Introduction

In recent years, “smart beta” and factor investing have transitioned from academic curiosities into essential tools in many quantitative portfolios. Robo-advisors, seeking scalable edges over plain indexing, increasingly incorporate factor strategies into their allocations. But what exactly are these approaches, how well do they work, and what should investors watch out for?

In this article, you'll learn:

• What smart beta and factor investing are

• The typical factors and how they’ve performed

• How robo-advisors integrate factor strategies

• The opportunities and risks

• How platforms like Surmount might use them thoughtfully

What Are Smart Beta & Factor Investing?

Factor Investing: Basics

“Factor investing” refers to targeting systematic return drivers (or factors) beyond the market’s baseline exposure. These factors are characteristics that have historically shown predictive power for risk-adjusted returns. Common factors include:

• Value (cheap valuations, e.g. low P/E or low price/book)

• Momentum (recent winners continuing to outperform)

• Quality (companies with stronger profitability, balance sheets)

• Low Volatility / Minimum Variance (stocks with lower price volatility)

• Size / Small Cap (smaller firms, though this has become more contested)

These factor premia have been studied extensively in academic finance, and many asset managers package them into “smart beta” or factor-tilted ETFs. Verified Investing gives a good narrative of this evolution.

Smart Beta: The Middle Ground

Smart beta (also called strategic beta) sits between passive indexing and active stock picking. It uses rules-based, transparent factor tilts rather than purely cap-weighted benchmarks. The idea: capture factor excess returns (alpha-ish) while maintaining the low cost and transparency of passive investing.

Instead of weighting stocks by market cap, smart beta strategies might weight by fundamentals (e.g. dividends, earnings), volatility, or combined factor signals.

The term “smart beta” has occasionally faced criticism as marketing jargon — but the underlying discipline is rooted in factor theory and quantitative indexing. Robo-Advisory: From Investing Principles and Algorithms to Future Developments discusses how robo-advisors are increasingly using smart beta and factor ETFs in their models.

Factor Performance & Challenges

Historical Evidence & Cyclicality

Factor premiums tend to be persistent over long horizons but cyclical in the short term. Some factor styles outperform in certain regimes and underperform in others. Research organizations warn that smart beta has underdelivered relative to early expectations, especially when valuation-based tilts become overly crowded. VettaFi outlines how many factor strategies underperformed in the 2017–2024 period, and warns about overvaluation and mean reversion risks.

Still, proponents argue that when valuations revert or factor dispersion increases, factor strategies may regain tailwinds — positioning for a resurgence. Advisor Perspectives covers this view.

Advanced models attempt to adapt dynamically. For instance, a regime-aware model using Hidden Markov Models (HMM) applied to smart beta strategies showed improved risk-adjusted returns by switching factor exposures across regimes. “A novel dynamic asset allocation system using Feature Saliency Hidden Markov models for smart beta investing” is one such example.

Another study combined multiple independent smart beta strategies (e.g., momentum + low vol) to generate a more stable “composite” strategy with better diversification among factor draws. “Combining Independent Smart Beta Strategies for Portfolio Optimization” describes how combining factors can reduce volatility of factor-based returns.

Practical Issues & Implementation Drag

• Factor crowding / valuation risk: When many investors pile into the same factor, its expected premium may shrink or reverse.

• Trading costs / turnover: Factor strategies often require frequent rebalancing, especially momentum or volatility signals, which can erode net returns.

• Slippage and model error: The ideal factor exposures implied by theory may not translate cleanly in real markets.

• Correlation breakdowns: In crises, classic factor relationships can break down, e.g. momentum turning negative, low-vol techniques failing.

• Overfitting risk: Factor models tuned to historical data may not generalize in future regimes.

How Robo-Advisors Use Factor / Smart Beta Approaches

Robo-advisors, aiming to deliver institutional-quality quant strategies at scale, have been integrating factor-based funds into their offerings. Some common patterns include:

Tiered & Blended Portfolios

Many robo platforms blend standard index ETFs with factor-tilted ETFs. For example, allocating 70% to core broad-market ETFs and 30% to a smart beta/factor ETF overlay.

Vanguard’s Robo-Advisor Landscape 2023 report notes that some robo portfolios include “Smart Beta portfolios” as optional tiers, using factor-tilted ETFs in place of plain market-weighted ones (Vanguard Robo-Advisor Landscape 2023).

Dynamic Factor Allocation / Weighting

Rather than static tilts, some robos dynamically adjust factor weights depending on regime indicators (e.g. momentum strength, volatility states). Grealish & Kolm’s review of robo models mentions that robo-advisors increasingly integrate systematic factor and smart beta strategies as part of their algorithmic toolkits (Grealish & Kolm, Robo-Advisory: From Investing Principles and Algorithms).

In effect, these platforms try to "turn on" or emphasize factors when conditions are favorable, and reduce exposure when they seem expensive or risky.

Custom Factor Models & Smart Beta Variants

Some academic/industry research suggests using machine learning or risk-adjusted factor portfolios tailored to risk categories. For instance, a recent model categorized stocks by risk and constructed smart beta portfolios per risk class using ML, showing higher returns at lower volatility. “Developing a Security Risk Assessment based Smart Beta Portfolio Model for Robo Advising”

Platforms may also exclude certain factor exposures that conflict with investor constraints (ESG, liquidity, volatility budgets) and adjust weighting schemes accordingly.

Hybrid Oversight & Risk Controls

To mitigate factor disasters, many robo-advisors embed risk limits or human oversight triggers. If a factor tilt is performing poorly over time, the system might scale it back or override allocations. Because factor tilts can exacerbate drawdowns, blending with broad passive exposures helps stabilize performance.

Robo-advisors also monitor factor crowding, valuation spreads, and signal decay to decide whether to include or exclude certain factor exposures dynamically.

Considerations for Investors & What to Watch

When evaluating robo platforms that use smart beta / factor strategies, watch these elements:

• Transparency of factor models: Which factors? How are they weighted? Are the models published or explained?

• Rebalancing frequency & turnover assumptions: High turnover eats into net returns.

• Drawdown risk mitigation: How does the platform limit damage when factors underperform?

• Factor entry/exit criteria: Are tilts constant, or do they adapt based on market signals or valuation thresholds?

• Costs & fees: Factor ETFs often carry modestly higher fees than plain index funds — know the net drag.

• Correlation and diversification: Blends of uncorrelated factors help reduce factor “boom-bust” risk.

• Backtesting vs live performance: Ask to see how factor tilts did in adverse regimes, not just in good years.

• Customization & constraint flexibility: Ability to exclude certain factors or sectors (e.g. ESG, volatility limits).

Why & When Factor Tilts Might Add Value — And When They Might Not

When They’re More Likely to Help

• In regime shifts or factor dispersion increases, where classic factor premia diverge

• When one factor is undervalued (low valuation) and expected to mean-revert

• When portfolio size is large enough to absorb turnover and trading costs

• When combined with core passive holdings to stabilize volatility

• For investors who want a systematic tilt edge rather than pure cap weighting

When They Might Be Detrimental

• Overcrowded factors already fully priced in

• In crisis periods where factors break down or correlation rises

• For smaller portfolios where turnover / transaction costs are a large relative drag

• When the strategy is overfitted or lacks robust risk controls

• If the investor demands consistency and low variance over seeking occasional outperformance

How Surmount Uses Smart Beta to Elevate Retirement & Wealth Strategies

At Surmount, smart beta isn’t a gimmick — it’s built into how we design portfolios to balance long-term stability with opportunities for excess return. Unlike platforms that treat factor investing as an afterthought or chase the “factor of the month,” Surmount applies it with discipline and transparency:

• Core + factor overlay: Every Surmount portfolio is anchored in a stable broad-market core, with factor tilts layered on strategically — never in isolation, always in balance.

• Dynamic factor weighting: Our models adjust exposure based on valuations and market regimes, tilting in when factors are attractive, scaling back when they’re not.

• Multi-factor approach: Surmount blends value, momentum, quality, and low volatility so users aren’t reliant on any single factor cycle.

• Risk management built in: Factor tilts are constrained by drawdown limits and volatility thresholds to avoid runaway exposure.

• Radical transparency: Investors can always see which factors are active, why they’re included, and what they contribute — no black boxes, no surprises.

• Evidence over hype: Surmount’s models are grounded in robust research and forward-looking signals, not overfitted backtests.

• Market-aware: We track factor crowding and valuation spreads to avoid overheated trades, positioning clients to benefit when cycles turn.

In short: Surmount treats smart beta as a strategic enhancement, not speculation. The result is a disciplined, research-driven edge that fits seamlessly into our broader mission — making sophisticated portfolio design accessible to every investor, without sacrificing clarity or control.