1. Hybrid Fund Market Timing
By Ryan Marshall
MSc Finance & Investments
Student number: 440469
Coach: Egemen Genc
Co-reader: Florian Madertoner
Date of submission: 7th June 2016
2. i
Abstract
In this paper I examine the market timing ability of a sample of hybrid funds. In particular, I
investigate market timing ability with respect to stocks and to bonds. I use a new methodology that
combines a characteristic-based market timing measure for stock funds with an equivalent measure
for bond funds. With this new methodology I find that over the time period of 2000 to 2014, hybrid
funds exhibit significant evidence of market timing ability. Furthermore, this market timing abilit y
is primarily driven by balanced funds, with a smaller contribution also coming from the ability of
flexible portfolio funds. These findings are in line with the theory and the pre-existing literature.
The copyright of the master thesis rests with the author. The author is responsible for its contents.
RSM is only responsible for the educational coaching and cannot be held liable for the content
4. 1
1 INTRODUCTION
The mutual fund industry has grown significantly in recent years. The increasing size of
the industry has led to heightened scrutiny – largely based on the question of whether or not mutual
funds are a smart investment. There is a vast literature that questions if, and how, mutual funds do
indeed add value for their investors. A mutual fund’s investment portfolio can differ from its
benchmark portfolio in two general ways: through selectivity and market timing (or both).
Selectivity relates to picking individual assets that the mutual fund expects to outperform, whereas
market timing involves anticipating future factors that impact asset returns and rebalancing the
portfolio accordingly.
Because of this, the performance of a mutual fund can be divided into selectivity and
market timing ability. As suggested by Kacperczyk, Van Nieuwerburgh and Veldkamp (2014),
selectivity and market timing ability are not talents that one is born with. “They are the result of
time spent working, analysing data. Like workers in other jobs, fund managers may choose to
focus on different tasks at different points in time.” This means that some mutual fund managers
are better able to time the market than others.
The vast majority of the market timing literature is covered by equity funds, with much of
the remaining literature focusing on bond funds. Both older papers, Treynor and Mazuy (1966),
and newer papers, Jiang (2003), find no evidence of equity fund market timing ability. Similarly,
no evidence of bond fund timing ability is found by Ferson, Henry and Kisgen (2006). For hybrid
funds, for which we would expect to be superior market timers, the literature is sparse. Hybrid
funds are a special category of mutual fund that invests its assets amongst stocks and bonds. So
due to this, a hybrid fund is affected by more market factors (that is, factors affecting the returns
of stocks and bonds) than the average mutual fund, which is concerned only with market factors
that affect stocks or bonds. This perhaps indicates that market timing ability is more crucial for
hybrid fund performance than for the performance of other categories of mutual fund. We would
expect a hybrid fund to have superior market timing ability over equity funds and bond funds as
they have greater flexibility in allocating assets (in particular, shifting between stocks and bonds
– non-hybrids cannot do this) and because they should have a greater and wider knowledge of
market factors.
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Measuring the ability of hybrid funds to successfully time the market has profound
implications for the efficient market hypothesis. As we suspect that market timing ability may be
of huge importance for hybrid funds, there is a possibility that hybrid funds hint at a significant
anomaly for the efficient market hypothesis. For a mutual fund investor, it is clearly important to
identify fund managers with investment ability and to improve their own understanding of the
funds that they are investing in. Particularly for hybrid funds, where it has already been argued
that market timing ability is critical, investors want to identify this ability. For example, a risk
averse investor may look to invest in a hybrid fund because of the promise of lower volatility.
Because of this they seek a fund with market timing ability, in particular, the ability to mitigate
losses in a down market by reallocating assets. For other mutual funds it is of interest to know if
hybrid funds can successfully time the market. If they are successful, then managers of equity
funds or bond funds may be incentivized to expand their own portfolios and to become hybrid
funds themselves, in order to reap the same benefits that hybrid funds possibly receive (a wider
knowledge of market factors, for example).
Hybrid funds have grown in importance due to the rapidly increasing number of them in
existence over the last 20 years. Their growing importance and my prior argument that market
timing ability is, in theory, of more importance for hybrid funds than for equity funds or bond
funds leads to the main research question that I would like to answer:
Are hybrid funds good market timers?
In particular, how much ability do hybrid funds have to anticipate changes in market factors and
to appropriately shift assets into or out of stocks and bonds? Most mutual fund market timing
literature has focused on either equity funds or bond funds. However, there is a collection of works
that include hybrid funds as part of their more general fund sample. Comer (2006) lists some of
these papers: Treynor and Mazuy (1966), Kon (1983), Chang and Lewellen (1984), Henriksson
(1984), Lee and Rahman (1990), Chan and Chen (1992), Ferson and Schadt (1996), Bello and
Janjigian (1997), Becker, Ferson, Myers and Schill. (1999), Edelen (1999), Volkman (1999),
Goetzmann, Ingersoll and Ivkovic (2000), and Kosowski (2002). The caveat to this literature is
that the market timing models used in these papers look at the portfolio allocation between stocks
and cash, disregarding the bond holdings completely.
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To the best of my knowledge the only literature on market timing, specifically for hybrid
funds, is by Comer (2006). He finds evidence of significant market timing ability in hybrid funds
during the period 1992-2000, but no evidence of significant market timing ability in the period
1981-1991. Comer’s methodology involves a factor-based quadratic regression that measures the
convexity of the relationship between the excess return of the fund and the excess return of the
market. However, as the author himself acknowledges, outlining four potential problems, his
methodology is far from ideal when it comes to detecting market timing ability. This leaves a clear
gap in the literature to apply a different methodology – I apply a holdings-based methodology
formed from a combination of Daniel, Grinblatt, Titman and Wermers (1997) and Moneta (2009)
for measuring the market timing ability of hybrid funds.
In particular, I apply this holdings-based methodology for hybrid funds over the period
2000-2014.I obtain quarterly asset allocation data, including allocations for different types of bond
(government, corporate, etc.) and cash on all hybrid funds from the CRSP Mutual Fund database.
For the equity portion of the funds, I also obtain quarterly stock holdings data from CRSP, as well
as from the Thomson Reuters Mutual Fund Holdings database.
I find that hybrid funds are indeed good market timers, in particular, they exhibit
significantly positive market timing ability over the sample period. This finding is in line with both
the theory and the prior literature. Ability to time stock markets and bond markets both contribute.
I also find the surprising result that balanced funds are better market timers than the less restricted
flexible portfolio funds. The finding of hybrid fund market timing ability has real economic
significance and must encourage fund managers to consider a move to the hybrid fund model for
the benefits of a wider knowledge of market factors and the greater flexibility of more investment
instruments. My findings also put more doubt on the efficient market hypothesis.
The rest of this paper is as follows: Section 2 provides the literature review. Section 3
introduces the data used. Section 4 contains an in-depth description of the methodology applied.
Section 5 presents the empirical results. Section 6 concludes the paper. Finally, included at the end
of the paper is the reference list and the appendix.
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2 LITERATURE REVIEW
In this section I start by clearly defining what a hybrid fund is, and what separates them
from other categories of mutual fund. Then, I introduce the concept of mutual fund performance –
what it is and what encompasses it. Focusing in on market timing specifically, I look at the
literature that discusses the market timing ability of equity funds and bond funds separately, as it
is vast and wide-ranging when compared to the hybrid fund literature. This will give a solid
background before starting the discussion on hybrid fund market timing. This discussion will allow
a critical evaluation of the hybrid fund market timing literature, particularly regarding the available
methodology, and will allow me to hypothesise on what kind of market timing ability we should
expect from a hybrid fund.
2.1 HYBRID FUNDS
Hybrid funds are a special subset of mutual funds that focus on trading both stocks and
bonds, whereas most mutual funds tend to focus on just one or the other. Hybrid funds can be
further separated into two different categories: balanced funds and flexible portfolio funds. The
difference between these two categories of hybrid fund comes from differing investment objectives
and contrasting levels of market timing aggressiveness. Balanced funds tend to have a relatively
fixed proportion of stocks and bonds in their portfolio. In doing this, balanced funds look for a
combination of income growth and risk aversion. In fact, to be classified as a balanced fund, a fund
must maintain at least 25% of its assets in bonds at all times. Despite this, as Comer (2006) reasons,
balanced funds do actively engage in market timing, generating high portfolio turnover. On the
other hand, flexible portfolio funds seek to maximize the highest possible total return at any given
time, often with no restrictions. Due to this, they may change their portfolio composition at any
given time, shifting between stocks and bonds whenever appropriate to realize their goals.
I note that hedge funds share some similarities with hybrid funds. Hedge funds hold
portfolios invested in various asset classes and actively look to time the market. However, a big
difference between the two types of fund is that hedge funds often hold derivatives in their portfolio
and have a tendency to take substantial short positions – at a contrast to hybrid funds. I leave the
discussion of hedge funds here.
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2.2 FUND PERFORMANCE
Before I can discuss mutual fund performance at depth it is important to note that there are
two main ways that a mutual fund’s portfolio can differ from a benchmark portfolio, as touched
on in Section 1. Firstly, a fund can differentiate its portfolio from that of a benchmark through
selectivity. For example, in an equity fund, the manager can select different stocks from those that
make up the benchmark portfolio. Therefore, mutual funds attempt to apply their selectivity ability
to successfully pick well-performing assets. The second way that a fund portfolio may differ from
its benchmark is through market timing. A fund could hold the exact same portfolio as in the
benchmark – exact same assets and in the exact same proportions – but the timing of asset
purchases and sales could vary. For example, at the start and end of a given quarter the fund
portfolio and benchmark portfolio could be identical. However, it could be the case that within this
quarter, the fund sold some of the assets before purchasing them back, or perhaps it increased the
portfolio weight on a certain asset before decreasing the weight again. That is, market timing
entails rebalancing the portfolio between different securities and different asset classes to increase
the weight on the best performing assets.
So, a fund portfolio can differ from a benchmark portfolio through selectivity and market
timing (or a combination of the two). Because of this, we can separate mutual fund performance
into selectivity performance and market timing performance. Isolating and separating these two
drivers of performance is a large part of the mutual fund performance literature. Wermers (2006)
reasons that it is important to measure fund management ability before expenses and costs as a
fund may hold performance talent but is too small-scale and hence, too expensive to support. Fund
returns before costs and fees can be benchmarked, leaving a measure of fund performance – the
ability of the fund to select assets plus the ability of the fund to time the market. Of course,
hypothetical returns based on the holdings of mutual funds overestimate the returns left for
investors. However, they are fitting for measuring the ability of mutual funds to select securities
or time the market as they are benchmarked against portfolios that also ignore transaction costs
and other expenses. The difficulty then comes from separating these two elements of performance.
One method of separating market timing ability from selectivity ability is the return gap of
Kacperczyk, Sialm and Zheng (2008). The return gap is defined as the difference between the
returns of a hypothetical portfolio containing the same securities held by a mutual fund, minus the
disclosed returns of the mutual fund. The authors argue that this measures the “unobserved actions
9. 6
of mutual funds.” One particular “observed action” that does not impact the return gap is selectivity
ability, as both portfolios, hypothetical and actual, are identical. The differences come from market
timing ability, expenses, trading costs, investor externalities and agency costs. Investor
externalities and agency costs can be argued to be part of market timing ability as they affect the
flexibility of the fund to invest as they so desire. Therefore, one way that the market timing ability
of a mutual fund can be measured is by computing the size of the return gap and subsequently
subtracting expenses and trading costs. This can be done at every point the fund discloses its
returns and holdings, allowing us to observe a time series of market timing ability.
Another approach is to attempt to measure market timing ability directly. One such way is
the Characteristic Timing methodology of Daniel et al. (1997). This methodology was originally
introduced for measuring the market timing ability of equity funds and was later extended by
Moneta (2009) for use with bond funds. This characteristic-based approach uses benchmark
portfolios that were constructed to replicate the characteristics of different assets held by a mutual
fund. Doing this, the authors are able to decompose the performance of a fund into Average Style,
Characteristic Selectivity (𝐶𝑆) and Characteristic Timing (𝐶𝑇), where 𝐶𝑆 + 𝐶𝑇 is the performance
added by fund managers. The 𝐶𝑆 measure works by detecting whether or not the fund can select
stocks that outperform the average stock with the same characteristics. On the other hand, the 𝐶𝑇
measure works by detecting whether mutual funds time their portfolio weights appropriately on
these characteristics.
One more methodology that attempts to measure market timing ability directly is that of
Treynor and Mazuy (1966). Their model of market timing considers the convexity of the
relationship between a fund's return and the return of a benchmark as a measurement of ability to
time the market. The assumption behind this model is that a mutual fund uses its superior
information about the future of different market characteristics to adjust the market exposure of its
portfolio.
With several methods of isolating and measuring market timing ability in place, I will take
a look at how some previous works have applied them. The goal of this paper is ultimately to
measure market timing ability and so this is the focus of coming discussions. But before I do this
I briefly touch on what the literature has to say regarding the extent of selectivity ability.
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2.2.1 Selectivity
Most of the mutual fund performance literature focuses on equity funds, and further, it
concentrates on the ability of these funds to pick the right stocks. Both Grinblatt and Titman (1989,
1993) and Wermers (1997) consider performance before fees and expenses are deducted. They
find that mutual funds do have an ability to successfully pick stocks that outperform their
benchmarks. Considering after-expenses performance, Malkiel (1995) and Gruber (1996) find the
disappointing result that stock-picking ability is not significant enough to eclipse fees and expenses.
Using their characteristic-based performance measure, Daniel et al. (1997) show that stocks that
are picked by mutual funds outperform a characteristic-based benchmark, where the level of
outperformance is close to the fund management fee. Another disappointing result is that Grinblatt,
Titman and Wermers (1995) find that the majority of mutual funds use momentum as a stock
selection criterion, rather than having a superior ability to pick winning stocks. More recent papers
such as Brand et al. (2005), Kacperczyk, Sialm and Zheng (2005) and Cremers and Petajisto (2009)
all show that the divergence of the fund’s portfolio composition from that of the fund’s benchmark
index actually enhances fund performance, hinting at selectivity ability. Moneta (2009) examines
bond funds specifically and finds that bond funds have an ability to select and hold securities that
outperform their benchmarks, again, almost by enough to cover fees and expenses. Findings on
selectivity ability appear consistent.
2.2.2 Market Timing
Before evaluating what the literature has to say about market timing ability I will quickly
make the definition of market timing more explicit. I consider market timing ability as the ability
to successfully use superior information about future factors that impact asset returns to
successfully rebalance the portfolio among different asset classes. Specifically, Moneta (2009)
says that market timing ability is the ability of the fund to increase (decrease) the portfolio
investment on an asset class that is likely to perform better (worse) during the subsequent months.
Note that I used the term “superior information,” Ferson and Schadt (1996) claim that if mutual
funds can successfully time the market using publicly available information then it may not be
appropriate to view this as superior performance. An example of market timing would be a fund
altering the proportion of its portfolio dedicated to bonds in anticipation of an interest rate change,
that is acting on a prediction of a market-wide factor.
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There is a significant literature on mutual fund performance, most of it focused on equity
funds – with a proportion dedicated to measuring market timing ability. In the context of an equity
fund, market timing ability can largely be considered as holding stocks when they outperform cash,
and holding cash when it outperforms stocks (and also moving in and out of individual stocks
when appropriate). Sensoy and Kaplan (2005) point out that there are at least two reasons why
equity funds may not be successful at market timing. Equity funds may have market timing ability
from the sense of being able to successfully predict future market factors but may not be able to
change the cash proportion of their portfolio to exploit this knowledge. Firstly, inflows and
outflows by fund investors affect the proportion of cash – there is a lag before the fund can invest
new money in stocks or sell stocks to replenish the cash balance. Secondly, many fund companies
restrict their managers from holding excess cash balances.
One of the first known empirical studies on equity fund market timing ability was by
Treynor and Mazuy (1966). Their TM measure computes market timing ability using a factor-
based quadratic regression, which was explained earlier. Using this methodology, Treynor and
Mazuy find no evidence of stock timing ability in 1953-1962. The TM model laid down the
foundations of the market timing literature, leading to different extensions and applications. This
ranged from a conditional version that separates a manager’s response to public and private
information, Ferson and Schadt (1996) and Becker et al. (1999), to a state-dependant version that
examines performance of funds during recessionary and expansionary periods, Kosowski (2002).
Jiang (2003) reached the same conclusion as Treynor and Mazuy, using a nonparametric test on a
more up-to-date sample of funds in 1980-1999. Daniel et al. (1997) looked at equity fund market
timing ability from a different angle, directly considering the holdings of funds. Using their
methodology, which I introduced previously, they find that on average mutual funds in their
sample period of 1975 to 1994 do not exhibit market timing ability. Using different methodologies
on equity funds from different time periods, all of these papers have reached the same unsurprising
conclusion – a conclusion that fits with the theoretical reasoning of Sensoy and Kaplan (2005).
Most of the discussion so far has focused on equity funds, much like the mutual fund
literature as a whole. And because of this, unfortunately, the literature on bond fund timing is
sparse. For bond funds, market timing consists of moving the portfolio composition between bonds
and cash, and at a lower level, changing between differently performing bonds. Theoretically we
would expect bond funds to be bad market timers. The arguments of Sensoy and Kaplan (2005)
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regarding the cash balance of equity funds also apply to bond funds. Further, bonds are much less
liquid than equities, making them more difficult to trade as desired.
Chen, Ferson and Peters (2008) specifically looked at the market timing ability of US bond
funds and found no evidence of market timing ability. They come to this conclusion by extending
the factor-based methodology of Treynor and Mazuy (1966) to be applied to bond funds over the
period 1962 to 2007. The methodology of Chen et al. proves to be more complex than the original
TM measure. The authors reason that for bond funds the regression must now include a vector on
control variables, along with lagged values of factor changes. Similarly, Ferson et al. (2006) do
not find evidence of superior performance of bond funds with respect to market timing ability,
using stochastic discount factors from continuous-time term structure models on government bond
funds during 1986 to 2000. Boney, Comer and Kelly (2009) consider the market timing ability of
high quality corporate bond funds in 1994-2003 and find evidence of negative market timing
ability, their methodology working by measuring changes in portfolio allocations using the
quadratic programming technique of Sharpe (1992). Moneta (2009) uses a holdings-based
methodology analogous to the characteristic-based methodology of Daniel et al. (1997),
specifically for bond funds. Using this methodology, Moneta finds that some subgroups of bond
funds do provide evidence of significant positive market timing ability during 1997-2006.
Most of the bond fund literature finds no market timing ability, or even negative market
timing ability – both successfully explained by the theory. Moneta’s results are surprising however,
but he also adds that for the average fund his market timing results have no significance. This hints
that there may be a minority of bond funds that are good market timers but are lost in the average
due to poor-performing subgroups.
With a greater understanding of what the literature has to say regarding equity fund and
bond fund market timing ability, plus some knowledge of how this ability was measured, I can
begin to form a conceptual framework for this study.
2.3 CONCEPTUAL FRAMEWORK
With the knowledge gained in the previous subsection we can begin to look specifically at
hybrid fund market timing, beginning with a discussion of the relevant literature. This will then
lead to a development of the approach that I take to measure the market timing ability of hybrid
funds. With a methodology in place I will then form the hypotheses that I wish to test.
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2.3.1 Hybrid Fund Market Timing
So far we have seen empirical evidence that on average, equity funds and bond funds are
not good market timers. On first reflection this might lead us to expect that hybrid funds, which
are for all intents and purposes a combination of an equity fund and a bond fund, are also not good
market timers. On the other hand, as hybrid funds get to invest in more types of asset classes they
have greater control over their ability to allocate assets (recall equity funds having little control
over their cash balance, for example). Specifically, for flexible portfolio funds we might expect
them to be successful market timers because they have the ability to completely change their asset
allocation as market factors change. Another argument could be made that hybrid funds have to
possess a superior knowledge of the market (they do not just focus on macro factors that affect
stock returns, for example) in order to be successful with both equities and bonds, leading them to
be better market timers. For these reasons it is possible that hybrid funds would have significant
market timing ability, at least above that of equity funds and bond funds.
There are a number of works in the mutual fund literature that include hybrid funds as part
of their more general fund sample. Lee and Rahman (1990) find evidence of market timing ability
in hybrid funds over the period 1977-1984. Ferson and Schadt (1996) found that an equally
weighted portfolio of balanced and income funds from 1968 to 1990 also provided evidence of
market timing ability. Volkman (1999) finds that half of the balanced funds in his sample
showcased significant market timing ability from 1980 to 1990. However, none of these papers
analyse the bond portion of the funds in detail. Comer (2006), who lists these works, reasons that
these results may be driven more by an absence of analyses on the bond portions of the portfolios
rather than by evidence of actual market timing ability.
Building on these works, Comer (2006) looks specifically at the market timing ability of
hybrid funds using a factor-based quadratic regression methodology (henceforth referred to as
Factor Timing) based on Treynor and Mazuy (1966). He investigates two separate samples of
hybrid funds and finds evidence of market timing skill in the 1992-2000 sample but no significant
evidence in the 1981-1991 sample. He finds that the 1992-2000 sample is largely driven by the
market timing success of balanced funds over this period. Comer argues that this result is intuitive
as it is difficult to consistently excel at timing the market and balanced funds tend to have less
portfolio turnover than flexible portfolio funds. The 1992-2000 sample includes a bull market
which is reasoned to drive the success in this period and not in the previous period. This is an
14. 11
interesting result; as balanced funds face more restrictions in their ability to time the market but
witness a greater performance than flexible portfolio funds.
However, Comer’s study has its limitations. As mentioned by the author himself, the
methodology of Treynor and Mazuy is not optimal for detecting market timing ability. Comer
provides a summary of four potential problems with his approach. First of all, Goetzmann et al.
(2000), Bollen and Busse (2001), and Chance and Hemler (2001) all conclude that the use of
monthly data may fail to fully detect ability to time the market if timing decisions occur during the
month (although this often applies to other methodologies too). Second, Jagannathan and
Korajczyk (1986) prove that the relationship between portfolio skewness and benchmark skewness
may lead to a significant positive market timing coefficient even in the absence of any market
timing ability. Third, Edelen (1999) finds that funds exhibit significant negative market timing
coefficients from a Factor Timing methodology when that fund experiences cash inflows. Finally,
Bollen and Busse also explain that if funds time the market according to a specification other than
the Factor Timing model, then using this model violates various regression assumptions. Due to
these limitations, I consider other options for my own hybrid fund market timing measure.
Although, as the Factor Timing methodology is the only one that has currently been applied to
hybrid funds I will still look to apply it as a robustness test, parallel to a new methodology.
The conceptual framework (see Figure 2.1.) for this study really accentuates how limited
the hybrid fund market timing literature is. Comer has the only contribution and so it is crucial to
bring in new ideas from the market timing literature outside of hybrid funds. The next logical step
is to evaluate hybrid fund market timing using a different methodology. I apply a holdings-based
methodology formed from a combination of Daniel et al. (1997) and Moneta (2009), the previously
mentioned Characteristic Timing methodology. Daniel et al. use their methodology for measuring
the market timing ability of equity funds, whereas Moneta’s measures the market timing ability of
bond funds. The idea behind the Characteristic Timing methodology is that it measures how funds
react to the changing characteristics of different investment assets – this is explained in much
greater detail in Section 4. The measures with respect to each investment asset are then weighted
by their portfolio presence to create an overall market timing measure, also allowing the equity
measure and the bond measure to be combined in a logical way. That is, the methodology I use
computes the Characteristic Timing measure of Daniel et al. for stocks, Moneta’s Characteristic
Timing measure for bonds, and then weights the two based on a given fund’s portfolio composition.
15. 12
I use this methodology as the authors of the respective papers find results that appear accurate and
significant, and the two Characteristic Timing methodologies are intuitive to combine. In summary,
I am taking a successful equity fund market timing methodology and another successful
methodology from the bond fund timing literature, combining them to make a new hybrid fund
market timing measure.
2.3.2 Hypothesis Development
With a market timing methodology in place I can now develop the hypotheses of my study.
My main research question, stated in Section 1, asks if hybrid funds are good market timers. So a
good starting hypothesis, before working towards deeper findings, should allow me to answer this
question. I have argued previously that we might expect hybrid funds to be good market timers
because of their greater flexibility to move between differently performing investment assets and
because of a potentially wider knowledge of market factors. These arguments fit in line with the
findings of Comer (2006), that there is evidence of hybrid fund market timing ability in one of his
sample periods. Based on the theory and the empirical examination I can now form my first
hypothesis (henceforth known as H1), which will allow me to answer my main research question:
H1: On average, hybrid funds are successful market timers.
Note: This figure illustrates the conceptual framework of the methodologies involved in my study. This study combines
Characteristic Timing methodologies for equity funds and bonds funds. My application of the Characteristic Timing
methodology runs parallel to the pre-existing hybrid fund market timing literature that applies the Factor Timing methodology.
Figure 2.1. Conceptual framework for this study
This study
Daniel et al. (1997)
Comer (2006)
Moneta (2009)
Characteristic Timing (hybrid funds)
Characteristic Timing (equity funds) Characteristic Timing (bond funds)
Factor Timing (hybrid funds)
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Moneta (2009) examines different investment objectives and finds that some mutual funds
are successful in making some allocation decisions but not in making others. This suggests that
mutual funds with different investment objectives have varying skill specialisations and potentially
different abilities to time the market. Balanced funds and flexible portfolio funds have very
different investment objectives (minimizing risk versus maximizing total return, respectively) and
so we might expect them to have a different level of ability to time the market.
Once I have tested H1 it would be of interest to understand what drives the result. That is,
I would like to see if balanced funds or flexible portfolio funds are more crucial to the average
market timing ability of hybrid funds. For example, if I find that on average hybrid funds are
indeed successful market timers, is this driven by balanced funds, flexible portfolio funds or both?
We saw above that Comer (2006) found that the market timing ability of hybrid funds in his second
sample was primarily driven by balanced funds, but we must recognize his use of a flawed
methodology and consider the theory. Because flexible portfolio funds have the ability to
aggressively alter their asset allocations, whereas balanced funds have to maintain target
allocations, we would expect flexible portfolio funds to exhibit greater market timing ability. This
leads to my second hypothesis (henceforth known as H2):
H2: Flexible portfolio funds are more successful market timers than balanced funds.
In other words, this hypothesis implicitly states that any market timing ability of hybrid funds
found on average is driven predominately by flexible portfolio funds.
It would also be very interesting to see which fund characteristics in the cross-section can
be associated with positive (or negative) market timing ability for hybrid funds. If I find that one
of flexible portfolio funds or balanced funds is a superior timer, then it would be important to find
out why. Investigating the relationship between market timing ability and fund characteristics such
as returns, size, turnover, expenses and portfolio composition can lead to important conclusions,
particularly for fund managers. Unfortunately, it is difficult to motivate some of these relationships.
I will now introduce new theoretical explanations but empirical explanations cannot be applied.
Most market timing papers find no evidence of market timing ability and so clearly evidence of
relationships between fund characteristics and market timing ability are difficult to find. As
previously stated, the only existing work on hybrid fund market timing is by Comer (2006). He
finds evidence of market timing ability and so is able to investigate two different relationships,
17. 14
specifically for hybrid funds. He finds no statistically significant relationship between portfolio
turnover and market timing skill. However, he does find evidence that hybrid funds with lower
expenses have a better ability to time the market. Now we can discuss some theoretical
relationships.
It is reasonable to expect a positive relationship between market timing ability and returns.
It appears nonsensical that hybrid funds (or any other kind of fund, for that matter) that are
successful market timers would make lower returns than their worse-at-timing peers. However,
there are indeed reasons why this could be the case. First of all, selectivity ability must also be
considered. It is entirely possible that funds that can successfully time the market do so, but based
on poor asset choices. This could lead to a situation where poorer market timers that make better
asset decisions could enjoy higher returns. Additionally, superior market timing ability could lead
to overtrading. In a world with transaction fees, making extensive use of superior market timing
ability can be destructive. It is important to remember these points, but it is still reasonable to
expect that better market timing ability leads to higher returns.
A strong relationship between returns and market timing ability tells a great deal. Earlier I
introduced the concept of fund performance being divided between market timing ability and
selectivity ability. A strong positive relationship between returns and market timing ability could
imply that returns are driven primarily by the ability of funds to time the market, with selectivity
ability being of little consequence. The converse could also be the case. Either way, this would be
a profound finding from the perspective of the manager of a hybrid fund as it would give them a
clear guideline of where they should be focusing their time, effort and resources.
Other cross-sectional relationships are not likely to have as profound an impact as that of
returns but could still be important for fund managers to be aware of. For example, a positive
relationship between fund size and market timing ability incentivizes smaller funds to scale up
operations. A portfolio turnover relationship will indicate whether more passive or more
aggressive trading strategies are ideal. A relationship between market timing ability and expenses
will allow us to see if the most reputable funds (those able to charge higher expenses) have the
most market timing talent. And finally, looking at the relationship between market timing ability
and portfolio composition allows us to see if holding a high proportion of any particular type of
asset (in other words, specialising in an asset type) is beneficial or not.
18. 15
For hybrid funds in particular we may expect larger funds to be better market timers than
smaller funds for two reasons. The first reason is that a bigger fund can more easily diversify its
portfolio. In particular, it can allocate its portfolio amongst different bonds much more easily than
a small fund. As bonds are so expensive it is difficult to get the desired allocation of each bond
category for a fund with less resources. The second reason is that larger funds are less affected by
investor clientele moving their cash. For example, a smaller fund may have to change from its
desired portfolio allocations because a large investor has withdrawn its funds, an effect that would
be much smaller for a large hybrid fund.
As a hybrid fund is subjected to more changing market factors than a standard equity fund
or bond fund, we expect that in order to sustain positive abnormal returns it must successfully time
the market incredibly often. The changing market factors would imply changing the portfolio
composition between stocks, bonds and cash on a regular basis. Because of this we would expect
that the most active hybrid funds, with the highest portfolio turnover, are the best market timers.
I have reasoned that market timing is a considerably important ability for hybrid funds and
so we would assume that the funds that have the best market timing ability would have the highest
expenses. This is because the higher expenses indicate that the fund itself believes it has superior
market timing ability and that investors, who are willing to pay the higher expenses, also agree.
Portfolio composition is also of interest to investigate, despite a lack of strong theory on
how this would affect market timing ability. Most of any potential relationship is going to be driven
by the performance of the assets themselves. For example, if stocks perform badly over a period
then the best market timers are likely to have held a small proportion of stocks in their portfolios.
This also applies to bond and cash holdings. But it is also true that bonds are much less liquid than
stocks, perhaps making them more difficult to time the market with.
Based upon the above theoretical arguments, it would not be a huge surprise to find that at
least one of the fund characteristics mentioned above has an effect on the market timing ability of
hybrid funds. Combining this with Comer’s significant finding regarding expenses, we expect that
different fund characteristics do affect hybrid fund market timing ability. This leads to my third
and final hypothesis (henceforth known as H3):
H3: Cross-sectional differences in fund characteristics affect the market timing ability of
hybrid funds.
19. 16
3 DATA
My database covers hybrid funds quarterly from January 1999 to December 2014. I obtain
all available asset allocation data for all known hybrid funds over this period from the CRSP
Mutual Fund database. Note that this sample period was chosen as CRSP only possesses quarterly
asset allocation data from 1999. Fund quarters are included in my sample if the fund’s objective
category for a given quarter is either “balanced” or “flexible portfolio”. The respective Lipper
objective codes for these fund categories are B and FX. Observations are dropped if the Lipper
objective code is missing and if the fund’s objective category cannot be found another way (using
CRSP objective codes or the fund name, for instance). Funds with total net assets of less than $5m
are dropped as most small fund holdings are not well reported. To be included in the sample each
fund also needs to have survived at least 2 years in order to avoid the incubation bias as discussed
by Evans (2007). Also to address this incubation bias, all observations before the starting year
reported in CRSP are eliminated, as well as observations with a missing fund name. Observations
are also dropped if the asset allocation data is missing. Quarterly stock holdings data for the equity
portion of the funds is also obtained from CRSP for 2002 onwards and from the Thomson Reuters
Mutual Fund Holdings database for 1999-2001.
For each fund quarter I obtain from CRSP the fund name, management company, Lipper
objective code, returns, total nets assets, expense ratio, turnover ratio and portfolio allocation data.
CRSP supplies the amount of the fund that is invested in common stocks, preferred stocks,
convertible bonds, corporate bonds, municipal bonds, government bonds, cash and other securities.
The quarterly stock holdings data from Thomson Reuters (for 1999-2001) had to be matched to
the data from the CRSP database by hand. I followed the process of Wermers (2000),
predominantly matching funds between the two databases by matching fund names. Management
company names and other fund characteristics, such as total net asset data, were also used to ensure
accuracy. All funds were able to be matched successfully between the two databases.
Descriptive statistics of hybrid funds for the 1999-2014 period are presented in Table 3.1.
In my sample I consider over 1,000 hybrid funds. Approximately half of the sample is represented
by balanced funds, with the other half being represented by flexible portfolio funds. Balanced
funds are a slightly more important category for the sample, partially due to the higher number of
balanced funds but mainly due to their significantly larger average size. We can also clearly see
that bonds do indeed form a significant proportion of hybrid fund portfolios. In particular, I
20. 17
mentioned previously that balanced funds have to maintain a bond proportion of the portfolio
above 25%, the descriptive statistics appear to fall in line with this.
The CRSP database reports summary data for all share classes of each fund separately. As
Kacperczyk et al. (2008) state, mutual fund families introduced different share classes in the 1990s
and their use has risen prominently since. Each fund in my sample has, on average, just over 3.5
share classes. Different share classes for the same fund entail different types of fees and loads, and
hence, different expense ratios. However, I do not want to consider multiple share classes for the
same fund and I need to be able to match each fund with the quarterly stock holdings in the
Thomson Reuters database, which only has one share class for each fund. To combat this
duplication, I follow the methodology of Kacperczyk et al. (2005). I only include the dominant
class of shares, which I take as the class with the biggest total net assets according to the CRSP
data. As each different share class for the same fund has the exact same portfolio holdings this
should not impact my testing of H1 and H2. Note that for H3 it would have been ideal to combine
share classes as done by Wermers (2000), but this is extremely time consuming and out of the
scope of my work. Instead, I started with a sample of portfolio allocation data that included 50,674
quarterly observations, before removing excess share classes by hand to leave 25,544 quarterly
observations remaining.
Note that we would expect deleting excess share classes, rather than combining them,
would impact some of the descriptive statistics. In particular, all total net asset values will be biased
downward as my data is based on the total net assets of the largest share class, rather than by the
All Hybrid Funds Balanced Funds Flexible Portfolio Funds
Number of funds 1,059 538 521
Number of share classes 3,776 1,965 1,811
Average total net assets ($millions) 1,006.37 1,255.58 715.80
Return mean (%/year) 0.69 0.70 0.68
Return standard deviation (%/year) 2.93 2.70 3.18
Average stock portfolio weight (%) 56.42 56.13 56.79
Average bond portfolio weight (%) 27.16 29.17 24.78
Average cash portfolio weight (%) 4.96 3.67 6.48
Average portfolio turnover (%/year) 73.04 68.80 78.53
Average expense ratio (%) 0.85 0.87 0.82
Note: This table contains the descriptive statistics for my entire sample of hybrid funds for the period of 1999 to 2014.
Averages are computed by calculating the cross-sectional mean over each fund every quarter and then calculating the time
series mean. Values are also computed separately for balanced funds and flexible portfolio funds. All data is obtained
from CRSP.
Table 3.1. Descriptive statistics for hybrid funds (1999-2014)
21. 18
total net assets of a given fund. Similarly, my data represents the expense ratio of the largest share
class, rather than a weighted average of all of the available share classes of a fund. As a knock on
effect we would expect this to impact the results of my third hypothesis. However, I do not expect
these results to have a profound impact as all funds in my sample have been treated in the same
way.
I find that the average expense ratio (the annual fee that funds charge their investors) of a
hybrid fund is 0.85%, which is low when compared to other categories of mutual fund. This may
come as a surprise. I have already discussed that hybrid funds have to be knowledgeable on a wider
range of market factors than standard equity funds or bond funds. One might expect this additional
responsibility of hybrid funds to imply a higher reward, in this case the reward being that hybrid
funds are the recipient of a higher expense ratio. It is certainly interesting that this is not the case
here, but perhaps the expense ratio is driven by other factors. CRSP calculates the expense ratio as
the total investment that shareholders pay for a fund’s operating expenses, 12b-1 fees included.
My data also gives some interesting information regarding the comparison of balanced
funds and flexible portfolio funds. The descriptive statistics show that over the sample period,
balanced funds have averaged a higher return than flexible portfolio funds. Recalling that the
objective of a flexible portfolio fund is to maximize returns, whilst balanced funds aim to minimize
risk, we may find this result surprising. However, for a large portion of the sample period
(approximately 2007 onwards) we endured a strong recession – perhaps an objective of minimizing
risk was more appropriate over this period. The higher return volatility for flexible portfolio funds
is arguably more intuitive, their greater flexibility likely means more regular changes between
holdings of differently performing assets. Additionally, as balanced funds aim to minimize risk we
would fully expect them to have lower volatility in their returns.
We can also observe a higher turnover ratio, the percentage of the fund portfolio that has
been turned over in a given year, for flexible portfolio funds. We might have expected this from
their greater ability to move between different asset classes. However, what we might not have
expected is that the difference in turnover ratio between balanced funds and flexible portfolio funds
is relatively small. Similarly, we might have expected flexible portfolio funds to have a higher
expense ratio than balanced funds, representing their status as more active traders.
Interestingly, the size of the stock portion of balanced funds and flexible portfolio funds is
very similar. The differences in portfolio composition come from balanced funds holding slightly
22. 19
more bonds and less cash. Presumably flexible portfolio funds have held less bonds due to the low
yields late in my sample period and the booming stock markets of the early 2000s, whereas
balanced funds have less of a license to be able to do this. Also, the higher cash holdings for
flexible portfolio funds perhaps is indicative of a quick action strategy, enabling the funds to be
liquid enough to be able to quickly buy into any profitable opportunity as they may appear.
The only other hybrid fund database that I am aware of belongs to Comer (2006). He looks
at two separate samples, one from 1981 to 1991 and the other from 1992 to 2000. Comer only has
one requirement for a hybrid fund to be included in his sample and that is that the fund must survive
for a minimum of five years (due to his methodology). Because of this, the sample runs the risk of
being subjected to survivorship bias, see Brown, Goetzmann, Ibbotson and Ross (1992). The 1981-
91 sample is composed of only 56 hybrid funds. Similarly, the 1992-2000 sample consists of 58
hybrid funds. My database agrees with this, consisting of very few funds over the early part of the
sample period, before rapidly increasing around 2003 (see Table 5.1. in Section 5). Comer’s fund
summary data comes from Weisenberger’s Mutual Funds Panorama and he collects portfolio
holdings data from CRSP (for his first sample) and Morningstar (for his second sample).
I chose to use predominantly CRSP data for a variety of reasons. CRSP allows for easily
collecting data exclusively on hybrid funds via the use of the Lipper objective codes. The database
contains a wide variety of summary data, such as expense ratios and portfolio weights, allowing
me to test more fund characteristics for H3. But perhaps most importantly of all, gathering the
equity holdings data from CRSP allows for easy matching between summary data and holdings
data. Many papers in the mutual fund literature recount the arduous process of matching different
fund databases by hand. Unfortunately, I had to do this manual process for the first years of my
sample using Thomson Reuters data, due to the unavailability of CRSP holdings data for those
years. As CRSP contains the bond holdings weights, Morningstar was not required. One reason
that Comer turned to Morningstar for his second sample was due to the CRSP data only having
annual asset allocation data at the time, a problem that does not affect my sample.
Other definitions of fund categories that are classed as a hybrid fund exist. As an example,
Comer also includes “asset allocation funds” as a category of hybrid fund. I note that this difference
likely comes from my use of Lipper objective codes, whereas Comer uses the listings of
Weisenberger. My sample does include asset allocation funds, as Lipper objective codes have them
included amongst balanced funds. My sample is all inclusive in the context of hybrid funds.
23. 20
4 METHODOLOGY
There are many different ways to approach measuring the ability of mutual funds to time
the market. Most methodologies in the mutual fund performance literature look at market timing
only with respect to equities, and often for varying reasons these methodologies cannot be
extended to include bonds. Less often, the opposite also applies. In particular, this makes it difficult
to measure the market timing ability of hybrid funds. It is because of this that I combine two
different methodologies, one specifically for equity market timing and another specifically for
bond market timing. Both methodologies are successful in their own right and they can be
combined in a logical way to create a new methodology for measuring the market timing ability
of hybrid funds and testing my hypotheses. As a means of a robustness check, I also plan to apply
the only pre-existing methodology for measuring hybrid fund market timing.
4.1 VARIABLES AND MEASURES
In this section I discuss two methodologies for measuring market timing in-depth. The first
methodology is a holdings-based methodology that measures a variable know as Characteristic
Timing. The second methodology uses a factor-based quadratic regression, called Factor Timing,
to measure market timing ability. Both methodologies were introduced in Section 2, but this
discussion goes deeper and explains how I apply them to my sample. I use Characteristic Timing
as my primary methodology and the Factor Timing methodology will then be applied as a
robustness test for the Characteristic Timing results.
4.1.1 Characteristic Timing
In order to measure the market timing ability of hybrid funds I need to be able to measure
their market timing ability with respect to equity holdings and to bond holdings. In order to do this,
I combine two different methodologies, both based on the original Characteristic Timing measure
of Grinblatt and Titman (1993). I will use the methodology of Daniel et al. (1997) for equity
holdings and the methodology of Moneta (2009) for bond holdings. This methodology works by
measuring the extent that funds time their exposures to the changing characteristics of different
investment assets. The methodologies of Daniel et al. and Moneta both appear to generate accurate
findings and so applying an intuitive combination of the two is a natural next step.
24. 21
The Daniel et al. Characteristic Timing measure works by creating 125 benchmark
portfolios that are matched to each fund’s equity holdings every year. The equities are matched to
a benchmark portfolio across three dimensions: size (market value of equity), book-to-market ratio
and momentum (prior year return). Daniel et al. state that these three characteristics are the best
ex-ante predictors of cross-sectional patterns in common stock returns, summarizing Fama and
French (1992, 1996), Jegadeesh and Titman (1993), and Daniel and Titman (1997). In order to do
this, I gather the quarterly stock holdings data of each hybrid fund from the Thomson Reuters and
CRSP databases. Then for all of the stocks held by the hybrid funds I gather their monthly stock
price and return, their annual market value of equity and their annual book values of assets and
liabilities from Compustat.
To form the benchmark portfolios, I group all of the stocks available on Compustat into
three quintile groupings, one at a time, based on firm size, book-to-market ratio and momentum
(re-sorting each year of the sample). In particular, I group the stocks into quintiles based on firm
size, then within each size quintile I group the stocks into quintiles based on book-to-market ratio,
and I complete one final grouping on momentum within each of these 25 groups. Doing this gives
a 5 x 5 x 5 sorting; that is, 125 benchmark portfolios each year. Then monthly returns of these 125
portfolios are calculated by value weighting each of the stocks in the portfolios. More details on
the construction of these benchmark portfolios are found in the appendix. Each equity in a fund’s
portfolio is then matched to one of the 125 benchmark portfolios at the beginning of each year,
using the same three characteristics used for creating the benchmark portfolios.
Once the benchmark portfolios have been formed I can measure a fund’s ability to time the
market with respect to stocks. For a given fund at month 𝑡, the 𝐶𝑇 𝑆
measure is
𝐶𝑇𝑡
𝑆
= ∑(𝑤𝑗,𝑡−1 𝑅𝑗,𝑡
𝑇
𝑁
𝑗=1
− 𝑤𝑗,𝑡−13 𝑅𝑗,𝑡
𝑇−1
) (4.1)
where 𝑤𝑗,𝑡−1 is the portfolio weight of stock 𝑗 at the end of month 𝑡 − 1 and 𝑅𝑗,𝑡
𝑇
is the month
𝑡 return of the characteristic portfolio that is matched to stock 𝑗 at the beginning of year 𝑇 (the year
corresponding to month 𝑡). The weights are computed using the CRSP stock holdings data,
combined with monthly stock prices from Compustat. The time series average value of the 𝐶𝑇𝑡
𝑆
scores over all existing fund months is the 𝐶𝑇 𝑆
measure for that fund.
25. 22
Now I introduce Moneta’s methodology for the 𝐶𝑇 𝐵
measure of bond funds. It is fairly
similar to the methodology of Daniel et al., but the main difference is that asset class weights are
used rather than information about individual securities that make up the portfolio. For example,
a certain weight of a portfolio may consist of corporate bonds and I represent this by the returns
from a corporate bond index. I do this due to data feasibility; finding return and holdings data for
each individual bond in a portfolio is particularly difficult.
Using the quarterly portfolio weights in CRSP I can divide the bond portion of a fund’s
portfolio into: convertible bonds, corporate bonds, municipal bonds, government bonds and cash.
Each of the bond categories is then represented by a relevant bond index. All of the bond portfolios
are based on bond indices available from Bank of America Merrill Lynch (via Bloomberg).
Convertible bonds are represented by the US Convertible Index, Corporate bonds are represented
by the US Corporate Index, Municipals are represented by the US Municipal Securities Index,
Government bonds are represented by the US Treasury Index and finally cash is represented by
the 3-month Treasury Bill Index. Some observations had their bond holdings collected together
under just one category in CRSP – called “all bonds.” These observations are removed as it is not
possible to calculate the 𝐶𝑇 𝐵
score from this data.
For a given fund at month 𝑡, the 𝐶𝑇 𝐵
measure is
𝐶𝑇𝑡
𝐵
= ∑(𝑤𝑗,𝑡−1
𝑁
𝑗=1
− 𝑤𝑗,𝑡−13
∗
)𝑅𝑗,𝑡 (4.2)
where 𝑅𝑗,𝑡 is the total return of the bond index for bond type 𝑗 (corporate, government, etc.) during
month 𝑡, 𝑤𝑗,𝑡−1 is the weight of the fund invested in bond type 𝑗 during month 𝑡 − 1 and 𝑤𝑗,𝑡−13
∗
is the buy-and-hold weight of the fund invested in bond type 𝑗 during month 𝑡 − 13, that is:
𝑤𝑗,𝑡−13
∗
=
𝑤𝑗,𝑡−13 ∏ (1 + 𝑅𝑗,𝑡−𝜏)12
𝜏=1
∑ 𝑤𝑗,𝑡−13 ∏ (1 + 𝑅𝑗,𝑡−𝜏)12
𝜏=1
𝑁
𝑗=1
. (4.3)
As before, I compute the average value over all of the existing fund months to compute the
𝐶𝑇 𝐵
measure for that fund. The overall 𝐶𝑇 scores are then computed from the separate 𝐶𝑇 𝑆
and
𝐶𝑇 𝐵
scores. Each month, I compute 𝐶𝑇𝑡 as the weighted average of 𝐶𝑇𝑡
𝑆
and 𝐶𝑇𝑡
𝐵
, where the
weights are the proportion of the portfolio invested in stocks and the proportion invested in bonds
26. 23
(including cash). This is the logical way to combine the two methodologies, as the 𝐶𝑇𝑡
𝑆
measure
is a weighted average of the fund’s ability to time each stock it holds and 𝐶𝑇𝑡
𝐵
is the weighted
average of its ability to time each bond category it holds. Then, as before, a fund’s overall 𝐶𝑇 score
is simply the time series average of its monthly 𝐶𝑇𝑡 scores. Weighting the measures at the monthly
level gives an accurate reflection of the market timing ability of the fund with respect to each asset
it holds. Simply computing overall 𝐶𝑇 as some kind of average of overall 𝐶𝑇 𝑆
and 𝐶𝑇 𝐵
implies
the restricting assumption that funds do not vary the proportion of their portfolio invested in stocks
or bonds over time.
In short, the Characteristic Timing methodology works by comparing the current weighted
returns from characteristic portfolios matched to holdings this year to current weighted returns
from characteristic portfolios matched last year. This means that the 𝐶𝑇 measure captures the
ability of funds to time the market based on stock and bond characteristics. I take a positive 𝐶𝑇
score as evidence of abnormal performance – that is, evidence that hybrid funds successfully time
the market.
4.1.2 Factor Timing
The second methodology that I use allows us to measure the market timing ability of mutual
funds regarding both stocks and bonds simultaneously. This is the methodology of Comer (2006).
The model that Comer uses is an extension of the TM model created by Treynor and Mazuy (1966).
The TM model works on the assumption that if a mutual fund is not engaged in market timing,
thus maintaining a constant beta, then there is a linear relationship between fund return and the
return on the benchmark. But if the fund does indeed engage in market timing then this relationship
will be nonlinear. Hence, Treynor and Mazuy test for market timing ability by measuring the
nonlinearity of the relationship between fund return and the return on the benchmark. Although
this is the only pre-existing hybrid fund market timing methodology, giving us reason to examine
it further, we must recall the limitations mentioned in Section 2 regarding this methodology. These
limitations provide the reasoning for proceeding with the Characteristic Timing measure as my
primary methodology, relegating the Factor Timing approach to a robustness test.
Treynor and Mazuy measure the nonlinearity of the fund return to benchmark return
relationship by estimating the following regression:
27. 24
𝑟𝑖 = 𝛼𝑖 + 𝛽𝑖 𝑟 𝑚 + 𝑐𝑖 𝑟 𝑚
2
+ 𝑒𝑖, (4.4)
where 𝑟𝑖 is the excess return of fund 𝑖, 𝑟 𝑚 is the excess return on the market benchmark and 𝑐𝑖 is
the measure of nonlinearity. If the fund is successful at market timing, then fund return will be
higher than the return on the market benchmark regardless of whether or not the market benchmark
return is high or low. Because of this, I consider 𝑐𝑖 > 0 as evidence of market timing ability.
As it is currently stated, the Treynor and Mazuy model is not appropriate for use with
hybrid funds. This model only measures market timing ability with respect to a market portfolio
of stocks. That is, the model only measures equity market timing ability and not the ability to time
the bond market. So I need to add a timing variable for bonds (and similarly, I can add timing
variables for any other asset category). Beyond this, it is also important to include more variables
in order to better model hybrid fund returns. Below, I state the variables that I include in my Factor
Timing model, and Comer provides the reasoning for selecting these variables.
For the return-generating process of the stock portion of the fund, I use four indices that
represent the S&P 500, small stocks, value stocks, and growth stocks. I also use four indices to
model the bond portion of the fund, indices that represent high-quality bonds, low-quality bonds,
long-maturity bonds, and short-maturity bonds. This leads to an eight-factor model that represents
the expected excess returns for a hybrid fund:
𝑟𝑖 = 𝑎𝑖 + 𝑏𝑖,𝑠𝑝 𝑟𝑠𝑝 + 𝑏𝑖,𝑠𝑚 𝑟𝑠𝑚 + 𝑏𝑖,𝑔𝑟 𝑟𝑔𝑟 + 𝑏𝑖,𝑣 𝑟𝑣 + 𝑏𝑖,𝑙 𝑟𝑙 + 𝑏𝑖,𝑠ℎ 𝑟𝑠ℎ + 𝑏𝑖,ℎ𝑞 𝑟ℎ𝑞
+ 𝑏𝑖,𝑙𝑞 𝑟𝑙𝑞 + 𝑒𝑖.
(4.5)
Here, 𝑟𝑖 is the excess return for fund 𝑖, 𝑟𝑠𝑝 is the excess return on the S&P 500 index, 𝑟𝑠𝑚 is the
excess return on a small stock portfolio, 𝑟𝑔𝑟 is the excess return on a growth stock portfolio, 𝑟𝑣 is
the excess return on a value stock portfolio, 𝑟𝑙 is the excess return on a long-maturity bond
portfolio, 𝑟𝑠ℎ is the excess return on a short-maturity bond portfolio, 𝑟ℎ𝑞 is the excess return on a
high-quality bond portfolio, and 𝑟𝑙𝑞 is the excess return on a low-quality bond portfolio.
I represent the S&P 500 by the CRSP value weighted S&P 500 index. All of the remaining
stock portfolios are based on the benchmark portfolios created by Fama and French (1993). The
small stock portfolio is the average of the small-value, small-neutral, and small-growth portfolios.
The growth stock portfolio is the average of the small-growth and big-growth portfolios. Finally,
the value stock portfolio is the average of the small-value and big-value portfolios.
28. 25
All of the bond portfolios are based on bond indices available from Bank of America
Merrill Lynch (via Bloomberg), just as before. The long-maturity bond portfolio is represented by
the 15+ Year US Corporate, Government & Mortgage Index. The short-maturity bond portfolio is
represented by the 1-3 Year US Corporate, Government & Mortgage Index. The high-quality bond
portfolio is represented by the US Corporate & Government Index. Finally, the low-quality bond
index is represented by the US High Yield Index. All portfolios for both the stock and bond
portions of the model are total return portfolios.
Next, Comer uses the eight-factor model to derive a market timing model. Comer reasons
that the S&P 500 portfolio is the best representation of stock portfolios in which hybrid funds
invest. Similarly, for bonds, the best representation is given by the short-maturity bond index.
Because of this I will include quadratic terms (that represent market timing ability) for the S&P
500 portfolio and the short-maturity bond index. The coefficient of the quadratic S&P 500 term
represents stock timing ability and the coefficient of the quadratic short-maturity bond index term
represents bond timing ability. Of course more quadratic terms can be included, but at the expense
of making computations more intensive. This leaves us with the market timing model that I will
proceed with:
𝑟𝑖 = 𝑎𝑖 + 𝑏𝑖,𝑠𝑝 𝑟𝑠𝑝 + 𝑏𝑖,𝑠𝑚 𝑟𝑠𝑚 + 𝑏𝑖,𝑔𝑟 𝑟𝑔𝑟 + 𝑏𝑖,𝑣 𝑟𝑣 + 𝑏𝑖,𝑙 𝑟𝑙 + 𝑏𝑖,𝑠ℎ 𝑟𝑠ℎ + 𝑏𝑖,ℎ𝑞 𝑟ℎ𝑞
+ 𝑏𝑖,𝑙𝑞 𝑟𝑙𝑞 + 𝜆 𝑖,𝑠𝑝 𝑟𝑠𝑝
2
+ 𝜆 𝑖,𝑠ℎ 𝑟𝑠ℎ
2
+ 𝑒𝑖,
(4.6)
where 𝜆 𝑖,𝑠𝑝 represents stock timing ability of fund 𝑖 and 𝜆 𝑖,𝑠ℎ represents its bond timing ability. I
utilize this model in a panel regression, with cross-section and time fixed effects, over all existing
fund months using monthly fund and index return data.
4.2 HYPOTHESIS TESTING
Now that I have explained the methodologies that I will move forward with, I can outline
how I apply them for testing my hypotheses. Recall my first hypothesis:
H1: On average, hybrid funds are successful market timers.
As stated above, a positive 𝐶𝑇 measure is taken as evidence of positive abnormal performance. I
compute the 𝐶𝑇 score for my entire sample of hybrid funds and a (significant) positive 𝐶𝑇 measure
is taken as evidence that hybrid funds are indeed successful market timers on average.
29. 26
Recall my second hypothesis:
H2: Flexible portfolio funds are more successful market timers than balanced funds.
I repeat the above procedure separately for balanced funds and flexible portfolio funds in order to
determine their respective 𝐶𝑇 scores. The fund category with the larger 𝐶𝑇 measure is taken to be
the more successful market timer.
Now recall my third and final hypothesis:
H3: Cross-sectional differences in fund characteristics affect the market timing ability of
hybrid funds.
I sort all of the hybrid funds into quartiles based on the following fund characteristics: returns,
total net assets, turnover ratios, expense ratios and portfolio holdings of stocks, bonds and cash.
Then I can look to see if a relationship exists between the characteristic-sorted quartile portfolios
and the 𝐶𝑇 measures. Similarly, I sort all of the hybrid funds into quartiles based on their
respective 𝐶𝑇 scores to investigate the converse of the above relationship. Lastly, I also regress
the 𝐶𝑇 scores on the seven cross-sectional characteristics, leading to a thorough testing of H3.
I check the validity of the 𝐶𝑇 results primarily by comparing them to the Factor Timing
results. In particular, I compare 𝜆 𝑖,𝑠𝑝 to 𝐶𝑇 𝑆
and 𝜆 𝑖,𝑠ℎ to 𝐶𝑇 𝐵
for all hybrid funds and also
separately for balanced funds and flexible portfolio funds. If both methodologies post significant
and qualitatively similar results, then we can have some confidence in the 𝐶𝑇 scores. I also check
the validity of the 𝐶𝑇 results by examining portfolio allocations during up and down stock markets,
applying a methodology called Portfolio Allocation Analysis.
30. 27
5 RESULTS
In this section I present the results I computed using the previously outlined methodologies.
First I will disclose the main results from my Characteristic Timing methodology, which I will
then support using the results from the Factor Timing methodology and the previously mentioned
Portfolio Allocation Analysis. This will then allow me to thoroughly evaluate the Characteristic
Timing methodology. Once we have some confidence in the ability of the Characteristic Timing
methodology to provide meaningful and accurate results I can answer my main research question.
I will test hypotheses H1, H2 and H3, before taking a look at other interesting results that my
methodology provides.
5.1 CHARACTERISTIC TIMING
Recall that my primary methodology for this study is Characteristic Timing – an
amalgamation of the stock holdings and bond holdings Characteristic Timing measures of Daniel
et al. (1997) and Moneta (2009), respectively. I first use equation (4.1) to compute the 𝐶𝑇 𝑆
score
for each fund every month in my sample. The next step was to compute the time series average
𝐶𝑇 𝑆
score for each individual hybrid fund over the entire sample period, then the aggregate 𝐶𝑇 𝑆
was calculated as an equal weight average of each fund’s 𝐶𝑇 𝑆
score. I computed equal weights as
I believe it is appropriate to consider the market timing ability of each fund equally, it is
nonsensical to make a conclusion on overall market timing ability when that conclusion is largely
driven by a minority of large funds. For yearly 𝐶𝑇 𝑆
scores, the time series average for each
individual fund was computed solely over the given year before the aggregate score was computed
– this ensures that the 𝐶𝑇 𝑆
score for a given year is completely independent of scores from other
years. 𝐶𝑇 𝑆
scores for the entire sample period, as well as for each individual year, are provided in
Table 5.1. under the Stocks column.
Note that there are only 𝐶𝑇 𝑆
scores for the period 2000 to 2014, despite my sample starting
in 1999. Recalling equation (4.1), we can see that the 𝐶𝑇 𝑆
score for a given year is calculated using
data from the prior year. Hence, data from 1999 is required in the computation of the 𝐶𝑇 𝑆
score
for 2000 and it is not possible to have 𝐶𝑇 scores for 1999.
31. 28
Period Number of Funds Annual Return Overall Stocks Bonds
2000-2014 1,059 0.69% 0.20290*** 0.18891 0.00125*
(2.97) (0.64) (1.88)
2000 5 2.07% 0.41308 2.07441* 0.00002
(1.34) (1.91) (1.04)
2001 9 -1.98% 0.30299 4.37982** 0.00000
(1.26) (2.01) (-1.05)
2002 71 -3.09% 0.02755 6.46495 0.00002
(0.78) (0.84) (0.68)
2003 259 1.21% 0.02367 1.97692 -0.00144**
(0.08) (1.08) (-2.41)
2004 281 1.23% -1.57044*** -4.33501*** -0.00015**
(-3.38) (-4.28) (-1.96)
2005 351 0.22% 0.94542*** 3.51373*** -0.00020*
(3.99) (3.77) (-1.89)
2006 401 0.80% 2.46100*** 6.03386*** -0.00004
(9.92) (11.32) (-0.47)
2007 490 0.68% 1.06607** 3.14303** -0.00006
(2.33) (2.37) (-1.44)
2008 722 -2.22% 0.63680* 1.84237* 0.00096*
(1.81) (1.67) (1.86)
2009 744 2.78% -0.33952* -1.70216*** 0.02759
(-1.75) (-2.96) (1.55)
2010 777 2.81% -0.14477*** -0.92408*** 0.00065
(-4.90) (-5.66) (0.90)
2011 786 -1.56% 0.17677*** 1.05367*** 0.00084
(4.46) (4.25) (1.53)
2012 772 1.55% 0.01393 0.00074 -0.00011
(0.27) (0.07) (-1.33)
2013 733 1.07% 0.29526*** 1.53744*** 0.00011
(4.69) (4.93) (1.40)
2014 703 -0.28% -0.06479 -0.68003 0.00005
(-1.47) (-1.47) (1.44)
Table 5.1. Characteristic Timing scores with respect to stocks, bonds and overall ability (2000-2014)
Note: This table contains the Characteristic Timing scores (overall scores, as well as stock scores and bond
scores) of all hybrid funds between 2000 and 2014. These Characteristic Timing scores are calculated as an
equally weighted average of all relevant Characteristic Timing scores from individual hybrid funds. The
annual return is calculated as the equally weighted average of each fund's annual return over the appropriate
year or period.
* Significant at the 10% level.
** Significant at the 5% level.
*** Significant at the 1% level.
32. 29
The next step is to compute the 𝐶𝑇 𝐵
scores using equation (4.2). This is subtly different to
the use of equation (4.1). The main difference here is the use of equation (4.3), the buy-and-hold
weight in the 𝐶𝑇 𝐵
calculation, which helps to make up for the limited bond holdings disclosures
of mutual funds. Once equations (4.2) and (4.3) have been applied to compute the monthly 𝐶𝑇 𝐵
scores for each individual hybrid fund, all that was mentioned previously regarding how the
aggregate and yearly 𝐶𝑇 𝑆
scores were computed applies here. The 𝐶𝑇 𝐵
scores for the entire
sample period, as well as for each individual year, are provided in Table 5.1. under the Bonds
column.
Finally, and perhaps most importantly, I computed the overall 𝐶𝑇 scores using the separate
𝐶𝑇 𝑆
and 𝐶𝑇 𝐵
scores. I aggregated the two scores in a way that is consistent with the separate
calculations of the two measures. Each month, for every individual hybrid fund, the overall 𝐶𝑇
score is computed as the weighted average of the appropriate monthly 𝐶𝑇 𝑆
and 𝐶𝑇 𝐵
scores. Here
the weights are the proportion of the portfolio invested in stocks and the proportion invested in
bonds. Then as before, I calculate the time series average 𝐶𝑇 score for each fund before computing
the aggregate 𝐶𝑇 score as an equal weighted average.
This means that for a given year or for the entire sample, the overall 𝐶𝑇 score is not equal
to the sum or a weighted average of the relevant 𝐶𝑇 𝑆
and 𝐶𝑇 𝐵
scores. The weighted average is
always computed at the monthly level first, as any other calculation would give unfair weightings
on the 𝐶𝑇 𝑆
and 𝐶𝑇 𝐵
scores. Note that it is possible to see an overall 𝐶𝑇 score that is larger than
the sum of 𝐶𝑇 𝑆
and 𝐶𝑇 𝐵
. Overall 𝐶𝑇 scores can also be found in Table 5.1., this time under the
Overall column. I also computed 𝐶𝑇 scores (overall, as well as 𝐶𝑇 𝑆
and 𝐶𝑇 𝐵
) separately for
balanced funds and flexible portfolio funds. These results are displayed in Table 5.2.
The main takeaways from Table 5.1. and Table 5.2. are that on average, hybrid funds did
indeed exhibit significant market timing ability over the period of 2000 to 2014. This ability was
primarily driven by stock timing ability and further supported by a smaller amount of bond timing
ability. Further, balanced funds are found to be better market timers than flexible portfolio funds.
Before I initiate an in-depth discussion of these results I would like to check that they are realistic
and reasonable. The next step is to test the robustness of these results.
34. 31
5.2 ROBUSTNESS TESTS
In this subsection I test the robustness of the 𝐶𝑇 scores. The primary robustness test that I
have discussed in detail is the Factor Timing methodology. I will introduce the Factor Timing
results and compare them to my 𝐶𝑇 scores. After this I will introduce and implement a procedure
known as Portfolio Allocation Analysis.
5.2.1 Factor Timing
I explained the Factor Timing methodology in Section 4 with a similar level of detail to the
level that I explained the Characteristic Timing methodology. The reason for this is that
Characteristic Timing, to the best of my knowledge, has never been applied to hybrid funds. That
is, no pre-existing research has combined the Characteristic Timing methodology for the stock and
bond holdings of mutual funds. This means that it is incredibly important to test the robustness of
the results from this unknown and unproven methodology. Factor Timing, once again to the best
of my knowledge, is the only methodology that has been applied to measuring the market timing
ability of hybrid funds, by Comer (2006). This makes Factor Timing incredibly important as a
robustness test – it is the only methodology that has been proven to be accurate and effective for
what I am attempting to achieve.
Recall that the Factor Timing methodology works by running a panel regression of monthly
fund returns on the returns of various stock and bond benchmarks, along with two quadratic terms.
The regression equation used for Factor Timing is equation (4.6). It is the coefficients of the two
quadratic terms that measure the market timing ability of hybrid funds with respect to stocks and
bonds. The results of these regressions are in Table 5.3. I ran the regressions over the entire sample
period of 15 years, as well as separately in increments of 5 years. Note that smaller increments
would not have been too meaningful – 5 years of monthly data is perhaps the smallest increment
size that would still provide worthwhile results, a notion that Comer himself agrees with.
The results from Table 5.3. should be able to allow us to test the results that are required
for H1, that is the aggregate 𝐶𝑇 scores for all hybrid funds. In order to test the relevant 𝐶𝑇 scores
for H2 I also ran the Factor Timing regressions separately for balanced funds and for flexible
portfolio funds – these results are presented in Table 5.4.
35. 32
Referring back to Table 5.1. and the entire sample period of 2000 to 2014, we see a 𝐶𝑇 𝑆
score of 0.18891 and a 𝐶𝑇 𝐵
score of 0.00125, for all hybrid funds. This largely fits in line with the
Factor Timing results. As we can see in Table 5.3., the coefficients for both stocks and bonds are
positive. The difference comes from the bond coefficient being much higher than the stock
coefficient. The coefficients from the Factor Timing regression are significant at the 5% level for
stocks and at the 1% level for bonds, whereas for Characteristic Timing only the 𝐶𝑇 𝐵
score is
significant, in particular at the 10% level. We can certainly be confident about the signs of the 𝐶𝑇
scores as they are both positive, just like the Factor Timing coefficients. Because the separate 𝐶𝑇
scores and Factor Timing coefficients are all positive we would expect the aggregate overall 𝐶𝑇
score for 2000-2014 to also be positive – and it is. Note that there is no meaningful way to combine
the Factor Timing stock and bond coefficients together, which makes the previous statement the
best argument available.
To further compare the results from Table 5.1. to the results from Table 5.3. I average the
𝐶𝑇 scores for stocks and bonds over the same five-year increments to make them more comparable
to the five-year Factor Timing coefficients. All of the five-year scores are positive, barring the
𝐶𝑇 𝐵
score for 2000-2004. This means that only two (the 2005-2009 𝐶𝑇 𝑆
and 𝐶𝑇 𝐵
scores) of the
six five-year 𝐶𝑇 scores have signs that agree with the signs of the relevant Factor Timing
Period Stocks Bonds
2000-2014 0.02862** 1.24631***
(2.27) (7.11)
2000-2004 -0.21721*** 2.60264***
(-6.30) (4.93)
2005-2009 0.10281*** 0.32896
(4.05) (1.09)
2010-2014 -0.21926*** -1.87692
(-8.24) (-0.27)
Table 5.3. Factor Timing coefficients for all hybrid funds (2000-2014)
Note: This table contains the Factor Timing coefficients, separately with respect to stocks and
bonds, of all hybrid funds between 2000 and 2014. The adjusted R-squared for 2000-2014 is
87.41%.
* Significant at the 10% level.
** Significant at the 5% level.
*** Significant at the 1% level.
36. 33
coefficients. This does not give us confidence in these figures, but perhaps the differences can be
explained by the relatively small number of observations included in these five-year regressions.
Now looking at balanced funds and flexible portfolio funds separately, the 2000-2014
overall 𝐶𝑇 scores are 0.24817 for balanced funds and 0.15237 for flexible portfolio funds. The
Factor Timing coefficients in Table 5.4. seem to agree with these scores in large part. The stock
and bond coefficients for both types of hybrid fund are all positive. Balanced funds have a Factor
Timing coefficient that is much higher with respect to stocks and only a little smaller with respect
to bonds than flexible portfolio funds do.
It is much more difficult to use Factor Timing to verify the information I need for H3,
regarding the cross-sectional relationships of hybrid fund market timing. But the 𝐶𝑇 results that I
have been able to test have been justified for the most relevant time frame; the entire fifteen- year
sample period. The Factor Timing results support the Characteristic Timing results.
5.2.2 Portfolio Allocation Analysis
Comer (2006) uses a test that he refers to as Portfolio Allocation Analysis to support his
Factor Timing findings – here I look to apply it as a robustness test for the Characteristic Timing
results. The test combines the methodology of Henriksson and Merton (1981) with the quadratic
programming analysis of Sharpe (1992). As Comer himself discusses, “The intuition behind
Period Stocks Bonds Stocks Bonds
2000-2014 0.03772*** 1.20210*** 0.00497 1.32025***
(2.86) (6.74) (0.22) (4.07)
2000-2004 -0.22951*** 1.56548*** -0.20322*** 4.38655***
(-6.05) (2.69) (-3.07) (4.35)
2005-2009 0.08733*** -0.08899 0.115504** 0.85697
(3.41) (-0.29) (2.54) (1.56)
2010-2014 -0.20529*** 9.40490 0.23391*** -13.17967
(-8.37) (1.49) (-4.93) (-1.07)
Table 5.4. Factor Timing coefficients for balanced funds and flexible portfolio funds (2000-2014)
Balanced Funds Flexible Portfolio Funds
Note: This table contains the Factor Timing coefficients, separately with respect to stocks and bonds, of all
balanced funds and flexible portfolio funds between 2000 and 2014. For balanced funds the adjusted R-
squared for 2000-2014 is 92.03%, for flexible portfolio funds it is 82.86%.
* Significant at the 10% level.
** Significant at the 5% level.
*** Significant at the 1% level.
37. 34
Portfolio Allocation Analysis is that a fund that demonstrates stock timing ability will have a
higher percentage of the portfolio allocated to stocks during the months in which stock returns are
higher than bond and cash returns. Likewise, the fund should have a lower portfolio allocation to
stocks during the months in which stocks underperform bonds and cash. By estimating portfolio
allocations during up and down stock markets, I can examine whether differences in stock
allocations are correlated with the stock timing ability estimated by the [Factor Timing] model.”
I will now sketch out how I implement the Portfolio Allocation Analysis. Recall that in
equation (4.5) there are four portfolios representing stock returns and four portfolios representing
bond returns. I collected the total returns (not excess returns) of these eight portfolios as well as
the total returns of cash (as used in equation (4.2)), for the duration of the 2000-2014 sample period.
Now I apply Henriksson and Merton’s dummy variable approach to create two different data sets.
For each monthly observation I compute the maximum and the minimum total return of the four
stock portfolios. I also do this for the four bond portfolios, combined with the cash portfolio. When
the minimum total return of the stock portfolios is greater than the maximum total return of the
bond and cash portfolios, I add that observation to the data set of the months where stocks are the
best-performing asset. Similarly, when the maximum total return of the four stock portfolios is less
than the minimum of the five bond and cash portfolios, that observation is added to the data set
containing the months where stocks are the worst-performing asset. Over the 15-year sample
period I found that there were 55 months where stocks were the best-performing asset and 39
months were they were the worst-performing asset.
At the next step Comer employs Sharpe’s quadratic program, but this is unnecessary here
as I already have data on the size of fund stock holdings (as a percentage of the overall portfolio).
I calculate the average stock portfolio weight during the months when stocks are the best-
performing asset and subtract the average stock portfolio weight from months where stocks are the
worst-performing asset. Doing this, I find that over the sample period, the average stock portfolio
weight of hybrid funds is 0.25% higher during the months where stocks are the best-performing
asset. This indicates that the market timing ability found by the Characteristic Timing and Factor
Timing methodologies are not spurious.
38. 35
5.2.3 Evaluation of the Characteristic Timing Methodology
After computing the 𝐶𝑇 scores I ran two different robustness tests to check the validity of
my findings: Factor Timing and Portfolio Allocation Analysis. I found that both of these robustness
tests supported my findings. I can now be confident enough to use my Characteristic Timing results
to test my three hypotheses. But before doing this I will introduce a short discussion of some of
the advantages and disadvantages of this methodology and how they might have impacted my
results.
Daniel et al. (1997) outline some advantages of methodologies that use fund portfolio
holdings. They argue that by analysing the portfolio holdings of funds it is more possible to design
benchmarks that do a better job of capturing fund investment styles. Another point that they make
is that using hypothetical returns, computed from fund holdings, is more appropriate for measuring
market timing ability as fees, expenses and trading costs are not taken account – making a fair
comparison with benchmarks that also ignore these costs. Specifically regarding Characteristic
Timing, Daniel et al. reason that empirical evidence suggests characteristics provide better ex-ante
forecasts of cross-sectional patterns of future returns than factor-based models. Furthermore, they
say characteristic-matching is better at matching future realized returns, leading to more statistical
power for detecting abnormal performance than factor-based models.
The first disadvantage of my methodology is that the holdings data is quarterly. This means
that I have to operate on the assumption that a stock or bond held in a given quarter is held for the
entirety of that quarter. For example, a stock that may have only been held for one day will wrongly
be assumed to have been held for much longer. This is a disadvantage of the methodology as it
does give a false representation of exact fund holdings. However, I do not believe this has a
profound impact on the 𝐶𝑇 scores. Each 𝐶𝑇 score is the holdings-based return of a year compared
to the holdings-based return from the previous year. So when computed for each month for 1,059
hybrid funds, most of this impact will be averaged out. Another disadvantage is that the
methodology measures the 𝐶𝑇 score using all transactions, and of course not all transactions are
due to market timing. It is possible that a fund has to make trades that negatively contribute to its
𝐶𝑇 measure due to diversification reasons, for example. In other words, Characteristic Timing
does not solely measure market timing ability, but this problem also applies to other market timing
measures.
39. 36
Overall, Characteristic Timing has proved to be a reliable and accurate methodology for
measuring market timing ability. But it is also important to compare this methodology to others,
and for hybrid funds the only other methodology is Factor Timing. Back in Section 2 I outlined
the four reasons that make Factor Timing less than ideal for measuring market timing ability.
Above, I also outlined some reasons why characteristic-based methodologies are better than factor-
based methodologies for the same purpose. One other advantage is the richness of the
Characteristic Timing results. Factor Timing only allows us to compute (meaningful) coefficients
for periods of no less than five years. The Characteristic Timing methodology supplies us with 𝐶𝑇
scores for each individual fund, for each fund month. This gives us greater flexibility to, for
example, compute the 𝐶𝑇 𝐵
measure for only balanced funds with high expenses in August 2007.
I believe that all of the above makes Characteristic Timing the most effective methodology for
measuring the market timing ability of hybrid funds.
It is also important to consider the ease of use and computation time of the Characteristic
Timing methodology. Characteristic Timing is much more difficult to implement than Factor
Timing and also takes up considerably more time. The Factor Timing methodology is simply a
regression. Once the relevant data has been collected and sorted, all that remains is a regression
analysis, which is relatively simple and rapid. Characteristic Timing, however, is not so straight
forward and even requires more data. For Factor Timing, monthly fund returns and the returns of
the eight benchmark indices are required. Whereas for Characteristic Timing we require holdings
information for each fund, including complete stock holdings. For each individual stock held by a
hybrid fund, each month, we require prior year returns (for momentum), market value of equity
and the book-to-market ratio in order to assign the stock to a characteristic portfolio – leading to
millions of pieces of data. Sorting the universe of stocks into 125 portfolios each year, as well as
calculating the returns of these portfolios, is equally time-consuming. Fortunately, matching bond
holdings to relevant bond indices is much less computationally intense. The accuracy and
effectiveness of the Characteristic Timing methodology comes at the price of being
computationally slow.
40. 37
5.3 HYPOTHESIS TESTING
Now that I have tested the robustness of the Characteristic Timing methodology, and
concluded that it is providing us with reliable and reasonable results, I would like to apply it to
testing the three hypotheses that I introduced in Section 2. Recall H1:
H1: On average, hybrid funds are successful market timers.
Table 5.1. provides the evidence needed to test this hypothesis. It states the aggregate overall 𝐶𝑇
score for hybrid funds over the period from 2000 to 2014: 0.20290, at the 1% level of significance.
Since I already provided reasoning for finding this result accurate, we cannot reject this hypothesis.
The answer to my main research question is yes – hybrid funds are good market timers.
It makes sense that hybrid funds are successful market timers. I argued previously from a
theoretical point of view that, as hybrid funds can invest in more types of asset classes, they have
greater control over their ability to allocate assets. Also, particularly more so for flexible portfolio
funds, hybrid funds have a great opportunity to adjust their asset allocations as market factors
change (recall non-hybrid funds can only alter the size of their cash allocation). One more reason
why this result should not surprise us is that hybrid funds should, in theory, possess a superior
knowledge of market factors. For example, hybrid funds must have a great understanding of factors
that affect stock returns as well as those that affect bond returns, whereas as most funds only have
to focus on one of these.
It is interesting to recall that most of the past empirical research has found that equity funds
and bond funds are not good market timers, whereas my research has found that hybrid funds have
exhibited stock and bond timing ability (albeit, not significantly). Earlier works that included
hybrid funds as part of a much larger and more general sample of mutual funds agree with my
conclusion that hybrid funds are good market timers. These works include the previously
mentioned papers of Lee and Rahman (1990), Ferson and Schadt (1996) and Volkman (1999). All
of the sample periods for these papers end at or before 1990, meaning there is no overlap at all
with my sample period. As I stated in Section 2, none of these studies analyse the bond portions
of the portfolios.
Comer (2006), the only pre-existing work on hybrid fund market timing, applies the Factor
Timing methodology to two sample of hybrid funds; 1981-1991 and 1992-2000. Comer finds
evidence of market timing ability in the 1992-2000sample, but no significant evidence in the 1981-
