Model Portfolio Masterclass Series

History of Factor Investing & the Birth of Smart Beta ETFs

The investment landscape offers a multitude of strategies, with factor funds designed as tools specifically for portfolio optimization. By strategically incorporating these approaches, investors can exploit market inefficiencies and align their portfolios with macroeconomic and other trends. This masterclass delves into the foundations of factor investing.

Factor investing has its roots in mid-20th-century academic research, with its foundation laid by the Capital Asset Pricing Model (CAPM) in the 1960s. CAPM introduced market beta as a key determinant of returns. This concept was expanded in the early 1990s through the pioneering work of Eugene Fama and Kenneth French, who identified additional factors, such as Size and Value, that could explain stock returns beyond market beta. Over time, further research brought factors like Momentum, Quality, and Low Volatility into the fold, solidifying factor investing as a systematic approach to harnessing these return drivers.

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The first factor-based ETFs emerged in the 2000s, often targeting single factors such as Value or Growth within indices like the S&P 500. These were soon followed by a new category known as Smart Beta ETFs, which typically tracked an index that looked to identify one or more factors in an effort to outperform traditional market-cap-weighted indices. However, while these ETFs aimed to enhance performance, their methodologies often varied, even when they appeared similar on the surface. Investors needed to delve into the specifics: Which factors are included? How is Value defined? Every Value ETF is probably using the price-to-earnings ratio (PE) as one of the fundamentals, but is that the trailing or forward-looking PE ratio? And which other fundamentals are included? For a multi-factor strategy, which factors are part of the mix? This complication suggests each ETF’s unique strategy requires scrutiny to determine whether it alignes with an investor’s objectives.

What Smart Beta strategies essentially achieved was a systematic replication of techniques long employed by hedge funds, albeit with distinct approaches. Choosing between these flavours of factor-based strategies became a critical investment decision for fund managers.

Today, the term Smart Beta has largely fallen out of favour and is replaced by multi-factor ETFs for those incorporating multiple factors. Of course, let’s not confuse these ETFs with multi-asset ETFs, which are a completely different proposition. Some of these multi-factor ETFs are actively managed, providing flexibility to deviate from rigid index rules by applying discretionary algorithms to stock selection. On the other hand, it makes them less transparent and harder to determine the details.

What is Factor-Based Investing?

Factor investing is a quantitative strategy that selects securities based on specific attributes, or factors, empirically associated with enhanced risk-adjusted returns. The growing popularity of these approaches is driven by advancements in data analytics, the expansion of ETFs and the rising demand for tailored investment solutions. In navigating today’s complex markets, factor investing provides a means to refine portfolio exposure, enhance returns, and manage risks with greater precision.

These factors are typically categorized into style classifications, providing a versatile framework for portfolio construction:

• Value: Identifying securities trading below intrinsic value using metrics like price-to-earnings or price-to-book ratios.
• Growth: Targeting companies with substantial growth potential, characterized by high revenue expansion and innovation. These tend to be high-beta stocks.
• Quality: Emphasizes financially strong companies with attributes like high return on equity and consistent earnings.
• Momentum: Focusing on assets with sustained positive price trends, driven by behavioural finance phenomena like herd behaviour.
• Size: Investing in small-cap stocks with higher growth potential, albeit at increased volatility.
• Low Volatility: Selecting securities with consistent performance and minimal price fluctuations, appealing to conservative investors. However, excessive popularity of low-volatility ETFs can sometimes lead to higher price swings, undermining their objective.
• Income: Emphasizing income-generating securities, which pay a steady and above average income stream. Often associated with blue-chip stocks, this factor may also appeal to investors seeking tax-efficient income streams.
• Multi-Factor: Combines several of the above factors, tailored to align with varying market conditions and phases of the economic cycle.

Factor-Based Investing: Quantitative Insights and Strategic Selection

Factor-based strategies offer a flexible and systematic approach to portfolio construction, allowing fund managers to adapt to shifting market dynamics while aligning with specific investment goals. The basic idea is that by using pre-built factor ETFs, a Discretionary Fund Manager, can then decide which factors should be included within one’s portfolio and what the weights should be. Rooted in decades of academic research and historical data, these strategies offer robust evidence supporting the effectiveness of various factors. By enabling customized portfolio designs, factor-based investing caters to diverse risk-return profiles and investment goals. Furthermore, combining multiple complementary factors can, in principle, help mitigate systemic risks, enhancing portfolio resilience.

A heatmap of the most popular annual factor returns illustrates the significant dispersion among factors over time. It is worth noting that a factor that leads the pack one year may find itself lagging behind in the next. Identifying the optimal factor, or combination of factors, for a specific economic environment remains a critical challenge for fund managers.

For instance, while Growth has been the dominant factor for over a decade, Value investing has shown periodic success. Warren Buffett's approach exemplifies this, blending a Value focus with tactical adjustments to align with prevailing market conditions. Notably, over the past ten years, only three factors – Growth, Quality, and Momentum – consistently outperformed the S&P 500, underscoring the importance of strategic selection and timing in factor-based investing.

Analyzing factor performance across various macroeconomic regimes reveals a clear correlation between factor returns and prevailing economic conditions. This suggests that incorporating macroeconomic signals into a factor-based strategy could enhance its predictive power. However, this raises a critical question: Is a straightforward factor rotation strategy within equity exposure sufficient, or would a more robust multi-factor approach yield better results?

The key challenge lies in accurately identifying the economic regime and linking it to factor returns with statistical significance. This task is inherently complex and not always feasible. Furthermore, with a limited universe of approximately seven commonly used factors, the strategy often lacks the breadth required to deliver consistent and meaningful outperformance compared to a traditional market-cap-weighted equity benchmark. Achieving superior results demands both precision in economic regime identification and thoughtful diversification across complementary factors.

Strategic Framework for Implementation

Based on our experience, factor-based investing is most effective when used as tactical tilts within model portfolios, leveraging macroeconomic conditions, major market trends, and geopolitical developments. During periods of heightened market risk or when a recession appears close, overweighting Value, Quality, Income, or Low Volatility ETFs – or a combination thereof – can provide defensive positioning. Conversely, in an expansionary phase, Growth stocks and potentially Momentum-oriented strategies often deliver stronger returns.

Should you opt for a Multi-Factor ETF with more or less fixed factor weightings that targets several factors simultaneously? If the desired factor set is available in ETF or fund format, a multi-factor fund is often the better choice to avoid overconcentration, which can occur when combining overlapping factors like Growth and Momentum.

Or should you opt for a Multi-Factor ETF that uses a mix of factors and is able to change its factor weightings based on economic conditions? I would say yes, if you believe that the methodology the ETF uses can more likely than not outperform the general market-cap weighted exposure. This brings us right back to Smart Beta ETFs – for an ETFs that track a benchmark – or to hedge fund models that are now also listed in an ETF wrapper. The million dollar question is whether this investment is better than the market-cap weighted equivalent, or is it just a feature that justifies that the issuer can charge higher fees? I’ll let you decide for yourself.

Regardless of the approach, it is essential to carefully examine the benchmark index definitions for passive funds or the investment mandate for active funds. Definitions of individual factors vary widely, and multi-factor strategies can diverge, not just in how factors are defined or which underlying fundamentals are considered, but also in how stocks are weighted within the final strategy. A thorough understanding of these nuances is crucial to align factor strategies with your overall model portfolio objectives.

Note 1: Average annualized returns in USD from 23 February 2015 to 19 January 2025. Factor returns via Factor ETFs. 2015 is the return from 23 February 2015 to 31 December 2015. The following ETFs were used to represent the different factor exposures: Value (IVE), Growth (IVW), Quality (SPHQ), Momentum (MTUM), Size (MDY), Low Volatility (SPLV), Income (DVY), Multi-Factor (ROUS) and finally S&P 500 (SPY). Amongst the multi-factor funds, the Hartford Multifactor US Equity ETF, ROUS, was chosen as it has the longest history of any multi-factor ETF. It represents 50% Value, 30% Momentum and 20% Quality.

Note 2: Average annualized returns in USD from 28 February 2015 to 31 December 2024. Factor returns via Factor ETFs.

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*The podcast provided by Allan Lane & Irene Bauer has been converted from their own original content, into a podcast using Generative AI tools and the voices used in the podcast are not their own. All information provided has been fact checked.

The investments referred to in this podcast is targeted at professional Wealth Managers & Financial Advisors and may not be suitable for all investors. Twenty20 Solutions Ltd does not provide, and nothing in this podcast should be construed as, investment or other advice. It is not intended that anything stated in this podcast should be construed as an offer, or invitation to treat, or inducement for you to engage in any investment activity. The information in this podcast relating to model portfolios & individual funds suggested by Algo-Chain is purely for research and educational purposes only.