Keywords: Global mutual funds; Market timing performance
1. Introduction
The main goal of this study is to evaluate the market timing ability of global asset allocationfunds. Also known as multi-asset global, these funds are part of the family of asset allocation,hybrid, and balanced mutual funds. These funds differ from traditional global or internationalfunds in that they face fewer investment constraints and are known to actively shift assets across awide variety of asset classes. Global asset allocation funds allow one-stop shopping for individualinvestors looking for a diversified global fund.
Traditional market timing studies assume that mutual fund managers restrict their decisionsto transfer assets only from stock to cash and vice versa. Given the investment philosophy of
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global asset allocation funds, a multi-factor model is necessary to measure market timing abilityand should include timing variables for both the stock and bond markets. This multi-factor modelmust span the investment choices these managers face. The purpose is to contribute to the markettiming literature by evaluating the market timing ability of this special kind of fund. This paper willapply the multi-factor market timing model first introduced by Comer (2006). Comer examinedthe market timing ability of two samples of hybrid mutual funds during two sample periods. Hereported significant market timing ability during the 1992–2000 time period.
Global asset allocation funds have received very limited attention. Most academic studiesinclude global asset allocation funds as part of the sample, and very few studies are solely devotedto global funds. Shukla and Singh (1997) is one of a few papers that has examined global funds.They reported evidence of over-performance by world equity mutual funds when compared witha world index. They also reported that global funds performed better during periods when theS&P 500 did poorly. Bhargava et al. (2001) studied 20 global mutual funds as part of their overallsample. Consistent with Shukla and Singh (1997), they also reported that global funds performedbetter than the Morgan Stanley Capital International Index (MSCI) World Index.
The objective of this study is to empirically examine the timing ability of global asset allocationfunds. I consider three model specifications: the classical Treynor and Mazuy (1966) modeland two multi-factor extensions based Comer’s (2006) work on domestic hybrid mutual funds.Based on the Treynor and Mazuy specification, I find evidence of poor market timing ability.However, this evidence disappears when two multi-factor extensions of the classical model areemployed.
The remainder of the paper is organized as follows. The next section presents a brief literaturereview. Section 3 describes the three timing models plus the style methodology used to identifythe focus market or the market where most of the trading and timing occurs. The fourth sectiondiscusses the data and presents some descriptive statistics. A fifth section presents the empiricalresults and a final section concludes.
2. Literature review
The seminal work of Treynor and Mazuy (1966) (hereafter TM) introduced a nonlinear modelthat measures the ability of fund managers to decrease (increase) market exposure prior to a marketfall (rise). The TM model tests for a nonlinearity effect as a result of timing ability. A mutualfund with a constant β that does not engage in market timing will have returns that are linearlyrelated to those of a market benchmark. However, a fund managed by a successful market timerwill have a nonlinear relationship with the market. The TM approach has become a standard inthe mutual fund literature to test the timing ability of fund managers.
Two influential studies of market timing include Henriksson and Merton (1981) and Lehmannand Modest (1987). Henriksson and Merton defined market timing as a put option strategy, wherethe perfect timer will be fully invested in the market during an up market and completely out ofthe market during a down market. Lehman and Modest introduced one of the first multi-factortiming models. They extended the TM model to include cross factors and quadratic terms to testfor timing ability in each of the markets (factors) included in the model.
More recent studies of market timing include Ferson and Schadt (1996) and Becker et al.(1999). These studies use a conditional TM model to compensate for the difference betweenpublic and private information. Timing and selectivity are examined in the models by Bello andJanjigian (1997) and Volkman (1999). The vast majority of the timing studies cited above fail tofind evidence of market timing. Bollen and Busse (2001) and Comer (2006) are two exceptions.
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Bollen and Busse (2001) have argued that most evidence of poor timing ability reported in previousstudies was due to the test of monthly, instead of daily, data. They found evidence of markettiming for equity funds during the 1985–1995 time period. Comer (2006) found that multi-factorextensions of the TM model better explain the return variability of hybrid mutual funds and reportssignificant timing ability for the sample covering the 1992–2000 time period. More will be statedabout Comer’s approach below.
Chance and Hemler (2001), Goetzmann et al. (2000), and Bollen and Busse (2001) proposedthe use of daily fund data to evaluate timing ability as well as mutual fund performance in general.These studies attributed the lack of evidence of market timing ability reported in previous studiesto the use of non-daily data. Chance and Hemler (2001) found evidence of market timing, but thisevidence disappears if monthly, instead of daily, data is used. Goetzmann et al. (2000) have shownhow the Henriksson and Merton (1981) model is biased when using monthly data to measure thetiming ability of managers who time the market on a daily basis. These findings motivate the useof daily data in this study.
Comer (2006) presented a multi-factor extension of the TM model to better examine the timingability of hybrid mutual fund managers. His model includes several stock and bond indices andtiming variables for both stock and bond markets to show the importance of using a multi-factormodel to examine the stock market timing ability of funds that clearly trade in both markets.The stock portion of the timing model in Comer (2006) is based on Elton et al. (1996a) andincludes four indices. Comer included indices that represent the S&P 500 as well as small, value,and growth stocks. Rather than relying on previous studies to develop the bond portion of themodel, Comer turned to bond classifications instead. He developed a new bond index modelbased on the different bond maturities and quality levels. The bond portion of the timing modelincludes indices that represent high quality bonds, low quality bonds, long maturity bonds, andshort maturity bonds.
Based on a comparison between the results from the traditional TM model and his multi-factor extension, Comer postulated that the inclusion of the bond indices in the multi-factormodel leads to different conclusions regarding the timing ability of hybrid mutual funds. Hefound that the timing coefficients based on the TM model are biased due to a strong correlationbetween bond indices and the quadratic term used to examine timing ability. The results fromthe multi-factor timing model provide evidence of significant market timing ability during the1992–2000 time period and less market timing ability during 1981–1991 than that based on the TMspecification.
The present study adds to the debate presented by Bollen and Busse (2001), Glassman andRiddick (2006), and Comer (2006). This paper investigates whether timing ability is measuredbetter when additional factors are included. By adding carefully chosen additional factors to theTM model and increasing the frequency of data, this study differs from previous studies and addsto the current literature.
3. Methodology
In this section, three market timing models are presented: the classical TM model, a stock-extended multi-factor timing model, and a stock- and bond-extended multi-factor timing model.Since the two multi-factor models include several indices to represent different sectors of thestock and bond market, it is imperative to determine which market is the focus market. The focusmarket is the market where the fund manager trades more actively. For this purpose, I use thestyle analysis of Sharpe (1992).
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3.1. Market timing models
Three timing models are employed to test for the market timing ability of global asset allocationfunds. The first model is the traditional TM model. This model calls for the estimation of thefollowing nonlinear regression:
ri = αi + βirm + δir2m + ei (1)
where ri is the excess return of the fund and rm is the excess return on the market index. Astatistically significant δi > 0 provides evidence of a fund manager with market timing ability.The TM model tests for the nonlinearity effect as a result of timing ability. A mutual fundthat does not engage in market timing will have returns linearly related to those of a marketbenchmark.
As previously stressed, the original TM model does not seem appropriate to measure the timingability of a manager that is known to shift funds between several asset classes. This motivates theuse of multi-factor timing models as proposed by Lehmann and Modest (1987) and later expandedby Comer (2006). The second model used here takes into consideration the different investmentoptions a manager faces in the stock market.
Elton et al. (1996b, 1999) used a model for stock returns that included an overall marketindex, a size index, and a growth versus value index. These two studies differentiate from otherstudies of market returns (see for example Fama and French, 1993) in that they used indices thatare accessible to individual investors through index funds. This fact allows for the comparisonof fund managers with alternative index strategies. My second timing model will include threeglobal stock indices: a value index, a growth index, and a small cap index. This multi-factor timingmodel takes the following form:
ri = αi + βirm + δir2m +
2∑j=1
λjrj + ei (2)
where ri is the excess return of the fund; rm is the excess return on a stock index representing thefocus market; and rjs are the excess returns on the two additional global stock indices. Again, apositive and statistically significant δi provides evidence of a fund manager with timing ability.Notice that in order to have a parsimonious model, no cross terms are included. This is consistentwith Comer (2006).
As it will become evident later in the paper, a significant portion of the portfolio of globalasset allocation funds is devoted to bond investment. To better measure the timing ability ofthese globally diversified portfolios, it is imperative to include bond indices that represent theinvestment choices that these managers have. Blake et al. (1993) used various bond indices thatcapture the specific features of this market. They used bond indices for corporate and governmentbonds as well as indices to capture differences in maturity. Also, they advocate the use of indicesthat represent low quality bonds and mortgage-backed securities. The approach here is that ofComer (2006). Comer proposed a model for bond returns that included indices that represent longand short maturities and high and low quality. To that specification, I also include an aggregatetreasury index to represent this sector of the bond market.1
1 I want to thank an anonymous referee for this suggestion.
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The second multi-factor model used here to measure market timing ability is an expandedversion of the stock-extended timing model presented above. The stock market part of the multi-factor model includes global indices for value and growth stocks and also a global small capindex. Global bond indices of short and long maturity, high and low quality, as well as a treasuryindex, comprise the bond part of the model:
ri = αi + βirm + δir2m + γirb + φir
2b +
6∑j=1
λjrj + ei (3)
where ri is the excess return of the fund; rm is the excess return on a stock index representing thefocus market; rb is the excess return on a bond index representing the bond-focus market; andrjs are the excess returns on the additional stock and bond indices. As previously mentioned, if amanager is successful at timing the market, δi will be positive and significant.
3.2. Fund exposure to market indices
To better measure the market timing ability of this sample of mutual funds, it is important todetermine the average exposure to each of the market indices included in the multi-factor timingmodel. Once the average exposure is determined, then the index with the largest average exposureis the one used in measuring timing ability.
In order to estimate the average fund exposure, the style methodology (first introducedby Sharpe, 1992) is used. Style analysis allows for the estimation of the exposure of eachfund’s portfolio to each market index from the publicly available daily fund and index returns.To implement this methodology, it is assumed that daily fund returns can be expressedas:
ri =n∑
j=1
wi,jrj + ei (4)
where ri is the daily total return of fund i; wi,j is the exposure of fund i to index j; rj is the dailytotal return of index j; and ei is the unexplained component of fund return.
The portfolio weights are the solution of a quadratic programming problem. These weightsrepresent factor loadings on an index strategy that does the best job explaining the fund’sreturn:
min
⎡⎣var
⎛⎝ri −
n∑j=1
wi,jrj
⎞⎠
⎤⎦
subject to
0 ≤ wi,j ≤ 1 ∀jn∑
j=1
wi,j = 1
(5)
The focus market, where most trading is likely to occur, is the resulting market with the largestexposure.
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Table 1Descriptive statistics
Variable Mean
Expense ratio 1.3178Turnover ratio 86.1848Net assets (MM) 529.9907
US stocks 28.3775Non-US stocks 25.0122Bonds 25.5571Cash 13.1952
Developed markets 86.3416Emerging markets 7.8329
Asia 15.0136Europe 27.8435Americas 56.8434
This table presents some descriptive statistics of the global asset allocation funds in the sample. These numbers are basedon the values reported in the quarterly Morningstar Principia CD during the 2001–2004 sample period. All numbers,except Net Assets, are in percentages. To calculate the mean value of each variable, first the mean of the time series ofeach fund was computed and then the cross sectional mean was calculated.
4. Data
4.1. Fund sample
The study covers the January 17, 2001 to August 31, 2005 time period. The start of the timeperiod was determined by the first day that data on the daily global bond indices was available.2
The fund sample is composed of all mutual funds classified as global asset allocation funds inthe December 2000 Morningstar Principia CD. To be eligible for the fund sample, a mutual fundmust have daily Net Asset Values (NAV) and distributions available starting January 17, 2001.The daily NAV and distribution data comes from Bloomberg. Funds provide their data to theNational Association of Security Dealers, which in turn provides the data to Bloomberg. For fundgroups with multiple classes of the same fund, only one fund is included in the sample.3
The final sample includes 27 mutual funds.4 Descriptive statistics for the funds in the sampleare presented in Table 1. The data presented in Table 1 show how diversified these funds really are.Based on average portfolio allocations and geographical exposures presented as percentages ofassets, more than half of their portfolios are invested in stocks and the stock part of the portfoliois almost evenly divided between domestic and international markets. Also, on average, thesefunds are heavily invested in developed markets but have some exposure to emerging countries.Moreover, there is a clear preference for the markets in North and South America, followed bythe markets in Europe and Asia. Finally, a significant portion of these portfolios is invested inbonds. This amounts to an average exposure of 25%.
2 For understandable reasons, the sample period excludes the days surrounding September 11, 2001.3 For further details see Livingston and O’Neal (1998) and O’Neal (1999).4 To avoid the survivorship bias problems presented in Elton et al. (1996a), all non-surviving funds are included in the
analyses.
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4.2. Index data
For the implementation of the single and multi-factor market timing models, data on severalglobal market indices is needed. For the stock part of the model, the MSCI global indices areused. For the traditional TM model, the market is represented by the MSCI global stock index.The stock-extended timing model (Eq. (2)) includes global indices for value and growth stocks,plus a global small cap index. All these indices are also provided by MSCI. On January 17, 2001,Lehman Brothers’ daily global bond indices became available for the first time, and this datemarks the start of the sample period of this study.
The main goal is to test the market timing ability of global asset allocation funds by firstpostulating new models of portfolio returns that do a better job explaining fund returns. Asmentioned above, the data on global bond indices was just recently available. This is one possibleexplanation of why a study like this has never been attempted before. The stock- and bond-extendedmulti-factor timing model includes the global stock indices mentioned above, plus global bondindices for long and short maturity, high and low quality, and an aggregate treasury bond index.All bond indices come from the Lehman Brother’s global family of bond indices.
5. Empirical results
5.1. Classical Treynor and Mazuy model
As a first step, I examine the timing ability of global asset allocation funds using the classicalTM timing model. The MSCI global stock index is used as the market index. The results fromthis estimation can be found in Table 2. As a group, these fund managers have poor markettiming ability as evidenced by a negative and statistically significant mean timing coefficient. Themean timing coefficient is −0.7542 and this value is statistically significant at the 10% level. The
Table 2Distribution of timing coefficients from the Treynor and Mazuy model
Stock timing coefficientsMean −0.7542*
Median −0.7258***
Standard deviation 1.9620
No. of positive coefficients 4No. of negative coefficients 23
No. of positive and significant coefficients 0No. of negative and significant coefficients 9
Mean adjusted r2 0.6545
This table reports the results from the estimation of the Treynor and Mazuy market timing model:
ri = αi + βirm + δir2m + ei, (1)
where ri is the excess return on the fund and rm is the excess return on the MSCI global stock index. A δi > 0 providesevidence of market timing ability. The sample of funds consists of 27 global asset allocation mutual funds from January 17,2001 to August 31, 2005. The statistical significance of the timing coefficients is based on heteroskedasticity-consistentstandard errors.
* Statistical significance at the 10% level.*** Statistical significance at the 1% level.
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Wilcoxon Rank Test was used to test the significance of the median. The results show that theestimated median coefficient is −0.7258 and significant at the 1% level.
Further evidence of poor market ability is evident when the distribution of the timing coeffi-cients is closely examined. Only 4 timing coefficients (or 14% of the sample) are positive, while23 coefficients (or 85% of the sample) are negative. None of the positive coefficients are signifi-cant. Nine of the negative coefficients are statistically significant: there are two coefficients thatare significant at the 1%, five at the 5%, and two timing coefficients that are significant at the10% level. Consistent with the majority of the timing literature, this sample of funds lacks timingability.
These results are based on a single-market timing model. Part of the motivation of this studyis to employ multi-factor timing models that include several markets (indices) that representthe investment opportunities these fund managers have. Next, a stock-extended timing model isconsidered. This alternative timing model includes global indices on growth, value, and small capsectors of the market.
5.2. Stock-extended timing model
Next, I estimate a stock-extended multi-factor model that includes three stock indices. Thismodel better represents the stock-investments options that these fund managers have. Since this isa multi-factor model, it is important to determine which sector of the stock market to use in testsof market timing. To address this issue, I use the style methodology in Sharpe (1992) describedabove.
Table 3 (panel A) shows the results of the style analysis. The weights on the table are theresults from the quadratic program in Eq. (5) estimated using an equally weighted portfolio of allthe funds in existence in any given day. Two important facts arise from panel A of Table 3. First,regardless of the constraints imposed by the quadratic program, this multi-factor model explainsthe variability in stock returns fairly well as evidenced by the 0.8133 adjusted r2. Second, theresults on the table show that the stock index with the highest average exposure is the globalsmall cap index with an exposure of 22.29%. Since the portfolio of funds displayed the highestsensitivity to this market, it is likely that most of the timing and trading of these fund managerstakes place in this market. This sector is going to be used as the stock index to measure timingability in this stock-extended multi-factor timing model.
Table 4 shows the results from the estimation of the stock-extended multi-factor timing model.This timing model does a slightly better job explaining the return process of these funds since theaverage adjusted r2 is 0.6868, which is higher than the 0.6545 for the TM model above. Althoughthere is some evidence of poor market timing, the significance of the evidence is lower than in theclassical TM case. As a group, no definitive conclusion can be made. The mean timing coefficientis −1.6865, but this value is not significant. Again, the Wilcoxon Rank Test is used to computethe median. This value of −1.6616 is also not statistically significant.
The distribution of the signs of the individual timing coefficients is very similar to the TMcase. Only five funds (or 18% of the sample) attained a positive timing coefficient. None of themare significant though. The remaining 22 funds have negative timing coefficients, with 7 beingsignificant. Two coefficients are significant at the 1% level, three at the 5%, and two at the 10%level. Overall, the results from a stock-extended multi-factor timing model that includes severalstock indices are very close to what the TM model reveals. But, although most of the timingcoefficients are negative, as a group, no significant evidence of poor market timing ability isevident.
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This table presents the solution from the style analysis performed on an equally weighted portfolio of all the funds inthe sample and in existence in any given day of the complete sample period. The weights presented here are the averagefactor loadings on an index strategy that best explains the return of the portfolio.*** Statistical significance at the 1% level.
Table 4Distribution of timing coefficients from the stock-extended timing model
No. of positive coefficients 5No. of negative coefficients 22
No. of positive and significant coefficients 0No. of negative and significant coefficients 7
Mean adjusted r2 0.6868
This table reports the results from the estimation of a multi-factor timing model that includes global stock indices for:value stocks, growth stocks, and small cap stocks. The following equation is the timing model estimated:
ri = αi + βirm + δir2m +
2∑j=1
λjrj + ei. (2)
δi > 0 provides evidence of market timing ability. The market in this case is represented by the small cap stock index.The sample of funds consists of 27 global asset allocation mutual funds from January 17, 2001 to August 31, 2005. Thestatistical significance of the timing coefficients is based on heteroskedasticity-consistent standard errors.
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Table 5Distribution of timing coefficients from the stock- and bond-extended timing model
No. of positive coefficients 5No. of negative coefficients 22
No. of positive and significant coefficients 2No. of negative and significant coefficients 2
Mean adjusted r2 0.72921
This table reports the results from the estimation of a multi-factor timing model that includes global indices for severalstock and bond indices. For the stock portion, the multi-factor timing model includes global indices for: value stocks,growth stocks, and small cap stocks. The bond part of the model includes global indices for: low and short maturity bonds,high and low quality bonds, and an aggregate treasury index. The following equation is the timing model estimated:
ri = αi + βirm + δir2m + γirb + φir
2b +
6∑j=1
λjrj + ei. (3)
δi > 0 provides evidence of stock market timing ability. For the stock timing variable the market is represented by the smallcap stock and for the bond timing variable the market is represented by the treasury index. The sample of funds consistsof 27 global asset allocation mutual funds from January 17, 2001 to August 31, 2005. The statistical significance of thetiming coefficients is based on heteroskedasticity-consistent standard errors.
5.3. Stock- and bond-extended model
The final timing model considered in this article is a multi-factor timing model that includesseveral stock and bond indices. This multi-factor model better spans the investment choices thatthese fund managers face. The model includes the same indices included in the stock-extendedmodel of the previous section, plus five bond indices. The model includes bond indices for longand short maturities, for bonds of high and low quality, and a treasury index.
Again, to determine the focus market, where it is hypothesized that most of the trading occurs,a style analysis on the equally weighted portfolio is estimated. The results in Table 3 (panelB) reveal that still, on the stock part of the model, the global small cap index has the highestsensitivity: 20.93%. Also, two facts are important to notice from the bond part of the model. First,bonds are an important part of these funds portfolios. The overall sensitivity to the set of bondindices is more than 21% and three out of five bond indices have non-zero exposure.5 The bondindex with the highest sensitivity is the aggregate treasury index. The weight on this factor is17.78% and is statistically significant at the 1% level.
The results from the estimation of the timing coefficients for the stock- and bond-extendedmodel can be found in Table 5. This model better explains the return variability of these funds. Theaverage adjusted r2 is 0.7292, which is higher than the two previous models. The results in Table 5are similar to that of the stock-extended model. There is no conclusive evidence of either poor orgood timing ability. The average stock timing coefficient is −1.2840 and the estimated median is
5 Although the long and short maturity indices have estimated zero weights, it is important to include these indices inthe timing model since many individual funds have a non-zero exposure to them.
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−0.9429; both of these numbers are not significant. The number of positive and negative timingcoefficients is equal to that of the stock-extended model; 5 are positive and 22 are negative. Thereare two positive coefficients at the 10% level while two negative coefficients are significant: oneat the 5% level and one at the 10% level.
In sum, the results show that the multi-factor timing models better explain the return vari-ability of this sample of global asset allocation funds. Based on these two formulations, noevidence of either good or poor timing ability is found. A somewhat surprising result is the evi-dence of poor market timing ability found when the single-market TM model is used. Thisresult is spurious since the TM formulation disregards the fact that these funds are knownto actively trade in several stock and bond sectors of the market. Moreover, when comparedwith the multi-factor formulations, the classical TM timing model has the lowest explanatorypower.
6. Conclusion
This paper empirically examines the market timing ability of global asset allocation funds.These globally diversified funds belong to the family of hybrid, balanced, and asset allocationmutual funds. As such, fund managers face fewer investment restrictions than the typical globalor international mutual fund. To the best of my knowledge, this is the first time the timingability of these funds is examined. Since daily data on global indices has just been available,data restrictions might be one of the reasons why a study like this has not been previouslyattempted.
Three market timing models are employed: the classical Treynor and Mazuy model and twomulti-factor extensions first introduced by Comer (2006). I examined the timing ability of a sampleof 27 global asset allocation funds during the January 17, 2001 to August 31, 2005 time period.Based on the classical Treynor and Mazuy timing model, there is evidence of poor market timingas indicated by negative mean and median timing coefficients. But the main goal of this studywas to test the market timing ability of these funds by postulating new models of portfolio returnsthat better explain fund returns.
This paper uses a stock-extended multi-factor timing model that includes global indices forvalue, growth, and small cap stocks. The model better explains the return variability of these funds.However, based on this alternative formulation, no definitive conclusion can be made regardingthe timing ability of these funds. Although the majority of the timing coefficients are negative,the mean and median coefficients are not significant.
Finally, a multi-factor model that includes indices representing several stock and bond sectorsof the market is employed. In addition to the stock indices included in the stock-extended tim-ing model mentioned above, this model also includes bond indices for short and long maturity,high and low quality, and an aggregate treasury index. This model works better than the previ-ous two models in explaining fund returns. However, the results based on this formulation aresimilar to those based on the stock-extended model. As a group, there is no evidence of eithergood or poor timing ability. The number of positive and negative timing coefficients is identicalto that found with the stock-extended model and I find no significant mean or median timingcoefficients.
The evidence of poor market timing ability based on the Treynor and Mazuy timing modelis somewhat troubling. The result is based on a single-factor timing model that ignores a listof market sectors where these fund managers actively trade. Moreover, based on the investmentobjective of these funds and the statistical evidence presented here, a multi-factor timing model is
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more appropriate to examine the timing ability of these fund managers. Finally, the results fromboth multi-factor timing models are consistent. There is no conclusive evidence of either good orpoor market timing ability.
Acknowledgements
I am grateful for the comments from Ike Mathur, editor, and an anonymous referee. Specialthanks go to George Comer for providing some of the data used in this study along with veryuseful insights. I also would like to thank Rauli Susmel, James C. Brau, and Jimmy Torrez, forcomments and suggestions. Finally, the outstanding research assistance of Mari Carmen Ferreiraand Angelica Rodrıguez is greatly appreciated.
References
Becker, C., Ferson, W., Myers, D., Schill, M., 1999. Conditional market timing with benchmark investors. Journal ofFinancial Economics 52, 99–148.
Bello, Y., Janjigian, V., 1997. A reexamination of the market-timing and security-selection performance of mutual funds.Financial Analysts Journal, 24–30.
Bhargava, R., Gallo, J.G., Swanson, P.E., 2001. The performance, asset allocation, and investment style of internationalequity managers. Review of Quantitative Finance and Accounting 17, 377–395.
Blake, C., Elton, E., Gruber, M., 1993. The performance of bond mutual funds. Journal of Business 66, 371–403.Bollen, N., Busse, J., 2001. On the timing ability of mutual fund managers. Journal of Finance 56, 1075–1094.Chance, D., Hemler, M., 2001. The performance of professional market timers: daily evidence from executed strategies.
Journal of Financial Economics 62, 377–411.Comer, G., 2006. Hybrid mutual funds and market timing performance. Journal of Business 79, 771–797.Elton, E., Gruber, M., Blake, C., 1996a. Survivorship bias and mutual fund performance. Review of Financial Studies 9,
1097–1120.Elton, E., Gruber, M., Blake, C., 1996b. The persistence of risk-adjusted mutual fund performance. Journal of Business
69, 133–157.Elton, E., Gruber, M., Blake, C., 1999. Common factors in active and passive portfolios. European Financial Review 3,
53–78.Fama, E., French, K., 1993. Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 33,
3–56.Ferson, W., Schadt, R., 1996. Measuring fund strategy and performance in changing economic conditions. Journal of
Finance 51, 425–461.Glassman, D., Riddick, L., 2006. Market timing by global fund managers. Journal of International Money and Finance
25, 1029–1050.Goetzmann, W., Ingersoll, J., Ivkovic, Z., 2000. Monthly measurement of daily timers. Journal of Financial and Quantitative
Analysis 35, 257–290.Henriksson, R., Merton, R., 1981. On market timing and investment performance II: statistical procedures of evaluating
forecast skills. Journal of Business 54, 513–533.Lehmann, B., Modest, D., 1987. Mutual fund performance evaluation: a comparison of benchmarks and benchmark
comparisons. Journal of Finance 42, 233–265.Livingston, M., O’Neal, E.S., 1998. The cost of mutual fund distribution fees. The Journal of Financial Research 21,
205–218.O’Neal, E.S., 1999. Mutual fund share classes and broker incentives. Financial Analysts Journal 55, 76–87.Sharpe, W., 1992. Asset allocation: management style and performance measurement. Journal of Portfolio Management
18, 7–19.Shukla, R., Singh, S., 1997. A performance evaluation of global equity mutual funds: evidence from 1988–1995. Global
Finance Journal 8, 279–293.Treynor, J., Mazuy, K., 1966. Can mutual funds outguess the market? Harvard Business Review 44, 131–136.Volkman, D., 1999. Market volatility and perverse timing performance of mutual fund managers. Journal of Financial
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