Factor Investing

Factor Investing

Factor investing (Factor Investing) is a systematic approach to portfolio construction based on academically grounded characteristics (factors) that explain differences in the returns of securities. Unlike traditional stock picking, where selection relies on the subjective judgment of an analyst, factor investing is based on quantitative metrics substantiated by decades of empirical research. The pioneers of the factor approach are Eugene Fama and Kenneth French (Fama-French Three-Factor Model, 1992), who extended the CAPM model with additional factors: Size (small capitalization) and Value (value). Later, the model was expanded to five factors (Fama-French Five-Factor Model, 2015), including Profitability and Investment (investment conservatism). For a manager of a large portfolio, factor investing offers a systematic framework for increasing returns and reducing risk without being dependent on individual forecasts.

Quality Factor

The quality factor (Quality Factor) identifies companies with sustainable profitability, a strong balance sheet, and stable growth. Academic rationale: investors systematically underestimate the sustainability of competitive advantages (Sustainable Competitive Advantages) of quality companies and overpay for speculative growth stories.

Key metrics of the Quality Factor:

• High return on equity (ROE > 15%);
• Earnings stability — low EPS growth volatility over the last 5 years;
• Low financial leverage (Net Debt/EBITDA WACC);
• Dividend consistency and growth.

Empirical data from the Kenneth French Data Library demonstrate that a portfolio of high-quality companies (High Quality) outperformed a portfolio of low-quality companies (Junk) by 3.5–5.0% per annum over the period 1963–2023.

It is critically important: the Quality Factor shows the best relative performance during periods of economic stress and heightened volatility—precisely when portfolio protection is most needed. During the 2008–2009 financial crisis, the Quality portfolio outperformed Junk by 15–20%.

Major indexes and ETFs for gaining exposure to the Quality Factor: MSCI World Quality Index (ETF: QUAL), S&P 500 Quality Index, FTSE Quality Factor Index.

Momentum Factor

The momentum factor (Momentum Factor) is based on the empirical observation that stocks showing high returns over the past 6–12 months continue to outperform the market in the next 3–6 months, and conversely—underperformers continue to lag.

Academic rationale: behavioral finance explains the momentum effect through underreaction (investors react slowly to new information), disposition effect (the tendency to sell rising stocks too soon), and herding (herd behavior amplifies trends).

Jegadeesh and Titman (1993) in their seminal work documented an average abnormal return for momentum strategy of 1.0–1.5% per month.

Practical implementation of the Momentum Factor:

• Ranking stocks by total return over the past 12 months (excluding the most recent month to eliminate the short-term reversal effect);
• Forming a long portfolio from the top decile (Top Decile Winners) and, if possible, a short portfolio from the bottom decile (Bottom Decile Losers);
• Monthly rebalancing.

Key risks: Momentum Crash—a sharp reversal when yesterday’s underperformers surge and leaders fall. The most notable example: March 2009, when the momentum strategy lost 40% in one month amid a reversal from the bottom of the financial crisis. To reduce the risk of a momentum crash, sector concentration is capped (Sector Neutrality) and the factor is combined with others.

Value Factor

The value factor (Value Factor)—one of the oldest and most documented factors—is based on buying stocks trading at a discount to intrinsic value as measured by multiples such as P/E (Price-to-Earnings), P/B (Price-to-Book), EV/EBITDA (Enterprise Value to EBITDA), and Dividend Yield.

Academic rationale: Fama and French (1992) found that stocks with a high B/M (Book-to-Market) ratio systematically outperformed stocks with low B/M by 4–5% per annum during 1926–1991. Explanations include: rational compensation for higher risk (risk-based explanation) or behavioral error—investors extrapolate short-term trends too far into the future, overvaluing “star” growth stocks and undervaluing “boring” value companies.

However, the period 2007–2020 was a real test for the Value Factor: growth stocks systematically outperformed value stocks for 13 years— the longest period of underperformance in recorded history. The cumulative Growth vs Value gap reached 200+ percentage points.

Reasons: dominance of tech mega-caps (FAANG/Magnificent 7), exceptionally low interest rates (favoring long-duration growth stocks), structural changes in the economy (shift to asset-light business models).

Since 2022, there has been a reversal in favor of Value: rising interest rates increase the discount rate, which disproportionately depresses the valuations of growth stocks with distant cash flows.

Low Volatility Factor

The low volatility factor (Low Volatility Factor, Min Vol)—one of the most paradoxical anomalies of financial markets—shows that stocks with historically low volatility (Low Beta) systematically deliver higher risk-adjusted returns than stocks with high volatility (High Beta). This outcome directly contradicts the predictions of the CAPM model, which presumes that higher risk (beta) should be compensated by higher expected returns.

This phenomenon, known as the Low Volatility Anomaly, was first documented by Black, Jensen, and Scholes (1972) and has been confirmed in numerous subsequent studies across various markets and time periods. Baker, Bradley, and Wurgler (2011) showed that $1 invested in a portfolio of low volatility stocks in 1968 would have grown to $59 by 2008, while $1 in high volatility stocks would have grown only to $7.

Explanations for the Low Volatility Anomaly:

• Lottery preference (investors prefer high volatility stocks as “lottery tickets” with a small chance of a huge win, overpaying for them);
• Benchmark constraints (institutional managers mandated to beat the index avoid low-beta stocks, creating their systematic undervaluation);
• Leverage constraints (investors without access to leverage buy high-beta stocks to increase expected returns, instead of leveraging low-beta stocks).

Combining Factors to Reduce Portfolio Volatility

Individual factors are characterized by cyclicality (Factor Cyclicality): each factor goes through periods of outperformance and underperformance. Value dominates in the early stages of economic recovery; Momentum—in a sustained trend; Quality—in the late-cycle and in recessions; Low Volatility—in periods of market stress.

Combining multiple factors (Multi-Factor Portfolio) significantly reduces portfolio volatility and drawdown due to low or negative correlation between factors. The correlation of Value and Momentum historically ranges from –0.5 to –0.6: when one factor underperforms, the other often outperforms, providing natural diversification.

Practical implementation of a Multi-Factor Portfolio for large capital: an equally weighted combination of Quality + Value + Momentum + Low Volatility achieves a Sharpe ratio of 0.8–1.0 at volatility of 10–12%—substantially better than any one factor alone or a capitalization-weighted index.

Backtesting via the Kenneth French Data Library confirms the sustainability of results over 60+ years and across various markets (US, Europe, Japan, EM).

Implementation tools: multifactor ETFs (GSLC, LRGF, ACWF), specialized SMAs (Separately Managed Accounts) through AQR, Dimensional Fund Advisors (DFA), Robeco, or custom portfolios via prime broker. For portfolios of $50M+, SMA solutions are preferred due to tax optimization (Tax-Loss Harvesting) and customization of factor exposures.