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Investment Strategies of Constructing FTSE 100 Index Funds with Exchange-Traded Derivative Instruments
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Passive and enhanced index funds are two important options for investors. Vanguard is the largest provider of passive indexed funds, and DFA is one of the major providers of enhanced indexed funds, with uniquely close ties to academic financial research and an illustrious board of directors. Vanguard has low fees and investors can buy Vanguard funds directly. DFA’s fees are higher and one can invest in DFA funds only through an advisor, who charges for the service. Moreover, one must pay transactions fees to a custodian. We ask whether DFA has outperformed Vanguard by enough to justify the additional fees. Passive and enhanced index funds are two important options for investors. It is worthwhile to compare the performance of the two, and such a comparison is best between particularly reputable fund families offering these instruments. Vanguard is the largest provider of passive indexed funds and it has low fees. DFA is one of the major providers of enhanced indexed funds (using stock screens which are unique to them), with uniquely close ties to academic financial research and an illustrious board of
This article uses portfolios of Vanguard index funds to study the optimal portfolio allocation strategy for long-term investors who are saving for retirement. The optimization, conducted using both a single-index-hybrid model (SIHM) and the Markowitz–Sharpe optimization method, suggests that in the long run, an optimized allocation strategy will yield cumulative returns equivalent to those of a passive allocation strategy with significantly less risk. In addition, the optimized allocation strategy achieves the favorable risk and reward profile using fewer funds than the passive strategy.
We analyze 16 mutual funds that are self-proclaimed or media-identified disciples of behavioral finance to determine whether: (a) they successfully attract investment dollars and (b) their strategies earn abnormal returns for their investors. We find these funds are successfully attracting investment dollars, outperform S&P 500 index funds, load especially heavily on the HML factor, but fail to earn risk-adjusted abnormal returns. Our results suggest behavioral mutual funds are tantamount to value investing and not much more.
This article describes the role of exchange-traded funds (ETFs) in the implementation of exposure to various asset classes, as shown by a recent survey of European professional investors and asset managers. It provides insights into the current use of liquid index trackers and into the prospects, as revealed by survey responses, for growth in the use of these instruments. The survey is based on a questionnaire that was addressed to industry participants in Europe in early 2008. The study generated responses from 111 Europe-based asset managers and institutional investors. It is worth noting that the survey garnered responses from the major industry players, as is evidenced by the 12.3% of institutions that manage in excess of €100 billion. Selected findings of the survey are highlighted in this article. The findings show that liquidity is perceived as a key advantage of ETFs over indexing vehicles like traditional index funds and total returns swaps. Likewise, the tracking quality of ETFs is judged to be ...
We provide evidence on the debate of `Exchange traded funds (ETFs) versus Index Funds' using data of ETFs and index funds belonging to the same investing family. Data used involve the Vanguard funds and results indicate that ETFs and index funds present, on average, similar return and risk records. In addition, the risk of ETFs and index funds is similar to the risk of the tracking indices. However, the return of these alternative investing tools is slightly inferior to the return of benchmarks. Moreover, a positive relationship between return and risk is revealed. Further research demonstrates that ETFs and index funds are fully invested in their benchmarks. As a result, the tracking error for both ETFs and index funds is low. Finally, the tracking error is found to be positively affected by expenses.
The paper provides a critical review of empirical findings on the performance of mutual funds, mainly for the US and UK. Ex-post, there are around 0-5% of top performing UK and US equity mutual funds with truly positive-alpha performance (after fees) and around 20% of funds that have truly poor alpha performance, with about 75% of active funds which are effectively zero-alpha funds. Key drivers of relative performance are, load fees, expenses and turnover. There is little evidence of successful market timing. Evidence suggests past winner funds persist, when rebalancing is frequent (i.e., less than one year) and when using sophisticated sorting rules (e.g., Bayesian approaches) - but transactions costs (load and advisory fees) imply that economic gains to investors from winner funds may be marginal. The US evidence clearly supports the view that past loser funds remain losers. Broadly speaking results for bond mutual funds are similar to those for equity funds. Sensible advice for most investors would be to hold low cost index funds and avoid holding past ‘active’ loser funds. Only sophisticated investors should pursue an active ex-ante investment strategy of trying to pick winners - and then with much caution.
This study focuses on S&P500 inclusions and deletions, examining the impact of potential overnight price adjustment after the announcement of an S&P500 index change. We find evidence of a significant overnight price change that diminishes the returns available to speculators although there are still profits available from the first day after announcement until a few days after the actual event. More importantly, observing the tick-by-tick stock price performance and volume effects on the key days during the event window for the first time, we find evidence of consistent trading patterns during trading hours. A separate analysis of NASDAQ and NYSE listed stocks allows for a detailed examination of the price and volume effect at an intra-day level. We find that index funds appear to cluster their rebalancing activities near to and after the close on the event date, suggesting that they are more concerned with tracking error than profit.
We apply a recent nonparametric methodology to test the market timing skills of UK equity and balanced mutual funds. The methodology has a number of advantages over the widely used regression based tests of Treynor-Mazuy (1966) and Henriksson-Merton (1981) . We find a relatively small number of funds (around 1%) demonstrate positive market timing ability at a 5% significance level while around 19% of funds exhibit negative timing and on average funds miss-time the market. However, controlling for publicly available information we find very little evidence of market timing ability based on private timing signals. In terms of investment styles, there are a small number of successful positive market timers amongst Equity Income and 'All Company' funds but not among either Small Stock funds or Balanced funds, although a few small stock funds are found to time a small stock index rather than a broad market index. Copyright (c) 2009 The Authors Journal compilation (c) 2009 Blackwell Publishing Ltd.
Explicit mutual fund fees are typically less than 1% of the assets under management. By comparison, the typical hedge fund charges a base fee of 2% plus a performance fee equal to 20% of net profits. Thus, hedge funds appear to charge far more for even comparable performance—unless one takes account of the following:
Using these two assumptions, the author provides a simple example that shows that a representative mutual fund's performance can be replicated by combining an index fund, which represents the mutual fund's passive component, with a hedge fund, representing the mutual fund's active component. When analyzed in this way, the fee of the combined fund turns out to be remarkably close to the actual fee of the mutual fund. This in turn suggests that the implicit fee for the mutual fund's small active component is comparable to the fees of the hedge fund.
Using a returns-based style analysis approach, we develop a dominant timing indicator to measure each fund's ability to take advantage of movements in their dominant passive index. We apply this to a sample of Australian multi-sector funds over the period 1990 to 2005. We find evidence that the dominant timing metric presents a more positive picture of fund timing ability in comparison to traditional timing measures; however, the majority of funds are still unable to time their dominant index effectively.
Many researches indicate that the Black-Scholes (BS) option-pricing model demonstrates systematic biases due to some unreasonable assumptions. In practice, implied volatilities tend to differ across exercise prices and time to maturities. To solve the problem, Heston and Nandi (HN) (2000) develop closed-form Generalized Autoregressive Conditional Heteroscedasticity (HN-GARCH) model. In this study, we apply their model on Financial Time Stock Exchange (FTSE) 100 index option. As a benchmark, we employ the ad hoc BS model which uses a separate implied volatility for each option to fit the smirk/smile in implied volatilities. The test finds that the HN GARCH has smaller valuation errors than the ad hoc BS model.