This paper aims to assess the ability of New Zealand (NZ) actively managed funds to generate risk-adjusted outperformance using portfolio holdings data. Focusing on domestic equity allocations addresses the benchmark selection issue, particularly for funds with national and international exposures.
The authors assess performance using several asset pricing models including the CAPM, three-factor and four-factor models. The authors also assess performance across funds with different characteristics such as fund size, size of local holdings, type of fund provider, past returns and fees. The authors further examine whether funds engage in any stock-picking or market timing by considering the active share and tracking error.
The returns on NZ equity holdings of NZ actively managed funds from 2010 to 2017 provide little evidence of risk-adjusted outperformance and stock-picking skill. These exposures yield pre-cost returns that have a nearly perfect correlation with the market index and an insignificant alpha. Funds show little tendency to bet on any of the main characteristics known to predict stock returns, such as size, book-to-market and momentum. In addition, the authors show that the average active shares and tracking errors are low, suggesting that the majority of funds hold NZ equity portfolios that closely mimic the market index.
Existing studies rely on returns data which aggregate performance across all asset classes with varying exposures. This may lead to benchmark selection issues (particularly for funds with international exposures) which may obscure the fund manager’s true stock-picking skills. Assessment using holdings data would enable suitable performance measurement by researchers and industry analysts.
Frijns, B. and Indriawan, I. (2018), "On the ability of New Zealand actively managed funds to generate outperformance in their domestic equity allocations", Pacific Accounting Review, Vol. 30 No. 4, pp. 463-481. https://doi.org/10.1108/PAR-10-2017-0079Download as .RIS
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Copyright © 2018, Emerald Publishing Limited
The New Zealand (NZ) mutual fund industry has grown considerably over the past decade with institutional investors playing a greater role. According to the 2017 JBWere Equity Ownership Survey, the share of NZ equity held by NZ managed funds has increased from 15.6 per cent of total market value in 2005 to 21.4 per cent in 2017. Given the significant proportion of assets under management and the important role of professional investment firms in providing investment services, performance measurement is critical to determine whether investment objectives are being met. This is particularly important for actively managed funds, as investors expect these managers to provide returns that exceed passive returns, after fees and expenses.
Even though active funds claim the ability to outperform (Scobie, 2017), existing NZ focused studies document that active funds do not outperform the market after costs (Bauer et al., 2006; Fowler et al., 2010; Trainor, 2014; Frijns and Tourani-Rad, 2015). Most of these studies use return data, which complicate performance measurement, as funds invest in various asset classes and have both national and international exposures, making the selection of an appropriate benchmark difficult. We resolve this issue by using portfolio holdings data. Specifically, we zoom in on the domestic equity part of NZ-based funds and assess whether there is evidence of skill in the selection of NZ equities. While various studies use portfolio holdings to investigate performance in other markets, such as the USA (Da et al., 2010; Lewellen, 2011; Busse and Tong, 2012; Anand et al., 2013) or Australia (Bennett et al., 2016), to the best of our knowledge, ours is the first to study NZ mutual funds using NZ equity holdings data.
In this study, we focus on the NZ equities held by NZ actively managed funds and assess whether NZ fund managers have the ability to generate outperformance in their domestic equity allocations. Using monthly holdings data from September 2010 to February 2017, we find no evidence of outperformance over various market indices (NZX50, NZXAll, NZX50P). The aggregate portfolio of NZ equities held by active funds has a return correlation of 97.3 per cent with the NZX50 index and a beta that is indifferent from one. In addition, we find no evidence of outperformance before fees and expenses based on the CAPM, Fama and French’s (1993) three-factor and Carhart’s (1997) four-factor model. This suggests that the aggregate NZ equity portfolio of NZ actively managed funds closely tracks the market index without generating additional returns. When we group funds based on fund size, size of local holdings, type of fund provider (banks, insurance companies or investment companies), past returns and fees, we find almost no evidence of outperformance across the different categories.
Subsequently, we assess whether funds engage in stock-picking or market timing by considering the active share (Cremers and Petajisto, 2009) and tracking error. We find an active share of 26.4 per cent (Cremers and Petajisto (2009) label funds with active shares below 20 per cent as pure index trackers) and a tracking error of 2.8 per cent for the aggregate portfolio. Similar results are obtained when funds are grouped by different characteristics, suggesting that NZ actively managed funds engage little in stock-picking and market timing in their NZ equity allocation. In a direct test for stock-picking skills, we assess whether changes in the weights in a particular stock are related to future returns in that stock. We find no evidence of any stock-picking skill, but this analysis reveals that NZ active managers engage in return chasing behavior by increasing allocations to stocks that have performed well in the previous month.
Overall, our study shows that active fund managers, in aggregate, do little more than hold the market portfolio, and earn almost identical pre-cost returns to the market. An important implication for investors is that they should be cautious when funds claim to have abilities to outperform. Investors should be wary of the fees charged for these active strategies.
Our study contributes to a large body of literature on performance measurement of fund managers (Sharpe, 1966; Jensen, 1968; Hendricks et al., 1993; Carhart, 1997; Chen et al., 2004; Barras et al., 2010). Many of these studies focus on comparing returns of a fund to a benchmark index or a peer group of funds ( Carhart, 1997; Barras et al., 2010; Fama and French, 2010). These studies find little evidence of persistence in outperformance in US funds after controlling for risk and in some cases find persistent underperformance (Carhart, 1997).
The return-based evidence for the NZ fund industry is pretty much in line with the US evidence. Bauer et al. (2006) examine the performance of 143 NZ funds over the period 1990-2003 and find that the alphas for equity funds are not significantly different from zero. Fowler et al. (2010) examine the performance of NZ actively managed funds for the period 1999-2006 and find that active funds barely earn their fees and that passive investments might do just as well or better. Trainor (2014) documents that KiwiSaver funds do not outperform and in some cases underperform the benchmark constructed using various asset classes. Frijns and Tourani-Rad (2015) investigate the performance of KiwiSaver growth funds for the period 2007-2013. Similar to previous studies, they find no evidence of risk-adjusted outperformance of these funds and in several cases document significant underperformance.
While the above studies provide little evidence on the ability of active managers to generate outperformance, they are based on return data which aggregate performance across all asset classes. This may lead to benchmark selection issues (particularly for funds with international exposures), which may obscure a manager’s true stock-picking skills. To resolve this issue, recent studies focus on portfolio holdings data to more precisely measure fund manager skill (Cremers and Petajisto, 2009; Chen et al., 2010; Lewellen, 2011; Bennett et al., 2016).
Unlike the USA, NZ does not have a mandatory portfolio holdings disclosure regime [see Brown and Gregory-Allen (2012) for a discussion on the need for mandated portfolio disclosure]. However, Morningstar records holdings that are disclosed voluntarily by funds. Based on the market capitalization of funds, we obtain NZ equity holdings data for about 65 per cent of actively managed NZ funds. We use these data to assess the performance of the NZ equity portion of active funds, capturing the lion share of NZ actively managed equities. Our paper therefore complements existing return-based studies on the NZ market by using NZ equity holdings data and provides much needed research on this market that has received relatively little attention.
The remainder of this paper is structured as follows. Section 2 describes the methodologies used in this study to assess performance. Section 3 details the data and provides descriptive statistics. We report our findings in Section 4. Section 5 concludes.
To investigate the risk-adjusted performance of NZ-based investment funds, we compare the returns of the NZ equity portion of the portfolios held by actively managed funds with the returns of several NZ market capitalization weighted stock market indices.
The first model we consider compares the performance of the domestic equity portfolio of NZ-based actively managed funds with the market index. Specifically, we estimate the following CAPM regression:
The CAPM assumes that only market risk is priced. However, in addition to market risk, there are other well-established factors that affect stock returns and therefore the performance of investment funds. Fama and French (1993) posit that the cross-section of average returns can be explained by two additional factors: size factor and book-to-market factor. We therefore augment the CAPM with these factors and estimate the so-called Fama and French (1993) three-factor model:
Another well-established factor that is known to explain mutual fund returns is momentum. Carhart (1997) introduces a four-factor model which includes the previous three factors plus an additional factor to capture the momentum effect (Jegadeesh and Titman, 1995). As such, we consider the following four-factor model:
In this section, we discuss the data used in this study. We first discuss the holdings data that are obtained from Morningstar. Second, we discuss the return data, along with the portfolio and factor construction approach we follow.
3.1 Portfolio holdings data
Portfolio holdings data for NZ actively managed funds are obtained from Morningstar. Specifically, we obtain monthly holdings data that are disclosed by NZ actively managed funds. We limit our sample to the period from September 2010 to February 2017, when portfolio holdings information is more readily available. From the list of actively managed funds, we focus on those funds that have a NZ equity allocation; hence, this includes NZ equity and balanced funds and funds with international allocations but some proportion in NZ equities. We exclude funds of funds as their holdings are already considered through the underlying funds. In total, there are 134 NZ funds that fulfill our criteria, consisting of 58 managed (open-end) funds and 76 retirement (including KiwiSaver) funds.
Table I presents summary statistics for the portfolio holdings data. In Panel A, we report holdings by fund type (managed and retirement funds). As can be seen, the average number of NZ stocks held by NZ-based funds is 30. We observe that these funds, on average, invest 51 per cent of their equity holding in NZ equities or about 42 per cent of their total assets under management. In comparison, the average allocations to international equities is 31 per cent of the total asset value and around 28 per cent for fixed income securities. When we consider managed and retirement funds separately, we observe that the retirement funds have a broader allocation to NZ equities, investing in, on average, 34 stocks versus 28 stocks for the managed funds. As a percentage of total assets, managed funds allocate a substantially larger percentage to NZ equities than retirement funds, 48 versus 34 per cent, respectively. This relatively low percentage of total assets allocated to NZ equities of retirement funds is likely a consequence of the default investment option in the KiwiSaver scheme, which predominantly invest in fixed income securities.
In Panel B, we report portfolio holdings by year. The average number of stocks held by NZ-based equity funds increased over time from 19 in 2010 to 35 in 2017. However, the percentage holdings in NZ equities has decreased over time from 63 per cent in 2010 to 47 per cent in 2017. In addition, the value of local holdings as a percentage of total asset under management has also decreased from 56 to 36 per cent. The proportion of the fixed income component has increased, whereas the international equity component remains relatively constant. This downward trend in domestic equities and upward trend in fixed income securities is again driven by the growth of the default KiwiSaver investment option.
3.2 Return data
To assess the performance of the funds in our sample, we construct a value-weighted portfolio based on the actual reported holdings of NZ equities of each mutual fund. We compute the value weights of all funds in each stock, i, as:
As detailed in Section 2, performance is evaluated by comparing the excess return of this portfolio (in excess of the 90-day bank bill rate obtained from DataStream) to the excess returns of the market. In this case, we use three main indices in NZ to proxy for the market return, the NZX All index, the NZX50 index and the NZX50 portfolio index. Data for these indices are obtained from Datastream.
In our analysis, we also control for other factors such as size, book-to-market (BM) and momentum. As these factors are not readily available for the NZ market, we construct these factors manually, following the methodology of Fama and French (1993) and modifications based on Bauer et al. (2006) and Frijns and Tourani-Rad (2015) implemented to deal with the small cross-section of the NZ market. We first screen all listed NZ stocks at the end of each calendar year from 2010 to 2017. A stock must have a price record at the end of the year and publicly available accounting data for June of that year to be included in the factor portfolios. In line with common practice, we exclude foreign companies, unit trusts and stocks with negative BM ratios (Fama and French, 1993; Gaunt, 2004; Nartea et al., 2009). Accounting and stock market data are obtained from Datastream.
To construct the size factor, stocks are ranked by market capitalization as of December each year and sorted into two groups (we exclude companies with less than $5m in market capitalization). From these companies, the 20 per cent of smallest stocks are assigned to the small portfolio whereas the 80 per cent of the largest stocks are assigned to the large portfolio. The SMB factor is constructed by computing the difference between the small and large cap portfolio.
For the book-to-market factor, we follow a similar approach. Stocks are independently ranked by book-to-market ratio (shareholder equity divided by market capitalization as of December that year). The 30 per cent of stocks with the highest book-to-market value are assigned to the high book-to-market portfolio, whereas the 30 per cent of stocks with the lowest book-to-market value are assigned to the low Book-to-Market portfolio. The HML factor is computed as the difference between the high minus low portfolio. Both SMB and HML factors are value-weighted portfolios that are rebalanced annually.
To investigate the momentum effect, we again rank stocks at the end of each year, but this time according to their 11-month past returns lagged one month. This is consistent with the common practice of skipping a month between stock ranking and the investment period. The 30 per cent of stocks with the highest cumulative returns are assigned to the winners portfolio, whereas the 30 per cent of stocks with the lowest cumulative returns are assigned to the losers portfolio. The MOM factor is then computed as the difference between the winners minus losers portfolio. The selection of the cut-off points for the SMB, HML and MOM factors are based on Bauer et al. (2006) and Frijns and Tourani-Rad (2015).
Table II reports the annualized excess returns of the NZ equity portion of active funds and the market returns over the NZ 90-day Bank Bill rate, the SMB, HML and MOM factors. The portfolio based on all actively managed funds yields a return of 11.1 per cent p.a. over the risk-free rate. On average, retirement funds perform better than managed funds with annual returns of 11.6 per cent versus 10.7 per cent, respectively. The portfolio return has a standard deviation of 9.3 per cent, a slightly positive skewness and a kurtosis of around 3, suggesting that the distribution is close to normal.
The second block of results in Table II shows summary statistics for the market indices used in this study. We note that the equity strategy of the funds is slightly higher than the returns of NZX50 and NZXAll indices (10.4 and 10.2 per cent, respectively) and is comparable to the average return of the NZX50P index (11.2 per cent). We further note that the actively managed funds have slightly higher standard deviations relative to the indices, which can be due to the fact that the funds are under diversified relative to the indices or due to the active risk that funds take.
For the factors, we observe that the SMB factor has a negative return of −2.1 per cent p.a. over the sample period, suggesting that small caps underperformed large caps. The HML factor has a positive annual return of 5.3 per cent p.a., indicating that over the sample period, value stocks outperformed growth stock. Finally, the MOM factor has a positive return of 2.1 per cent p.a., suggesting that a trading strategy of buying winners and selling losers will yield positive returns. However, we note that these factor returns are insignificant.
4. Empirical results
In this section, we assess the risk-adjusted performance of NZ investment funds by comparing the returns of the portfolio of NZ equities of actively managed funds with various benchmarks. We further assess whether actively managed funds deviate substantially from the market portfolio.
4.1 Fund performance
To assess the relative performance of NZ actively managed funds, we start by looking at the performance of the funds relative to several benchmarks. Our main comparisons are to the NZX50, NZXAll, and the NZX50P indices. We then subtract the returns of the benchmark portfolio from the fund returns.
As a preliminary analysis, we plot the excess returns of funds relative to the various benchmarks in Figure 1. Over the sample period, average excess returns are positive against the NZX50 and NZXAll and zero against the NZX50P. None of these excess returns, however, are significant, suggesting that the performance of NZ actively managed funds could have been replicated, on average, by simply investing in the market portfolios.
We continue with formal analyses of performance using the asset pricing models detailed in Section 2. In Table III, we report the results for equation (1). In Panel A, the excess portfolio returns are compared to the NZX 50 index. The first column in each panel shows the results for a, the risk-adjusted outperformance over the market index. For the Aggregate portfolio, we observe that a is positive at 0.05 per cent per month (0.60 per cent p.a.), but insignificant. This suggests that there is no statistical evidence for outperformance of actively managed funds relative to the NZX 50 index. The coefficient b, which measures the degree of market risk, is close to and not significantly different from 1, suggesting that the portfolio mimics the market portfolio very closely. This is further confirmed by the adjusted R2, which is high at 95 per cent, indicating that market returns strongly explain the returns of the portfolio of actively managed funds. The evidence from portfolios constructed using the managed and retirement funds shows similar results, albeit that retirement funds have slightly better outperformance than managed funds. Both alphas, however, are insignificant.
Panels B and C report the results in comparison with the NZXALL and NZX 50P indexes, respectively. As documented in Panel A, estimated a’s are insignificant for all regressions, and the exposures relative to the market index, b, are not significantly different from one. In addition, the adjusted R2s are high, between 89 and 93 per cent. In sum, Table III suggests that NZ actively managed funds have returns on their NZ equity strategies that track the returns on market indices very closely. We do not find evidence supportive of the notion that actively managed funds are able to generate significant outperformance relative to these indices. The results confirm the earlier findings by Bauer et al. (2006), who focused on return performance of NZ funds, and Trainor (2014) and Frijns and Tourani-Rad (2015), who focus on KiwiSaver funds.
Table IV reports the results for the three-factor model [equation (2)]. The results show that the alphas are positive against the NZX50 and NZXAll indices, while the alpha is negative against the NZX50P index. However, none of the alphas are significant. As with the CAPM results, the coefficients b are close to one, suggesting that these portfolios follow the market index closely. For the SMB, the coefficients s are insignificant, suggesting that investment funds do not follow a size-based strategy [a finding also observed by Frijns and Tourani-Rad (2015) for KiwiSaver funds]. Similar to Frijns and Tourani-Rad (2015), for the HML factor, coefficients are also insignificant, suggesting that investment funds do not follow a specific growth or value strategy.
In Table V, we report the results of the four-factor model in equation (3). The coefficients m are negative and significant when we compare the portfolio with the NZX50 and NZX50P indexes, suggesting that investment funds do not follow a strategy of buying past winner, but focus on a strategy that buys past losers. Bauer et al. (2006) also find a negative exposure to the momentum factor for domestic-focused equity funds. However, we note that the inclusion of the momentum factor has no material impact on the results previously presented, i.e. the alphas from the four-factor model remain insignificant, betas are indistinguishable from one and adjusted R2’s remain virtually the same. These results again highlight the inability of actively managed funds to outperform the market index on aggregate.
4.2 Fund characteristics and outperformance
Section 4.1 documents that a portfolio made up of the NZ equity allocations of NZ-based investment funds tracks the NZ market indices closely and is not able to outperform these market indices. However, the finding that the aggregate portfolio, on average, does not beat the market does not prove that NZ-based mutual funds have no stock-picking skills. Indeed, if some of the active funds outperform at the expense of other active funds, we would expect the overall portfolio to show no outperformance. Hence, an important follow-up question is whether particular types of funds have stock-picking ability, even if the industry overall does not. To address this question, we categorize funds based on several characteristics and assess the performance of these funds. The characteristics we consider are fund size (to assess whether funds benefit from economies of scale), local holdings size, business type, past returns, fees and fund age.
In Table VI, we sort funds into different categories and report the performance relative to the NZXAll index. We focus on the aggregate holdings across all the funds within the group, which includes managed and retirement funds. The last column in the table reports the fraction of funds in each group.
In Panel A, we report results for portfolios based on average fund size, where Large represents the top one-third of funds with highest assets under management, etc. We observe that medium- and small-sized funds earn better returns than large funds. For the medium-sized funds, we observe that the CAPM, three- and four-factor alphas are around 0.12 per cent per month, whereas for the small-sized funds, the alphas are between 0.04 and 0.06 per cent. However, in line with the results presented in Tables III,Tables V we find that none of these alphas are significant and funds of different size almost perfectly replicate the market indices, with market betas indistinguishable from one and adjusted R2s around 90 per cent. In addition, no evidence is found for exposures to either SMB or HML, while a negative exposure to momentum is observed across all size categories.
In Panel B, we group portfolios by their value of local holdings. The idea behind this is that funds with large NZ equity holdings will have better industry knowledge. We observe that funds with less exposure to the local market perform better than funds that have greater exposure. In particular, funds with small local holdings have a positive and significant (at the 10 per cent level) monthly return of 0.22 per cent (based on the CAPM). This alpha coefficient, however, becomes insignificant in the three- and four-factor models, while the loading on the HML factor becomes significant. This observation indicates that the significant positive alpha observed in the CAPM regression can in part be explained by the exposure to value stocks. In contrast, funds with large local holdings have an insignificant monthly return of 0.01 per cent.
In Panel C, we group funds by business type (we focus on investment funds provided by banks, insurance companies and investment companies). Overall, banks appear to have the best performance, with a CAPM alpha of 0.09 per cent, a three-factor alpha of 0.07 per cent and a four-factor alpha of 0.08 per cent per month. However, again none of the alphas are significant.
In Panel D, we group funds by past returns to assess whether past winners have better stock-picking skills than past losers. Funds with high past returns perform somewhat better than funds with low past returns, and funds with medium past returns seem to outperform the other two, but again none of the alphas are significant. These results indicate that past performance is not persistent.
We group funds by management fees in Panel E. The results suggest that funds with the highest fees (higher than 1.5 per cent) perform the worst as the alphas are negative. It is the funds with medium fees (between 1 and 1.5 per cent) which are the best performer with a CAPM alpha of 0.04 per cent, a three-factor alpha of 0.03 per cent and a four-factor alpha of 0.04 per cent per month. Again, none of the alphas are significant.
Finally, we assess fund performance in relation to fund age in Panel E. We make three equal-sized groups based on fund age (high, medium and low). The high group has an average age of 155 months, the medium group has an average age of 93 months and the low group has an average age of 43 months. We do not find that fund age affects fund performance as indicated by the insignificant alphas.
The basic conclusion from Table VI is that while we observe some small differences across funds with different characteristics, there is virtually no evidence of funds with specific characteristics outperforming the market based on the various benchmark models. The results suggest that the majority of groups hold portfolios that closely mimic the market.
4.3 Deviations from the market portfolio
The previous subsection demonstrates that there is little evidence on outperformance of funds with different characteristics, and that all fund types closely track the market index. In this section, we specifically address the question of how closely funds track benchmark returns.
We particularly examine whether active funds take positions that are different from the benchmark. The motivation for this analysis comes from the fact that an active manager can only add value relative to the index by deviating from it. This deviation may come either through stock selection, factor timing or both. Stock selection involves picking stocks that the manager expects to outperform their peers. Factor timing involves taking time-varying positions in broader factor portfolios.
We follow Cremers and Petajisto (2009) to quantify an active manager’s effort to engage in stock selection or factor timing. For stock selection, we compare the holdings of a mutual fund with the holdings of its benchmark index. This approach is labeled active share and is constructed as follows:
In addition to the active share, we also compute the fund’s tracking error, which is the time-series standard deviation of the difference between a fund’s return (Rfund,t) and its benchmark index return (Rindex,t):
Tracking error is widely used in practice to evaluate active portfolio management. A typical active manager strives for an expected return higher than the benchmark index, but at the same time a low tracking error to minimize the risk of significantly underperforming the index.
Table VII reports the results for active share and tracking error. We report the active share against the NZXAll index as this index provides the lowest active share and tracking error amongst the three indices. The result for our aggregate portfolio suggests that the mutual fund sector as a whole gives investor an active share of 26.4 per cent with a tracking error of 2.8 per cent p.a. [in their sample of US equity funds, Cremers and Petajisto (2009) report tracking errors that, for the majority, are in the range of 0-14 per cent]. These findings suggest that NZ actively managed funds, on average, track the market index very closely.
Similar to Table VI, we split the active share and tracking error of the aggregate portfolio into various groups based on fund size, local holdings size, business type, past returns, management fees and fund age. Based on these different sorts, the active share rarely exceeds 30 per cent and tracking errors largely stay below 4 per cent.
Overall, our results show that active shares and tracking errors are low, implying that the majority of portfolios have holdings that deviate very little from the market index. Our findings provide little evidence of active management, either in terms of stock-picking or market timing.
4.4 Can mutual funds predict winners?
The previous sections have shown that the investment strategies of NZ-based mutual funds largely track benchmark returns and that active shares of these funds are relatively low. In this section, we address the question of stock-picking skills of mutual funds even more directly by considering whether the weights that active funds allocate to stocks have any predictive power over future returns of these stocks. Specifically, we estimate the following regression:
Table VIII reports the results for equation (8). As can be seen, the coefficient on forward-looking returns is insignificant, showing that there is no predictive relation between the weights allocated to specific stocks and their future returns. We also do not observe any significant relation between current returns and changes in weights. However, we do observe that there is a positive and significant relation between lagged returns and changes in portfolio weights. This suggests that funds tend to increase their allocations to funds that have performed relatively well in the previous month, and thus that they display return chasing behavior.
In this study, we examine the performance of NZ actively managed funds using portfolio holdings data. We focus on the returns of the aggregate NZ equity portfolio held by NZ active fund managers and compare them with the returns of market benchmarks such as the NZX50, NZXAll, NZX50P, as well as the returns of stock portfolios with similar characteristics. We further use the CAPM and three- and four-factor models to measure risk-adjusted performance. Our findings suggest that the aggregate portfolio held by active fund managers is highly correlated with the main NZ stock market indexes with portfolio betas that are statistically indifferent from 1. We find no evidence of outperformance for this aggregate portfolio before costs and fees, with a CAPM alpha of 0.05 per cent, three-factor alpha of 0.05 per cent and four-factor alpha of 0.06 per cent per month. We therefore concur with previous studies that NZ active portfolio managers do not possess stock-picking skill. Funds with specific characteristics appear to demonstrate better performance than others, but alphas remain insignificant.
Further analysis reveals that funds have relatively low active shares, close to the cut-off point of what Cremers and Petajisto (2009) refer to as passive index trackers, and tracking errors seem to be relatively low as well. This shows that active funds deviate their holdings relatively little from the actual market portfolio. In an even more direct test to examine stock-picking skills, we assess whether future stock returns are related to changes in weights, but find no relation between future returns and changes in portfolio weights. In fact, our results suggest that active funds engage in return chasing behavior, increasing the weights in stocks that have performed well in the previous month.
Our study shows that NZ active fund managers, in their NZ equity allocation, on aggregate, do little more than hold the market portfolio, presumably generating significant costs and fees in the process. Despite funds being actively managed, they earn almost identical pre-cost returns to passive market portfolios for their NZ equity allocation. Although this does not provide a complete picture on their ability to generate outperformance (as they may be skilled in the investment they make in non-NZ equities), a direct implication of this is that investors in NZ mutual funds should be very wary of self-proclaimed investment skill and fees charged that are beyond the level of fees that one would expect to pay for a passively managed fund. Our findings further highlight the importance of portfolio disclosure as it allows for monitoring to assess whether funds that claim to be active indeed follow active investment strategies.
|Fund type||#funds||#Stocks||HoldingsNZ Equity (‘000) ($)||HoldingsEquity (‘000) ($)||
|HoldingsTotal (‘000) (%)||
|Panel A. Holdings by fund type|
|Median across funds||30||44,316||98,331||48||158,080||40||31||28|
|Min across funds||19||29,284||46,060||43||58,118||25||24||9|
|Max across funds||40||62,446||177,093||72||327,489||64||38||44|
|Panel B. Holdings over time|
This table provides summary statistics of the portfolio holdings data. Panel A reports holdings information by fund type and Panel B reports the holdings information over time. #funds is the number of funds, #Stocks is the average number of stocks for each fund, HoldingsEquity is the average holdings value in equities, HoldingsNZEquity is the average holdings value in NZ equities, HoldingsINT Equity is the average holdings value in international equities, HoldingsFixed is the average holdings value in fixed income securities and HoldingsTotal is the average total asset value across funds
Summary statistics (annual excess returns over NZ 90-day bank bill)
|Portfolio||Average (%)||SD (%)||t-stat (NW)||Skewness||Kurtosis|
This table reports average (value-weighted) annual excess returns, standard deviation and t-statistics for the aggregate portfolios held by investment funds and for the market indexes and the size, book-to-market and momentum factors
Results from CAPM
|Portfolio||a (%)||t-stat||b||Wald test (b = 1)|
|Panel A: Against NZX50|
|Panel B: Against NZXAll|
|Panel C: Against NZX50P|
This table reports regression results from equation (1); the benchmark is the NZ market indexes; a measures the risk-adjusted performance relative to the benchmark, b measures the coefficient for the market excess return and measures the goodness of fit. Figures in parentheses are Newey–West corrected t-statistics;
denotes significance at the 10% level
Results from three-factor model
|Portfolio||a (%)||t-stat||b||Wald test (b = 1)||s||t-stat||h||t-stat|
|Panel A: Against NZX50|
|Panel B: Against NZXAll|
|Panel C: Against NZX50P|
This table reports regression results from equation (2); the benchmark is the NZ market indexes; a measures the risk-adjusted performance relative to the benchmark market index, b is the coefficient for the market excess return, s is the coefficient for the size factor, h is the coefficient for the book-to-market value factor and measures the goodness of fit; figures in parentheses are Newey-West corrected t-statistics
Results from four-factor model
|Portfolio||a (%)||t-stat||b||Wald test
(b = 1)
|Panel A: Against NZX50|
|Panel B: Against NZXAll|
|Panel C: Against NZX50P|
This table reports regression results from equation (3); the benchmark is the NZ market indexes; a measures the risk-adjusted performance relative to the benchmark market index, b is the coefficient for the market excess return, s is the coefficient for the size factor, h is the coefficient for the book-to-market value factor, m is the coefficient for the momentum factor and measures the goodness of fit; figures in parentheses are Newey–West corrected t-statistics;
denotes significance at the 5% level
The cross section of fund performance
|a (%)||t-stat||b||a (%)||t-stat||b||s||h||a (%)||t-stat||b||s||h||m||%Funds|
|Panel A: Group by fund size|
|Panel B: Grouped by market cap of local share holdings|
|Panel C: Group by firm type|
|Panel D: Grouped by past performance|
|Panel E: Grouped by fees|
|Medium (1- 1.5%)||0.04||(0.44)||1.02||0.91||0.03||(0.32)||1.02||0.017||0.042||0.91||0.04||(0.39)||1.01||0.02||0.04||−0.02||0.91||42.1|
|Panel F: Grouped by fund age|
This table reports monthly CAPM, three- and four-factor regressions for investment funds grouped by fund size, market value of local holdings, business type, past returns, management fees, and fund age; a is the funds’ alpha; b, s, h and m are the coefficients for the market risk (NZXAll), size, book-to-market value and momentum factors, respectively; %Funds is the fraction of funds in each group. Figures in parenthesis are the Newey-West corrected t-statistics
denote statistical significance at 1%, 5% and 10% levels, respectively
Active share and tracking error
|Active share (%)||Tracking error (%)|
|Grouped by fund size|
|Grouped by market cap of local share holdings|
|Grouped by business type|
|Grouped by past returns|
|Grouped by fees|
|Grouped by fund age|
Determinants of portfolio rebalancing
This table reports the results from equation (8); γ1, γ2 and γ3 are the regression coefficients for the future, current and lagged returns, respectively; γ4 is the regression coefficient for the number of disclosures; we apply both firm- and time-fixed effects in the panel regression; figures in parentheses are t-statistics corrected using clustered standard error;
denote statistical significance at 1%, 5% and 10% levels, respectively
About $26 billion as of September 2016.
We expect a NZ fund manager to be more informed about the NZ equity market than a foreign market. Banegas et al. (2013) study European mutual funds and show that local fund managers are more skilled than Pan-European funds managers. NZ fund managers may also generate outperformance in their bond strategy. However, as bonds generally are less volatile than equities, generating substantial outperformance in a bond strategy is difficult.
Trainor (2014) deals with the benchmark issue by constructing a value-weighted benchmark from several asset classes (NZX50, ASX 200, Fama and French Global Equity Index, 10-yr NZ Government bond, JP Morgan Global Bond Index and the 90-day NZ Government treasury rate). In contrast, we focus on the NZ equity part obtained from portfolio holdings data. This allows for an accurate comparison with NZ equity market benchmarks.
In 2010, the NZ Ministry of Economic Development started a discussion on mandatory holdings disclosure regulation for KiwiSaver funds. Periodic disclosure regulation was introduced on 1 July 2013 but was revoked on 1 December 2014.
About 75 per cent of these funds disclose holdings on a continuous basis. For the remaining 25 per cent , disclosure is mainly determined at the fund provider level. This suggests that funds do not time disclosure depending on performance.
We use the variable ‘Index Fund’ in Morningstar to distinguish between active and passive funds. Morningstar defines an “Index fund” as a fund that tracks a particular index and attempts to match its returns. By definition, index funds are purely passive funds that do not outperform an index. Non-index funds, on the other hand, can follow active strategies to achieve outperformance over a benchmark by deviating from it. This distinction is in line with studies that typically exclude index funds and refer to the remainder as active funds (Agarwal et al., 2015). To prevent survivorship bias we consider both live and dead funds.
The S&P/NZX 50 portfolio index has the same constituents as the S&P/NZX 50 Index, but with a 5 per cent cap on float-adjusted market capitalization. The capped methodology is designed to provide exposure to a diversified portfolio that is more aligned with what investors may hold.
We exclude the years 2010 and 2017 from these plots as we only have a few months of data in these years.
In unreported tests we also confirm that excess returns are not significantly different from the benchmark for the Open-End Funds and the Pension Funds.
We obtain similar results to those presented in this paper when using the other market indexes, but do not present these for the sake of brevity. These results can be provided on request.
In our case, we compute the sum across stock positions only, as we apply the measure exclusively to all-equity portfolios.
In this regression, we would expect a “mechanical” positive relation between contemporaneous changes in weights and returns if a fund follows a simple buy-and-hold strategy, because a positive return for a stock in month t would result in a relative weight increase in that month. However, this mechanical effect should not affect the relations between changes in weights and lagged or future returns.
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