Passively managed funds, including exchange-traded funds (ETFs) and index funds, have grown 73 percent over a six-year period, increasing from 11 percent of global assets under management (AUM) in 2009 to 19 percent in 2015. Within this segment, ETFs have been growing at an annual growth rate of approximately 25 percent over the past decade, with AUM above $3 trillion. Institutions, too, are increasingly using ETFs for core exposures and access to smart beta strategies. In a study by Greenwich Associates, 36 percent of the institutional equity ETF users expected to increase their allocations to ETFs in the year ahead, while that number was 35 percent for institutional bond ETF users.
Accompanying this growth has been a sharp fall in fees. Investing in the U.S. stock market through an index-based fund in 1976 would have cost an upfront fee of 6 percent, as compared with just 0.05 percent for many index-based funds today. The downward trend in fees can be seen across active and passive funds, across geographies and across product categories — bonds, equities, target date and hybrid funds. The trend is much more pronounced in the case of index funds and ETFs. According to a report from Deloitte, actively managed funds in Europe charged 5 percent less in 2012 than they did in 2002, whereas that number was closer to 42 percent lower in the case of passive products. Investors have also simultaneously shown a strong preference for low-fee funds, with 95 percent of new flows going into funds in the lowest-cost quintile over the past 10-year period, further reducing asset-weighted expense ratios.
An important enabler for offering passive exposure with fees in the single basis points has been the significant technological changes in the asset management industry. Improvements in computing, data storage and processing power have created significant cost efficiencies across the value chain of product creation, execution and fund distribution. Back in the 1970s, when Wells Fargo attempted to create an equal-weighted index using stocks listed on the New York Stock Exchange, the project was abandoned due to the difficulty of managing non-market-cap-weighted indices — think dividend calculations, cash management, back-office functions and trading performed without today’s technology. On the trading side, bid-ask spreads also have fallen dramatically over the past 20 years, with markets becoming electronic and more liquid. And a reason specific to ETFs is the innovative way their shares are created and redeemed. This process requires fewer intermediaries and trading, thus reducing the administrative burden significantly. On the distribution side, funds incur lower marketing costs than in the past due to modern channels and online platforms. Technological changes are only increasing, and similar to every other industry, from travel to publishing, technology could create winners and losers in the asset management industry. Put in the context of increasing automation, just as robo-advisory is likely to be an important new player on the distribution side, indexing, which is highly process driven, may continue to be one of the strongest beneficiaries on the product side.
It is perhaps not surprising that demand for low-fee products has increased, given their relevance in a low-return environment. With a reasonable assumption that the equity risk premium over a five-year period will be 4 percent annualized, a 1 percent active management cost would be 25 percent of the overall expected gain from equity investing. That margin would be even larger in the case of bonds, where expected returns are near zero. Low fees aside, there are other parts to the demand equation as well. Regulation has been favorable toward passive investing, with a strong push for greater cost transparency and big changes being made in how advisers and investment platforms are paid. In addition, disappointing active management results have driven investors to vote with their dollars in favor of low-cost passive funds. Funds reap the benefits of scale as their AUMs increase, resulting in lower custodial fees, better IT systems, advanced risk management, collateral management, transaction cost savings through internal crossing networks and spreading out the anticipated high regulatory burden. Funds that offer passive beta exposure especially benefit, as index calculation and passive tracking are areas where technology may be powerfully used to remodel a low-margin business into a highly scaled, efficient enterprise. The table on this page shows the strong relationship between lower fees and funds that have high AUM.
With increasing competition, passive strategies are expected to remain low cost. In particular, for traditional market-cap-weighted index funds, the return of two funds covering the same market should be equivalent (by definition), resulting in beta being commoditized and fund managers left competing only for price. (To a lesser extent, this commoditization is taking place in factor investing.) Thus, the barrier to entry becomes the scale and is no longer about the product. If a product has $100 million AUM and the cost of replicating the market for an additional $100,000 is modest, in order to maximize profit the eventual price target also should trend to a modest point.
Some firms might consider a “loss leader” approach to retain AUM. This pricing strategy could be to offer their core pure passive range at relatively low rates in order to retain their scale benefits. Many investors also seek to complement their passive low-cost beta with active management using a core satellite approach. Additionally, securities lending on a fund’s assets, even if a large proportion of it is returned to the fund, can be of significant magnitude for providers with scale. Blocher and Whaley, in the report titled Passive Investing: The Role of Securities Lending, show that ETFs can earn significant revenue from securities lending, on the order of the size of the ETF’s expense ratio.
Though the cost of investing in most marketcap funds is decreasing, investors are still willing to pay higher fees for products that beat their benchmark. Indeed, the main goal of traditional long-only active management was to outperform market-cap-weighted indices by picking certain stocks. However, research that began more than two decades ago shows that outperforming managers were generating excess returns through tilting toward certain sources of returns or risk premia. These systematic sources of returns exist due to behavioral anomalies, systematic risks and structural inefficiencies in the market. As these systematic sources of returns (or factors) are both identified and quantified, they become more readily investable. The cost of accessing these factors also decreases; once the drivers are understood, they can be systematically harvested in an inexpensive, transparent, rules-based approach.
This demystification of investing is an ongoing process, where sources of returns are broken down through quantitative models and then commercialized through better technology. Technology and quantitative models are a powerful combination, as together they could potentially create investment strategies that are cheaper, more reliable, with reduced human bias and greater economies of scale. Automation makes a large proportion of the human labor involved in tracking a passive index redundant and, in much the same way, quantitative models attempt to do the same to the traditional judgment and decision-
making activity of an active manager.
Portfolio returns were initially considered to be solely due to active manager skill, and hence any return was treated as “alpha.” In 1963, the capital asset pricing model explained the cross-sectional differences of stock returns with only a single factor — the market beta — and portfolio returns in excess of this were attributed to skill. Later, academic research began to identify other sources of return, with the most important being the Fama-French Three-Factor Model in 1993, which included market beta, value and size (and was later expanded to four factors adding momentum). There has been a steady evolution of research into the best measures to capture these factors, their performance in different cycles and new models that offer compelling alternatives to the four-factor model.
ALPHA IS CONTINUALLY SHRINKING
As returns are broken down further and further, alpha shrinks as it is the portion of a portfolio’s returns that is unexplained by exposure to systematic risks or betas. We have witnessed the continuous and ongoing process of alpha being recategorized as beta and, consequently, the portion of return attributed to manager skill reduced.
As interest in factors grows, there is likely to be a renaissance in demand for quantitative factor investing. This quantitative-based investing could look quite different from the active quant boom of 2004–2007, as much of the growth would be in the form of transparent “white-box,” low-cost indices.
The indications are already well in evidence as demand grows for smart beta, hedge fund replication strategies, passive retirement funds, sustainable beta and quantitative techniques that target carry or volatility, or are trend-following. In 2011, Internet entrepreneur and venture capitalist Marc Andreessen notably stated that software was steadily eating the world, disrupting industries such as music, healthcare and more. Indeed in much the same way, technology in the form of systematic indexing appears to be taking over the investing landscape, encroaching deeply into active management.
The increased scope of passive investing is most evident in the growth of smart beta — the AUM in smart beta indices increased by a compound rate of nearly 40 percent from 2010 to 2015 (compared with market-cap indices, which grew 19 percent), with a proliferation of new indices. The ideas on which smart beta is based — factors such as size, dividends, value, low volatility, momentum and quality — have existed for a long time, but the remarkable aspect is the way these factors have democratized investing. They provide inexpensive, transparent and easy access to sources of return that were previously considered dependent on managerial skill and only available from active managers. Though smart beta so far has been largely an equity story, certain styles in fixed income have emerged, such as quality, value and, in the case of sovereign bonds, GDP weighting. Recently, the focus of the industry has been on the optimal way to blend the risk premia together in order to create well-diversified “all weather” portfolios and other sophisticated factor combinations that can be dynamic and extendable over asset classes.
HEDGE FUND BETA
Hedge fund or alternate beta further expands the scope of systematic beta to the less well-represented risk premia generally targeted by hedge fund managers. Passive hedge fund approaches attempt to capture that portion of hedge fund returns driven by systematic risk exposures (beta) versus non-replicable manager skill (alpha) in a cheaper, transparent and rules-based format. Several studies have questioned the skill of hedge fund managers, indicating that their returns are driven by “passive” linear systematic risk factors. Implementation difficulties exist in harvesting these factors passively, especially in terms of liquidity and cost efficiency. However, many hedge fund styles — such as long/short, merger arbitrage, global macro and relative value credit — invest in liquid securities that trade and are priced daily, much the same as a traditional long-only fund.
Long/short equity risk premia is likely to be a primary new product because of high liquidity and it being the natural extension of current smart beta factor indices.
While the collective performance of the hedge fund industry has been in question, the actual investment characteristics of alternate beta and hedge funds are still of extreme interest. The current low-return world presents a daunting obstacle to asset owners, especially those with plan targets. The projected return on investments is a crucial assumption that can be pivotal in the asset owner’s ability to meet its contributions. This issue can be seen with U.S. corporate pension plans, where the expected return on plan assets is on average 7.3 percent per year. In the United Kingdom, this expected return is 6.8 percent. It is almost impossible to find such returns in fixed income, which is traditionally an important asset class for both pensions and insurance — the U.S. 10-year Treasury bond rate was approximately 1.9 percent and the corresponding U.K. 10-year bond yield was at 1.5 percent in May 2016. Equity allocations that are too high could make all the difference between funded and unfunded status, given their high volatility. Thus, matching these pension liabilities requires other sources of uncorrelated returns with less downside, which may explain the surprisingly strong flows into alternate beta strategies and hedge funds in the recent few years. Hedge fund factors tend to have low correlation with one another and with market beta, and thus they can be used to improve portfolio
diversification, returns and risk management. The demand for these factors (but with a cheaper fee), greater transparency and increased liquidity are key drivers for hedge fund beta indices.
Due to the macro-economic and regulatory environment in the recent years, there has been a marked global trend for strategies that align investors with their desired outcome rather than an arbitrary market index. Investment outcomes that suit specific needs such as growth, income, inflation hedging, risk management or capital preservation resonate far more with individual and institutional market participants. Pension funds and asset managers have woken up to the realization that their end consumers are less worried about benchmarks and do not think in terms of risk/reward.
McKinsey & Co.’s 2013 report Outcomes Are the New Alpha states that more than 80 percent of asset managers now place “solutions” among their top three growth priorities. The average firm interviewed by McKinsey for the survey expected its solutions business to deliver more than one-quarter of its inflows and one-sixth of its revenues in the near future.
The “outcome” preferences of individuals is also becoming critical, as pension liabilities move from the state and employers to individuals. According to a report by Casey Quirk, individual investors represented 90 percent of the asset management industry’s net new inflows in 2014. The report also predicted that they would dominate growth in the industry, accounting for almost 120 percent of the asset management inflows through 2020. In most fund market jurisdictions, governments and employers are looking to reduce the cost of retirement benefits and to decrease the risk in their balance sheets by ending pension plans that result in volatile liabilities linked to final year salaries. As employers move away from defined benefit (DB) plans in favor of defined contribution (DC) plans, a progressively greater share of investment risk is being transferred back to end-investors, with commensurately less being borne by intermediaries and companies. The end result is that individuals are forced to bear the burden of every aspect of decision making and risk in retirement planning.
THE OUTCOMES TREND FAVORS INDEXING
With the big focus on fees, retirement plan fiduciaries and end-investors may find low-cost index products that aim to meet their investment goals appealing. Automated advisory platforms (robo-advisers), which are set to increase in popularity, also primarily use ETFs for their transparency and low cost in order to offer market exposure. We expect multi-asset indices that allocate dynamically to a variety of risk premia and target particular outcomes to benefit greatly from this trend. Essentially, in outcome-oriented indexing, it is the index that performs the asset allocation and risk management role by first targeting a specific result and then arranging all the component index blocks to achieve the desired cash flows. Through advanced quantitative models, passive portfolios can be created which target customized risk/return profiles that evolve over time, such as those of individuals planning for retirement. Consider the modern sophistication available to address the challenges faced by a DC participant. For example, the S&P STRIDE Index Series combines a target-date glide path with risk management that allows investors to start with an asset growth strategy and then shift to income generation as they progress from their working years into retirement.
MASS CUSTOMIZATION AND LONG TAIL INVESTORS
Mass customization is defined as the process that enables the creation of products with enough customization that nearly everyone can find what they want at a low price. There are two main reasons for why mass customization for specific financial outcomes based on age, demographics, income, future choices and risk tolerance may be soon offered.
The first reason is the array of index building blocks that are available and in the innovation pipeline — everything from broad equities across geographies and sliced-up portions of the same — small-cap, large-cap, value, growth, quality, low volatility, high beta, defensive, risk controlled, factors. Within passive fixed income as well, combinations of government bonds, high-yield and emerging-market debt with different weighting schemes are available. Additionally, there are specialized asset classes such as commodities, infrastructure, timber, listed real estate, natural resources and so on. Secondly, robo-advisers can offer affordable, sophisticated, outcome-oriented index solutions and use simple quantitative models to decide optimal allocation to various index funds, thus cutting out the intermediary fees of financial advisers and active managers.
In the first phase of investment mass customization, simple goal-based models that factor in the individual’s age and risk tolerance will be used to target the large number of fee-sensitive retail investors in employee-sponsored 401(k) or DC plans. The graph on page 56 illustrates the “investor long tail” that can have access to heavily customized solutions through the potent tool of robo-advisers combined with rules-based strategies. While automated investment advice is currently targeted mainly for mass retail, as awareness of its benefits increases, it could eventually be used heavily by the high-net-worth category as well.
One important innovation has been the offering of index strategies and themes via a basket of stocks that can be purchased through a single order, without the need for a fund structure. Instead of buying the basket via an ETF or mutual fund wrapper, the order automates the purchase of all the constituent stocks specified by the index directly on the exchange. Though the sharp fall in transaction costs over the past decade has made funds cheaper, it also makes the savings from pooling funds together less material. If investors can access an investment strategy without going through a fund wrapper, significant hurdles, such as seeding the ETF, are removed, and granular tailoring for particular individual needs becomes possible. As a concept, this is currently only possible in equities due to the low transaction costs and high liquidity. Although it is in the early phases and not without risk, this trend points to the ability to offer highly bespoke and specialized solutions to retail investors in the future.
Angana Jacob (firstname.lastname@example.org) is associate director, global research and development at S&P Dow Jones Indices.