- Factor investing has qualities of both active and passive management.
- Like passive investing, it is rules-based and cost-efficient. Like active investing, it attempts to allocate assets to factors that have achieved excess returns.
- Our research shows that investment success is mostly due to exposure to favourable factors and, to a smaller extent, stock selection.
In 1976, the first index fund was launched. Today, a staggering one-third of European equity funds are passively managed. Nearly half of all equity funds in Asia and the US passively mirror an index.1 As passive investing has risen in popularity amid the longest bull market in history2, active managers — the driving force behind market efficiency (that is, share pricing that accurately reflects available information) — are on their way to becoming the minority.
But investors have options outside of these traditional investment approaches. Modern technology and data offer new ways to invest in a cost-efficient manner without relying on active trading to ensure price efficiency. Factor investing is one of those alternatives.
Factor investing is a simple, rules-based and transparent approach to building portfolios that provides consistent exposure to characteristics that have historically been associated with excess risk-adjusted returns.3
Traditional capitalisation-weighted indexes — and the passive investment vehicles that replicate them — have a single rule: The bigger the capitalisation size, the better. As share prices of successful (or overpriced) companies appreciate, their weights are automatically increased in the index. Out-of-favour, low-priced stocks are relegated to the bottom of the list.
As such, market bubbles (that is, when prices of stocks within a certain sector like technology, for example, are dramatically inflated) inevitably acquire prominent status, while underpriced companies are underweighted in the index and, therefore, in passive funds. Buying high and selling low is not typically a winning strategy. With the growing scarcity of active managers to ensure that share prices reflect available information (the market efficiency mentioned earlier), those bubbles are harder to foresee.
Some rules are better than others. If “buying high” is not a winning strategy, doing the opposite may work. Allocating a larger weight to companies with lower price-to-earnings (P/E) ratios — or, perhaps, to those that are more profitable — are just a few examples of such potentially “better” rules of building portfolios (rather than those based on size alone).
Consider the P/E ratio of a security (a “factor” in investment geek talk). By consistently buying the lowest-priced stocks (as measured by the P/E ratio) in the US market,4 we are not only able to steer away from the most prominent bubbles of the last three decades but also realise returns that typically exceed those of the traditional capitalisation-weighted index.
Relying on just one rule — even a good one, like not putting all your eggs in one basket — would be foolish, of course. Through review and validation of academic research and best practices amongst active investment management firms, we are able to collate a myriad of indicators (factors) — such as low P/E or high profit margins — that are both time- and science-tested to have shown better returns or lower risks. As Exhibit 3 shows, multiple factors don’t all work well at the same time; and yes, there are periods when they don’t work at all.
Most importantly, we use unemotional computers (albeit with humans watching over them) to crunch the data, rank the stocks and optimise portfolios to balance risk and return while reducing the transaction costs of managing it all.
This approach has qualities of both traditional passive and active management. Factor investing, similar to passive investing, is rules-based and cost-efficient. And like active investing, it seeks to allocate assets to the factors that have historically provided excess return (Exhibit 4).
Why now? Evolution.
The concept of unemotional, rules-based investing has existed since the 1930s, promoted by Columbia Business School professors Benjamin Graham and David L. Dodd. For the investment geeks amongst us, Graham’s 1949 book The Intelligent Investor describes how factors can be easily mapped to value, quality or momentum factor families. For example, it says:
- To capture quality, focus on companies with total debt-to-current-assets ratios of less than 1.10.
- To capture momentum, focus on companies with positive earnings-per-share growth in the last five years with no earnings deficits.
- To capture value, focus on companies with P/E per-share ratios of 9.0 or less.
Today, we have better technology to deploy when applying such rules to investing. We also better understand the sources of active manager outperformance. In Exhibit 5, we analyse outperforming managers in the US market.
It turns out that success (remember, we are only looking at outperforming managers here) is mostly a function of exposure to favourable characteristics (factors) and, to a much smaller extent, specific stock selection.
This should not be a surprise. It agrees with our 1986 research by Gary Brinson and Gilbert Beebower about the dominance of allocation as a determinant of success. Stock selection has its place, and skilful active managers should be able to deliver stock-specific excess returns. However, factor allocation should be able to leverage modern technology and science to provide access to attractive areas of the market in a cost-efficient manner.
IMPORTANT NOTE: The opinions and views contained in this document are solely those of SEI and are subject to change; descriptions relating to organisational structure, teams, and investment processes herein may differ significantly from those prescribed by underlying managers regarding their own investment houses and investments.
1 Morningstar Direct Asset Flows
3 Kahn, R., Lemmon, M. 2016. The Asset Manager’s Dilemma: How Smart Beta is Disrupting the Investment Management Industry. Financial Analysts Journal 72 (1): 15-20
4 The US market defined here is the SEI-constructed capitalisation-weighted US Equity Index. The portfolio is constructed by selecting the largest 33% constituents by weight. P/E ratios are calculated by SEI, using data from company income statements collated and provided by FactSet Fundamentals database. Securities are capitalisation-weighted with a weight cap. Returns do not reflect any management fees, transaction costs or expenses. Data refer to past performance. This information is for illustrative purposes only and does not represent actual Fund performance.
SEIGL is part of the SEI Investments Company (SEI) group of companies, which was founded in 1968 and is headquartered in Oaks, Pennsylvania. SEI Investments Management Corporation (SIMC) is the investment advisory arm and wholly-owned subsidiary of SEI. As at 30 June 2019, through its subsidiaries and partnerships in which the company has a significant interest, SEI manages, advises or administers $970 billion in hedge, private equity, mutual fund and pooled or separately managed assets, including $335 billion in assets under management and $630 billion in client assets under administration. For more information, visit seic.com.
Alpha source: Alpha source is a term used by SEI as part of our internal classification system to categorise and evaluate investment managers in order to build diversified fund portfolios. An alpha source is the investment approach taken by an active investment manager in an effort to generate excess returns. Another way to define an alpha source is that it is the inefficiency that an active investment manager seeks to exploit in order to add value.
Momentum: Momentum stocks are those whose prices are expected to keep moving in the same direction (either up or down) and are not likely to change direction in the short-term.
Price-to-earnings ratio: Price-to-earnings ratio is the ratio of a company’s share price to its earnings over the past 12 months, which can be is used to help determine whether a stock is undervalued or overvalued.
Quantitative: Quantitative analysis is based on computer-driven models.
Value: Value stocks are those that are considered to be cheap and are trading for less than they are worth.
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IMPORTANT NOTE: The opinions and views contained in this document are solely those of SEI and are subject to change; descriptions relating to organisational structure,
teams, and investment processes herein may differ significantly from those prescribed by underlying managers regarding their own investment houses and investments.