1. Introduction and Literature Review
In the field of mutual funds one important strand of research deals with money flows and the relation between investor flows and performance. Within this area there is broad agreement with the fact that investors “chase performance” (e.g., Ivković and Weisbenner [
1,
2]; Fulkerson
et al. [
3]; and public media, e.g., Clements [
4]; Gaffen [
5]; Israelsen [
6]), meaning that fund investors reward superior past performance with new money inflows, while inferior performance is punished by money outflows (e.g., Ippolito [
7]). Early studies document a convex, non-linear shape to this relation, showing that good performance leads to high flows of new money while bad performance produces only low outflows (e.g., Chevalier and Ellison [
8]; Sirri and Tufano [
9]; Del Guercio and Tkac [
10]; Shrider [
11]). This asymmetry is often attributed to the existence of uninformed (unsophisticated) investors making irrational investment decisions (e.g., Gruber [
12]), or to the expectation of rational investors that fund firms will replace poorly performing fund managers or at least change their investment strategy to avoid poor performance in future (e.g., Lynch and Musto [
13]). Huang
et al. [
14] also argue that participation costs (information or search costs + transaction costs) are sometimes cited as the reason for staying with a poorly performing fund.
Newer studies on the performance flow relation find evidence for a more linear relation by using gross money flows instead of net money flows. Among others, Ivković and Weisbenner [
1] find that “old money” punishes poor performance as much as it awards good performance, but “new money” flows into both poorly performing and well performing funds, thereby disguising the punishment and amplifying the award on an aggregate net flow level. Similar results are documented in studies by Ivković and Weisbenner [
2], Shrider [
11], Cashman
et al. [
15,
16]. An important reason for these steady new-money flows to poorly performing funds could be the growing popularity of mutual fund-based savings and retirement plans (e.g., Cohen and Schmidt [
17]; Holden and Van Derhei [
18]; Goda
et al. [
19]) which, once chosen, are difficult to change.
On the reverse relation between flow and performance, however, there is less agreement in the literature. Ferson and Schadt [
20] find that fund flows affect fund betas and thereby the funds’ expected returns. As for the direction, Berk and Green [
21] argue from a theoretical point of view that the (rational) allocation of new money to previously well performing funds causes diminishing returns to scale, thereby decreasing subsequent performance. On the other hand, poorly performing funds face money outflows and either enhance performance or disappear, such that in equilibrium all surviving funds earn equal returns.
Liquidity service costs and liquidity trading are the most popular explanations for diseconomies of scale caused by fund flows (e.g., Green and Hodges [
22]; Coval and Stafford [
23]; Edelen
et al. [
24]; Benson
et al. [
25]). Besides offering a widely diversified portfolio at a reasonable cost, mutual funds provide a high level of liquidity to shareholders who can buy and sell at the fair net asset value on a daily basis (e.g., Gruber [
12]; Edelen [
26]). This liquidity service is costly to fund investors, as a fund facing high money inflows might trigger a price impact and therefore have to buy new assets above fundamental value (e.g., Coval and Stafford [
23]). This is pronounced for large funds or funds investing in illiquid markets (e.g., Chen
et al. [
27]; Pollet and Wilson [
28]). On the other hand, a fund facing high money outflows is also likely to cause a price impact and may have to sell assets below fundamental value. Interestingly, Coval and Stafford [
23] find price impact by liquidity trading (“asset fire sales and purchases”) even in the most liquid of asset markets. In addition, Warther [
29], Edelen and Warner [
30], and Bhargava and Konku [
31] report significant price pressure and price changes in the equity market caused by high aggregate flows to the mutual fund market.
Edelen [
26] even finds that it is predominantly due to liquidity service costs that mutual funds usually underperform their benchmarks. Adjusting for liquidity-motivated trading, he finds significant levels of skill among mutual fund managers. Further confirmation of these results is reported in related studies by, e.g., Alexander
et al. [
32], who analyze returns to trading on information
vs. returns to trading on flows. They find significant outperformance for the former and significant underperformance for the latter. Pollet and Wilson [
28] analyze the different strategies reacting to growth, specifically scaling
vs. diversification. They find that funds reacting to growth by simply scaling their existing holdings significantly underperform funds that diversify, especially in more illiquid markets such as small-cap equity.
In recent studies, e.g., Rakowski and Wang [
33], Rakowski [
34], Benson
et al. [
25], and Rohleder
et al. [
35] find that the dynamics in fund flows, high flow volatility and unexpected flows significantly erode fund returns, as these variables cause the fund to perform more liquidity-motivated trades or to hold larger positions of cash as an alternative. However, both Rakowski and Wang [
33] and Benson
et al. [
25] find that price pressure through liquidity trading primarily affects fund returns in the short run. For the long-term, both studies document a positive effect of fund flows on mutual fund returns.
Regardless of its nature, there might be some information content in past mutual fund flows relevant to future returns. In this context, using portfolio holdings, Grinblatt and Titman [
36,
37] show that mutual fund managers often incorporate momentum in their investment decisions. Thus, if they use new money inflows to purchase past winners, then this might translate into higher fund returns going forward. However, to my best knowledge, there is no study analyzing the relation between flows and performance for a wide variety of different equity, bond, and money market fund groups. In addition, I am aware of no study systematically testing simple investment strategies for exploiting the information content in past fund flows in order to earn abnormal returns, so-called “flow chasing”.
In this empirical study I therefore first examine whether there is a significant relation between fund size and performance, specifically economies or diseconomies of scale. Then, I briefly analyze performance persistence or “hot hands” in mutual funds (e.g., Hendricks
et al. [
38]), because if investors chase past performance, this can only be beneficial if past performance is persistent. After this, I assess whether there is a systematic relation between past flows and future performance for different objectives of equity, bond, and money market funds before testing simple investment strategies based upon this information against uninformed strategies.
I find larger funds to significantly outperform smaller ones, especially among bond and money market funds. In addition, I find significant short-term performance persistence as well as significant performance persistence over longer horizons for money market funds. As for the flow-performance relation, I find high-flow funds significantly outperform low-flow funds over one month, and also find significant outperformance of high-flow funds over longer horizons for bond funds. However, there is no advantage to be gained in using simple investment strategies based upon this information, as the abnormal returns earned by the strategies are eaten up by the high associated costs.
The remainder of this paper is organized as follows.
Section 2 describes the methods used in the empirical study.
Section 3 describes the data and reports summary statistics and performance.
Section 4 presents the results from the empirical study as well as interpreting remarks.
Section 5 concludes.
4. Empirical Analysis
4.2. Performance Persistence
Another issue in the relation between fund flows and future returns is performance persistence. It is clearly established that flows chase performance such that in periods following superior returns fund flows should be high and in periods following inferior returns fund flows should be low, if not negative (e.g., Ippolito [
7]; Ivković and Weisbenner [
2]). This is only beneficial if performance persists (e.g., Hendriks
et al. [
38]; Brown and Goetzmann [
52]; Carhart [
47]).
Table 4 gives a first indication of the validity of this relation by showing correlation coefficients between fund portfolio returns and future flows. For bond funds and equity funds, the table clearly confirms the relation as future absolute and relative flows are positively correlated with returns while correlation coefficients decrease with increasing time lag. The table shows unexpected relations for money market funds. While absolute flows leading 1, 2, (and 3) months are positively correlated as expected, relative flows and absolute flows leading by more than 3 months are highly negatively correlated—meaning that superior returns are followed by relative outflows rather than by increased inflows. However, as money market funds have very low returns on average, with very low return variability, these correlations might not be too meaningful.
Table 4.
Correlation coefficients between returns and future flows.
Table 4.
Correlation coefficients between returns and future flows.
| Bond Fund Returns | Equity Fund Returns | Money Market Fund Returns |
---|
| Equal-Weighted | Value-Weighted | Equal-Weighted | Value-Weighted | Equal-Weighted | Value-Weighted |
---|
Future absolute flows |
(t + 1m) | 40.91 | 42.54 | 37.62 | 37.79 | 12.48 | 26.55 |
(t + 2m) | 22.49 | 23.34 | 27.34 | 26.05 | 2.76 | 17.56 |
(t + 3m) | 14.95 | 15.21 | 13.41 | 14.92 | –1.44 | 10.87 |
(t + 4m) | 10.16 | 11.06 | 28.55 | 29.55 | –13.91 | –2.47 |
(t + 5m) | 9.37 | 9.00 | 21.84 | 21.54 | –12.83 | –5.05 |
(t + 6m) | 5.33 | 4.48 | 8.92 | 10.28 | –16.36 | –5.52 |
Future relative flows |
(t + 1m) | 31.38 | 30.11 | 24.05 | 22.57 | –10.79 | 2.46 |
(t + 2m) | 28.88 | 27.86 | 18.52 | 17.42 | –8.77 | 3.49 |
(t + 3m) | 22.44 | 20.94 | 7.90 | 9.41 | –14.54 | –3.71 |
(t + 4m) | 17.86 | 16.59 | 11.71 | 12.28 | –16.62 | –8.75 |
(t + 5m) | 16.44 | 13.96 | 18.37 | 18.06 | –28.06 | –21.57 |
(t + 6m) | 19.95 | 18.32 | 7.58 | 7.96 | –21.02 | –12.94 |
Following the liquidity cost argument from the literature (e.g., Edelen [
26]; Rakowski [
34]), high fund flows should erode fund returns because funds are likely to face problems in allocating their new money advantageously. This means that, if flows follow superior performance which
Table 4 confirms for bond funds and equity funds, there should be no performance persistence. However, for money market funds, where correlations in
Table 4 are low or negative, I expect to find at least some evidence for performance persistence. To test for performance persistence
Table 5 shows performance measures and flows for decile portfolios ranked by past returns as well as performance differences between the deciles, or zero-investment portfolios based upon the deciles, respectively.
6 Panel A shows results for deciles ranked by returns lagged one month (
t − 1
m). In the lower part the table, negative flows follow low performance and positive flows follow high performance, as expected.
Table 5.
Performance of deciles ranked by lagged return.
Table 5.
Performance of deciles ranked by lagged return.
| Bond Funds | Equity Funds | Money Market Funds | Bond Funds | Equity Funds | Money Market Funds |
---|
| A. Ranked by Monthly Return (t − 1m) | B. Ranked by monthly return (t − 2m) |
Monthly mean excess return (MER) |
Decile 1 (lowest) | −0.1652 | −0.0720 | −0.1038 *** | 0.1185 | 0.1929 | −0.1077 *** |
Decile 10 (highest) | 0.3692 *** | 0.8043 ** | 0.0235 *** | 0.1697 | 0.5728 | 0.0257 *** |
Highest − lowest | 0.5343 *** | 0.8763 *** | 0.1273 *** | 0.0511 | 0.3799 | 0.1334 *** |
Higher 30% − lower 30% | 0.3183 *** | 0.5103 ** | 0.0963 *** | 0.0498 | 0.1889 | 0.0984 *** |
Middle 40% − outer 60% | 0.0393 | −0.0259 | 0.0202 *** | 0.0179 | −0.0213 | 0.0202 *** |
Monthly multi-factor alpha |
Decile 1 (lowest) | −0.4576 *** | −0.5638 *** | −0.0087 | −0.1140 | −0.1520 | −0.0054 |
Decile 10 (highest) | 0.1655 * | 0.2580 * | 0.0337 *** | −0.0979 | −0.0488 | 0.0298 *** |
Highest − lowest | 0.6231 *** | 0.8218 *** | 0.0425 *** | 0.0161 | 0.1032 | 0.0352 *** |
Higher 30% − lower 30% | 0.3655 *** | 0.4625 *** | 0.0330 *** | 0.0337 | 0.0120 | 0.0297 *** |
Middle 40% − outer 60% | 0.0668 ** | 0.0189 | −0.0129 *** | 0.0145 | 0.0117 | −0.0129 *** |
Relative [absolute] fund flows |
Decile 1 (lowest) | −0.39 [−5.14] | −0.31 [−14.71] | 0.07 [−1.26] | −0.36 [−7.92] | −0.21 [−17.07] | 0.06 [−29.37] |
Decile 10 (highest) | 0.30 [31.70] | 1.52 [55.34] | 1.36 [156.64] | 0.24 [39.35] | 1.27 [51.22] | 1.22 [65.18] |
| C. Ranked by monthly return (t − 3m) | D. Ranked by monthly return (t − 4m) |
Monthly mean excess return (MER) |
Decile 1 (lowest) | 0.0587 | 0.2058 | −0.1089 *** | 0.0348 | 0.2206 | −0.1072 *** |
Decile 10 (highest) | 0.3402 *** | 0.5336 | 0.0272 *** | 0.2281 ** | 0.4669 | 0.0248 *** |
Highest − lowest | 0.2814 * | 0.3278 | 0.1361 *** | −0.1933 | −0.2463 | −0.1320 *** |
Higher 30% − lower 30% | 0.2153 ** | 0.1780 | 0.1005 *** | −0.1213 | −0.1742 | −0.0971 *** |
Middle 40% − outer 60% | −0.0272 | −0.0087 | 0.0186 *** | 0.0344 | 0.0368 | 0.0201 *** |
Monthly multi-factor alpha |
Decile 1 (lowest) | −0.1803 * | −0.1057 | −0.0045 | −0.2444 ** | −0.1145 | −0.0045 |
Decile 10 (highest) | 0.1186 | −0.1948 | 0.0272 *** | 0.0409 | −0.2026 | 0.0274 *** |
Highest − lowest | 0.2989 * | −0.0891 | 0.0317 *** | 0.2853 | −0.0881 | 0.0319 *** |
Higher 30% − lower 30% | 0.2260 * | −0.1086 | 0.0261 *** | 0.1842 | −0.0295 | 0.0277 *** |
Middle 40% − outer 60% | −0.0045 | 0.0342 | −0.0130 *** | 0.0433 * | 0.0834 * | −0.0141 *** |
Relative [absolute] fund flow |
Decile 1 (low) | −0.35 [−5.70] | −0.14 [−20.87] | 0.25 [−47.18] | −0.41 [−7.02] | −0.10 [−24.98] | 0.16 [−33.29] |
Decile 10 (high) | 0.36 [43.03] | 1.16 [45.66] | 0.99 [46.15] | 0.45 [50.31] | 1.26 [62.51] | 0.94 [49.15] |
| E. Ranked by monthly return (t − 5m) | f. Ranked by monthly return (t − 6m) |
Monthly mean excess return (MER) |
Decile 1 (lowest) | 0.1884 | 0.4380 | −0.1077 *** | 0.2263 * | 0.2663 | −0.1058 *** |
Decile 10 (highest) | 0.1917 | 0.2935 | 0.0252 *** | 0.0755 | 0.5963 | 0.0258 *** |
Highest − lowest | 0.0032 | −0.1445 | 0.1328 *** | −0.1509 | 0.3300 | 0.1316 *** |
Higher 30% − lower 30% | 0.0099 | −0.0457 | 0.0987 ** | −0.0983 | 0.2259 | 0.0979 *** |
Middle 40% − outer 60% | −0.0188 | −0.0319 | 0.0200 *** | 0.0070 | −0.0050 | 0.0184 *** |
Monthly multi-factor alpha |
Decile 1 (lowest) | −0.0542 | 0.1453 | −0.0045 | 0.0290 | −0.0965 | −0.0061 |
Decile 10 (highest) | −0.0636 | −0.4108 *** | 0.0279 *** | −0.2263 ** | −0.0536 | 0.0276 *** |
Highest − lowest | −0.0094 | −0.5561 *** | 0.0324 *** | −0.2553 | 0.0427 | 0.0337 *** |
Higher 30% − lower 30% | −0.0057 | −0.3355 ** | 0.0264 *** | −0.1674 | 0.0429 | 0.0273 *** |
Middle 40% − outer 60% | −0.0075 | 0.0008 | −0.0142 *** | 0.0380 | 0.0149 | −0.0135 *** |
Relative [absolute] fund flow |
Decile 1 (low) | −0.20 [6.82] | −0.18 [−17.93] | 0.24 [−11.82] | −0.15 [4.08] | −0.14 [−11.09] | 0.12 [36.52] |
Decile 10 (high) | 0.26 [35.06] | 1.22 [60.63] | 1.16 [73.76] | 0.23 [46.03] | 1.14 [50.45] | 1.02 [84.91] |
Concerning performance, Panel A shows a clear performance persistence structure, with decile 1 shows the lowest performance, and decile 10 the highest. In addition, I find a positive and significant performance by decile 10, even on a risk-adjusted basis, as well as significant and positive returns to both zero-investment “highest − lowest” and “higher 30% − lower 30%” portfolios for all three fund groups. This is in line with the short-term persistence findings by, among others, Bollen and Busse [
55].
Panel B of
Table 5 shows results for deciles ranked by returns lagged two months (
t − 2
m). Consistent with
Table 4, decile 1 experiences outflows while decile 10 experiences inflows for all the fund groups. As for performance, I find significant and positive returns for the zero-investment portfolios as well as positive and significant performance in decile 10 only for money market funds. Consistent with Berk and Green [
21], this is not the case for bond funds and equity funds.
Panels C and D show results for deciles ranked by returns lagged three and four months, respectively. In the lower part, flows show the same structure as in the panels before. As to performance, I find clear evidence for significantly persistent money market fund performance with significantly positive alphas for decile 10. Surprisingly, I also find significant and positive performance persistence for bond funds. For equity funds, neither panels shows any evidence of performance persistence.
Panels E and F show results for deciles ranked by returns lagged five and six months, respectively. Especially in Panel F the table shows positive flows also for decile 1 of bond funds and money market funds. Concerning performance persistence, I find evidence for significant and positive returns to the zero-investment portfolios and positive performance for decile 10 for money market funds. Notably, the performance of the zero-investment portfolios is more or less constant over all six panels. For equity funds, Panel E shows a significant performance reversal on a risk-adjusted basis, but no significant results for bond funds. Panel F shows no noteworthy results for equity or bond funds.
From this, I expect to find significant relations between past flows and future returns for bond funds but not for equity funds in the next section, especially for short horizons. Even though money market funds show significant performance persistence, the correlations with past flows are very low or even negative (
Table 4). Thus, I do not expect significant results for money market funds.
4.3. The Flow-Performance Relation
In previous sections I show that there are positive correlations between fund returns and future fund flows, confirming that investors partly ground investment decisions upon past returns. In addition, I show performance persistence in bond fund and money market fund performance. As a consequence, I expect fund flows to have at least some information content relevant to future returns. Such findings would be consistent with mutual fund managers using inflows to buy past winner funds (momentum) which in turn causes fund returns to persist over the short horizons (Grinblatt and Titman [
36,
37]). To test for the validity of this expectation,
Table 6 shows the performance of decile portfolios ranked by past relative flows as well as performance differences between the highest and the lowest deciles, between the higher 30% and the lower 30%, and between the middle 40% and the outer 60%.
7 Panel A of
Table 6 shows results of flow-deciles ranked by relative flows lagged one month. As a first finding, all fund groups show positive “highest – lowest” and “higher 30% − lower 30%” differences, respectively, showing that funds with high past flows outperform funds with low past flows. This is particularly pronounced for bond funds, where most results are statistically significant except for government bond funds. Money market funds show significant but very small outperformance of high past flow deciles for MER but not for alpha. As for equity funds, only small-cap funds show statistically significant differences, while all other groups are insignificant. This pattern in small-cap equity funds could be related to particularly strong momentum patterns of small stocks documented by, e.g., Fama and French [
56].
8
Table 6.
Deciles ranked by relative flows.
Table 6.
Deciles ranked by relative flows.
| Bond Funds | Equity funds | Money Market Funds |
---|
| All | Corporate | Govern-ment | Mortgage-Backed | Municipal | General | All | Aggressive Growth | Growth | Growth/Income | Mid-Cap | Small-Cap |
---|
A. Ranking by monthly relative flows lagged 1 month (t − 1m) |
Monthly mean excess return (MER) |
Decile 1 (lowest) | 0.0176 | 0.0363 | 0.1005 | 0.0019 | −0.0380 | 0.0367 | 0.2546 | 0.3275 | 0.1996 | 0.2429 | 0.5724 | 0.3242 | 0.0004 |
Decile 10 (highest) | 0.2191 *** | 0.2518 *** | 0.1399 * | 0.2300 *** | 0.1161 * | 0.2894 *** | 0.4333 | 0.5157 | 0.3441 | 0.3715 | 0.8162 ** | 0.7938 ** | 0.0043 * |
Highest − lowest | 0.2015 *** | 0.2155 *** | 0.0394 | 0.2281 *** | 0.1541 ** | 0.2528 ** | 0.1787 | 0.1882 | 0.1444 | 0.1286 | 0.2438 | 0.4695 *** | 0.0039 ** |
Higher 30% − lower 30% | 0.1502 *** | 0.1395 *** | 0.0297 | 0.1346 *** | 0.0932 ** | 0.1794 *** | 0.2122 | 0.1484 | 0.1656 | 0.1432 * | 0.2209 | 0.3542 *** | 0.0046 *** |
Middle 40% − outer 60% | 0.0273 | 0.0504 | −0.0051 | 0.0394 ** | 0.0535 *** | 0.0322 | 0.0086 | 0.0421 | 0.0509 | 0.0239 | −0.1284 *** | −0.0100 | −0.0080 *** |
Monthly multi-factor alpha |
Decile 1 (lowest) | −0.1810 *** | −0.1433 ** | −0.0504 | −0.1343 * | −0.2106 *** | −0.2432 ** | −0.1433 * | −0.1439 | −0.1156 | −0.1400 * | 0.0050 | −0.3391 *** | 0.0066 |
Decile 10 (highest) | 0.0377 | 0.0691 | −0.0460 | 0.0205 | −0.0154 | 0.0428 | −0.1190 | −0.1089 | −0.0739 | −0.1012 ** | 0.1211 | 0.0469 | 0.0100 * |
Highest − lowest | 0.2186 *** | 0.2124 *** | 0.0044 | 0.1548 ** | 0.1952 ** | 0.2860 * | 0.0244 | 0.0350 | 0.0416 | 0.0388 | 0.1161 | 0.3860 *** | 0.0034 |
Higher 30% − lower 30% | 0.1689 *** | 0.1525 *** | 0.0168 | 0.0877 ** | 0.1179 ** | 0.1978 *** | 0.0713 | 0.0706 | 0.0746 | 0.0792 | 0.0891 | 0.2213 ** | 0.0032 |
Middle 40% − outer 60% | 0.0162 | 0.0029 | −0.0189 | 0.0124 | 0.0480 *** | 0.0213 | 0.0208 | 0.0514 | 0.0341 | 0.0130 | −0.1362 *** | −0.0118 | −0.0035 * |
B. Ranking by monthly relative flows lagged 2 month (t − 2m) |
Monthly mean excess return (MER) |
Decile 1 (lowest) | 0.1050 | 0.1596 | 0.1149 | 0.0034 | −0.0186 | −0.0248 | 0.4881 | 0.4913 | 0.4501 | 0.4829 | 0.6854 * | 0.5566 | −0.0012 |
Decile 10 (highest) | 0.2079 *** | 0.2212 ** | 0.1595 ** | 0.2649 *** | 0.1061 | 0.3154 *** | 0.5368 | 0.3619 | 0.5754 | 0.5010 * | 0.7564 * | 0.8022 * | 0.0022 |
Highest − lowest | 0.1029 | 0.0616 | 0.0446 | 0.2614 *** | 0.1247 * | 0.3402 *** | 0.0487 | −0.1294 | 0.1253 | 0.0181 | 0.0710 | 0.2456 * | 0.0034 * |
Higher 30% − lower 30% | 0.0547 | 0.0283 | 0.0175 | 0.1370 *** | 0.0557 | 0.2089 *** | 0.0322 | −0.0530 | 0.0239 | 0.0905 | 0.1548 | 0.1880 | 0.0041 *** |
Middle 40% − outer 60% | −0.0046 | 0.0047 | 0.0034 | 0.0139 | 0.0426 *** | 0.0460 | 0.0280 | 0.0233 | 0.0536 | −0.0054 | −0.0521 | −0.0182 | −0.0149 *** |
Monthly multi-factor alpha |
Decile 1 (lowest) | −0.0949 * | −0.0694 | −0.0544 | −0.1601 *** | −0.1723 ** | −0.3170 *** | −0.1360 | −0.0023 | −0.1357 | 0.0951 | −0.0308 | −0.2368 ** | 0.0045 |
Decile 10 (highest) | −0.0019 | 0.0170 | −0.0207 | 0.0532 | −0.0421 | 0.0433 | −0.0729 | −0.2952 | 0.0514 | −0.0725 * | 0.0437 | −0.0223 | 0.0079 |
Highest − lowest | 0.0930 | 0.0865 | 0.0337 | 0.2133 *** | 0.1303 * | 0.3603 *** | 0.0632 | −0.2929 | 0.1871 | 0.0226 | 0.0745 | 0.2145 | 0.0035 |
Higher 30% − lower 30% | 0.0625 | 0.0437 | 0.0182 | 0.1068 *** | 0.0774 | 0.1966 *** | 0.0037 | −0.0965 | 0.0405 | 0.0769 | 0.1179 | 0.1604 | 0.0020 |
Middle 40% − outer 60% | −0.0202 | −0.0214 | −0.0042 | −0.0024 | 0.0223 * | 0.0188 | 0.0524 | −0.0489 | 0.0581 | 0.0109 | −0.0575 | 0.0115 | −0.0104 *** |
C. Ranking by monthly relative flows lagged 3 month (t − 3m) |
Monthly mean excess return (MER) |
Decile 1 (lowest) | 0.0809 | 0.1214 | 0.1266 * | 0.0444 | −0.0251 | 0.0953 | 0.3424 | 0.7466 ** | 0.1701 | 0.3669 | 0.3693 | 0.5681 | −0.0005 |
Decile 10 (highest) | 0.1830 *** | 0.2052 *** | 0.0799 | 0.2255 *** | 0.0878 | 0.1914 * | 0.4981 | 0.3003 | 0.3578 | 0.3556 | 0.7429 * | 0.7493 * | 0.0029 |
Highest − lowest | 0.1020 * | 0.0838 | −0.0467 | 0.1812 *** | 0.1129 ** | 0.1146 | 0.1557 | −0.4463 | 0.1877 | −0.0114 | 0.0374 * | 0.1813 | 0.0034 ** |
Higher 30% − lower 30% | 0.0906 * | 0.0710 | 0.0182 | 0.0854 *** | 0.0553 | 0.0975 | 0.0979 | −0.1415 | 0.0945 | 0.0501 | 0.2376 * | 0.1322 | 0.0052 *** |
Middle 40% − outer 60% | −0.0084 | −0.00186 | 0.0199 | 0.0124 | 0.0422 ** | 0.0512 | 0.0005 | −0.0178 | 0.0650 ** | 0.0153 | −0.0199 | −0.0104 | −0.0149 *** |
Monthly multi-factor alpha |
Decile 1 (lowest) | −0.1117 *** | −0.0649 | −0.0533 | −0.0997 | −0.1714 *** | −0.1904 *** | 0.1098 | 0.2485 | −0.1977 * | −0.0215 | −0.1948 | −0.1697 | 0.0104 |
Decile 10 (highest) | −0.0031 | −0.0046 | −0.0974 * | 0.0226 | −0.0645 ** | −0.0256 | −0.0774 | −0.3887 * | −0.0972 | −0.0935 ** | 0.0651 | −0.0469 | 0.0085 |
Highest − lowest | 0.1085 ** | 0.0603 | −0.0441 | 0.1223 * | 0.1069 * | 0.1679 * | 0.0324 | −0.6372 ** | 0.1006 | −0.0720 | 0.2598 | 0.1228 | −0.0019 |
Higher 30% − lower 30% | 0.0915 ** | 0.0707 * | 0.0119 | 0.0495 | 0.0600 | 0.1040 ** | −0.0167 | −0.2142 | 0.0353 | −0.0004 | 0.1162 | 0.0189 | −0.0012 |
Middle 40% − outer 60% | −0.0240 | −0.0567 | 0.0108 | −0.0088 | 0.0288 ** | 0.0257 | 0.0532 | 0.0419 | 0.0688 ** | 0.0189 | −0.0324 | 0.0044 | −0.0088 *** |
D. Ranking by monthly relative flows lagged 4 month (t − 4m) |
Monthly mean excess return (MER) |
Decile 1 (lowest) | 0.1084 | 0.0943 | 0.1192 | 0.1138 ** | 0.0312 | 0.1172 | 0.3102 | 0.4478 | 0.1954 | 0.3572 | 0.6688 * | 0.5298 | −0.0005 |
Decile 10 (highest) | 0.2050 *** | 0.2438 *** | 0.1258 | 0.2321 *** | 0.0759 | 0.1886 * | 0.3936 | 0.3428 | 0.3113 | 0.3441 | 0.5930 | 0.7633 * | 0.0034 |
Highest − lowest | 0.0966 * | 0.1495 ** | 0.0067 | 0.1184 ** | 0.0447 | 0.0952 | 0.0834 | −0.1050 | 0.1160 | −0.0131 | −0.0758 | 0.2335 * | 0.0039 ** |
Higher 30% − lower 30% | 0.0864 ** | 0.1179 ** | −0.0264 | 0.0782 *** | 0.0181 | 0.0737 | 0.0763 | 0.0583 | 0.0499 | 0.0758 | 0.0344 | 0.0614 | 0.0046 *** |
Middle 40% − outer 60% | −0.0083 | 0.0222 | 0.0287 | 0.0218 | 0.0283 * | 0.0602 | 0.0129 | 0.0795 | 0.0581 * | 0.0232 | −0.0570 | −0.0014 | −0.0154 *** |
Monthly multi-factor alpha |
Decile 1 (lowest) | −0.0933 ** | −0.1135 ** | −0.0441 | −0.0189 | −0.1294 ** | −0.1161 | −0.1693 | 0.0224 | −0.2199 * | −0.0808 | 0.0949 | −0.1320 | 0.0093 |
Decile 10 (highest) | 0.0098 | 0.0420 | −0.0822 | 0.0351 | −0.0670 ** | −0.0191 | −0.1741 ** | −0.2809 | −0.1289 | −0.0937 ** | −0.1146 | −0.0455 | 0.0067 |
Highest − lowest | 0.1031 * | 0.1555 *** | −0.0381 | 0.0541 | 0.0624 | 0.1040 | -0.0048 | −0.3033 | 0.0909 | −0.0129 | −0.2095 | 0.0864 | −0.0027 |
Higher 30% − lower 30% | 0.0973 *** | 0.1337 *** | −0.0315 | 0.0445 | 0.0244 | 0.0806 | 0.0287 | −0.0461 | 0.0457 | 0.0657 | −0.0746 | −0.0607 | −0.0011 |
Middle 40% − outer 60% | −0.0165 | 0.0129 | 0.0192 | −0.0112 | 0.0132 | 0.0449 | 0.0508 | 0.0805 | 0.0572 * | 0.0361 | −0.0614 | 0.0036 | −0.0083 ** |
E. Ranking by monthly relative flows lagged 5 month (t − 5m) |
Monthly mean excess return (MER) |
Decile 1 (lowest) | 0.1271 * | 0.1184 | 0.1875 ** | 0.1100 ** | 0.0633 | 0.0601 | 0.9344 | 0.4521 | 0.3688 | 0.2444 | 0.5428 | 0.5058 | 0.0002 |
Decile 10 (highest) | 0.1836 ** | 0.2148 ** | 0.1491 * | 0.2144 | 0.0986 | 0.1997 * | 0.4104 | 0.3182 | 0.3382 | 0.3692 | 0.4303 | 0.5211 | 0.0025 |
Highest − lowest | 0.0564 | 0.0964 | −0.0383 | 0.1045 * | 0.0353 | 0.1555 | 0.0160 | −0.1339 | −0.0306 | 0.1249 | −0.1125 | 0.0153 | 0.0023 |
Higher 30% − lower 30% | 0.0438 | 0.0547 | −0.0205 | 0.0854 ** | −0.0178 | 0.1316 ** | −0.0043 | −0.1439 | −0.0152 | 0.0784 | 0.0429 | 0.0459 | 0.0048 *** |
Middle 40% − outer 60% | 0.0046 | −0.0191 | −0.0058 | 0.0214 | 0.0266 * | 0.0870 ** | 0.0028 | 0.1307 | 0.0737 ** | 0.0163 | −0.0979 * | 0.0399 | −0.0150 *** |
Monthly multi-factor alpha |
Decile 1 (lowest) | −0.0331 | −0.0425 | −0.0034 | −0.0147 | −0.0869 ** | −0.1868 ** | −0.0816 | −0.0910 | −0.0590 | −0.1785 ** | 0.0185 | −0.2084 ** | 0.0083 |
Decile 10 (highest) | −0.0161 | 0.0003 | −0.0458 | 0.0038 | −0.0383 | −0.0139 | −0.1100 | −0.2676 * | −0.0567 | −0.0560 | −0.1923 | −0.2469 ** | 0.0073 |
Highest − lowest | 0.0170 | 0.0429 | −0.0423 | 0.0185 | 0.0485 | 0.1726 | −0.0284 | −0.1766 | 0.0022 | 0.1225 | −0.2109 | −0.0386 | −0.0010 |
Higher 30% − lower 30% | 0.0332 | 0.0409 | −0.0298 | 0.0448 | 0.0052 | 0.1110 * | −0.0514 | −0.2263 | −0.0091 | 0.0715 | −0.0757 | −0.0270 | −0.0017 |
Middle 40% − outer 60% | −0.0164 | −0.0487 | −0.0149 | −0.0093 | 0.0022 | 0.0379 | 0.0511 | 0.1141 | 0.0826 ** | 0.0336 | −0.1464 *** | 0.0395 | −0.0084 ** |
F. Ranking by monthly relative flows lagged 6 month (t − 6 m) |
Monthly mean excess return (MER) |
Decile 1 (lowest) | 0.0904 | 0.1337 | 0.1746 ** | 0.1252 ** | 0.0578 | 0.1276 | 0.3912 | 0.7350 ** | 0.2887 | 0.3332 | 0.3860 | 0.5021 | −0.0007 |
Decile 10 (highest) | 0.1735 *** | 0.2239 *** | 0.0606 | 0.2347 *** | 0.0811 | 0.1621 | 0.3664 | 0.5269 | 0.3246 | 0.3580 | 0.7283 * | 0.6532 | 0.0025 |
Highest − lowest | 0.0831 | 0.0902 | −0.1139 | 0.1095 * | 0.0233 | 0.0515 | −0.0248 | −0.2083 | 0.0359 | 0.0249 | 0.3423 * | 0.1511 | 0.0031 * |
Higher 30% − lower 30% | 0.0323 | 0.0405 | −0.0528 | 0.0551 * | −0.0308 | 0.1000 * | 0.0185 | 0.0508 | 0.0121 | 0.0680 | 0.0768 | 0.0521 | 0.0042 *** |
Middle 40% − outer 60% | 0.0055 | 0.0040 | −0.0011 | 0.0092 | 0.0337 *** | 0.0715 * | 0.0047 | −0.0887 | 0.0832 ** | 0.0029 | −0.0885 | −0.0358 | −0.0150 *** |
Monthly multi-factor alpha |
Decile 1 (lowest) | −0.1098 ** | −0.0592 | −0.0083 | −0.0269 | −0.1261 ** | −0.1212 | −0.0802 | 0.2092 | −0.0631 | −0.1100 * | −0.1325 | −0.2455 ** | 0.0075 |
Decile 10 (highest) | −0.0086 | 0.0266 | −0.1263 ** | 0.0134 | −0.0551 ** | −0.1266 | −0.1899 ** | −0.0737 | −0.1489 | −0.0644 * | 0.0157 | −0.1258 | 0.0073 |
Highest − lowest | 0.1012 * | 0.0858 | −0.1180 | 0.0402 | 0.0711 | −0.0054 | −0.1097 | −0.2829 | −0.0858 | 0.0457 | 0.1482 | 0.1197 | −0.0002 |
Higher 30% − lower 30% | 0.0332 | 0.0388 | −0.0652 | 0.0270 | 0.0053 | 0.0838 | −0.0303 | 0.0159 | −0.0185 | 0.0661 | −0.0000 | 0.0253 | −0.0002 |
Middle 40% − outer 60% | −0.0119 | −0.0327 | −0.0004 | −0.0205 | 0.0302 ** | 0.0348 | 0.0671 ** | −0.0917 | 0.1036 *** | 0.0269 | −0.0684 | 0.0313 | −0.0064 * |
Interestingly, Panel A shows significant outperformance by the outer deciles over the middle deciles of mid-cap funds, which is in sharp contrast to the hypothesis that extreme inflows or outflows force managers to invest or disinvest inefficiently, respectively, while moderate flows allow optimal investment decisions. Evidence in favor of this hypothesis is shown for municipal bond funds.
Panel B shows results for flow-deciles ranked by relative flow lagged two months. Here, the evidence in general weakens, as can be expected, but still I find significant outperformance of high past flow deciles over low past flow deciles for mortgage-backed and general bond funds. In addition, I find significant outperformance of middle deciles over outer deciles for municipal bond funds and for growth funds. But in general, there is no relevant information content regarding equity fund returns or money market fund returns.
Panels C and D show results for flow-deciles ranked by relative flow lagged three and four months, respectively. Again, I find significant outperformance for high past flow mortgage-backed and general bond funds. In addition, I find evidence for outperformance of high past flow corporate bond funds. For growth funds, I find outperformance of the middle deciles over the outer deciles in both panels, whereas I find a significant relation only in Panel C for municipal bond funds. Panels E and F show results for flow-deciles ranked by relative flows lagged five and six months, respectively, but results show no systematic relationship and only weak evidence for outperformance of high past flow mortgage-backed bond funds.
In a similar fashion
Table 6A reports performance and performance differences of deciles ranked by past absolute fund flows. For flows lagged one month, Panel A shows significant outperformance of high past flow deciles for mortgage-backed, municipal and general bond funds, as well as for growth funds and money market funds. Panel B, which reports results for deciles ranked by absolute flows lagged two months, shows significant outperformance of high past flow deciles for mortgage-backed and general bond funds, but no significant results for equity or money market funds. The remaining panels for deciles ranked by absolute flows lagged more than two months show most unsystematic results with the exception of significant outperformance of high past flow corporate bond funds in Panel D. As the absolute flow results are similar in direction but weaker in significance than the relative flow results, I do not present
Table 6a here, but it is available on request.
From these results I conclude that there is significant information about future returns in the flows to at least some fund groups. Especially high inflow mortgage-backed bond funds and general bond funds show significant outperformance. In addition, corporate bond fund flows provide some information. As for equity funds the information content is weak.
4.4. “Flow Chasing” Investment Strategies
In this section, I test whether the information content in fund flows regarding future returns can be exploited using simple investment algorithms. To do so, I compare the returns for four different strategies, two of which are uninformed and two that are informed. One strategy rebalances monthly the full sample of a respective fund group, one buys and holds the funds existing in a specific fund group at the beginning of the sample period (“initial funds”, e.g., Rohleder
et al. [
44]), and two invest according to past flow ranking information. All of these strategies account for front-end and rear-end loads charged for rebalancing transactions.
Of the flow-decile-based algorithms, strategy three initially invests in flow-decile 10 (highest past flows) and replaces monthly the funds that have migrated to deciles lower than 8, so that after a warm-up period there are stable weights of deciles 8, 9, and 10 in the portfolio as well as a constant monthly turnover ratio. Strategy four replaces funds that have migrated to deciles lower than 8 only every second month in order to reduce load payments. In this way, again after a warm-up period, there are alternating but bi-monthly constant weights between all deciles in one month, or between deciles 8, 9, and 10 in the other month, respectively, as well as a bi-monthly constant turnover ratio.
For both strategies, I calculate the portfolio weights and the turnover ratios from the empirical monthly and bi-monthly migration matrices. As an example,
Table 7 shows the migration rates between relative-flow deciles for all small-cap equity funds. Variances and standard deviations are calculated using the empirical covariances between the flow-decile returns and the portfolios weights of the strategies. An illustration of the strategies and the covariance matrix of small-cap funds is given in the
Appendix.
Table 7.
Empirical migration matrices between flow deciles, small-cap equity funds.
Table 7.
Empirical migration matrices between flow deciles, small-cap equity funds.
Decile (t + 1) Decile (t) | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
---|
A. Empirical 1-month migration matrix |
1 (lowest) | 29.32 | 15.69 | 8.75 | 7.05 | 5.93 | 5.53 | 4.97 | 5.62 | 5.86 | 11.28 |
2 | 17.29 | 25.54 | 17.29 | 10.28 | 7.60 | 5.53 | 5.09 | 4.28 | 4.22 | 2.88 |
3 | 10.24 | 17.80 | 23.36 | 16.47 | 10.06 | 7.36 | 5.44 | 4.10 | 2.80 | 2.37 |
4 | 6.91 | 11.17 | 17.07 | 21.05 | 15.87 | 10.45 | 6.67 | 5.21 | 2.94 | 2.65 |
5 | 6.03 | 7.60 | 11.04 | 16.89 | 19.69 | 15.86 | 10.11 | 6.23 | 3.65 | 2.92 |
6 | 5.31 | 6.22 | 7.46 | 10.52 | 16.83 | 20.01 | 15.44 | 9.46 | 5.41 | 3.34 |
7 | 5.47 | 4.99 | 5.47 | 7.80 | 10.46 | 16.08 | 20.94 | 16.01 | 8.54 | 4.23 |
8 | 5.72 | 4.52 | 4.13 | 4.78 | 6.81 | 9.83 | 17.22 | 23.68 | 16.63 | 6.68 |
9 | 6.27 | 3.91 | 3.51 | 3.13 | 4.15 | 5.78 | 9.05 | 17.79 | 30.53 | 15.88 |
10 (highest) | 8.13 | 3.01 | 2.20 | 2.16 | 2.62 | 3.47 | 4.87 | 7.36 | 19.03 | 47.15 |
B. Empirical 2-month migration matrix |
1 (lowest) | 26.59 | 16.19 | 9.65 | 7.35 | 6.56 | 5.49 | 5.51 | 5.79 | 6.31 | 10.54 |
2 | 17.42 | 23.67 | 17.15 | 11.25 | 7.41 | 6.43 | 5.44 | 4.36 | 3.71 | 3.15 |
3 | 10.69 | 17.36 | 21.56 | 16.06 | 10.53 | 7.73 | 5.75 | 4.51 | 3.07 | 2.74 |
4 | 8.13 | 11.05 | 16.50 | 19.82 | 14.83 | 10.33 | 7.32 | 5.11 | 3.66 | 3.24 |
5 | 6.81 | 8.06 | 11.56 | 15.97 | 18.59 | 14.85 | 9.76 | 6.78 | 4.37 | 3.25 |
6 | 6.31 | 6.57 | 8.07 | 10.47 | 15.48 | 18.55 | 14.88 | 9.80 | 5.94 | 3.94 |
7 | 6.03 | 4.84 | 5.39 | 7.85 | 11.07 | 15.39 | 18.84 | 15.46 | 9.86 | 5.27 |
8 | 5.54 | 5.06 | 4.06 | 5.11 | 7.40 | 10.41 | 16.87 | 21.78 | 16.26 | 7.51 |
9 | 5.67 | 4.55 | 3.62 | 3.56 | 4.94 | 6.63 | 9.85 | 17.58 | 27.32 | 16.28 |
10 (highest) | 7.49 | 3.09 | 2.72 | 2.69 | 3.21 | 4.15 | 5.63 | 8.49 | 19.08 | 43.45 |
Table 8 shows average loads per transaction and returns for the four investment strategies in the period from January 1993 through December 2009. Regarding the loads, bond funds charge lower loads on average than equity funds. Money market funds charge almost no loads. Front-end loads are distinctly higher than rear-end loads. The average front-end load for bond and equity funds range from 0.8634% of the amount invested for government bond funds to 1.3868% for aggressive growth funds.
Table 8.
Returns to different investment strategies and loads.
Table 8.
Returns to different investment strategies and loads.
| Average Loads per Transaction | Investment Strategies |
---|
1. All Funds Rebalanced Monthly | 2. Buy and Hold Initial Funds | 3. Flow Deciles Rebalanced Monthly | 4. Flow Deciles Rebalanced Bi-Monthly |
---|
| Frond | Rear | Round Trip | MER | MER − Loads | Turnover Ratio *,+ | St.dev. | MER | St.dev. | MER | MER − Loads | Turnover Ratio * | St.dev. | MER | MER − Loads | Turnover Ratio ** | St.dev. |
---|
Bond funds |
All | 0.9146 | 0.3645 | 1.2791 | 0.1308 | 0.1227 | 0.63 | 0.9571 | 0.1309 | 1.0138 | 0.2149 | −0.2462 | 36.05 | 0.9298 | 0.1960 | −0.0511 | 38.63 | 0.9485 |
Corporate | 0.8865 | 0.3345 | 1.2211 | 0.1567 | 0.1478 | 0.73 | 1.0419 | 0.1531 | 1.2112 | 0.2408 | −0.2272 | 38.32 | 1.0524 | 0.2234 | −0.0225 | 40.27 | 1.0920 |
Government | 0.8634 | 0.3277 | 1.1912 | 0.1153 | 0.1089 | 0.54 | 0.9356 | 0.1323 | 0.9832 | 0.1447 | −0.3705 | 43.25 | 0.9805 | 0.1414 | −0.1272 | 45.10 | 0.9555 |
MBS | 0.9739 | 0.3221 | 1.2960 | 0.1596 | 0.1538 | 0.44 | 0.7510 | 0.1433 | 0.7758 | 0.2191 | −0.2130 | 33.34 | 0.8865 | 0.2020 | −0.0271 | 35.36 | 0.8520 |
Municipal | 0.9778 | 0.4127 | 1.3906 | 0.0922 | 0.0858 | 0.46 | 1.2017 | 0.1045 | 1.2647 | 0.1190 | −0.3530 | 33.94 | 0.9562 | 0.1116 | −0.1415 | 36.41 | 1.0108 |
General | 0.9451 | 0.6766 | 1.6217 | 0.1694 | 0.1462 | 1.43 | 1.3899 | 0.1399 | 1.4423 | 0.2818 | −0.1601 | 27.25 | 1.3979 | 0.2642 | 0.0143 | 30.82 | 1.4060 |
Equity funds |
All | 1.2537 | 0.3935 | 1.6472 | 0.4036 | 0.3849 | 1.13 | 4.5269 | 0.3814 | 4.4598 | 0.4656 | −0.0151 | 29.18 | 4.9602 | 0.4516 | 0.1957 | 31.06 | 4.8085 |
Aggr. Gr. | 1.3868 | 0.4596 | 1.8464 | 0.3647 | 0.3412 | 1.27 | 4.7550 | 0.3926 | 5.0633 | 0.4843 | −0.0752 | 30.30 | 5.3015 | 0.4635 | 0.1525 | 33.70 | 5.1313 |
Growth | 1.3573 | 0.3756 | 1.7329 | 0.3334 | 0.3132 | 1.16 | 4.5397 | 0.3393 | 4.5149 | 0.3772 | −0.1336 | 29.48 | 5.0829 | 0.3728 | 0.1024 | 31.21 | 4.9237 |
Gr./Income | 1.2020 | 0.3679 | 1.5700 | 0.3530 | 0.3372 | 1.01 | 4.0211 | 0.3426 | 4.0382 | 0.4001 | −0.0310 | 27.26 | 4.0759 | 0.3965 | 0.1741 | 28.33 | 4.0507 |
Mid-Cap | 1.2445 | 0.4284 | 1.6728 | 0.5165 | 0.4876 | 1.73 | 5.2671 | 0.4057 | 5.4806 | 0.7627 | 0.2100 | 33.04 | 5.7144 | 0.7077 | 0.4196 | 34.44 | 5.6198 |
Small-Cap | 1.1057 | 0.4202 | 1.5260 | 0.5643 | 0.5478 | 1.08 | 5.4632 | 0.5232 | 5.3911 | 0.7530 | 0.2727 | 31.47 | 5.6719 | 0.7005 | 0.4409 | 34.03 | 5.6234 |
Money market funds |
Money M. | 0.0359 | 0.0822 | 0.1181 | −0.0272 | −0.0276 | 0.34 | 0.0341 | −0.0326 | 0.0341 | 0.0029 | −0.0739 | 65.00 | 0.0312 | −0.0006 | −0.0390 | 65.00 | 0.0314 |
The table shows a very clear picture of the investment strategies. Compared to the uninformed strategies, both flow-decile-based strategies show superior—or abnormal—returns before loads are accounted for. At the same time, the standard deviations do not change significantly, so that these strategies earn higher returns facing the same risk as the uninformed strategies. However, after loads are accounted for the table shows underperformance of the investment algorithms indicating that the abnormal returns are over-compensated by the associated loads charges.
It is, therefore, not possible to exploit the information content in lagged flows via these simple investment strategies. Of the two uninformed strategies neither is superior to the other. Strategy 1 yields the highest return for MBS, general bond, mid-cap, and small-cap funds. Strategy 2 shows the highest return for corporate, government, and municipal bond funds as well as for aggressive growth, growth, and growth/income funds.
Noteworthy is the fact that the highest abnormal returns from investing on past flow information are shown for small-cap and mid-cap funds. This is because, first, the flow-decile results in
Table 6 are less significant for these fund groups than for bond funds and, second, because from a theoretical point of view funds investing in small-cap or mid cap-funds based upon past-flow data should not do well because these are considered to be less liquid than other equity funds.
5. Conclusions
In the mutual fund literature, the relationship between flows and performance is a widely discussed topic. There is a consensus that investors chase past performance, but the opposite direction is not as clear. I, therefore, investigate the relationship between past flows and future performance for a variety of bond, equity, and money market funds, as well as examine related economic issues like performance persistence and the size-performance relationship. I find clear evidence for economies of scale in the mutual fund industry, especially for bond and money market funds, but also to a lesser extent for equity funds. Moreover, I find short-term persistence for all fund groups as well as performance persistence over several months for money market funds.
Overall, these results lead to the conclusion that funds with high past inflow significantly outperform funds with low past inflow. This is especially true for mortgage-backed and general bond funds over several months. For equity funds, however I do not find significant results over more than one month. Based on this information, I used simple investment algorithms and find abnormal returns to these strategies for all fund groups, where the highest abnormal returns are possible for mid-cap and small-cap equity funds, as well as for general and corporate bond funds. At the same time, the standard deviations remain more or less unchanged. However, when Account for the load charges associated with these algorithms, the abnormal returns are so eaten up that simple informed investment strategies underperform uninformed strategies.
As a policy implication, I must advise against “Flow Chasing” as an investment strategy. My empirical results show that there is some informational content in past flows that are relevant to the future performance of funds. However, this is not sufficient to offset the transaction costs associated with “chasing” and acting on this information.