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The paper aims to examine the disclosure practices of KiwiSaver retail schemes in New Zealand. The aim is to investigate the level of comparability of KiwiSaver disclosures in the annual reports provided to members.

Using data from KiwiSaver annual reports, the paper addresses three research questions using a disclosure index method. First, in the absence of standardised performance measures, will schemes adopt comparable disclosure practices? Second, will larger schemes disclose more information than smaller schemes? Third, will better performing schemes disclose more information than poorer performing schemes?

The analysis indicates that KiwiSaver schemes' disclosure practices are not comparable and there are no evident patterns between size or performance and disclosure quality or quantity.

As this is an exploratory study, a sample of schemes is used in the research, thereby limiting generalisability of the research. In addition, different schemes have chosen different permitted forms for reporting to scheme members, exacerbating the lack of comparability this paper seeks to study; with some schemes providing abridged annual reports and others providing full annual reports to members. Moreover, the use of a disclosure index for classification introduces an element of subjectivity into the research method.

Overall savings, of which retirement savings is a subset, are low in New Zealand. One of the mechanisms to encourage saving is to provide an environment that supports effective investment decision making. This research highlights some of the inconsistencies that exist in current reporting practices and limit comparability between KiwiSaver schemes.

The purpose of this paper is probe the early data emerging from the KiwiSaver market and to draw insights on KiwiSaver investor behaviour, particularly in respect of the unique default provider feature of the scheme.

The primary source of data for this study is a purpose-built database compiled using data from KiwiSaver providers’ annual reports for the period 2009-2011.

The study finds that KiwiSaver members, like other investors, are chasing performance and seeking to avoid fees. However, an unexpected negative relation is found for bank ownership.

The key limitations of this data source include the low frequency, differing formats and levels of detail disclosed in various annual reports.

Chasing past performance indicates a need for investor education for KiwiSaver members.

The study provides an initial empirical examination of the KiwiSaver market, and the determinants of the flow of funds and members. The results can be used to guide policymakers and providers in their future decision-making around the scheme and individual offerings.

International evidence of economies of scale in mutual funds is mixed. KiwiSaver offers an interesting opportunity to examine economies of scale given its growth from a new scheme with few members and low balances, where fund costs should be high, to a much larger scheme that should be cheaper to run. As a defined contribution superannuation scheme, fees play an important role in determining the eventual retirement savings members achieve. This paper aims to examine whether the anticipated economies of scale are passed onto members.

The authors use a sample of 267 KiwiSaver funds over 2013-2018 and relate fund fees to assets under management (AUM) and the number of participants using regression analysis and a translog cost function.

The authors find evidence to suggest funds are passing on cost savings. Specifically, the authors observe that fees increase slower as the number of members grows, suggesting economies of scale are driven by the number of members, but not the size of the assets being managed. All else held constant, a 1 per cent increase in fund participants increases fees by 0.93 per cent on average. In contrast, a 1 per cent increase in AUM results in effectively 1 per cent increase in fees, all else held constant.

While KiwiSaver has been an undeniable boost to the local funds management industry, regulators are increasingly under pressure to ensure fees are appropriate. In 11 years, New Zealand-based KiwiSaver has grown to over $50b in AUM, with over $400m in total fees per year. This paper provides evidence that economies of scale are partially present in the KiwiSaver sector, although not where it arguably counts: in the size of the AUM.

This paper aims to investigate the risk-adjusted performance of the KiwiSaver Growth funds for the period 2007-2013 in New Zealand.

Performance attribution regressions are used to measure risk-adjusted performance of KiwiSaver funds.

This paper found that there is no evidence of systematic risk-adjusted outperformance of KiwiSaver Growth funds, and in several cases, there is evidence of significant underperformance. This paper further reports substantial variation in the amount of risk-taking, and local and international stock market exposure of KiwiSaver Growth funds.

KiwiSaver is becoming an increasingly important investment vehicle for many New Zealanders saving for retirement. This is the first paper that considers the performance of KiwiSaver funds.

This paper aims to examine the performance and benchmark asset allocation policy of 70 KiwiSaver funds catergorised as growth, balanced or conservative over the period October 2007-June 2016. The study focuses on the sources for returns variability across time and returns variation among funds.

Each fund is benchmarked against a portfolio of eight indices representing eight invested asset classes. Three measures were used to examine the after-fee benchmark-adjusted performance of each fund: excess return, cumulative abnormal return and holding period returns difference. Tracking error and active share were used to capture manager’s benchmark deviation.

On average, funds underperform their respective benchmarks, with the mean quarterly excess return (after management fees) of −0.15 per cent (growth), −0.63 per cent (balanced) and −0.83 per cent (conservative). Benchmark returns variability, on average, explains 43-78 per cent of fund’s across-time returns variability, and this is primarily driven by fund’s exposures to global capital markets. Differences in benchmark policies, on average, account for 18.8-39.3 per cent of among-fund returns variation, while differences in fees and security selection may explain the rest. About 61 per cent of balanced and 47 per cent of Growth funds’ managers make selection bets against their benchmarks. There is no consistent evidence that more actively managed funds deliver higher after-fee risk-adjusted performance. Superior performance is often due to randomness.

This study makes use of a unique data set gathered directly from KiwiSaver managers and captures the long-term strategic asset allocation target which underlines the investment management process in reality. The study represents the first attempt to examine the impact of benchmark asset allocation policy on KiwiSaver fund’s returns variability across time and returns variation among funds.

The purpose of this paper is to explore the differences in KiwiSaver portfolio composition between investors who receive financial advice and those who do not.

Using proprietary data which contain information of 405,107 individual KiwiSaver accounts, this paper examines who receives advice, compares the asset allocations of advised accounts with non-advised accounts, explores the relation of asset allocation with demographic characteristics and compares differences in returns between advised and non-advised investors.

Three key findings are presented in this paper. First, female investors, relatively older investors and investors with higher levels of funds under management (invested wealth) are more likely to receive financial advice. Second, advised investors hold more equity assets. Third, differences in performance between advised and non-advised accounts are marginal.

Panel data are not used, which prohibit investigating asset allocation choices overtime. The time series for returns is short, as KiwiSaver has only been operating since 2007. The total portfolio that people own is not known; thus, the values on investment fund information do not represent the total wealth of each person, as other accounts elsewhere may exist.

There are broad implications for the New Zealand capital market, retirement policy, financial advice industry and development of financial literacy programmes.

The paper examines individual investor behaviour on a nationwide sample and explores how receiving financial advice relates to asset allocation.

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.