The impact of portfolio holdings disclosure on fund returns

Pacific-Basin Finance Journal 57 (2019) 101172

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The impact of portfolio holdings disclosure on fund returns Russell Gregory-Allena, , Hatice Ozer Ballia, Kathleen Thompsonb ⁎

a b

T

Massey University, New Zealand JBWere, Investment Strategy Group, Auckland, New Zealand

ARTICLE INFO

ABSTRACT

JEL: G11 G15 G23 G28

Portfolio holdings disclosure has been a controversial subject for many years. Disclosure requirements in the USA were relaxed in 1985 from quarterly to semi-annual, then in 2004 returned to quarterly. Today, some countries do not require holdings disclosure, while others are considering making it compulsory. New Zealand has made this change for KiwiSaver funds and Australia is giving it consideration. Furthermore, in the U.S., there are current discussions about whether hedge funds should be subject to more disclosure. Our study examines the impact of disclosure on fund returns in two ways. First, in Australia and New Zealand, disclosure is not required of all funds but some fund managers choose to disclose. This affords us a natural experiment to compare funds that disclose with those that do not. Second, in New Zealand in 2013, a special class of funds called KiwiSaver was newly required to disclose holdings. This allows us to compare the same funds before and after the disclosure requirement. Examining these funds in several ways, controlling for past performance, risk, size and endogeneity, we find no evidence that disclosing fund holdings harms portfolio returns.

(Portfolio choice, investment decisions) (International financial markets) (non-bank financial institutions) (Government policy & Regulation)

Keywords: Disclosure Voluntary disclosure Mandatory disclosure Portfolio disclosure Portfolio holdings Fund performance Fund flows Front-running Agency cost Australia New Zealand

1. Introduction From 2009, Morningstar, Inc. has conducted a bi-annual Global Fund Investor Experience Report, which among other things rates international mutual fund industries on a scale from F to A+, overall and on several dimensions. Initially the covered countries were mostly components of the MSCI EAFE+US, but that expanded each year as Morningstar coverage and data became more available. In the 2009 and 2011 reports, New Zealand earned a D-, largely due to a very low score in the Disclosure component. In that Disclosure component, Australia also earned a D-. Scores in both Australia and New Zealand have improved somewhat, but disclosure remains an area in which they lag many international peers. This has triggered our interest in whether and to what extent fund holdings1 disclosure is important for the industry. Conventional wisdom among mutual fund managers is that disclosing information is harmful to fund performance. Although they recognise investors need a certain amount of information in order to make investment decisions, it is often regarded that too much information allows other fund managers to copycat or front run. One type of information, portfolio holdings, can be particularly helpful to those other fund managers in this endeavour, potentially harming performance of the disclosing fund. The costs and benefits of the disclosure of portfolio holdings have been the focus of longstanding debate among practitioners, Corresponding author. E-mail addresses: [email protected] (R. Gregory-Allen), [email protected] (H.O. Balli), [email protected] (K. Thompson). 1 The securities that are held within the portfolio ⁎

https://doi.org/10.1016/j.pacfin.2019.101172 Received 5 April 2018; Received in revised form 4 July 2019; Accepted 4 July 2019 Available online 09 July 2019 0927-538X/ © 2019 Elsevier B.V. All rights reserved.

Pacific-Basin Finance Journal 57 (2019) 101172

R. Gregory-Allen, et al.

regulators, researchers and academics. This paper provides a fresh perspective, exploring implications of the disclosure of portfolio holdings by examining a unique environment where disclosure is not mandatory,2 but in which some fund managers choose to disclose to a commercial database provider (Morningstar). We compare performance of disclosing funds to non-disclosing funds and, for another set of funds, we compare performance before and after they were required to disclose. Arguments in favour of portfolio disclosure include the following: First, it provides information to a commercial database provider, allowing improved oversight and monitoring on behalf of investors. Second, the transparency enables shareholders to monitor the compliance of a fund with its stated investment objectives and to detect style drift. Third, disclosure enhances the ability to track whether funds are engaging in portfolio manipulation such as window-dressing or portfolio pumping.3 Fourth, disclosure has the side effect of providing more extensive information in support of academic enquiry. On the other hand, portfolio disclosure may have the following drawbacks: First, it may enable increased front-running4 by professional investors and speculators. Second, it may increase copycat investing (free-riding),5 thus restricting a fund's ability to fully benefit from its research. Third, there are direct costs associated with producing and distributing timely and accurate information. Empirical research regarding the costs of disclosure regimes has investigated the free-riding of investors in the U.S. market by constructing copycat strategies. Frank et al. (2004) find that disclosure is costly for funds as copycat funds dilute the ability of the underlying fund to fully exploit their proprietary information. Verbeek and Wang (2013) find that the cost of disclosure is higher for increased disclosure frequency because copycat funds have more information on which to free-ride. Other research based on the U.S. market looks at the effect of disclosure on fund returns and finds that high-performing funds can have their performance impaired by disclosure (Ge and Zheng (2006), Parida and Teo (2018)). Academic interest in the field of disclosure of fund holdings has been prompted by the U.S. Securities and Exchange Commission's reform of holding disclosure regulations in 2004. This was done in a move toward increased transparency. The reform required funds to report quarterly rather than the previous requirement of semi-annual reporting. Most of the studies specifically about disclosure have centred on this regulatory change, including Wermers (2001), Frank et al. (2004), Wermers et al. (2007), Ge and Zheng (2006), Verbeek and Wang (2013), Elton et al. (2010), Parida and Teo (2018), and Agarwal et al. (2015). Following the implementation in 2004 of the requirement in the U.S. to disclose portfolio holdings on a quarterly basis, Ge and Zheng (2006) and Parida and Teo (2018) extended Wermers (2001) study by performing qualitative examinations of the effects of the change in reporting frequency. Agarwal et al. (2015) examine the impact of this regulatory change on the liquidity of stocks and fund performance. The literature examining copycat behaviour (or free-riding) includes Verbeek and Wang (2013), Frank et al. (2004), and Chen et al. (2017). These studies have yielded mixed results, but among the top-performing funds there does seem to be evidence of postfee abnormal performance for the copycat funds. Brown and Schwarz (2011), Schwarz and Potter (2016), Shi (2017), and Aragon et al. (2013) are among those who examine front-running. They generally find that front-running is possible when funds disclose, but the evidence on efficacy is mixed. Australia and New Zealand are the only two countries of 25 surveyed in the biennial Morningstar Global Investor Experience6 reports (2009–2017) which do not have compulsory disclosure requirements. Australia has never required holdings disclosure by fund managers, and New Zealand only began requiring disclosure for a certain class of so called “KiwiSaver” funds (a government-sponsored voluntary retirement savings scheme) in late 2013. In both Australia and New Zealand however, some fund managers have chosen to voluntarily disclose. This provides an opportunity to explore the impact of this choice, comparing the performance of funds that disclose to those that do not. Our completely new and unique contribution is that we now have sufficient data from the period post the 2013 regulatory change in New Zealand to examine before and after disclosure requirements for KiwiSaver funds. This is similar in concept to the previous studies examining performance before and after the increase of disclosure frequency in the U.S., but we have an even more profound discrete change from no requirement at all to quarterly disclosure. We undertake three key investigations, comparing: 1) Australian open-end funds that disclose to those that do not, 2) New Zealand open-end funds that disclose to those that do not, and 3) New Zealand KiwiSaver funds before and after they had a regulatory

2 While this study focuses on the potential effects of mandatory disclosure, it does not examine other important facets of mandatory disclosure regulation such as the lag period allowed following the reporting period and the frequency of disclosure. For example, U.S. regulations call for quarterly disclosure within 60 days of the end of the period. 3 Portfolio pumping is the act of bidding up the value of a fund's holdings before the end of a reporting period in order to raise the fund's performance results. 4 Front-running refers to the practice of outside investors buying (selling) securities in anticipation of buying (selling) trades by the fund. 5 Free-riding occurs when outsiders are able to observe a fund's investment strategies, allowing them to either copy a fund's holdings or to adopt the investment strategies of the fund. 6 The biennial Morningstar reports rate (2009–2015: F to A+; in 2017 a 5-point scale) the mutual fund industries in various countries around the world (16 in the 2009 report, 25 in the 2017 report) along four dimensions – Regulation, Disclosure, Fees and Expenses, Sales and Media. (In the original 2009 report, there were 6 dimensions: Investor Protection, Transparency in Prospectus and Shareholder Reports, Transparency in Sales Practices and Media, Fees and Expenses, Taxation, Distribution and Choice.) Over most of these reports for the Disclosure dimension, Australia and New Zealand have ranked at or near the bottom of all countries with scores of D+ or D and even D-. Finally, in the 2015 report, NZ rose to C+ and in 2017 to “Average”. The New Zealand improvement in 2015 was almost certainly due to the KiwiSaver (Periodic Disclosure) Regulations of 2013, almost immediately superseded by the Financial Markets (Repeals and Amendments) Act 2013. While these regulations only apply to a certain class of “KiwiSaver” fund (a government-sponsored voluntary retirement savings scheme), there is a possibility this will be extended eventually to all funds.

2

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requirement to disclose. We use various measures of fund performance, various econometric models, and control for risk, size and endogeneity. In all attempts, we cannot find evidence that disclosing portfolio holdings detracts from return. 2. Data and methodology 2.1. Data The data were obtained from the Morningstar database.7 To the best of our knowledge, this is the most comprehensive database for fund characteristics and disclosed holdings of Australian and New Zealand funds. It is also the most widely used database among financial advisors and is the main conduit for retail investors to monitor their funds' activity. The Morningstar database records holdings that have been voluntarily disclosed proactively by the fund. Additionally, on an adhoc basis, Morningstar makes holdings requests directly to selected funds. This introduces a potential bias as we do not know for which funds holdings data have been requested and, therefore, we do not know for which funds holdings were requested but declined.8 Despite this bias, the disclosed holdings reported by Morningstar are the most comprehensive record that is available in New Zealand and Australia to measure holdings disclosed to the public. We have three sets of data. In New Zealand and Australia, we have open-end funds, which are not subject to the KiwiSaver regulation and have been in existence for many years. It is these funds for which we compare those which disclose to those which do not. In New Zealand, we also have KiwiSaver funds, which began in October 2007. For this set, we can compare disclosure of the same funds before and after the increased disclosure requirement which became effective September 2013. The final sample spans the period January 2003 to December 2017. Prior to 2005, Morningstar did not record portfolio holdings, but we include two years prior in order to have more “pre-disclosure” comparisons. Where funds have multiple share classes we only use the oldest, we exclude funds with less than 24 monthly returns, and we exclude fund data from the first year of a fund's operation.9 We use only Active funds, eliminating all which state their objective as either Index, or Enhanced Index, and we only include equity funds. For the Australian and New Zealand open-end funds, we determine “equity status” by Morningstar's “Global Broad Category,”10 but for KiwiSaver these had insufficient data, so we use “Asset Allocation Equity %” greater than 80% to make that determination.11 Finally, we eliminate survivorship bias using data from both alive and discontinued funds. Table 1 provides an overview of our datasets and a break-down of how our sample was trimmed down with respect to total data availability, equity, oldest share class, active, assets greater than 10 million, and having more than 24 months of returns. In our final sample we have 1124 Australian funds, 106 New Zealand funds, and 114 New Zealand KiwiSaver funds. Table 1 Data description.

Overall database description All funds, including dead funds Equity and Oldest Share Class and Active and Net Assets > $10 million and at least 24 monthly returns

Australia

New Zealand

New Zealand

Open-End

Open-End

KiwiSaver

6942 3359 2333 2215 1225 1124

690 286 280 262 138 106

214 153 153 153 119 114

2.1.1. Benchmarks Benchmarks are an important consideration for Index funds, Enhanced Index funds, and Active funds which specify a benchmark in their Prospectus (called the “Primary Benchmark”). However, even for Active funds which do not have a Primary Benchmark, some benchmark is needed for making risk adjusted return comparisons across different funds. To this end, for all funds Morningstar uses analysis of the holdings and prospectus to assign what they call the “MPT” Benchmark, used for estimating alpha, beta, etc.12 7 An anonymous reviewer cautioned us to note the concern about Morningstar's potential survivor bias as noted in Elton et al. (2001). That study uses the older CD-based platform Principia Pro from 1999, whereas we use the modern Direct product. We have carefully examined this issue and have determined that our Australia and New Zealand data are free of survivor bias. Details are available from the authors upon request. 8 We did ask for these data but, unsurprisingly, Morningstar declined to give us this information. 9 In the early months of a fund's existence, we find the flow to be extremely volatile, making any inferences suspect at best. 10 We also investigated the open-end funds with “greater than 80%,” but found no appreciable difference in results; however, there were much fewer data. 11 We thank an anonymous reviewer for this suggestion. 12 When a fund has a Primary Benchmark, the MPT Benchmark is usually the same.

3

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While it would be ideal to always use the Primary Benchmark to gauge a fund, in our sample this often does not exist. For example, in our Australian sample 920 specify a Primary Benchmark. That would be useable, but in New Zealand active funds with a primary benchmark are nearly non-existent – for New Zealand open-end funds there are only 10 such funds, and for our KiwiSaver funds only three. As a result, for consistency we use Morningstar's MPT Benchmark for all three datasets. 2.1.2. Performance metrics We have two types of non-risk adjusted return: Raw Return (henceforth simply called “return”), and Active Return,13 which is the outperformance relative to the MPT Benchmark. In addition, we consider two specifically risk-adjusted returns to find outperformance. For the first, we use the 4-factor model of Carhart (Carhart, 1997) and Fama-French (Fama and French, 1993); in the tables we call this Alpha1. For the second, since the factors available from the Ken French website are for the generic Asia-Pacific region, we also consider this model using factors specific to Australia and New Zealand.14 In this model, we have factor data from AQR, a global investment management firm,15 and the resulting alpha we call Alpha2. Therefore, we have four performance metrics: Return, Active, Alpha1 and Alpha2. We also categorise funds as Top Quantile or Bottom Quantile funds,16 as measured by each of these performance metrics. 2.1.3. Quarterly data Most of our data are reported monthly, but disclosure of security holdings is generally only reported quarterly (and not always on the same quarterly cycle). Therefore, we aggregate17 all data on calendar quarters and use these quarterly data in all our regressions. 2.2. Methodology 2.2.1. Naïve approach We start with a naïve examination of the impact of disclosure on returns, including variables other researchers have found to potentially impact return: risk, style, size, and past performance to capture persistence. Our initial regression is the following panel setup:

Perfi, t =

0

+

1 Discli, t 1

+

2 TopQi, t 1

+

3 BotQi, t 1

+

4 SEi, t 1

+

5 Sizei, t 1

+

6 Stylei, t 1

+

i

+

t

+

i, t

(1a)

The dependant variable is one of four: Return, Active, Alpha1, or Alpha2, each as defined in the previous section. Discl is a dummy variable equal to 1 if a fund provides at least one voluntary disclosure during each quarter and zero otherwise. TopQ and BotQ are dummy variables, each equal to one if a fund's average performance over the previous 12 months (using the relevant performance metric) is in the top quantile18 and the bottom quantile, respectively. To help control for risk, SE is the standard deviation of the relevant performance measure over the previous 12 months, and Size is the natural logarithm of the total net assets at the end of each quarter. We use as “Style” Morningstar's “Equity Style Box”, which classifies funds on a 9-point scale from Large Growth to Mid Blend to Small Value. ϑi and δt refer to fund fixed effect and time fixed effect coefficients, respectively. Finally, εi, t is the error term, assumed independent and normally distributed. All of the independent variables are lagged at one quarter. In addition, we suspect that a manager's “to disclose or not to disclose” decision may be related to past performance therefore we interact TopQ and BotQ dummies with Discl:

Perfi, t =

0

+ +

1 Discl i, t 1 7 Discli, t

1

+

2 TopQi, t 1

TopQi, t

1 +

+

3 BotQi, t 1

8 Discli, t

1

+

4 SEi, t 1

BotQi, t

1

+

+ i

5 Sizei, t 1

+

t

+

i, t

+

6 Stylei, t 1

(1b)

2.2.2. Addressing endogeneity However, this naïve method ignores the very likely possibility that past performance may have some impact on a manager's decision about whether or not to disclose. For example, a good performing manager may not want to reveal her holdings for fear of being copied. Since the direction of the causal relationship between performance and disclosure is not clear, we suspect the Discl may be endogenous in estimating performance. To account for this bias, we use 2SLS, with instrumental variables (IVs) as appropriate for each dataset.19 In addition, there are two more interaction terms we must consider as they are potentially endogenous as well due to being interacted with Discl. Therefore, we solve for 2SLS in following two steps: 13

Active Return is sometimes called “Excess Return”, but this can be confused with return in excess of the risk-free rate (which is also called excess return). We prefer the term “Active Return” because it is unambiguous. 14 We thank an anonymous reviewer for this suggestion. 15 Data were obtained from https://www.aqr.com/Insights/Datasets/Betting-Against-Beta-Equity-Factors-Monthly. We thank the reviewer for informing us about these data. 16 Australian high-rank funds are the 20% of funds with the highest performance over the previous six months. For New Zealand (due to far fewer funds) we use the top 25%. 17 Sum, average, at least 1, or last, as appropriate for the data item. 18 Quintile for Australia, Quartile for the much smaller New Zealand data sets. 19 Some might suggest using probit regressions to estimate the 1st stage results, however fixed fund and time effects are important and highly significant in our estimations, and probit does not yield viable results in the presence of fixed effects [Wooldridge, 2010]. 4

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The 1st stage equations, without interaction terms (for Eq. (1a)):

Discli, t

1

=

+

+

0

1 TopQi, t 1

6 IVi, t 1

+

+

i

t

+

2 BotQi, t 1

+

i, t

+

3 SEi, t 1

+

4 Sizei, t 1

+

5 Stylei, t 1

(2a)

The 1st stage equations, with interaction terms (for Eq. (1b)):

Discli, t

=

1

+ Discli, t

TopQi, t

1

+ Discli, t

0

+

1 TopQi, t 1

1

=

+ 0

6 IVi, t 1

BotQi, t

1

+

6 IVi, t 1

1

=

0

6 IVi, t

+

+ +

+

1

+

7 IVi, t 1

2 BotQi, t 1

+

3 SEi, t 1

TopQi, t

+

8 IVi, t 1

1 TopQi, t 1 7 IVi, t 1

2 BotQi, t 1

TopQi, t

1 TopQi, t 1 7 IVi, t

+ +

1

+

1

+

+

+

+

8 IVi, t

4 Sizei, t 1

BotQi, t

3 SEi, t 1

8 IVi, t 1

2 BotQi, t 1

TopQi, t

1

1

+

BotQi, t

3 SEi, t 1

+

BotQi, t

1

+

+

1

i

5 Stylei, t 1

+

t

4 Sizei, t 1

+

1

i

+

+

i

+

+ t

4 Sizei, t 1 1

+

5 Stylei, t 1

+

+ t

i, t

i, t 5 Stylei, t 1

+

i, t

(2b)

In these eqs. IV refers to the chosen instrument for each specific dataset which we will clarify later. Here and in later regressions when interactions are included, we use additional instruments for the interacted terms, in order to avoid a “forbidden regression” problem. For example for the Australian dataset, later we use FirmAge, FirmAge*TopQ and FirmAge*BotQ as three instruments for three endogeneous variables Discl, Discl*TopQ and Discl*BotQ, respectively, within Eq. (1b).20 And then the 2nd stage equations,21 without interaction terms (Eq. (3a)) and with interaction terms (Eq. (3b)):

Perfi, t =

0

+ +

Perfi, t =

0

+ +

1 Discl i, t 1 i

+

t

+

+

2 TopQi, t 1

+

3 BotQi, t 1

+

4 SEi, t 1

+

5 Sizei, t 1

+

6 Stylei, t 1

(3a)

i, t

1 Discl i, t 1 7 Discli, t 1

+

2 TopQi, t 1

TopQi, t

1

+

+

3 BotQi, t 1

8 Discli, t 1

+

4 SEi, t 1

BotQi, t

1

+

+ i

+

5 Sizei, t 1 t

+

i, t

+

6 Stylei, t 1

(3b)

Applying 2SLS techniques is somewhat tricky, as finding the perfect candidate Instrumental Variable requires a thorough search and validation process. For each dataset, we need at least one IV satisfying the necessary conditions to be a perfect candidate as an instrument for Discl. The main two conditions are that the chosen instrument must be an exogenous variable (corr(IV, ε) = 0) and it must be strongly correlated with the endogenous variable (corr(IV, Discl) ≠ 0). We use the first stage regressions above to validate whether the chosen IV has a strong correlation with the endogenous variable - Discl - or not. For the main equation (Eq. (1a)), we can easily test this by determining if the coefficient of IV is highly statistically significant when Discl is regressed on just TopQ, BotQ, SE, Size, Style, and the IV of our choice (Eq. (2a)). However, for Eq. (1b) (the main equation with interaction terms) to check the same issue, we need an F test of joint significance test of IV, IV*TopQ, and IV*BotQ on each of the reduced form equations (Eq. (2b)). Stata's ivreg2 command (for panel setup) prints the “Kleibergen-Paap LM: Underidentification Test” for this purpose. The null hypothesis of this test is that the model is under-identified. Therefore, rejecting the null hypothesis (with small p-values) tells us that the model is identified using these instruments. We also check for weak instrument robustness with the Anderson-Rubin Wald (F) Test. Additionally we apply another test for endogeneity, the commonly used Generalized Method of Moments (GMM) Distance Test.22 The null hypothesis for this is that the defined endogenous regressors can be used as exogenous (just assuming OLS is consistent) against the alternative hypothesis that these endogenous regressors are actually endogenous (implying 2SLS might produce more unbiased results). Therefore, if we reject the null hypothesis, then we might rely on the 2SLS results. If we fail to reject the null hypothesis, it simply tells us that OLS and 2SLS results are highly similar and there is no evidence to choose 2SLS over OLS. For other diagnostic checks within any Panel regressions (either OLS or 2SLS), we further use a Wald (F) Test for the joint significance of Fund Fixed Effect (or Time Fixed effect). Further, for checking Fixed Effect (FE) versus Random Effect (RE), we use the Hausman test (1978). The null hypothesis is that using Fixed Effect regressions and Random effect regressions are similar against the alternative hypothesis that using FE regressions is favourable and more consistent. The P-values for both the Wald test and Hausman test are printed in the Notes section of each table. In all the estimations, we have very high evidence for using fixed effect regression with both Fund and Time Fixed effects. We do not report it here, but we have also calculated Pearson correlations between explanatory variables to identify the basic relationship among all variables exploring any potential multi-collinearity.23 Using a benchmark of 0.70 (Kennedy, 2008), no multicollinearity problem is identified. Panel Unit root tests are also applied and we have no evidence for a unit root in any of our sample data. 20

For a discussion of this method, please see Balli and Sørensen (2013). In the above and in the results section, we have written out 1st and 2nd stage equations for clarity of procedure, but we did not manually insert the 1st stage estimations into the 2nd stage, which would result in incorrect standard errors. To avoid this we use STATA's ivreg2 command for panel data, which handles this correctly. 22 For example, see Hayashi (2000). 23 These tables are quite numerous but are available on request. 21

5

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Table 2 Australia Open-End Funds - All Models, 12 month Horizon with 1 quarter lag - Active. Panel

Discl TopQ BotQ SE Size Style Discl*TopQ Discl*BotQ

2SLS

(1)

(2)

(3)

(4)

(5)

(6)

0.021 (0.73) 0.148*** (3.85) −0.096*** (−2.97) 0.123*** (7.07) 0.002 (0.61) 0.013 (1.46) – – –

−0.033** (−1.97) 0.042*** (2.94) −0.008 (−0.60) 0.121*** (8.38) −0.059*** (−9.29) 0.014 (1.57) – – –

−0.029 (−1.59) 0.048*** (3.40) −0.006 (−0.50) 0.085*** (4.99) −0.058*** (−9.02) 0.007 (0.79) – – –

−3.838 (−1.50) 0.016 (0.45) −0.036 (−1.13) 0.019 (0.33) 0.000 (−0.01) −0.007 (−0.35) –

41,085 0.03

−0.021 (−1.14) 0.057*** (2.63) 0.003 (0.15) 0.085*** (4.97) −0.058*** (−8.99) 0.007 (0.81) −0.017 (−0.61) −0.018 (−0.72) 41,085 0.03

41,083

−3.348 (−1.30) 0.506 (1.09) −0.010 (−0.02) 0.015 (0.26) 0.003 (0.08) −0.001 (−0.02) −0.901 (−1.05) −0.031 (−0.03) 41,083

Yes Yes

Yes Yes

0.015 0.061 0.063 Yes Yes

0.017 0.071 0.082 Yes Yes

Sample 41,085 41,085 R2 0.03 0.01 Kleibergen-Paap LM Test; Ho = Underidentification; p-value Weak Instrument-Robust Inference Test; p-value Endogeneity Test; Ho = Endogeneous regressors are exogeneous; p-value Time FE Yes – Fund FE – Yes

–

Notes: The dependent variable is Active. All 2SLS estimations are calculated using “FirmAge” as the instrumental variable. All the explanatory variables are lagged one quarter. Discl is one if the fund provides at least one voluntary disclosure during each quarter and zero otherwise. BotQ and TopQ are dummy variables, each equal to one if a fund's performance for the quarter is in the bottom quintile or top quintile respectively. SE is the standard deviation of the relevant fund performance metric. Size is the natural logarithm of the total net assets at the end of each quarter. Style is Morningstar's “Equity Style Box”, which classifies funds on a 9-point scale from Large Growth to Mid Blend to Small Value. Clustered standard errors are calculated and t statistics are printed in parenthesis accordingly. ***, ** and * represent that the coefficient is statistically significant at the 1%, 5% and 10% significance levels, respectively. Also a Wald Test for the joint significance of Time effect (or Fund effect) is calculated and strong evidence for Time effect (or Fund Effect) is found in relevant regressions with a p-value =0.00. Finally a Hausman test is calculated for Ho = FE and RE are similar, for all regressions and we found strong evidence to use FE regressions with a p-value = 0.00.

2.2.3. KiwiSaver For New Zealand's KiwiSaver funds, we have an additional variable of interest – Regulation. This takes the form of a dummy variable, REG, equal to 1 after the legislation went into effect September 2013, and 0 before. Because we need to compare KiwiSaver results with and without legislation, henceforth we refer simply to “KiwiSaver with REG” and “KiwiSaver without REG”. We first examine these funds without the REG dummy, using baseline regressions just as above for Australia and New Zealand open-end funds. When we add REG, we then have triple interaction terms. The first stage regressions are similar in approach to the above, so we just show the resulting 2nd stage:

Perfi, t =

0

+

1 Discl i, t 1

+

8 Discli, t 1

+

11 TopQi, t 1

+ +

13 Discli, t i

+

t

+

1

+

2 REGi, t 1

REGi, t REGi, t TopQi, t

1

+ 1 1

+

+

3 TopQi, t 1

9 Discli, t 1 12 BotQi, t 1

REGi, t

1 +

+

4 BotQi, t 1

TopQi, t

1

+

REGi, t

1

14 Discl i, t

1

+

5 SEi, t 1

10 Discli, t 1

BotQi, t

1

+

6 Sizei, t 1

BotQi, t REGi, t

+

7 Stylei, t 1

1

1

(4)

i, t

3. Results 3.1. Naïve approach For the initial naïve approach regressions (Eq. (1a)) using Active return, 12 month horizon and independent variables lagged 1 quarter (Tables 2, 3, 4 & 5 for Australia, New Zealand, KiwiSaver without REG, and KiwiSaver with REG, respectively) we see in columns 1–3 that the significance of Discl varies depending on the dataset and on including both fund and time fixed effects.24 24

For all the estimations, we calculate clustered standard errors for correcting autocorrelation and heteroscedasticity biases. 6

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Table 3 New Zealand Open-End Funds - All Models, 12 month Horizon with 1 quarter lag - Active. Panel

2SLS

(1)

(2)

(3)

(4)

(5)

(6)

Discl*TopQ

0.068 (1.34) 0.103 (1.44) −0.068 (−1.00) 0.06* (1.85) 0.013 (0.79) 0.039*** (2.81) –

−0.099 (−1.34) −0.032 (−0.61) 0.045 (0.83) 0.144*** (3.22) −0.074* (−1.84) 0.060*** (2.83) –

−0.061 (−0.63) −0.025 (−0.51) 0.050 (0.94) 0.121** (2.41) −0.062 (−1.47) 0.057** (2.51) –

−0.757 (−0.70) 0.037 (0.64) 0.124** (2.32) 0.098* (1.92) −0.076 (−1.52) 0.033 (0.98) –

Discl*BotQ

–

–

– 3867 0.09

−0.076 (−0.78) 0.005 (0.08) −0.007 (−0.1) 0.119** (2.40) −0.060 (−1.43) 0.061*** (2.63) −0.075 (−0.76) 0.185* (1.70) 3867 0.09

2447

−0.596 (−0.58) −0.329 (−0.53) 0.223 (0.51) 0.100** (2.00) −0.077 (−1.44) 0.018 (0.46) 0.648 (0.58) −0.220 (−0.23) 2447

Yes Yes

Yes Yes

0.030 0.468 0.407 Yes Yes

0.039 0.819 0.636 Yes Yes

Discl TopQ BotQ SE Size Style

Sample 3867 3867 R2 0.01 0.01 Kleibergen-Paap LM Test; Ho = Underidentification; p-value Weak Instrument-Robust Inference Test; p-value Endogeneity Test; Ho = Endogeneous regressors are exogeneous; p-value Time FE Yes – Fund FE – Yes

–

Notes: The dependent variable is Active. All 2SLS estimations are calculated using “Cost” as the instrumental variable. All the explanatory variables are lagged one quarter. Discl is one if the fund provides at least one voluntary disclosure during each quarter and zero otherwise. BotQ and TopQ are dummy variables, each equal to one if a fund's performance for the quarter is in the bottom quintile or top quintile respectively. SE is the standard deviation of the relevant fund performance metric. Size is the natural logarithm of the total net assets at the end of each quarter. Style is Morningstar's “Equity Style Box”, which classifies funds on a 9-point scale from Large Growth to Mid Blend to Small Value. Clustered standard errors are calculated and t statistics are printed in parenthesis accordingly. ***, ** and * represent that the coefficient is statistically significant at the 1%, 5% and 10% significance levels, respectively. Also a Wald Test for the joint significance of Time effect (or Fund effect) is calculated and strong evidence for Time effect (or Fund Effect) is found in relevant regressions with a p-value =0.00. Finally a Hausman test is calculated for Ho = FE and RE are similar, for all regressions and we found strong evidence to use FE regressions with a p-value = 0.00.

We have also calculated the Wald Test for joint significance of time effects (or fund fixed effect) for each of the different combinations of the models, recording a highly significant joint time effect (or fund effect) with a p-value 0.00. To make a further decision about estimating models with Firm Fixed Effects or with Firm Random Effects, we applied the Hausman test (Hausman, 1978). We have enough evidence to reject the null hypothesis that FE and RE are equally appropriate to use at the 0.01 significance level (pvalue = 0.00). In Column 4 of each of these tables, once we account for interaction terms (Eq. (1b)), we have no evidence of joint impact of disclosure on fund performance for Active Return for all data sets except KiwiSaver without REG.25 3.2. Addressing endogeneity To consider endogeneity, we first need to determine valid instrumental variables. To select candidate instruments, we looked for variables that were conceptually related to disclosure. As noted earlier, a fund's past performance is possibly related, but that is also potentially correlated with current performance, so we need something else. As we also mentioned earlier, one of the complaints managers have about disclosure is the operational cost of complying. Since this reporting is most easily done in a firm's back-office, a firm's size or age26 (as opposed to fund size or age) is likely to be related with the size (or existence) of that back-office; the idea here is that the larger or older the firm, the more likely they are to have a back-office that can handle disclosure reporting with little marginal cost. Another reasonable proxy of back-office costs (and therefore back-office size) would be expenses unrelated to transactions or 25 Whenever we include interaction terms, the impact of Discl on Fund performance is equivalent to a joint (total) impact of Discl on Fund Performance which is either β1 + β7 or β1 + β8, for TopQ firms or BotQ firms respectively for equation 3b. Therefore a further F-test is applied for joint significance of those coefficients and accordingly, we interpret the results. 26 Morningstar does not have a variable for firm size or firm age, so we had to construct these ourselves, taking care to include all (equity) funds at a given firm regardless of share class, style, size or age.

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Table 4 KiwiSaver without REG - All Models, 12 month Horizon with 1 quarter lag - Active. Panel

2SLS

(1)

(2)

(3)

(4)

(5)

(6)

Discl*TopQ

0.119*** (2.94) 0.003 (0.04) −0.021 (−0.56) 0.149*** (2.67) −0.013 (−1.63) 0.026** (2.26) –

0.081** (2.13) −0.061** (−2.51) 0.056** (2.36) 0.251*** (6.33) −0.009 (−0.52) 0.018 (1.34) –

0.157*** (3.95) −0.062*** (−2.84) 0.052*** (2.63) 0.204*** (5.55) −0.060** (−2.18) 0.017 (1.28) –

−0.206 (−0.64) −0.051** (−2.14) 0.044* (1.96) 0.217*** (5.37) −0.054* (−1.83) 0.023 (1.48) –

Discl*BotQ

–

–

– 3198 0.3

0.096*** (2.82) −0.155*** (−3.81) 0.034 (1.20) 0.218*** (5.95) −0.053** (−2.07) 0.015 (1.16) 0.221*** (3.37) 0.034 (0.84) 3198 0.31

3075

−0.240 (−0.71) −0.040 (−0.63) 0.021 (0.52) 0.216*** (4.95) −0.057* (−1.88) 0.024 (1.58) −0.025 (−0.21) 0.060 (0.81) 3075

Yes Yes

Yes Yes

0.001 0.487 0.212 Yes Yes

0.001 0.760 0.102 Yes Yes

Discl TopQ BotQ SE Size Style

Sample 3198 3198 R2 0.05 0.04 Kleibergen-Paap LM Test; Ho = Underidentification; p-value Weak Instrument-Robust Inference Test; p-value Endogeneity Test; Ho = Endogeneous regressors are exogeneous; p-value Time FE Yes – Fund FE – Yes

–

Notes: The dependent variable is Active. All 2SLS estimations are calculated using “LnFirmSize” as the instrumental variable. All the explanatory variables are lagged one quarter. Discl is one if the fund provides at least one voluntary disclosure during each quarter and zero otherwise. BotQ and TopQ are dummy variables, each equal to one if a fund's performance for the quarter is in the bottom quintile or top quintile respectively. SE is the standard deviation of the relevant fund performance metric. Size is the natural logarithm of the total net assets at the end of each quarter. Style is Morningstar's “Equity Style Box”, which classifies funds on a 9-point scale from Large Growth to Mid Blend to Small Value. Clustered standard errors are calculated and t statistics are printed in parenthesis accordingly. ***, ** and * represent that the coefficient is statistically significant at the 1%, 5% and 10% significance levels, respectively. Also a Wald Test for the joint significance of Time effect (or Fund effect) is calculated and strong evidence for Time effect (or Fund Effect) is found in relevant regressions with a p-value =0.00. Finally a Hausman test is calculated for Ho = FE and RE are similar, for all regressions and we found strong evidence to use FE regressions with a p-value = 0.00.

returns (and not fees). Morningstar records “Representative Cost ex Transaction fee”, which we have also examined (we'll call this Cost).27 In our estimations we found that FirmAge and the natural log of Firm Size (LnFirmSize) were valid instruments for Australia and KiwiSaver, respectively, and for New Zealand open-end funds Cost was the best, each determined as follows. In order to check if a potential candidate is a good instrument, we must check its relationship with our variable of interest – Discl. In Table 6 we report the coefficients from the reduced form equations (first stage regressions), for each of the four measures of performance, and note that these chosen IVs are significantly related to Discl.28 We also want to determine that the candidate IVs are not highly correlated to those performance metrics themselves so that they don't have direct impact on performance metrics, and in Table 7 we see that they are not. Using these IVs, we also supply a detailed table to represent one example29 of reduced form regressions in the presence of endogeneity. In Table 8, cols 1–3, we report the 1st stage results for each of Australia, New Zealand, and KiwiSaver without REG. For KiwiSaver with REG, we also have the interaction terms to deal with (eq. 4). From this we see that the IVs are highly significant, and therefore they are valid IVs for Australia, New Zealand, and KiwiSaver without REG. When we account for legislation in KiwiSaver with REG, we need to have two reduced form equations due to the interacted term (Discl*REG) and to apply a joint significance F-test on the coefficients of the IV and IV*REG terms. The P-values for the F-test are equal to 0.00, implying that both IV and IV*REG are valid IV's for Discl and Discl*REG for KiwiSaver with REG. For the 2nd stage equations (Eqs. (3a, 3b and 4)), in columns 5 & 6 of Tables 2-5 we report the results with and without interaction terms for each of Australia, New Zealand, KiwiSaver without REG, and KiwiSaver with REG. After thoroughly checking for 27 From the Morningstar data point definition: “this measure does not include one off costs, or cost levied by third parties such as investment advisors or platforms” 28 In Table 6, we only focus on the naïve approach and the related first stage equations, as these are sufficient to make the point of correlation of IV's with Discl. 29 Again, complete tables are extensive but are available upon request.

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Table 5 KiwiSaver with REG - All Models, 12 month Horizon with 1 quarter lag - Active. Panel

2SLS

(1)

(2)

(3)

(4)

(5)

(6)

0.118 (1.54) †

Discl*TopQ

0.003 (0.04) −0.021 (−0.56) 0.149*** (2.65) −0.013 (−1.62) 0.026** (2.20) 0.002 (0.03) –

0.183*** (3.77) 0.137*** (3.49) −0.061** (−2.47) 0.055** (2.36) 0.269*** (6.73) −0.035* (−1.65) 0.009 (0.68) −0.168*** (−4.18) –

0.177*** (3.50) 0.155* (1.89) −0.062*** (−2.84) 0.052*** (2.64) 0.205*** (5.59) −0.059** (−2.17) 0.016 (1.23) −0.032 (−0.78) –

−0.297 (−0.75) 0.122 (0.85) −0.049** (−2.00) 0.045** (2.03) 0.210*** (5.06) −0.059* (−1.93) 0.031 (1.61) 0.270 (1.06) –

Discl*BotQ

–

–

–

TopQ*REG

–

–

–

BotQ*REG

–

–

–

Discl*TopQ*REG

–

–

–

Discl*BotQ*REG

–

–

– 3198 0.30

0.064 (1.23) 0.116 (1.48) −0.187*** (−3.15) 0.026 (0.86) 0.223*** (6.16) −0.052** (−2.04) 0.014 (1.06) 0.055 (1.05) 0.361*** (3.71) 0.070 (0.90) 0.088 (0.97) 0.020 (0.35) −0.236** (−2.05) −0.059 (−0.59) 3198 0.31

3075

−0.131 (−0.30) 0.229 (1.34) −0.003 (−0.04) 0.071 (1.36) 0.207*** (4.46) −0.061** (−2.01) 0.028 (1.56) −0.007 (−0.02) −0.224 (−0.85) −0.166 (−0.75) −0.159 (−1.28) −0.161 (−1.50) 0.409 (1.36) 0.399 (1.48) 3075

Yes Yes

Yes Yes

0.001 0.471 0.411 Yes Yes

0.002 0.444 0.226 Yes Yes

Discl REG TopQ BotQ SE Size Style Discl*REG

Sample 3198 3198 R2 0.05 0.04 Kleibergen-Paap LM Test; Ho = Underidentification; p-value Weak Instrument-Robust Inference Test; p-value Endogeneity Test; Ho = Endogeneous regressors are exogeneous; p-value Time FE Yes – Fund FE – Yes

– – – – –

Notes: The dependent variable is Active. All 2SLS estimations are calculated using “LnFirmSize” as the instrumental variable. All the explanatory variables are lagged one quarter. Discl is one if the fund provides at least one voluntary disclosure during each quarter and zero otherwise. BotQ and TopQ are dummy variables, each equal to one if a fund's performance for the quarter is in the bottom quintile or top quintile respectively. SE is the standard deviation of the relevant fund performance metric. Size is the natural logarithm of the total net assets at the end of each quarter. Style is Morningstar's “Equity Style Box”, which classifies funds on a 9-point scale from Large Growth to Mid Blend to Small Value. REG is a dummy for the new disclosure requirements, equals 1 after September 2013. Clustered standard errors are calculated and t statistics are printed in parenthesis accordingly. ***, ** and * represent that the coefficient is statistically significant at the 1%, 5% and 10% significance levels, respectively. Also a Wald Test for the joint significance of Time effect (or Fund effect) is calculated and strong evidence for Time effect (or Fund Effect) is found in relevant regressions with a p-value =0.00. Finally a Hausman test is calculated for Ho = FE and RE are similar, for all regressions and we found strong evidence to use FE regressions with a p-value = 0.00. †Due to multi-collinearity this variable dropped out.

Under identification, Weak instrument and Endogeneity tests printed under each table, we see that we have mostly high statistical evidence for relying on the results of 2SLS and validity of used IVs. Thus in all cases, we have no evidence that Discl significantly impacts fund performance. 3.3. Robustness analysis 3.3.1. Examining the performance measure We next turn to the various methods of measuring performance, to determine if our results are sensitive to that choice. In Tables

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Table 6 Coefficient estimates of the impact of Instruments on Disclosure through Reduced Form Equations -12 month Horizon with 1 quarter lag.

Return Active Alpha1 Alpha2

Australia Open-End

New Zealand Open-End

KiwiSaver (w/o Reg)

0.296** (2.44) 0.285** (2.39) 0.296** (2.43) 0.305** (2.51)

−1.107*** (−2.88) −1.062*** (−2.69) −1.093*** (−2.77) −1.127*** (−2.87)

−0.132*** (−3.41) −0.137*** (−3.48) −0.129*** (−3.36) −0.128*** (−3.29)

Notes: This table only prints estimations from the first stage (reduced form) equations for four performance metrics (listed in first column) for 12 month Horizon with one quarter lag. Specifically, the impact of Instrumental variable on Disclosure (to indicate the validity and strong correlation of instrument with Disclosure) is calculated while controlling for included exogenous variables for each model separately. The dependent variable is Disclosure (lagged) variable and “FirmAge”, “Cost”, and “LnFirmSize” are used as the instrumental variables for Australia open-end funds, New Zealand open-end funds and KiwiSaver funds respectively. Both time and fund fixed effects are controlled for all the regressions. Clustered standard errors are calculated and t statistics are printed in parenthesis accordingly. ***, ** and * represent that the coefficient is statistically significant at the 1%, 5% and 10% significance levels, respectively. Table 7 Correlations for Australia Open-End, New Zealand Open-End and KiwiSaver funds. Australia Open-End

Return Active Alpha1 Alpha2 Discl FirmAge

Return

Active

Alpha1

Alpha2

Discl

FirmAge

1.000 0.198 0.746 0.756 0.036 0.002

1.000 0.232 0.222 0.004 −0.052

1.000 0.929 0.029 0.028

1.000 0.036 0.033

1.000 0.135

1.000

New Zealand Open-End

Return Active Alpha1 Alpha2 Discl Cost

Return

Active

Alpha1

Alpha2

Discl

Cost

1.000 0.324 0.789 0.809 0.059 −0.023

1.000 0.304 0.303 −0.001 −0.040

1.000 0.945 0.009 −0.007

1.000 0.017 −0.009

1.000 −0.115

1.000

KiwiSaver

Return Active Alpha1 Alpha2 Discl LnFirmSize FundAge

Return

Active

Alpha1

Alpha2

Discl

LnFirmSize

FundAge

1.000 0.443 0.940 0.972 0.097 0.065 0.143

1.000 0.431 0.418 0.044 −0.012 −0.027

1.000 0.955 0.059 0.092 0.170

1.000 0.061 0.070 0.132

1.000 0.502 0.444

1.000 0.503

1.000

Notes: This table prints pairwise correlation coefficient between all the dependent variables (the level of the data) and the Instrumental variables (one quarter lag) used in estimations. “FirmAge”, “Cost”, and “LnFirmSize” are used as the instrumental variables (IV) for Australia open-end funds, New Zealand open-end funds and KiwiSaver funds, respectively. On rare occasions, for KiwiSaver, to get a better identification with estimations, we used “FundAge” as another IV. ***, ** and * represent that the coefficient is statistically significant at the 1%, 5% and 10% significance levels, respectively.

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Table 8 Reduced Form Regressions − 12 month Horizon with 1 quarter lag - Active. Australia Open-End

New Zealand Open-End

KiwiSaver (w/o Reg)

KiwiSaver (w/ Reg)

(1)

(2)

(3)

(4)

(5)

Dependent variable

Discl

Discl

Discl

Discl

Discl*Reg

TopQ

−0.010* (−1.66) −0.008 (−1.40) −0.017** (−2.27) 0.016*** (3.33) −0.004 (−0.84) 0.285** (2.39) –

0.032* (1.88) −0.003 (−0.22) 0.017 (−0.86) 0.005 (0.18) 0.018** (2.15) −1.062*** (−2.69) –

0.025 (1.59) −0.045*** (−3.32) 0.051** (2.30) 0.065** (1.99) 0.016** (2.05) −0.137*** (−3.48) –

41,498 0.017 0.017

2447 0.008 0.03

3075 0.000 0.001

0.018 (1.10) −0.041*** (−3.09) 0.024 (1.15) 0.055* (1.82) 0.017** (2.22) −0.151*** (−4.31) 0.073*** (6.70) 3075 0.000 0.001

−0.015 (−1.35) −0.011 (−1.01) −0.016 (−0.77) 0.018 (0.77) −0.021** (−2.53) −0.076** (−2.05) 0.160*** (8.63) 3075 0.000

BotQ SE Size Style IV IV*Reg Sample Ho = Instruments exclusion; F-test(p-value) Kleibergen-Paap LM Ho = Underidentification; p-value

Notes: This table only prints the estimations of first stage (reduced form) equations for Active for 12 month Horizon with one quarter lag. The dependent variable is Disclosure (lagged) variable for the columns (1) to (4) and for the fifth one it is Discl*Reg (lagged). “FirmAge”, “Cost”, and “LnFirmSize” are used as the instrumental variables for for Australia open-end funds, New Zealand open-end funds and KiwiSaver funds, respectively. All the explanatory variables are lagged one quarter. Discl is one if the fund provides at least one voluntary disclosure during each quarter and zero otherwise. BotQ and TopQ are dummy variables, each equal to one if a fund's performance for the quarter is in the bottom quintile or top quintile respectively. SE is the standard deviation of the relevant fund performance metric. Size is the natural logarithm of the total net assets at the end of each quarter. Style is Morningstar's “Equity Style Box”, which classifies funds on a 9-point scale from Large Growth to Mid Blend to Small Value. REG is a dummy for the new disclosure requirements, equals 1 after September 2013. Both time and fund fixed effects are controlled for all the regressions. Clustered standard errors are calculated and t statistics are printed in parenthesis accordingly. ***, ** and * represent that the coefficient is statistically significant at the 1%, 5% and 10% significance levels, respectively.

9-11, we report 2SLS results with interaction terms for each of Return, Active, Alpha1 and Alpha2, for Australia, New Zealand, and KiwiSaver (with and without REG). In all cases, again we can find no evidence of (joint) significant impact of Discl on any type of performance and our diagnostic checks indicate that our results are reliable. 3.3.2. Robustness check on horizon and lags The above analysis has been done with performance metrics averaged over 12 months and all dependent variables lagged 1 quarter. We now examine that 12 month horizon with a 2 quarter lag, and a 24 month horizon with lags of 1 and 2 quarters. In all cases we report Active as the performance metric and use the 2SLS model with interaction terms. Results are printed in Tables 12-14 for Australia, New Zealand, and KiwiSaver. For KiwiSaver, we show results with and without REG. Once again, in all cases we have no evidence of (joint) significant impact of Discl on performance. 3.3.3. Diff in Diff estimates To identify the average treatment impact of Disclosure on Fund Performance for KiwiSaver before and after legislation, we also exploit a very common approach called Difference in Difference30 or DID. This technique is useful to measure the differences of the changes in the outcome variable (Performance) over time between the treatment and control group. To define the treatment group we use the Discl dummy, and to reflect time changes we use the REG dummy within the DID model along with covariates (as in Eq. (1a)): risk, style, size, and past performance. Table 15 records the results for the average treatment effect of mandatory disclosure over a total 4-year window (two years before and after mandatory disclosure) to control for any other confounding effects which might mask the true effect of disclosure. Basically with demonstrating the average treatment effect, we are indicating the difference in the total impact of mandatory disclosure on performance of funds before and after the regulation. So, this tells us if the impact changes significantly with regulation. Table 15 column (1) records DID coefficients over different horizons (12 month horizon with lags of 1 and 2 quarters and 24 months with lags of 1 and 2 quarters) and for different performance metrics. Overall results demonstrate that we have no 30

This method, as well as the 2-year window, was suggested by an anonymous referee as a method to control for possible confounding events. 11

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Table 9 Australia Open-End Funds - All Fund Performance Metrics - Robustness Analysis.

Discl TopQ BotQ SE Size Style Discl*TopQ Discl*BotQ Sample Kleibergen-Paap LM Test; Ho = Underidentification; p-value Weak Instrument-Robust Inference Test; p-value Endogeneity Test; Ho = Endogeneous regressors are exogeneous; p-value

Return

Active

Alpha 1

Alpha 2

−3.655 (−1.04) 0.404 (0.47) −0.926 (−1.29) −0.014 (−0.33) −0.030 (−0.59) 0.039 (1.53) −1.245 (−0.77) 1.917 (1.40) 41,398 0.008 0.005 0.012

−3.348 (−1.30) 0.506 (1.09) −0.010 (−0.02) 0.015 (0.26) 0.003 (0.08) −0.001 (−0.02) −0.901 (−1.05) −0.031 (−0.03) 41,083 0.017 0.071 0.082

−1.947 (−0.81) 0.565 (1.28) −0.988 (−1.49) −0.069*** (−3.23) −0.052 (−1.39) 0.000 (0.01) −1.27 (−1.43) 1.971 (1.54) 41,116 0.004 0.003 0.006

−0.270 (−0.13) 0.384 (1.00) −0.923* (−1.92) −0.044*** (−2.65) −0.077** (−2.28) 0.027 (1.64) −0.871 (−1.14) 1.842** (1.97) 41,141 0.003 0.008 0.014

Notes: The dependent variables are indicated in the first row. All 2SLS estimations are calculated using “FirmAge” as the instrumental variable. All the explanatory variables are lagged one quarter. Discl is one if the fund provides at least one voluntary disclosure during each quarter and zero otherwise. BotQ and TopQ are dummy variables, each equal to one if a fund's performance for the quarter is in the bottom quintile or top quintile respectively. SE is the standard deviation of the relevant fund performance metric. Size is the natural logarithm of the total net assets at the end of each quarter. Style is Morningstar's “Equity Style Box”, which classifies funds on a 9-point scale from Large Growth to Mid Blend to Small Value. Clustered standard errors are calculated and t statistics are printed in parenthesis accordingly. ***, ** and * represent that the coefficient is statistically significant at the 1%, 5% and 10% significance levels, respectively. Also a Wald Test for the joint significance of Time effect (or Fund effect) is calculated and strong evidence for Time effect (or Fund Effect) is found in relevant regressions with a p-value =.00. Finally a Hausman test is calculated for Ho = FE and RE are similar, for all regressions and we found strong evidence to use FE regressions with a p-value = .00. Therefore, both time and fund fixed effects are controlled for all the regressions.

statistical evidence for average treatment effect of disclosure on fund performance while also controlling for other characteristics of funds as listed above. These results are robust to our previous findings and comparable to the impact of interacted term on fund performance in Table 5 (column 3), in which the whole sample period was used. We have also applied a triple diff in diff for further concentrating on fund performance - TopQ and BotQ - to check the robustness of our regressions in which triple interactions are controlled for. The last two columns in Table 15 record the results for the average treatment effect of mandatory disclosure, conditional on being in either TopQ or BotQ separately. Specifically, the recorded results in column (2) are equivalent to calculating the impact of the triple interacted term for TopQ on the fund performance for Eq. (4). That is, the difference in the total impact of mandatory disclosure on fund performance before and after regulation for the top performing funds (as compared to the middle performing funds). Column (3) is similar, but with BotQ. Results demonstrate that except for most of Active, there is a negative significant difference before and after the regulation, for the total effect of mandatory disclosure on fund performance between the top performing funds compared to middle performing funds. In column (3), we have no consistent evidence for a significant difference in the effect of mandatory disclosure on fund performance before and after regulation, between the bottom performing funds compared to middle performing funds. Both of these results are robust with the rest of the results in the paper before endogeneity correction.31 4. Summary and conclusion Our research adds to the body of literature on the balance between the optimal level of transparency that allows investors to monitor their fund manager, versus disclosure that impacts a manager's ability to generate outperformance without competitors taking advantage of proprietary knowledge. In both New Zealand and Australia, the governments are considering changes to disclosure regulations, making disclosure more broadly required. This study can be viewed as an examination of the potential effects of a mandatory disclosure regime. For these countries, and others considering either a change to such a rule or an increase in requirements (such as hedge funds in the U.S.), the results have implications for: regulators for determining the potential effects of mandatory disclosure; investors when making 31 When different horizons and lags are considered for various performance metrics, mostly we find that the endogeneity test is rejected favouring the use of 2SLS over OLS.

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Table 10 New Zealand Open-End Funds - All Fund Performance Metrics - Robustness Analysis.

Discl TopQ BotQ SE Size Style Discl*TopQ Discl*BotQ Sample Kleibergen-Paap LM Test; Ho = Underidentification; p-value Weak Instrument-Robust Inference Test; p-value Endogeneity Test; Ho = Endogeneous regressors are exogeneous; p-value

Return

Active

Alpha 1

Alpha 2

−0.725 (−0.50) 0.746 (0.93) 1.640 (1.26) 0.065 (0.84) −0.088 (−1.07) 0.097 (1.56) −1.558 (−0.99) −3.067 (−1.05) 2524 0.186 0.156 0.296

−0.596 (−0.58) −0.329 (−0.53) 0.223 (0.51) 0.100** (2.00) −0.077 (−1.44) 0.018 (0.46) 0.648 (0.58) −0.220 (−0.23) 2447 0.039 0.819 0.636

−1.480 (−1.43) −0.971 (−1.44) 0.212 (0.48) −0.035 (−0.52) −0.051 (−1.14) 0.042 (1.00) 1.944 (1.45) −0.293 (−0.35) 2515 0.047 0.108 0.098

−0.935 (−0.93) −0.520 (−1.17) 0.318 (0.78) 0.006 (0.10) −0.059 (−1.38) 0.042 (1.23) 0.981 (1.14) −0.451 (−0.59) 2518 0.006 0.348 0.334

Notes: The dependent variables are indicated in the first row. All 2SLS estimations are calculated using “Cost” as the instrumental variable. All the explanatory variables are lagged one quarter. Discl is one if the fund provides at least one voluntary disclosure during each quarter and zero otherwise. BotQ and TopQ are dummy variables, each equal to one if a fund's performance for the quarter is in the bottom quintile or top quintile respectively. SE is the standard deviation of the relevant fund performance metric. Size is the natural logarithm of the total net assets at the end of each quarter. Style is Morningstar's “Equity Style Box”, which classifies funds on a 9-point scale from Large Growth to Mid Blend to Small Value. Clustered standard errors are calculated and t statistics are printed in parenthesis accordingly. ***, ** and * represent that the coefficient is statistically significant at the 1%, 5% and 10% significance levels, respectively. Also a Wald Test for the joint significance of Time effect (or Fund effect) is calculated and strong evidence for Time effect (or Fund Effect) is found in relevant regressions with a p-value =0.00. Finally a Hausman test is calculated for Ho = FE and RE are similar, for all regressions and we found strong evidence to use FE regressions with a p-value = 0.00. Therefore, both time and fund fixed effects are controlled for all the regressions. Table 11 KiwiSaver with and without REG - All Fund Performance Metrics - Robustness Analysis. Return

Discl REG TopQ BotQ SE Size Style Discl*REG Discl*TopQ

Alpha 1

Alpha 2

(1)

(2)

(1)

(2)

(1)

(2)

(1)

(2)

−0.617 (−1.51) –

−0.739 (−1.07) 0.946*** (4.54) −0.024 (−0.23) 0.077 (0.70) 0.075 (1.03) −0.069* (−1.90) 0.040** (2.05) 0.487 (1.22) −0.055 (−0.12) −0.185 (−0.38) −0.043 (−0.30) −0.193 (−1.02) 0.150 (0.34)

−0.240 (−0.71) –

−0.131 (−0.30) 0.229 (1.34) −0.003 (−0.04) 0.071 (1.36) 0.207*** (4.46) −0.061** (−2.01) 0.028 (1.56) −0.007 (−0.02) −0.224 (−0.85) −0.166 (−0.75) −0.159 (−1.28) −0.161 (−1.50) 0.409 (1.36)

−0.590 (−1.54) –

−0.633 (−1.29) 0.735*** (4.01) −0.083 (−1.11) −0.029 (−0.24) 0.086 (1.52) −0.058* (−1.80) 0.036** (2.07) 0.403 (1.47) −0.042 (−0.15) −0.095 (−0.19) −0.008 (−0.07) −0.092 (−0.56) 0.274 (0.91)

−0.547 (−1.51) –

−0.562 (−1.10) 0.757*** (3.83) −0.079 (−1.15) 0.016 (0.14) 0.064 (1.18) −0.067** (−2.14) 0.036** (2.01) 0.469* (1.66) −0.084 (−0.41) −0.344 (−0.62) 0.058 (0.48) −0.166 (−0.88) 0.191 (0.68)

−0.033 (−0.46) 0.058 (0.64) 0.109* (1.65) −0.057 (−1.56) 0.024* (1.83) –

TopQ*REG

0.053 (0.35) −0.117 (−0.51) –

BotQ*REG

–

Discl*TopQ*REG

–

Discl*BotQ

Active

−0.040 (−0.63) 0.021 (0.52) 0.216*** (4.95) −0.057* (−1.88) 0.024 (1.58) – −0.025 (−0.21) 0.060 (0.81) – – –

−0.105 (−1.27) −0.063 (−0.72) 0.124** (2.30) −0.048 (−1.47) 0.024* (1.80) – 0.241* (1.94) 0.026 (0.17) – – –

−0.066 (−0.95) −0.045 (−0.65) 0.109** (2.27) −0.057* (−1.77) 0.023* (1.83) – 0.146 (1.64) −0.049 (−0.35) – – –

(continued on next page)

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Table 11 (continued) Return

Active

Alpha 1

Alpha 2

(1)

(2)

(1)

(2)

(1)

(2)

(1)

(2)

Discl*BotQ*REG

–

–

0.234 (0.46) 3056 0.004 0.104 0.014

–

3075 0.001 0.279 0.211

0.399 (1.48) 3075 0.002 0.444 0.226

–

Sample Under Identification Test; p-value Weak Instrument Test; p-value Endogeneity Test; p-value

0.395 (0.73) 3075 0.002 0.215 0.051

0.481 (0.78) 3055 0.006 0.007 0.002

3075 0.001 0.760 0.102

3056 0.002 0.103 0.258

3055 0.003 0.094 0.165

Notes: The dependent variables are indicated in the first row. All 2SLS estimations are calculated using “LnFirmSize” as the instrumental variable. All the explanatory variables are lagged one quarter. Discl is one if the fund provides at least one voluntary disclosure during each quarter and zero otherwise. BotQ and TopQ are dummy variables, each equal to one if a fund's performance for the quarter is in the bottom quintile or top quintile respectively. SE is the standard deviation of the relevant fund performance metric. Size is the natural logarithm of the total net assets at the end of each quarter. Style is Morningstar's “Equity Style Box”, which classifies funds on a 9-point scale from Large Growth to Mid Blend to Small Value. REG is a dummy for the new disclosure requirements, equals 1 after September 2013. Clustered standard errors are calculated and t statistics are printed in parenthesis accordingly. ***, ** and * represent that the coefficient is statistically significant at the 1%, 5% and 10% significance levels, respectively. Also a Wald Test for the joint significance of Time effect (or Fund effect) is calculated and strong evidence for Time effect (or Fund Effect) is found in relevant regressions with a p-value =0.00. Finally a Hausman test is calculated for Ho = FE and RE are similar, for all regressions and we found strong evidence to use FE regressions with a p-value = 0.00. Therefore, both time and fund fixed effects are controlled for all the regressions.

Table 12 Australia Open-End Funds - Different Horizons - Active - Robustness Analysis.

Discl TopQ BotQ SE Size Style Discl *TopQ Discl *BotQ Sample Under Identification Test;p-value Weak Instrument Test; p-value Endogeneity Test; p-value

12 month Horizon 1 qtr Lag

12 month Horizon 2 qtr Lag

24 month Horizon 1 qtr Lag

24 month Horizon 2 qtr Lag

−3.348 (−1.30) 0.506 (1.09) −0.010 (−0.02) 0.015 (0.26) 0.003 (0.08) −0.001 (−0.02) −0.901 (−1.05) −0.031 (−0.03) 41,083 0.017 0.071 0.082

0.378 (0.20) 0.042 (0.18) 0.136 (0.50) 0.081** (2.08) −0.060** (−1.96) 0.006 (0.54) −0.051 (−0.11) −0.254 (−0.48) 40,552 0.020 0.961 0.961

−3.837 (−1.32) 0.172 (0.24) −0.269 (−0.44) 0.004 (0.04) 0.000 (0.00) −0.005 (−0.19) −0.305 (−0.25) 0.602 (0.51) 39,628 0.027 0.307 0.319

0.495 (0.26) −0.172 (−0.50) 0.153 (0.52) 0.099 (1.60) −0.067** (−2.28) 0.006 (0.49) 0.223 (0.37) −0.245 (−0.42) 39,069 0.028 0.951 0.954

Notes: The dependent variable is Active over different horizons (12 or 24 month) and lag length (1 quarter or 2 quarter lagged). All Panel estimations are calculated using 2SLS with “FirmAge” as the instrumental variable. All the explanatory variables are lagged one quarter. Discl is one if the fund provides at least one voluntary disclosure during each quarter and zero otherwise. BotQ and TopQ are dummy variables, each equal to one if a fund's performance for the quarter is in the bottom quintile or top quintile respectively. SE is the standard deviation of the relevant fund performance metric. Size is the natural logarithm of the total net assets at the end of each quarter. Both time and fund fixed effects are controlled for all the regressions. Style is Morningstar's “Equity Style Box”, which classifies funds on a 9-point scale from Large Growth to Mid Blend to Small Value. Clustered standard errors are calculated and t statistics are printed in parenthesis accordingly. ***, ** and * represent that the coefficient is statistically significant at the 1%, 5% and 10% significance levels, respectively. Also a Wald Test for the joint significance of Time effect (or Fund effect) is calculated and strong evidence for Time effect (or Fund Effect) is found in relevant regressions with a p-value =0.00. Finally a Hausman test is calculated for Ho = FE and RE are similar, for all regressions and we found strong evidence to use FE regressions with a p-value = 0.00. Therefore, both time and fund fixed effects are controlled for all the regressions.

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Table 13 New Zealand Open-End Funds - Different Horizons - Active - Robustness Analysis.

Discl TopQ BotQ SE Size Style Discl *TopQ Discl *BotQ Sample Under Identification Test; p-value Weak Instrument Test; p-value Endogeneity Test; p-value

12 month Horizon 1 qtr Lag

12 month Horizon 2 qtr Lag

24 month Horizon 1 qtr Lag

24 month Horizon 2 qtr Lag

−0.596 (−0.58) −0.329 (−0.53) 0.223 (0.51) 0.100** (2.00) −0.077 (−1.44) 0.018 (0.46) 0.648 (0.58) −0.220 (−0.23) 2447 0.089 0.819 0.636

−0.729 (−0.46) 0.106 (0.19) 0.477 (0.90) 0.042 (0.73) −0.072 (−1.42) 0.003 (0.06) −0.036 (−0.04) −0.704 (−0.60) 2366 0.201 0.669 0.390

−0.819 (−0.67) −0.037 (−0.06) 0.699 (0.83) 0.056 (0.55) −0.045 (−0.67) 0.035 (0.80) −0.198 (−0.18) −1.029 (−0.51) 2412 0.215 0.797 0.500

−1.193 (−0.65) 0.195 (0.26) 0.722 (0.90) −0.020 (−0.19) −0.042 (−0.55) 0.009 (0.21) −0.448 (−0.34) −1.561 (−0.77) 2331 0.271 0.384 0.320

Notes: The dependent variable is Active over different horizons (12 or 24 month) and lag length (1 quarter or 2 quarter lagged). All Panel estimations are calculated using 2SLS with “Cost” as the instrumental variable. All the explanatory variables are lagged one quarter. Discl is one if the fund provides at least one voluntary disclosure during each quarter and zero otherwise. BotQ and TopQ are dummy variables, each equal to one if a fund's performance for the quarter is in the bottom quintile or top quintile respectively. SE is the standard deviation of the relevant fund performance metric. Size is the natural logarithm of the total net assets at the end of each quarter. Both time and fund fixed effects are controlled for all the regressions. Style is Morningstar's “Equity Style Box”, which classifies funds on a 9-point scale from Large Growth to Mid Blend to Small Value. Clustered standard errors are calculated and t statistics are printed in parenthesis accordingly. ***, ** and * represent that the coefficient is statistically significant at the 1%, 5% and 10% significance levels, respectively. Also a Wald Test for the joint significance of Time effect (or Fund effect) is calculated and strong evidence for Time effect (or Fund Effect) is found in relevant regressions with a p-value =0.00. Finally a Hausman test is calculated for Ho = FE and RE are similar, for all regressions and we found strong evidence to use FE regressions with a p-value = 0.00. Therefore, both time and fund fixed effects are controlled for all the regressions. Table 14 KiwiSaver with and without REG - Different Horizons - Active - Robustness Analysis.

Discl REG TopQ BotQ SE Size Style Discl*REG Discl*TopQ

12 month Horizon 1 qtr Lag

12 month Horizon 2 qtr Lag

24 month Horizon 1 qtr Lag

24 month Horizon 1 qtr Lag

(1)

(2)

(1)

(2)

(1)

(2)

(1)

(2)

−0.240 (−0.71) –

−2.157 (−1.16) −0.681 (−0.75) −0.006 (−0.04) 0.087 (0.84) 0.208*** (2.92) −0.064 (−1.11) 0.091 (1.32) 2.260 (1.17) 0.315 (0.48) −0.804 (−1.11) 0.270 (0.56) 0.193 (0.75) −0.873 (−0.93)

−0.233 (−0.59) –

−2.695 (−1.15) −1.017 (−1.21) −0.282 (−1.36) 0.124 (0.96) 0.201** (2.44) −0.033 (−0.62) 0.091 (1.03) 2.951 (1.38) 1.477** (1.98) −1.268 (−1.23) 0.944 (1.23) 0.276 (1.07) −2.754* (−1.67)

−1.804 (−1.27) –

−2.262 (−1.19) 0.226 (0.37) 0.218 (0.79) 0.109 (0.44) 0.090 (0.62) −0.025 (−0.35) 0.079 (1.41) 1.204 (0.76) −0.133 (−0.15) 0.022 (0.02) −0.604 (−1.25) −0.079 (−0.26) 0.583 (0.57)

−1.316 (−0.98) –

−1.118 (−0.70) −0.118 (−0.37) −0.232 (−0.81) −0.109 (−0.65) 0.223** (2.16) −0.011 (−0.39) 0.045 (1.17) 0.995 (1.21) 0.897 (1.59) 0.524 (0.62) 0.017 (0.06) 0.337 (1.34) −0.606 (−0.88)

−0.040 (−0.63) 0.021 (0.52) 0.216*** (4.95) −0.057* (−1.88) 0.024 (1.58) –

TopQ*REG

−0.025 (−0.21) 0.06 (0.81) –

BotQ*REG

–

Discl*TopQ*REG

–

Discl*BotQ

−0.049 (−0.96) 0.078 (1.37) 0.180*** (3.96) −0.018 (−0.77) 0.036** (2.20) – 0.034 (0.34) 0.015 (0.14) – – –

15

0.064 (0.37) −0.027 (−0.22) 0.193** (2.29) −0.001 (−0.02) 0.047 (1.46) – −0.031 (−0.20) 0.408 (1.50) – – –

0.197 (1.20) −0.015 (−0.13) 0.232** (2.44) −0.015 (−0.33) 0.050* (1.72) – −0.259* (−1.69) 0.215 (0.85) – – –

(continued on next page)

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R. Gregory-Allen, et al.

Table 14 (continued) 12 month Horizon 1 qtr Lag

12 month Horizon 2 qtr Lag

24 month Horizon 1 qtr Lag

24 month Horizon 1 qtr Lag

(1)

(2)

(1)

(2)

(1)

(2)

(1)

(2)

Discl*BotQ*REG

–

–

0.215 (0.19) 2908 0.065 0.211 0.213

–

3075 0.001 0.760 0.102

0.738 (0.71) 3117 0.059 0.010 0.102

–

Sample Under Identification Test; p-value Weak Instrument Test; p-value Endogeneity Test; p-value

0.401 (0.52) 3198 0.039 0.288 0.301

−0.681 (−0.70) 2823 0.062 0.535 0.540

2998 0.003 0.897 0.478

2797 0.063 0.108 0.079

2716 0.081 0.053 0.017

Notes: The dependent variable is Active over different horizons (12 or 24 month) and lag length (1 quarter or 2 quarter lagged). All column (1) and Column (2) estimations are calculated using 2SLS with “LnFirmSize” and “FundAge”as the instrumental variables respectively. All the explanatory variables are lagged one quarter. Discl is one if the fund provides at least one voluntary disclosure during each quarter and zero otherwise. BotQ and TopQ are dummy variables, each equal to one if a fund's performance for the quarter is in the bottom quintile or top quintile respectively. SE is the standard deviation of the relevant fund performance metric. Size is the natural logarithm of the total net assets at the end of each quarter. Both time and fund fixed effects are controlled for all the regressions. Style is Morningstar's “Equity Style Box”, which classifies funds on a 9-point scale from Large Growth to Mid Blend to Small Value. REG is a dummy for the new disclosure requirements, equals 1 after September 2013. Clustered standard errors are calculated and t statistics are printed in parenthesis accordingly. ***, ** and * represent that the coefficient is statistically significant at the 1%, 5% and 10% significance levels, respectively. Also a Wald Test for the joint significance of Time effect (or Fund effect) is calculated and strong evidence for Time effect (or Fund Effect) is found in relevant regressions with a p-value =0.00. Finally a Hausman test is calculated for Ho = FE and RE are similar, for all regressions and we found strong evidence to use FE regressions with a p-value = 0.00. Therefore, both time and fund fixed effects are controlled for all the regressions. Table 15 Difference in Difference estimates of effect of Disclosure on Fund Performance. KiwiSaver with REG (1)

(2)

(3)

Dependent variable

# Horizon in month

# lag in quarter

DID

DID with TopQ

DID with BotQ

Return Return Return Return Active Active Active Active Alpha1 Alpha1 Alpha1 Alpha1 Alpha2 Alpha2 Alpha2 Alpha2

12 12 24 24 12 12 24 24 12 12 24 24 12 12 24 24

1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2

0.004 −0.051 −0.013 −0.054 −0.007 −0.012 0.005 −0.005 −0.002 −0.024 −0.046 −0.047 0.000 −0.047 −0.045 −0.062

−0.311** −0.442*** −0.571*** −0.080 −0.329* −0.146 −0.116 −0.102 −0.305** −0.416** −0.665*** −0.239* −0.328** −0.329* −0.594*** −0.182

0.340 0.253 −0.327 0.236 −0.013 0.384** −0.347* 0.249 0.265 −0.020 0.398** 0.146 0.144 −0.114 0.275 0.055

Notes: The dependent variables are indicated in the first column. DID reflects the size of the average treatment effect of Disclosure on fund performance. Disclosure, is one if the fund provides at least one voluntary disclosure during each quarter and zero otherwise. BotQ and TopQ are dummy variables, each equal to one if a fund's adjusted performance for the quarter belongs to the bottom quintile and the top quintile respectively. Both time and fund fixed effects are controlled for all the regressions. We estimate a triple diff in diff analysis to estimate the effect of disclosure for TopQ and BotQ funds separately. These are listed in the last two columns. ***, ** and * represent that the coefficient is statistically significant at the 1%, 5% and 10% significance levels, respectively. The appropriate equation for column (1), where the reported coefficient is β8: Perfi, t =

0

+

1 Discli, t 1

+

2 REGi, t 1

+

3 TopQi, t 1

+

4 BotQi, t 1

+

5 SEi, t 1

+

6 Sizei, t 1

+

7 Stylei, t 1

+

8 Discli, t 1

REGi, t

1

REGi,t

1

For column (2), where the coefficient is β11 (for column 3 is similar, with BotQ replacing TopQ in the second line): Perfi, t =

0

+

1 Discl i, t 1

+

9 Discli, t

+

i

+

t

+

1

+

2 REGi, t 1

TopQi,t

1

+

+

3 TopQi, t 1

10 TopQi, t

1

+

4 BotQi, t 1

REGi, t

1 +

+

5 SEi, t 1

11 Discli, t

i, t

16

1

+

6 Sizei, t 1

TopQi, t

1

+

7 Stylei, t 1

REGi, t 1+

+

8 Discl i, t 1

+

i

+

t

+

i, t

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R. Gregory-Allen, et al.

investment decisions about funds; and fund managers when making transparency choices. We examine a unique situation where, in Australia and New Zealand, most fund managers are not required to disclose holdings but some voluntarily do. This allows us to examine funds which disclose against those that do not. In addition, in New Zealand there is a class of funds called KiwiSaver which began in October 2007, and then since September 2013 have been required to disclose. This allows us to compare the same funds before and after they began disclosing. Although we model fund performance with four different performance measures, control for past performance, standard deviation and size, and check for robustness with endogeneity, different horizons and lags, and interaction between past performance, legislation (for KiwiSaver), and disclosure, we find no convincing evidence that disclosure impacts return, let alone harms return. We find that, in this case of funds in New Zealand and Australia, for holdings disclosure - beneficial to regulators and investors for the monitoring function – there is no evidence for a negative return impact. This has implications for governments considering expansion of laws requiring holdings disclosure. Acknowledgements This paper has been through several iterations, most recently waiting for time to elapse to allow us to have sufficient data after the KiwiSaver regulation went into effect. We acknowledge and appreciate comments on this and earlier versions from participants at sessions at Australasian Finance & Banking Conference 2012, AUT Capital Markets 2013, Academy of Financial Services 2013, University of North Texas Finance Seminar 2014, New Zealand Finance Colloquium 2018, and particularly Faruk Balli, Henk Berkman, Stephen Brown, Charles Corrado, Hung Do, Bart Frijns, Reggie Gregory-Allen, Paul Hutchison, Imre Karafiath, Tomas Mantecon, Bent Sørensen, and Nuttawat Visaltanachoti. References Agarwal, V., Mullally, K.A., Tang, Y., Yang, B., 2015. Mandatory portfolio disclosure, stock liquidity, and mutual fund performance. J. Financ. 70 (6), 2733–2776. Aragon, G.O., Hertzel, M., Shi, Z., 2013. Why do hedge funds avoid disclosure? Evidence from confidential 13F filings. J. Financ. Quant. Anal. 48 (5), 1499–1518. Balli, H.O., Sørensen, B.E., 2013. Interaction effects in econometrics. Empir. Econ. 45 (1), 583–603. Brown, S., Schwarz, C., 2011. The Impact of Mandatory Hedge Fund Portfolio Disclosure. (Available at SSRN 1683628). Carhart, M.M., 1997. On persistence in mutual fund performance. J. Financ. 52 (1), 57–82. Chen, Z., Gallagher, D.R., Lee, A.D., 2017. Testing the effect of portfolio holdings disclosure in an environment absent of mandatory disclosure. Account. Financ. 57 (S1), 101–116. Elton, E.J., Gruber, M.J., Blake, C.R., 2001. A first look at the accuracy of the CRSP mutual fund database and a comparison of the CRSP and Morningstar mutual fund databases. J. Financ. 56 (6), 2415–2430. Elton, E.J., Gruber, M.J., Blake, C.R., Krasny, Y., Ozelge, S.O., 2010. The effect of holdings data frequency on conclusions about mutual fund behavior. J. Bank. Financ. 34 (5), 912–922. Fama, E.F., French, K.R., 1993. Common risk factors in the returns on stocks and bonds. J. Financ. Econ. 33 (1), 3–56. Frank, M.M., Poterba, J.M., Shackelford, D.A., Shoven, J.B., 2004. Copycat funds: information disclosure regulation and the returns to active management in the mutual fund industry. J. Law Econ. 47 (2), 515–541. Ge, W., Zheng, L., 2006. The Frequency of Mutual Fund Portfolio Disclosure. Hausman, J.A., 1978. Specification tests in econometrics. Econometrica 1251–1271. Hayashi, F., 2000. Econometrics. Princeton University Press. Kennedy, P., 2008. A guide to econometrics, 6th ed. Blackwell Publishing, Malden, MA. Parida, S., Teo, T., 2018. The impact of more frequent portfolio disclosure on mutual fund performance. J. Bank. Financ. 87, 427–445. Schwarz, C.G., Potter, M.E., 2016. Revisiting mutual fund portfolio disclosure. Rev. Financ. Stud. 29 (12), 3519–3544. Shi, Z., 2017. The impact of portfolio disclosure on hedge fund performance. J. Financ. Econ. 126 (1), 36–53. https://doi.org/10.1016/j.jfineco.2017.06.001. Verbeek, M., Wang, Y., 2013. Better than the original? The relative success of copycat funds. J. Bank. Financ. 37 (9), 3454–3471. Wermers, R., 2001. The potential effects of more frequent portfolio disclosure on mutual fund performance. Perspective 7 (3), 1–11. Wermers, R., Yao, T., Zhao, J., 2007. The Investment Value of Mutual Fund Portfolio Disclosure. Unpublished working paper. University of Maryland. Wooldridge, J.M., 2010. Econometric Analysis of Cross Section and Panel Data. MIT press.

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