Closed-End Investment Companies: Historic Returns and Investment Strategies

Abstract

Studies conducted in the past have identified inefficiencies in the market for Closed-End Investment Company (CEIC) shares. In addition, studies have demonstrated the potential for trading strategies to exploit these inefficiencies. The purpose of this paper is to investigate the possibility of achieving excess returns through the utilization of relatively simple strategies not requiring continuous monitoring of discount(s) or frequent trading. Our investigation demonstrates that realizing excess returns through the use of simple mechanical trading strategies will not be possible.

Full text

FINANCIAL SERVICES REVIEW. 7(2): 83-93 Copyright © 1998 by JAI Press Inc.
ISSN: 1057-0810 All rights of reproduction in any form reserved.
Closed-End Investment Companies: Historic
Returns and Investment Strategies
Carolyn Reichert and J. Douglas Timmons
Studies conducted in the past have identified inefficiencies in the market for Closed-
End Investment Company ( CEIC) shares. In addition, studies have demonstrated the
potential for trading strategies to exploit these inefficiencies. The purpose of this paper
is to investigate the possibility of achieving excess returns through the utilization of rel-
atively simple strategies not requiring continuous monitoring of discount(s) or frequent
trading. Our investigation demonstrates that realizing excess returns through the use of
simple mechanical trading strategies will not be possible.
I. INTRODUCTION
The purpose of this study is to test for Closed-End Investment Company (CEIC) market
inefficiency, and therefore the possibility of excess profits. Prior studies have developed
complex trading strategies that outperformed the market. Our concern is whether small
investors can expect to exceed market returns using uncomplicated techniques to select
funds. The plan to be tested is much like the "Dow Dogs" strategy of buying the five Dow
Jones Industrial stocks that have the highest yield. Investors who bought these Dow stocks
at the beginning of each year, without consideration of any other factors, have fared well
(U.S. News & World Report, 1996). Should CEIC investors who buy discounted funds at
the beginning of the year, and rebalance their investments after one-year holding periods
expect to profit?
This study evaluates four simple CEIC trading strategies. Using one year holding peri-
ods, the plans select funds trading at either maximum discounts or higher than average dis-
counts. The appeal of these investment strategies is their simplicity. They provide a longer
holding period and fewer transactions than previous studies. The initial investment is
smaller, and short selling is not required. This makes the strategies particularly appealing
to small investors, who are active in the CEIC market. These investors lack the time and
resources to follow the more complex systems developed in earlier studies. In fact, any
investor could easily duplicate these strategies.
Carolyn Reichert ° Southwest Texas State University. J. Douglas Timmons ° Department of
Economics and Finance, P.O. Box 27, Middle Tennessee State University, Muffreesboro, TN 37132.

84 FINANCIAL SERVICES REVIEW 7(2) 1998
II. LITERATURE REVIEW
Premiums and discounts on CEIC shares are determined by comparing the market price of
the shares to the Net Asset Value (NAV) of the fund' s holdings on a per share basis. If the
fund's market price is less than the NAV, then the fund sells at a discount; if it is greater
than the NAV, it trades at a premium. When the discount or premium exceeds a value that
can be adequately explained by market forces (Malkiel, 1977; Pratt, 1966), the departure
from NAV is deemed excessive and suggests market inefficiency in setting fund prices.
Thus, the size of the discount or premium is a key consideration in selecting CEIC shares.
Most closed-end funds sell at a discount, and this discount has persisted over time (Pontiff,
1994). This paper focuses on profitable trading strategies, rather than explaining the exist-
ence of the discount.
Several researchers have devised trading strategies that attempt to select profitable
funds based on the size of the discount or premium. Thompson (1978) reveals that funds
trading at a premium are bad investments. He creates portfolios that incorporate all funds
trading at a discount. He finds significant performance that is consistent across benchmark
portfolios, even after adjusting the returns for tax receipt distributions and reinvestment
options. Richards, Fraser, and Groth (1980) focus on two strategies: buy and sell trading
points and filter rules. Their strategies involve buying CEIC shares trading at a discount
and shorting CEIC shares trading at a premium. Using weekly rebalancing, all of their
strategies outperform the S&P 500. They find that the extreme buy and sell points and larg-
est filters produce the highest returns. Anderson (1986) extends their study to investigate
three different time periods. He demonstrates that only the buy and sell points provide
excess returns for all three periods. Brauer (1988) constructs a frequency distribution based
on the potential for open ending to get a cutoff for inclusion in the portfolio. His portfolios
outperform the S&P 500. Pontiff (1994) separates the funds into groups based on the size
of the premium. He finds that premium funds are bad investments. He shows that funds
with 20% discounts have expected twelve-month returns that are 6% greater return than
non-discounted funds.
Prior studies of closed-end funds have not explicitly considered transaction costs,
although Pontiff (1994) does state that the round trip transaction costs needed to eliminate
the abnormal returns in his study are 8.25% for buying securities and 3.13% for shorting
them. Pesaran and Timmerman (1994) use .5% and 1% rates in their paper on stock market
trading with transaction costs. This produces round trip transaction costs of 1% to 2% per
CEIC share. Taxes are another important consideration. Morris and Scanlon (1996), Malk-
iel (1977), and Kim (1994) examine the impact of taxes in explaining the discount. They
use a variety of tax rates including 25%, 31% and split rates (31% on dividends and 28%
on capital gains). They find that taxes are an important factor in explaining the fund's dis-
count.
Small investors are important in the closed-end fund market. Palomino (1996) hypoth-
esizes that small (or noise) traders may earn higher expected utility than rational investors
(by deviating from the Nash equilibrium strategy) because they create additional market
volatility. Thus, rational investors are reluctant to trade in small markets, and noise trader
risk persists. Some CEIC funds are thinly traded, and small investors have a greater impact
in these markets. The initial public offering (IPO) evidence supports the idea that small
investors are active in the closed-end fund market. An examination of closed-end fund
IPOs reveals no abnormal performance in the first two days of trading (Barry & Jennings,

Closed.End Investment Companies
85
1993). Subsequently, the price declines sharply as large traders sell to small "noise" traders
(Barry & Peavy, 1990; Weiss-Hanley, Lee, & Seguin, 1996).
The primary goal of any trading strategy is to outperform the market portfolio on a
risk-adjusted basis. Brickley and Schallheim (1985), Brauer (1984, 1988), Thompson
(1978) and Pontiff (1994) all use market and risk-adjusted returns (market model). How-
ever, there is some evidence that the market model may be inadequate for examining CEIC
funds. Thompson (1978) observes that the two-parameter market model does not describe
the return generating process for CEIC funds. Brickley and Schallheim (1985) find evi-
dence that the market model may be inadequate if new (uncertain) information is likely to
occur in the marketplace. Barry and Peavy (1990) discover that the IPOs of CEIC funds
have low betas in the first trading months due to extensive price stabilization. Beta
increases as funds season in the after market. Pontiff (1994) finds that beta increases as
fund premiums increase. If new information about the CEIC fund is contained in the pre-
mium, this could bias the market model results. Instead of using the market model, Rich-
ards, Fraser, and Groth (1980) and Anderson (1986) compare the return and standard
deviation of the proposed strategies to the S&P 500. This avoids some of the market
model's problems.
Closed-end funds that convert to open-end could bias the results. If the fund liquidates
or open-ends, the value of the fund's shares should return to the NAV. Brickley and Schall-
heim (1985) and Brauer (1984, 1988) present evidence that funds with higher than average
discounts are more likely to open-end. After the announcement date, the discount narrows.
Investors can earn abnormal returns if they buy the fund (even after the announcement) and
hold it until it is open-ended or liquidated.
III. STRATEGIES AND DATA
Previous studies use strategies with complex investing rules, weekly trading and rebalanc-
ing, large initial investments, or short selling. In contrast, small investors typically want
simple trading rules with a minimum of rebalancing. They lack the time for frequent trad-
ing and the money for large initial investments. Small investors are often encouraged to
buy assets and hold them for long periods of time to minimize transaction costs. Risk aver-
sion and limited investment knowledge deter them from shorting shares. This paper tests
four simple trading strategies to determine if small investors can earn abnormal returns
with minimal effort. They are variations of buy and hold strategies with annual rebalanc-
ing. This allows the investor to minimize transaction and monitoring costs. They require
five or fewer funds, reducing the investment size, and they do not involve shorting CEIC
shares.
Four investment strategies are tested: (1) single most discounted CEIC; (2) single most
relatively discounted CEIC; (3) five most discounted CEICs; and (4) five most relatively
discounted CEICs. For the first strategy, the investor selects the fund trading at the largest
percentage discount from the NAV. This is the most discounted CEIC. These shares are
purchased and held for one year. The process is repeated at the end of each year. If the year-
end calculations show that the most discounted issue has changed, then the original shares
are sold and the new most discounted shares are purchased, using all of the funds released
by the sale of the old issue.

86 FINANCIAL SERVICES REVIEW 7(2) 1998
The second strategy involves a similar process, except that each issue's discount is
compared to its five-year running average. The issue that is most discounted compared to
its average is the most relatively discounted CEIC. When five years of history are unavail-
able, the running average is based on available data. The third and fourth strategies are
multiple fund strategies that involve selecting the five most discounted or relatively dis-
counted funds. When using multiple funds, the investor holds an equal amount of all five
funds in a portfolio. Carryover funds are held without transactions. If a fund is replaced, the
proceeds from the sale of that fund are evenly distributed into the new funds placed in the
portfolio.
TABLE 1
F-Test for Equal Variances
Fund Name Classification Period of Inclusion
Adams Express Diversified 1971 - 1995
Advance Investors Diversified 1974-1976
America South Africa (ASA) Ltd. Specialized 1971 - 1995
American Utility Shares Specialized 1973-1978
Carriers-General Diversified 1971 - 1981
Central Fund of Canada International 1988-1990
Central Securities Corp. Specialized 1986-1995
Cypress Fund Specialized 1987-1990
Dominick Fund, Inc., The Diversified 197 l- 1974
Drexel Utility Shares Specialized 1973-198 l
Duff & Phelps Sel. Utility Specialized 1988-1990
Emerging Medical Specialized 1986-1987
First Australia International 1987-1995
General American Investors Diversified 1971-1994
Germany Fund International 1987-1995
Griesedieck Co., The Diversified 1971 - 1975
Highland Capital Corp. Specialized 1976-1982
International Holdings Diversified 1971 - 1975
Japan Fund, Inc., The International 1971-1987
Keystone Inc. Specialized 1973-1977
Korea Fund International 1986-1995
Lehman Corp., The Diversified 1971 - 1990
Madison Fund, Inc. Diversified 1971 - 1982
Malaysia Fund International 1988-1990
Mexico Fund International 1987-1990
National Aviation Corp. Specialized 1971 - 1979
Nautilus Fund Diversified 1980-1985
Niagara Share Corp. Diversified 197 l- 1990
Petroleum & Resources Corporation Specialized 1971-1995
Pilgrim Regional Specialized 1987-1995
Precious Metals Specialized 1975-1983
Providence Investors, Inc. Diversified 1971-1976
REIT Income Fund Specialized 1973-1980
S-G Securities, Inc. Specialized 1974-1979
Source Diversified 1975-1995
Surveyor Fund Specialized 1971 - 1973
Thai Fund International 1989-1990
Tri-Continental Corp. Diversified 1971 - 1995
U.S. & Foreign Securities Corp. Diversified 1971-1984
Value Line Development Specialized 1976-1979
Zweig Fund Diversified 1987-1995

Closed.End Investment Companies
87
If funds that subsequently liquidate or open-end have larger discounts than those that
do not, our strategies would tend to pick these funds. If we find positive abnormal returns,
it could be due to the influence of these funds on the results. Since the focus of the paper is
making abnormal profits using a simple trading rule, this would support the use of our rules
rather than detract from them.
The data set consists of funds from the Weisenberger
Investment Company Survey
(1965-1990) defined as "Specialized," "International" or "Diversified." Specialized funds
consist predominantly of securities from one industry or group of closely related industries,
or stocks of a specific type (i.e., letter stock). International funds invest chiefly in the secu-
rities of foreign companies. Diversified funds hold a well-diversified portfolio of invest-
ments. Due to changes in reporting by Weisenberger, the
S&P NYSE Stock Reports (1995-
1996) are used for the 1991-1995 data.
The final database includes 41 funds: 17 Specialized, 8 International, and 16 Diversi-
fied. A complete listing of the funds originally included in the database is provided in
Table 1. The data spans December 31, 1965, to December 31, 1995. Five years of data are
needed to construct the relatively most discounted strategies, thus all trading strategies
begin at year-end 1970 and end at year-end 1995. The data collected for each fund includes
classification, year-end price and discount, and distributions during the year. Capital gains
distributions are included as distributions. The year-end price is the actual transaction
price.
For informational purposes, results are provided for four "pooled" portfolios: (1) all
specialized funds; (2) all diversified funds; (3) all international funds; and (4) all funds
(aggregate). Since these portfolios are for comparative purposes and assume rebalancing
each year, they are assessed transaction costs annually at the full value of the portfolio.
This is necessary to avoid complex distribution decision processes that, in the end, would
appear arbitrary and would cloud the results of the strategies above.
For comparative purposes the S&P 500 Index is also presented. Data for the S&P 500
Index comes from the
S&P Security Price Index Record
and the
S&P Stock Market Ency-
clopedia. The
investor starts at year-end 1970 by purchasing the S&P 500 Index, which is
held for the entire sample period. Taxes and transaction costs are applied to the index (on
both capital gains and dividends). Annualized yields on three month T-bills are used to
estimate the risk-free rate. This information is obtained from
Business Statistics,
(1963-
91), and the Statistical Abstract of the United States (1996).
IV. METHODOLOGY
Annual holding period returns are computed as follows:
Where
Rl
Po
Pl
D1
(PI - P0) + Dl (1)
R1 = P0
= Annual return
= Price at end of previous year
= Price at end of current year
= Distributions made during the current year

88 FINANCIAL SERVICES REVIEW 7(2) 1998
Two types of costs are considered in computing the holding period returns: transaction
costs and taxes. Each time a fund is bought or sold, transaction costs are assessed. For sim-
plicity, a flat fee is applied to the entire amount purchased or sold by the investor. When
purchasing a fund, the investor pays an additional percentage B to the broker, and P0(1 +
B) is used in place of P0. When selling a fund, the investor loses percentage B to the broker,
and PI(1 - B) is used in place ofP 1. Two rates are tested: .5% for low transaction costs and
1% for high transaction costs.
Taxes are more problematic. The data spans a longer time period than previous studies
of taxes and CEIC funds, and individual tax rates vary considerably over the study period.
In order to evaluate trading strategies rather than tax rate changes, this study uses a fiat tax
rate of 40%. The flat rate allows consideration of the tax impact from trades without adding
variability from changing tax rates. The 40% rate accommodates the wide range of tax
rates on dividends and capital gains over the 1965 to 1995 time period (other tax rates are
also tested, with no significant difference in the results).
When a fund is sold or liquidated, capital gains (losses) are realized. For convenience,
capital losses are assumed to offset other capital gains (so the loss results in tax savings).
Brokerage fees are included in assessing the taxable amount of the gain (loss). Two differ-
ent methods are used to compute capital gains. The actual method uses the price change
over the fund's entire holding period. Since the tax is only paid when the fund is sold, there
could be complications with using the actual gain in computing annual returns. Annual
returns examine the annual change in price, but the taxable amount is based on a gain
earned over multiple years. To remedy this problem, the approximate method uses the
fund's price change over the last year to compute the taxable gain. Since both methods pro-
duce similar results, only the approximate method is reported. All distributions by the fund
are adjusted for taxes. For informational purposes, before-tax returns with no transaction
costs are also computed.
The annual returns are used to calculate both geometric and arithmetic means. For sta-
tistical analysis requiring an estimate of the mean, the arithmetic mean is used. The sample
standard deviation is calculated for each strategy to estimate overall risk. To assess market
TABLE 2
F-Test for Equal Variances
After-Tax Returns Before-Tax Returns
1% Transaction No Transaction
Costs Costs
Strategy Computed F-Value Computed F-Value
Most Discounted 1.67 3.89*
Most Relatively Discounted 3.46* 5.32"
5 Most Discounted 1.38 2.25**
5 Most Relative Discounted 1.60 2.13**
Specialized 0.79 2.12**
Diversified 0.52 1.39
International 1.88*** 5.07*
Aggregate 0.58 1.58
Notes:
*Significant at the 1% level (Critical F-value = 2.66).
**Significant at the 5% level (Critical F-value = 1.98).
***Significant at the 10% level (Critical F-value = 1.70).

TABLE 3
Summary Statistics for After-Tax Returns with 1% Transaction Costs
r~
,ig
Strategy
Trading Strategies:
Most Discounted
Most Relatively Discounted
5 Most Discounted
5 Most Relative Discounted
Pooled Portfolios:
Specialized
Diversified
International
Aggregate
Comparison:
S&P 500
T-Bills
Standard
Average Minimum Maximum Deviation Beta
13.87% -11.85% 78.88% 0.196 0.61
14.54% -27.47% 77.04% 0.282 0.79
12.84% -20.81% 47.74% 0.178 0.95
13.23% -20.69% 70.16% 0.192 0.82
7.57% - 19.29% 39.20% O. 134 0.54
7.09% -13.45% 26.08% 0.109 0.60
11.72% -21.89% 58.92% 0.208 0.82
8.15% -15.50% 32.56% O.ll6 0.63
10.85% -26.50% 34.00% 0.152 1.O0
2.79% 1.21% 5.63% 0.011
Coefficient of
Variation
1.41
1.94
1.39
1.45
1.77
1.54
1.77
1.42
1.40
0.38
Sharpe
Index
1.33
0.98
1.33
1.28
0.84
0.93
1.01
1.09
1.00
Geometric
Mean
12.40%
11.34%
11.47%
11.78%
6.78%
6.57%
9.95%
7.56%
9.78%
2.78%
Two Sample
t-statistic
0.6093
0.5766
0.4245
0.4868
-0.8102
-1.0069
0.1694
-0.7072
Ill
OO

TABLE 4
Summary Statistics for Before-Tax Returns with No Transaction Costs
Strategy
Trading Strategies:
Most Discounted
Most Relatively Discounted
5 Most Discounted
5 Most Relative Discounted
Pooled Portfolios:
Specialized
Diversified
International
Aggregate
Comparison:
S&P 500
T-Bills
Standard
Average Minimum Maximum Deviation Beta
21.70% -18.16% 136.04% 0.309 0.92
22.41% -24.24% 98.67% 0.362 0.94
20.97% -18.80% 67.92% 0.235 1.14
21.28% - 18.65% 81.89% 0.229 0.90
14.81% -30.83% 68.61% 0.228 0.88
13.97% -20.95% 46.25% 0.185 0.98
21.85% -35.31% 102.13% 0.353 1.24
15.78% -24.42% 57.29% 0.197 1.01
13.04% -24.35% 36.41% 0.157 1.00
6.97% 3.02% 14.08% 0.027
Coefficient of
Variation
1.43
1.61
1.12
1.08
1.54
1.33
1.62
1.25
1.20
0.38
Sharpe Index
1.54
1.38
1.92
2.02
1.11
1.22
1.36
1.44
1.00
Geometric
Mean
18.54%
17.60%
18.80%
19.34%
12.65%
12.53%
17.11%
14.16%
11.93%
6.94%
Two Sample t-
statistic
1.249
1.189
1.404*
1.486*
0.319
0.190
1.140
0.545
Z
>
Z
<
Note: Sigmficam at the 10% level.
<
co

Closed.End Investment Companies 91
risk, each strategy's beta is computed. The coefficient of variation and the Sharpe Index
allow investors to analyze return in conjunction with risk. Investors want a small coeffi-
cient of variation since it indicates a smaller standard deviation for a given return. In con-
trast, they want a large Sharpe Index because it means a better yield with respect to the
standard deviation. The Sharpe Index focuses on the risk premium, while the coefficient of
variation considers the total return. For each fund, the Sharpe Index is divided by the S&P
500's Sharpe Index to produce a relative index value.
It is important to determine if the proposed strategies outperform the market on a risk-
adjusted basis. The market model requires either a pre-event estimation period for deter-
mining beta or co-estimating beta and abnormal returns through the construction of portfo-
lios. Data limitations prevent either of these techniques from being used in this paper. In
addition, the market model may be inadequate for examining CEIC funds. Consequently,
this paper compares the return and standard deviation of the proposed strategies to the S&P
500. A two-sample t-test is performed to determine if the returns for the proposed strategies
are significantly different from the S&P 500.
V. RESULTS
Before performing the two-sample t-test, an F-test is used to determine if the S&P 500 and
the test strategy have equal variances. If the variances are unequal, it is necessary to adjust
the test statistic to reflect this fact. The results are presented in Table 2. Because transaction
costs of .5% and 1% produced virtually identical results, only the statistics for the higher
transaction costs are reported in Tables 2 and 3. Most of the after-tax returns have equal
variances to the S&P 500. However, the before-tax returns do not. When the two-sample t-
test for equal means are re-run assuming unequal variances, similar results occur. Only the
equal variance results are reported.
Table 3 contains summary statistics for the after-tax returns using transaction costs of
1%. The four trading strategies have higher arithmetic and geometric means than either the
S&P 500 or the pooled portfolios. All four also have higher standard deviations than the
S&P 500. Since the tested strategies require annual rebalancing while the S&P buy and
hold strategy does not, taxes and transaction costs are incurred more frequently. This could
confound the after-tax measurement of beta. The coefficient of variation and Sharpe Index
provide mixed results. Three of the trading strategies have a higher Sharpe Index than the
S&P 500. Since a higher Sharpe Index is desirable, this supports the use of these trading
strategies. With 1% transaction costs, the pooled portfolios have a higher coefficient of
variation than the S&P 500. Since investors want a lower coefficient of variation, this
result does not support the use of pooled portfolios. The two-sample t-test determines if a
trading strategy or pooled portfolio outperforms the S&P 500 on a risk-adjusted basis.
None of the t-statistics are significant, even at the 10% level. This suggests that none of the
simple trading strategies outperform the S&P 500.
The summary statistics for the before-tax returns (no transaction costs) are presented
in Table 4. Both the trading strategies and the pooled portfolios have higher arithmetic and
geometric means than the S&P 500. In addition, the geometric means of the trading strate-
gies exceeds that of the pooled portfolios. All of the trading strategies have a higher mini-
mum and maximum than the S&P 500. The S&P 500 has a lower standard deviation than

92 FINANCIAL SERVICES REVIEW 7(2) 1998
any of the tested strategies or pooled portfolios. Betas for the trading strategies range from
.90 to 1.14. Most of the results for the coefficient of variation are mixed, although the
pooled portfolios do have higher coefficients of variation than the S&P 500. This suggests
pooled portfolios should not be used. Both the trading strategies and the pooled portfolios
have Sharpe Indexes in excess of 1.0. This suggests the use of neither of these investing
methods. Thus, the coefficient of variation and the Sharpe Index provide conflicting evi-
dence on the use of pooled portfolios. The two-sample t-test is significant at the 10% level
for only two of the tested strategies. None of the t-statistics for the pooled portfolios are
significant. Although two of the trading strategies marginally outperform the S&P 500 on
a risk-adjusted basis, taxes and transaction costs erode away these marginal benefits.
VI. CONCLUSION
The most obvious differences between this study and others are the temporal distribution
of the data and the consideration of taxes and transaction costs. The strategies tested in this
paper are simple and relatively inexpensive. Investors need to purchase at most five funds
to implement the trading plan. Short selling is not used, and rebalancing occurs annually.
The purpose of the study is to determine if a simple trading strategy can yield superior per-
formance without substantially increasing risk.
The results do not support the use of a simple mechanical strategy. Even when margin-
ally significant before-tax returns are available, transaction costs and taxes erode the ben-
efits. Excess returns are not possible for investors lacking the time or resources to actively
trade in the marketplace. Small investors following simple trading rules with a minimum
of rebalancing are unlikely to earn the abnormal returns documented in earlier studies. This
should serve as a warning to investors lured by the promise of excess returns from CEIC
funds selling at discounts. It is important for small investors to be aware of the need for
additional monitoring, more frequent trading, larger initial investments, or short selling if
they want to use CEIC funds to outperform the market. Investors wanting to avoid these
complications should consider alternative investments.
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