are politically sensitive hedge fund managers...
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Are Politically Sensitive Hedge Fund Managers Better?
Honghui Chen, Alok Kumar, Yan Lu, Ajai Singh
March 2018
Abstract: This paper uses political sensitivity of hedge fund managers to identify managerial skill.
On average, hedge fund managers trade in anticipation of Presidential election outcomes and adjust
the political sensitivity of their portfolio accordingly. Importantly, managers who adjust their
portfolio political sensitivity more successfully generate significantly higher alpha than those who
are politically less sensitive. The superior performance of politically-sensitive funds is
economically significant and persists for about a year. We also find that funds with greater political
sensitivity are more likely to survive. Together, these results suggest that hedge-fund managers
who are more responsive to shifts in the political environment are better skilled.
Keywords: Hedge funds, political sensitivity, Presidential elections, portfolio adjustments,
performance evaluation, managerial skill.
JEL Codes: G11, G14, G23.
_____________ Please address all correspondence to Alok Kumar, University of Miami, School of Business Administration, Coral Gables, Florida 33124, USA. E–mail: [email protected]. Tel: +1–305–284–1882. Honghui Chen ([email protected] Tel: +1–407–823–0895), Yan Lu ([email protected]. Tel: +1–407–823–1237) and Ajai Singh ([email protected] Tel: +1-407-823-0761) are from the University of Central Florida, College of Business, 12744 Pegasus Drive, Orlando FL 32816, USA. We thank the seminar participants at the University of Central Florida and China International Conference in Finance 2017, Carina Cuculiza, and especially Zhi Da for their helpful comments. Chen, Lu, and Singh gratefully acknowledge the financial support from the SunTrust Endowment.
Are Politically Sensitive Hedge Fund Managers Better?
March 2018
Abstract: This paper uses political sensitivity of hedge fund managers to identify managerial skill.
On average, hedge fund managers trade in anticipation of Presidential election outcomes and adjust
the political sensitivity of their portfolio accordingly. Importantly, managers who adjust their
portfolio political sensitivity more successfully generate significantly higher alpha than those who
are politically less sensitive. The superior performance of politically-sensitive funds is
economically significant and persists for about a year. We also find that funds with greater political
sensitivity are more likely to survive. Together, these results suggest that hedge-fund managers
who are more responsive to shifts in the political environment are better skilled.
Keywords: Hedge funds, political sensitivity, Presidential elections, portfolio adjustments,
performance evaluation, managerial skill.
JEL Codes: G11, G14, G23.
1
1. Introduction
According to Hedge Fund Research, the industry’s assets under management (AUM)
jumped from just about $500 billion in 2005, to over $3 trillion as of December 2016. The
explosive growth in hedge funds’ AUM has naturally piqued the interest of financial economists.
In tandem with the industry’s substantial growth, related academic research addressing specific
questions has witnessed a sharp increase over the past decade.1 Several studies examine whether
hedge-fund managers have superior aptitude; and if indeed they do, whether it is feasible to identify
skilled fund managers.
In this paper, we propose a new method for identifying skilled hedge fund managers on an
ex ante basis. Our key innovation is to use changes in the political environment to detect
managerial skill. We are specifically interested in the active-change component of the “theta”2
around Presidential elections. We posit that portfolio adjustments made by hedge-fund managers
in response to changing political environment reflect managerial skill.
We examine this proposition by measuring hedge-fund managers’ response to changing
political environment around U.S. Presidential elections. We also calculate the impact of those
portfolio adjustments on the investment performance of their funds. Using the findings from these
tests, we assess whether skilled hedge-fund managers recognize the changing political climate and
adjust their portfolio holdings accordingly.
1 The evidence from prior studies supports the common perception that hedge-fund managers possess relatively superior investment picking abilities. Both Kosowski, Naik, and Teo (2007) and Chen, Cliff and Zhao (2012) find evidence suggesting superior skills of fund managers. Brown, Goetzmann, and Ibottson (1999), Edwards and Caglayan (2001), Aggarwal and Jorion (2010), and Ter Horst and Verbeek (2007) document persistence in hedge fund performance. Brunnermeier and Nagel (2004) document that hedge-fund managers possess both stock selection and market timing skills. Agarwal, Jiang, Tang and Yang (2013) show that hedge funds possess skills in selecting certain winning stocks and try to hide information about their holdings. 2 Theta is a measure of political sensitivity of a portfolio, i.e., changes made by fund managers through active buying and selling of individual stocks in their portfolios. See Section 2.3 for additional details.
2
To measure the changing political environment, we adopt the methodology developed in
Addoum and Kumar (2016) to estimate the aggregate political sensitivity (i.e., political theta) for
each industry portfolio. We find systematic time-series variation in aggregate political theta,
particularly around Presidential elections, and especially those Presidential elections that are
associated with a change in the political party occupying the White House. The change in political
theta is generally in the direction of the winning party.
Focusing on the portfolio decisions of hedge fund managers, we find that, in aggregate,
hedge-fund managers adjust their holdings in anticipation of as well as in response to Presidential
election outcomes. While these active portfolio theta adjustments may signal managerial skill, the
political theta of a portfolio could also change because of: (i) weight change resulting from changes
in the price of the portfolio’s assets, and (ii) changes in individual stock theta. To ensure that
portfolio adjustments reflect managerial skill, we investigate whether there are systematic and
substantial changes in the hedge fund portfolio theta.
To identify the active-change component, we develop a new method to decompose the
change in political theta for each fund into the three distinct components, as discussed above,
which include the measure of active portfolio theta adjustments. We find systematic and
substantial changes not only in the hedge fund portfolio theta, but also in the active-change
component of theta around Presidential elections. These changes are especially notable around
Presidential elections associated with a change in the incumbent party occupying the White House.
Next, we directly examine whether the systematic portfolio adjustments affect hedge fund
performance around Presidential election cycles. In each calendar quarter, we sort each domestic-
equity-focused hedge fund into quartiles by the change in its political theta (i.e., the active
component of change in theta). The funds in the top quartile exhibit greatest political sensitivity
3
while the funds in the lowest quartile exhibit the least amount of adjustments in response to
changes in the political climate. We measure the average alpha for each quartile portfolio.
We find that the top quartile of equally-weighted funds generate an average alpha of 49
basis points (bps) per month, whereas the lowest quartile generate an average alpha of only 30 bps
per month. The average alpha increases from 10 bps for the bottom theta change quartile to 47 bps
for the top theta change quartile in the four quarters around Presidential elections, and from 14 bps
to 69 bps around Presidential elections associated with a change in the political party. Sorting by
active-theta change produces similar results. These findings are all the more notable given that our
estimates of political theta are based only on long positions, while hedge funds often take short
position as well. Consequently, our results are likely understated.
The relation between alpha and the change in political theta for hedge funds remains
positive even when we control for other fund characteristics and risk factors that might affect fund
performance. This relation is only significant during Presidential election cycles, which suggests
that significant economic information is revealed during the period around Presidential elections.
Collectively, these results indicate that hedge funds that actively adjust their portfolio holdings in
anticipation of, or in response to, Presidential elections generate significant abnormal performance.
Our findings compare favourably with prior skill-related evidence of superior performance.
For example, Sun, Wang and Zheng (2012) find that funds with “Strategic Distinctiveness Index”
(SDI) in the highest quintile significantly outperform funds in the lowest quintiles by 3.5% in the
following year, adjusting for the Fung and Hsieh (2004) seven risk factors. Cao, Chen, Liang, and
Lo (2013) document that top liquidity timing hedge funds outperform the bottom ones by about
4.0-5.5% annually. Further, our results are consistent with the average performance documented
4
in the literature.3
In the last part of the paper, we use the average of four quarterly changes in political theta
around each Presidential election for each hedge fund as a proxy for its manager’s skill, and use it
to predict the fund’s alpha over the following two years. We find that our skill proxy can
significantly predict managerial alpha during the following year. Its ability to predict returns
beyond one year deteriorates quickly and becomes insignificant over the two-year window.
Since our analysis based on future alphas could potentially be subject to a survivorship
bias, we also examine the ability of our skill proxy to predict hedge-fund survival. The results are
consistent with our expectation that funds managed by better skilled managers survive longer. We
find that our skill proxy can significantly predict the hedge-fund survival rate for up to two years
after the election year. Specifically, hedge funds that actively adjust their portfolio theta are more
likely to survive the following two years. These findings further support our prediction that hedge-
fund managers’ ability to successfully adjust the portfolio theta is an ex ante indicator of skill.
In the last part of the paper, we investigate whether skills in exploiting changes in political
environment are restricted to domestic-equity-focused hedge funds only. Accordingly, we examine
the changes in political theta and active changes in political theta for other hedge funds and mutual
funds to evaluate their effect on performance relative to that of our sample of domestic-equity-
focused hedge funds. We find similar active changes in political theta for other hedge funds,
especially around party-switching presidential elections, suggesting these skills are not exclusive
to our sample of hedge fund managers. We find similar changes in overall political theta for mutual
funds, but their effect on performance is much weaker and statistically insignificant. This finding
3 Agarwal, Daniel and Nail (2009) combine four commercial databases from 1994 through 2002 and document an annualized alpha of 4.5% for the combined data. Ibbotson, Chen and Zhu (2011) show that their sample of all hedge funds free of survivorship and backfill bias, generates an average alpha of three percent per year from 1995 through 2009. Kosowski, Naik, and Teo (2007) document an average alpha of 42 basis points per month in their sample.
5
is consistent with the notion that hedge fund managers are better skilled.
These results are consistent with prior evidence that hedge-fund managers are responsive
to political news. Our findings contribute to this literature on the importance of political
information and the investment implications of such news.4
Our findings also enrich the existing evidence on the investment skill of hedge-fund
managers, which are conventionally thought to be manifested in the alpha, or at least some portion
of the total return that is not attributable to systematic risk.5 Our proposition predicts a spike in the
performance of hedge funds (their alpha) around changes in political power, for funds whose
managers employ information related to Presidential-election outcomes. The finding of
significantly higher alpha generated by funds that successfully adjust their political theta around
Presidential election cycles confirms this expectation.
The remainder of our paper is organized as follows. In Section 2, we provide a description
of our data and the estimation method. We present the main empirical results in Section 3, and
conduct various robustness checks in Section 4. In Section 5, we provide additional evidence in
support of our proposition. We conclude in Section 6 with a brief summary.
2. Data and Methods
In this section, we describe various datasets used in our empirical analysis. We also develop
a new method to decompose changes in political sensitivity into three distinct components.
2.1. Stock Price and Hedge Fund Holdings
4 See Hochberg, Sapienza, and Vissing-Jørgensen (2009), Karolyi (2009) and Gao and Huang (2016). 5 See Kosowski, Naik, and Teo (2007); Jagannathan, Malakhov, and Novikov (2010); Agarwal, Jiang, Tang and Yang (2013).
6
We obtain monthly stock returns, stock prices, stock shares outstanding and Standard
Industry Classification (SIC) codes from the Center for Research on Security Prices (CRSP).
Consistent with prior literature, we only consider common stocks with a CRSP share code of 10
or 11. We also obtain Fama-French factor returns, 48 SIC industry classifications, and 48 industry
monthly value weighted portfolio returns from Kenneth French’s data library.
Quarterly hedge fund stock holding data are obtained from Thomson Reuters institutional
holdings dataset compiled from 13F filings by various institutions. Form 13F provides position
level disclosure of all institutional investment managers with more than $100 million assets under
management (AUM). We then link hedge-fund management company holding data with monthly
stock price and returns from CRSP.
2.2. Hedge Fund Dataset
We use monthly net-of-fee returns and assets under management data reported in the TASS
dataset from January 1990 to December 2016 to evaluate hedge-fund performance. TASS starts
reporting its data in 1994. Hence, a survivorship bias could arise for certain funds that ceased to
exist before December 1993, because no information would be available on these funds. However,
such a bias should have little impact on our analysis because our focus is on data from January
1994 onward. Nonetheless, our analyses includes both live and dead funds.
Fung and Hsieh (2000; 2009) and Aiken, Clifford and Ellis (2012) discuss potential biases
that commercial hedge-fund data often suffer. It is generally known that, unlike other institutional
investors, hedge funds are relatively free of regulations. Reporting to commercial databases
including TASS is a voluntary decision. Therefore, there is a self-reporting bias. Incubated funds
with internal capital choose to report their performance data after accumulating successful tracking
records. As a result, funds that report their performance to commercial databases significantly
7
outperform non-reporting funds. To mitigate the incubation and the backfilling bias, in robustness
tests, we repeat the baseline analysis excluding the first 12-month’s performance of each hedge
fund.6
To understand the risk-adjusted abnormal return of hedge funds around political elections,
we control for the risks of hedge funds using the Fung and Hsieh (2004) seven-factor model,
throughout this paper. The Fung and Hsieh seven factors include three trend-following factors on
bonds, currencies and commodities; two equity-oriented risk factors, the Standard & Poor’s 500
index monthly total return and the difference of Russell 2000 index monthly total return and
Standard & Poor’s 500 monthly total return; two bond-oriented risk factors: the monthly change
in the 10-year treasury constant maturity yield and the monthly change in the Moody’s Baa yield
less 10-year treasury constant maturity yield.7
Since Form 13F disclosures contain only quarterly stock holdings of long positions, we
restrict the hedge fund investment style to be equity focused.8 The screening results in 1,279
domestic hedge funds in 450 fund families with equity-focused investment style. Table 1 presents
the summary statistics. We report time-invariant variables including management fee, incentive
fee, high-water mark, lockup period, redemption period, leverage dummy and AUM. Hedge-fund
performance is explained by a myriad of factors. These include fund incentives (Agarwal, Daniel
and Naik, 2009), share restrictions (Aragon, 2007) and size (Berk and Green, 2004). Time-variant
variables reported include raw returns, seven-factor alpha, total risk and idiosyncratic risk. For our
6 Some recent studies used hedge fund datasets combined from different vendors. Although combing multiple databases could increase the size of the fund universe, it does not necessarily mitigate the abovementioned biases. TASS itself has many advantages compared to other commercial datasets. It includes most fund characteristics, and the nonacademic version also offers detailed information on fund managers and management companies. TASS is also the largest component of the union dataset (if we count both unique and repetitive funds). 7 We download the trend-following factors from David Hsieh’s data library at http://faculty.fuqua.duke.edu/ ~dah7/DataLibrary/TF–Fac.xls. 8 We carefully screen the investment styles provided by TASS database, and include only hedge fund with investment style of “Equity Market Neutral” and “Long/Short Equity Hedge”.
8
sample, the average monthly raw return is 0.59% and the monthly seven-factor adjusted return is
0.49%. On the risk side, the total risk is 4.94%, while the idiosyncratic risk is 3.61% per month.
Average time-series performance and risk of funds within our sample period are largely consistent
with those in past literature.9
2.3. Changes in Political Sensitivity
We estimate political sensitivity using the method proposed in Addoum and Kumar (2016).
Specifically, we estimate political theta for each industry portfolio using the following time-series
regression:
, , , , ,( )i t f t i i mkt t f t i i tr r r r RepDummyα β θ ε− = + − + + (1)
where 𝑟𝑟𝑖𝑖,𝑡𝑡 is the industry portfolio returns. 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 is the Presidential party indicator, which
takes a value of one when the U.S. President is a Republican and zero when a Democrat is in the
White House. We estimate the model each month for a rolling window of past 180 months.
Next, we map the time-series estimation of 48-industry thetas to stocks that belong to the
industry to get the individual stock level sensitivity 𝜃𝜃𝑖𝑖.10 We define a stock as a “red” stock if 𝜃𝜃𝑖𝑖 >
0 and as a “blue” stock if 𝜃𝜃𝑖𝑖<0. The political sensitivity for the hedge-fund management company
(𝜃𝜃𝑝𝑝) is the value-weighted average of individual stock political sensitivity. To be specific, end of
quarter holdings are used to calculate 𝜃𝜃𝑝𝑝 for the next three months. For example, 𝜃𝜃𝑝𝑝 for March
2012 is based on holdings information from December 2011, and the June 2012 𝜃𝜃𝑝𝑝 is based on
holdings from March 2012. Therefore, our computation approach ensures that there is no look
ahead bias in the estimates of changes in 𝜃𝜃𝑝𝑝. Moreover, we recognize that hedge fund managers
9 For example, see Agarwal, Fos and Jiang (2014). 10 Our measure of theta is effectively the industry theta.
9
might also take advantage of political sensitivity changes by short selling stocks. However, 13F
holdings do not reflect short positions. As such, we may be underestimating the changes in hedge
fund political sensitivity, which would bias against our findings.
Our measure of theta change from September to December of each election year (including
2016) does not reflect potential portfolio adjustments by hedge fund managers after election
outcome is available.
Table 1 shows that the average 𝜃𝜃𝑝𝑝 is 0.096. Figure 1 illustrates the changes in 𝜃𝜃𝑝𝑝 over the
sample period. We observe that the changes in the average political sensitivity coincide with the
results of political election, especially during the election cycles where there is a change in the
incumbent party in the White House. For example, during the 2000 election cycle when President
Bush took office, political sensitivity jumped from -0.30 in June 2000 to -0.08 in June 2001, a 74%
increase. In contrast, political sensitivity dropped from the peak of 0.21 in June 2008 to 0.04 in
June 2009, an 83% decrease, during the 2008 election cycle when President Obama took office.
2.4. Decomposition of Political Sensitivity
To better assess whether the change in political sensitivity is at least partially caused by
active holding change, we develop a method to decompose Δ𝜃𝜃𝑝𝑝. The change of hedge fund political
sensitivity is:
( )
( )
( )
, 1 1
, ,1 1 1 1 1 1 1 1
, ,
, ,,1 1 1
, ,
1 11 1
11 1
p t it it it it
i t i tit it it it it it it it it it it it
p t p t
i t p ti tit it it it it it it
p t p t
w w
r rw w w w w w
r r
r rrw w w w
r r
θ θ θ
θ θ θ θ θ θ
θ θ θ
− −
− − − − − − − −
− − −
∆ = −
+ += − + − + − + +
−+= − + − + + +
∑
∑
( )
1 1
, p,,1 1 1 1 1
, p,
(r )11 1
it it
i t ti tit it it it it it it it
p t t
w
rrw w w w
r r
θ
θ θ θ θ
− −
− − − − −
−+= − + − + + +
∑
∑ ∑ ∑
,
10
where itw and itθ are the weight and θ of stock i at time t, respectively, ,i tr and ,p tr are the
return for stock i and portfolio from t-1 to t, 1itw − and 1itθ − are defined similarly.
The three distinct parts in the decomposition are:
(1) Changes in individual stock political sensitivity 𝜃𝜃𝑖𝑖: ( )1it it itw θ θ −−∑ ,
(2) Active holding changes by managers: ,1 1
,
11
i tit it it
p t
rw w
rθ− −
+− +
∑ , and
(3) Changes in weights due to stock price change: , p,1 1
p,
(r )1i t t
it itt
rw
rθ− −
−
+∑ .
3. Empirical Results
3.1. Decomposition of Changes in Political Sensitivity
In this section, we examine the quarterly changes in the political sensitivity of hedge funds,
as well as the components of these changes during each Presidential election cycle from 1996 to
2016. The political sensitivity at the management company level 𝜃𝜃𝑝𝑝 is the value-weighted average
of 𝜃𝜃𝑖𝑖 of all stocks held by the fund. As we show in the previous section, the quarterly changes in
political sensitivity can be decomposed into three parts: (1) changes due to 𝜃𝜃𝑖𝑖, (2) active holding
change by hedge-fund managers, and (3) changes due to stock price. Since part (1) and part (3) are
passive changes, we focus on part (2). If hedge-fund managers are able to correctly predict the
winning party, they would actively adjust their holdings to be in line with the political sentiment
because these stocks are expected to benefit from the changed political environment.11
11 Given that industrial political theta is assigned to individual stocks, active changes in stock weights are the same as the changes in industry weights. Our approach does not distinguish between stock selection skills and industrial skills.
11
During each Presidential election cycle, we calculate quarterly changes in political
sensitivity (𝛥𝛥𝜃𝜃𝑝𝑝) from the previous quarter and its three components for each quarter ending in
June, September, December, of the election year and in March of the year after election. We first
calculate the change for each fund family, and report the average for all the equity fund families
with valid estimates of (𝛥𝛥𝜃𝜃𝑝𝑝). We use two different approaches to average across a fund family.
The first is to weight 𝛥𝛥𝜃𝜃𝑝𝑝 with number of stock positions in the fund portfolio (count weighted).
The second approach uses asset under management (AUM) for each fund family as the weight
(value weighted).12
Table 2 reports the results. In Panel A, we find the number-of-position weighted change in
political sensitivity is large around Presidential election cycles, and is at least partially caused by
active change of hedge-fund holdings. For example, during the 2000 Presidential election when
the winning party is Republican, changes in political sensitivity are positive for all four quarters
examined, and active changes are all in the same direction.
More importantly, the economic magnitude of active change is quite large. For example,
𝛥𝛥𝜃𝜃𝑝𝑝 in from June 2000 to September 2000 is 0.074 and the active change is 0.021. 𝛥𝛥𝜃𝜃𝑝𝑝 from
September to December 2000 is 0.114 and active change component is 0.026. During 2008 election
cycle when the Democratic Party won the White House, changes in political sensitivity are
negative for three quarters, and active changes are negative for all four quarters. The economic
magnitudes of active change during 2008 election year are relatively small. During other election
years (without a change in the control of the White House), changes in political sensitivity 𝛥𝛥𝜃𝜃𝑝𝑝
and active change are not always in line with the direction of winning party, indicating weaker
12 Aragon and Martin (2012) document that option usage by hedge funds is informative and produce superior risk adjusted returns. Given our reliance on the stock positions reported in 13F, we may underestimate the true political sensitivity if hedge funds use options to take advantage of the changing political environment.
12
incentive to make portfolio adjustments based on political sentiment. Panel B reports changes in
value-weighted average political theta. Our results are robust to different weighting schemes for
the average.
Results from Table 2 suggest that, in aggregate, hedge funds actively adjust the political
sensitivity of their portfolios around Presidential elections when there is a change in the incumbent
party in the White House. Around other Presidential elections when there is no change in the party
in control, we do not observe a systematic aggregate change in political sensitivity of hedge funds.
However, given the heterogeneity in hedge funds, not observing systematic changes in aggregate
does not preclude the possibility that some hedge funds might still be successful in adjusting their
portfolio theta. Thus, in the next section we turn to examine how cross-sectional variation in
changes in hedge-fund political sensitivity affects fund performance.
3.2. Political Sentiment and Hedge Fund Performance
How do the changes in political sensitivity and active holding change affect hedge-fund
performance? To answer this question, we first sort hedge-fund management companies by
changes in political sensitivity 𝛥𝛥𝜃𝜃𝑝𝑝 and active holding change 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝 into quartiles for each
quarter. We then evaluate the portfolio performance for funds in each quartile.
Each month, we estimate out-of-sample seven-factor alphas (Fung and Hsieh, 2004) for
each domestic equity-focused fund, using a rolling 24-month window. Next, we obtain equally-
and value-weighted alphas at the management company level. Our choice of alpha at the
management company level is based on the fact that equity holding data are reported by hedge
fund companies. Since each hedge-fund company may manage multiple hedge funds, fund families
with multiple funds will be over represented if we merge the company level political sensitivity
data with individual hedge funds and analyze at the individual fund level.
13
To pool political sensitivity estimates and active change from different elections cycles for
our analysis, we define a conditional political sensitivity and active change measure 𝛥𝛥𝜃𝜃𝑝𝑝_𝐴𝐴 and
𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝_𝐴𝐴 following Addoum and Kumar (2016). Specifically,
𝛥𝛥𝜃𝜃𝑝𝑝_𝐴𝐴 = 𝛥𝛥𝜃𝜃𝑝𝑝 and 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝_c= 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝 when the winning party is Republican, and
𝛥𝛥𝜃𝜃𝑝𝑝_𝐴𝐴 = −𝛥𝛥𝜃𝜃𝑝𝑝 and 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝_𝐴𝐴=−𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝 when the winning party is Democratic.
Next, we sort 𝛥𝛥𝜃𝜃𝑝𝑝_𝐴𝐴 and 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝_c into quartiles and compute the average of company-level
alpha for each quartile. Since equity holding data are reported quarterly, we match quarterly 𝛥𝛥𝜃𝜃𝑝𝑝_c
and 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝_c with monthly alphas in the quarter. For example, we use holdings data from
December 1995 and March 1996 to compute ∆𝜃𝜃𝑝𝑝_𝐴𝐴 , 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝_𝐴𝐴 from March 1996 to June 1996,
and then link these changes to hedge fund performance in April, May and June 1996. We repeat
the analysis for the full sample during our sample period, for subsamples around elections and for
subsamples around party-change elections.
We present our results in Table 3. In Panel A for the full sample, we find a monotonic
increase in hedge fund performance along with the increase of 𝛥𝛥𝜃𝜃𝑝𝑝_𝐴𝐴 and 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝_𝐴𝐴. The time
series average of equally-weighted alpha is 49 basis points per month for the hedge funds in the
highest quartile of 𝛥𝛥𝜃𝜃𝑝𝑝, 19 basis points higher than that for the funds in the lowest quartile.13 Our
results are similar if we sort by active holding change (𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝_𝐴𝐴). The average equally-weighted
alpha is 47 basis points per month for the funds in the highest quartile, compared with the 35 basis
points for the funds in the lowest quartile, a difference of 12 basis points.14
13 To obtain statistical significance for the difference in alpha, we use the t-statistics for the time-series average of the differences. 14 The results based on value-weighted alpha are qualitatively similar. We observe that value-weighted alphas for each quartile are lower than the corresponding equally-weighted alpha. The difference in alphas for the highest and lowest quartiles is smaller as well. The smaller magnitude for value-weighted alphas relative to the equally-weighted alphas is consistent with the observation that smaller hedge funds generate higher alphas.
14
Next, we turn our attention to Panel B, which presents the alphas for the four political theta
change quartiles and active change quartiles around Presidential elections. We find that the
positive effects of active change on hedge fund performance are more significant. In general, the
average alpha increases from 10 basis points for the lowest theta change quartile to 47 basis points
for the highest theta change quartile. In Panel C, when we focus our analysis on the two party
changing election cycles during our sample period (years 2000, 2008 and 2016), we find that both
the levels and differences in alphas are consistently higher. For example, the average of the
equally-weighted alpha is 57 basis points for the highest quartile and 15 basis points in the lowest
quartile of active theta changes, a difference of 42 basis points. These results show that the effects
of political sentiments are strongest when there is a change in the control of the White House.
3.3. Political Sentiment and Hedge Fund Performance-Regression Results
It is possible that the sorting results are driven by other known factors that explain hedge-
fund returns. These include management fee, performance fee and assets under management
(AUM). Accordingly, we control for other factors in the multivariate regressions of hedge fund
company performance on change in political theta 𝛥𝛥𝜃𝜃𝑝𝑝_𝐴𝐴 and active change 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝_𝐴𝐴.
, 0 1 2 1 3 1 4 1, 3
5 6 7
8 9 10 11 , 1 ,
_ _
( )
p t t p t p t t t t
p p p
p p p p t p t
Alpha Election c c Election Alpha
ManagementFee PerformanceFee HighWaterMark
Leveraged Redemption Lockup Log Size
β β β θ β θ β
β β β
β β β β ε
− − − −
−
= + + ∆ + ∆ × +
+ + +
+ + + + +
, 0 1 2 1 3 1
4 1, 3 5 6
7 8 9
10 11 , 1 ,
_ _
( )
p t t p t p t t
t t p p
p p p
p p t p t
Alpha Election Active c Active c Election
Alpha ManagementFee PerformanceFee
HighWaterMark Leveraged Redemption
Lockup Log Size
β β β β
β β β
β β β
β β ε
− −
− −
−
= + + ∆ + ∆ ×
+ + +
+ + +
+ + +
15
where the dependent variable is the equally-weighted or value-weighted monthly alpha at the
management company level. Election takes a value of one if the month is from April of the election
year to March of the following year. _p cθ∆ is the conditional political sensitivity and
𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝_𝐴𝐴 is the conditional active change at the end of the previous quarter, as defined in section
3.2. Independent variables also include interactions of Election and 𝛥𝛥𝜃𝜃𝑝𝑝_c and 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝_𝐴𝐴. To
control for mean reversion, we include Alphat-1, t-3, which is the average alpha in the previous
quarter.
Fund characteristics are included as independent variables in the regression. All fund-
family level characteristics are equally-weighted or value-weighted averages of domestic equity-
focused hedge funds under their management. We include year-quarter fixed effect and cluster the
standard errors at the management company level.
The results are presented in Panel A of Table 4. Our key variable of interest is the
interaction term of changes in political sensitivity/active change and Election. We find that during
Presidential election, increase in political sensitivity and active change have a statistically
significant positive effect on hedge-fund performance. This finding suggests that skilled hedge-
fund managers predict election results successfully and adjust their holdings accordingly to boost
the performance of their funds. Our results are robust whether we use equally- or value-weighted
average.
We next explore if the positive effect is more significant during election cycles with party
change. To address this question, we estimate the following multivariate regression:
16
, 0 1 2 3 1
4 1 5 1
6 1, 3 7 ,
_
_ _p t t t p t
p t t p t t
t t p t
Alpha ElectionPartyChange ElectionNoPartyChange c
c ElectionPartyChange c ElectionNoPartyChange
Alpha FirmChacteristics
β β β β θ
β θ β θ
β β ε
−
− −
− −
= + + + ∆
+ ∆ × + ∆ ×
+ + +
, 0 1 2 3 1
4 1
5 1
6 1, 3 7 ,
_
_
_
p t t t p t
p t t
p t t
t t p t
Alpha ElectionPartyChange ElectionNoPartyChange Active c
Active c ElectionPartyChange
Active c ElectionNoPartyChange
Alpha FirmChacteristics
β β β β
β
β
β β ε
−
−
−
− −
= + + + ∆
+ ∆ ×
+ ∆ ×
+ + +
where ElectionPartyChange is an indicator variable that takes the value of one if the month is
during the election cycle of 2000 and 2008 and zero otherwise. ElectionNoPartyChange is an
indicator variable that takes the value of one if the month is during the election cycle of 1996, 2004
and 2012. We are interested in the coefficients of interaction terms between two Election indicator
variables and political sensitivity change, and between two Election indicator variables and active
holding change.
As shown in Panel B of Table 4, changes in political theta and active changes only have
statistically significant positive effects on hedge fund performance at ten percent or five percent
level when there is a party change. During elections without a party change, we observe an
insignificant effect.
3.4. Active Hedge Funds
In the previous section, we find a positive relation between changes in political sensitivity
of hedge funds and abnormal performance for hedge funds. However, we know little about the
hedge funds with large changes in political sensitivity around Presidential elections. In this section,
we examine characteristics of these funds.
17
We classify a fund as an “active” fund if the average rank of 𝛥𝛥𝜃𝜃𝑝𝑝_c and 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝_𝐴𝐴 of its
management company during an election cycle is in the highest quartile and as an “inactive” fund
if the average rank of 𝛥𝛥𝜃𝜃𝑝𝑝_c and 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝_𝐴𝐴 of its management company during the election cycle
is in the lowest quartile. We report our results in Table 5.
We find that active funds are not statistically different from inactive funds in terms of
management fees, performance fees, lock-up periods, redemption periods, leverage, and asset
under management. They tend to take on greater total risk relative to inactive funds. We do find
that total risk is 7.04% for the active funds, statistically significantly higher than that of 4.30% for
the inactive funds and that these funds are rewarded by superior overall returns. On average, active
funds deliver raw returns that are 107 basis points greater than those for inactive funds. Even after
adjusting for the known systematic factors, we find active funds generate abnormal returns that are
90 basis points higher per month. The active funds also have greater idiosyncratic risk, lower
proportion of high water mark feature and a shorter lockup period.
3.4.1. Political Inclinations
We next examine whether managers’ personal political inclinations affect their portfolio
political theta adjustment around Presidential elections. To do this, we follow Hong and
Kostovetsky (2012), and obtain information on political contributions from the Federal Elections
Committee (www.fec.gov) website, which contains all contribution data since 1979. After merging
the dataset of individual political contributions with the recipient party information, we are able to
obtain the donor’s name, address (city and state), employer, occupancy, donation date, donation
amount and recipient’s affiliated party. Using the names and address of hedge-fund managers’
management companies, we employ a two-stage matching method to search for each manager’s
political donations. In the first stage, we match by first name, last name, state and city. This
18
process results in 446 fund managers. In the second stage, we remove these 446 fund managers
from our pool, and further match by first initial, last name and confine the matches to be within
100 miles of geographical distance between reported home city and company city. This
accommodates situations where fund managers live in a different city/state from their offices (for
example, fund managers might live in New Jersey while their office is in New York City).
Next we manually compare the donors’ first names and exclude donors whose employers
are clearly not hedge funds.15 The second stage identifies political donations from an additional
198 fund managers, which gives us a total of 644 managers. We next restrict our political donation
sample to the managers who are associated with domestic equity funds that also have 13F holdings
data. These screens yield a final sample of 100 managers associated with 174 distinct funds.
We sum up all political contributions during our sample period 1994 to 2016 for each hedge
fund manager. We then compute the net political donation to Republican Party. A manager is
classified as “Politically red” if the net donation is positive and “Politically blue” if it is negative.
In results not tabulated here, we find that on average, the political theta of portfolios are positively
related to the political inclinations of the managers. In other words, “red” managers tend to hold
more “red” stocks in their portfolio and likewise for the “blue” managers. This finding is consistent
with Hong and Kostovetsky (2012).
We also find that regardless of their political inclinations, both “red” and “blue” managers
successfully adjust their portfolio holdings consistent with the outcome of the Presidential
elections. These results are consistent with our hypothesis that changes in portfolio political thetas
are an indication of superior skills. Moreover, such skills appear to be independent of managers’
15 For example, IDT Communications, Microsoft, AT&T can be clearly identified as not being hedge funds.
19
political inclinations. These results should be interpreted with caution because of the small sample
size for which the requisite data are available.
3.5. Additional Evidence
The regression analysis in Section 3.3 is based on the relation between the changes in
political theta and the fund returns over the subsequent quarter. While such evidence is consistent
with the notion that the managers for these funds are skilled, we look for additional direct evidence
in support of the hypothesis.
3.5.1. Persistence of Fund Performance
We first examine the relation between (active) changes in hedge fund political theta during
the election year and alphas for the funds over the following one and two years. If (active) change
in political theta around election is indeed an indicator of superior skills, we expect that the (active)
changes in hedge-fund political theta during the election year should be able to predict future fund
performance.
In Table 6, we use multivariate regression to analyze hedge fund performance persistence.
The dependent variables are the monthly company level seven factor alphas for the one year and
two years following each election year during our sample period. Average Δθp_c is the average
corrected political sensitivity over the four quarters for each hedge fund during each election year
in our sample period. Average ΔActivep_c is similarly defined. The independent variables also
include age of the fund company at event time and other fund characteristics.
Examining Table 6, we find that in the regression of alphas for the one year after election,
the coefficient for the average Δθp_c is 0.707, statistically significant at 1% level. The evidence
suggests that average change in political sensitivity strongly predict future fund returns for the
20
following year, providing further support for our hypothesis that such changes are an indicator of
managerial skills. The coefficient for the average Δθp_c decreases to 0.294 when we include the
performance from the second year following the election, suggesting that the ability of the election
year change in political sensitivity to predict future returns quickly disappears after one year. The
coefficient for the average ΔActivep_c displays a similar pattern. The persistence of the ability for
change in political sensitivity and active changes to predict abnormal performance over the
following year and the disappearance of such ability in year two are consistent with the evidence
from Ter Horst and Verbeek (2007), who confirm hedge fund performance persists for two to four
quarters.
3.5.2. Fund Survival
The evidence from the previous section shows that active funds are more likely to deliver
higher abnormal returns. However, such evidence suffers from a survivorship bias. That is, if some
of these active funds decide to close their funds on account of poor performance over the next year
or two, the poor performance that would otherwise be observed, would not be included in the
regression. To address this potential bias, we introduce the ability to survive the next year or two
as another measure. Funds managed by skilled managers are expected to survive longer. If the
average change in political theta for a fund during the Presidential election year is an indicator of
skill, we expect active funds to survive longer. We explore this question by estimating the
following logistic multivariate regressions:
0 1 2 3
4 5 6
7 8 9 , 1
_
( )
p p t p
p p p
p p p t p
SurviveNyear Average c Age ManagementFee
PerformanceFee HighWaterMark Leveraged
Redemption Lockup Log Size
β β θ β β
β β β
β β β ε−
= + ∆ + +
+ + +
+ + + +
21
0 1 2 3
4 5 6
7 8 9 , 1
_
( )
p p t p
p p p
p p p t p
SurviveNyear Average Active c Age ManagementFee
PerformanceFee HighWaterMark Leveraged
Redemption Lockup Log Size
β β β β
β β β
β β β ε−
= + ∆ + +
+ + +
+ + + +
where SurviveNyear is the indicator variable which takes a value of one if the fund company
survives at least N years after December of the election year and zero otherwise. Aget is the age of
fund companies in December of the election year.
We repeat the analysis for one year and two years and report the results in Table 7. We end
up with 506 company-election cycle observations for the one-year and two-year analysis, and find
that fund companies with higher average 𝛥𝛥𝜃𝜃𝑝𝑝_c and 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝_𝐴𝐴 are more likely to survive for the
one-year and two-year period after the election cycle. The regression coefficient is more significant
in the one-year survival analysis than in the two-year survival analysis. In untabulated results, we
find that fund companies are also more likely to survive for three years and four years after
election, but the statistical significance is only marginal.16
4. Robustness Tests
In this section, we conduct several additional tests to evaluate the robustness of our results.
We examine the performance based on stock holdings data. Furthermore, we employ alternate
measures of abnormal returns for hedge fund management companies.
4.1. Holding-based Alpha
16 This finding is consistent with the fact that the average half-life of hedge funds is only thirty months.
22
Our baseline results employ returns reported by the hedge funds. In this section, we
compute portfolio returns derived from the firm-level stock holdings reported in Thomson
Financial 13-F holdings data. Because the exact timing of the trades is unknown, returns are
estimated assuming that the trades take place at the end of the quarter. Before proceeding further,
we would like to note that returns derived from holdings data could underestimate the true returns
generated by the fund managers. Agarwal et al. (2013) show that hedge funds sometimes hide their
valuable holdings information from the 13F filings, and these secret holdings generate superior
performance. Aragon and Martin (2012) document that option usage by hedge funds, unreported
in Thomson Financial 13F database, typically generate superior performance. Kacperczyk, Sialm,
and Zheng (2008) term the difference between the mutual fund’s reported return and its holding
based return as the “return gap”, and suggest that it is a measure of skill.
Since returns derived from 13F filings are equity based, we employ the Carhart four-factor
model (Carhart, 1997) to generate abnormal returns, using a 24-month rolling window regression
method. Next, we repeat the analysis in Table 3 for the full sample, the subsample around elections
and the subsample around party change elections and report our results in Appendix A. We find
that while the differences in abnormal returns between the highest and lowest quartiles are all
positive, the magnitudes are much smaller. We do not observe the monotonic decreases of alphas
from the highest quartile to the lowest quartile of 𝛥𝛥𝜃𝜃𝑝𝑝_c and 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝_𝐴𝐴. The disappearance of a
significant relation using holding-based return is consistent with the possibility that these hedge-
fund managers are adding value via “unobserved actions” a la Kacperczyk, Sialm, and Zheng
(2008).
23
4.2. Additional Risk Factors
Recent literature shows that although the Fund and Hsieh (2004) seven-factor model has
significant explanatory power of hedge fund return, additional risk factors also exist. Mitchell and
Pulvino (2001) find that risk arbitrage generates excess returns due to the exposure to option-based
strategies. We repeat the analysis with two additional risk factors: out-of-the-money S&P 500 call
and put option-based factors. Additionally, we also re-estimate the model after adding the Pástor
and Stambaugh (2003) liquidity factor to account for the exposure to liquidity risk (Sadka, 2010;
Teo, 2011; Aragon and Strahan, 2012). Finally, we repeat our analysis by augmenting the seven-
factor model with MSCI Emerging Markets Index. Our results are robust to all these variations.
The coefficient estimates on the interaction terms of Active holding change and Election remain
economically and statistically significant.
4.3. Pre-Fee Returns
Monthly returns are reported after fees. To further confirm the value of active management
in the hedge fund industry, it is important to also estimate risk-adjusted alphas using pre-fee fund
returns. Since the fee contract includes performance fees and high-water mark, we need to make
some assumptions. Following Appendix A of Agarwal, Daniel, and Naik (2009) that capital leaves
the fund on a first-in, first-out basis, we re-estimate our baseline models. Our results are robust
when using alphas generated by pre-fee returns.
4.4. Incubation Period
It is generally known that hedge funds are relatively free of regulations. Reporting to
commercial databases including TASS is a voluntary decision. Therefore, there is a self-reporting
bias. Incubated funds with internal capital choose to report their performance data after
24
accumulating successful track records. As a result, funds that report their performance to
commercial databases significantly outperform non-reporting funds. To mitigate the backfilling
bias resulting from the incubation period, we repeat the baseline analysis by excluding the first 12-
month’s performance of each hedge fund. In un-tabulated results, we find that our results are robust
to the exclusion of the first 12-month’s performance.
5. Additional Evidence
In this section, we highlight our results by contrasting our results with the behavior and
performance of non-equity hedge funds as well as mutual funds. We also discuss our results in
light of the 2016 presidential election.
5.1. Non-Equity Hedge Funds
In this study, we focus on domestic equity hedge funds for two reasons. First, there is a
more direct link between active changes in equity holdings and abnormal returns generated by
reported returns. Second, the political theta changes are measured using stocks. In general, we
should expect the effect of political theta changes on non-equity hedge funds to be smaller.
However, to the extent that the non-equity hedge funds invest some portion of their portfolio in
equity securities, we might observe more aggressive active political theta adjustments around party
changing presidential election. Hence, the effect of active political theta changes on abnormal
performance around party-switching presidential election could still be positive and significant.
We repeat the analysis in table 3 using all other hedge funds and report the results in
Appendix B. Investment styles of hedge funds are more complicated than other investment
vehicles such as mutual funds and pension funds. For these funds, returns are mainly driven by
25
other strategic and tactical investment methods. The first two columns of Appendix B show the
effects of theta changes on abnormal performance for non-equity hedge funds to be smaller than
the corresponding results for the equity hedge funds, reported in Table 3. The last two columns of
Appendix B report the effect of active theta change on fund performance. Interestingly, the
magnitude of these effects is similar to the corresponding effect observed for the equity hedge
funds in Table 3, especially around party-changing presidential elections. This result suggests that
domestic equity hedge funds are not the only hedge funds who actively exploit the political
sensitivity effect around presidential elections.
5.2. Mutual Funds
In our analysis so far, we have only focused on hedge funds. However, as pointed out in
Section 2.3, hedge funds can use many different investment approaches. We recognize that hedge
fund managers might also take advantage of political sensitivity changes by short selling stocks.
However, holdings reported in 13F filings do not reflect short positions. As such, we may be
underestimating the changes in hedge fund political sensitivity, which would bias against our
findings of superior skills of hedge fund managers.
In contrast, mutual funds primarily take long positions in their equity investments, and thus
provide for better estimates of true theta changes. If mutual fund managers are as skilled as hedge
fund managers, we should expect theta changes to have similar or stronger effects on fund
performance.
We estimate mutual fund theta using data from Center for Research in Security Prices
(CRSP) survivor-bias free mutual funds database and Thomson Reuters Mutual Funds Holdings
26
data,17 using the same approach as for hedge fund. This results in 3,819 non-indexing equity
mutual funds. Since mutual fund holdings data are at fund level, we compute alphas at the fund
level for mutual funds, instead of computing average alphas sorted by theta changes and active
changes at fund family level for hedge funds.
We present our results in Figure A.1 and Appendix C. A side-by-side comparison of Figure
A.1 and Figure 1 shows a very similar pattern of changes in political sensitivity for mutual funds
and hedge funds. However, the response is much quicker and stronger for hedge funds. Further
comparison of results from Table 3 shows that both the theta changes and the active theta changes
for mutual funds have a weaker effect than those for hedge funds. Interestingly, the effect of active
theta change becomes positive, though not significant, around party-switching elections. These
findings suggest that mutual fund managers are not as skillful as their counterparts in the hedge
fund industry. These results are particularly noteworthy in view of the fact that we likely
underestimate changes in political sensitivities for hedge fund managers.
5.3. Election of 2016
The outcome of 2016 presidential election was contrary to the predictions by the majority
of the contemporaneous political polls. This event presents an interesting opportunity to evaluate
the validity of our measure of skill. Both theta changes and active theta changes are positive in
June, September, and December 2016, which indicates that hedge fund managers in aggregate are
correctly adjusting their portfolios in anticipation of the eventual outcome. Interestingly, these
changes are especially large from September through December 2016. 18 In contrast, the
17 Thomson-Reuters Mutual Fund Holdings database provides security holding information for all registered mutual funds that report their holdings with the SEC, plus 3,000 global funds 18 It is worth noting that our measure of theta change and active theta change does not reflect potential portfolio adjustments by hedge fund managers after election outcome is available.
27
corresponding changes for mutual fund are smaller and even negative in several instances. This
provides further support to our proposition that politically sensitive hedge fund managers have
superior skills.
6. Summary and Conclusion
In this paper, we develop and test the hypothesis that hedge-fund managers who actively
and successfully adjust the political sensitivity of their portfolios around Presidential elections are
skilled. We demonstrate that hedge-fund managers exploit the political environment around
Presidential elections and on average trade in anticipation, or in response to, the Presidential
election outcome by increasing their holdings of stocks that are more likely to benefit from (and
decreasing holdings of stocks that are likely adversely affected by) the new political environment.
Further, we find that hedge funds that are successful in adjusting their portfolio political sensitivity
generate higher alpha than those who are politically inactive. The better performance is more
pronounced around Presidential elections and is most significant during Presidential elections that
involve a switch in the political party incumbent in the White House.
We show that the average adjustment of political sensitivity around a Presidential election
continues to predict fund performance for up to one year after the election and hedge-fund survival
for up to two years. We document a positive correlation between managers’ political inclinations
and the political sensitivity of their portfolios. However, we find that regardless of their political
inclinations, hedge-fund managers successfully adjust their portfolio holdings consistent with the
outcome of the Presidential elections.
We find that these skills are not exclusive to our sample of hedge fund managers. We find
similar changes in overall political theta for mutual funds. However, their effect on performance
28
is much weaker and statistically insignificant, consistent with the notion that hedge fund managers
are better skilled.
Our results provide additional evidence supporting the information effects of Presidential
elections and indicate that skilled hedge-fund managers exploit political information around
Presidential election to generate superior performance. Further, our results demonstrate that
arbitrageurs may eventually correct the systematic mispricing induced around Presidential
elections. Hedge funds are often considered as one of the potential groups of astute arbitrageurs.19
They are expected to trade around Presidential elections. If hedge-fund managers take advantage
of the predictable return patterns around the Presidential election cycle, we would expect such
activity to be reflected in the hedge-fund performance. Our findings suggest that hedge-fund
managers indeed successfully exploit such opportunities and potentially correct the mispricing.
19 Fu and Huang (2016) find that the post-event drift documented in several earlier studies related to stock repurchases and seasoned equity offerings is not found in more recent time periods. Among other things, they ascribe the improved information environment to the increased presence and activity of hedge-fund managers in removing market inefficiencies and faster adjustment to intrinsic values. Such evidence suggests that hedge funds can arbitrage away mispricing in the stock markets and bring security prices back to their fundamental values.
29
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32
Figure 1: Plot of Political Sensitivity This figure plots the political sensitivity (θp) by hedge fund management companies from January 1994 through December 2016. Computation of company level political sensitivity is discussed in section 2.2.
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
1994
0319
9409
1995
0319
9509
1996
0319
9609
1997
0319
9709
1998
0319
9809
1999
0319
9909
2000
0320
0009
2001
0320
0109
2002
0320
0209
2003
0320
0309
2004
0320
0409
2005
0320
0509
2006
0320
0609
2007
0320
0709
2008
0320
0809
2009
0320
0909
2010
0320
1009
2011
0320
1109
2012
0320
1209
2013
0320
1309
2014
0320
1409
2015
0320
1509
2016
0320
1609
Political Sensitivity
Blue won
Red won
Red won
Blue won
Blue won
Red won
33
Table 1: Summary Statistics
Characteristics of hedge fund and political sensitivity 𝜃𝜃𝑝𝑝are presented in this table. Both management and performance fees are reported in percentage. High-water mark is an indicator variable which takes the value of one if the hedge fund uses high-water mark and zero otherwise. The lock-up period is in months if the records are non-zero. Redemption period is in days. Leveraged is an indicator variable which equals one if the hedge fund uses leverage and zero otherwise. We report the Fung and Hsieh (2004) seven-factor monthly alpha. Total risk is the standard deviation of raw monthly returns. Idiosyncratic risk is the residual standard deviation from the seven-factor regressions. Risk and alphas are estimated using a 24-month rolling window. The sample period is from January 1994 to December 2016. * Significant at the 10% level; ** Significant at the 5% level; *** Significant at the 1% level.
Variable Mean
Number of funds 1,279 Management fee (%) 1.41 Performance fee (%) 18.36 High-water mark (dummy) 0.72 Fraction of funds with lock-ups 0.29 Lock-up period (months) 12.46 Redemption period (days) 34.03 Leveraged (dummy) 0.59 Assets under management ($ million) 185.06
Returns (%) 0.59
Alpha (%) 0.49
Total risk (%) 4.94 Idiosyncratic risk (%) Political Sensitivity (𝜃𝜃𝑝𝑝)
3.61
0.096
34
Table 2: Decomposition of Changes in Political Sensitivity
This Table reports the change and decomposition of political sensitivity of hedge fund management companies during each election cycle from 1992 to 2016. Political sensitivity (𝜃𝜃𝑝𝑝) is defined in section 2.2. Changes in political sensitivity can be decomposed into (1) changes in stock 𝜃𝜃𝑖𝑖 (2) active change by hedge-fund managers 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅 and (3) stock price change. During each election cycle, 𝛥𝛥𝜃𝜃𝑝𝑝 and the decomposition are computed for four quarter-pairs during each election cycle: June- March, September-June, December-September and March-December. Panel A is the aggregate result weighted using total number of stocks used in theta calculation in each hedge fund family. Panel B is the aggregate result weighted using total asset value of each hedge fund family.
Panel A: Count weighted 𝛥𝛥𝜃𝜃𝑝𝑝 Change in Stock 𝜃𝜃𝑖𝑖 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅 Price Change
199206 June-March -0.048 -0.010 -0.029 -0.010 199209 September-June 0.046 0.038 0.009 -0.005 199212 December-September 0.015 0.011 -0.008 0.013 199303 March-December -0.046 -0.017 -0.017 -0.013 199606 June-March -0.040 -0.043 -0.001 0.004 199609 September-June 0.008 0.023 -0.006 -0.008 199612 December-September -0.013 -0.005 0.012 -0.020 199703 March-December -0.019 -0.027 -0.001 0.009 200006 June-March 0.023 -0.014 0.034 0.006 200009 September-June 0.074 0.004 0.021 0.050 200012 December-September 0.114 -0.005 0.026 0.093 200103 March-December 0.090 0.045 0.003 0.043 200406 June-March 0.000 -0.001 -0.002 0.002 200409 September-June 0.026 0.012 -0.002 0.015 200412 December-September 0.016 0.019 0.000 -0.003 200503 March-December 0.024 0.013 0.002 0.008 200806 June-March 0.049 0.016 -0.002 0.036 200809 September-June -0.098 -0.014 -0.010 -0.074 200812 December-September -0.049 -0.012 -0.009 -0.028 200903 March-December -0.047 -0.029 -0.005 -0.013 201206 June-March 0.002 0.018 -0.001 -0.015 201209 September-June 0.019 0.019 0.002 -0.001 201212 December-September 0.007 -0.012 0.004 0.015 201303 March-December -0.014 -0.015 0.004 -0.003 201606 June-March 0.004 -0.006 0.006 0.004 201609 September-June 0.010 -0.002 0.004 0.008 201612 December-September 0.024 -0.009 0.018 0.016
35
Panel B: Value Weighted
𝛥𝛥𝜃𝜃𝑝𝑝 Change in Stock 𝜃𝜃𝑖𝑖 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅 Price Change
199206 June-March -0.049 -0.010 -0.029 -0.011 199209 September-June 0.045 0.037 0.012 -0.008 199212 December-September 0.020 0.009 -0.002 0.013 199303 March-December -0.047 -0.020 -0.015 -0.013 199606 June-March -0.038 -0.041 -0.002 0.004 199609 September-June 0.005 0.021 -0.007 -0.009 199612 December-September -0.014 -0.004 0.011 -0.021 199703 March-December -0.018 -0.027 -0.001 0.009 200006 June-March 0.035 -0.022 0.041 0.017 200009 September-June 0.081 0.006 0.028 0.047 200012 December-September 0.089 -0.007 0.011 0.085 200103 March-December 0.092 0.046 0.002 0.045 200406 June-March -0.003 -0.002 -0.003 0.001 200409 September-June 0.023 0.011 -0.003 0.014 200412 December-September 0.016 0.018 0.000 -0.003 200503 March-December 0.025 0.015 0.002 0.009 200806 June-March 0.050 0.015 0.001 0.034 200809 September-June -0.093 -0.012 -0.010 -0.071 200812 December-September -0.051 -0.015 -0.009 -0.026 200903 March-December -0.042 -0.024 -0.006 -0.012 201206 June-March 0.001 0.018 -0.001 -0.016 201209 September-June 0.020 0.019 0.002 -0.001 201212 December-September 0.006 -0.012 0.003 0.014 201303 March-December -0.014 -0.016 0.005 -0.002 201606 June-March 0.004 -0.006 0.006 0.004 201609 September-June 0.010 -0.002 0.004 0.008 201612 December-September 0.024 -0.009 0.018 0.016
36
Table 3: Hedge Fund Performance sorted by Changes in Political Sensitivity and
Active Change
This table reports hedge fund seven-factor alphas (Fung and Hsieh, 2004) sorted by changes in political sensitivity 𝛥𝛥𝜃𝜃𝑝𝑝_c and active change 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝_𝐴𝐴. Alphas are estimated using a 24-month rolling window using returns of domestic equity funds from TASS. Changes in political sensitivity 𝛥𝛥𝜃𝜃𝑝𝑝_c and active change 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝_𝐴𝐴 are grouped into quartiles, from highest to lowest. Alphas of hedge fund management companies are computed using equally or value weighted alphas of hedge funds under their management. To obtain statistical significance for the differences, we first obtain the difference in the variables of interest, then obtain the t-statistics for the time-series average of the differences. * Significant at the 10% level; ** Significant at the 5% level; *** Significant at the 1% level.
Panel A: Full sample
Sort by 𝛥𝛥𝜃𝜃𝑝𝑝_c Sort by 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝_𝐴𝐴
Equally Weighted Value Weighted Equally Weighted Value Weighted
Highest 0.494% 0.419% 0.469% 0.395% 2 0.450% 0.417% 0.437% 0.391% 3 0.356% 0.279% 0.346% 0.302%
Lowest 0.304% 0.287% 0.351% 0.313% Highest-Lowest 0.190%*** 0.132%*** 0.118%*** 0.082%***
Panel B: Around Election
Sort by 𝛥𝛥𝜃𝜃𝑝𝑝_c Sort by 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝_𝐴𝐴
Equally Weighted Value Weighted Equally Weighted Value Weighted
Highest 0.469% 0.438% 0.425% 0.331% 2 0.323% 0.290% 0.408% 0.381% 3 0.274% 0.222% 0.211% 0.231%
Lowest 0.099% 0.111% 0.114% 0.111% Highest-Lowest 0.370%*** 0.327%*** 0.311%*** 0.220%***
Panel C: Around Party-change Election
Sort by 𝛥𝛥𝜃𝜃𝑝𝑝_c Sort by 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝_𝐴𝐴
Equally Weighted Value Weighted Equally Weighted Value Weighted
Highest 0.694% 0.670% 0.571% 0.487% 2 0.411% 0.396% 0.620% 0.606% 3 0.325% 0.303% 0.206% 0.283%
Lowest 0.139% 0.175% 0.152% 0.159% Highest-Lowest 0.555%*** 0.495%*** 0.419%*** 0.328%***
37
Table 4: Multivariate regression of political sensitivity and active change on hedge fund performance
Results from regression analysis of changes in political sensitivity and active change on fund performance are reported in this table. Dependent variables are management company level Fung and Hsieh (2004) seven-factor alpha, which are computed using equally weighted or value weighted alphas of hedge funds under their management. Election takes a value of one if the observation is from March of the election year to March of next year. 𝛥𝛥𝜃𝜃𝑝𝑝_c is the conditional political sensitivity. 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝_𝐴𝐴 is the conditional active change. The independent variables also include interactions of Election and 𝛥𝛥𝜃𝜃𝑝𝑝_c and 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝_𝐴𝐴 . Alphat-1, t-3 is the average lagged alpha of last quarter. The t-statistics, derived from standard errors clustered by hedge fund management companies, are in parentheses. The sample period is from January 1994 to December 2014. * Significant at the 10% level; ** Significant at the 5% level; *** Significant at the 1% level.
38
Panel A Equal weighted Value weighted (1) (2) (3) (4) Election 1.203** 1.193** 1.341* 1.369*
(2.003) (1.999) (1.844) (1.863) 𝛥𝛥𝜃𝜃𝑝𝑝_c 0.163 0.164 (0.755) (0.749) 𝛥𝛥𝜃𝜃𝑝𝑝_c ×Election 0.925** 0.896** (2.359) (2.308) 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝_c -0.009 0.004
(-0.046) (0.018) 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝_c ×Election 0.752** 0.696**
(2.108) (1.979) Alphat-1, t-3 0.006 0.006 0.005 0.005
(1.390) (1.385) (1.251) (1.245) Management Fee 0.102 0.103 0.060 0.062
(1.010) (1.020) (0.419) (0.431) Incentive Fee -0.002 -0.003 -0.012 -0.012
(-0.305) (-0.316) (-1.169) (-1.184) High-Water Mark 0.060 0.058 0.066 0.065
(0.612) (0.598) (0.500) (0.494) Leveraged 0.107 0.107 0.161 0.161
(1.358) (1.363) (1.440) (1.449) Redemption Period 0.003** 0.003** 0.002 0.002
(2.037) (2.021) (1.050) (1.033) Lockup Period 0.001 0.002 0.004 0.004
(0.208) (0.264) (0.401) (0.445) log(size) 0.058*** 0.058*** 0.073*** 0.073***
(2.863) (2.874) (3.383) (3.393) Year-Quarter fixed effect Yes Yes Yes Yes Firm level clustering Yes Yes Yes Yes R-squared 0.049 0.048 0.065 0.064 N 21,342 21,342 20,449 20,449
39
Panel B
Equal weighted Value weighted (1) (2) (3) (4) Election w/ party change 1.470** 1.448** 0.310 0.288
(2.214) (2.183) (0.713) (0.661) Election w/o party change 1.185** 1.172* 0.167 0.165
(1.981) (1.958) (0.499) (0.492) 𝛥𝛥𝜃𝜃𝑝𝑝_c 0.170 0.173 (0.811) (0.816) 𝛥𝛥𝜃𝜃𝑝𝑝_c ×Election w/ party change 0.993** 0.929** (2.165) (2.083) 𝛥𝛥𝜃𝜃𝑝𝑝_c ×Election w/o party change 0.116 0.148 (0.319) (0.401) 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝_c 0.010 0.026
(0.054) (0.139) 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝_c ×Election w/ party change 0.806* 0.723*
(1.934) (1.784) 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝_c ×Election w/o party change -0.194 -0.212
(-0.653) (-0.721) Alphat-1, t-3 0.006 0.006 0.005 0.005
(1.390) (1.384) (1.252) (1.246) Management Fee 0.100 0.101 0.058 0.059
(0.989) (0.999) (0.401) (0.412) Incentive Fee -0.003 -0.003 -0.012 -0.012
(-0.333) (-0.340) (-1.174) (-1.189) High-Water Mark 0.062 0.059 0.068 0.065
(0.637) (0.606) (0.516) (0.492) Leveraged 0.107 0.108 0.161 0.162
(1.355) (1.361) (1.442) (1.457) Redemption Period 0.003** 0.003** 0.003 0.002
(2.048) (2.040) (1.062) (1.059) Lockup Period 0.001 0.001 0.003 0.004
(0.161) (0.210) (0.372) (0.408) log(size) 0.058*** 0.058*** 0.073*** 0.073***
(2.855) (2.874) (3.369) (3.382) Year-Quarter fixed effect Yes Yes Yes Yes Firm level clustering Yes Yes Yes Yes R-squared 0.049 0.048 0.064 0.063 N 21342 21342 20449 20449
40
Table 5: Characteristics of Active Funds and Inactive Funds
This table reports hedge fund characteristics sorted by changes in political sensitivity and active change. A hedge fund is defined as “Active” if the average change in political sensitivity 𝛥𝛥𝜃𝜃𝑝𝑝_c or active change 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝_𝐴𝐴 of its management company during an election cycle is in the highest quartile. A hedge fund is defined as “inactive” if the average changes of its management company in political sensitivity or active change during an election cycle is in the lowest quartile. Both management and performance fees are reported in percentage. High-water mark is an indicator variable which takes the value of one if the hedge fund uses high-water mark and zero otherwise. The lock-up period is in months if the records are non-zero. Redemption period is in days. Leveraged is an indicator variable which equals one if the hedge fund uses leverage and zero otherwise. We report the Fung and Hsieh (2004) seven-factor monthly alpha. Total risk is the standard deviation of raw monthly returns. Idiosyncratic risk is the residual standard deviation from the seven-factor regressions. Risk and alphas are estimated using a 24-month rolling window. The sample period is from January 1994 to December 2016. * Significant at the 10% level; ** Significant at the 5% level; *** Significant at the 1% level.
Panel A: Sort by Political Sensitivity
Variable Active Inactive Active-Inactive
Number of funds 219 242 Management fee (%) 1.38 1.46 -0.08 Performance fee (%) 18.45 18.16 0.29 High-water mark (dummy) 0.67 0.75 -0.08** Fraction of funds with lock-ups 0.27 0.31 -0.04 Lock-up period (months) 11.70 12.72 -1.02 Redemption period (days) 32.58 34.41 -1.83 Leveraged (dummy) 0.65 0.58 0.07 Assets under management (US $m) 173.99 191.43 -17.44
Returns (%) 1.32 0.25 1.07* Alpha (%) 0.95 0.05 0.90*** Total risk (%) 7.04 4.30 2.74* Idiosyncratic risk (%) 4.57 3.02 1.55*
41
Panel B: Sort by Active Change Variable Active Inactive Active-Inactive
Number of funds 314 313 Management fee (%) 1.36 1.56 -0.20 Performance fee (%) 17.47 18.46 -0.99 High-water mark (dummy) 0.66 0.75 -0.09** Fraction of funds with lock-ups 0.20 0.25 -0.05* Lock-up period (months) 11.90 12.78 -0.88 Redemption period (days) 30.62 31.67 -1.05 Leveraged (dummy) 0.61 0.50 0.11 Assets under management (US $m) 147.12 166.85 -19.73
Returns (%) 1.55 0.22 1.33* Alpha (%) 1.04 0.16 0.88*** Total risk (%) 8.15 3.63 4.52* Idiosyncratic risk (%) 4.99 2.62 2.37*
42
Table 6: Persistence Analysis This table reports multivariate regression analysis hedge fund performance persistency. Dependent variables are monthly company level seven factor alphas, one year and two years after the election cycle. Average Δθp_c is the average corrected political sensitivity during this election cycle. Average ΔActivep_c is the average corrected active change during this election cycle. The independent variables include age of the fund company at event time and other fund characteristics. The t-statistics, based on standard errors clustered by hedge fund management companies, are in parentheses. The sample period is from January 1994 to December 2014. * Significant at the 10% level; ** Significant at the 5% level; *** Significant at the 1% level.
One year Two years One year Two years Average Δθ_c 0.707*** 0.294 (2.734) (1.410) Average ΔActive_c 0.823** 0.330 (2.478) (1.319) Fund company age at event time -0.002 -0.000 -0.002 -0.000 (-1.489) (-0.149) (-1.401) (-0.113) Management Fee -0.071 0.169 -0.067 0.170 (-0.491) (1.031) (-0.463) (1.033) Incentive Fee -0.009 -0.011 -0.009 -0.011 (-0.761) (-0.796) (-0.766) (-0.798) High-Water Mark 0.677*** 0.302* 0.685*** 0.306* (3.321) (1.930) (3.335) (1.934) Leveraged -0.068 -0.070 -0.063 -0.069 (-0.521) (-0.679) (-0.480) (-0.669) Redemption Period -0.001 0.002 -0.001 0.002 (-0.403) (1.171) (-0.361) (1.180) Lockup Period 0.000 0.004 0.001 0.004 (0.041) (0.486) (0.071) (0.500) log(size) 0.050* 0.017 0.047 0.016 (1.702) (0.594) (1.593) (0.552) Year Dummies (-1.510) (0.675) (-1.488) (0.702) R-squared 0.015 0.010 0.015 0.010 N 4831 9039 4831 9039
43
Table 7: Survival Analysis
This table reports multivariate logistic regression analysis of changes in political sensitivity and active change on hedge fund survival probabilities. Dependent variables are indicator variables that equal one if the fund company survives for more than one year and two years, and zero otherwise. Average Δθp_c is the average corrected political sensitivity during this election cycle. Average ΔActivep_c is the average corrected active change during this election cycle. The independent variables include age of the fund company at event time and other fund characteristics. The t-statistics, based on standard errors clustered by hedge fund management companies, are in parentheses. The sample period is from January 1994 to December 2014. * Significant at the 10% level; ** Significant at the 5% level; *** Significant at the 1% level.
One year Two years One year Two years Average Δθ_c 1.004*** 0.457*
(2.921) (1.872) Average ΔActive_c 1.205*** 0.423*
(3.457) (1.808)
Fund company age at event time -0.011*** -0.010*** -0.010*** -0.008***
(-4.740) (-4.761) (-4.513) (-4.232)
Management Fee -0.579 -0.473 -0.458 -0.497*
(-1.620) (-1.625) (-1.312) (-1.748)
Incentive Fee 0.011 -0.012 0.004 -0.022
(0.380) (-0.477) (0.150) (-0.877)
High-Water Mark 0.832** 0.618** 0.803** 0.535*
(2.256) (2.054) (2.279) (1.822)
Leveraged -0.437 -0.085 -0.236 -0.055
(-1.333) (-0.331) (-0.757) (-0.221)
Redemption Period -0.006 -0.004 0.001 -0.004
(-0.858) (-0.816) (0.086) (-0.776)
Lockup Period -0.029 0.004 -0.028 -0.005
(-1.258) (0.208) (-1.249) (-0.274)
log(size) 0.150** 0.131** 0.007 0.124**
(2.045) (2.188) (0.097) (2.129)
R-squared 0.092 0.057 0.085 0.048
N 506 506 506 506
44
Appendix A: Hedge Fund Performance by Sorting of Changes in Political Sensitivity and Active Change using Holding Based Alpha
This table reports hedge fund four-factor alphas (Carhart, 1997) sorted by changes in political sensitivity and active change. Alphas are estimated using a 24-month rolling window based on value weighted holding based portfolio returns reported in 13F. Changes in corrected political sensitivity (Δθp_c) and active change (ΔActivep_c) are grouped into quartiles, from highest to lowest. Δθp_c and ΔActivep_c are defined in section 2.2.
Panel A: Full sample
Sort by 𝛥𝛥𝜃𝜃𝑝𝑝 Sort by Active Change
Value Weighted Value Weighted
Highest 0.367% 0.222% 2 0.131% 0.171% 3 0.279% 0.325%
Lowest 0.126% 0.197% Highest-Lowest 0.241%*** 0.025%
Panel B: Around Election
Sort by 𝛥𝛥𝜃𝜃𝑝𝑝 Sort by Active Change
Value Weighted Value Weighted
Highest 0.836% 0.757% 2 0.396% 0.574% 3 0.539% 0.499%
Lowest 0.612% 0.582% Highest-Lowest 0.224% 0.175%
Panel C: Party Change Sort by 𝛥𝛥𝜃𝜃𝑝𝑝 Sort by Active Change
Value Weighted Value Weighted
Highest 0.933% 0.858% 2 0.066% 0.358% 3 0.643% 0.540%
Lowest 0.777% 0.725% Highest-Lowest 0.156% 0.133%
45
Appendix B: Hedge Fund Performance by Sorting of Changes in Political Sensitivity and Active Change using control funds
This table reports hedge fund seven-factor alphas (Fund and Hsieh, 2004) sorted by changes in political sensitivity and active change. Alphas are estimated using a 24-month rolling window using returns of not domestic equity funds (control group) from TASS. Changes in political sensitivity (𝛥𝛥𝜃𝜃𝑝𝑝) and active change (ΔActivep_c) are grouped into quartiles, from highest to lowest. 𝛥𝛥𝜃𝜃𝑝𝑝 and ΔActivep_c are defined in section 2.2. Alphas of hedgefund management company are computed using equally or value weighted alphas of hedge funds under their management.
Panel A: Full sample Sort by 𝛥𝛥𝜃𝜃𝑝𝑝 Sort by Active Change
Equally Weighted Value Weighted Equally Weighted Value Weighted
Highest 0.495% 0.466% 0.482% 0.469% 2 0.373% 0.377% 0.398% 0.386% 3 0.374% 0.369% 0.342% 0.356%
Lowest 0.397% 0.377% 0.416% 0.377% Highest-Lowest 0.098%** 0.089%** 0.066% 0.092%**
Panel B: Around Election
Sort by 𝛥𝛥𝜃𝜃𝑝𝑝 Sort by Active Change
Equally Weighted Value Weighted Equally Weighted Value Weighted
Highest 0.447% 0.417% 0.452% 0.406% 2 0.361% 0.332% 0.420% 0.369% 3 0.362% 0.409% 0.358% 0.389%
Lowest 0.203% 0.222% 0.220% 0.236% Highest-Lowest 0.244%*** 0.195%** 0.231%*** 0.170%**
Panel C: Party Change Sort by 𝛥𝛥𝜃𝜃𝑝𝑝 Sort by Active Change
Equally Weighted Value Weighted Equally Weighted Value Weighted
Highest 0.529% 0.497% 0.598% 0.629% 2 0.278% 0.355% 0.433% 0.395% 3 0.550% 0.640% 0.485% 0.615%
Lowest 0.326% 0.390% 0.166% 0.236% Highest-Lowest 0.203% 0.107% 0.432%*** 0.393%***
46
Figure A.1: Plot of Political Sensitivity of Mutual Funds This figure plots the political sensitivity (θp) by mutual fund management companies from January 1994 through December 2016. Computation of company level political sensitivity is discussed in section 2.2.
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
1994
0319
9409
1995
0319
9509
1996
0319
9609
1997
0319
9709
1998
0319
9809
1999
0319
9909
2000
0320
0009
2001
0320
0109
2002
0320
0209
2003
0320
0309
2004
0320
0409
2005
0320
0509
2006
0320
0609
2007
0320
0709
2008
0320
0809
2009
0320
0909
2010
0320
1009
2011
0320
1109
2012
0320
1209
2013
0320
1309
2014
0320
1409
2015
0320
1509
2016
0320
1609
Political Sensitivity
Bluewon
Red won
Red won
Bluewon
Bluewon
Red won
47
Appendix C: Mutual Fund Performance by Sorting of Changes in Political Sensitivity and Active Change
This table reports mutual fund four-factor alphas (Carhart,1997) sorted by changes in political sensitivity 𝛥𝛥𝜃𝜃𝑝𝑝_c and active change 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝_𝐴𝐴 . Alphas are estimated using a 24-month rolling window using returns of domestic equity funds from CRSP. Changes in political sensitivity 𝛥𝛥𝜃𝜃𝑝𝑝_c and active change 𝛥𝛥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑅𝑅𝑝𝑝_𝐴𝐴 are grouped into quartiles, from highest to lowest.
Panel A: Full sample Sort by 𝛥𝛥𝜃𝜃𝑝𝑝 Sort by Active Change
Highest -0.029% -0.005% 2 -0.005% -0.015% 3 -0.009% -0.009%
Lowest -0.009% -0.001% Highest-Lowest -0.020% -0.004%
Panel B: Around Election
Sort by 𝛥𝛥𝜃𝜃𝑝𝑝 Sort by Active Change Highest 0.027% 0.104%
2 0.004% -0.058% 3 0.004% 0.080%
Lowest -0.014% -0.037% Highest-Lowest 0.041% 0.141%
Panel C: Party Change Sort by 𝛥𝛥𝜃𝜃𝑝𝑝 Sort by Active Change
Highest 0.194% 0.316% 2 0.189% -0.015% 3 0.320% 0.449%
Lowest 0.161% 0.118% Highest-Lowest 0.033% 0.198%