Investor Timing Performance in the Dutch Mutual Fund Industry

(1)

Investor Timing Performance in the Dutch

Mutual Fund Industry

Rik Prikken S1611984

University of Groningen Faculty of Economics and Business

MSc Business Administration Specialization: Finance Supervisor: dr. A. Platinga

(2)

R. Prikken, 1611984 Page 2

Investor Timing Performance in the Dutch

Mutual Fund Industry

Rik Prikken

S1611984

JEL classification: G11,G20

Keywords: Mutual funds, Investor timing, Fund cash flows.

Abstract:

(3)

1. Introduction

In the last two decades the mutual fund industry has grown dramatically. According to the Investment Company Institute (2012), the world wide fund industry held assets worth of $23.8 trillion at the end of 2011. This dramatic growth justifies the enormous amount of studies published in the academic literature. Wermers (2000) summarized the state of the US equity mutual fund research as follows: “The majority of studies now conclude that actively managed funds underperform their passively managed counterparts.” More recent research almost always draws the same conclusion. On the other hand, according to Otten and Schweitzer (2002) and Otten and Bams (2002), European mutual funds, and especially small cap funds, outperform their US counterparts and sometimes even the benchmark. Ter Horst, Nijman and de Roon (1998) found that Dutch mutual funds that mainly invest in Dutch equity show outperformance relative to a passive portfolio of indices. European mutual funds and especially Dutch mutual funds are not investigated substantially while according to figures provided by the Dutch National Bank (2012), the Dutch mutual fund industry holds assets worth of €504 billion at the end of 2011 of which €179 billion in equity funds only.

However, besides the average performance of a mutual fund, also the timing and selecting abilities of mutual fund investors is important. Whether investors are able to enhance their returns by usefully timing their cash flows, selecting the superior funds, or both, is unclear. Grinblatt and Titman (1992), Hendricks et al (1993), Gruber (1996), Zheng (1999) and Keswani and Stolin (2008) find that the short-term performance of funds experiencing positive net cash flows appears better than those experiencing negative net cash flow. This suggests investors have the ability to select funds with superior subsequent performance, which is also known as the ‘smart money’ effect. This effect however, is explained by stock return momentum over the short term (Sapp and Tiwari, 2004). Whether the investment returns are influenced by investor timing decisions over a longer term is less clear. By comparing the time-weighted return of a mutual fund and the money-weighted return of that particular fund, the effectiveness of cash flow timing can be determined. The result of this comparison is known as the performance gap.

(5)

industry. By analyzing investor timing at the individual fund level, my methodology preserves cross-sectional differences in the timing performance of investors in individual funds. In addition the potential determinants of this performance gap will be studied to give more insight in this issue.

The following section will give the theoretical basis of this study including the potential determinants of the performance gap. This is followed by the data and methodology section which gives the descriptive statistics and the different models to study the performance gap. In section 4 the results of these data and methodology are given and section 5 explores whether return chasing behavior is present in the Dutch mutual fund industry. Section 6, finally, concludes.

2. Literature review

Investors can enhance their returns by selecting the superior fund and by considerably time their investment decisions, or both. The papers referred to in the previous section all focus on picking the superior fund while other studies examined the timing ability of fund managers whereas only a few have studied the timing ability at the individual fund level (Friesen and Sapp, 2007; Hou, 2012). In this section I will present the theoretical basis of my study and declare the different determinants under investigation.

(6)

(2012) state that ‘investors may purchase additional or well performing funds during good times, and redeem additional or poorly performing funds during bad times’. The net purchases or sales in a particular fund are the net cash flow. These flows are always initiated by investors and no counterparty action has to be considered. Therefore, to study the investment decisions of investors, the unique features of open-end funds provide a valuable tool. (Rakowski and Wang, 2009).

Several studies have examined the market-timing abilities of mutual fund investors and found mixed results. According to Treynor and Mazuy (1966), Jensen (1968) and Henriksson and Merton (1981) timing has no beneficial results when examining timing activities in mutual performance. Treynor and Mazuy added a quadratic term to the Capital Asset Pricing Model to test fund managers’ market-timing ability. They found no evidence to support the belief that mutual fund managers can outguess the market. Furthermore, Chang and Lewellen (1984) found negative market timing skills of fund managers. Meanwhile, Jiang et al. (2007) analyze portfolio holdings by applying holdings-based tests and find that, on average, actively managed US domestic equity funds have a positive timing ability. They argued that previous studies revealed insignificant market-timing ability because the return-based test such as the Treynor-Mazuy model demonstrates an artificial timing bias due to the dynamic trading effect and option-like features of stock returns. Finally, Dellva et al. (2001) studied the selectivity and timing performance of the Fidelity sector mutual funds during the 1989-1998 time period. When using the Dow Jones Industry benchmark, the results indicate that many sector fund managers have positive selectivity skills but negative timing ability skills.

(7)

annual returns generated lower returns compared to buy-and-hold returns over the testing period. Friesen and Sapp (2007) studied the performance gap of US mutual equity funds, e.g. the difference between the money-weighted returns and the time-weighted returns. Their results show that equity fund investor timing decisions reduce fund investor average returns by 1.56% annually based on a study over a period from 1991 to 2004. In other words, the average investor lost 1.56% by timing their cash flows relative to a buy-and-hold strategy. This final result is in line with the results of the study of Frazzini and Lamont (2008). They argue that investors fail to benefit from superior performance due to entering and exiting at the wrong moment. Hou (2012) also studied the performance gap on individual fund level and also by comparing the time-weighted returns with the money-weighted returns. His study contained 304 Taiwan’s mutual equity funds and the general conclusion is that investor timing performance is negative and significant in well-performing funds, but positive and significant in poorly performing funds.

(8)

show that the performance gap is negatively correlated with best-performing funds. Investors selecting a well-performing fund exhibit poor timing in their cash flows. The determinants I study are the raw total return, the risk-adjusted return according to the 3-factor alpha from the model of Fama and French, the volatility in both the time-weighted returns and the monthly net cash flows, the length of return history of the fund, the total size of the fund, the total expense ratio of the fund, whether the fund charges a fee when purchasing or selling the fund and, finally, whether the fund distributes an income as a dividend or reinvests this income.

3. Data and methodology

3.1. Sample description

This study uses a sample of 90 Dutch mutual equity funds for the period from June 2008 until March 2012. For all of these funds, at least 24 succeeding months of total net asset values are available. The selection of the funds to include is done with the help of Datastream and the data is collected from Morningstar and Bloomberg. I have chosen to exclude funds with fewer observations and all non-equity funds. According to studies of Friesen and Sapp (2007) and Hou (2012), the performance gap for equity funds is much larger and significant. In addition, benchmarking models employed for non-equity funds may not be appropriate. Table A in the Appendix contains a detailed list of all the funds included in this research

Since I include only Dutch mutual equity funds, my sample has only 90 funds. Therefore, the majority of my analysis is done on the fund level. I also study the funds on group level to avoid the possibility of making the wrong conclusion caused by a major asset transfer between 2 of more mutual funds within 1 sector. A bad performance of one of the fund managers can lead to such a transfer. I do this by defining five strategies and six investment region.

(9)

Table 1: Summary statistics.

Mean Median 25th Percentile 75th Percentile Standard deviation Total net assets (€ Millions) 265.56 73.33 27.09 276.52 558.2 Monthly net cash flow (€ Millions) -1.03 -0.21 -1.87 4.39 1.72 Arithmetic return (% per month) 0.22% 0.18% -0.07% 0.53% 0.65% Geometric return (% per month) -0.01% 0.02% -0.31% 0.37% 0.88% The sample contains all Dutch mutual equity funds with at least 24 monthly observations and which still exist. All non-equity funds are excluded. The final sample contains 90 funds. The monthly total net assets (TNA) are obtained from Bloomberg and the monthly net cash flow (NCF) is computed with ( ), with obtained from Datastream. The monthly arithmetic and geometric return are also computed with obtained from Datastream. The median and percentiles are ranked on

size (TNA and NCF) and on percentage (returns).

1, the 25th percentile smaller funds face a much lower average geometric return compared to the biggest 25th percentile(-0.31% to 0.37%). Whether this is of influence on the average performance gap, will be addressed later. I also observe that the arithmetic return is positive with an average of 0.22% and the geometric return is almost zero with an average of -0.01%.

3.2. Measurement of the performance gap

This study uses the performance gap as a measure of investor timing. The performance gap is defined as the difference between the time-weighted return and the money-weighted return of a particular fund. I use the time-weighted return to measure the performance of a fund and the money-weighted return, derived as the internal rate of return, to measure the performance of fund investors. In more precise words, the performance gap is the difference between the average monthly geometric return and the average monthly money-weighted return of a fund. When there are no transactions in and out of the fund over the entire period, the time-weighted return and the money-time-weighted return are the same.

The geometric average monthly return of a fund is calculated as

(10)

Where rj,t is the return of fund j in the month t. This geometric return measures the average return of a Euro invested over the entire sample period, including dividends. For this measure the data is obtained from Datastream.

The money-weighted average return captures the average return earned by fund investors. It measures the return weighted by the amount of money invested at each point in time. The money-weighted average monthly return for fund j is defined as the rate of return at which the accumulated value of the initial total net assets, plus the accumulated value of net cash flows, equals the actual total net assets at the end of the sample period:

: ( ) ∑ ( )( ) (2)

Here, TNAt is the total net assets at time t and with as:

( ) (3)

Here, is the net cash flow of fund j over the month t. I follow Gruber (1996) and

assume that when two or more funds merge into one surviving fund, investors place their money in the surviving fund and continue to earn the return on the surviving fund. All investor cash flows are implicitly assumed to occur at the end of each month. My measure for investor timing skills or performance gap is computed by subtracting the money-weighted return from the time-weighted return, or Eq. 1 minus Eq. 2:

(4)

(11)

3.3. Fund performance measurement

To examine whether a relationship exists between the timing performance of an investor and the performance of a fund, I classify the funds according to their risk-adjusted performance. The risk-adjusted performance will be evaluated by using a common employed benchmark model: the 3-factor model of Fama and French (1993).

The 3-factor model of Fama and French is specified by:

(5)

Where is the monthly total return on fund j in excess of the monthly return on a

German Bond (close to risk free) and Bench is the excess return on a value-weighted market portfolio. SMB (small minus big) is a size factor expressed in terms of market capitalization and is calculated as the difference between the return of a portfolio of small caps and the return of a portfolio of large caps. HML (high minus low) is a book-to-market factor and is calculated as the difference between the return of a portfolio of value stocks with a high book-to-market ratio and the return of a portfolio of growth stocks with a low book-to-market ratio. Fama and French (1993) have shown that these 2 factors massively improve the explanatory power of the regression. They argue that ‘the intercept in the time-series regression of the managed portfolio’s excess return […] is the average abnormal return needed to judge whether a manager can beat the market’, this is exactly what I need to study whether a relationship exists between the timing performance of an investor and the performance of a fund. I obtained the relevant data from the webpage of French.1

3.4. Determinants

In addition to the identification of a performance gap, I also explore the determinants of this performance gap. The potential determinants are the raw total return, the risk-adjusted

1 For the regression equation given by eq. (5) data can be obtained from the website of French:

(12)

return according to the 3-factor alpha from the model of Fama and French, the total size of the fund, the length of return history of the fund, the volatility in both the time-weighted returns and the monthly net cash flows and the total expense ratio of the fund. For the fund size determinant, I use the ratio of the total net assets of a fund versus the total net assets of all the funds in the sample and for fund age I count the number of returns since the introduction of the fund. For the volatility in the time-weighted returns, I calculate the standard deviation of the returns over the sample period, and for the volatility in the monthly net cash flow, I calculate the standard deviation of the flows over the entire sample period. The total expense ratio is obtained from Morningstar. In addition, I will explore weather a fund distributes or accumulates dividend and weather a fund charges a fee when purchasing or selling the fund or not are significant determinants of the performance gap. The data for these two potential determinants are also obtained from Morningstar.

Table 2: Summary statistics of the performance gap determinants.

Mean Median Standard Deviation

Arithmetic return (% per month) 0.23% 0.18% 0.65%

Alpha (F&F) (% per month) -0.36% -0.25% 0.68%

Fund size (size/average size) 1.00 0.27 2.10

Fund age (no. of returns) 175.5 150 134.9

Volatility of returns 0.064 0.058 0.026

Volatility of flows 0.760 0.332 1.464

Total expense ratio (% per year) 1.51% 1.47% 0.63%

Income Accumulate

Dividend 74 16

Load No-load

Load vs No-load 70 20

(13)

4. Results

4.1 Timing performance

For all the funds in the database the arithmetic, geometric and money-weighted returns are calculated in months and in quarters. With these numbers the performance gap is calculated according to the methodology presented. In panel A of table 3, the monthly average, median, 25th percentile, 75th percentile and the standard deviation of the returns of all the funds in my sample are presented. For the average fund, the performance gap is -0.02 percent per month or -0.24% annually. This means that investors have positive timing skills. According to table 1, only a few funds have a high percentage of total net assets under management and many smaller funds are part of the industry. For the smaller 25th percentile the performance gap is positive (0.05% monthly), e.g. a negative result because of timing decisions and a negative performance gap for the biggest 25th percentile (-0.16%). In other words, investors selecting the bigger funds benefit from timing their investments, while investors selecting the smaller funds face a loss due to timing. For the median fund, the performance gap is close to zero percent.

(14)

Table 3: Fund returns per month.

Mean Median 25th Percentile 75th Percentile Standard deviation

Panel A: All funds (n=90)

Arithmetic return (% per month) 0.22% 0.18% -0.07% 0.53% 0.65%

Geometric return (% per month) -0.01% 0.02% -0.31% 0.37% 0.88%

Dollar-weighted (% per month) 0.01% 0.01% -0.35% 0.53% 0.79%

Performance gap (% per month) -0.02% 0.00% -0.11% 0.15% 0.32%

(p-value) 0.

Panel B: Load funds (n=70)

Arithmetic return (% per month) 0.30% 0.19% -0.04% 0.63% 0.55%

Geometric return (% per month) 0.12% 0.04% -0.28% 0.38% 0.60%

Dollar-weighted (% per month) 0.11% 0.01% -0.30% 0.54% 0.63%

Performance gap (% per month) 0.01% 0.01% -0.08% 0.13% 0.25%

(p-value) 0.09

Panel C: No-load funds (n=20)

Arithmetic return (% per month) -0.02% 0.08% -0.22% 0.39% 0.87% Geometric return (% per month) -0.46% -0.06% -0.45% 0.17% 1.42%

Dollar-weighted (% per month) -0.35% -0.07% -0.76% 0.29% 1.16%

Performance gap (% per month) -0.11% -0.07% -0.20% 0.18% 0.47%

(p-value) -0.03

For each fund, the arithmetic, geometric and money-weighted returns per month are calculated. The performance gap is the difference between the geometric and money-weighted return. Panel A presents the entire sample, Panel B the load funds and Panel C the no-load funds. The medians are ranked by percentage. The probabilities for the mean performance gap are reported between the parentheses.

In table 4 the same results are presented as in table 3 but now in quarters of a year. According to table 4, Panel A the average performance gap is -0.56% per quarter or -2.2% per year which is considerably higher than the average performance gap calculated per month. The difference between the performance gap per month and per quarter is even bigger within the load funds. Panel B shows the average performance gap for load funds is -0.62% per quarter. For no-load funds the outcomes are almost identical for per month as for per quarter.

4.2 Fund size

(15)

Table 4: Funds returns per quarter

Mean Median 25th Percentile 75th Percentile Standard deviation

Panel A: All funds (n=90)

Arithmetic return (% per quarter) 1.00% 0.93% 0.26% 1.86% 1.47%

Geometric return (% per quarter) 0.05% 0.27% -0.69% 1.20% 2.01%

Money-weighted (% per quarter) 0.61% 0.54% -0.60% 1.85% 2.21%

Performance gap (% per quarter) -0.56% -0.16% -1.09% 0.13% 1.24%

(p-value) 0

Panel B: Load funds (n=70)

Geometric return (% per quarter) 0.21% 0.27% -0.61% 1.21% 1.50%

Money-weighted (% per quarter) 0.83% 0.67% -0.53% 2.18% 1.99%

(p-value) -0.01

Panel C: No-load funds (n=20)

Geometric return (% per quarter) -0.53% 0.26% -0.72% 0.90% 3.21%

Money-weighted (% per quarter) -0.16% 0.47% -1.60% 1.23% 2.79%

(p-value) 0

For each fund, the arithmetic, geometric and money-weighted returns per quarter are calculated. The performance gap is the difference between the geometric and money-weighted return. Panel A presents the entire sample, Panel B the load funds and Panel C the no-load funds. The medians are ranked per percentage. The p-value for the mean performance gap are reported between the parentheses.

positive timing skills and benefit from timing their investment decisions while the investors that are investing in the smaller fund do not. To study this more thoroughly, I will divide my sample into 5 deciles based on the fund’s total net assets. For every decile I calculate the arithmetic, the geometric and the money-weighted return. The performance gap is based on the difference between the geometric return and the money-weighted return. Results are shown in table 5.

(16)

Table 5: Performance gap based on fund size.

Decile 1 (small) 2 3 4 decile 5 (large)

Average TNA (€ million) 12.2 38.3 74.4 210 993

Arithmetic return -0.13% 0.32% 0.37% 0.37% 0.24%

Geometric return -0.44% -0.03% 0.16% 0.16% 0.11%

Money-weighted return -0.43% -0.08% 0.17% 0.23% 0.17%

Performance gap -0.01% 0.05% -0.02% -0.07% -0.06%

The entire sample is divided into 5 deciles based on fund size with decile 1 containing the smallest funds and decile 5 the larger funds. Average TNA is based on fund size per 3-2012. The performance gap is obtained by the difference between the geometric return and the money-weighted return.

4.3 Fund strategy and region

I also study the funds on group level. By presenting groups based on a strategy or investment region separately, I can study whether differences occur between these groups. In addition, by studying groups separately, I eliminate the possibility of making the wrong conclusion because of a major asset transfer between 2 or more funds within 1 strategy or region. Such a transfer can occur because of a poor performance of a fund manager of one of the funds and does not necessary have to occur because of a changing investment strategy which includes timing influence. I differentiate the database in several groups of funds based on two factors.

(17)

Table 6: Performance gap based on strategy and investment region.

Panel A: Strategy

Mean Values Income Mixed Growth Sustainable Sector

Geometric monthly return -0.04% 0.07% -0.97% 0.29% 0.53%

Money-weighted monthly return -0.08% 0.05% -0.66% 0.31% 0.49%

Performance gap 0.04% 0.02% -0.30% -0.01% 0.03%

(p-value) -0.06 0 -0.82 -0.83 -0.42

Median Values

Geometric monthly return -0.10% 0.02% -0.08% 0.15% 0.71%

Money-weighted monthly return -0.18% 0.02% -0.14% 0.20% 0.68%

Performance gap 0.05% 0.01% -0.12% -0.01% 0.05%

Standard deviation of performance

gap 0.003 0.002 0.006 0.001 0.003

Panel B: Investment region

Mean Values Global Europe Netherlands Pacific North

America Emerging

Geometric monthly return 0.48% -0.26% -0.27% -0.85% 0.90% -0.20%

Money-weighted monthly return 0.50% -0.25% -0.25% -0.64% 1.01% -0.36%

Performance gap -0.01% -0.01% -0.03% -0.21% -0.10% 0.16%

(p-value) 0 -0.39 -0.01 -0.62 -0.73 -0.82

Median Values

Geometric monthly return 0.31% -0.21% -0.42% 0.03% 0.91% -0.08%

Money-weighted monthly return 0.48% -0.19% -0.31% -0.26% 0.97% -0.30%

Performance gap 0.01% 0.00% -0.03% 0.01% -0.07% 0.13%

Standard deviation of performance

gap 0.002 0.003 0.002 0.006 0.001 0.003

For each fund the geometric and money-weighted returns are calculated. The performance gap is the difference between the 2. Funds are grouped according to strategy (Panel A) and investment region (Panel B). The strategies and investment regions of the funds are obtained from Morningstar. Summary statistics are reported per fund strategy and fund investment region. The standard deviations of the performance gaps are reported. Finally, the probabilities for the mean performance gap are reported between the parentheses.

response to positive returns than investors in conventional funds, but a smaller response to negative returns.

(18)

positive performance gap for the regions ‘Global’ and ‘Pacific’, although these are reasonable small.

4.4 Relation between the returns and the performance gap

The results so far have considered the difference between the time-weighted and money-weighted returns of the mutual funds. In other studies the ability of investors to select the better fund is examined frequently by measuring the risk-adjusted return of a mutual fund. Since the performance gap is measured at the individual fund level, it is possible to examine whether the timing performance and the ability of selecting the better fund is correlated with each other. To do this I need to measure the risk-adjusted return of the individual fund which can be done by the Fama and French (1993) 3-factor model. The alpha estimated by the model of Fama and French represents the risk-adjusted return. To examine whether a relation between the 2 performance indicators exists, I sort all funds into 10 deciles based on the alpha and present the corresponding performance gap of the decile. Besides the relation between the risk-adjusted return and the performance gap, I also study whether a relation is apparent between the arithmetic return and the performance gap.

Table 7 presents in 2 panels the results. Panel A considers the risk-adjusted return and contains 10 deciles, from worst to best. First of all, it can be seen that only the two best deciles have a positive alpha, which means that only the funds in these deciles are outperforming the benchmark based on the risk-adjusted return. Since the performance gaps are random and the Spearman Rank correlation is close to zero, there is no evidence of a significant relation between the risk-adjusted return and the performance gap.

(19)

Table 7: Performance gap of portfolios based on performance.

Performance

decile Panel A Panel B

F&F alpha

Performance

Gap Arithmetic return Performance Gap

1 worst -0.0177 -0.34% -0.0097 -0.37% 2 -0.0073 -0.07% -0.0027 0.03% 3 -0.0057 0.08% -0.0009 0.04% 4 -0.0043 0.01% 0.0001 0.10% 5 -0.0028 -0.04% 0.0014 -0.08% 6 -0.0023 0.06% 0.0025 0.02% 7 -0.0017 -0.08% 0.0036 -0.01% 8 -0.0006 0.08% 0.0056 0.05% 9 0.001 0.11% 0.0089 -0.04% 10 best 0.0053 -0.02% 0.0135 0.07% All funds Spearman Rank correlation -0.0036 0.122 0.0022 0.09

The entire fund sample is divided into 10 deciles based on the funds risk-adjusted return (Panel A) and arithmetic return (Panel B). The alphas are calculated according eq. (5). Of these deciles the average alpha and the combined average performance gap are presented.

4.5 Determinants

In this section I analyze the determinants of the performance gap controlling for a number of fund characteristics. The potential determinants are the raw total return and the risk-adjusted return, the total size of the fund, the length of return history of the fund, the volatility in both the time-weighted returns and the monthly net cash flows, the total expense ratio of the fund, weather a fund distributes or accumulates dividend and weather a fund charges a fee when purchasing or selling the fund. For each fund, the mean level of each fund characteristic over the sample period is employed. To examine the determinants, two models are used. Panel A in table 8 includes among the regressors’ the mean total return of the fund over the sample period as a measure of fund performance. Panel B in table 8 includes among the repressors’ the alpha from the Fama and French (1993) 3-factor model to replace the mean total return of the fund as a measure of risk-adjusted performance.

(20)

Table 8: Performance gap determinants.

Model I Model II

Panel A Panel B

Coefficient t-stat Coefficient t-stat

Intercept 0.001 0.34 0.001 -0.27 Arithmetic return 0.058 0.95 Alpha (F&F) 0.185* 2.67 Fund size 0.000 0.02 0.000 0.16 Fund age 0.000 1.63 0.000* 2.06 Volatility of returns -0.057* -3.78 -0.029 -1.65

Volatility of fund flows 0.000 -0.34 0.000 -0.46

Total expense ratio 0.137* 2.51 0.152* 2.88

dividend distributing 0.000 -0.07 -0.001 -0.50

Load vs. No-load 0.000 0.09 0.002 0.30

Adj. R² 0.229 0.284

Jarque-Bera (residuals) 4.29 (-0.117) 3.3 (-0.192)

The performance gap is the dependent variable in a linear regression and the independent variables are listed in the first column of the table. Model I uses the arithmetic return as the characteristic of fund performance. Model II uses the Fama and French 3-factor model risk-adjusted return according to Eq. (5) as the characteristic of fund performance.

* Significant at the 5% level.

(21)

5. Return-chasing behavior.

In this section I will study whether mutual fund investors are making timing decisions based on past fund performance which is also known as return-chasing behavior. First I will take a look at the published literature followed by my study and the accessory results.

The studies of Grinblatt and Titman (1992) and Hendricks et al. (1993) presents results which indicate that there is a positive persistence in mutual fund performance. According to studies of Gruber (1996), Zheng (1999) and Lynch and Musto (2003) there is a convex relation between past returns and fund flows of mutual funds. Gruber (1996) found that a strong and significant positive relationship is apparent between past performance and subsequent cash flows. Zheng (1999) found evidence that funds that have positive net cash flows subsequently perform significantly better. This effect occurs only on the short term and is almost entirely explained by betting on winners. Fund flows amongst poor performers are examined by Lynch and Musto (2003) who find that flows are less sensitive to past performance when past performance is relatively poor. Hou (2012) found for the well-performing funds a negative and significant investor timing performance and for the poorly performing funds a positive and significant investor timing performance. This suggests that investor return-chasing behavior exists in Taiwan’s mutual funds industry. A return-chasing behavior simulation done by Friesen and Sapp (2007) shows that investor behavior is broadly consistent with the negative timing ability and performance gap found empirically by their study.

5.1 Methodology

(22)

⁄ (6)

Where ⁄ is the ratio between the average monthly net cash flow of portfolio

p and the total average monthly net cash flow and is the average total return of portfolio p in the previous month. The SMB and HML factors are the same as in the Fama and French

Table 9: Summary statistics.

Panel A Mean Median Standard Deviation

NCF P1 (€ millions) 0.83 1.10 1.81

NCF P2 (€ millions) 0.99 0.85 1.83

Return P1 (% per month) 2.83 2.42 4.75

Return P2 (% per month) -2.8 -2.07 5.43

Panel B

NCF P1 (€ millions) 0.40 0.47 1.15

NCF P2 (€ millions) 1.65 1.39 1.57

Return P1 (% per month) 5.69 5.30 11.51

Return P2 (% per month) -4.10 -3.15 11.53

The sample is divided into two portfolios which are rebalanced every month. Panel A considers the monthly study and Panel B considers the quarterly study. P1 includes the better performing funds and P2 the poorer performing funds.

model and the relevant data is again obtained from the webpage of French. I include these two factors to improve the explanatory power of the regression.

In addition, I will also study the return chasing behavior from quarter-to-quarter. I will do this by taking t in quarters of a year and rebalancing is done every quarter. In total, this study has 14 quarters and is from June 2008 until March 2012. Table 9 reports the descriptive statistics of the simulation. Panel A of the table shows the average and the median of the NCF and the total return of both the portfolios month-to-month. P1 represents the portfolio including

the funds which performed better than the defined level in the previous month and P2 represents

(23)

5.2 Results

I have estimated the regressors for both portfolios. Table 10 presents the outcomes and I will first consider the monthly study. It can be seen that for Portfolio P1 the variable for return

chasing behavior is positive and significant. This means, according to my results, a positive return in the previous month leads to a higher net cash flow. This phenomenon is better known as return chasing behavior. For Portfolio P2, the return chasing behavior variable is not

significant. These two outcomes are consistent with the academic literature since former studies (Gruber 1996; Zheng 1999; Lynch and Musto 2003) showed the same convex relation between previous returns and net cash flows.

For the quarterly study, the outcomes are assuming that no return chasing behavior is present. The coefficients are for both portfolios not significant. Since the quarterly study only includes 14 quarters, this may be due to a lack of succeeding values.

Table 10: Return chasing behavior regression outcomes.

P1 P2

Panel A Coefficient t-stat Coefficient t-stat

Intercept 0.670* 2.10 0.840* 2.75 Return Chasing Behavior 8.064* 2.42 -4.003 -0.80 SMB -0.066 -0.60 -0.004 -0.04 HML 0.123 0.09 -0.176 -1.95 Panel B Intercept 0.143* 0.33 1.640* 3.09 Return Chasing Behavior 3.331 0.98 -2.106 -0.45 SMB 0.091 0.56 -0.068 -0.30 HML _-0.059 _0.68 _0.021 _0.18

The ratio between the average net cash flow of a portfolio and the total average net cash flow is the dependent variable in a linear regression and the independent variables are listed in the first column of the table. Panel A is on a monthly basis and Panel B is on a quarterly basis.

(24)

6. Conclusion

This study investigates the timing ability of mutual fund investors in the Dutch mutual equity fund industry by using cash flow data at the individual fund level. I do this by comparing the geometric return and the money-weighted return over the period 2008 to 2012 and I do not find substantial proof of a performance loss due to timing the investment decisions. When I take the entire sample and use a month-to-month basis, the performance gap is 0.02% negative per month or 0.24% per year which suggests a really small positive result of timing the investment decisions by investors. When I take the same sample and calculate the performance gap on a quarter-to-quarter basis, the performance gap increases to 0.56% negative or 2.2% per year. Different results are found when the sample is divided into sub-categories. The performance gap is small but positive for load funds while it is negative for no-load funds. Investors timing their investment decisions in no-load funds gain up to 1.3% per year compared to the geometric return of these funds. A comparison based on fund size suggests that larger mutual funds have a lower performance gaps than smaller funds, but the results also show that this relation is not significant. When the sample is sorted by fund strategy, I find performance gaps for all strategies which are really small, both positive as negative except for the strategy growth. The average active fund investor substantially outperforms the growth of a Euro invested in a fund with a growth strategy, on average 3.6% per year. Sorting on investment region, the same phenomena are found. For the regions Global, Europe and the Netherlands, the performance gap is negative but small. For the regions Pacific and North-America, the performance gap is negative too but more substantial, up to 1.2% and 2.5% per year respectively. For mutual funds investing in Emerging Markets, the performance gap is almost 2% per year positive. These results suggests that investors choosing mutual funds with investment regions Pacific and North-America gain from timing their investments while for Emerging Markets funds the opposite is true.

(25)

present in the Dutch mutual fund industry. This return chasing behavior leads to a convex relation between fund return and the succeeding net cash flow, which is consistent with former studies.

(26)

7. References

Bollen, N.., Busse, J., 2001, Short-term persistence in mutual fund performance, Review of

Financial Studies, Vol. 18, No. 2, pp. 569 – 597.

Bollen, N., 2007, mutual fund attributes and investor behavior, Journal of Financial and Quantitative analysis, Journal of Financial and Quantitative Analysis, Vol. 42 No. 3, pp. 683-708.

Braverman, O., Kandel, S., Wohl, A., 2005, The (bad?) timing of mutual fund investors, Discussion Paper, 5243, Centre for Economic Policy Research, London.

Bullard, M., Friesen, G., Sapp, T., 2007, Investor timing and fund distribution channels, viewed June 13, 2012. Available at SSRN: http://ssrn.com/abstract=1070545

Chang, E., Lawellen, W., 1984, Market timing and mutual fund investment performance,

Journal of Business, Vol. 57, pp. 57-72.

Dellva, W., Demaskey, A., Smith, C., 2001, Selectivity and market timing performance of Fidelity sector mutual funds, Financial Review, Vol. 36, No. 1, pp.39-54.

DNB, 2012, netto inleg en belegd vermogen in beleggingsinstellingen naar sector, Amsterdam, the Netherlands, viewed 25 September 2012,

<javascript:xls.open('t6.2nk.xls','nexbelinst1',800,600)>

Elton, E., Gruber, M., Das, S., Blake, C., 1996, The persistence of risk-adjusted mutual fund performance, Journal of Business, Vol. 69, pp. 133-157.

Fama, E., and French, K., 1993, Common risk factors in the returns on stocks and bonds,

(27)

Friesen, G., Sapp, T., 2007, mutual fund flows and investor returns: an empirical examination of fund investor timing ability, Journal of Banking & Finance, Vol. 31, pp. 2796-2816.

Frazzini, A., Lamont, O., 2008, Dumb money: mutual fund flows and the cross-section of stock returns, Journal of Financial Economics, Vol. 88, 299-322.

Grinblatt, M., Titman, S., 1992, The persistence of mutual fund performance, Journal of

Finance, Vol. 47 No. 1, pp. 1977-1984.

Gruber, M., 1996, Another puzzle: the growth in actively managed mutual funds, Journal of

Finance Vol. 51, 783-810.

Hendricks, D., Patel, J., Zeckhauser, R., 1993, Hot hands in mutual funds: short-run persistence of relative performance, Journal of Finance, Vol. 48 No. 1, pp. 93-130.

Henriksson, R., Merton, R., 1981, On market timing and investment performance: statistical procedures for evaluating forecasting skills, Journal of Business, Vol. 54, pp. 513-534.

Hou, T., 2012, Return persistence and investment timing decisions in Taiwanese domestic equity mutual funds, Managerial Finance, Vol. 38 No. 9, pp. 873-981.

Huang, J., Wei, K.D., Yan, H., 2007, Participation costs and the sensitivity of fund flows to past performance, Journal of Finance, Vol. 62, No. 3, pp. 1273-1311.

Investment Company Institute, 2011, Worldwide mutual fund assets and flows fourth quarter 2011. [ONLINE] Available at: http://ici.org/iciglobal/stats/worldwide/ww_12_11. [Viewed 25 September 12]

(28)

Jensen, M., 1968, The performance of mutual funds in the period 1945-1964, Journal of

Finance, Vol. 23, pp. 389-416.

Jiang, G., Yao, T., Yu, T., 2007, Do mutual funds time the market? Evidence from portfolio holdings, Journal of Financial Economics, Vol. 86, pp. 724-758.

Johnston, K., Hatem, J, Carnes, T., 2010, Investor education: how plan sponsors should report your returns, Managerial Finance, Vol. 36, No. 4, pp.354-363.

Keswani, A., Stolin, D., 2008, Which money is smart? Mutual fund buys and sells of individual and institutional investors, Journal of Finance, Vol. 63, pp. 85-118.

Lynch, A.W., Musto, D.K., 2003, How investors interpret past fund returns, Journal of Finance, Vol. 58, No. 5, pp. 2033-2058.

Mamaysky, H., Spiegel, M., Zhang, H., 2007, Improved forecasting of mutual fund alphas and betas, Review of Finance, Vol. 11, pp. 359-400.

Navone, M., Pagani, M., 2009, Loads and investment decisions, working paper, Carefin research paper no. 2/09

Nesbitt, S., 1995, Buy high, sell low: timing errors in mutual fund allocations, Journal of

Portfolio Management, Vol. 22, pp. 57-60.

Otten, R., Bams, D., 2002, European mutual fund performance, European Financial

Management, Vol. 8, pp. 75-101.

(29)

Rakowski, D., Wang, X., 2009, The dynamics of mutual fund flows and returns: a time-series and cross-sectional investigation, Journal of Banking & Finance, Vol. 33, pp. 2102-2109.

Sapp, T., Tiwari, A., 2004, Does stock return momentum explain the smart money effect?

Journal of Finance, Vol. 59, pp. 2605-2622.

Sirri, E., Tufano, P., 1998, Costly search and mutual fund flows, Journal of Finance, Vol. 53 No. 5, pp. 1589-1622.

Ter horst, J., Nijman, T. and de Roon, F., 1998, Return based style analysis and performance evaluation of Dutch mutual funds, Center Discussion paper 9850.

Treynor, J., Mazuy, K., 1966, Can mutual funds outguess the market?, Harvard Business

Review, Vol. 44, pp. 131-136.

Warhter, V., 1995, Aggregate mutual fund flows and security returns, Journal of Financial

Economics, Vol. 39, pp. 209-235.

Wermers, R., 2000, Mutual fund performance: An empirical decomposition into stock-picking talent, style, transaction costs, and expreses, The Journal of Finance, Vol. 55 No. 4, pp. 1655-1695.

Zheng, Lu., 1999, Is money smart? A study of mutual fund investors’ fund selection ability,

(30)

Appendix

Table A: detailed list of all the funds included in this research with the ISIN codes.

Name ISIN Name ISIN

Achmea Eurolanden Aandelenfonds NL0006259988 Intereffekt India Warrants NL0000817161

AEGON Equity Europe NL0006517278 Intereffekt Frontier Vietnam A NL0006489189

AEGON Euro Fonds NL0000210599 Intereffekt China Warrants NL0000290427

AEGON Europees Aandelen Fonds NL0000685519 Intereffekt Japanse Warrants Inc NL0006477440

Allianz Holland Europe NL0000286979 SNS Azië Aandelenfonds NL0000291128

ABN AMRO Multi Mgr Profiel 6 NL0000604759 Delta Lloyd Eur Deelnemingen Fds NL0006056509

Delta Lloyd Europa Fonds NL0000292712 Hof Hoorneman European Income Fund NL0009864495

ING Europe NL0000292332 Insinger de Beaufort Eurp Mid Cap NL0009787894

Optimix Europe Inc NL0000287092 ING Europe Small Caps NL0006311730

Rolinco NL0000289817 Kempen European Smallcap NL0000288504

SNS Euro Aandelenfonds NL0000291086 Kempen SeNSe NL0000113082

Achmea Wereld Aandelenfonds NL0006259996 ING Daily Consumer Goods NL0000289767

AEGON World Equity NL0000685477 ING Luxury Consumer Goods NL0000289684

Allianz Duurzaam Wereld Fonds NL0009497965 ING Energy NL0000289791

BNP Paribas OBAM NL0006294035 ING Utilities NL0000289668

Delta Lloyd Investment Fund NL0000286318 ING Financials NL0000286169

Friesland Bank Aandelen Fonds NL0000285955 ING Industrials NL0000289841

ING Global NL0006311805 ING Basic Materials NL0000289882

ING Global Opportunities NL0009265404 ING Information Technology NL0006311821

IdB Multi-Manager International Equity NL0000284883 ING Telecom Services NL0000289999

SNS Wereld Aandelenfonds NL0000291144 ING Health Care NL0000292274

Insinger de Beaufort Sustainable Val NL0006239006 Kempen Best Selection European Property NL0009296649 Achmea Nederland Aandelenfonds NL0006259970 BNP Paribas Small Companies Netherlands NL0006294134

Add Income Fund N.V. NL0009388743 Kempen Orange NL0000289627

AEGON Equity Holland NL0000685444 Kempen Oranje Participaties NL0000440675

AEGON Index Plus Fonds NL0006354151 Delta Lloyd Deelnemingen Fonds N.V. NL0000288389

Allianz Holland NL0000286920 ASN Duurzaam Aandelenfonds NL0000441301

BNP Paribas AEX Index NL0006294142 ING Duurzaam Aandelen Fonds NL0006311789

BNP Paribas Netherlands NL0006294084 Robeco Duurzaam Aandelen NL0000288843

Delta Lloyd Nederland Fonds NL0000286334 ASN Duurzaam S&Mid Cap NL0000290518

ING Dutch NL0000287993 ASN Milieu & Waterfonds NL0000280501

Robeco Hollands Bezit NL0000286615 Delta Lloyd Donau Fonds NL0000286326

SNS Nederlands Aandelenfonds NL0000291037 ING Emerging Europe NL0000292225

AEGON Emerging Markets NL0006354235 BNP Paribas Global High Income Equity NL0006294167

ING Global Emerging Markets NL0006311771 BNP Paribas Premium Global Dividend NL0006294050

Optimix Emerging Markets Inc NL0000287126 Delta Lloyd Select Dividend NL0000292746

SNS Opkomende Landen Aandelenfonds NL0000293371 ING Hoog Dividend Aandelenfonds NL0000289858

(31)

Robeco Afrika Fonds A NL0006238131 Kempen European High Dividend NL0000293348

AEGON Equity Pacific NL0006354391 Kempen Global High Dividend NL0006089229

BNP Paribas Asia Pacific High Income Eq NL0006294175 SNS Hoogdividend AandelenFds NL0000291102

Finles Lotus Fonds NL0006326936 Allianz Holland Amerika Fonds NL0000286896

Allianz Holland Pacific NL0000286938 ING North America NL0000286045

ING Far East NL0006311755 Optimix America Inc NL0000287118

Investor Timing Performance in the Dutch Mutual Fund Industry

Investor Timing Performance in the Dutch

Mutual Fund Industry

Investor Timing Performance in the Dutch

Mutual Fund Industry

Contents

1. Introduction

2. Literature review

3. Data and methodology

4. Results

5. Return-chasing behavior.

6. Conclusion

7. References

Appendix