Smart Beta and Factor Investing: The Evidence-Based Approach to Active Returns

What Is Factor Investing?

Factor investing is an approach to portfolio construction grounded in academic research: it identifies characteristics — "factors" — that explain why certain groups of securities have historically delivered returns above the broad market, and then systematically tilts a portfolio towards those characteristics.

This sits between pure passive investing (owning the market at zero active judgement) and traditional stock picking (concentrated bets on individual securities based on a manager's judgement). Factor investing is rules-based and systematic — the factor defines which securities to buy and in what proportion — but it is not market-cap-weighted passivity. It is a disciplined form of active tilting.

The key claim is that factors are not simply luck. They reflect either a genuine risk premium (investors are compensated for bearing a risk that other investors avoid) or a persistent behavioural anomaly (investors systematically make mistakes that create exploitable patterns). If true, these premiums should persist even after they are published — though the evidence on this is mixed.

The Core Factors

Value: Cheap Stocks Outperform Expensive Ones

Value investing — buying stocks that trade cheaply relative to their fundamentals — has the longest academic pedigree. Benjamin Graham formalised the concept in "Security Analysis" in 1934. The empirical evidence goes back decades: stocks with low price-to-book (P/B) ratios, low price-to-earnings (P/E) ratios, or low enterprise value-to-EBITDA have historically outperformed expensive growth stocks over long periods.

The Fama-French three-factor model, introduced in 1992, formalised this by showing that a model combining market exposure, a size factor, and a value factor explained a much larger portion of equity returns than the single-factor Capital Asset Pricing Model (CAPM). This paper is among the most cited in financial economics.

The economic rationale for the value premium is debated. Risk-based explanations argue that value companies are riskier — they are more financially distressed, more cyclical, and more sensitive to economic downturns — and investors must be compensated. Behavioural explanations argue that investors are systematically over-optimistic about growth companies and over-pessimistic about distressed companies, creating a systematic mispricing.

Both explanations predict the premium will persist, though for different reasons.

Size: Small Companies Outperform Large

The size effect — that small-cap companies have historically delivered higher returns than large-cap companies — was documented in the early 1980s. It is included in the Fama-French model.

The size premium is more contested than value. It has been small and inconsistent in recent decades in developed markets, particularly the US. In international markets and over very long time periods, small companies do appear to outperform, particularly when combined with other factors (small-cap value stocks have a particularly strong return history).

The size premium, if it exists, is likely a risk premium: small companies are less liquid, more vulnerable to economic downturns, and have less certain earnings. Investors require higher returns to hold them.

Momentum: Recent Winners Continue to Win

Momentum is one of the most robust factors in academic finance, documented across decades, geographies, and asset classes. Stocks that have performed well over the past 6–12 months tend to continue to outperform over the next 3–12 months. Stocks that have performed poorly tend to continue to underperform.

The momentum factor was formalised by Jegadeesh and Titman in 1993 and has been extensively replicated. It is also one of the most difficult to explain from an efficient markets perspective — the efficient market hypothesis suggests that past price movements should not predict future returns.

Behavioural explanations dominate: investors are slow to update their beliefs about improving companies (underreaction), and later become overly enthusiastic (overreaction), creating a trend that eventually reverses. Momentum is therefore a medium-term phenomenon; it tends to reverse over longer horizons as prices overshoot and correct.

The drawback of momentum is its crisis risk: momentum strategies can suffer sharp, sudden reversals when market conditions change abruptly. The "momentum crash" phenomenon — documented in 2009 and in March 2020 — is a real risk.

Quality: Strong, Profitable Businesses Outperform

The quality factor captures the finding that companies with high profitability, stable earnings, low financial leverage, and strong cash flows have historically outperformed. This seems paradoxical from a traditional risk/return perspective: why would "safer" companies deliver higher returns?

The behavioural explanation: investors undervalue quality, perhaps because stable, boring businesses receive less attention than exciting growth stories. Quality companies also tend to hold up better in market downturns, reducing the actual volatility of a quality-tilted portfolio.

The quality factor has been commercially developed by index providers including MSCI (Quality Indices) and FTSE. The profitability factor was formally incorporated into the Fama-French model via the five-factor model published in 2015.

Low Volatility: Paradoxical Outperformance

The low-volatility anomaly is perhaps the most surprising factor. Standard finance theory predicts that higher risk should bring higher returns. Yet empirical research consistently shows that low-volatility stocks — those with smaller price swings — have historically matched or outperformed high-volatility stocks, with significantly lower drawdowns.

This paradox has several plausible explanations. Institutional investors with mandates benchmarked to market-cap-weighted indices are forced to hold high-beta stocks; this creates excess demand for volatile stocks and depresses their returns. Individual investors are attracted to lottery-like, high-volatility stocks, bidding up their prices. Institutional investors also face borrowing constraints that prevent them from fully exploiting the anomaly.

MSCI Minimum Volatility indices, and similar products from other providers, have been commercially successful. The low-volatility ETF category has attracted substantial assets, which itself may have reduced some of the future premium.

The Factor Zoo Problem

The number of factors published in academic finance journals has grown to several hundred. A now-famous study estimated that researchers discovered over 300 factors by 2018. Most financial economists believe the majority of these are spurious — the result of data mining (running many statistical tests on the same dataset until something appears significant) rather than genuine economic mechanisms.

The factors with the strongest case for persistence share several characteristics:

• Documented across multiple geographies and time periods, not just the US
• Survive out-of-sample testing (i.e., perform after the original paper was published)
• Have a plausible economic rationale (either risk-based or behavioural)
• Remain significant after adjusting for transaction costs and realistic implementation

The core factors — value, quality, momentum, and low volatility — meet these criteria most robustly. Size is more contested. The proliferation of new factors is a reason for scepticism: the more factors proposed, the more likely any individual factor is a statistical artefact.

Smart Beta ETFs

Smart beta is the commercial name for factor-based index investing — systematic, rules-based exposure to academic factors through low-cost ETFs. The term is disputed (some argue that factor ETFs are simply a cheaper form of active management), but the products have been enormously successful in attracting investor assets.

Major smart beta ETF categories include:

Value ETFs: iShares Edge MSCI World Value Factor, Xtrackers MSCI World Value. These tilt portfolios towards stocks with low price-to-book, price-to-earnings, and enterprise value-to-cashflow multiples.

Quality ETFs: iShares Edge MSCI World Quality Factor. Tilts towards high-ROE, stable-earnings companies with low leverage.

Momentum ETFs: iShares Edge MSCI World Momentum Factor. Holds recent outperformers; rebalanced frequently (typically twice per year) to capture near-term trends.

Low-Volatility ETFs: MSCI World Minimum Volatility (offered by iShares, Amundi). Constructs a portfolio optimised to minimise overall volatility, not just selecting individually low-volatility stocks.

Multifactor ETFs: Combine multiple factors in a single product, typically value, quality, and momentum. Examples include JPMorgan's multifactor ETF range and various iShares products.

The costs of smart beta ETFs sit between pure passive (0.05–0.2%) and active funds (0.5–1.5%) — typically in the range of 0.2–0.5% annually.

The Factor Cycle and the Need for Conviction

The most important practical lesson about factor investing is that factors go through long, painful underperformance cycles. No factor works every year. Investors who rotate away from a factor after a period of underperformance — just when the factor is cheapest and the subsequent expected return is highest — destroy value.

The most striking recent example is value. From approximately 2010 to 2020, the value factor significantly underperformed growth. Technology companies — expensive by traditional metrics — delivered extraordinary returns. Value-tilted portfolios dramatically lagged the market. Many commentators declared that value investing was "dead." Between 2021 and 2023, as interest rates rose and speculative technology valuations contracted, value staged a significant recovery.

Investors who abandoned value in 2019 or 2020, after a decade of underperformance, missed the recovery. This is a classic behavioural mistake: capitulating after the worst of the underperformance, just before the reversion.

Momentum suffers sudden, sharp reversals. Quality and low volatility underperform in strong bull markets when riskier assets lead. Size is inconsistent.

The cycle problem makes factor investing psychologically demanding. It requires conviction in the academic evidence through periods when that evidence appears to have stopped working.

Multifactor Investing and Factor Timing

If each factor has cycles, combining multiple factors reduces the risk of being in the wrong factor at the wrong time. Value and momentum, for example, tend to be negatively correlated — value looks for cheap assets, momentum looks for recently rising assets (which are by definition becoming more expensive). Holding both simultaneously smooths the ride.

Multifactor portfolios have the academic support of portfolio diversification: even if the timing of individual factors is unpredictable, diversifying across factors with low correlations improves the risk-adjusted expected return.

Factor timing — trying to predict which factor will outperform next and tilting accordingly — is appealing but has a poor track record. The evidence for successful factor timing is thin. Most practitioners recommend maintaining a balanced multifactor exposure rather than rotating tactically.

Applying Factor Investing

A practical approach to factor investing for an internationally mobile investor:

A core global index position (capturing market beta) can be complemented by modest factor tilts. For example: a global equity index ETF comprising 70% of the equity allocation, with a 15% tilt to a global value ETF and a 15% tilt to a global quality ETF. Over 10+ years, this combination has the potential to add modestly to returns relative to the plain index, at similar or modestly higher cost.

Alternatively, a multifactor ETF provides a single-product solution with internal diversification across factors, at a slightly higher cost than a single-factor product.

Factor tilts should be sized modestly. Given the evidence that factors underperform for years at a time, a 100% factor-tilted portfolio requires a level of conviction and patience that very few investors sustain. Starting with a 20–30% factor-tilt allocation and a 70–80% plain index core is a more realistic approach.

Risks and Limitations

Factor investing is not guaranteed to outperform. Specific risks include:

• Capacity: as more money chases the same factor signals, the premium may be arbitraged away over time
• Crowding: popular smart beta ETFs can become crowded, meaning all holders face similar risks in a market dislocation
• Implementation costs: factor ETFs rebalance more frequently than plain index funds, incurring trading costs not always captured in the OCF
• Factor timing risk: choosing the wrong moment to implement a factor tilt can crystallise a multi-year underperformance period

Capital can fall as well as rise. Past factor performance does not guarantee future factor performance. Tax treatment and investment regulations may change. Seek independent financial advice before implementing any factor strategy.

How Global Investments Can Help

Our advisers understand evidence-based investing approaches including factor investing and smart beta. We can help you assess whether a factor tilt is appropriate for your portfolio given your investment horizon and risk tolerance, select cost-effective implementations, and build a portfolio you can maintain through inevitable underperformance periods. Contact us to discuss your approach.