Skip to main content

You are here

Clear Path Analysis Report: The evolution of factor investing

A quant heritage

Investors have long followed rules-based approaches to put together investment portfolios.

In fact, quantitative asset management has a heritage of at least eight decades. The merits of a simple, rules-based approach—choosing “value” stocks on the basis of accounting metrics like price to book value or price to earnings ratios—were set out by Benjamin Graham and David Dodd in their seminal book “Security Analysis”, published in 1934.

In the 1970s, particularly in the U.S., a new generation of investment analysts started to focus on techniques like momentum trading, statistical arbitrage and portfolio optimization; led by advancements in computer processing, greater access to data and spurred by developments in financial theory. Even non-quantitative asset managers have long had preferences, spoken or unspoken, towards particular types of stock, whether value, growth or small-cap, in their portfolios.

So why has factor investing suddenly achieved renewed popularity?

As in so many other areas of the economy, computing power has played a major role. Whereas, the first portfolio optimizations, undertaken in the 1950s, may have taken several days to run on a mainframe computer, these days it’s possible to run sophisticated back-tests in seconds. Data mining in investment carries risks, of course. There’s the apocryphal economist who claimed, “If I torture the data long enough, I can make it confess to anything.” We can see patterns where they don’t exist, in other words.
 

Understanding the sources of markets’ returns

In finance theory, a factor is a common driver of securities’ returns. The component of stocks’ returns that is driven by factor exposure can be understood as resulting from a shared source of risk, called systematic risk, which carries an associated return premium. The remaining component of any individual stock’s returns derives from its stock specific (non-systematic) risk.

Classical investment theory assumed that there is a single type of systematic risk, called market risk. Under the Capital Asset Pricing Model (CAPM), introduced in the 1960s, a single market factor explains stocks’ returns. The associated risk premium is called the equity risk premium.

However, more recent theories suggest the single factor model has limitations. There is empirical evidence to support theories that other characteristics, such as stocks’ valuation and size, also help explain their performance over time. For example, empirically, stocks with lower price-to-earnings ratios have high returns over the long term, compared to those with higher price-to-earnings ratios, and similarly smaller capitalization stocks have produced high returns compared with the shares of larger companies.

1993, Eugene Fama and Kenneth French published a paper in which they examined three factors: a market factor, a size factor and a value factor. The authors concluded that this three-factor model is a better representation of stocks’ real-life performance than the single-factor model.

Over time, other factors, such as momentum and volatility, have been identified empirically and rationalized theoretically, achieving acceptance amongst investment practitioners.

Factors via indexes

A factor index targets factor return premia in a transparent, rules-based and investable format. It can be used both as a benchmark for the performance of actively managed funds and as the underlying target for an index-replicating investment strategy.

Factor indexes draw upon the heritage of equity style indexes, which were first introduced by Russell in 1987 for the US equity market. They offer market participants alternative tools for use in implementing their investment strategies. Interest in the factor approach now extends beyond equities and into other asset classes, such as fixed income, currencies and commodities.

FTSE Russell’s 2016 Smart Beta survey, which covered global asset owners with an estimated US $2 trillion under management, revealed that 52% of European institutions and 28% of North American institutions now have an allocation to smart beta strategies, a category that includes factor indexes.

Amongst the largest investors—those with over $10 billion under management—seeking specific factor exposure was cited by almost half of survey respondents as their primary objective when evaluating smart beta strategies.

—Download the complete paper—