All topics

An Introduction to Factor Investing

1.  The Academic Underpinning to Factor Investing

In the last 60 years, academic research has driven a gradual evolution in our understanding of the decomposition of equity market returns. 

In the 1960s, the CAPM model1 provided an explanation of portfolio return as:
                                                                           Market return (beta) + Manager skill (alpha) + noise

Factor-based investing dives deeper into what was previously understood to be alpha, identifying common risk factors, which tend to drive the returns of individual securities. A factor-based analysis decomposes what was hitherto considered “alpha” into returns driven by common risk factors and a residual component which represents “true alpha.” 

Today, it is widely accepted that equity portfolio returns are better expressed as:
                                                       Market return (beta) + Factor returns + Manager skill (true alpha) + noise

The groundwork for factor returns was set in 1992, when Eugene Fama & Kenneth French2 demonstrated the existence of two factors, “value” and “size”, which historically have been significant drivers of equity market performance. Since that time, the race has been on amongst quant researchers to identify other “style factors” associated with excess returns. In the last five years, progress has accelerated due to improving technology and availability of data.

1 The Capital Asset Pricing Model: Theory and Evidence (Fama and French, 2004)
2 The Cross-Section of Expected Stock Returns (Fama and French, 1992)


It is important that such quant work is robust and intellectually rigorous. Quant researchers refer to a “factor zoo” of possible contenders for the next, bright new factor to follow. Not all back-testing is reliable, so it is important that factors: a) can prove themselves across different geographies, time spans and economic regimes, and b) have a credible explanation for why they work.

Claims for new factors have been researched and counter-tested by competing firms and academics, and over time, some kind of academic consensus is beginning to emerge.

There are perhaps five main equity market factors, which have gained widespread acceptance as long-term, sustainable drivers of excess returns.  In most cases, the rationale for their efficacy lies in an understanding of behavioural finance.

But not all researchers agree on the selection of factors. And even where there is a consensus around the validity of an individual factor, there will be differing views on the best way of specifying that factor in order to extract value.

In the pages that follow, we aim to provide you with some of the best academic research and professional papers, so that you can make up your own mind about whether, and how, to best employ a factor investing strategy.


factor investing - different factors - style investing

2.  Turning Academic Concepts into Investment Strategies

The concept of factor investing dates back to the 1970s, but it has only been gaining traction over the past few years. Factor-based allocation has its roots in copious empirical findings, accumulated over many decades, that document the existence of factor premiums in financial markets. These premiums can be systematically harvested in order to achieve higher risk-adjusted returns and better diversification than traditional market capitalization-weighted indices.

Prominent institutional investors have publicly embraced more systematic approaches to portfolio allocation and securities selection, based on these insights, and the popularity of factor investing has grown rapidly among professional investors around the world. Meanwhile, asset managers and market index providers have also dived in and dramatically increased the breadth of their offering in this field.

A recent survey of international investors by Invesco illustrated the rise of factor investing worldwide. It reported a broad-based increase in allocations to factor-related products, with overall factor allocations increasing from 12% of assets under management in 2016, to 14% in 2017. All key institutional and retail segments and geographies experienced an increase. In terms of absolute figures, an article published in February 2018 by “The Economist” magazine estimated that more than USD 1 trillion is currently invested in an explicitly factor-based fashion.


For investors, the obvious starting point is to ensure they understand the theory. It is important to understand the major empirical findings on which factor investing is based, before forming investment beliefs and goals. In the process, investors should explicitly determine the kinds of risks they are comfortable with. They should also make clear the role that factor investing should play in achieving their objectives, in accordance with their own investment policy.

The factor investing label encompasses a wide variety of investment products that can be put to work in many different ways. The possible solutions range from generic single-factor smart beta ETFs to more sophisticated offerings based on bespoke factor indices and actively managed multi-factor and multi-asset funds. This is important, as the needs and priorities related to factor exposures or flexibility with regard to a reference index can differ greatly from one investor to another.

For example, while some asset owners may be willing to fully embrace factor investing, others may only be looking to reduce downside risk in their overall equity portfolio. And while some investors may already be considering risk from an absolute perspective, others may not be ready to abandon their benchmarked investment approach. Broadly speaking, products can be classified into two major categories: those designed to generate enhanced returns through explicit exposure to well-rewarded factor premiums, and those with a clear focus on risk reduction.

Within the enhanced returns category, investors will typically find diversified multi-factor solutions. These strategies are designed to build portfolios that generate higher long-term returns through a balanced exposure to several premiums. There are also single-factor strategies that focus on value or momentum premiums, for example. These offer investors greater leeway to manage individual factor exposures in their portfolios, frequently at a lower cost. Meanwhile, risk-oriented products aim to achieve higher risk adjusted returns, usually through volatility or drawdown risk reduction.


Factors represent different characteristics or attributes of a financial security – such as the size of its market capitalization, its valuation, or its price momentum and volatility – that are important determinants of its risk and return in the long run. Factor investing can therefore be described as an evidence-based investment approach, which identifies the factors that are rewarded with superior risk-adjusted performance and seeks exposure to them.

Harvesting factor premiums requires extensive empirical testing and verification over long periods of time and in different markets. It is also important to look beyond mere statistical patterns and aim to understand the economic drivers behind factor premiums. Risks that are not adequately rewarded with higher returns should be avoided.

The debate over why factors exist is still an ongoing one among academics. Generally speaking, there are three main types of explanations that are not mutually exclusive. First, factor premiums may simply be a compensation for taking on more risk. For example, prominent academics such as Nobel Prize winner Eugene Fama have argued that the value premium represents a compensation for being exposed to companies with higher distress risk.

Another type of explanation relates to the structural features of financial markets. These include restrictions or limitations some investors may be bound to, for example due to financial regulation. Finally, investors have been proven to be subject to numerous behavioural biases that result in mispricing. They make mistakes, that don’t pay attention, they frequently under-react or overreact, etc. Many empirical studies document these biases.


In recent years, the combination of cheap computing power and greater market data availability for researchers in quantitative finance has led to a dramatic rise in the number of market anomalies reported in academic literature. Purported factors have become so numerous that a growing number of experts have warned about a so-called “zoo” of new factors. This term was coined by John Cochrane, of the University of Chicago, in his presidential address to the American Finance Association, back in 2011.

However, most of these reported factors tend to be related to one another. They frequently turn out to be simply different, maybe more exotic, ways to measure the same phenomenon. In fact, empirical research shows that it is possible to bring the number of anomalies included in the zoo down to a handful of relevant factors.

Investors should therefore be selective and focus on a small number of well-established factors. To qualify as relevant, a factor should (1) show a strong premium over long periods of time and across different markets and asset classes. And (2) it should have survived rigorous falsification attempts, both in academia and in-house. There should also (3) be an economic rationale with strong academic underpinnings for each factor. Finally, a relevant factor should be (4) implementable in practice, that is generate superior risk-adjusted returns in real life conditions – after trading costs.

This is why asset managers and index providers usually consider only a limited number of factors in their product offering. Factors such as value, size, momentum, income or dividend yield, low volatility and quality, for example, are typically among the most commonly selected factors. These are well-rewarded risk premiums, persistent over time, that have been documented in many different markets and across multiple asset classes.


The table below gives an overview of the equity factors considered by some of the key players in the factor investing arena:


FTSE Russell


Research Affiliates





Low Volatility





Low Volatility

Dividend Yield





Low Volatility

Dividend Yield





Low Volatility



S&P Dow  Jones Indices






Low Volatility

Enhanced Value



Low Volatility

Dividend Yield

Equal Weight





Low Volatility




Min volatility



3.  The Five Equity Market Style Factors


What it means: Tilting towards under-valued companies. The value factor identifies companies which are relatively cheap, based on metrics such as price/earnings, price / cash flow, price/sales or price/book value.

Why it may work: Investors have a tendency to fall in love with certain companies and overprice them. Investors require an unreasonably high incentive in order to hold vulnerable companies or unfashionable companies, so less attractive companies become too cheap. “Value” and “size” were the first two factors identified by Eugene Fama and Kenneth French in their seminal 1992 paper.6


What it means: Favouring companies with smaller stock market capitalisations. Why is may work: Investors may systematically under-price smaller companies because they are less liquid and have higher transaction costs. A higher risk premium may also be demanded by investors because smaller companies tend to carry higher business risk and are under-researched – and therefore less well understood.

Why it may work: Investors may systematically under-price smaller companies because they are less liquid and have higher transaction costs. A higher risk premium may also be demanded by investors because smaller companies tend to carry higher business risk and are under-researched – and therefore less well understood.


What it means: Selecting stocks with upward trends in their prices, typically based on returns over the last 12 months.

Why it may work: Investors feel good when they take profits but have an aversion to taking losses. This dampens the size of the price trend, so that the direction is sustained for longer.


What it means: Biasing a portfolio towards securities with historically lower price fluctuations, or low market beta.

Why it may work: For portfolio managers tasked with outperforming an equity index, low beta stocks may be seen as “risky” stocks to hold in a rising market. For that reason, a higher risk premium may ironically be required, in order to hold them.


What it means: Investing in companies with steady earnings, low leverage and solid balance sheets.

Why it may work: The Quality Factor is perhaps the most intuitive factor. High quality companies may be better at allocating capital and generating shareholder returns. Equally, it may be that investors systematically underprice quality companies because they are perceived as dull and unexciting.

4.  References and Further Reading

Below, we provide links to key papers, both academic and commercial, which cover key topics within the world of factor investing. In many sections, we begin by citing some of the seminal academic works, before moving into more recent, more practical white papers from asset managers.


Factor investing smart beta white papers


Factor Investing: Made Simple Guide (PLSA, 2017)

This 16-page guide explains the most widely used factors, the nature of factor premiums and how this knowledge can be applied within the pension universe. Factors and their application in the credit markets is also included.

The Essentials of Factor Investing (Robeco, 2019)

This primer from Robeco explains the basics elements of factor investing, using a learning module composed of 9 sections. This report can also be used to earn CPD credits.


A Five-Factor Asset Pricing Model (Fama & French, 2014)

Lauded researchers in finance, Fama and French, expand upon their 3-factor model to study average stock returns.

Concerns regarding the new Fama-French 5-factor model (Robeco)

Nobel prize laureate Eugene Fama and fellow researcher Kenneth French have revamped their famous 3-factor model by adding two new factors to analyse stock returns: Profitability and Investment. However, this model excludes Low Volatility and Momentum raising questions from the authors at Robeco.

Choosing Factors (Eugene Fama & Kenneth French, 2017)

Renowned researchers, Fama and French add to their compendium of knowledge related to factor selection in this 54-page research piece from the University of Chicago Booth School of Business.

Factor Investing: Lucky Factors (Harvey & Liu, June 2017)

This recent academic research proposes a method to select factors for investment purposes via a detailed and rigorous statistical approach expanding upon the principles of previous academic research in this area of finance.

Factors – Theory, Statistics, and Practice (Stephen A. Ross, 2017)

In this journal editorial comment, Stephen A. Ross raises questions over the investment community's current fixation with factors arguing that economic theory, not just data mining of stock return statistics is required to fully explain the factor phenomenon.

A Framework for Assessing Factors and Implementing Smart Beta Strategies (2015)

In this Journal of Index Investing report, the authors illustrate that premium bearing factors can be identified via a series of heuristics and not just via endless data mining. Genuine factors can therefore be identified when their characteristics appear regularly over time and across investment geographies.

Pseudo-Mathematics: Effects of Backtest Overfitting on Out-of-Sample Performance (2014)

The creation of factor indices can have its roots in back-testing. This detailed journal article from the American Mathematical Society informs readers on minimum back-test lengths and the perils of back-test overfitting.


Investment Performance of Common Stocks in Relation to their PE Ratios: A Test of the Efficient Market Hypothesis (S. Basu, 1977)

From a database of 1400 NYSE firms for the period 1956-1971, this influential Journal of Finance article empirically analyses stock investment performance in relation to P/E ratios and the implications for the efficient market hypothesis.

The Cross-Section of Expected Stock Returns (Fama and French, 1992)

Acclaimed finance academics, Fama and French explore the Size factor and book to market equity; and how these relate to mean stock returns when examining beta, size, leverage, book to market equity and P/E ratios.

Fact, Fiction, and Value Investing (Cliff Asness et al, 2015)

Value investing has existed for over 30 years. AQR investigates the facts and myths surrounding the Value factor as it stands today.


Returns to Buying Winners and Selling Losers: Implications for EMH (Jegadeesh and Titman, 1993)

Referencing the Momentum factor indirectly, this 1993 Journal of Finance paper looks at the abnormal returns generated on a series of strategies based on relative strength of NYSE and AMEX stocks.

On Persistence in Mutual Fund Performance (Mark Carhart, 1997)

In this detailed journal research of mutual funds from 1962-1993, Carhart reminds investors to avoid consistent poorly performing funds, that performing funds tend to have a higher expected return in the following year and that mutual fund costs have a negative impact on performance.

Why Invest in Momentum as a Factor? (SSGA, 2017)

This short 7-page report from State Street Global Advisors discusses the Momentum factor, its portfolio churn tendency and how churn can be mitigated via optimisation. The Momentum factor is also reviewed in combination with the Value and Size factors.

When It Comes to Momentum, Don’t Cramp My Style (Axioma, Jan 2018)

Axioma conduct a study on their “medium-term Momentum factor” comparing the attributes of 3 types of momentum portfolio using stocks from the Russell 1000 index.

Momentum: A Practitioner’s Guide (S&P Dow Jones Indices, Jan 2017)

S&P Dow Jones visit the Momentum factor, referencing Carhart’s expansion upon the Fama French 3 Factor Model in the calculation of their own momentum indices.


Do Stock Prices Fully Reflect Information about Future Earnings? (Richard G. Sloan, 1996)

Value and Quality are factors which are well-known today. This academic paper, which is indirectly related to these factors, studies how accruals and cash flow are reflected in the future earnings of US companies for the period 1962 to 1991.

Value Investing: Use of Financial Information to Separate Winners from Losers (Piotroski, 2000)

This in-depth paper examines the book to market ratio and how mean returns can be enhanced by investment in financially strong book to market firms - particularly those that are small and medium-sized firms, with low share turnover with no analyst coverage.

Asset Growth and the Cross-Section of Stock Returns (Cooper, Gulen and Schill, 2008)

Analysing a multitude of US stocks from 1963-2003, the co-authors from this 2008 Journal of Finance report find that a company’s annual asset growth rate is a statistically significant predictor of US stock returns.

Quality Investing (Robert Novy-Marx, May 2014)

This academic research piece from Robert Novy-Marx of Rochester University evaluates the Quality factor as an add on to the Value factor and its utility for long only investors.

How Quality Sharpens the Factor Premiums Approach (Robeco, 2016)

Robeco address the numerous factor premiums, their risk adjusted return potential and optimal factor combinations in this 17-page report.

Quality: A Practitioner’s Guide (S&P Dow Jones Indices, Jan 2017)

The Quality factor is investigated by S&P Dow Jones using 3 measures: the return on equity, the financial leverage ratio and the accruals ratio. All are used to establish which firms are worthy of inclusion in the S&P 500 Quality Index.

Investing in the Quality Factor (SSGA, 2017)

State Street Global Advisors investigate the Quality factor with their own tilting methodology to represent this factor in its best form. The correlation of Quality with other investment factors is also considered.


The Capital Asset Pricing Model: Some Empirical Tests (Black, Jensen, Scholes, 1972)

Widely considered a "classic", this academic appraisal by Black, Scholes and Jensen on the CAPM theory paved the way for the development of factor investing today.

Evidence on the Existence of Risk Premiums in the Capital Market (Haugen and Heins, 1972)

This influential paper from the University of Wisconsin-Madison considers whether sufficient evidence exists for the presence of a risk premium in financial markets and whether systematic or unsystematic risk confers a merited reward.

The Cross-Section of Volatility and Expected Returns (Ang, Hodrick, Xing, Zhang, 2005)

The authors of this 56-page comprehensive journal article analyse the Size, Value, Momentum and Liquidity factors with respect to volatility and expected returns.

Low Volatility in historical perspective: Fund investing since 1774 (Robeco)

Robeco portfolio managers, Jan Sytze Mosselaar CFA and Pim van Vliet CFA embark on an analysis of financial market and mutual fund history, explaining how the Low Volatility factor is of particular interest today given the prevalence of passive, quant and low risk funds.

Inside Low Volatility Indices (S&P Dow Jones Indices)

Post the GFC and 2010 Flash Crash, low volatility indices have garnered considerable market interest. This 21-page research piece explores the differences between the rankings-based S&P 500 Low Volatility Index and the optimization-based S&P 500 Minimum Volatility Index.

Low-Volatility Investing – Theory and Practice (Research Affiliates, 2015)

In the wake of the GFC, Flash Crash and Taper Tantrum, Feifei Li, Professor at Stanford University, considers two ways to constructing low volatility portfolios; one based on minimum variance and the other based on heuristic measures to exclude volatile companies.


The Relationship Between Return and Market Value of Common Stocks (Banz, 1980)

This classic paper from the Journal of Financial Economics analyses the existence and possible origin of the Size factor utilising a CAPM derived model on NYSE common stocks.

The Cross-Section of Expected Stock Returns (Fama and French, 1992)

Acclaimed finance academics, Fama and French explore the Size factor and book to market equity; and how these relate to mean stock returns when examining beta, size, leverage, book to market equity and P/E ratios.

Size Matters, If You Control Your Junk (AQR, 2015)

This detailed team-authored academic piece dispels the common misconceptions regarding the Size factor in investment in a study of a long/short equity strategy across global markets.


The Merits and Methods of Multi-Factor Investing (S&P Dow Jones Indices, 2017)

S&P Dow Jones examine the historical risk/return characteristics of the S&P 500 Quality, Value & Momentum Multi-Factor Index versus their own traditional single factor indices.

Multi-factor indexes: the power of tilting (FTSE Russell, 2017)

FTSE Russell investigates the merits of factor index creation via a top-down “mixed” or bottom-up “integrated” method. Presenting their proprietary tilt-tilt methodology, they infer a solid probability of achieving strong factor exposures together with high diversification.

A Smoother Path to Outperformance with Multi-Factor Investing (Research Affiliates, 2017)

The team at Research affiliates highlight 6 factors that have historically facilitated equity market outperformance and how systematic and dynamic rebalancing of factors can further improve returns.


Ten things you should know about factor investing (Robeco, 2017)

In this comprehensive and intelligible 27-page guide, Robeco discusses how to select the right factors; in turn addressing topics such as biases, portfolio turnover, weightings and overall performance in a factor-driven strategy.

Three ways to successfully implement factors and smart beta (Robeco, 2017)

Using their in-house quant research, Robeco explain how to adopt a factor strategy via three practical approaches: enhanced indexing, multi-factor and low volatility; and how these approaches are applied in their fund range.

Implementation considerations for factor investing (FTSE Russell, March 2018)

Capitalisation weighted indices confer exposure primarily to the Momentum factor. This paper from FTSE Russell looks at how to select and implement other factors into the portfolio mix for improved portfolio diversification and performance.

Factor investing case studies – the merits of tailor made solutions (Robeco, 2016)

In this 36-page study, Robeco publish their findings on three clients who have adopted custom-made multi-factor portfolio strategies with success, namely a pension fund, a sovereign wealth fund and a retail bank.

Optimal Holdings of Active, Passive and Smart Beta Strategies (QMA, 2017)

QMA investigates the utility of enhanced index and smart beta strategies in reducing investment risk for institutional equity portfolios.

Factor Exposure and Portfolio Concentration (FTSE Russell, May 2017)

FTSE Russell takes an in-depth academic view of multi-factor portfolio construction techniques addressing the perils of optimisation when tilting towards or away from certain individual and blended factors.

For Style Factors, One Size Does Not Fit All (Axioma, 2017)

If using factors to generate alpha, investors may need to switch between using local and regional models. Axioma warn investors to take note of pure risk factors and that "alpha factor users" should not be overly concerned when factor returns go negative.


Factor investing challenges: factor timing (Robeco, Aug 2017)

Should investors try to time their exposure to different factors? Robeco advise that individual factors can experience bouts of underperformance and thus recommend a diversified approach unless a particular factor is of interest.

Forecasting Factor and Smart Beta Returns – Research Affiliates, 2017

This 16-page paper by Research Affiliates discusses how to forecast factor and smart beta returns. The authors argue that using past performance to forecast future performance is likely to disappoint.

Contrarian Factor Timing is Deceptively Difficult (AQR Capital Management, 2017)

Despite the valuation concerns and fears of imminent mean-reversion of factor based performance strategies, the AQR authors in this 21-page journal piece find that Value, Momentum and Defensive factors are not as over-valued when measured by their value spreads.

The Promises and Pitfalls of Factor Timing (Jacobs Levy Center, 2017)

The University of Pennsylvania investigates “factor timing” highlighting the relationships between equity factor performance and different groupings of predictors namely: sentiment, valuation, trend, economic conditions and financial conditions.

How Can Smart Beta Go Horribly Wrong? (Rob Arnott, 2016)

The team at Research Affiliates state that most of the alpha produced by smart beta can be attributed to rising valuations and that performance chasing could lead to a smart beta crash due to the rising popularity of factor-tilt strategies.


Smart beta: 2017 global survey findings from asset owners (FTSE Russell, 2017)

This is FTSE Russell’s fourth comprehensive survey of almost 200 global asset owners on the themes influencing the adoption, evaluation and implementation of smart beta. Insight into smart sustainability and multi-factor index investing is included.

Invesco Global Factor Investing Study 2017

From over 100 in-depth face-to-face interviews with consultants, pension funds, insurers, sovereign investors and private banks globally, this 28-page study offers unique insights into the growth of factor investing.

related content