentiment is relatively higher in developed markets as compared with
emerging markets, it is also relatively higher among PE/VC funds as compared with angel investors
and the recent financial crisis did reduce the investor sentiment in the PE market while the priors
of this investor sentiment can provide mitigating effects during the crisis period.
In addition, I investigate the predictability of this new investor sentiment measurement and
find that it stands to reveal significant results in terms of predicting short-run positive financial
performances and earnings potential at investee firms and reflecting the possible misvaluations
effect in the long run. It suggests that PE investors might be overoptimistic to some extent. With
regards to the success of divestment strategies, I find that PE investors have market timing abilities
to divest successfully and prefer using acquisition strategy as compared with initial public offering
(IPO) strategy in the market. For the investor sentiment in the U.S. financial market, I find that the
public equity market sentiment will have strong and positive impact on the PE market investor
sentiment implying such sentiment can be physically and psychological spread across markets. My
tests results are robust under various clustering methods to correct standard errors while controlling
fixed effects.
Theoretically, the prospect theory developed by Kahneman and Tversky (1979) has built
the foundation for modern behavioral economics and finance studies and Dr. Daniel Kahneman
won the Nobel Prize in Economic Sciences in 2002 “for having integrated insights from
psychological research into economic science, especially concerning human judgment and
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decision-making under uncertainty”1. This extraordinarily influential theory is based on the idea
that people evaluate gains or losses from some neutral or status quo point, termed anchor or
reference point per se. And this theory inspired many more theoretical studies in the literature to
explain investors’ behaviors such as equity premium puzzle (Mehra and Prescott, 1985; Benartzi
and Thaler, 1995), loss aversion (Odean, 1998) and under-/over-confidence and under-/over-
reactions in the market (Griffin and Tversky, 1992; Daniel, Hirshleifer and Subrahmanyam, 1998;
Barberis, Shleifer and Vishny, 1998). My investor sentiment measurement in the PE market is also
inspired by the results of Tversky and Kahneman (1974) on the important behavioral heuristic
known as anchoring which draws on the decision-making process to attach or “anchor” our
thoughts and beliefs to a reference point.
Empirically, many scholars and their studies have applied the prospect theory and the
psychological heuristics to design confidence and sentiment indices in explaining different
investment decisions. The application of investor’s psychology, sentiment and attitude towards
risks and uncertainties has become more and more popular in the literature and has been empirically
confirmed to be important. For example, Lashgari (2000) discussed the TED spread and confidence
index impacts on the stock prices and found the rise/fall of the TED spread is associated with
low/high Barron’s investor confidence index. Dennis and Mayhew (2002) used the put-call option
ratio to proxy for trading pressure and market sentiment but found no strong link to the risk-neutral
skewness. Randall, Suk and Tully (2003) utilized the net cash flow in mutual funds to capture the
investor sentiment in the public equity market and used it as a significant factor in explaining the
monthly movements in the stock market returns. Baker and Wurgler (2006, 2007) have constructed
a novel composite investor sentiment index based on six individual sentiment proxies: the number
of IPOs, the average first-day returns of IPOs, the dividend premium, the closed-end fund discount,
the New York Stock Exchange (NYSE) turnover, and the equity share in new issues. Their famous
1 http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/2002/kahneman-facts.html.
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composite index is the most used market sentiment index in the U.S. and the authors argue that this
index has a greater effect on securities and demonstrates the intriguing patterns in the cross-section
of security returns across different sentiment states. In a further study by Baker, Wurgler and Yuan
(2012), the authors extended the previous studies to construct a global sentiment index and
documented the contagious effect from the sentiment across the six markets covered.
My paper adds to the large behavioral finance literature and the growing private equity
literature by documenting the investor sentiment in the PE market. While the existing literature has
focused most on the investor sentiment in the traditional financial and public equity market (Neal
and Wheatley, 1998; Whaley, 2000; Lashgari, 2000; Dennis and Mayhew, 2002; Kumar and
Persaud, 2002; Randall et al., 2003; Brown and Cliff, 2004, 2005; Baker and Wurgler, 2006, 2007;
Schmeling, 2009; Nayak, 2010; Baker, et al., 2012; Stambaugh, Yu and Yu, 2012; Spyrou, 2013),
the investor sentiment in the alternative investments market has received relatively little attention.
Only handful studies examine the investor sentiment in this market such as commercial real estate
(Zheng, Sun and Kahn, 2015), collectable arts (Pénasse, Renneboog and Spaenjers, 2014) and
investable wines (Cossutta, Masset and Weisskopf, 2013). As such, more research on this area is
clearly warranted. The most basic reason, I believe, for lack of work on the alternative investments
market is the fact that data are readily available on traditional investments but scantly available on
those alternative investments.
My paper builds on these important prior theoretical and empirical studies and fills in the
blanks in the literature by providing large sample international empirical evidence on the investor
sentiment in the PE market. To the best of my knowledge, this study is the first to use a multi-
country PE deal-level database to observe the investor sentiment across the world. I not only design
an innovative and simple measurement to capture the PE investor sentiment but also document the
strong legal and cultural environments as determinants and the characteristics of this investor
sentiment around the world. My paper also contributes to the private equity literature by applying
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behavioral finance concepts to explain the investee firm-level performances and earnings potential
as well as the success of divestment strategies of IPOs and acquisitions.
The remainder of the paper is organized as follows. Section 2 discusses the prior literature
and develops my hypotheses. Section 3 presents the data and the summary statistics, while section
4 covers the regression analyses and robustness checks. Section 5 discusses some limitations of the
dataset. Section 6 offers a conclusion and provides an outlook for future research.
2. Literature Reviews and Hypotheses
There is extant research discussing the investor sentiment in the traditional financial market
and its measurements as well as the associated impacts on the stock prices and returns. There are
five main approaches have been documented in the literature to capture the investor sentiment,
mostly for the investor sentiment in the public equity market.
Financial market-based measurements are used the most as the first main approach to proxy
for investor sentiment. The TED spreads (Lashgari, 2000), trading volumes (Gervais, Kaniel and
Mingelgrin, 2001; Hou, Xiong and Peng, 2009), put-call option ratios (Dennis and Mayhew, 2002),
extreme one-day returns (Barber and Odean, 2008), closed-end mutual fund premiums (Zweig,
1973; Lee, Shleifer and Thaler, 1991), net cash flows in mutual funds (Randall et al., 2003),
Chicago Board Options Exchange’s (CBOE) volatility index (VIX) (Whaley, 2000; Baker and
Wurgler, 2007), Credit Suisse Fear Barometer (CSFB) index (Da, Engelberg and Gao, 2015),
dividend premiums (Baker and Wurgler, 2004; Vieira, 2011), retail investors’ trading transactions
(Kumar and Lee, 2006) and pre-IPO grey market prices, IPO first-day returns as well as the volume
of IPOs (Cornelli, Goldreich and Ljungqvist, 2006; Baker and Wurgler, 2007) and investment
amounts in advertisement (Grullon, Kanatas and Weston, 2004) are all proved to be strong proxies
for investor sentiment. Baker and Wurgler (2006) proposed their influential composite sentiment
index which based on six individual sentiment proxies and it is widely used as a good and major
sentiment index for the U.S. public equity market.
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The second approach is to use survey-based investor sentiment index to predict financial
market indicators. The UBS/Gallup index of investor optimism, the conference board consumer
confidence index and the University of Michigan consumer sentiment index are among the most
used and refereed survey-based indices to proxy for investor sentiment. Although those three
indices receive support in the literature to provide good predictions for financial market indicators
(Brown and Cliff, 2005), their restrictions and survey study drawbacks cannot be neglected as the
frequency of survey-based investor sentiment index is usually weekly or monthly updated while
the financial market-based index is daily or more frequently updated and the survey respondents
might not answer questions in a truthful and careful way (Da, et al., 2015).
The third approach is developed with the technical advances in data mining and social
media coverage. Many scholars apply text mining and sentiment analysis techniques to extract
information and moods among investors, consumers and users from those news and social media
platforms. Information extracted from news media such as financial newspapers and journalist
columns will have significant impact on the investor sentiment when pitching the stocks in the
market and thus affect the stock prices and returns (Tetlock, 2007; Barber and Odean, 2008;
Dougal, Engelberg, García and Parsons, 2012; Ahern and Sosyura, 2015). Text mining algorithms
are mainly used to extract similar sentiment information from the most trending and popular social
media platforms such as Facebook and Twitter. Such investor sentiment information has been
found to help predict stock prices, market indices and trading volumes (Bollen, Mao and Zeng,
2011; Zhang, Fuehres and Gloor, 2011; Karabulut, 2013). Although interesting resources of
sentiment data can be found using the third approach, the data mining knowledge and data
collection procedure is somewhat difficult for researchers.
Another interesting, new and promising approach is to extract sentiment information using
internet search behaviors. Search engines like Google and Yahoo provide the search volume data
which can be useful to track the moods and sentiment changes among investors. More and more
studies have utilized these independent search data to proxy for investor sentiment and empirically
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confirm the usefulness and predictive power of investor attentions as well as the associated impacts
on market returns and volatilities, both in the U.S. and globally (Da, et al., 2015; Dimpfl and Jank,
2016; Gao, Ren and Zhang, 2016). Besides those studies with focus on the public equity market,
this approach also receives support to track the relationships between investor sentiment and the
foreign currency market volatilities (Smith, 2012).
Non-economic factors other than those economic related factors as above-mentioned will
also affect people’s moods and thus, in turn, to influence our investment decision process, risk
aversion levels and trading behaviors. Weather related factors have been discussed the most as
temperatures (Cao and Wei, 2005; Dong and Tremblay, 2015), sunshine exposure hours
(Hirshleifer and Shumway, 2003; Akhtari, 2011; Dong and Tremblay, 2015) and rainfalls, snow
depth as well as wind (Dong and Tremblay, 2015) will all have strong and pervasive effect on
investor sentiment in the public equity market. Kamstra, Kramer and Levi (2003) also found that
the seasonal affective disorder (SAD) has a profound effect on the stock market returns which
indicate that medical and health conditions will also affect investor sentiment. Although these
factors seems to be all related to weather conditions or seasonality, other non-economic factors
such as sports events (Edmans, García and Norli, 2007), aviation disasters (Kaplanski and Levy,
2010), lunar phases (Dichev and Janes, 2003; Yuan, Zheng and Zhu, 2006) and even geomagnetic
storms (Robotti and Krivelyova, 2003) have also been documented to be useful sentiment factors
to affect the stock and market returns.
Although there are many measurements and approaches can be applied to capture the
investor sentiment in the financial market, it is a little surprising that none of the prior private equity
literature has ever documented the investor sentiment and the associated impacts. I believe that the
data availability issue is the most basic reason for that lack of work in this area. The very few
studies in the alternative investments literature have documented the investor sentiment impact on
various asset classes. Zheng, et al., (2015) constructed a confidence index by using Google search
texts to proxy for the investor sentiment in 35 Chinese city real estate markets and found that the
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index can predict subsequent housing price appreciations and new housing constructions. Pénasse,
et al., (2014) used panel survey data to proxy for the art market participants’ confidence levels in
an outlook for a set of artists and found the existence of a slow-moving fad component in art prices
and this investor sentiment can predict short-term returns. Cossutta, et al., (2013) estimated the
investable wine indices by using hammer prices from auction house Acker Merrall and found such
indices can predict the future evolution of wine prices. These studies inspired me to apply the
special features of the PE market to find an appropriate approach to measure the investor sentiment
in this market.
Simon (1955) concludes that people start decision-making process by gathering relevant
information. Tversky and Kahneman (1974) introduce the concept of anchoring as a heuristic which
draws on the tendency to attach thoughts to a reference point. When an anchor is available with a
relevant value, people tend to make estimates by starting from this anchor of initial value to yield
final answers as a decision-making process. Such cognitive biases have been discussed in
Kahneman and Tversky (1979) and the prospect theory is useful to explain many finance and
economics related questions. Thus the reference point level is a critical factor when people make
decisions. Inspired by the prospect theory and the anchoring heuristic (Tversky and Kahneman,
1974; Kahneman and Tversky, 1979), I propose to design a simple and novel measurement at the
PE deal level to capture the investor sentiment in the PE market. Private equity investments are
primarily composed of relatively high risk, illiquid securities in private companies (Cumming and
Johan, 2007; Johan and Zhang, 2016), and the lock-up period might be up to 7 to 10 years which
is quite different from the public equity investments that the trading behaviors can happen in any
short time frame. In the private equity investments, each transaction is deal based, in this way, a
natural reference point or anchor that investors can refer is the investee firm valuation at the point
when they make the final decision to close and complete this deal at a final price. Inspired by the
prior studies that use the hammer prices for the investable wines (Cossutta, et al., 2013) and the
catalogue prices for the collectable stamps (Dimsona and Spaenjers, 2011) as reference points when
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calculating the price premiums or investor confidence levels, my method utilizes the investee firm
valuations and the completed PE deal prices to calculate the new measurement for the investor
sentiment in the PE market – I call it “Price-to-Valuation (PV) ratio”. I will present the details of
this new measurement in the next section.
Sentiment reflects the investor’s attitudes towards risks and uncertainties, thus it is
important to find out what determinates such sentiment and what factors will have impacts on this
decision-making process, especially for an international study. Prior literature already confirms that
legal environment is important for investors if they can be better protected (La Porta, Lopez-de-
Silanes, Shleifer and Vishny, 1997, 1998, 2006), and legal rules that can protect investors from
expropriation by insiders will also affect the investors’ willingness to participate in the equity
markets. A stronger legal environment is highly valued by investors (Fernandes, Lel and Miller,
2010) and thus increases the participation of investors and predicts a stronger sentiment effect (Yuk
Ying, Faff and Hwang, 2012). Similar to the public equity market, better legal conditions will also
facilitate better enforcement of PE contracts, and the associated information asymmetries between
PE investors, investee firms and outside investors during both investment and divestment periods
can be alleviated in a more efficient way (La Porta et al., 1997, 1998, 2006; Lerner and Schoar,
2005; Cumming, Fleming and Schwienbacher, 2006). Therefore, all else being equal, I believe that
the PE investor sentiment will be relatively higher in countries with better legal environments. In
addition to the legal environments, more and more international studies consider national culture
as one of the important factors affecting business decisions (Shane, 1993; Hayton, George and
Zahra, 2002; Cumming, Johan and Zhang, 2014). One of the six Hofstede’s cultural dimensions is
uncertainty avoidance (Hofstede, 2001) and it has been documented to be linked to entrepreneurs’
risk-taking behaviors and proactiveness as well as having impacts on the PE investment and
divestment activities (Kreiser, Marino, Dickson and Weaver, 2010; Holm, Opper and Nee, 2013;
Johan and Zhang, 2016). Thus, I believe that the uncertainty avoidance level will also affect
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people’s perception of and motivation to pursue opportunities and thus affect the PE investor
sentiment. I therefore hypothesize the following Hypothesis H1a:
Hypothesis 1a: Legal and cultural environments that protect and facilitate risk-taking
behaviors under uncertainty are strong determinants of the PE investor
sentiment.
Moreover, although I use anchoring heuristic as a main method to generate the PV ratios
that aim to capture the investor sentiment in the PE market, another cognitive bias people tend to
have when making decisions is the belief bias that may cause people to tend to over-depend on
prior knowledge in arriving at decisions. Kuhnen and Knutson (2011) find that marketplace features
or outcomes of past choices may change emotions and thus influence future financial decisions. If
this is the same case for PE investors, their previous PE deal sentiment will be a critical factor that
they might refer when they are in process of making and completing the current deal. I thus
hypothesize the following Hypothesis H1b:
Hypothesis 1b: Prior sentiment will have positive impact on the current PE investor
sentiment.
Investor sentiment in the public equity market has been confirmed to be a powerful and
good predictor and has profound effect on the stock market prices, volatilities and returns (Lashgari,
2000; Randall, et al., 2003; Baker and Wurgler, 2006, 2007; Barber and Odean, 2008, Baker, et al.,
2012; Da, et al., 2015). However, as mentioned before, PE investments are primarily composed of
relatively high risk, illiquid securities in private companies (Cumming and Johan, 2007; Johan and
Zhang, 2016), and the lock-up period might be up to 7 to 10 years long, your investment returns
will be better realized only if the investee firm is acquired or goes for an IPO. The returns cannot
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be easily calculated and monitored. Thus, one way the investors can utilize to forecast their future
returns is to look at the profitability and earnings potential of their investee firms. If those investors
present relatively higher confidence and interest levels in their investee firms, those firms may
perform financially well and present huge earnings potential. Therefore, I expect that by controlling
for international differences, the investor sentiment in the PE market will positively affect the
investee firm-level profitability and earnings potential in the short run. In addition, if a stock is
inefficiently mispriced initially, it would eventually earn negative abnormal returns when such
misvaluation is corrected (Baker and Wurgler, 2006; Dong, Hirshleifer, Richardson and Teoh,
2006). Similar to the public equity market, if there is presence of potential misvaluation in the PE
deal, the reversal of financial performances might be observed in the long run, as posited in
Hypothesis 2a:
Hypothesis 2a: Investor sentiment in the PE market will positively affect the investee firm-
level profitability and earnings potential in the short run and experience
a long-run performance reversal.
PE investors divest their holdings or exit from their investments in five ways: IPOs,
acquisitions, secondary sales, buybacks and write-offs (Black and Gilson, 1998; Cumming and
Johan, 2013; Johan and Zhang, 2016). The last two methods of exits are deemed to be the least
successful from the perspective of the PE investor and the investee firm as they do not result in any
significant inflow of additional capital into the firm. IPO is deemed to be the most successful
divestment strategy from all parties concerned due to the potential for new capital inflow to the
investee firm and the potential for profit for the PE investors (Black and Gilson, 1998; Cumming
and MacIntosh, 2003a, b; Fleming, 2004; Schwienbacher, 2008; Cumming and Johan, 2013; Johan
and Zhang, 2016). I focus on the first two main strategies not only in unison but separately as well
to test whether investor sentiment will affect successful divestment strategies, and to acknowledge
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the investor sentiment differences between public exits (IPOs) versus private exits (acquisitions).
In this sense, I expect when the PE investor sentiment level is relatively higher, their intentions to
divest and get their returns back will be higher and thus the probability of successful exit rates will
be also higher. Therefore, I expect that by controlling international differences, the successful exit
rates will be relatively higher when the PE investor sentiment level is higher, as posited in
Hypothesis 2b:
Hypothesis 2b: Investor sentiment in the PE market will positively affect the successful
exits rates, for both acquisition and IPO strategies.
3. Data and Summary Statistics
My analysis exploits the comprehensive data collected at the deal level of investee firms
from PitchBook, which comprise 12,457 completed PE deals in 10,094 investee firms from 68
countries over the period from 1992 to 2012. The dataset allows me to investigate the PE investor
sentiment at both the investee firm and country level to shed more lights on this area in academia.
Before I introduce the details of my new proxy for the PE investor sentiment, I first outline
the overall trends for the PE deals in the dataset. In Figure 1A, I present the trends for the total PE
deal sizes and the total number of PE deals in my data sample from 1992 to 2012. Over the 21-year
period, we can find that the overall trends for both indicators co-move with each other and present
an upward slope, with conspicuous downfalls that capture the dot-com bubble and the recent
financial crisis. The PE market trend seems to quite resemble the global macroeconomic conditions.
In Figure 1B, I present the trends for the average PE deal sizes and the average investee firm
valuations over the same period. The PE market is booming in the past decade with more and more
deals completed as shown in Figure 1A and both the deal sizes and the investee firm valuations are
reaching new-level highs. Although those two indicators present two almost identical trend lines,
what makes me noticed and interested is that the average PE investee firm valuations are always
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above the average PE deal sizes all over the 21-year sample period, and the distances between those
two trend lines become wider and wider. I have to think more deeply to investigate what happened
to the investor sentiment in this “hot” market.
[Insert Figures 1A and 1B About Here]
Inspired by the famous prospect theory and the anchoring heuristic (Tversky and
Kahneman, 1974; Kahneman and Tversky, 1979), I propose and design a simple and novel
measurement to capture the investor sentiment in the PE market. As shown in Figure 1B, the natural
question I ask myself is why, on average, given a higher investee firm valuation, the deal size is
lower. If investors take the investee firm valuation as their judgmental anchor before making the
final decision to close the deal, the final completed deal price will be the outcome from this
anchoring heuristic by using firm valuation as a reference point. In this way, I present the new
measurement in the following formula to capture the PE investor sentiment given each specific deal
for firm i at time t:
𝑃𝑟𝑖𝑐𝑒 − 𝑡𝑜 − 𝑉𝑎𝑙𝑢𝑎𝑡𝑖𝑜𝑛 (𝑃𝑉)𝑅𝑎𝑡𝑖𝑜𝑖𝑡 =𝐷𝑒𝑎𝑙 𝑆𝑖𝑧𝑒𝑖𝑡
𝐼𝑛𝑣𝑒𝑠𝑡𝑒𝑒 𝐹𝑖𝑟𝑚 𝑉𝑎𝑙𝑢𝑎𝑡𝑖𝑜𝑛𝑖𝑡
Easily this variable can help me further investigate the sentiment whether PE investors present
relatively more or less confidence and interest in each deal. I first use the Patek Philippe watch
auction as an example: the PV ratio for this case is 1.60. That is 60% above the estimated valuation
of this watch and this PV ratio represents the huge interest in this investment. Using this
measurement, I can calculate the related PV ratios in the dataset.
Table I summarizes the key distributions associated with the PV ratios across the world in
the dataset. I first present the year distribution of this variable over the period from 1992 to 2012.
With a more vivid view from Figure 2, we can find the trend for the PE investor sentiment is
relatively stable with an average PV ratio of about 0.80 over the 21-year period. But we still can
16
find that during the dot-com bubble and financial crisis period, two lowest points of PV ratio can
be easily identified in the chart. I then present the country distribution for the PV ratios of the 68
countries and territories in the sample and I also accompany a world map of Figure 3 to provide a
clearer picture. The PV ratio is widely distributed around the world, with a smallest value of 0.03
for Kuwait and the largest value of 1.00 for several countries such as Albania and Latvia. In Figure
3, I present the country distribution with red colors, the darker the red area shown on the world
map, the higher the PV ratio for this country. The western hemisphere shows relatively higher level
of PE investor sentiment as compared with the eastern hemisphere on the map. Moreover, I also
present the industry distribution in Table I to show the industry preferences in terms of the PV
ratios. Although the average PV ratio is about 0.80 among all industry groups, I find that PE
investors present relatively lower sentiment in some industries such as capital markets/institutions
and commercial banks, but relatively higher sentiment in other industries like information
technology and healthcare.
[Insert Table I, Figures 2 and 3 About Here]
Table II summarizes the main variables in the dataset. I aim to show the PE investor
sentiment around the world by investigating the determinants of the PV ratios and further to explore
whether this sentiment variable can proxy for the true interests of PE investors and thus has any
impacts on the investee firm-level performances as well as the divestment strategy choices. The PV
ratio at the deal level will be the main dependent variable. The explanatory variables include the
GDP growth rate for the macroeconomic conditions, the MSCI returns for each country’s stock
market conditions, the minority shareholders protection index for the legal environment, the
Hofstede’s cultural dimensions of uncertainty avoidance, as well as a variety of other control
variables to capture investee firm and industry characteristics. From the data, I can empirically
confirm that the PE investor sentiment is dynamic in different industries and countries over the
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sample period. This provides me with a unique opportunity to explore all the possible reasons
behind the PE investment behaviors and attitudes towards each specific deal. Moreover, the
PitchBook database provides detailed information on testing international differences across
countries over time, which can shed further light on investors’ behaviors in the PE market.
[Insert Table II About Here]
In Table III, I present a pair-wise correlation matrix for each of variables used for this
study. Note that the correlations highlight some potential collinearity issues across different
explanatory variables, which I explore in the following multivariate empirical tests in the next
section. I choose the most related variables and those ones having the most explanatory power in
the multivariate tests.
[Insert Table III About Here]
Before I start the multivariate regression analysis, I want to first show some of the
highlighted details from the PitchBook database and provide the preliminary means difference tests
results regarding the different characteristics between several subgroups in Table IV. In the first
subpanel of Table IV, I show that the U.S. PE market is preferable and mature for investors as
compared with the non-U.S. PE market. Although the U.S. deals have smaller sizes and those U.S.
investee firms are smaller and receiving lower company valuations, this market is much more
preferable and mature in terms of having higher PV ratio for each deal and higher successful exit
rates. The U.S. is a quite different market in terms of country characteristics in this subpanel, which
further emphasizes the importance of this study as compared with previous studies, which mainly
focus only on the U.S. market. My study provides more evidence that shows how PE investor
sentiment differs internationally. In the next subpanel, I compare those deals in the developed
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markets with those in the emerging markets. Although emerging market is a hot spot for various
types of investments, it seems that investors present much lower sentiment and interest in this
booming PE market. Besides the main PV ratio is much smaller for the emerging markets, we can
also find that the deal sizes are smaller given much higher investee firm valuations. Developed
markets still seem to be more preferable in this sense.
Furthermore, I divide the sample by all angel deals versus pure PE/VC deals. I want to
investigate whether different investor types will have different preferences in the PE market. Angel
investors, as active players in the initial funding stages, present relatively lower sentiment in terms
of smaller PV ratio values as compared with PE/VC funds, who act as financial intermediaries to
invest on behalf of their institutional customers. Although most of the variables are insignificantly
different between those two subsamples, PE/VC funds still perform relatively better in terms of
higher successful exit rates. Another interesting area to explore before moving to the multivariate
tests is to divide the sample into pre- and post-financial crisis period to find out whether this crisis
has any impact on the investor sentiment in the PE market. In the last subpanel of Table IV, I find
that the PV ratio is smaller after the recent financial crisis given the average PE deal sizes and
investee firm valuations are bigger. This finding actually reflects the real investor sentiment in the
PE market: larger valuations and larger deal sizes cannot be good proxies to show the real investor
attitude towards the investment; they actually become more cautious after the financial crisis.
Moreover, the overall environment for the PE market has changed a lot after the financial crisis
with deals occurring in countries with lower GDP growth rate, lower stock market returns but in a
little better legal environment. I also find that the recent financial crisis hit the IPO market harder
than the acquisition market. During the post-financial crisis period, all exit rates decreased, and this
is mainly driven by the huge decrease in the IPO exit rates. The IPO exit rates significantly declined
after the financial crisis and such trend reflects possible prudent and cautious moods among
entrepreneurs and PE investors to bring private firms public.
19
[Insert Table IV About Here]
4. Regression Analyses and Robustness Checks
Now that I have laid out some of the unique and interesting results from the means
difference tests, I perform multivariate regression analyses in this section, mainly using clustered
OLS models by controlling company and year effects in addition to controlling the industry and
country fixed effects. I also perform several subsample tests and show several robustness checks
before drawing my conclusions.
4.1. What factors determine the PE investor sentiment?
In the main regression analyses, as shown in Table V, I use the newly developed PV ratio
as the main dependent variable throughout all models. I add different explanatory variables from
various facets each separately and all together in order to test how different factors will have impact
on the PE investor sentiment. The regressions include explanatory variables for macroeconomic
and stock market conditions, legal environments, investee firm characteristics, as well as for
Hofstede’s cultural dimensions. The main regression models in Table V use the following
specification:
PV ratio = f (Macroeconomic and Stock Market Conditions, Legal Environments, Investee
Firm Characteristics, Hofstede’s Cultural Dimensions, Industry and Country Dummies)
Most of the major variables are defined in Table II. Note that there are a large number of
explanatory variables that I could have included but chose to exclude. The primary reasons for the
parsimonious specification are as follows. First, the selected variables are plausibly pertinent to PE
investor sentiment and are chosen for the purpose of testing Hypothesis 1a and 1b and the following
20
hypotheses. Second, note that the excluded variables are highly collinear. Hence, any additional
control variables for the available sets of countries and years would not be perfectly suitable without
potentially introducing spurious results into the regressions. Examples include some of the other
dimensions of Hofstede’s cultural variables, as well as other legal and institutional variables. The
selection and reporting of variables was conducted to assess the factors that directly capture the
major differences of investee firms and institutional environments across the world.
In order to present a clear picture for what factors determine PE investor sentiment given
different institutional environments, I test different facets impact one by one separately first in
Models (1) to (5) and combine all the factors together in Model (6) of Table V Panel A. From the
results of Models (1) and (2), I use the GDP growth rate to proxy the macroeconomic condition
and the MSCI returns to proxy the stock market conditions, respectively. However, I did not find
any statistically significant results.
In Model (3), I use the minority shareholders protection index to capture the legal
environments. This index is the coded weighted average index on the ten key legal provisions
identified by legal scholars as most relevant to the protection of minority shareholder rights
(Guillén and Capron, 2015): 1) the power of the general meeting for de facto changes; 2) an agenda-
setting power; 3) the anticipation of a facilitated shareholder decision; 4) the prohibition of multiple
voting rights; 5) independent board members; 6) the feasibility of directors’ dismissal; 7) private
enforcement of directors’ duties (derivative suit); 8) shareholder action against resolutions of the
general meeting; 9) mandatory bid; and 10) disclosure of major share ownership (Lele and Siems,
2007; Siems, 2008). Higher values indicate “better” degree of minority shareholders’ protection
and legal systems.2 From the results in Model (3), I find that the coefficient of the minority
shareholders protection index is positive and statistically significant at 1% level. A one standard
2 The author is grateful to Mauro Guillén and Laurence Capron for sharing their minority shareholders
protection index. This legal index is dynamic over the years to capture a more comprehensive legal
environment with more countries and years covered.
21
deviation increase in the minority shareholders protection index increases the PV ratio by 7.33%.
Better legal environments will increase the PE investor sentiment.
There are more and more international studies determining that cultural dimensions cannot
be neglected in exploring institutional differences around the world. Following the literature
confirming that cultural dimensions are related to entrepreneurship and private equity investment
at the national level (Shane, 1993; Hayton, George and Zahra, 2002; Cumming, Johan and Zhang,
2014), I choose one of the six Hofstede’s cultural dimensions: uncertainty avoidance index in my
study to test the cultural environment impact on the PE investor sentiment. The results of Model
(4) show that the national cultural environment seems to have statistically significant (at 1% level)
impact on the PE investor sentiment. A one standard deviation decrease in the uncertainty
avoidance index increases the PV ratio by 3.77%. A national cultural environment favoring more
risk-taking will increase the PE investor sentiment.
Then I move on to test any impact from investee firm level characteristics, I utilize two
variables to capture the size and activeness of the investee firm. The natural logarithm of the
number of employees is used to capture the firm size effect, and the number of deals per year for
each investee firm is used to capture the investee firm and entrepreneurs’ activities within a
calendar year. With the results of Model (5), I find that those investee firms with less-active
entrepreneurs and in smaller sizes (both statistically significant at 1% level) will receive relatively
higher interest from their investors. The economic significance of Model (5) also indicate that with
one standard deviation decrease in the natural logarithm of the number of employees, the PV ratio
will be 18.56% higher, and a one standard deviation decrease in the number of deals per year will
increase the PV ratio by 21.03%.
In Model (6), I combine the five facets all together to find whether those factors can be
good determinants of the PE investor sentiment. From the results of Model (6), we can find that the
investee firm level characteristics and the legal environment are strong and good determinants of
the PV ratio. The other three factors return statistically insignificant results. Model (6) indicates
22
that those investee firms residing in better legal environments with less active entrepreneurs and in
smaller sizes (all statistically significant at 1% level) will receive relatively higher PE investor
sentiment. The economic significance for these three factors is large and comparable as I discussed
in Models (3) and (5). Thus, my Hypothesis 1a is partially supported from the tests in Table V
Panel A.
As the PV ratio is aiming to capture the PE investor sentiment on a specific deal, itself is
calculated based on the prospect theory and the anchoring heuristic when people make decisions.
In this way, a natural reference point for such decision-making procedure is anchoring to the prior
of this sentiment variable. As a robustness check, I present my regression tests by adding the lagged
value of the PV ratio as an explanatory variable in Panel B of Table V. Throughout Models (7) to
(12) in Panel B, we can find that the interested variable, the lagged value of PV ratio to proxy for
prior PE investor sentiment, returns consistently positive and statistically significant at 1% level
results in all models. The prior PE investor sentiment will have large and positive impacts on the
current PE investor sentiment, a one standard deviation increase in the lagged value of PV ratio
will increase the current PE investor sentiment by 29.07% (Model (12)) to 35.46% (Model (7)). In
addition to such strong impact from prior PE investor sentiment across all test models, the five
facets I used in Panel A still return qualitatively similar results in Panel B. Taken the results from
Model (12) as a main example, a one standard deviation increase in the minority shareholders
protection index will increase the PE investor sentiment by 10.79%; a one standard deviation
decrease in the uncertainty avoidance index will increase the PE investor sentiment by 14.30%; a
one standard deviation decrease in the natural logarithm of the number of employees will increase
the PE investor sentiment by 20.78%; and a one standard deviation decrease in the number of deals
per year will increase the PE investor sentiment by 14.57%. My Hypotheses 1a and 1b are fully
supported: better legal environments well protecting investors and national cultural environments
favoring risk-taking will increase the level of PE investor sentiment and prior investor sentiment in
the PE market will positively affect the current PE investor sentiment. These results are also
23
qualitatively unchanged when considering non-U.S. and non-Financial-Crisis subsamples as
additional robustness checks.
[Insert Table V About Here]
4.2. Is PE investor sentiment different in developed and emerging markets?
As I have found in the previous section that, on average, the PE investor sentiment is
relatively higher in the developed markets than that in the emerging markets, I then test whether
this finding can be further validated in my regression analysis. Before showing the regression
results, I use Figures 4A to 4E to present clearer pictures for the trends in terms of several important
indicators in both PE markets. As more mature economies like the developed markets, the scale
and size of the PE market is way bigger for these markets than emerging markets (Figures 4A and
4B). But when I look at the trends for the average deal sizes and the investee firm valuations in
Figures 4C and 4D, I can discover two interesting results: 1). The trend line for the average deal
sizes for developed markets is mostly above the trend line for emerging markets; 2). After 2002,
the average investee firm valuations become larger and larger in the emerging markets as compared
with developed markets. Superficially, I could have thought that the emerging market is developing
so well to attract more and more attentions and become a hot spot in the PE market. However, from
Figure 4E, which outlines the trend lines for the actual PE investor sentiment over the 1992 to 2012
period, I can find that the trend line of PV ratio for developed markets is relatively stable at an
average value of 0.82 and it is mostly above the trend line for the emerging markets. The trend line
for the emerging markets is more volatile and has a downward slope. The distances between those
two trend lines are also becoming wider and wider implying the investor sentiment is still relatively
higher in the developed and mature PE markets.
24
Thus, in Table VI Panel A, I keep all the original settings used in Model (6) of Table V
Panel A, I then add the developed and emerging markets dummies into the models separately. From
the results of Models (1) and (2), we can find that if the PE market in located in the developed
countries, the PE investor sentiment will be higher (both statistically significant at 1% level).
Similarly, the results of Models (3) and (4) indicate that the PE investor sentiment will be lower
(both statistically significant at 1% level) in the emerging markets. Furthermore, in order to rule
out the concerns that the results above might be driven by the U.S. subsample, I excluded all U.S.
deals in all of the models in Table VI Panel B as a robustness check. Consistent with the findings
in Panel A, Models (5) to (8) also confirm my proposition that the PE investor sentiment will be
relatively higher in the developed markets as compared with the emerging markets. To some extent,
PE market in developed countries will be more preferable to investors.
[Insert Figures 4A to 4E and Table VI About Here]
4.3. Is PE investor sentiment different for angel inventors versus PE/VC funds?
In order to complement my previous study regarding the comparisons between angel
investors and PE/VC funds around the world (Cumming and Zhang, 2016), I extend this paper to
find out whether the investor sentiment level will be different between these two major types of
players in the PE market. Angel investors are different from PE/VC funds in five major ways
(Cumming and Zhang, 2016), their different natures and characteristics determine the different
investment styles in the PE market. Angel investors invest their own money in initial funding stages
while PE/VC funds act as financial intermediaries usually invest in later stages on behalf of their
institutional investors. In this way, the investor sentiment levels between angels and PE/VC funds
are deemed to be different.
25
In Figure 5A, I present the trend lines for the PV ratios between angel investors and PE/VC
funds. Interestingly, the sentiment for angel investors is more volatile as compared with that of
PE/VC funds. And for most of the time in the sample period, the sentiment of angel investors is
relatively lower than that of PE/VC funds and it becomes lower and lower in recent years. And in
Figure 5B, I have shown that over the funding rounds, angel investors’ sentiment is lower than that
of PE/VC funds and the distances between the two trend lines are becoming wider and wider,
indicating that PE/VC funds, relative to angel investors, will have much higher interest and
confidence in later stage deals.
In Table VII, I keep all the original settings used in Model (6) of Table V Panel A, then I
add the angel investor and PE/VC funds dummies into the models in Panels A and B, respectively.
From the results of Model (1), we can find that relative to PE/VC funds, angel investors will present
relatively lower sentiment in PE deals (statistically significant at 5% level). Furthermore, in Models
(2) to (4), I use subsamples of only the 1st/2nd/3rd round deals to provide more robust results and to
find out whether the investor sentiment will be different and larger in later stages. In terms of
economic significance, we find that if angel investors are involved in the 1st round, their PV ratio
will be 1.22% lower relative to PE/VC funds (statistically insignificant); if they are involved in the
2nd round, their PV ratio will be 6.95% lower relative to PE/VC funds (statistically significant at
5% level); and if they are involved in the 3rd round, their PV ratio will be 10.87% lower relative to
PE/VC funds (statistically significant at 1% level). The results echo my proposition and the picture
I have shown in Figure 5B, both the statistical significance and the coefficients for the angel
investor dummy from Models (2) to (4) become more and more negative and larger. Angel
investors’ sentiment in PE deals will be indifferentiable in the 1st round, but it becomes less
interested in investing later stages as compared with PE/VC funds, who are more professional
managers and players in the PE market. The results from Models (5) to (8) also confirm these
findings by presenting the tests using PE/VC funds dummy as a robustness check.
26
[Insert Figures 5A to 5B and Table VII About Here]
4.4. Is there any change of PE investor sentiment by the recent financial crisis?
In order to explore whether the recent global financial crisis as a market shock will
have any impacts on the PE investor sentiment, I use the dummy variable which equals one
for all completed PE deals occurred after August 2007 and equals zero for rest of the deals
throughout all of the model specifications. I expect the sign for the financial crisis dummy
to be negative, indicating less interest and confidence for PE investors towards their
investments after the financial crisis. Combining the means difference tests results in Table
IV, I did find that the PE investor sentiment is lower after the financial crisis. I present my
regression tests in Panel A of Table VIII using full sample, and present the subsamples of
emerging markets and developed markets in Panels B and C, respectively. The coefficients
for the financial crisis dummy of Models (1), (4) and (7) are all negative and statistically
significant at 1% level, indicating that the PE investor sentiment is lower after the financial
crisis, whatever it is for developed markets or for emerging markets or for all over the
world.
As a robustness check, I also consider prior PE investor sentiment as an important
reference point when making future financial decisions, Model (2) of Panel A returns
qualitatively unchanged results for the lagged PV ratio, but the financial crisis dummy
becomes statistically insignificant, although the sign stays negative. If prior PE investor
sentiment is higher, the future investor sentiment will be higher and the negative impact
from financial crisis will be less. Similar results can be found in Panels B and C, but mainly
for developed markets but not for emerging markets.
27
Inspired by those tests in Models (2), (5) and (8), I further use interaction tests to
provide additional robustness checks. I interact the financial crisis dummy with the lagged
PV ratio and I expect the sign of the interacted variable to be positive, which indicates the
mitigating effect from prior sentiment of PE investors. The results in Models (3) and (9)
confirm my proposition (both statistically significant at 1% level), but Model (6) for
emerging markets return insignificant result. The global financial crisis can have huge
negative impact on the PE investor sentiment across the world, but the prior high level of
investor sentiment can serve to provide significant mitigating effect on such negative crisis
impact and this effect is more significant for developed markets. Taken Model (3) as an
example, we can find that the financial crisis will decrease the PV ratio by 15.09% in terms
of economic significance, but if the prior sentiment is high and are within the financial
crisis period, the mitigating effect will increase the PV ratio by 15.08%, it almost offsets
all of the negative impact from the financial crisis.
[Insert Table VIII About Here]
4.5. Does PE investor sentiment affect investee firm-level profitability and earnings potential?
After I discussed several interesting results regarding the PE investor sentiment
characteristics, I need to further validate my Hypothesis H2a to find out whether this investor
sentiment can help us indicate or predict the financial performances for those investee firms. The
investor sentiment in the public market usually can be used to predict future earnings, cross-
sectional stock returns and market returns as discussed in previous literature. The judgmental
anchors or reference points can also be utilized to explain the many aspects of merger and
28
acquisition activities (Baker, Pan and Wurgler, 2012). On the one hand, if the investor sentiment is
high in the PE deal, the investee firm’s earnings potential and profitability might also be high in
that PE investors expect future returns to be higher in those investee firms that perform financially
well and present huge earnings potential. But on the other hand, such sentiment might also reflect
the possible overoptimism from the PE investors. The investee firms might receive potential
misvaluations from those overoptimistic PE investors and in the long run, the effect of such
overoptimism on subsequent financial performances might be evident. Therefore, I use four
financial accounting variables related to profitability and earnings potential to perform regression
analysis related to the PE investor sentiment impact on the investee firm profitability for time
horizons of current period and one year to five years after the PE deal was completed.
Throughout Panels A to D in Table IX, the dependent variables are EBITDA, net income,
revenue and gross profit, respectively. In Model (1) of those panels, I perform the contemporaneous
tests to first find out whether PV ratio can indicate good financial performances in the investee
firms. Only Panel B for net income returns statistically significant and positive results. When the
PV ratio is higher, the net income is higher in the investee firm. When the time horizons are
extended to the next three-year period, the PV ratio still predicts statistically significant and positive
financial performances results in some of my model settings, such as EBITDA in Models (2) and
(3) of Panel A, net income in Model (3) of Panel B and gross profit in Models (2) to (4) of Panel
D. Other models either return statistically insignificant results or show the PV ratio negatively
predict the future financial performances at the investee firms. Therefore, I further extend the time
horizons to be four years and five years after the PE deal was made and completed. From the results
of Models (5) and (6) across all four panels, I find that the existence of possible misvaluations
might be driven by the overoptimism of the PE investors as the PV ratio can predict performance
reversal in the long run. From the results in Model (6) of Panels A, B and D, after five years, the
EBITDA, net income and gross profit all return statistically significant and negative results. My
new measurement can reflect the possible misvaluations and the sentiment of PE investors. PV
29
ratio can indicate and predict the positive financial performances and earnings potential at the
investee firms in the short run and it can also predict the performance reversals in the long run. My
Hypothesis 2a is confirmed.
[Insert Table IX About Here]
4.6. Does PE investor sentiment affect successful exits choices?
In addition to the analysis of PE investor sentiment impact on the financial performance of
the investee firms, another important consideration is the performance on the investors’ side. Are
they receiving higher or lower returns from their investments in those investee firms? Since it is
hard to get creditable performance measures like internal rate of returns (IRRs) or performance
multiples, I follow previous literature (Wiltbank, 2005; DeGennaro and Dwyer, 2010) to explore
the successful exit rates as an alternate measure for the performances. The successful exits include
either an IPO exit or an acquisition exit. I use clustering PROBIT models by controlling the
company and year effects in addition to controlling the industry and country fixed effects. In Table
X, I report the results of the successful exit test first in Model (1) and then perform IPO and
acquisition exit tests in Models (2) and (3), respectively. When a divestment strategy is executed,
I expect the sign to be positive for the PV ratio in that the investor sentiment is expected to be
higher at that moment when PE investors can finally get their returns back. The results from my
tests confirm my proposition and are interesting to show the possible market timing abilities of PE
investors. Model (1) returns statistically significant (at 1% level) and positive marginal effect of
the PV ratio, indicating that the PE investor sentiment is higher when the successful exit strategies
are executed. If there is a 1% increase in the PV ratio, the successful exit rates will be 4.29% higher.
But when I further perform the subsample tests using IPO exits dummy in Model (2) and acquisition
exits dummy in Model (3), I find out that the results in Model (1) can be explained due to different
30
sentiment levels reflected when different divestment strategies are chosen. The sign of the PV ratio
in Model (2) for IPO exits is still positive but statistically insignificant. The sign of the PV ratio in
Model (3) for acquisition exits is positive and statistically significant at 1% level. A 1% increase in
the PV ratio will increase the IPO exits rate by 1.34% but it will increase the acquisition exits rate
by 5.13%. When the investor sentiment level is higher, PE investors tend to prefer divestment
strategy of an acquisition instead of an IPO. As documented in Kim and Ritter (1999) and Ritter
(2003), it is more expensive to go public than to exit via other vehicles due to the obligatory legal,
financial, and other professional advisors required to initiate the process, the transaction costs of
preparing a prospectus, and the well documented “under-pricing effect” of IPOs, not to mention
the ongoing costs of reporting requirements for publicly listed firms. Considering such more
expensive costs associated with IPOs, numerous IPO market professionals have expressed that
regulatory changes together with changes in investor sentiment, have changed the dynamic of the
IPO marketplace (Henry and Gregoriou, 2013), my results above concur those views and provide
the support by using investor sentiment as reasons.
In addition to the related costs explanation with regards to the insignificant results of IPO
exits, another possible explanation for the above results might be linked to the market timing
abilities of the PE investors. As my PV ratio is taking investee firm valuation as reference point, if
the PE investors are timing the market, they will tend to exit their investments when the firm
valuation is higher and in turn, their returns from such divestment strategies could be higher. This
can explain why, when the PV ratio is higher, PE investors will tend to successfully exit their
investments, in either IPO or acquisition. Although my Hypothesis H2b cannot be fully supported,
I still find positive impact from the PE investor sentiment on the overall successful exit rates,
especially for acquisition strategies and document the different sentiment levels when choosing
divestment strategies.
[Insert Table X About Here]
31
4.7. Will public market investor sentiment have any impacts on the PE investor sentiment – a U.S.
case study?
In order to complement my study which focuses on the PE market, I want to investigate
whether the investor sentiment in the public equity market will have any impact on the PE investor
sentiment. Those two markets are deemed to be correlated with each other and the investor
sentiment might be contagious between them. Given the size and scale of the public equity market,
the investor sentiment in this market might have big impact on those investors in the PE market,
especially for those who invest in both markets. Therefore, my study uses U.S. as a special case to
provide evidence supporting my proposition by applying Baker and Wurgler (2006)’s two
composite sentiment indices into my regression analysis.
From the Figures 6A and 6B, I outline the trends for both my PE investor sentiment index
together with the Baker and Wurgler (2006)’s composite index (U.S. investor sentiment index 1
and 2, respectively). Although the PE investor sentiment index is not co-moving with the U.S.
investor sentiment index 1 or 2 in a perfect way, we still can find that my PE market sentiment
index is good and capturing the downturn in the financial market, the dot-com bubble and the recent
financial crisis. But after the dot-com bubble burst, the PE market seems to be rebounded more
quickly than the public equity market.
In Model (1) of Table XI, I add the U.S. investor sentiment index 1 into the original settings
used in Table V, the results confirm my thought that the public market investor sentiment will have
positive impact on the PE investor sentiment (statistically significant at 5% level). A one standard
deviation increase in this sentiment index, the PV ratio will increase by 3.53%. Put it in another
way, if the investors have bullish sentiment in the public equity market, such bullish sentiment will
be spread into the PE market and will drive the interest and confidence level of PE investors to a
higher level. As I discussed in the section 4.1 that prior PE investor sentiment is a very important
32
determinant and reference point when PE investors make decisions. I thus add the lagged value of
PV ratio in Model (2) to find out whether public equity market investor sentiment will still affect
the PE investor sentiment. I find similar results in Model (2) that the positive impact still persists
(statistically significant at 5% level) and the economic significance is even larger. In Models (3)
and (4), I substitute the U.S. investor sentiment index 1 with the U.S. investor sentiment index 2 to
provide additional robustness. The results are qualitatively unchanged using both tests. It seems the
public equity markets and the private equity markets are complements to each other instead of being
substitutes to each other in the U.S.
[Insert Table XI About Here]
5. Limitations
For the first time, I present large sample evidence on the PE investor sentiment around the
world. However, my data are not without limitations. I hope my work will inspire others to continue
to research in the future. It is hard and infeasible to get the data for the entire universe of the
alternative investments, especially for those investments in the PE market. My study is innovative
to design and propose a new measurement to quantify the investor sentiment in the alternative
investments market by using PE data. It is also important to study the investor sentiment for other
alternative investment asset classes such as collectables like art, wine, antiques, coins or stamps
and film productions as well as other alternative financial assets such as real estates and hedge
funds.
I also cannot rule out endogeneity fully, and my dataset suffers some problems that might
cause concerns for the results. For example, I cannot identify the substantial heterogeneity across
the angels and PE/VC funds. It would be better to know the different type of angels, whatever they
are in angel groups, business angels or just wealthy individuals. And the heterogeneity across
different institutional investors behind those PE/VC funds will be another interesting area to
33
investigate to find out more exciting results. Given the summary statistics, the magnitude of the
impact I have documented in this study is likely to capture some of the large and successful
investors in the PE market. In most of my regression models, I include industry, country and year
fixed effects as controls for possible unobserved firm-level heterogeneity over time (Linck, Netter
and Yang, 2009). Such fixed-effects models can help to alleviate the endogeneity problem caused
by omitted variables to some extent (Campa and Kedia, 2002).
Moreover, as an international study, my country observation still suffers insufficiency to
cover all the countries and territories on earth. However, my sample to include 68 countries is still
good to provide international evidence to support my hypotheses and my findings are robust to
subsamples of the data and randomly kick out different countries from of the sample. My
conclusions are based on the data I have from PitchBook. It might be better to consider other
datasets in the future when available. My data also have the limitations on variables regarding the
performances of the investors, whether the investor sentiment will affect and be linked to their
performances in terms of IRRs or other performance multiples both in the short and long periods
will be interesting to explore and find additional support for the possible overoptimism when PE
investors make investment and divestment decisions.
6. Conclusions and Future Research Directions
My analysis exploits the comprehensive data collected at the deal level of investee firms
from PitchBook, which comprise 12,457 completed PE deals from 68 countries over 1992 to 2012.
The dataset allows me to investigate the PE investor sentiment at both the investee firm and country
level. The data indicate that, institutional environments and firm-specific characteristics are both
strong determinants of the PE investor sentiment. Such investor sentiment will be higher in smaller
entrepreneurial firms in countries with better legal environments and with cultures characterized
by higher levels of risk-taking. Such behaviors are robust when accounting for the prior sentiment.
I also document the differences of this PE investor sentiment across different markets and investor
34
types as well as the impact from the recent financial crisis on this sentiment. I find that the PE
investor sentiment is relatively higher in developed markets as compared with emerging markets,
it is also relatively higher among PE/VC funds as compared with angel investors and the financial
crisis did reduce this sentiment in the PE market while prior sentiment can provide mitigating
effects during the crisis period.
In addition, I investigate the predictability of this new investor sentiment measurement and
find that it stands to reveal significant results in terms of predicting short-run positive financial
performances and earnings potential at investee firms and reflecting the possible misvaluations
effect in the long run. It suggests that PE investors might be overoptimistic to some extent. With
regards to the choice success of divestment strategies, I find that PE investors have market timing
abilities to divest successfully and prefer using acquisition strategy as compared with initial public
offering (IPO) strategy in the PE market. For the sentiment in the U.S. financial market, I find that
the public equity market sentiment will have strong and positive impact on the PE market investor
sentiment implying sentiment can be physically and psychological spread across traditional and
alternative financial markets. My tests results are robust under various clustering methods to correct
standard errors while controlling fixed effects.
To investigate and document investor sentiment in the alternative investments market is a
promising and interesting area for study in the academia. With more creditable and different
alternative asset classes’ data becoming available in the future, researchers can explore more in this
area and shed more lights on the investor sentiment characteristics by using my newly developed
measurement. It will be fascinating to find out what the true investor attitudes towards different
financial assets, what the returns will be from such sentiment (especially those returns over the
post-IPO or post-acquisition periods), and whether the contagious effect is more profound globally
or locally. More questions and answers are waiting to be asked and solved.
My study mainly investigates the economic related factors and the associated impacts on
the PE investor sentiment, it will also be interested to learn whether the non-economic factors as I
35
discussed for the public equity market can be utilized to explain the sentiment levels when investors
are involved in PE deals. Weather, health/medical conditions, news or disasters might be other
factors to determine and affect the PE investor sentiment, although it might be a totally different
case from previously documented for the public equity market.
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0
200
400
600
800
1000
1200
1400
1600
0
100000
200000
300000
400000
500000
600000
700000
800000
900000
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012
Total Number of PE Deals Total PE Deal Sizes (in $ Millions)
Figure 1A: Trends for Total PE Deal Sizes and Total Number of PE Deals (1992 to 2012)
0
200
400
600
800
1000
1200
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012
Average PE Deal Sizes (in $ Millions) Average PE Investee Firm Valuations (in $ Millions)
Figure 1B: Trends for Average PE Deal Sizes and Investee Firm Valuations (1992 to 2012)
45
Figure 3: International Map for the PE Investor Sentiment
0
0.2
0.4
0.6
0.8
1
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012
PV Ratios
Figure 2: Trends for Average PV Ratios (1992 to 2012)
(.833333,1]
(.687453,.833333](.495735,.687453][.032,.495735]No data
46
0
100000
200000
300000
400000
500000
600000
700000
800000
900000
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012
Developed Markets Emerging Markets
Figure 4A: Total PE Deal Sizes (in $ Millions) Comparison: Developed vs. Emerging Markets (1992 - 2012)
0
200
400
600
800
1000
1200
1400
1600
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012
Developed Markets Emerging Markets
Figure 4B: Total Number of PE Deals Comparison: Developed vs. Emerging Markets (1992 - 2012)
47
0
100
200
300
400
500
600
700
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012
Developed Markets Emerging Markets
Figure 4C: Average PE Deal Sizes (in $ Millions) Comparison: Developed vs. Emerging Markets (1992 - 2012)
0
500
1000
1500
2000
2500
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012
Developed Markets Emerging Markets
Figure 4D: Average Investee Firm Valuations (in $ Millions) Comparison: Developed vs. Emerging Markets (1992 - 2012)
48
0
0.2
0.4
0.6
0.8
1
1.2
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012
Developed Markets Emerging Markets
Figure 4E: Average PV Ratios Comparison: Developed vs. Emerging Markets (1992 - 2012)
49
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1.1
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012
PV Ratio - Angel Investors PV Ratio - PE/VC Funds
Figure 5A: Average PV Ratios Comparison: Angel Investors vs. PE/VC Funds (1992 - 2012)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1 2 3
PV Ratio - Angel Investors PV Ratio - PE/VC Funds
Figure 5B: Average PV Ratios Comparison over the 1st-Three Rounds: Angel Investors vs. PE/VC Funds (1992 - 2012)
50
Table I. PV Ratio Country, Industry and Year Distributions
-1
-0.5
0
0.5
1
1.5
2
2.5
0.7
0.72
0.74
0.76
0.78
0.8
0.82
0.84
0.86
0.88
0.9
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012
U.S. Investor Sentiment Index 1 PV Ratio
Figure 6A: Average U.S. Sentiment Comparison: PV Ratio vs. Public Market Sentiment 1 (1992 - 2012)
-1
-0.5
0
0.5
1
1.5
2
2.5
0.7
0.72
0.74
0.76
0.78
0.8
0.82
0.84
0.86
0.88
0.9
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012
U.S. Investor Sentiment Index 2 PV Ratio
Figure 6B: Average U.S. Sentiment Comparison: PV Ratio vs. Public Market Sentiment 2 (1992 - 2012)
51
This table summarizes the key distribution features for the main variable: PV Ratio in our sample. In this table, we show the distributions
of PV Ratio for 68 countries and territories around the world, 40 industry groups and 21 years of sample period.
Year
Distribution Country Distribution Industry Distribution
1992 0.805 Albania 1.000 Kuwait 0.032 Agriculture 0.834
1993 0.867 Argentina 0.587 Latvia 1.000 Apparel and Accessories 0.726
1994 0.846 Australia 0.683 Lithuania 0.936 Capital Markets/Institutions 0.639
1995 0.791 Austria 0.854 Luxembourg 0.796 Chemicals and Gases 0.819
1996 0.786 Bahamas 0.877 Malaysia 0.867 Commercial Banks 0.660
1997 0.756 Barbados 0.350 Mexico 0.512 Commercial Products 0.882
1998 0.799 Belgium 0.755 Monaco 0.478 Commercial Services 0.808
1999 0.731 Bermuda 0.575 Mongolia 0.250 Commercial Transportation 0.829
2000 0.754 Brazil 0.510 Netherlands 0.737 Communications and Networking 0.791
2001 0.815 Bulgaria 0.637 New Zealand 0.738 Computer Hardware 0.757
2002 0.818 Canada 0.736 Norway 0.848 Construction (Non-Wood) 0.887
2003 0.851 Cayman Islands 0.568 Peru 0.315 Consumer Durables 0.848
2004 0.854 Chile 0.478 Philippines 0.653 Consumer Non-Durables 0.806
2005 0.836 China 0.446 Poland 0.841 Containers and Packaging 0.873
2006 0.834 Colombia 0.431 Portugal 1.000 Energy Equipment 0.745
2007 0.806 Cyprus 0.299 Romania 0.833 Energy Services 0.747
2008 0.787 Czech Republic 0.844 Russia 0.369 Exploration, Production and
Refining 0.758
2009 0.731 Denmark 0.692 Singapore 0.632 Forestry 0.799
2010 0.753 Egypt 0.403 Slovakia 0.510 Healthcare Devices and Supplies 0.804
2011 0.770 Estonia 0.778 South Africa 0.950 Healthcare Services 0.817
2012 0.725 Finland 0.875 South Korea 0.522 Healthcare Technology Systems 0.858
France 0.754 Spain 0.651 Insurance 0.745
Germany 0.790 Sweden 0.830 IT Services 0.789
Greece 0.496 Switzerland 0.791 Media 0.836
Hong Kong 0.525 Taiwan 0.581 Metals, Minerals and Mining 0.686
Hungary 0.645 Tanzania 0.148 Other Business Products and
Services 0.894
Iceland 0.320 Thailand 0.266 Other Consumer Products and
Services 0.862
India 0.288 Turkey 0.591 Other Energy 0.884
Indonesia 0.402 United Arab
Emirates 0.630 Other Financial Services 0.723
Ireland 0.719 United Kingdom 0.828 Other Healthcare 1.000
Israel 0.605 United States 0.826 Other Information Technology 1.000
Italy 0.697 Uruguay 1.000 Other Materials 0.820
Jamaica 1.000 Venezuela 1.000 Pharmaceuticals and
Biotechnology 0.737
Japan 0.618 Vietnam 0.301 Restaurants, Hotels and Leisure 0.789
Retail 0.711
Semiconductors 0.730
Services (Non-Financial) 0.810
Software 0.757
Textiles 0.737
Transportation 0.847
Utilities 0.737
52
Table II. Variable Definitions and Summary Statistics
This table provides definitions of the main variables in the dataset, the data sources, and summary statistics.
Variable Name Definition Mean Median Standard
Deviation Minimum Maximum
Number of
observations
Main Dependent Variables
PV Ratio Firm-level multiple of deal size to investee company valuation. 0.795 1.000 0.346 0.000 8.793 12499
PV Ratio (Lagged 1-Year) Lagged one year value of firm-level multiple of deal size to
investee company valuation. 0.729 1.000 0.349 0.002 1.428 2938
Deal and Investee Company
Characteristics
Deal Size Firm-level deal size (in M$) for the investee companies. 339.609 80.000 1539.205 0.010 101003.000 12499
No. of Deals per Year Firm-level number of deals has been made in a year for the
investee companies. 1.124 1.000 0.482 1.000 11.000 12499
Company Valuation Firm-level valuation (in M$) for the investee companies at the
time of deal completed. 607.655 121.000 2809.904 0.010 118803.000 12499
No. of Employees Firm-level number of employees in the investee companies. 2640.827 350.000 14573.310 1.000 805600.000 8751
LN of No. of Employees Natural logarithm of the firm-level number of employees in the
investee companies. 5.781 5.858 2.169 0.000 13.599 8751
EBITDA
EBITDA stands for earnings before interest, taxes, depreciation
and amortization. EBITDA is one indicator of a company's
financial performance and is used as a proxy for the earning
potential of a business.
49.856 25.320 3724.313 -
200014.000 21522.000 2975
Net Income
Net income is a company's total earnings or profits and it is
calculated by taking revenues and subtracting the costs of doing
business. This number appears on a company's income
statement and is an important measure of how profitable the
company is over a period of time.
-56.975 3.150 3884.255 -
199843.000 28000.000 2738
Revenue Revenue is the amount of money that a company actually
receives during a specific period. 1127.055 76.100 38569.880 -1266.950 3200000.000 7319
Gross Profit
Gross profit is a company's total revenue or total sales minus
the cost of goods sold. Gross profit is the profit a company
makes after deducting the costs associated with making and
selling its products, or the costs associated with providing its
services.
347.093 92.340 1232.560 -1702.000 35066.000 1983
Country Level Characteristics
GDP Growth Rate
Annual percentage growth rate of GDP at market prices based
on constant local currency. Aggregates are based on constant
2010 U.S. dollars. GDP is the sum of gross value added by all
resident producers in the economy plus any product taxes and
minus any subsidies not included in the value of the products. It
is calculated without making deductions for depreciation of
fabricated assets or for depletion and degradation of natural
resources. Data are in annual %. Source: World Bank.
2.493 2.586 2.114 -7.821 15.240 12423
53
MSCI Returns
The country-specific Morgan Stanley Capital International
index return, a proxy for stock market conditions in each
country.
0.072 0.094 0.159 -0.606 1.437 12370
Minority Shareholders Protection Index
The minority shareholders protection index is the coded
weighted average index on the ten key legal provisions
identified by legal scholars as most relevant to the protection of
minority shareholder rights (as per Guillen and Capron, 2015):
powers of the general meeting for de facto changes; agenda-
setting power; anticipation of shareholder decision facilitated;
prohibition of multiple voting rights; independent board
members; feasibility of directors’ dismissal; private
enforcement of directors’ duties (derivative suit); shareholder
action against resolutions of the general meeting; mandatory
bid; and disclosure of major share ownership (as per Lele and
Siems, 2007 and Siems, 2008). Higher values indicate “better”
degree of minority shareholders’ protection and legal systems.
6.919 7.250 0.618 1.750 8.250 11948
UAI
Hofstede’s index of uncertainty avoidance. The Uncertainty
Avoidance dimension expresses the degree to which the
members of a society feel uncomfortable with uncertainty and
ambiguity. The fundamental issue here is how a society deals
with the fact that the future can never be known: should we try
to control the future or just let it happen? Countries exhibiting
strong UAI maintain rigid codes of belief and behaviour and are
intolerant of unorthodox behaviour and ideas. Weak UAI
societies maintain a more relaxed attitude in which practice
counts more than principles. Source: http://geert-
hofstede.com/national-culture.html.
47.443 46.000 9.891 8.000 112.000 12428
Financial Crisis Dummy A dummy variable equals to 1 for deals completed after August
2007. 0.383 0.000 0.486 0.000 1.000 12499
US dummy A dummy variable equals to 1 for deals in the U.S. 0.786 1.000 0.410 0.000 1.000 12499
Developed Markets Dummy A dummy variable equals to 1 for deals in the developed
markets. 0.949 1.000 0.220 0.000 1.000 12419
Emerging Markets Dummy A dummy variable equals to 1 for deals in the emerging
markets. 0.051 0.000 0.220 0.000 1.000 12419
U.S. Investor Sentiment Index 1
It is the sentiment index in Baker and Wurgler (2006), using
updated version of Eq. (3) in that paper and based on first
principal component of five (standardized) sentiment proxies
where each of the proxies has first been orthogonalized with
respect to a set of six macroeconomic indicators.
0.192 0.189 0.596 -0.866 3.076 9799
U.S. Investor Sentiment Index 2
It is the sentiment index in Baker and Wurgler (2006), using
updated version of Eq. (2) in that paper and based on first
principal component of five (standardized) sentiment proxies.
0.027 -0.042 0.597 -0.932 2.837 9799
Investor Type Dummy
All Angels Dummy A dummy variable equals to 1 for deals with angel investor. 0.015 0.000 0.122 0.000 1.000 12499
Pure PE/VC Dummy A dummy variable equals to 1 for deals with PE/VC investors. 0.985 1.000 0.122 0.000 1.000 12499
54
Exit Outcomes
Successful Exits
A dummy variable equals to 1 for either IPO or Acquisition exit
and equals to 0 for unsuccessful exits such as distribution,
bankruptcy, secondary sales and repurchases.
0.988 1.000 0.108 0.000 1.000 10581
IPO Exits
A dummy variable equals to 1 for IPO exit and equals to 0 for
unsuccessful exits such as distribution, bankruptcy, secondary
sales and repurchases.
0.866 1.000 0.247 0.000 1.000 940
Acquisition Exits
A dummy variable equals to 1 for Acquisition exit and equals to
0 for unsuccessful exits such as distribution, bankruptcy,
secondary sales and repurchases.
0.987 0.000 0.437 0.000 1.000 9767
55
Table III. Pair-wise Correlations Matrix
This table provide correlations across the main variables in the dataset. * Significant at least the 5% level of significance.
[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22]
PV Ratio 1.00
PV Ratio (Lagged 1-
Year) 0.37* 1.00
No. of Deals per Year -0.26* -0.25* 1.00
LN of No. of
Employees -0.20* -0.12* 0.08* 1.00
EBITDA 0.00 -0.08* 0.02 0.05* 1.00
Net Income 0.00 0.03 0.01 0.02 0.99* 1.00
Revenue 0.00 0.00 0.01 0.04* 0.09* 0.13* 1.00
Gross Profit -0.04 -0.04 0.12* 0.33* 0.13* 0.04 0.76* 1.00
GDP Growth Rate -0.11* -0.04* -0.01 0.10* 0.01 0.02 -0.01 -0.04 1.00
MSCI Returns -0.04* 0.03 0.02 0.07* 0.04 0.04* -0.01 0.01 0.65* 1.00
Minority Shareholders
Protection Index 0.11* 0.03 0.02 -0.19* 0.00 -0.01 -0.06* 0.02 -0.24* -0.11* 1.00
UAI -0.05* -0.03 0.01 0.08* 0.01 0.00 0.01 0.04 -0.11* -0.01 -0.18* 1.00
Financial Crisis Dummy
-0.08* -0.12* 0.08* -0.02 0.02 0.01 -0.01 0.04 -0.40* -0.22* 0.21* 0.01 1.00
US dummy 0.17* 0.09* -0.02 -0.25* -0.02 -0.02 -0.04* -0.03 -0.15* -0.05* 0.42* -0.28* -0.11* 1.00
Developed Markets
Dummy 0.24* 0.19* -0.03* -0.16* -0.01 -0.02 -0.01 0.03 -0.46* -0.12* 0.27* -0.20* -0.11* 0.45* 1.00
Emerging Markets
Dummy -0.24* -0.19* 0.03* 0.16* 0.01 0.02 0.01 -0.03 0.46* 0.12* -0.27* 0.20* 0.11* -0.45* -1.00* 1.00
U.S. Investor
Sentiment Index 1 0.00 0.00 -0.04* 0.03* -0.01 -0.01 -0.02 -0.02 0.15* -0.04* -0.50* -0.38* 1.00
U.S. Investor
Sentiment Index 2 0.00 0.00 -0.04* 0.04* -0.02 -0.02 -0.03* -0.04 0.13* -0.10* -0.61* -0.37* 0.96* 1.00
All Angels Dummy -0.04* -0.04 0.02* 0.00 0.00 0.00 0.00 -0.02 -0.02* -0.01 0.00 -0.01 0.02 -0.01 0.01 -0.01 -0.01 -
0.01 1.00
Pure PE/VC Dummy 0.04* 0.04 -0.02* 0.00 0.00 0.00 0.00 0.02 0.02* 0.01 0.00 0.01 -0.02 0.01 -0.01 0.01 0.01 0.01 -1.00* 1.00
Successful Exits 0.22* 0.21* -0.20* -0.09* -0.01 -0.01 0.00 -0.06* -0.03* 0.00 0.02* -0.04* -0.06* 0.11* 0.12* -0.12* 0.01 0.01 -0.07* 0.07* 1.00
IPO Exits 0.03 0.37* -0.20* -0.15* -0.14* -0.19* -0.02 -0.10* 0.02 0.12* 0.04 -0.14* -0.23* 0.39* 0.23* -0.23* 0.06 0.05 -0.23* 0.23* 1.00* 1.00
Acquisition Exits 0.34* 0.22* -0.23* -0.09* -0.01 -0.01 0.00 -0.07* -0.03* 0.00 0.02* -0.04* -0.06* 0.12* 0.13* -0.13* 0.01 0.01 -0.06* 0.06* 1.00*
56
Table IV. Mean Descriptive Statistics by Main Characteristics
This table provides the main mean descriptive statistics across different main characteristics by different subsample groups. The table also provides the two-
sample means test results between those groups in our data. The first sub-panel presents the mean comparison tests between US deals and non-US deals; the
second sub-panel presents the mean comparison tests between developed markets deals and emerging markets deals; the third sub-panel presents the mean
comparison tests between all angel deals vs. pure PE/VC deals and the fourth sub-panel presents the mean comparison tests pre financial crisis deals and post
financial crisis deals. The means test is a two-sample t-test with equal variance. *, **, *** Significant at the 10%, 5% and 1% levels, respectively.
US Deals vs. Non-US Deals Developed Markets Deals vs. Emerging Markets Deals
US Deals Non-US Deals Mean
Differences
Developed Markets
Deals
Emerging Markets
Deals
Mean
Differences
Main Dependent Variables
PV Ratio 0.826 0.681 0.145*** 0.815 0.439 0.376***
PV Ratio (Lagged 1-Year) 0.748 0.679 0.069*** 0.746 0.494 0.252***
Deal and Investee Company Characteristics
Deal Size 311.387 443.304 -131.917*** 342.842 218.296 124.547**
No. of Deals per Year 1.120 1.140 -0.020* 1.121 1.191 -0.070***
Company Valuation 515.773 945.255 -429.482*** 580.632 1004.385 -423.753***
No. of Employees 2114.039 5148.881 -3034.842*** 2384.738 9748.100 -7363.362***
LN of No. of Employees 5.530 6.973 -1.442*** 5.714 7.534 -1.821***
EBITDA 15.801 230.908 -215.107 40.243 274.299 -234.056
Net Income -92.099 88.673 -180.771 -77.987 258.670 -336.656
Revenue 469.307 4332.916 -3863.609*** 1043.820 3139.769 -2095.949
Gross Profit 334.411 434.609 -100.198 352.089 165.087 187.001
Country Level Characteristics
GDP Growth Rate 2.331 3.098 -0.767*** 2.273 6.680 -4.406***
MSCI Returns 0.068 0.088 -0.020*** 0.068 0.154 -0.086***
Minority Shareholders Protection Index 7.042 6.362 0.680*** 6.954 6.140 0.815***
UAI 46.000 52.887 -6.887*** 46.973 55.857 -8.884***
Exit Outcomes
Successful Exits 0.994 0.962 0.032*** 0.990 0.915 0.076***
IPO Exits 0.932 0.605 0.327*** 0.887 0.574 0.313***
Acquisition Exits 0.994 0.960 0.034*** 0.990 0.904 0.086***
57
All Angel Deals vs. Pure PE/VC Deals Pre Financial Crisis Deals vs. Post Financial Crisis Deals
All Angel
Deals Pure PE/VC
Deals Mean
Differences
Pre Financial Crisis Deals
Post Financial Crisis Deals
Mean Differences
Main Dependent Variables
PV Ratio 0.689 0.796 -0.107*** 0.815 0.761 0.054***
PV Ratio (Lagged 1-Year) 0.636 0.731 -0.095** 0.763 0.674 0.088***
Deal and Investee Company Characteristics
Deal Size 345.021 339.526 5.495 318.211 374.036 -55.825**
No. of Deals per Year 1.200 1.123 0.077** 1.095 1.172 -0.077***
Company Valuation 608.915 607.636 1.279 490.609 795.965 -305.356***
No. of Employees 3772.815 2621.621 1151.194 2546.360 2805.185 -258.826
LN of No. of Employees 5.731 5.782 -0.050 5.814 5.722 0.092*
EBITDA 42.182 49.950 -7.769 -3.293 129.246 -132.539
Net Income 45.845 -58.152 103.997 -96.227 0.595 -96.822
Revenue 463.540 1136.433 -672.893 1387.030 675.454 711.576
Gross Profit 133.888 349.375 -215.487 301.893 404.330 -102.437*
Country Level Characteristics
GDP Growth Rate 2.137 2.499 -0.362** 3.153 1.436 1.717***
MSCI Returns 0.056 0.072 -0.016 0.100 0.027 0.073***
Minority Shareholders Protection Index 6.923 6.919 0.004 6.822 7.088 -0.266***
UAI 46.383 47.459 -1.076 47.327 47.629 -0.303*
U.S. Investor Sentiment Index 1 0.139 0.193 -0.054 0.361 -0.114 0.475***
U.S. Investor Sentiment Index 2 -0.011 0.028 -0.039 0.191 -0.269 0.461***
Exit Outcomes
Successful Exits 0.920 0.989 -0.069*** 0.993 0.979 0.014***
IPO Exits 0.100 0.874 -0.774*** 0.925 0.764 0.161***
Acquisition Exits 0.919 0.988 -0.069*** 0.993 0.978 0.015***
58
Table V. OLS Regression Models for Determinants of PE Investor Sentiment
This table presents the clustered OLS model results of the determinants of PE investor sentiment. All dependent variables across Model (1) to (12) are the PV Ratios as defined in
Table II. Panel A investigates the impact from each facet of determinants on the PE investor sentiment and panel B investigates the impact from each facet of determinants on the
PE investor sentiment in addition to considering the prior. *, **, *** Significant at the 10%, 5% and 1% levels, respectively.
Panel A: Without Prior Sentiment Variable
Model (1) Model (2) Model (3) Model (4) Model (5) Model (6)
PV Ratio PV Ratio PV Ratio PV Ratio PV Ratio PV Ratio
Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic
GDP Growth Rate -0.0012 -0.25 0.0017 0.40
MSCI Returns -0.0260 -0.63 0.0138 0.36
Minority Protection Index 0.0410 2.96*** 0.0526 4.07***
UAI -0.0013 -3.44*** 0.0000 0.01
LN of Number of Employees -0.0296 -12.28*** -0.0286 -10.62***
Number of Deals per Year -0.1507 -7.34*** -0.1505 -7.37***
Company Effects Yes Yes Yes Yes Yes Yes
Industry Effects Yes Yes Yes Yes Yes Yes
Country Effects Yes Yes Yes Yes Yes Yes
Year Effects Yes Yes Yes Yes Yes Yes
Number of Observations 12419 12366 11945 12423 8742 8467
R-squared 0.1013 0.1001 0.1009 0.1005 0.1637 0.1638
59
Table V. OLS Regression Models for Determinants of PE Investor Sentiment (Continued)
Panel B: With Prior Sentiment Variable
Model (7) Model (8) Model (9) Model (10) Model (11) Model (12)
PV Ratio PV Ratio PV Ratio PV Ratio PV Ratio PV Ratio
Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic
PV Ratio (Lagged 1-Year) 0.3512 15.92*** 0.3504 15.69*** 0.3520 15.82*** 0.3487 15.64*** 0.3487 15.64*** 0.2878 9.82***
GDP Growth Rate -0.0015 -0.29 -0.0026 -0.36 0.0009 0.14 0.0009 0.14 -0.0051 -0.76
MSCI Returns 0.0216 0.35 0.0130 0.21 0.0130 0.21 0.0937 1.37
Minority Protection Index 0.0575 3.57*** 0.0575 3.57*** 0.0604 3.18***
UAI -0.0020 -0.89 -0.0050 -1.85*
LN of Number of Employees -0.0331 -9.18***
Number of Deals per Year -0.1044 -4.03***
Company Effects Yes Yes Yes Yes Yes Yes
Industry Effects Yes Yes Yes Yes Yes Yes
Country Effects Yes Yes Yes Yes Yes Yes
Year Effects Yes Yes Yes Yes Yes Yes
Number of Observations 2933 2918 2906 2786 2785 2115
R-squared 0.2164 0.2135 0.213 0.2112 0.2105 0.2355
60
Table VI. OLS Regression Models for PE Investor Sentiment in Developed Markets vs. Emerging Markets
This table presents the clustered OLS model results to compare the PE investor sentiment between developed and emerging
markets. All dependent variables across Model (1) to (8) are the PV Ratios as defined in Table II. Panel A presents the
differences of PE investor sentiment in the developed and emerging markets and panel B presents the differences of PE
investor sentiment in the developed and emerging markets, but excluding the U.S. as a robustness check. For conciseness, we
exclude all control variables which contain the exact same variables in Table V: GDP Growth Rate, MSCI Returns, Minority
Protection Index, UAI, LN of Number of Employees and Number of Deals per Year. *, **, *** Significant at the 10%, 5%
and 1% levels, respectively. *, **, *** Significant at the 10%, 5% and 1% levels, respectively.
Panel A: All Sample Tests
Model (1) Model (2) Model (3) Model (4)
PV Ratio PV Ratio PV Ratio PV Ratio
Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic
Developed Markets Dummy 0.3668 6.40*** 0.2730 5.30***
Emerging Markets Dummy -0.3668 -6.40*** -0.2730 -5.30***
Controls No Yes No Yes
Industry Effects Yes Yes Yes Yes
Country Effects Yes Yes Yes Yes
Year Effects Yes Yes Yes Yes
Number of Observations 12414 8467 12414 8467
R-squared 0.0791 0.1508 0.0791 0.1508
Panel B: Subsample Tests - Without US
Model (5) Model (6) Model (7) Model (8)
PV Ratio PV Ratio PV Ratio PV Ratio
Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic
Developed Markets Dummy 0.3068 5.46*** 0.2472 4.58***
Emerging Markets Dummy -0.3068 -5.46*** -0.2472 -4.58***
Controls No Yes No Yes
Industry Effects Yes Yes Yes Yes
Country Effects Yes Yes Yes Yes
Year Effects Yes Yes Yes Yes
Number of Observations 2590 1255 2590 1255
R-squared 0.1862 0.2767 0.1862 0.2767
61
Table VII. OLS Regression Models for PE Investor Sentiment by Different Investor Types
This table presents clustered OLS model results to compare the PE investor sentiment between angels and PE/VC funds.
All dependent variables across Model (1) to (8) are the PV ratios as defined in Table II. Panel A presents the results for
angel investors and Panel B presents the results for PE/VC funds. For conciseness, we exclude all control variables which
contain the exact same variables in Table V: GDP Growth Rate, MSCI Returns, Minority Protection Index, UAI, LN of
Number of Employees and Number of Deals per Year. *, **, *** Significant at the 10%, 5% and 1% levels, respectively.
*, **, *** Significant at the 10%, 5% and 1% levels, respectively.
Panel A: For Angel Investors
Model (1) Model (2) Model (3) Model (4)
PV Ratio PV Ratio PV Ratio PV Ratio
All Rounds Deals 1st Round Deals 2nd Round Deals 3rd Round Deals
Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic
All Angels Dummy -0.0870 -2.54** -0.0346 -0.81 -0.1964 -2.12** -0.3071 -7.32***
Controls Yes Yes Yes Yes
Company Effects Yes Yes Yes Yes
Industry Effects Yes Yes Yes Yes
Country Effects Yes Yes Yes Yes
Year Effects Yes Yes Yes Yes
Number of Observations 8467 3779 2185 985
R-squared 0.1648 0.1807 0.1595 0.2316
Panel B: For PE/VC Funds
Model (5) Model (6) Model (7) Model (8)
PV Ratio PV Ratio PV Ratio PV Ratio
All Rounds Deals 1st Round Deals 2nd Round Deals 3rd Round Deals
Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic
Pure PE/VC Dummy 0.0870 2.54** 0.0346 0.81 0.1964 2.12** 0.3071 7.32***
Controls Yes Yes Yes Yes
Company Effects Yes Yes Yes Yes
Industry Effects Yes Yes Yes Yes
Country Effects Yes Yes Yes Yes
Year Effects Yes Yes Yes Yes
Number of Observations 8467 3779 2185 985
R-squared 0.1648 0.1807 0.1595 0.2188
62
Table VIII. OLS Regression Models for the Financial Crisis Impact on PE Investor Sentiment
This table presents the clustered OLS model results to investigate the financial crisis impact on the PE investor sentiment. All dependent
variables across Model (1) to (9) are the PV ratios as defined in Table II. Panel A presents the results for all samples, panel B presents the
results for the Emerging Markets subsample and panel C presents the results for the Developed Markets subsample. For conciseness, we exclude all control variables which contain the exact same variables in Table V: GDP Growth Rate, MSCI Returns, Minority Protection Index,
UAI, LN of Number of Employees and Number of Deals per Year. *, **, *** Significant at the 10%, 5% and 1% levels, respectively. *, **,
*** Significant at the 10%, 5% and 1% levels, respectively.
Panel A: For All Sample
Model (1) Model (2) Model (3)
PV Ratio PV Ratio PV Ratio
Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic
PV Ratio (Lagged 1-Year) 0.2859 9.55*** 0.2403 7.62***
PV Ratio (Lagged 1-Year) * Financial Crisis Dummy 0.1128 2.65***
Financial Crisis Dummy -0.0413 -3.71*** -0.0283 -1.30 -0.1073 -2.93***
Controls Yes Yes Yes
Company Effects Yes Yes Yes
Industry Effects Yes Yes Yes
Country Effects Yes Yes Yes
Year Effects Yes Yes Yes
Number of Observations 8467 2115 2115
R-squared 0.1655 0.2361 0.2382
Panel B: For Emerging Markets Subsample
Model (4) Model (5) Model (6)
PV Ratio PV Ratio PV Ratio
Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic
PV Ratio (Lagged 1-Year) 0.1666 0.68 0.2313 0.70
PV Ratio (Lagged 1-Year) * Financial Crisis Dummy -0.1254 -0.37
Financial Crisis Dummy -0.1335 -3.41*** -0.0869 -1.26 -0.0294 -0.35
Controls Yes Yes Yes
Company Effects Yes Yes Yes
Industry Effects Yes Yes Yes
Country Effects Yes Yes Yes
Year Effects Yes Yes Yes
Number of Observations 241 88 88
R-squared 0.3943 0.6422 0.6445
Panel C: For Developed Markets Subsample
Model (7) Model (8) Model (9)
PV Ratio PV Ratio PV Ratio
Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic
PV Ratio (Lagged 1-Year) 0.2847 8.93*** 0.2389 7.32***
PV Ratio (Lagged 1-Year) * Financial Crisis Dummy 0.1156 2.60***
Financial Crisis Dummy -0.0366 -3.20*** -0.0210 -1.05 -0.1041 -2.78***
Controls Yes Yes Yes
Company Effects Yes Yes Yes
Industry Effects Yes Yes Yes
Country Effects Yes Yes Yes
Year Effects Yes Yes Yes
Number of Observations 8226 2027 2027
R-squared 0.1324 0.1971 0.1993
63
Table IX. OLS Regression Models of PE Investor Sentiment Impact on Investee Firm Profitability
This table presents the clustered OLS model results to investigate the PE investor sentiment impact on the profitability and earnings potential of the investee companies. All
dependent variables across Panels A to D are the four main financial performance measurements: EBITDA, Net Income, Revenue and Gross Profit as defined in Table II
and from time t to t+5 across the six models in each panel. For conciseness, we exclude all control variables which contain the exact same variables in Table V: GDP
Growth Rate, MSCI Returns, Minority Protection Index, UAI, LN of Number of Employees and Number of Deals per Year. *, **, *** Significant at the 10%, 5% and 1%
levels, respectively. *, **, *** Significant at the 10%, 5% and 1% levels, respectively.
Panel A: EBITDA
Model (1) Model (2) Model (3) Model (4) Model (5) Model (6)
t t+1 t+2 t+3 t+4 t+5
Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic
PV Ratio 74.2377 1.29 3900.3360 13.78*** 61.5248 2.28** 17.2539 1.45 -7.0372 -0.58 -64.4394 -3.63***
Controls Yes Yes Yes Yes Yes Yes
Industry Effects Yes Yes Yes Yes Yes Yes
Country Effects Yes Yes Yes Yes Yes Yes
Year Effects Yes Yes Yes Yes Yes Yes
Number of
Observations 2557 1671 1274 1050 944 869
R-squared 0.0206 0.0176 0.1399 0.1645 0.1089 0.0945
Panel B: Net Income
Model (1) Model (2) Model (3) Model (4) Model (5) Model (6)
t t+1 t+2 t+3 t+4 t+5
Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic
PV Ratio 83.6869 2.30** -19.1002 -0.8 41.7556 2.42** 11.2293 1.55 -79.0567 -4.21*** -53.1512 -2.44**
Controls Yes Yes Yes Yes Yes Yes
Industry Effects Yes Yes Yes Yes Yes Yes
Country Effects Yes Yes Yes Yes Yes Yes
Year Effects Yes Yes Yes Yes Yes Yes
Number of
Observations 2298 1532 1207 1005 918 847
R-squared 0.0214 0.0494 0.0882 0.0267 0.0325 0.0308
64
Table IX. OLS Regression Models of PE Investor Sentiment Impact on Investee Firm Profitability (Continued)
Panel C: Revenue
Model (1) Model (2) Model (3) Model (4) Model (5) Model (6)
t t+1 t+2 t+3 t+4 t+5
Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic
PV Ratio 1329.1680 0.74 2818.4500 0.84 -1717.8440 -0.44 -3115.2630 -0.86 -3096.2590 -1.3 549.6620 0.25
Controls Yes Yes Yes Yes Yes Yes
Industry Effects Yes Yes Yes Yes Yes Yes
Country Effects Yes Yes Yes Yes Yes Yes
Year Effects Yes Yes Yes Yes Yes Yes
Number of Observations 6124 3755 3224 2929 2923 2865
R-squared 0.0144 0.0105 0.01 0.0131 0.019 0.0225
Panel D: Gross Profit
Model (1) Model (2) Model (3) Model (4) Model (5) Model (6)
t t+1 t+2 t+3 t+4 t+5
Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic
PV Ratio -38.3693 -0.62 1334.9010 4.73*** 1571.5810 4.36*** 1504.2480 4.76*** 1378.0590 6.03*** -97.7746 -3.37***
Controls Yes Yes Yes Yes Yes Yes
Industry Effects Yes Yes Yes Yes Yes Yes
Country Effects Yes Yes Yes Yes Yes Yes
Year Effects Yes Yes Yes Yes Yes Yes
Number of Observations 1750 1242 964 809 715 667
R-squared 0.1625 0.0111 0.0147 0.0176 0.0177 0.1531
65
Table X. PROBIT Regression Models of PE Investor Sentiment Impact on Exits Outcomes
This table presents clustering PROBIT model results of PE investor sentiment impact on successful exits
outcomes and we report the associated marginal effects on the interested variable: PV ratio. The
dependent variables across Models (1) to (3) are different exits dummy variable to capture all successful
exits, all IPO exits and all acquisition exits. For conciseness, we exclude all control variables which
contain the exact same variables in Table V: GDP Growth Rate, MSCI Returns, Minority Protection
Index, UAI, LN of Number of Employees and Number of Deals per Year. *, **, *** Significant at the
10%, 5% and 1% levels, respectively. *, **, *** Significant at the 10%, 5% and 1% levels, respectively.
Model (1) Model (2) Model (3)
Successful Exits IPO Exits Acquisition Exits
Marginal Effects z score Marginal Effects z score Marginal Effects z score
PV Ratio 0.0429 7.47*** 0.0134 0.77 0.0513 9.16***
Controls Yes Yes Yes
Company Effects Yes Yes Yes
Industry Effects Yes Yes Yes
Country Effects Yes Yes Yes
Year Effects Yes Yes Yes
Number of
Observations 5503 530 5011
Pseudo R2 0.4671 0.3825 0.5709
66
Table XI. OLS Regression Models for Public Market Investor Sentiment on PE Investor Sentiment - a U.S. Case
This table presents the clustered OLS model results to investigate the public market investor sentiment impact on the PE investor
sentiment. All dependent variables across Model (1) to (4) are the PV Ratios as defined in Table II. For conciseness, we exclude all
control variables which contain the exact same variables in Table V: GDP Growth Rate, MSCI Returns, Minority Protection Index,
UAI, LN of Number of Employees and Number of Deals per Year. *, **, *** Significant at the 10%, 5% and 1% levels,
respectively. *, **, *** Significant at the 10%, 5% and 1% levels, respectively.
Model (1) Model (2) Model (3) Model (4)
PV Ratio PV Ratio PV Ratio PV Ratio
Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic
PV Ratio (Lagged 1-Year) 0.2693 8.95*** 0.2693 9.00***
U.S. Investor Sentiment Index 1 0.0205 2.53** 0.0283 1.98**
U.S. Investor Sentiment Index 2 0.0272 2.78*** 0.0343 1.78*
Controls Yes Yes Yes Yes
Company Effects Yes Yes Yes Yes
Industry Effects Yes Yes Yes Yes
Year Effects Yes Yes Yes Yes
Number of Observations 7212 1698 7212 1698
R-squared 0.1282 0.1828 0.1286 0.1829
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