Active equity strategies - Factor investing and style tilts

Learning Outcomes

This article explains active equity factor investing and style tilts, including:

• Distinguishing between fundamental and quantitative approaches to active equity management, and identifying how each framework sources and implements style factor exposures.
• Describing the main rewarded equity style factors—value, growth, momentum, quality, and size—and linking their typical signals to long-term performance patterns tested in the CFA Level 3 exam.
• Explaining the rationale for factor investing and deliberate style tilts as tools for generating excess returns relative to a market-cap-weighted benchmark.
• Comparing portfolio construction techniques for implementing style tilts, such as long-only tilts, long/short factor portfolios, and diversified multi-factor strategies.
• Evaluating the key risks of factor-based strategies, including cyclic underperformance, factor crowding, increased tracking error, and the potential costs and pitfalls of factor timing.
• Assessing how combining multiple style factors can improve diversification, stabilize active returns, and better align portfolios with stated client objectives and constraints.
• Interpreting exam-style vignettes that require justification of a proposed factor tilt, evaluation of its implementation, and clear communication of the trade-off between expected return increase and additional active risk.
• Explaining how style analysis (holdings-based and returns-based) is used to measure and monitor factor exposures and detect style drift in active equity portfolios.

CFA Level 3 Syllabus

For the CFA Level 3 exam, you are required to understand active equity portfolio management and factor investing, with a focus on the following syllabus points:

• Differentiating between fundamental and quantitative active management approaches.
• Explaining major equity style factors (e.g., value, growth, momentum, quality, size) and their long-term performance patterns.
• Understanding rationale and methods for implementing style tilts in active equity portfolios.
• Assessing the trade-offs, risks, and expected outcomes when constructing factor-tilted portfolios relative to a benchmark.
• Evaluating the role of factor timing and diversification in achieving active equity returns.
• Using style analysis to assess a manager’s true factor exposures and to monitor for style drift.

Test Your Knowledge

Attempt these questions before reading this article. If you find some difficult or cannot remember the answers, remember to look more closely at that area during your revision.

1. Which statement best distinguishes a fundamental from a quantitative factor-investing approach?
   1. Fundamental approaches rely on long/short portfolios, while quantitative approaches are always long-only.
   2. Fundamental approaches rely on analyst judgment and company research, whereas quantitative approaches rely on systematic models using historical data.
   3. Fundamental approaches only use macroeconomic variables, whereas quantitative approaches only use firm-level data.
   4. Fundamental approaches cannot implement style tilts, whereas quantitative approaches can.
2. Which set lists only commonly rewarded equity style factors as used in active equity strategies?
   1. Value, momentum, quality, size
   2. Liquidity, term structure, inflation, currency
   3. Growth, volatility, interest rates, commodities
   4. Sector, country, duration, credit spread
3. Why might an active equity manager deliberately apply a style tilt relative to a market-cap-weighted benchmark?
   1. To exactly match the benchmark’s tracking error.
   2. To reduce all exposure to systematic risk.
   3. To exploit long-term factor premia or client preferences by overweighting certain rewarded style factors.
   4. To guarantee outperformance in all phases of the business cycle.
4. What is the primary risk associated with actively timing style factors (e.g., rotating between value and momentum)?
   1. Permanently eliminating tracking error.
   2. Increasing the probability of missing factor rebounds and damaging performance if timing signals are wrong.
   3. Reducing portfolio turnover to near zero.
   4. Eliminating the need for any benchmark.

Introduction

Active equity strategies commonly seek to outperform a market benchmark through factor investing and style tilts. Rather than holding a purely index-tracking portfolio, active managers identify and exploit systematic relationships in stock returns and adjust allocations accordingly. This article reviews how style factors such as value, growth, momentum, quality, and size are used in portfolio construction, and explains the rationale, implementation, and risks of factor investing and style tilting as evaluated in the CFA exam.

Key Term: factor investing
Factor investing is a systematic investment approach targeting specific, repeatable sources of return (factors), including but not limited to style signals such as value, momentum, growth, quality, and size.

Key Term: style factor
A style factor is a company or security characteristic—such as valuation ratios, past price performance, profitability, or market capitalization—that is systematically associated with differences in expected equity returns across stocks.

In the Level 3 exam context, factor investing and style tilts appear in both essay and item-set questions, typically embedded in broader portfolio construction or manager evaluation vignettes. Candidates are expected not only to define factors, but to judge whether a proposed factor tilt is consistent with a client’s investment policy, evaluate its risks, and explain its impact on active risk and tracking error.

THE OVERVIEW OF ACTIVE EQUITY STRATEGIES

Fundamental vs. Quantitative Approaches

Active equity strategies can be classified as fundamental or quantitative in their decision processes.

Key Term: fundamental approach
A fundamental approach is a human judgment-based process relying on company research, valuation, and discretionary stock selection.

Key Term: quantitative approach
A quantitative approach is a systematic, model-based process using historical data and algorithms to drive investment decisions with limited discretion at the individual-trade level.

Fundamental managers typically:

• Analyze company financial statements, business models, competitive position, management quality, and industry structure.
• Estimate fundamental value using discounted cash flow, dividend discount, or multiples-based models.
• Compare fundamental value (or relative value versus peers) to market price to decide whether to overweight, underweight, or avoid a stock versus the benchmark.

Style exposures in a fundamental process are often a by-product of the philosophy:

• A deep-value manager who buys stocks with low price-to-book and low price-to-earnings ratios will naturally tilt toward the value factor and often towards smaller, more cyclical companies.
• A growth-oriented manager focusing on companies with high forecast earnings growth, strong competitive advantages, and high reinvestment rates will tilt toward growth and sometimes quality factors.

The fundamental PM may not explicitly target numerical factor scores, but the investment philosophy creates persistent style tilts.

Quantitative managers typically:

• Define explicit factor signals (e.g., book-to-price, 12-month momentum, return on equity, earnings revisions).
• Rank or score the stock universe on these factors.
• Construct portfolios using systematic rules or optimization, subject to risk, turnover, and constraint controls.

For example, a quantitative value strategy may rank all stocks on composite value scores and buy the cheapest decile, while shorting or underweighting the most expensive decile.

Both approaches can be used to implement factor investing:

• A fundamental manager may deliberately emphasize certain styles (e.g., “high-quality value”) through security selection and position sizing.
• A quantitative manager may build explicit long/short factor portfolios or multi-factor models that directly target given factor exposures.

In practice, many strategies are hybrids: they employ quantitative screens to narrow the universe and fundamental analysis to select final holdings, or they use quantitative risk models to monitor the factor exposures of fundamentally selected portfolios.

Factor-Based Strategies and Style Investing

Style factors are variables systematically associated with cross-sectional differences in equity returns. The curriculum emphasizes several rewarded style factors commonly used in active equity strategies.

Key Term: rewarded factor
A rewarded factor is a systematic risk or behavioral variable with historic evidence of a long-term positive return premium, such as value, momentum, quality, or size.

Value

Key Term: value factor
The value factor captures the tendency for stocks with low prices relative to fundamentals (e.g., earnings, book value, cash flow, sales) to outperform stocks with high such ratios over the long term.

Typical value signals include:

• Low price-to-earnings (P/E)
• Low price-to-book (P/B)
• Low price-to-cash-flow (P/CF)
• Low price-to-sales (P/S)
• High dividend yield

Value strategies may be:

• Relative value (cheap versus industry peers).
• Deep value (extremely low valuations, often with financial distress risk).
• High-quality value (value combined with strong balance sheets and consistent profitability).

Economic and behavioral rationales include risk-based explanations (distressed companies are riskier) and behavioral biases (investors overreact to bad news and project poor recent performance too far).

Growth

Key Term: growth factor
The growth factor aims to capture the performance of companies with high expected or realized growth in key metrics such as revenues, earnings, cash flows, or book value.

Typical growth signals include:

• High forecast earnings per share (EPS) growth.
• High historical EPS or sales growth.
• High reinvestment rates and strong competitive positioning.

Academic evidence for a distinct long-term “growth premium” is weaker than for value, but growth style investing remains important in practice. In style classification systems, growth often represents the opposite end of the spectrum from value, and many benchmarks are split into value and growth sub-indexes.

Momentum

Key Term: momentum factor
The momentum factor captures the tendency for stocks with strong recent performance to continue outperforming over intermediate horizons (e.g., 6–12 months).

Typical momentum signals:

• Past 12-month total return (excluding the most recent month).
• Shorter-term signals (e.g., 6-month return) in some models.

Empirically, price momentum has delivered strong long-run returns in many markets, but with substantial cyclicality and occasional severe “crashes,” particularly following sharp market reversals. Behavioral explanations include investor underreaction to news and herding, while risk-based explanations emphasize exposure to crash risk.

Momentum strategies often require high turnover and can be sensitive to transaction costs and liquidity constraints.

Quality

Key Term: quality factor
The quality factor reflects characteristics such as strong and stable profitability, conservative leverage, earnings quality, and good corporate governance.

Typical quality signals:

• High and stable return on equity (ROE) or return on assets (ROA).
• Low leverage or strong interest coverage.
• Low earnings volatility.
• Positive and consistent free cash flow.
• Measures of earnings quality (e.g., accruals).

High-quality companies tend to be more resilient in downturns and may exhibit smaller drawdowns. Historically, quality has delivered attractive risk-adjusted returns, often with lower volatility than the general market, making it appealing for clients focused on capital preservation.

Size

Key Term: size factor
The size factor, or “size effect,” captures the historical tendency of small-capitalization stocks to outperform large-cap stocks over long horizons.

Size is typically measured by market capitalization. Small-cap stocks often:

• Have higher expected returns but also higher volatility and illiquidity.
• Receive less analyst coverage, potentially creating more mispricing opportunities.
• Are more sensitive to the business cycle and economic shocks.

In practice, a size tilt might be implemented by overweighting small- and mid-cap stocks relative to a large-cap benchmark.

Key Term: style tilt
A style tilt is a deliberate over- or underweight to securities with particular style factor characteristics (e.g., value vs. growth, small cap vs. large cap), relative to the portfolio benchmark.

Key Term: factor timing
Factor timing is dynamically adjusting portfolio exposures to style factors based on macro, valuation, or predictive signals, in an attempt to capitalize on forthcoming changes in factor performance.

Worked Example 1.1

Suppose an active equity manager believes value stocks will outperform in the next year due to attractive relative valuations. The manager tilts the portfolio by overweighting stocks with low P/E and P/B ratios and underweighting expensive growth stocks. What is this approach called, and what is the main risk they are taking?

Answer:
This approach is a style tilt toward the value factor. The main risk is factor-specific cyclicality: the value premium may fail to materialize or may even reverse during the period. If growth or high-multiple stocks continue to outperform, the portfolio will likely underperform the benchmark, increasing tracking error and potentially testing client tolerance for active risk.

IMPLEMENTING ACTIVE STYLE TILTS

Why Apply a Style Tilt

Traditional market-cap-weighted indexes provide broad exposure to many risks, but they weight stocks by market value rather than by expected return. Active managers employ style tilts to:

• Harvest long-run factor premia:
  For example, after an extended period of value underperformance, valuation spreads between cheap and expensive stocks may widen, making a value tilt attractive on both historical and forward-looking grounds.

• Express investment beliefs or macro views:
  A manager expecting an early-cycle expansion may tilt toward small-cap and cyclical value stocks; a manager worried about late-cycle risks may tilt toward quality and lower-volatility stocks.

• Improve diversification:
  Combining style factors that are imperfectly correlated (e.g., value and momentum) can produce more stable active returns than a concentrated single-factor exposure.

• Align with client objectives and constraints:

  • A capital-preservation-oriented client may favor a quality tilt to reduce drawdowns.
  • A long-horizon investor (e.g., a young accumulator or an endowment) may tolerate higher tracking error to pursue size and value premia.
  • ESG-oriented clients may prefer quality and growth names that meet sustainability criteria.

Key Term: active risk
Active risk, often proxied by tracking error, is the volatility of the difference between portfolio returns and benchmark returns.

Key Term: tracking error
Tracking error is the standard deviation of the portfolio’s active return (portfolio return minus benchmark return) over time.

Applying a style tilt is therefore a decision to accept higher active risk and tracking error in exchange for an expected improvement in long-term risk-adjusted returns or better alignment with the client’s preferences.

Portfolio Construction Techniques

Active approaches for style tilts include a spectrum of implementations, from conservative long-only tilts to more aggressive long/short factor portfolios.

Key Term: long-only tilt
A long-only tilt increases exposure to desired style factors by overweighting favored stocks and underweighting (or omitting) unfavored stocks, while holding only long positions and remaining broadly benchmark-constrained.

Key Term: long/short factor portfolio
A long/short factor portfolio seeks “pure” exposure to a factor by taking long positions in securities with high factor scores and short positions in those with low scores, often with limited net market exposure.

Key approaches include:

• Long-only tilts:

  • Overweight stocks with strong factor characteristics and underweight those with weak characteristics.
  • Maintain diversification and sector constraints so that the portfolio remains broadly similar to the benchmark.
  • Appropriate for many institutional and retail clients whose mandates or regulations prohibit short-selling or leverage.

• Long/short constructs and pure factor portfolios:

  • Construct portfolios that are dollar-neutral or beta-neutral to the broad market.
  • Example: long the top 20% of stocks by value score, short the bottom 20%, scaled to zero net investment.
  • Offer more concentrated factor exposure and can be combined as portable alpha overlays on top of a core index exposure.
  • Require tolerance for leverage, shorting, and potentially higher turnover.

• Multi-factor combination strategies:

  • Build diversified portfolios that jointly target multiple rewarded factors (e.g., value, momentum, and quality).
  • Can be implemented either as a single integrated optimizer (one portfolio balancing all factors and constraints) or as a combination of separate single-factor sleeves.
  • Typically aim to smooth cyclicality by including factors that perform well in different market environments.

Key Term: multi-factor strategy
A multi-factor strategy combines exposures to multiple rewarded factors in a single portfolio to stabilize and diversify active returns.

Portfolio construction for factor strategies typically relies on:

• A risk model describing how securities load on factors.
• Constraints on total expected tracking error, sector and country exposures, and turnover.
• Position limits to control concentration and liquidity risk.

Worked Example 1.2

A portfolio manager wants to add a momentum tilt. They allocate 60% to the benchmark index and 40% to the subset of stocks with the highest 12-month price return. What is this approach likely to improve and what is a possible downside?

Answer:
This blended approach is a long-only momentum tilt layered on top of a core index holding. It is likely to increase exposure to the momentum factor and, if momentum continues to be rewarded, to improve long-run expected returns versus the benchmark. The downside is increased exposure to momentum’s cyclicality and crash risk: the portfolio may experience sharp losses relative to the benchmark after market turning points, and turnover and transaction costs are likely to be higher.

Selecting Factor Signals

Implementation begins with clear factor definitions and robust empirical testing.

Steps include:

• Defining signals:
  For each factor, choose one or more measurable variables:

  • Value: composite of book-to-price, earnings yield, cash-flow yield, dividend yield.
  • Momentum: past 12-month return excluding the most recent month.
  • Quality: ROE, leverage, earnings volatility, accruals.
  • Size: market capitalization.
  • Growth: forecast EPS growth, historical sales growth.

• Combining signals:
  Many managers build composite scores that average or weight multiple related variables for robustness. For example, a value score might be the equal-weighted average of z-scores for B/P, E/P, and C/P.

• Backtesting and validation:

  • Test factor performance over long historical periods and across regions.
  • Check sensitivity to different definitions and parameter choices.
  • Guard against data-mining by using out-of-sample tests and economic reasoning, not just statistical fit.

• Portfolio construction and optimization:

  • Use scores to rank stocks and select a subset (e.g., top decile) or feed them into an optimizer that maximizes expected alpha subject to risk constraints.
  • Explicitly manage exposures to non-target factors (e.g., neutralize industry and country exposures when building a value or momentum factor portfolio).

Care is required to avoid unintended bets. For example, a simple value strategy based solely on P/B may inadvertently concentrate in distressed financials during a crisis. Including additional quality measures or sector constraints can mitigate such risks.

Measuring and Monitoring Style Exposures

Factor investing requires ongoing measurement of style tilts.

Key Term: style analysis
Style analysis is the process of estimating a portfolio’s exposures to style factors or style indexes to understand sources of risk and return and to monitor whether a manager is adhering to the stated style.

There are two main approaches:

• Holdings-based style analysis (HBSA):
  • Uses actual portfolio holdings and each stock’s style attributes (e.g., value and growth scores from an index provider).
  • Aggregates exposures across securities to estimate the portfolio’s factor tilts.
  • Provides an accurate, current snapshot and can detect concentrations and unintended exposures.

Key Term: holdings-based style analysis
Holdings-based style analysis infers a portfolio’s style exposures directly from its current holdings and the style characteristics of each security, aggregated on a position-weighted basis.

• Returns-based style analysis (RBSA):
  • Uses the portfolio’s historical returns and regresses them on returns of style indexes (e.g., large-cap value, small-cap growth).
  • Estimates average style exposures over the regression period.

Key Term: returns-based style analysis
Returns-based style analysis infers a portfolio’s style exposures statistically by regressing its historical returns on a set of style index returns, subject to constraints such as long-only weights summing to one.

RBSA is useful when holdings data are unavailable (e.g., hedge funds), but it may lag changes in style and can mask short-term style drift.

Key Term: style drift
Style drift is a significant deviation of a manager’s actual factor or style exposures from the stated or expected style, often detected through style analysis.

For CFA exam questions involving manager evaluation, candidates should use style analysis concepts to:

• Confirm whether a manager’s realized factor exposures match the stated style (e.g., a “large-cap value” manager who actually behaves like a growth or momentum manager).
• Discuss the implications of style drift for multi-manager portfolios and style allocation decisions.

Risks in Style Tilting

While rewarded factors offer potential for long-term excess returns, they come with distinct risks:

• Cyclic underperformance:
  Each factor experiences extended periods of underperformance relative to the market. For example, value underperformed growth for much of the late 1990s and mid-2010s. Clients and boards may lose patience, creating business risk for the manager.

• Factor crowding:
  When many investors chase the same factor (e.g., momentum or low volatility), valuations of factor-favored stocks can become stretched. Crowded factors are vulnerable to sharp reversals when conditions change or when flows reverse.

Key Term: factor crowding
Factor crowding occurs when many investors hold similar factor exposures, increasing fragility and the risk of abrupt, correlated unwinds in those positions.

• Implementation and turnover costs:
  Factors such as momentum require frequent rebalancing, leading to higher trading costs and market impact, particularly in less liquid small-cap names.

• Model risk and estimation error:
  The relationship between signals and returns can change over time. Poorly specified models or overfitted backtests can lead to disappointing out-of-sample performance.

• Elevated tracking error and client tolerance:
  Strong factor tilts increase tracking error relative to a cap-weighted benchmark. Even if the strategy is sound, short- to medium-term underperformance may be unacceptable for some clients, particularly those with low risk tolerance or short evaluation horizons.

Key Term: tracking error
Tracking error is the standard deviation of portfolio active returns; higher factor tilts generally increase tracking error versus the benchmark.

Factor Timing Considerations

Factor timing seeks to dynamically tilt toward or away from factors based on predictive signals (e.g., macro variables, valuation spreads, or factor momentum).

Possible timing signals include:

• Business cycle indicators (e.g., growth, inflation, yield curve slope).
• Relative valuation of factor portfolios (e.g., value vs. growth valuations).
• Interest rates, credit spreads, or volatility indices.

For example, an analyst might regress factor returns on normalized bond yields:

where $f_{i,t+1}$ is next month’s return on factor $i$ and $\text{Normalized Yield}_t$ is the deviation of the 10-year bond yield from its 12-month moving average.

Interpretation:

• A statistically significant $\beta_{i,1}$ suggests that current yields have some predictive power for next month’s factor returns.
• For example, higher normalized yields might be associated with lower subsequent returns to high-dividend or low-beta factors.

However, several challenges limit practical factor timing:

• Weak and unstable relationships:
  Relationships may be statistically weak or not robust out of sample. Structural changes in markets or policy regimes can break historical patterns.

• Estimation error and overfitting:
  With many potential predictors and relatively few cycles, data-mining risk is high. Exam answers should emphasize the need for economic rationale and out-of-sample tests.

• Transaction costs:
  Frequent adjustments to factor exposures can erode any incremental return.

• Type I and Type II errors:
  Mis-timing factors (e.g., cutting a losing factor just before it rebounds) can be costly, analogous to firing an underperforming manager just before mean reversion.

Given these issues, the curriculum generally views aggressive factor timing as risky and difficult to execute consistently. A more defensible approach is:

• Maintaining strategic, diversified factor exposures tilted toward the investor’s long-run beliefs.
• Making only gradual, valuation- or risk-based adjustments rather than frequent short-term rotations.

Worked Example 1.3

An equity portfolio combines 50% value, 25% momentum, and 25% quality, rebalanced quarterly. What practical benefits does this multi-factor approach offer over concentrating only on value?

Answer:
The multi-factor approach mitigates the cyclicality of any single factor. When value underperforms (e.g., during a growth-led market), momentum may still perform well by holding recent winners, and quality may provide downside protection. Because factor returns are less than perfectly correlated, combining value, momentum, and quality usually reduces the volatility of active returns and the depth of drawdowns compared with a pure value tilt, for a similar level of long-run expected excess return.

Worked Example 1.4

A conservative pension fund benchmarked to a broad large-cap index is considering two ways to gain value exposure:

• Strategy A: a benchmark-relative, long-only value tilt with maximum 4% tracking error.
• Strategy B: a leveraged, market-neutral long/short value factor portfolio targeting 12% volatility, implemented as an overlay on top of the existing index holdings.

Which strategy is more appropriate, and why?

Answer:
For a conservative pension fund with limited tolerance for tracking error and leverage, Strategy A is generally more appropriate. A benchmark-relative, long-only tilt increases value exposure while keeping overall market risk and tracking error within a specified limit, and it can usually be implemented without derivatives or explicit leverage. Strategy B introduces leverage, short-selling, and a high-volatility overlay, which may conflict with the fund’s risk tolerance, regulatory constraints, and governance capabilities. Even if the expected value premium is higher in Strategy B, its complexity and tail risk are likely inconsistent with a conservative institutional objective.

Worked Example 1.5

A quantitative team finds that when credit spreads widen sharply, subsequent 12-month returns to the quality factor are significantly positive, whereas returns to the size factor are weak. They propose timing factor exposures by overweighting quality and underweighting small caps whenever spreads widen.

What concerns should an investment committee raise before approving this factor timing proposal?

Answer:
The committee should question the robustness and economic rationale of the timing rule. Key concerns include:

• The sample period may be too short or dominated by a few crisis episodes, raising data-mining concerns.
• The relationship may not hold out of sample or under different policy regimes.
• Implementing large, rapid shifts in factor exposures after spread widening could be costly in illiquid markets.
• The fund’s governance process may not support rapid tactical decisions, and the rule might increase operational and model risk.
  A more prudent approach may be to recognize that quality tends to be defensive and small caps cyclical, but to incorporate this observation into strategic tilts rather than aggressive, short-horizon timing rules.

Exam Warning

Style tilts may boost risk-adjusted returns over long periods but can lag the market for extended stretches. For exam questions, always state both the potential benefits (e.g., higher expected return, better alignment with client objectives) and the risk of cyclically underperforming the benchmark and increasing tracking error.

Revision Tip

For CFA exam questions, you must be able to explain:

• Why style tilts may be appropriate for a given client and benchmark.
• How to choose and implement factor exposures (including long-only vs. long/short and single- vs. multi-factor approaches).
• How to assess the practical risks and limits of timing or concentrating factor bets, including business and behavioral risks when factors underperform.

Key Point Checklist

This article has covered the following key knowledge points:

• Factor investing and style tilts are key active equity strategies for outperforming a benchmark.
• Major style factors (value, momentum, quality, size, growth) have long-term empirical support but can underperform cyclically and exhibit distinct risk profiles.
• Fundamental and quantitative approaches offer different strengths for identifying and implementing factor signals; many real-world strategies are hybrids.
• Style tilts can be implemented via long-only benchmark-relative portfolios, long/short factor portfolios, or diversified multi-factor strategies.
• Style analysis (holdings-based and returns-based) is essential for measuring factor exposures, validating a manager’s stated style, and detecting style drift.
• Multi-factor portfolios help manage periods when individual style factors lag by exploiting imperfect correlations among factors.
• Factor timing adds model and implementation risk and can backfire if market regime changes are misjudged or historical relationships break down.
• Active style tilts require clear definition, validation, and disciplined implementation to manage tracking error, capacity, turnover, and unintended exposures relative to client objectives.

Key Terms and Concepts

• factor investing
• style factor
• fundamental approach
• quantitative approach
• rewarded factor
• value factor
• growth factor
• momentum factor
• quality factor
• size factor
• style tilt
• factor timing
• active risk
• tracking error
• long-only tilt
• long/short factor portfolio
• multi-factor strategy
• style analysis
• holdings-based style analysis
• returns-based style analysis
• style drift
• factor crowding