The Active Manager Paradox: High-Conviction Overweight Positions

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Is active management’s decade-long losing streak to passive management due to high fees, a lack of manager skill, or something else?

What’s required to answer this question is not rampant speculation but a fact-based assessment of manager decision making. As the saying goes, “You cannot manage what you cannot measure.”

Our research explored how active managers generate stock selection alpha. We conducted a multi-year analysis that covered 114 US equity mutual funds from 57 fund families and evaluated more than 400,000 individual rolling one-year performance periods. Combined, our sample represented about $2 trillion in assets under management (AUM).

Our key focus? Manager conviction. How committed is the manager to the different subgroupings of equities within each fund? To find out, we measured the scale of overweight and underweight positions rather than the raw size of the holdings, which tends to be biased by the benchmark weightings.

Research Design and Objective

The primary categories of stock positions based on a manager’s active intent are:

  1. High-Conviction Overweight
  2. Underweight
  3. Neutral Weight

We identified the constituents of these three categories by measuring real-time, daily mutual fund holdings and weights and rebalancing each group every 14 days. The fund holdings data came from Turing Technologies’ proprietary Hercules fund-replication system and corresponding Hercules Database.

Summary Results

The results, depicted below, feature two sets of data: the Success Rate of each category compared to the benchmark over rolling one-year periods, and the average annual excess return of those rolling periods.


The Impact of High-Conviction Overweights, Gross of Fees


The Impact of High Conviction Overweights, Net of 85 bps Fees



The High-Conviction Overweights, composed of the managers’ best ideas, is the only category that delivers stock selection alpha. High-Conviction Overweights achieved Success Rates of 84% gross of fees, and 74% net of a theoretical 85 basis points (bps) fees. Underweights and Neutral Weights, by comparison, generated a Success Rate of 50% gross of fees — the equivalent of a pure beta portfolio — and materially inferior Success Rates after fees.

That High-Conviction Overweights are the sole category through which active managers could add alpha defies the long-held assumption that managers can improve performance throughout the entire stock-selection and portfolio construction process.

Active Manager Paradox

While our data shows that fund managers can exhibit persistent skill through their high-conviction best ideas, it also reveals a portfolio design paradox.

As the sole source of excess return, High-Conviction Overweights need to be the main emphasis of all actively managed portfolios. Any allocation to anything else will reduce returns.

Yet, according to our research, the average manager sabotaged their returns by shrinking the High-Conviction Overweight stocks to an overall portfolio weight of 55%. The corresponding portfolio allocation to Underweights and Neutral Weights thus acts as a “Beta Anchor” that severely dilutes the alpha generated by High-Conviction Overweight positions.

To use a sports analogy, this is like an NFL football team voluntarily removing its star quarterback from the game after the first half. It does not constitute a winning strategy.

To be sure, a “Beta Anchor” has a variety of justifications. Allocating to a market-neutral component reduces the portfolio’s tracking error versus the benchmark. It also decreases the likelihood of a relative performance failure compared to a more highly concentrated portfolio. Nevertheless, any risk-management benefit is offset by a significant performance penalty.

Implications for Investors

We held off claiming to have the solution to the Active Manager Paradox in this paper. And we didn’t address the risk-management considerations. But this topic is not trivial.

Active management is, by definition, a premium service. Its fees are higher because the expectation is that it will deliver higher returns.

But our research indicates that the
current approach to actively managed fund design compromises the manager’s
ability to outperform.

Outside research supports the cause-and-effect implications of reduced allocations to High-Conviction Overweight stocks. Morningstar currently classifies mutual funds as either active or passive and provides summary return data for the average actively managed mutual fund by asset class. The chart below compares the relative performance of actively managed large-blend funds to the S&P 500 Index over rolling calendar years since 1990.

The results are bleak.


Actively Managed Large-Blend Mutual Funds vs. The S&P 500


Large-blend active managers have outperformed the S&P 500 in only five of the 29 years analyzed. On average, active managers underperformed by -1.7% per calendar year. 

The results are even worse for the most recent decade. Since 2010, active managers have failed to keep pace with the S&P 500 every year, lagging by -2.1% a year on average.

While it is industry convention to blame these outcomes on higher fees, our research suggests that fees are only a secondary contributor. Diluting the sole source of stock selection alpha to a minority component of a portfolio has far greater structural impact than higher fees.

The decade-long failure of active managers to compete with their passive counterparts has not gone unnoticed. End investors have voted with their feet: In the last five years, approximately $1.3 trillion has been taken out of active funds, while $1.3 trillion has flowed into passive funds and exchange-traded funds (ETFs), according to Morningstar.

Generating viable solutions to the Active Manager Paradox is of paramount importance to both the end investor and the active management industry itself. We believe this research can contribute to finding those solutions.

The good news is that active managers are creating real value. The bad news is that value is too often lost before it can be delivered.


Research Design Methodology

This analysis is based on a proprietary database of daily fund positions and portfolio weights constructed and maintained by Turing Technology Associates Inc. The specific funds used in the research dataset includes 114 unique US equity mutual funds, from 57 fund families, and represents $1.996 trillion in assets under management (AUM).

Fund Selection Process

The funds selected for use in the research came from the set of mutual funds included within a series of investment portfolios known as Ensemble Active Management (“EAM”) Portfolios. Turing licenses a series of proprietary technologies to clients to support their creation of such EAM Portfolios. Each EAM Portfolio is typically constructed from a set of 10 to 15 underlying mutual funds with a corresponding industry benchmark. As of early August, 2019, Turing had 24 client-designed EAM Portfolios in live production.

All 114 funds used within the study were selected by clients or prospects of Turing related to the design of an EAM Portfolio. Because Turing’s clients selected the underlying funds and corresponding benchmark, the fund selection process maintained independence from the researchers.

Each paired fund and benchmark is a subject of the analysis. Benchmarks included the S&P 500, Russell 1000, Russell 2000, Russell 1000 Value, and Russell 1000 Growth. The time periods used were either January 2014 through July 2019, or January 2016 through July 2019, depending on available data.

Source of Daily Fund Positions

To access daily fund holdings, Turing applied its proprietary fund-replication technology known as the Hercules System. Hercules is a machine learning-based platform processing a multitude of publicly available data, with core concepts behind the approach in use and development for more than a decade. Hercules is not a regression-based approach. Daily estimated positions are generated by the Hercules System, with the out-of-sample portfolios rebalanced every 14 days. 

For reference, the Hercules estimated fund holdings and weights for the funds used in this study typically generated a tracking error of less than 1%, and a correlation to the actual fund returns that was greater than 99.7%.

Isolating Manager Conviction

The focus of this research was to analyze the impact of manager conviction in security selection, and thus we embedded two critical design elements into the study. First, securities were categorized and evaluated based on portfolio weights relative to the benchmark. Rather than focus on actual portfolio weights, which are heavily influenced by benchmark weights, the emphasis was placed on a manager’s overweight and underweight decisions, and the scale of the over or underweight positions. Second, we divided each fund into multiple, non-overlapping subportfolios determined by the level of Manager Conviction involved, and evaluated their performance separately. Each subportfolio was rebalanced every 14 days, and treated as a distinct Model Portfolio. The three subportfolios analyzed were:

  • High Conviction Overweights: A subportfolio consisting of the largest overweight positions for stocks in the fund. The subportfolio was selected to cumulatively represent 80% of aggregate portfolio overweights relative to the benchmark.
  • Underweights: A subportfolio consisting of the largest underweight positions for stocks in the fund. The subportfolio was selected to cumulatively represent 80% of aggregate portfolio underweights relative to the benchmark.
  • Neutral Weights: A subportfolio consisting of overweight securities that are not included in the Overweight subportfolio, and underweight positions that are not included in the Underweight subportfolio.

All subportfolios capture distinct choices by a fund manager. The dynamic portfolio weights for each subportfolio are in proportion to the original fund weights, normalized to 100%. Securities outside of the benchmark were excluded as they cannot be properly evaluated in relation to a benchmark. All performance data was calculated both as gross of any fees and after factoring in a hypothetical 85 bps fee. Neither result reflected transaction costs.

The performance data presented represents rolling one-year data (daily step), which was evaluated to capture the percent of rolling periods where each subportfolio was able to outperform the corresponding benchmark (Success Rate), and the average excess (or negative) relative return.

A subportfolio consisting of securities included in the benchmark but not included in the mutual fund (i.e., Zero Weights) was built and analyzed. This fourth subgrouping was not included in the research results because the only way to capture any potential alpha would be through a 100% short portfolio, which is not allowed in a traditional mutual fund. For reference, the Zero Weight portfolio underperformed the benchmark by 78 bps, on average. Unfortunately, even a frictionless short portfolio of Zero Weight securities would not be able to earn the fees of even a standard long-only mutual fund.


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All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer.

Image credit: ©Getty Images/sompong_tom


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Alexey Panchekha, CFA

Over his nearly three-decade-long career, Alexey Panchekha, CFA, has spent 10 years in academia, where he focused on nonlinear and dynamic processes; 10 years in the technology industry, where he specialized in program design and development; and eight years in financial services.
In the latter arena, he specialized in applying mathematical techniques and technology to risk management and alpha generation. For example, Panchekha was involved in the equity derivative trading technology platform at Goldman Sachs, and led the creation of the multi-asset multi-geographies portfolio risk management system at Bloomberg. He also served as the head of research at Markov Process International, a leader in portfolio attribution and analytics. Most recently, Panchekha co-founded Turing Technology Associates, Inc., with Vadim Fishman. Turing is a technology and intellectual property company that sits at the intersection of mathematics, machine learning, and innovation. Its solutions typically service the financial technology (fintech) industry. Turing primarily focuses on enabling technology that supports the burgeoning Ensemble Active Management (EAM) sector supporting strategies targeting downside volatility management. Prior to Turing, Panchekha was managing director at Incapital, and head of research at F-Squared Investments, where he designed innovative volatility-based risk-sensitive investment strategies. He is fluent in multiple computer and web programming languages and software and database programs and is certified in deep learning software.
He earned a PhD from Kharkiv Polytechnic University with studies in physics and mathematics as well as an MS in physics. Panchekha is a CFA charterholder.



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