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Going viral: Portfolio performance of social media stocks

Wan-Jiun Paul Chiou1

Heather S. Knewtson2

John R. Nofsinger3

December 16, 2013

Abstract:

We identify social media stocks and study their return performance and contribution to portfolio

performance. Multivariate regression results demonstrate they have outperformed the overall

market but with high levels of risk. Therefore, we find that social media stocks improve mean-

variance efficiency for investors with higher risk appetites. We further investigate this risk/return

tradeoff by evaluating the impact of certain macroeconomic factors on the Sharpe ratio,

including maturity risk premium, default risk premium, volatility, and investor sentiment.

Causality tests indicate that the default risk premium, volatility, and investor sentiment have

incremental power in explaining the performance of social media stocks.

Keywords: Social media, Sharpe ratio, efficient portfolios, mean-variance relation, sentiment

JEL Classification: G11, G14

1 Wan-Jiun Paul Chiou is an Assistant Professor of Finance in the Department of Finance and Law, College of

Business Administration, Central Michigan University, 324 Sloan Hall, Mt. Pleasant, MI 48859, Phone: 989-774-

1262, Fax: 989-774-6456, Email: chiou1p@cmich.edu 2 Heather S. Knewtson (contact author) is an Assistant Professor of Finance in the Department of Finance and Law,

College of Business Administration, Central Michigan University, 205D Smith Hall, Mt. Pleasant, MI 48859,

Phone: 989-774-7554, Fax: 989-774-6456, Email: knewt1h@cmich.edu 3 John R. Nofsinger is a Professor and Nihoul Faculty Fellow of Finance in the Department of Finance and

Management Science, College of Business, Washington State University, P.O. Box 644746, Pullman, WA 99164-

7476, Phone: 509-335-7200, Email: john_nofsinger@wsu.edu

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“It took 38 years for the radio to attract 50 million listeners, and 13 years for television to gain

the attention of 50 million viewers. The Internet took only four years to attract 50 million

participants, and Facebook reached 50 million participants in only one-and-a-half years.”

(Nair, 2011)

1. Introduction and Motivation

Social media firms have drawn much attention from investors and the financial press. The

Facebook and Twitter IPOs represent examples of this intense investor interest. Facebook set

several market records by its IPO on May 18, 2012, including: (1) the largest venture backed IPO

debuting at over $100 billion, (2) the most venture capital raised with $2.2 billion in equity

financing acquired prior to IPO, and (3) the most active pre-IPO acquirer (Facebook acquired 13

venture-backed enterprises prior to its IPO).4 Facebook facilitates social networking around the

globe and is ubiquitous with the term social media. In November 2013, the IPO of Twitter also

drew a lot of attention as its stock rose from the IPO price of $26 to a first trading close of

$44.90. There are other social media stocks publicly listed, but they are currently categorized in

several industries.

Social media has changed and reshaped business models, the economy, politics, and

culture throughout the world. McKinsey Global Institute estimates that widespread use of social

media technologies will transform communications from one-to-one interactions into many-to-

many interactions, resulting in productivity gains of 20-25% amongst knowledge workers. They

further estimate this shift could result in a $900B to $1.3T increase in untapped economic surplus

annually, if industries fully embrace the benefits offered by adoption of social technologies

throughout their business models.5 Clearly, the anticipated impact of social media, as a new

industry, is more than merely a social matter. Cohen (2013) documents the impact of innovation

4 See http://venturebeat.com/2012/05/16/record-breaking-facebook-ipo/. 5 See McKinsey and Company (2012).

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on economic growth, on long-term corporate performance, and on security returns. Having a

better understanding of the risk and return implications of investing in social media firms can

provide evidence to better manage asset allocation.

The buzz surrounding the social media craze is reminiscent of the dot.com era from the

late 1990s to the early 2000s. Ofek and Richardson (2003), Griffin, et al. (2011), and Yu and

Yuan (2011) analyze how markets priced Internet stocks through the previous wave of cyber

euphoria. Customer to customer feedback about experiences gave birth to the term “going viral.”

By monitoring the social media chatter of their customers, firms can respond to customers more

quickly, accelerating both the pace of product response and obsolescence.

The infrastructure to support collaborative socializing provides opportunities for business

(from data mining) and more challenges for valuation. What drives value; is it the number of

clicks, the breadth of advertising dollars, the scope of an informal network? Whatever the source

of intrinsic value, financial markets are interpreting of the value of social media firms daily.

Since the definition and scope of social media is not completely clear, even for financial

regulators, a study on their stock performance remains unchartered territory. Given the fact that

large, private social media companies, such as Twitter, are becoming public companies, a study

of social media from the investment perspective seems timely and relevant to both academia and

Wall Street.

In this paper, we try to answer the following three questions: First, does being recognized

as a social media firm yield extra value? We construct an index of social media firms to evaluate

what benefits have been available to investors. Second, how does diversifying a portfolio

through adding social media stocks improve mean-variance efficiency? We consider contribution

to portfolio efficiency by investigating the relation to social media firms with the Fama and

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French (1997) industries’ performance. Finally, what factor loadings drive social media firm

value? We study the role of macroeconomic factors toward understanding the pricing of social

media stocks. We explore the impact of bond market risk premiums, forward market volatility

captured by the VIX, and investor sentiment on the performance of social media stocks. We

construct a sample of social media firms to answer these questions.

The sample includes obvious firms such as Facebook and LinkedIn. Missing from our

sample are several high profile social media firms such as Twitter, Second Life, and Pinterest,

which remain privately held or became public after our sample range. For example, Pinterest, an

online photo-sharing bulletin board, was valued at $2.5 billion in February 2013 following an

injection of capital by Valiant. This is larger than the market value of some of the current public

companies such as Zynga, Yelp, and Pandora.6

We find that social media firms outperform the market by providing a positive abnormal

return. We find that social media firms as an industry contribute to mean-variance efficiency, but

only for investors with a high risk tolerance. The outperformance of social media stocks are

related to macroeconomic factors, such as credit risk premiums, forward volatility, and investor

sentiment. However, the outperformance was lower in the most recent period. We interpret this

to mean that investors are getting more effective at pricing the securities of this newer business

model.

This rest of the paper is organized as follows. Section 2 contains a literature review of

social media firms and firms from the dot.com era. In Section 3, we explain our data and

methodology. Section 4 analyzes the performance of social media firms and investigates their

contribution toward portfolio diversification. In Section 5, we consider macroeconomic factors

that are associated with social media outperformance. Section 6 provides our conclusions.

6 http://www.bloomberg.com/news/2013-02-21/pinterest-gets-200-million-in-funding-at-2-5-billion-valuation.html.

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2. Literature Review

Social media, as a new communication platform, has had a tremendous impact on the

world economy. As more users enter into virtual gathering places, firms have had to transition

from reliance on old media (television, radio, and print media) and embrace this new media

awash in user-generated content. The impact of social media on business is wide ranging, as

noted by scholars in information systems (Luo, et al., 2013), marketing (Naylor, et al., 2012),

law (Janoski-Haehlen, 2011; Bellin, 2012), supply chain management (O'Leary, 2011), and

strategy (Baird and Parasnis, 2011). Several studies focus on the investment return of social

media capital expenditures (Fisher, 2009; Hoffman and Fodor, 2010).

Finance research on social media firms is also new. Hsieh and Walkling (2006) document

that there are certain overpriced “concept stocks” over time since the 1960’s. Social media stocks

can be classified as concept stocks after 2000, according to their definition. Cauwels and

Sornette (2012) suggest that Facebook and Groupon were overpriced according to their

fundamental value-based model of social media firms. Larcker, et al. (2012) investigate the role

social media can play in assisting boards with monitoring customer experiences. Specifically,

they consider the chatter offered freely by customers as an early warning system before poor

customer experiences go viral and damage a firm’s reputation. Chen, et al. (2013) conduct a

textual analysis of social chatter and investigate its relation to stock returns and earnings

surprises. Karabulut (2013) and Simon and Heimer (2012) study a proprietary database of retail

investors that share a common social platform. They find that network communications influence

more active trading. Most of the current research focuses on how the information revealed by

social media affects market behavior, but does not investigate the investment value of social

media stocks.

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Some of the firms in our sample have their origins in the dot.com era, and we consider

similarities between the findings of the dot.com era and our study of social media firms. The

mania for Internet stocks was followed by a crash, which Ofek and Richardson (2003) attribute

to lockup expiry for technology stocks precipitating a selloff. Griffin, et al. (2011) attribute the

selloff to institutions exiting the sector in a coordinated fashion. Ljungqvist and Wilhelm (2003)

studied the relation between severe IPO underpricing (like recently seen with Twitter) and firm

characteristics unique to the dot.com era. Overall, Hendershott (2004) found that value was

created in his sample of venture-backed dot.coms, with return on equity of 19% annually, even

accounting for the price run up and ensuing correction of the dot.com bubble and crash.

DeMarzo, et al. (2007) provide a theoretical model that explains how technological firms

tend toward overinvestment precipitating an asset bubble. The prediction of his model is for the

overinvestment to have arisen from an impulse for investors to herd into these stocks, creating

price pressure beyond the prediction of rational pricing models. This model explains why a new

type of industry creates furor in the markets and how asset price bubbles result.

3. Data and Methodology

Identifying social media firms that satisfies both investor intuition and academic rigor is

the first challenge of our study. A recent example illustrates this ambiguity. In July 2012, Netflix

CEO, Reed Hastings, posted material information about Netflix to his personal Facebook page.

The Security Exchange Commission (SEC) brought charges of improper disclosure against

Hastings in December 2012 for failing to make full disclosure through approved outlets.

Following its investigation, the SEC ruled in April 2013 that news could flow into social media

venues if investors are apprised that announcements are routinely made through these venues.

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The SEC recognizes the shift into social media spaces; however, it does not attempt to define

social media outlets in its guidance.

To study social media firms, we first identify our sample. Facebook seems to obviously

be a social media firm, but whether to include others, such as Angie’s List or Groupon, is

ambiguous. We apply a definition proposed by Kaplan and Haenlein (2010) to set our sample.

They propose that social media is characterized by a real-time updateable virtual space (Internet

or mobile) populated by user-generated content. Over the period from January 1, 1996 –

December 31, 2012, we identify 31 social media firms that traded on the NYSE, Amex or

Nasdaq. Return measures are constructed using data from CRSP, as well as industry membership

codes (SIC). The premiums of three factors (excess market returns, size, and value/growth) are

obtained from the data published on Ken French’s website.7

Social media firms do not emanate from a common SIC or NAICS industry code. Thus,

setting the sample is a laborious process of searching firms and testing against an objective,

academic definition. In this study, we apply the definition by Kaplan and Haenlein (2010) that

social media firms are webspaces (1) operating in a Web 2.0 environment (that is, real-time and

dynamically updateable to make online “social” technically feasible), (2) of user-generated

content defined by the Organization for Economic Cooperation and Development (Vickery and

Wunsch-Vincent 2007), and (3) demonstrating features of social presence, media richness, self-

presentation, and self-disclosure. We apply this definition to a subsample of firms that seemed

unequivocally to be members of the social media industry, such as Facebook, Google, and Yelp.

The common Yahoo! industry category for these firms is Internet Information Providers. Each of

the 217 firms from the Yahoo! list of this industry downloaded on January 16, 2013 was

considered as a potential member of social media (whether or not the firm was public). In

7 http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html

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addition, Global X Funds retails a social media ETF (SOCL) with 29 firms as of December 31,

2012. From these initial lists of candidate firms, we eliminate firms that do not meet the strict

social media definition.

While searching for industry codes (on Mergent Online, Lexis Nexis, and Value Line),

the various databases provided a list of competitors, which we added to our list as potential

sample firms. We compile a list of industry codes and search exhaustively in CRSP within these

18 SIC codes from the list of firms traded on the NYSE, Amex, and Nasdaq. We consider each

firm that appears as a firm in this SIC group as a candidate for the social media industry.

We manually investigate whether and when the firm had a user interface that fits the

three criteria defined by Kaplan and Haenlein (2010). These sites can also have a more

enhanced, fee-based model (like LinkedIn), but they do need to host a platform beyond merely a

comment portal in support of a normal product line for the firm. An example is that Amazon.com

has a very active user generated comment interface, but the portal exists to complement

Amazon’s business model of retailing products. Therefore, Amazon.com is not deemed to be a

social media firm. On the other hand, Google has a very active search interface that is widely

used. It also offers a social media interface known as Google +. The presence of the Google +

social space merits Google’s inclusion in the social media sample. Many firms fit the definition

of a social media firm, but remain either privately held (such as Linden Lab) or trade OTC

Bulletin Board (OTCBB) (such as Gree Inc.). Our final sample consists of 31 social media firms

between the years 1996 – 2012.

Table 1 contains the list of firms that form our social media sample. The early part of our

sample period overlaps with the tail of the dot.com era. The middle era includes the heyday of

the mortgage-backed securities (MBS) credit bubble, and the final portion of the study includes

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the MBS collapse and ensuing economic malaise. We study the full sample period and three

subsample periods.

<Insert Table 1 about here>

We formed weekly returns to measure excess and abnormal returns, which allow for

variation in the dependent variable and yet do not suffer from microstructure issues, as do daily

returns. We also use Friday prices to compute weekly returns for each social media stock in our

sample by following the approach of Fama and French’s (1993) weekly benchmark returns. To

compare social media firms to the industry in which they are members, we construct industry

benchmark returns and collect weekly returns (based on Friday prices) for all firms in the same

4-digit SIC group as the social media firms (exclusive of the social media firms). We then form

an average industry return by averaging the weekly returns within each SIC grouping. Table 2

contains summary statistics for social media firms (Panel A) and the industries that contained

social media firms (Panel B). For each panel, we provide the full sample period between 1996

and 2012, as well as three subsample periods.

<Insert Table 2 about here>

Comparing Panel A to Panel B for the entire sample period, social media firms have

higher mean returns and higher standard deviation, in addition to stronger skew and kurtosis than

the industries from which they come. The middle subperiod (2001 – 2006) appears to drive these

results. Figure 1 plots the cumulative returns of social media firms plotted against benchmark

returns for the S&P 500 and NASDAQ beginning on May 1, 1996. Social media firms beat both

these benchmark returns by a wide margin through December 31, 2012.

<Insert Figure 1 about here>

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We compute abnormal returns by controlling the risk factors known to influence returns.

Using the weekly return measure, we estimate (1) the market model and (2) the Fama French

(1993) three factor model to detect abnormal returns:

( ) , (1)

( ) , (2)

where is firm i’s return, is the risk-free rate, is the market benchmark return, SMB is

the size premium, HML is the value/growth premium, and is the weekly abnormal return

from the regression.8

We also construct Sharpe ratios to investigate the time variation in the risk-return

behavior of social media stocks. For each week, we formed Sharpe ratios using the past 52

weekly returns.9 The yield for the 3-month Treasury bill is used as the proxy for the risk-free

rate. We computed the expected return and standard deviation from the 52 weekly returns of the

social media stocks and their corresponding industries. We then formed the average Sharpe ratio

for social media firms by taking the mean Sharpe ratio each week t for all of the firms (i) in our

sample:

N

SR

SR

N

i

ti

t

1

,

. (3)

Figure 2 demonstrates the time-series behavior of the Sharpe ratio and its long-term

smoothing curve (H-P) proposed by Hodrick and Prescott (1997). It shows no specific direction

in the long term, but is volatile over time. The pattern seems to follow the business cycle, which

suggests that macroeconomic factors might be of impact on their performance.

8 We do not have the weekly Carhart (1997) momentum factor. However, we did estimate a daily return Carhart (1997) four factor model and found similar alpha estimates as the weekly three factor model shown in Table 3. 9 For robustness, we also formed Sharpe ratios using 26 weekly returns. The semi-annual results are similar to annual results and have been excluded for brevity.

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<Insert Figure 2 about here>

4. Empirical Results

4.1. Performance of social media index returns

We proceed by considering our sample of social media firms as their own group. We first

examine the risk-adjusted performance for our index of social media stocks. Note that toward the

end of our sample period, Global X Management Company LLC debuted a social media ETF on

November 11, 2011. Its portfolio includes non-social media firms, such as Angie’s List,

Groupon, and Nutrisystem.10

All of the firms in our social media sample are included in our

index. We consider both an equal-weighted and a value-weighted social media index and require

a minimum of five firms to populate the index.

Table 3 presents regression analysis for our social media index. Panel A contains results

for the equal-weighted social media index and Panel B contains results for the value-weighted

index. We control for risk factors by estimating the market model and the Fama-French three

factor model. Enthusiasm for social media as a new business paradigm does not imply

outperformance of social media stocks. Indeed, DeMarzo et al. (2007) predict strong investor

interest in technology firms can erode performance through investors herding into the new

investment opportunities. Although we find social media stocks in general to beat the market,

outperformance is dependent on the sample period. The results from Panel A indicate

outperformance for an equal-weighted index of social media firms. For the period 1996 – 2012,

the index shows a 20.3% abnormal annualized return for the market model, and a 21.6%

abnormal return for the three factor model, both significant at the 1% level.

<Insert Table 3 about here>

10 We downloaded member firms of the SOCL ETF on January 28, 2013 from this website: (http://www.globalxfunds.com/socialmediaetf/).

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In Panel B, value-weighted results are not statistically significant for the market model

and marginally significant for the three factor model. This evidence is consistent with small

firms being more influential in the computation for the equally-weighted index returns. Although

the evidence from social media indices reflects outperformance, the period between 2001 and

2006 seems to drive the relation. Neither the period between 1996 and 2000 nor the period

between 2007 and 2012 reflects outperformance. Our finding suggests that the social media firms

are viewed as “concept stocks,” at least during a certain era. It will be interesting to monitor

these firms after our sample as the Twitter IPO might suggest a new enthusiasm.

4.2. Diversification benefits of social media stocks

One of the critical questions to investors is whether social media stocks provide

diversification benefits in managing portfolios. Chen, Ho, Hsiao, and Wu (2010) examine the

diversification benefits of IPO stocks among stocks of various sizes and market-book ratios.

Given that many social media have become public in our sample period, we study whether social

media create portfolio diversification. Griffin, et al. (2011) and Ofek and Richardson (2003)

study the causes of pricing bubbles of Internet stocks. DeMarzo, et al. (2007) also analyze why

technological innovations may promote investment bubbles. Huang and Litzenberger (1988)

suggest that adding any nonredundant asset would improve mean-variance efficiency, but there

high volatility challenges the practical value of social media stocks in overall portfolio

management.

Table 4 reports the summary statistics of the weekly returns of 49 industries of Fama and

French (1997) and our value-weighted social media industry between March 11, 1998 and

December 31, 2012. Among them, social media stocks show the second highest return (annually

28.75%), only lower than precious metals. They also have the third highest volatility (annually

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43.55%), yielding a Sharpe ratio that is higher than those of over twenty industries. The social

media index also exhibits a high dispersion between the maximum and minimum of weekly

return, with a maximum of 22.9% (annualized) and minimum of -19.6% (annualized).

<Insert Table 4 about here>

We next analyze the diversification benefits provided by social media stocks. Our

industry benchmarks are the Fama-French Industry Portfolios and our social media index.

Following Markowitz (1952), the unconstrained optimal portfolio selection can be expressed as a

Lagrangian:

min ( ) ( ){ , , }w w Vw w w 1 1

21T T T

p , (4)

where p denotes the value-weighted mean return on the portfolio, the excess return, V the

variance-covariance matrix, and the shadow prices and , respectively. We also take into

account issues in portfolio weights to enhance feasibility of strategies. Green and Hollifield

(1992) question the usefulness of corner solutions of the non-constrained diversification.

Jagannathan and Ma (2003) evaluate portfolio bounds from the perspective of risk control and

document that weight constraints decrease volatility in portfolio return and increase feasibility of

asset allocation. The optimal portfolio is made more practical by setting a minimum number of

assets to avoid corner solutions and concentration. The investment (SS+DV(U)) is defined as a

portfolio with short-sales-prohibited and investing in at least U assets. The optimal weights are

then solved by applying the Kuhn-Tucker conditions when the complementary slackness

conditions, primal constraints, and gradient equations are fulfilled.

Figure 3 exhibits the efficient frontier that comprise of 49 industry portfolios and also the

efficient frontier formed by these industries with the index of social media stocks. To

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demonstrate their difference, we only show the short-sales-prohibited curves. Although the two

curves overlap in lower risk ranges, there is efficient frontier improvement in higher risk ranges

with social media stocks yielding higher expected portfolio returns than the curve that excludes

social media stocks. Our finding is similar to that of Chen, Ho, Hsiao, and Wu (2010), who show

that newly issued stocks yield higher volatility. The addition of these new investments provides

risk seekers new selections to pursue higher yield efficient portfolios.

<Insert Figure 3 about here>

Figure 4 shows the impact of adding portfolio diversity on mean-variance efficiency. The

constraint of at least 5 industries to form the portfolio does not significantly change the low-

volatility portfolio selections, but eliminates risky investing opportunities. This is consistent with

the risk reduction effect suggested by Jagannathan and Ma (2003). Similarly, when diversity

increases to necessitate at least 10 industries, the mean-variance efficiency drops and the possible

investing area also shrinks.

<Insert Figure 4 about here>

We follow Chiou (2008) to measure the benefits on the efficient frontier. The first

measure, the maximum Sharpe ratio (MSR), represents the highest mean-variance efficiency

achievable from the diversification. Since Elton, et al. (2009) suggest that investors may seek to

minimize the variance of a portfolio due to the lack of predictability of expected returns, the

second measure is the volatility of the minimum-variance portfolio (MV). Results are reported in

Table 5. For the MSR, the risk-adjusted return increases when an index of social media stocks is

included in the diversification portfolio. On the other hand, adding social media stocks does not

provide benefits to investors from a risk reduction perspective as they do not impact the MV

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portfolio. This conclusion holds for the diversity scenarios that require a minimum of at least five

and ten industries as a portfolio constraint, respectively.

<Insert Table 5 about here>

5. What drives social media performance?

5.1. GARCH regression analysis

Our previous analysis has shown that social media stocks have outperformed the market.

What drives this performance? In this section, we examine the relation between risk-adjusted

returns of social media stocks with macroeconomic factors. We now consider these factors to

understand which influence returns of this new industry. We initially consider bond market risk

premiums from Chen, et al. (1986) and the forward volatility captured by the VIX (the CBOE

S&P 500 Volatility Index) of Fleming, et al. (1995). Research suggests the maturity risk

premium (MRP) can be important to determine the equity premium (Fama and Gibbons, 1982;

Campbell, 1987; Rapach, et al., 2005). We measure MRP as the difference in yield between the

20-year Treasury bond and 3-month Treasury bill. Campbell, et al. (2008) and Vassalou and

Xing (2004) found the economic importance of default risk in asset pricing. We set the default

risk premium (DRP) as the difference in average interest rate between Moody’s Baa and Aaa

corporate bonds.

In addition, we consider the impact of consumer sentiment on the excess Sharpe ratio. Yu

and Yuan (2011) study the relation between investor sentiment and the mean-variance relation.11

They find variation in the risk-return relation when the investors have different attitudes about

prospects. There is a positive tradeoff between risk and returns during periods of low investor

11 Yu and Yuan (2011) use the Baker and Wurgler (2006) investor sentiment measure and check for robustness with the University of Michigan Consumer Sentiment Index. Results were similar with both measures; therefore we elected to use the index more readily available to us.

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sentiment. However, the mean-variance relation appeared to decouple during periods of high

investor sentiment. We collect the University of Michigan Consumer Sentiment Index to analyze

the impact of sentiment on excess mean-variance efficiency.12

Because of the time-rolling nature

of the Sharpe ratio calculation, it is appropriate to include the one-lag error in the regression. The

dependent variable is the Sharpe ratio ( ) of social media stocks. To control the time-variation

in error, we apply the following GARCH model for the regression:

1,

1

,0 )(

tttj

J

j

itt uuxSR , and

1

2

1 ttt huh , (5)

where xj’s are the macroeconomic variables. In addition to equation (5), we consider single-

variable regressions.

Table 6 reports the regression results. The GARCH regression results in Model 1 show

high statistically significant estimates for DRP and Sentiment. The findings are consistent with

the risk-adjusted returns for social media firms being associated with stress in credit market, as

modeled by the default risk premium. As credit markets require greater risk premiums for

liquidity and default, the Sharpe ratio for social media stocks decreases. The statistical

insignificance of the VIX suggests that investors of social media stocks in general are not

sensitive to higher forward risk environments. This may be because social media stocks provide

investors new opportunities for those who are not sensitive to market volatility. The single-

variable regressions show similar results found in the multiple regression.

<Insert Table 6 about here>

12 http://www.sca.isr.umich.edu/data-archive/mine.php

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In Models 1 and 5, sentiment is a significantly positive factor toward explaining the

Sharpe ratio of social media stocks. In periods of strong investor sentiment, such as the period

between 2001 and 2006, consistent with Yu and Yuan (2011), our finding suggests that investors

have a preference to riskier social media selections. As investors with strong risk appetites select

social media stocks, returns are elevated resulting in a strengthening Sharpe ratio. Social media

firms suffer in times of weak sentiment.

5.2. Causality tests

We further apply the Granger (1969) model to test whether these variables associated

with the dynamics of the macroeconomy and financial markets “cause” the risk-adjusted return

of social media stocks. We first confirm that the series are stationary by using augmented

Dickey–Fuller (1979) and Phillips–Perron (1988) tests. Using both the Akaike (1974) and the

Schwarz (1978) criteria, we set 12 lags as the optimum in the time-series to test whether these

variables Granger-cause SR.

In Table 7, the F-statistics show that the null hypotheses cannot be rejected for Granger

causality test only for the maturity risk premium. The regression results from Table 6 suggests

the statistical correlation between the Sharpe ratio and default risk and sentiment, Granger

causality tests further support the possibility that the changes in these variables determine the

performance of social media stocks. Interestingly, it appears that projected risk captured by the

VIX lends Granger causality to the performance of social media stocks.

<Insert Table 7 about here>

Our empirical results show that the return behavior of social media stocks is similar to

that of the dot-com bubble. Yu and Yuan (2011) suggest Internet stock pricing is associated with

investor confidence. Our results show that economic and financial factors, including sentiment,

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appear to determine the size of risk-adjusted returns. We further confirm statistical correlation of

these risk factors by showing Granger causality tests, which support the notion that any of these

factors determines the relation we find for social media firms.

6. Conclusion

Social media has accelerated the pace at which people communicate, socialize, learn, and

conduct business. Having a better understanding of the risk and return implications of investing

in social media firms has become important to both academia and Wall Street. We investigate the

performance of social media stocks by following a rigorous academic definition to set the sample

of social media firms.

We identify social media stocks by following the definition proposed by Kaplan and

Haenlein (2010). We find that social media firms generated alphas of about 21% annually over

the sample period. However, this outperformance is mostly driven by the period 2001 through

2006. We find that social media firms, as a new industry, contribute to mean-variance portfolio

management, but only for investors with a strong risk tolerance. Outperformance of social media

stocks are related to, and Granger caused by, macroeconomic factors, such as default risk

premiums, forward volatility, and investor sentiment.

Our paper contributes to the literature by studying whether recognition as a social media

firm yields extra value to stocks, controlling for various priced risks. We also investigate the

value of social media stocks in diversifying portfolios. The results of the impact of

macroeconomic factors on the excess industry-adjusted performance of social media stocks are

insightful to understating their pricing behavior.

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22

Table 1 Social Media Firms

The table reports social media firms that form the sample. Column A shows the name of the firm. Column B describes the rationale

for membership as a social media firm. Columns C and D show the beginning and ending dates for membership as social media firms,

listed by year and then month. We follow Kaplan and Haenlein (2010) to manually identify the 31 social media firms.

(A) Company Name (B) Description (C) Begin (D) End

A O L INC media platform, social networking (bebo and about.me) - after spun out of Time Warner 200912 201212 ACTIVEWORLDS CORP social gaming 200005 201212 ACTIVISION BLIZZARD INC handheld and online role-playing games 199501 201212 ANCESTRY COM INC genealogy community 200911 201212 ANSWERS CORP user generated wikianswers + reference materials 200410 201104 BAIDU COM INC news service, content and social chat 200508 201212 BEYOND COM CORP professional social networking 199806 200201 CHANGYOU COM LTD Chinese social gaming 200904 201212 EXCITE INC search applications and online social chat 199604 199905 FACEBOOK INC online social networking 201205 201212 FRIENDFINDER NETWORKS INC social networking 201105 201212 GIANT INTERACTIVE GROUP Chinese social gaming 200711 201212 GIGAMEDIA LIMITED Taiwanese online gaming and cloud computing 199801 201212 GOOGLE INC search engine/social networking (orkut, dodgeball, jaiku, googlewave, googlebuzz, google+) 200505 201212 HURRAY HOLDING CO LTD media platform, online gaming and social networking 200502 201212 IAC INTERACTIVECORP search applications, online dating, vimeo, ask.com 199501 201212 JIAYUAN COM INTERNATIONAL Chinese online dating social network space 201105 201212 LINKEDIN CORP professional social networking 201105 201212 LYCOS INC search applications, gaming and online social chat 199604 200010 NETEASE COM INC Chinese social gaming 200006 201212 PERFECT WORLD CO LTD social gaming 200707 201212 QUEPASA CORP Latino social networking 199906 201212 RENREN INC Chinese social networking service 201105 201212 SINA CORP Chinese social networking service, personal and professional 200004 201212 SOHU COM INC Chinese content community and social networking 200007 201212 WEBZEN INC social gaming 200312 201007 YAHOO INC news, content and social chat, Yahoo 360, yahoo groups, flickr, tumblr 199803 201212 YANDEX N V Russian search engine and social network 201105 201212 YELP INC business reviews and online yelper social space 201203 201212 YOUKU COM INC Chinese video content library; internet television & youtube 201012 201212 ZYNGA INC social gaming 201112 201212

23

Table 2 Summary statistics

The table reports the number of sample, minimum, median, maximum, mean, standard deviation, skewness, and kurtosis of the weekly

returns of social media stocks and industries during the period from January 1, 1996 – December 31, 2012. We follow Kaplan and

Haenlein (2010) to identify the 31 social media firms.

Period

N Min Median Max Mean Standard

Deviation

Skewness Kurtosis

Panel A: Weekly returns of social media Firms

1996-2012 9584 -0.69273 0.00000 1.46281 0.00298 0.10017 1.59195 16.10817

1996-2000 1391 -0.45455 -0.00581 0.76136 -0.00027 0.13284 0.85937 3.99316

2001-2006 2762 -0.69273 0.00170 1.46281 0.00893 0.10997 2.69436 28.57905

2007-2012 5431 -0.45385 -0.00153 0.77519 0.00078 0.08376 0.79653 6.34920

Panel B: Weekly returns of firms in Industries with social media Firms

1996-2012 5110 -0.32296 0.00233 0.54537 0.00252 0.04945 0.57852 7.85148

1996-2000 616 -0.32296 0.00307 0.20087 -0.00145 0.05615 -0.56746 3.87407

2001-2006 1786 -0.24638 0.00091 0.54537 0.00463 0.04901 1.69225 13.88303

2007-2012 2708 -0.28048 0.00288 0.28879 0.00204 0.04804 0.22922 4.77798

24

Table 3 Regression results for the social media index

This table contains regression results for indices of social media Firms for the period January 1,

1996 and December 31, 2012. The social media index is formed by equally weighting (Panel A)

or by value weighting (Panel B) weekly raw returns to form the indices. A minimum of 5 firms is

required to form the index. The index returns are regressed on the risk factors (excess market

return, size factor and value factor). The coefficients illustrated reflect the coefficient on the

intercept and their associated t statistic. Weekly returns are calculated by subtracting the adjusted

stock price from CRSP from the previous week’s adjusted stock price and dividing by the

previous week’s adjusted stock price. Panel A displays the coefficients and t values for the

market model and Panel B displays the coefficients and the t values for the Fama French three

factor model. The risk factors are obtained from Ken French’s website.

Panel A: Equal-weighted index returns

Period

Model 1: Jensen's alpha

Model 2: Fama French three factor

1996-2012

0.00390 0.00416

2.12 2.37

1996-2000

-0.00070 0.00006

-0.12 0.01

2001-2006

0.00961 0.00996

3.15 3.31

2007-2012

0.00043 0.00008

0.26 0.05

Panel B: Value-weighted index returns

Period

Model 1: Jensen's alpha

Model 2: Fama French three factor

1996-2012

0.00035 0.00051

1.23 1.91

1996-2000

0.00168 0.00176

1.39 1.47

2001-2006

0.00026 0.00068

0.77 2.12

2007-2012

-0.00012 -0.00013

-1.24 -1.39

25

Table 4 Social media and industry portfolios

The table reports the mean, standard deviation, Sharpe ratio, maximum, minimum, skewness,

and kurtosis of weekly returns of an index of social media stocks and the Fama and French

(1997) industries during the period from March 11, 1998 – December 31, 2012. Returns are

annualized for ease of interpretation.

Industry Mean St Dev SR Max Min Skew Kurtosis Social media 0.2875 0.4355 0.58 0.2291 -0.1964 0.39 4.76 Agriculture 0.1598 0.2510 0.50 0.1280 -0.1183 0.39 7.18 Food products 0.1663 0.1543 0.85 0.0906 -0.0730 0.01 7.57 Candy & soda 0.1593 0.2297 0.54 0.1243 -0.0845 0.27 5.66 Beer & liquor 0.2145 0.1875 0.96 0.1513 -0.0668 0.98 11.35 Tobacco products 0.2538 0.2952 0.74 0.2779 -0.1448 1.03 18.63 Recreation 0.1614 0.2245 0.56 0.1504 -0.0938 0.26 6.73 Entertainment 0.1732 0.2355 0.59 0.0860 -0.0923 -0.06 4.72 Printing & publishing 0.1278 0.2653 0.35 0.1296 -0.1038 0.44 7.55 Consumer goods 0.1574 0.2060 0.59 0.1364 -0.0901 0.14 7.88 Apparel 0.2000 0.2270 0.73 0.1426 -0.0936 0.16 7.41 Healthcare 0.2159 0.1941 0.93 0.0712 -0.0788 -0.47 4.51 Medical equipment 0.2559 0.1869 1.18 0.0789 -0.0786 -0.35 4.81 Pharmaceutical 0.2521 0.2431 0.89 0.1101 -0.1128 -0.12 4.69 Chemicals 0.1751 0.2403 0.58 0.1376 -0.1132 -0.12 7.62 Rubber & plastics 0.2014 0.2411 0.69 0.3364 -0.1016 2.96 67.90 Textiles 0.0892 0.2671 0.20 0.1270 -0.1092 0.16 5.12 Construction

materials

0.1487 0.2181 0.52 0.0980 -0.0966 -0.15 5.73 Construction 0.2014 0.3182 0.52 0.4140 -0.1243 2.42 51.96 Steel works 0.1357 0.3091 0.33 0.1580 -0.1451 -0.11 7.43 Fabricated products 0.1451 0.3188 0.35 0.1440 -0.1155 0.08 5.57 Machinery 0.1874 0.2502 0.61 0.1153 -0.1172 -0.20 6.15 Electrical equipment 0.1519 0.2255 0.52 0.1205 -0.0847 -0.10 4.79 Automobiles &

trucks

0.1161 0.2793 0.29 0.1105 -0.1332 0.00 7.01 Aircraft 0.2190 0.2476 0.74 0.1240 -0.1001 0.07 5.52 Ship & tail 0.1899 0.2945 0.53 0.1248 -0.1088 0.04 4.58 Defense 0.2503 0.2855 0.75 0.2207 -0.0860 1.77 18.55 Precious metals 0.4080 0.4962 0.75 0.2809 -0.1726 0.88 7.32 Industrial metals 0.2276 0.3185 0.60 0.1305 -0.1099 -0.06 3.70 Coal 0.2442 0.4707 0.44 0.2236 -0.2052 0.00 7.42 Petroleum & natural

gas

0.2241 0.3169 0.60 0.2023 -0.1631 -0.27 9.10 Utilities 0.1216 0.1697 0.51 0.1327 -0.0811 0.13 14.10 Communications 0.1398 0.2707 0.39 0.1295 -0.0880 0.09 4.47 Personal services 0.1470 0.2211 0.51 0.0839 -0.0989 -0.02 5.16 Business services 0.1995 0.2003 0.82 0.0780 -0.0908 -0.34 4.50 Computers 0.2092 0.2717 0.64 0.1138 -0.1155 -0.04 3.77 Computer software 0.2361 0.2546 0.79 0.1261 -0.1281 -0.14 5.19 Electronic equipment 0.2189 0.2809 0.65 0.1006 -0.1119 0.05 3.03 Measuring/control

equip

0.2395 0.2245 0.91 0.0876 -0.0831 -0.11 3.37 Business supplies 0.1153 0.2420 0.33 0.1471 -0.1092 0.16 9.95 Containers 0.1415 0.2607 0.41 0.1193 -0.0988 0.01 4.38 Transportation 0.1385 0.2328 0.44 0.0971 -0.1303 -0.20 5.47 Wholesale 0.2014 0.1966 0.85 0.0954 -0.0984 -0.28 6.08 Retail 0.1778 0.2311 0.62 0.0885 -0.0995 -0.07 5.20 Restaurants/hotels 0.1927 0.1994 0.79 0.0803 -0.0818 -0.11 6.69 Banking 0.1258 0.1595 0.57 0.0877 -0.0737 0.22 11.30 Insurance 0.1606 0.2191 0.57 0.1456 -0.1256 0.10 13.50 Real estate 0.1808 0.2612 0.56 0.2157 -0.1175 0.82 17.05 Trading 0.1757 0.2255 0.62 0.1060 -0.1103 0.12 7.40 Almost nothing 0.1826 0.1879 0.79 0.0638 -0.0801 -0.18 3.61

26

Table 5 The optimal portfolios

The table reports the maximum Sharpe ratio (MSR) and the minimum variance (MV) of the

efficient frontiers with and without the social media (SM) stock portfolio in the industry

portfolios during March 11, 1998 – December 31, 2012. We also present the weight of SM

(wSM) for each of optimal portfolios with different levels of diversity.

MSR wSM

MV wSM

SS

With SM 1.635 0.0093

0.1371 0.0000

Without SM 1.603 0.0000

0.1371 0.0000

SS+ Diversity (5)

With SM 1.619 0.0069

0.1377 0.0000

Without SM 1.549 0.0000

0.1377 0.0000

SS+ Diversity (10)

With SM 1.532 0.0252

0.1456 0.0000

Without SM 1.468 0.0000

0.1456 0.0000

27

Table 6 GARCH Regression of macroeconomic factors on Sharpe ratios of social media stock

The table reports the GARCH regression results of the variables affecting the Sharpe ratio of

social media stocks ( ) using weekly data from the period between January 1, 1996 and

December 31, 2012. The regression models are 1,

1

,0 )(

tttj

J

j

itt uuxSR , and the

variance equation 1

2

1 ttt huh , where xj’s are the macroeconomic variables. The

maturity risk premium (MRP) is the difference in yield between 20-year Treasury bond and 3-

month Treasury bill, the default risk premium (DRP) is the difference in average interest rate

between Moody’s Baa and Aaa corporate bond, and VIX is the CBOE S&P 500 Volatility Index.

Sentiment is taken as the University of Michigan Consumer Sentiment Index. Panel A shows the

results of the regressions. The variance equations are reported in Panel B.

Panel A: GARCH Regression

Model 1 Model 2 Model 3 Model 4 Model 5

b z value b z value b z value b z value b z value

C -0.053 -0.691 0.055 3.047 0.143 7.030 0.068 3.304 -0.148 -3.044

MRP 1.029 1.688 -0.007 -0.011

DRP -8.380 -3.751

-8.661 -4.808

VIX 0.001 1.037

-0.001 -0.803 Sentiment 0.002 2.805

0.002 4.468

ut-1 0.762 12.963 0.779 13.605 0.764 12.830 0.780 13.753 0.763 13.439

Adj R sq 0.602 0.587 0.608 0.590 0.592

Durbin-Watson 0.870 0.879 0.877 0.898 0.874

Panel B: Variance Equation

0.001 1.409 0.002 1.367 0.001 1.483 0.002 1.367 0.002 1.536

0.338 2.083 0.311 1.852 0.346 1.903 0.305 1.878 0.379 1.965

0.491 2.150 0.404 1.304 0.383 1.261 0.414 1.393 0.355 1.257

28

Table 7 Granger causality tests

The table reports the results of pairwise Granger (1969) causality tests. The null hypotheses are

the variables (MRP, DRP, VIX and Sentiment) do not Granger cause the Sharpe ratio of social

media stocks (SRSM). We first check that the series are stationary by using augmented Dickey–

Fuller (1979) and Phillips–Perron (1988) tests and find that all series are stationary. Using both

the Akaike (1974) or Schwarz (1978) criteria, we set 12 lags as the optimum in the time-series to

test whether these variables Granger-cause SR. The F-statistics and probabilities are shown.

F value Prob.

MRP does not Granger Cause SRSM 0.385 0.968

DRP does not Granger Cause SRSM 2.155 0.016

VIX does not Granger Cause SRSM 4.667 0.000

Sentiment does not Granger Cause SRSM 2.008 0.026

29

Figure 1 Social media index vs. Nasdaq and S&P 500

Social media index plotted against the returns on the Nasdaq and the S&P500 Index. Returns are

computed weekly for the period May 1, 1996 – December 31, 2012. The social media index is

computed as an equal-weighted index of the 31 social media firms that comprise the sample.

30

Figure 2

Mean of Sharpe ratio of social media stocks

The mean of Sharpe ratios of social media stocks between the period January 1, 1996 and

December 31, 2012 are plotted. The Sharpe ratio is computed by using the previous 50 weekly

return. The long-term trend is illustrated and smoothed by the filter proposed by Hodrick and

Prescott (1997) for actual time-series (H-P).

31

Figure 3 Efficient Frontiers and Industry Portfolios

The efficient frontiers are formed for the 49 industry portfolios of Fama and French (1997) that

includes (and excludes) an equally weighed index of social media firms. Sharpe ratios are

computed using weekly returns for the period between March 11, 1998 – December 31, 2012.

The dashed line includes the social media index in forming the frontier and the gray line

excludes the social media index of firms.

32

Figure 4 Mean-variance efficiency curve with diversity and short-sales-prohibited

The figure shows the curves of Sharpe ratios with various degrees of diversity. The investment

(SS+DV(U)) is a portfolio with short-sales-prohibited and investing at least in U (i.e., 5 or 10)

assets.