The Rise of Factor Investing

By Gary Mullins 

Correspondent, Economics Undergraduate 


In spite of the perennial pall of negativity and apprehension that seems to linger over the world economy, the reality is that things really aren’t going that badly. The S&P 500 stock price index recently recorded its longest ever bull run – almost nine and a half years without a fall of 20% or more. The technology-heavy NASDAQ and the US-focused Dow Jones Industrial Average indices have also recorded unprecedented highs. US inflation is hovering around the Fed’s two percent target and the unemployment rate is currently running at 3.9% – the lowest rate in over 18 years.

A humming world economy has a marked effect on investor sentiment, not least that investors are willing to stomach higher risk-return ratios. In the quest for higher yields, investors are accepting larger exposures to potential losses without the offset of greater potential compensation. This sort of investor exuberance has long been a telltale sign that the credit cycle is trundling towards a peak – with only one direction to go thereafter. However, setting the ominous foreshadowing to one side, there are benefits to a ‘fast and loose’ world economy. One such benefit is the resurgence of investor innovation. With the purse strings suitably loosened, there is huge demand to generate excess returns through innovative investment strategies.

Enter factor investing. Although not a new concept, factor-investing is experiencing a revival particularly amongst quantitatively-driven hedge funds and asset managers. The simple premise is that certain characteristics or “factors” are the primary underlying drivers of risk and return within a portfolio of securities. The behavior of a portfolio can be explained in terms of its exposure to these factors. A portfolio’s exposure to systematic factors can be quantified by analyzing its performance through the lens of linear regression modelling. Whereby, the return or volatility of a portfolio is broken down into a linear combination of the model factors. In industry jargon, funds tracking these factors are known as “smart beta”. BlackRock, the world’s largest asset management firm, estimates that there are $1.9tn of assets in dedicated factor strategies, and predicts this will swell to $3.4tn by 2022.

As is often the case, although the idea is relatively simple, the difficulty lies in the application of theory to reality. Over the past half century, a large body of academic research has identified hundreds of positive yield factors and savvy investment managers have reaped the reward by tilting their portfolios accordingly. Factor investing leverages the historical return drivers in portfolios in order to capture excess return without the corresponding downside risk. Portfolio construction based on these proven and persistent drivers of return is an essential weapon in an investor’s armory; allowing them to generate returns, reduce risk and improve diversification.

Andrew Ang, Head of Factor-Based Strategies at BlackRock, describes factors as being “the foundation of investing, just as nutrients are the foundations of the food we eat”. In order to best support our body’s needs, we need to ensure that we consume a variety of foods that contain the optimal mix of constituent nutrients. Correspondingly, factor investing encourages a more diversified, robust and well-balanced investment portfolio.

Factors are typically divided into a few broad categories – the global factor, country, industry, currency and style factors. Intuitively, every asset is exposed in some way to the global market. If there is a negative shock to the world economy, say due to bellicose trade-war rhetoric, that could reverberate through markets and impact an asset’s performance. Traditionally, fund managers attempted to beat the market by favouring one country, industry or currency over another and building their portfolios accordingly. However with the advancement in computing power and algorithmic trading, there are more systematic approaches to beating the market. Hence the evolution of style factors. These more sophisticated factors are based on market data such as trading volume, capitalization and balance sheet information. They are considered to be pertinent drivers of portfolio return. Asset managers have put extensive time and money into identifying innovative style factors that will differentiate their funds from the market. There are a handful of widely-accepted and empirically-justified style factors including: value, yield, momentum, volatility and size.


Smart beta funds lie on the spectrum between two traditional, yet extreme, investment strategies – pure active management and passive index tracking. Active fund managers frequently buy and sell stocks in an attempt to beat a benchmark. In comparison, passive fund managers employ a “buy and hold” mentality when constructing portfolios, thus securing far lower costs than their active counterparts. Historically, passive portfolio construction has revolved around capitalisation-weighted indexing – the size of the positions in a portfolio is weighted according to the market capitalisation of the company. For example, a cap-weighted index following the S&P 500 would have positions in the 500 biggest publicly-listed American companies and give a higher percentage weighting to the companies with the largest market capitalisation such as Apple, Amazon and Microsoft. Smart beta contrasts with traditional cap-weighted indices by selecting assets to capture investment factors in a rules-based, systematic way. Smart beta strategies are designed so as to reap the benefits of diversification and excess returns without the heavy price-tag associated with traditional active management.

The academic foundations of factor investing originated with the Arbitrage Pricing Theory (APT) developed by economist Stephen A. Ross in 1976. Prior to this, the Capital Asset Pricing Model (CAPM) held sway. The CAPM determines the theoretically adequate rate of return of an asset as a function of its risk. The riskier an asset is perceived to be, the larger the premium that an investor demands for taking on that risk. The introduction of the Arbitrage Pricing Theory allows for a more sophisticated approach to modelling asset prices. It’s based on the idea that an asset’s return can be predicted by a linear combination of a select number of macroeconomic variables that have a quantifiable effect on the asset’s performance. In contrast to the CAPM, the APT does not assume that markets are perfectly efficient. Arbitrageurs hope to take advantage of timeless human irrationalities which lead to imperfect, if fleeting, asset valuations.

In the decades that followed, a melange of factors that impact security performance have been identified. A 1981 paper by Rolf Banz established a size premium in stocks—that smaller company stocks outperform larger companies over long time periods. In 1992, economists Eugene Fama (of the Efficient Market Hypothesis) and Kenneth French published a seminal paper that demonstrated a value premium, whereby they illustrated the tendency for cheap assets to outperform expensive ones. Other significant factors that have been identified are measures of corporate profitability, asset growth, external financing, leverage and research and development costs.

Factor-based analysis has squeezed active managers because it has been shown to explain much of their returns and helped drive the rise of relatively inexpensive passive investing. Investors can access factors in equities through cheap index-tracking funds or exchange-traded funds (ETFs) from the likes of BlackRock, Vanguard and State Street. These index-tracking funds seek to replicate the holdings and performance of a designated index at a low cost. The popularity among investors for index funds has ballooned since they were first introduced by John Bogle of Vanguard in 1975.

Extensive research has indicated that the broadly accepted style factors have achieved superior returns when analysed over many decades. MSCI, a provider of equity indices and portfolio analysis tools, found that more than half of the performance of active fund managers can be explained with reference to the most common factors. However, performance is far from guaranteed. There are long historical periods where factors have underperformed compared to the market. To call factor-investing a mixed bag would be unfair. The best-known factors have been too successful for too long for it to be a statistical quirk.

As per Mr. Fama, asset prices should fully reflect all available information, directly implying that it is impossible to beat the market consistently on a risk-adjusted basis. Should such a strategy exist, everyone would follow it and any excess returns above and beyond the market average would be arbitraged away. But in the age of information technology, investors are better equipped to investigate, isolate and pounce on any mispricing that may surface in the dense labyrinth that is modern-day financial markets. Furthermore, humans cannot be assumed to be rational nor efficient. Man’s quest for a quick buck predates the birth of the modern-day financial system – not to mention Mr. Fama’s efficient market hypothesis. People will keep sifting through data in search of that elusive factor that captures human folly – and milk it for all it’s worth.


Featured photo provided by Pexels.

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