Quality Control: How to check for errors in your investment performance
Managing Partner
May 5, 2021
15 min
Recent investment performance calculation mistakes at Pennsylvania Public School Employees’ Retirement System (“PSERS”) have highlighted the importance of quality control reviews and raises questions about where risk exists, how these risks can be mitigated, and what role independent verifications should play in the quality control process.
What happened at PSERS?1
An error in the return calculation for Pennsylvania’s $64 billion state public school employee retirement plan has had serious implications for its beneficiaries and those involved in the calculation mistake.
In 2010, the plan, which was already underfunded, entered into a risk-sharing agreement where employees hired after 2011 would pay more into the plan if the return (average time-weighted return) over a specific time period fell below the actuarial value of asset (AVA) return of 6.36%.
In December 2020, the board announced that the plan had achieved a return of 6.38%, a mere 2 basis points above the minimum threshold. But in March the board changed its tune, announcing that the calculation was incorrect and the 100,000 or so employees hired since 2011 (and their employers) should have actually paid more into the plan.
What’s worse is PSERS also announced that the FBI is investigating the organization, although details of the probe have not yet been released.
According to PSERS, a consultant, that had calculated the return, came forward and admitted to the calculation error. But the board also said that it is looking into potential cover up by its staff. From what we know, at least 3 independent consultants were involved in providing data used for the calculations, calculating the returns, and verifying the returns. So, with all these experts involved, how could this happen and what can your firm do to avoid a similar situation?
Key issues to address in an investment performance quality control process
Firms should develop sound quality control processes to help identify errors before results are published. Often these processes either do not exist or are insufficient to identify issues. Following a robust quality control process that considers the key risks involved and then finds ways to mitigate these risks greatly increases the accuracy of presented investment performance.
Although we do not yet know the cause of the errors found in the PSERS case, we can highlight a few primary reasons errors occur in investment performance reporting. Primarily, errors found in published performance results are caused by:
- Key Issue # 1 – Issues in the underlying data (e.g., incorrect or missing prices, unreconciled data, missing transactions, misclassified expenses, or failing to accrue fixed income)
- Key Issue #2 – Mistakes in calculations (e.g., manual calculations that fail to match the intended methodology)
- Key Issue #3 – Errors in reporting (e.g., publishing numbers that do not match the calculated results)
A robust quality control process should specifically address all three of these areas.
Considerations when designing a robust quality control process
Key Issue #1 – Issues in the underlying data
As they say, garbage in, garbage out. It is important to ask and address questions confirming the validity of data before it is used to calculate performance. Specifically, consider how the data used in the calculations is gathered, prepared, and reconciled before completing the calculations. Is there any formal signoff from the operations team confirming that the data is ready for use? Has a review of the data been conducted by an operations manager prior to this confirmation being made?
While deadlines to get performance published can be tight, taking the time to ensure that the underlying data is final and ready to use before performance is calculated can prevent headaches later on.
The following is a list of issues to look for when testing data validity:
- Outlier performance – Portfolios performing differently than their peers may indicate a data issue or that the portfolio is mislabeled (i.e., tagged to a different strategy than it is invested in).
- Differences between ending and beginning market values – Generally, we expect a portfolio’s market value at the end of one month and the beginning of the next month to be equal (unless using a system where external cashflows are recorded between months and differences like this are expected). Flagging differences can help identify data issues.
- Offsetting decrease/increase in market value – Market values that suddenly increase or decrease and then return to the original value may have an incorrect price or transaction that should be researched.
- Gaps in performance – A portfolio whose performance suddenly stops and then restarts may have missing data.
- 0% returns – The portfolio may have liquidated and may no longer be under the firm’s discretionary management.
- Very low market values – The portfolio may have closed and is only holding a small residual balance, which should be excluded from the firm’s discretionary management.
- Net-of-fee returns higher than gross-of-fee returns – Seeing net returns that are higher than gross returns could indicate a data issue unless there are fee reversals you are aware of (e.g., performance fee accruals where previously accrued fees are adjusted back down).
- Gross-of-fee returns and net of-fee returns are equal – If gross-of-fee and net-of-fee returns are always equal for a fee-paying portfolio, it is likely that the management fees are paid from an outside source (paid by check or out of a different portfolio). The returns labelled as net-of-fee in a case like this should be treated as gross-of-fee returns.
Key Issue #2 – Mistakes in calculations
Mistakes happen, but there are ways to reduce their frequency and impact. First, you’ll want to consider how manual your performance calculations are as well as the experience of the person completing the calculations.
Let’s face it, Excel is probably the most widely used tool in performance measurement, especially for smaller firms. While many firms likely find Excel to be a user-friendly tool for calculating performance statistics, it has its limitations. Studies have shown that up to 90% of spreadsheets contain errors and spreadsheets with lots of formulas are even more likely to contain mistakes. Whether it’s not properly dragging down a formula or referencing the wrong cell, fundamentally, the biggest problem is that users do not check their work or have carefully outlined procedures for confirming accuracy.
Although this may seem obvious, having a second set of eyes on a spreadsheet can save you from the embarrassing headache of having to explain errors in performance calculations. It is even better if this review is a multi-layered process. Having someone review details as well as someone to do a high-level “gut-check” to make sure the calculations and results make sense can reduce this risk. Depending on the size of your firm, this may be easier to accomplish with a third-party consultant, where you serve as a final layer of review.
Having this final “gut-check” can help prevent avoidable errors prior to publication. We find that this final “gut-check” is best performed by someone who knows the strategy intimately rather than a performance or compliance analyst, as these individuals may be too focused on the calculation details to take a step back and consider whether the returns make sense for the strategy and are in line with expectations.
If you use software to calculate performance, you can significantly reduce the risk of manual error, but due diligence should still be performed from time to time to manually prove out the accuracy of the calculations completed in the program. This does not need to be done every time but should be conducted when introducing a new software system and any time changes are made to the program.
Key Issue #3 – Errors in reporting
It may seem silly, but many performance reporting errors come from transposing strategy and benchmark returns in presentations or placing the return of one strategy in the factsheet of another. Therefore, it is important to consider how the final performance figures make it from the system or spreadsheet into the performance presentations. Are they typed? Copy and pasted? Or are the performance reports generated directly out of a system? It’s not enough to complete the calculations correctly, the final reports must also be accurate, so adding a step to review this is crucial.
A similar review process to the one described above can really make a difference, but ultimately, understanding the vulnerabilities of your performance reporting will help you design quality control procedures that address any exposure.
Calculations completed by external performance consultants
Whether performance is calculated internally or by a third-party performance consultant, the same key issues should be considered when designing the quality control process. Due diligence should be done on the performance consulting firm to evaluate the level of experience the firm has with calculating investment performance and what kind of quality control process they follow prior to providing results to your firm. This information will help you determine what reliance you can place on their procedures and what your firm should still check internally.
For example, outsourcing performance calculations to an individual or single-person firm likely necessitates a more in-depth review since this individual would not have the ability to have a second set of eyes on the results prior to providing them to your firm. However, even larger performance consulting firms with robust quality control processes may not have intimate knowledge of your strategies, meaning that, at a minimum, a final “gut-check” should be done by your firm prior to publication.
Reliance on independent performance verification firms to find errors
Many firms that hire performance verification firms rely on their verifier to be their quality control check; however, this may not be a good practice for a variety of reasons. If this is a common practice at your firm, you may want to check the scope of your engagement before relying too heavily on your verifier to find errors.
Verification is common for firms that claim compliance with the Global Investment Performance Standards (GIPS®). But even firms that claim compliance with the GIPS standards and receive a firm-wide verification are required to disclose that, “…Verification does not provide assurance on the accuracy of any specific performance report.”
This is because verifiers are primarily focused on the existence and implementation of policies and procedures. While their review may help identify errors that exist in the sample selected for testing, it specifically does not certify the accuracy of presented results. While the verification process is valuable and often does turn up errors that need to be corrected, regardless of the scope of your engagement, a robust internal quality control process is likely still warranted.
Firms that are not GIPS compliant may engage verification firms for various types of attestation or review engagements like strategy exams or other non-GIPS performance reviews. In these situations, the scope of the engagement may be customized to meet the needs (and budget) of the firm seeking verification. A clear understanding of exactly what is in-scope and specifically what the verifier is opining on when issuing their report is key.
Situations where the engagement entails a detailed attestation tracing input data back to independent sources, confirming that calculations are carried out consistently, and verifying that published results match the calculations, allow for heavy reliance on the verifier as part of your quality control process.
Alternatively, when the scope merely consists of a high-level review confirming the appropriateness of the calculation methodology, a much more robust internal quality control process should be applied.
Knowing the scope of the engagement your firm has established with the verification firm is an important element in determining how much reliance can put on their review and findings, which can then be incorporated into the design of your own internal quality control procedures.
Key take-aways
Mistakes happen in investment performance reporting, but a robust quality control process can greatly mitigate this risk. Understanding the risks that exist, designing processes to test these risk areas, and understanding the role and engagement scope of all consultants involved are essential items in designing a quality control procedure that work for your firm – and hopefully one that will help you avoid situations like what happened with PSERS.
If you are not sure where to begin, we have tools and services available to help. Longs Peak uses proprietary software to calculate and analyze performance. Our software helps flag possible data issues and outlier performers and also produces performance reports directly from our performance system.
In addition, our performance consultants are available to work with your team to help identify potential vulnerabilities in your performance reporting process and can help you develop better quality control procedures, where needed.
Questions?
If you would like to learn more about our quality control process or any of the services we offer (like data and outlier testing) to help improve the accuracy and reliability of investment performance, contact us or email Sean Gilligan directly at sean@longspeakadvisory.com.
1 For more information on PSERS, please see this article from the Philadelphia Inquirer.
Popular Post
The Performance Problems That Cost Managers Allocations
August 23, 2026
15 min
Most managers assume that losing an allocation comes down to returns. Underperform the benchmark, underperform peers, and the mandate goes elsewhere. That happens, but it's not usually the reason a manager gets cut from a search after the numbers already looked competitive.
More often, it's something in how the performance was presented that made an allocator hesitate. A number that didn't match across two documents. A risk statistic nobody could explain. A question in due diligence that the manager couldn't answer cleanly. None of these are calculation errors. They're trust problems, and trust is what allocators are ultimately seeking when they write a check.
Here are the performance problems we see that cost managers allocations most often, and none of them start with the returns themselves.
The Numbers Don't Match Across Documents
An allocator pulls up your factsheet, your pitchbook, and your GIPS® Composite Report, and the composite's five-year return isn't quite the same in all three. Maybe it's a rounding difference, or the factsheet reflects a different "as of" date. The allocator doesn't know that, and they aren't going to assume the best. Inconsistency reads as carelessness, and carelessness in performance reporting raises an obvious question: what else isn't being checked?
This is why we push firms to treat marketing and GIPS compliance as one coordinated process rather than two departments working from different source files. Every document that leaves the building should trace back to the same underlying data.
This matters even more now that due diligence itself is being automated. Operational due diligence teams and consultants are increasingly running AI tools that cross-check pitchbooks, factsheets, DDQs, and regulatory filings against each other, flagging contradictions that used to slip through manual review. A rounding difference or a stale figure that a person might have missed a few years ago is exactly the kind of inconsistency these tools are built to catch instantly. Clean, consistent marketing materials aren't just good practice anymore — they're what it takes to pass a review that may happen before a person ever looks at your numbers.
Performance That Looks Selected, Not Reported
Showing your best-performing account, your best-performing period, or a composite with an unusually small number of accounts invites the question every allocator is trained to ask: what am I not being shown? Due diligence teams know that everyone can't be top quartile. The SEC Marketing Rule's anti-cherry-picking provisions exist because this pattern is common enough that regulators built rules around it, and sophisticated allocators are watching for it. If your performance can be read as overly flattering rather than representative, assume a diligence team will read it that way.
Wanting to lead with your best numbers is an understandable impulse. But diligence teams are trained specifically to spot it, and selective disclosure, even when every number in it is accurate, tends to read as a bigger warning sign than an honest, complete track record would. The stronger story is discipline: the periods where you held to your stated mandate and didn't deviate even while returns lagged. That's a harder story to tell than "we outperformed," but it's the one that actually holds up, because it shows you didn't drift toward whatever was working elsewhere just to keep pace. Chasing returns outside your stated process isn't skill, it's strategy drift, and allocators are trained to spot that just as readily as cherry-picked out performance.
Our advice: resist the instinct to lead with your best examples, and show the scenarios that build trust instead. We recommend showing the ones that demonstrate you stuck to your stated mandate, policies, and procedures, especially when the outcome wasn't your best quarter. Discipline under pressure is a more durable credential than a strong one-off time period, and it's the kind of evidence that holds up long after that number is forgotten.
Statistics You Show But Can't Explain
A page full of risk statistics doesn't build confidence on its own. It invites a follow-up question, and if the manager can't explain what a downside capture ratio of 85% says about the decisions actually made in the portfolio, the statistic becomes a liability instead of an asset. Allocators aren't just checking whether the numbers are favorable. They're checking whether the manager understands their own portfolio well enough to explain it. Statistics presented without interpretation signal that the second answer is “no.”
Likewise, a page of portfolio characteristics that have nothing to do with how the strategy is actually run are not doing you any favors. If you're not making decisions at the sector level, a sector breakdown doesn't tell an allocator anything about your process. If you don't manage individual position sizing, a top-ten holdings list is not adding value.
Your factsheet should be a roadmap for the conversation you want to have, not a checklist of everything other managers include. Every number on it should be something you can explain: how it got there, what decision it reflects, and what it says about how you manage money. A statistic that's only there because everyone else shows it likely isn't helping you if it doesn’t demonstrate active decision making. It's inviting a question you may not have a good answer to.
It's the same logic as a good resume. One padded with every certification, hobby, and unrelated past role doesn't read as impressive, it reads as overwhelming and maybe irrelevant, and it makes the reader work harder to find what actually matters to the job at hand. A factsheet works the same way. The strongest ones include only what's relevant to the case being made and make it easy to connect every line back to it.
No One Can Explain Why a Decision Was Made
This is the one that costs managers the most, and it's rarely about the numbers at all. An allocator asks why a composite was redefined, why a benchmark changed, or why a particular account was excluded, and the answer is a shrug or "that's how we've always done it." Undocumented decisions create the impression that performance is being managed reactively rather than governed intentionally. Firms that can point to a clear, contemporaneous record of why a judgment call was made close that conversation quickly. Firms that can't do this will leave the allocator wondering what other judgment calls haven't been documented either.
The Common Thread
None of these problems are really about whether the strategy performed well. It comes down to whether the story behind the numbers holds up consistently under scrutiny. Allocators aren't just buying returns. They're also buying confidence that what they're being shown today will still be true, and still explainable, a year from now.
The fix isn't more disclosure for its own sake. It's making sure everything across your performance reporting tells the same, well-documented story before an allocator ever has the chance to ask why it doesn't.
GIPS® is a registered trademark owned by CFA Institute. CFA Institute does not endorse or promote this organization, nor does it warrant the accuracy or quality of the content contained herein.
PMAR 2026: A Few Takeaways That Stuck With Me
June 1, 2026
15 min
Every Spring, the performance measurement community gathers for PMAR: The Performance Measurement, Attribution & Risk Conference, hosted by TSG. This year marked the twenty-fourth annual, and I left thinking about it differently than I have in years past.
Most years, the themes evolve gradually. This year, I felt like the ground was moving.
The theme nobody put on the agenda but ran underneath nearly every session was the pace of change. Specifically, what artificial intelligence is about to do to our work. And while I came away energized, I also came away with a healthy dose of " we (as in everyone) are not ready for how fast this is coming."
Here's what stayed with me.
AI Was the Undercurrent of the Whole Event
The session titled "AI, Anxiety, and Opportunity: What Performance Professionals Need to Know" was, predictably, one of the most sought-after sessions of the conference. The panel, which included practitioners from across the industry, did a nice job naming both sides of the coin: the anxiety of not knowing what your job looks like in five years, and the opportunity sitting right in front of us if we lean in.
Here's my honest read of the room, though. The mood was optimistic. Maybe a little too optimistic. There was a comfortable assumption that AI will mostly handle the tedious parts and leave the interesting work to us. Or that AI won’t take your job, someone that knows AI will. I'm not sure it'll be that tidy.
From what we're already seeing in our own work and across the firms we serve, the capabilities are advancing faster than most people can comprehend. The days where “our industry is just slower to adapt” are gone. Just last week, anthropic released Fable 5 and before it was shut down (temporarily?), we played around with it a little and its capabilities are dumbfounding. I don't think it will be long before these conferences look drastically different. Different sessions, different vendors, maybe a different sense of what the job even is. That's not a doom prediction. It's just a reason to pay closer attention than feels comfortable.
Separating Skill From Luck Just Got Harder and More Important
One of my favorite sessions was Michael Ervolini's "You Can't Find Skill in Returns: Distinguishing Performance From the Decisions That Generate Them." It's a deceptively simple premise: returns tell you what happened, not whether the manager was actually good. A great number can come from a great decision, or from luck. A bad number can hide genuine skill.
What I appreciate about PMAR is that the community keeps bringing fresh perspectives to this old, hard problem: how do we actually evaluate skill versus luck? It's a question that never fully resolves, and every year someone pushes the thinking forward.
It struck me that this question gets more important in an AI world, not less. As machines take over more of the calculation and even some of the decision-making, our value shifts toward judgment – knowing which decisions deserved credit, which results were noise, and what a number actually means in context. That's the kind of discernment a model can assist with but can't own. For more from Mr. Ervolini, here's a link to his latest book Skill vs. Luck.
The GIPS Challenges That Keep Coming Back
I'm biased here, but the "Common GIPS Challenges and How to Avoid Them" session was a highlight for us, in part because our own Matthew Deatherage, CFA, CIPM, was on the panel alongside peers from TSG, MassPRIM, and Strategic Investment Group.
What I always find striking about this topic is how consistent the challenges are. Firms pursuing compliance with the Global Investment Performance Standards (GIPS®)* tend to stumble on the same handful of issues year after year, and almost all of them are avoidable with the right foundation in place. That's a big part of why we do what we do at Longs Peak: helping firms get ahead of those pitfalls instead of discovering them during verification or, worse, during a regulatory exam.
Matt is a familiar face on these panels, and it's great to have our perspective in the mix. But the takeaway that stuck with me tied right back to the AI thread running through the whole conference.
Across several different panels, presenters talked about feeding the GIPS standards into their own AI models to churn out GIPS reports. And here's the thing, anyone can do that. You can drop the standards into a model in minutes. What a model can't do is provide critical judgment about how a principles-based framework should be applied to your specific facts and circumstances and whether those GIPS reports and statistics were calculated correctly. The GIPS standards aren't a checklist; they're a set of principles that require interpretation, and interpretation is exactly where experience earns its keep.
I'm not saying don't use AI to help build a framework. Use it. But like any model, if you don't really know what you're asking it to do, the output won't save you. Simply asking a model to "make my firm GIPS compliant" isn't going to make it so. At least not yet!
And there's one problem every performance professional already knows AI hasn't solved: data. As they say, garbage in, garbage out. Meaningful performance lives and dies on clean, well-organized data, and no software tool or AI model fixes messy inputs alone. At Longs Peak, we have spent the last 10 years working with clients to improve data quality through data integrity testing. For us, these AI models have only expanded what’s possible. We know one thing for sure: setting these tools up with the proper context (i.e., knowing what to look for) and then evaluating that context on an ongoing basis may turn out to be the most crucial piece of it all.
CFA Institute Is Listening on the CIPM
A session I didn't expect to find as interesting as I did was "CIPM Through the Practitioner Lens," facilitated by Rob Langrick of CFA Institute. Rather than simply presenting at the room, CFA Institute came to listen and gather candid feedback on the CIPM designation: where it's delivering value, where it's falling short, and how it should evolve to stay relevant to the work we actually do day to day.
The audience didn't hold back, and there were some genuinely thoughtful suggestions including how the code of ethics will evolve in this new AI era, some recommendations on reformatting the exam to break it into smaller chunks (going into greater detail on each) as well as adding a CIPM group within the CFA societies to encourage further connection. It was refreshing to see CFA Institute putting real energy behind a credential that so many of us have invested in and want to see grow in value. Given the pace of change in our field, willingness to adapt feels necessary. For anyone interested in contributing ideas to the CIPM, you can use this link to provide feedback.
A Quick Word on the Trivia
I'd be remiss not to mention that Performance Trivia got a much-needed upgrade this year. In past years, only a handful of contestants got to play while the rest of us watched (though in fairness, not all of us were clamoring for the spotlight). The new format this time allowed everyone to participate (without taking center stage), and it was a lot more fun for it. A small change, but it captured something I value about this community: it's competitive, but it's also genuinely collegial and prides itself on memorizing quirky names and vintage formulas.
Before PMAR Even Started: Women in Performance Measurement
For me, the week actually started the day before the conference, at the Women in Performance Measurement (WiPM) gathering. An event created just for the women in our industry. It's one of my favorite parts of this trip every year, and not only because the conversation is good. There's something energizing about being in a room full of women who do this work, comparing notes and reconnecting.
Fittingly, AI came up here too, though in a much more hands-on way than it would on the main stage. Practitioners shared real use cases, both personal and professional: the small ways AI is already saving them time day to day, and the bigger experiments they're running at their firms. It was practical, curious, and refreshingly free of hype.
We were also lucky to have a guest speaker, Lidia Arshavsky, who spoke on executive presence. She broke down how executive presence actually gets evaluated inside organizations (the signals people pick up on, often without realizing it) and offered practical recommendations for strengthening your own. It was the kind of talk that's useful no matter where you are in your career.
It was a great way to kick off PMAR, and an even better way to reconnect with women I only get to see a few times a year. Sometimes the most valuable part of a conference happens in these opportunities to network and reconnect within our niche performance community. A big thank you to TSG who donated the space for this event to take place and have done so for many years.
What AI Can't Take From Us
The conference's forward-looking sessions, including "Innovative Ways to Present Performance: Dashboards & Analytics," got me thinking. The tools are evolving so quickly and so much of the analysis, presentation, and reporting can now be automated. I am left wondering how long the traditional use of software in our space will last in its current form.
When the capabilities advancing fastest don’t always come from the established vendors, who benefits? My hope is that everyone does. That these tools level a playing field that used to tilt heavily toward the largest institutions, give smaller firms the ability to deliver high-caliber analytics previously out of reach, and push the whole field toward better solutions. That makes for a more competitive space and ultimately a clearer picture for investors to evaluate their options.
That's the optimistic case, and I believe it. But it only holds if we stay clear-eyed about where our own value comes from and that's the note I want to leave you on. The pace of change is a reason to focus, not to panic. The things that make us valuable are the things AI can't take: consciousness, judgment, and the human-in-the-loop accountability that clients ultimately trust. Machines will calculate faster and present prettier. They won't sit across the table from a client and take responsibility for what a number actually means.
So, by all means, get curious about the tools (Claude seemed to be most people’s favorite – mine as well). Experiment. Don't be the individual or firm that gets left behind. But anchor yourself in the part of this work that's irreplaceably human, because that's the part that was always the point.
See you at PMAR 2027. I suspect it'll look a little different.
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GIPS® is a registered trademark owned by CFA Institute. CFA Institute does not endorse or promote this organization, nor does it warrant the accuracy or quality of the content contained herein.
Understanding Tracking Error & R-Squared
March 27, 2026
15 min
Understanding Tracking Error & R-Squared
When evaluating the performance of an investment portfolio, it's essential to consider metrics that help measure the portfolio's consistency and alignment with its benchmark. Two critical metrics in this context are Tracking Error and R-Squared. These metrics provide insights into the portfolio's performance and risk characteristics. Let's explore what these metrics mean, how they are calculated, and their significance.
What is Tracking Error?
Tracking Error measures the deviation of a portfolio's returns from its benchmark returns. It indicates how closely a portfolio follows the benchmark to which it is compared. A lower tracking error suggests that the portfolio's returns are closely aligned with the benchmark, while a higher tracking error indicates greater deviation.
Tracking Error Formula
Tracking Error= standard deviation of (P-B)
where:
- P = Portfolio return
- B = Benchmark return
Annualized Tracking Error
When using monthly data, tracking error is annualized by multiplying the result by the square root of 12.
What is a Good Tracking Error?
A "good" tracking error depends on the investment strategy. For passive funds that aim to replicate a benchmark, a lower tracking error is desirable. For active funds that seek to outperform the benchmark, a higher tracking error might be acceptable, reflecting the manager's active bets.
What is R-Squared?
R-Squared (R²) measures the proportion of the portfolio's movements that can be explained by movements in its benchmark. It ranges from 0 to 100%, where a higher R-Squared indicates a greater correlation between the portfolio and its benchmark.
R-Squared Formula
R² = (Correlation of portfolio and benchmark returns)²
What is a Good R-Squared?
A higher R-Squared (closer to 100%) indicates that the portfolio's returns are highly correlated with the benchmark. For passive funds, a high R-Squared is preferred. For active funds, a lower R-Squared might indicate that the manager is taking independent positions relative to the benchmark.
How to Interpret Tracking Error and R-Squared
- Tracking Error: Indicates the consistency of the portfolio's returns relative to the benchmark. A lower tracking error is desirable for passive strategies, while active strategies might tolerate higher tracking errors.
- R-Squared: Shows the degree of correlation between the portfolio and benchmark returns. A high R-Squared suggests strong alignment, suitable for passive strategies, while a lower R-Squared might indicate active management.
While both Tracking Error and R-Squared are good measures to understand how closely a track record is managed to a benchmark, they should be analyzed with other available performance metrics. A high or low Tracking Error and R-Squared does not indicate if the performance was good or not. Therefore, using Tracking Error in combination with other metrics like Alpha or the Sharpe Ratio can help provide additional information on whether the strategy performed well while analyzing how closely it was managed to a benchmark. Any selected performance metric should also cover the same time periods as the calculated Tracking Error and R-Squared for the most relevant comparison.
Why are Tracking Error and R-Squared Important?
Both metrics are crucial for assessing portfolio performance and understanding the risk-return profile. They help investors gauge how well a portfolio is managed relative to its benchmark and assess the effectiveness of active versus passive management strategies.
Conclusion
Tracking Error and R-Squared are essential tools for evaluating portfolio performance. Understanding these metrics can help investors make informed decisions about their investment strategies and better manage their portfolios.
For more information on performance metrics and investment strategies, feel free to contact us or explore our other resources on investment performance.
A Practical Guide to Net vs. Gross Performance
February 1, 2026
15 min
Why “Net” Is Not a One-Size-Fits-All Answer
If you’ve worked in the investment industry, you’ve probably heard some version of this question:
“Should we show net or gross performance—or both?”
On the surface, the answer seems straight forward. The rules tell us what’s required. Compliance boxes get checked. End of story.
But in practice, presenting net and gross performance is rarely that simple.
How you calculate it, how you present it, and how you disclose it can materially change how investors interpret your results. This article goes beyond the rulebook to explore thepractical considerations firms face when deciding how to present net and gross returns in a manner that is clear, helpful, and in compliance with requirements.
Let’s Start with the Basics (Briefly)
At a high level, for separate account strategies:
- Gross performance reflects returns before investment management fees
- Net performance reflects returns after investment management fees have been deducted
Both gross and net performance are typically net of transaction costs, but gross of administrative fees and expenses. When dealing with pooled funds, net performance is also reduced by administrative fees and expenses, but here we are focused on separate account strategies, typically marketed as composite performance.
Simple enough. But that definition alone doesn’t tell the full story—and it’s where many misunderstandings begin.
Why Net Performance Is the Investor’s Reality
From an investor’s perspective, net performance is what actually matters. It represents the return they keep after paying the manager for active management.
That’s why modern regulations and best practices increasingly emphasize net returns. Investors don’t experience gross returns. They experience net outcomes.
And let’s be honest: if an investor chooses an active manager instead of a low-cost index fund or ETF tracking the same benchmark, the expectation is that the active approach should deliver something extra—after fees. Otherwise, it becomes difficult to justify paying for that active management.
Why Gross Performance Still Has a Role
If net returns are what investors actually receive, why do firms still talk about gross performance at all?
Because gross performance tells a different, but complementary, story: what the strategy is capable of before fees, and what investors are paying for that capability.
The gap between gross and net returns represents the cost of active management. Put differently, it answers a question investors are implicitly asking:
How much return am I giving up in exchange for this manager’s expertise?
Viewed this way, gross returns help investors assess:
- Whether the strategy is adding value before fees
- How much of the performance is driven by skill: security selection, asset allocation or portfolio construction
- Whether fees are the primary drag—or whether the strategy itself is struggling
When gross and net returns are shown together, they create transparency around both skill and cost. When shown without context, they can easily obscure the economic tradeoff.
Gross-of-fee returns are also most important when marketing to institutional investors that have the power to negotiate the fee they will pay and know that they will likely pay a fee lower than most of your clients have paid in the past. Their detailed analysis can more accurately be done starting with your gross-of-fee returns and adjusting for the fee they expect to negotiate rather than using net-of-fee returns that have been charged historically.
The Real-World Gray Areas Firms Struggle With
How to Present Gross Returns
Gross returns are pretty straightforward. They are typically calculated before investment management or advisory fees and usually include transaction costs such as commissions and spreads.
For firms that comply with the GIPS® Standards, things can get more nuanced—particularly for bundled fee arrangements. In those cases, firms must make reasonable allocations to separate transaction costs from the bundled fee. But, if that separation cannot be done reliably, gross returns must be shown after removing the entire bundled fee. [1]
Once you move from gross to net returns, however, the conversation becomes less straightforward. We’ve had managers question, “why show net performance at all?” This is especially the case when fees vary across clients or historical fees no longer reflect what an investor would pay today. Others complain that the “benchmark isn’t net-of-fees,” making net-of-fee comparisons inherently imperfect. These concerns highlight why presenting net returns isn’t just a mechanical exercise. In the sections that follow, we’ll unpack these challenges and walk through how to present net-of-fee performance in a way that remains meaningful, transparent, and fit for its intended audience.
How to Present Net Returns
This is where judgment and documentation matters most.
Not all “net” returns are created equal. Even under the SEC Marketing Rule, there is no single mandated definition of net performance—only a requirement that net performance be presented. Under the GIPS Standards, net-of-fee returns must be reduced by investment management fees.
In practice, firms may deduct:
- Advisory fees (asset-based investment management fees)
- Performance-based fees
- Custody fees
- Transaction costs
Two net-return series can look comparable on the surface while reflecting very different assumptions underneath. This lack of transparency is one of the main reasons institutional investors often require managers to be GIPS compliant—it simplifies comparison by requiring consistency in the assumptions used and how they are presented or additional disclosure when more fees are included in the calculation than what is required.
And context matters. A higher fee may be perfectly reasonable if it reflects broader services such as tax or financial planning, holistic portfolio construction, or access to specialized strategies. The problem isn’t the fee itself, it’s failing to use a fee scenario that is relevant to the user of the report.
Deciding Between Actual vs Model Fees
The next hurdle is deciding whether to use actual fees or a model fee when calculating net returns. Historically, firms most often relied on actual fees, viewing them as the best representation of what clients actually experienced. But that approach raises an important question: are those historical fees still relevant to what an investor would pay today? If the answer is no, a model fee may provide a more representative picture of current expected outcomes. Under the SEC marketing rule, there are cases where firms are required to use a model fee when the anticipated fee is higher than actual fees charged.
This consideration becomes even more important for strategies or composites that include accounts paying little or no fee at all. While the GIPS Standards and the SEC Marketing Rule are not perfectly aligned on this topic, they agree in principle—net performance should be meaningful, not misleading, and should reflect what an actual fee-paying investor should reasonably expect to pay. Thus, many firms opt to present model fee performance to avoid violating the marketing rule’s general prohibitions. [2]
Additional SEC guidance published on Jan 15, 2026 on the Use of Model Fees reinforced that the decision to use model vs actual fees is context-dependent. While the marketing rule allows net performance to be calculated using either actual or model fees, there are cases where the use of actual fees may be misleading. The SEC emphasized flexibility and that while both fee types are allowed, what’s appropriate depends on the facts and circumstances of the situation, including the clarity of disclosures and how fee assumptions are explained.
Which Model Fee Should Be Used?
Most firms offer multiple fee structures, typically based on account size, but sometimes also on investor type (institutional versus retail clients). That variability makes fee selection a key decision when presenting net performance.
If you plan to use a single performance document for broad or mass marketing, best practice—and what the SEC Marketing Rule effectively requires—is to calculate net returns using the highest anticipated fee that could reasonably apply to the intended audience. This helps ensure the presentation is not misleading by overstating what an investor might take home.
A common pushback is: “But the highest fee isn’t relevant to this type of investor.” And that may be true. In those cases, firms have a few defensible options:
- Create separate versions of the presentation tailored to different investor types, or
- Present multiple fee tiers within the same document, clearly explaining what each tier represents
Either approach can work—but only if disclosures are explicit and easy to understand. When multiple fee structures are shown, clarity isn’t optional; it’s essential.
In practice, many firms maintain separate retail and institutional versions of factsheets or pitchbooks. That approach is perfectly reasonable, but it comes with operational risk. If this becomes standard practice, firms need strong internal controls to ensure the right presentation reaches the right audience. That means:
- Clear internal policies
- Consistent naming and version control
- Training marketing and sales teams on when each version may be used
This often involves an overlap of both marketing and compliance to get it right because getting the fee right is only part of the equation. Making sure the presentation is used appropriately is just as important to ensuring net performance remains meaningful, compliant, and credible.
Which Statistics Can Be Shown Gross-of-Fees?
Since the introduction of the SEC Marketing Rule, there has been significant debate about whether all statistics must be presented net-of-fees—or whether certain metrics can still be shown gross-of-fees. Helpful clarity arrived in an SEC FAQ released on March 19, 2025, which confirmed that not all portfolio characteristics need to be presented net-of-fees. The examples cited included risk statistics such as the Sharpe and Sortino ratios, attribution results, and similar metrics that are often calculated gross-of-fees to avoid the “noise” introduced by fee deductions.
The staff acknowledged that presenting some of these characteristics net-of-fees may be impractical or even misleading. As long as firms prominently present the portfolio’s total gross and net performance incompliance with the rule (i.e., prescribed time periods 1, 5, 10 years),clearly label these characteristics as gross, and explain how they are calculated, the SEC indicated it would generally not recommend enforcement action.
Bringing it all Together
On paper, presenting net and gross performance should be a straight forward exercise.
In reality, layers of regulation, evolving expectations, and heightened scrutiny have made it feel far more complicated than it needs to be. But complexity doesn’t have to lead to confusion.
When firms are clear about:
- Who they are communicating with,
- What that audience expects,
- What the performance is intended to represent, and
- Why certain assumptions were chosen
…the decisions around what gets presented become far more manageable.
Net returns aren’t about finding a single “correct” number. They’re about telling an honest, well-documented story. And when that story is clear, investors don’t just understand the performance—they trust it.
[1] 2020 GIPS® Standards for Firms, Section 2: Input Data and Calculation Methodology(gross-of-fees returns and treatment of transaction costs, including bundled fees).
[2] See SEC Marketing Rule 2 026(4)-1(a) footnote 590 as well as the SEC updated FAQ from January 15, 2026. Available at: https://www.sec.gov/rules-regulations/staff-guidance/division-investment-management-frequently-asked-questions/marketing-compliance-frequently-asked-questions
Why Calculating Performance Isn’t the Same as Communicating It
January 20, 2026
15 min
In most investment firms, performance calculation is treated like a math problem: get the numbers right, double-check the formulas, and move on. And to be clear—that part matters. A lot.
But here’s the truth many firms eventually discover: perfectly calculated performance can still be poorly communicated.
And when that happens, clients don’t gain confidence. Consultants don’t “get” the strategy. Prospects walk away unconvinced. Not because the returns were wrong—but because the story was missing.
Calculation Is Technical. Communication Is Human.
Performance calculation is about precision. Performance communication is about understanding.
The two overlap, but they are not the same skill set.
You can calculate a composite’s time-weighted return flawlessly, in line with the Global Investment Performance Standards (GIPS®), using best-in-class methodologies. Yet if the only thing your audience walks away with is “we beat the benchmark,” you’ve left most of the value on the table.
This gap shows up all the time:
- A client sees strong long-term returns but fixates on one bad quarter.
- A consultant compares two managers with similar returns and can’t tell what truly differentiates them.
- A prospect asks, “But how did you generate these results?”—and the answer is a wall of statistics.
The math is necessary. It’s just not sufficient.
Returns Answer What. Clients Care About Why.
Returns tell us what happened. Clients want to know why it happened—and whether it’s likely to happen again.
That’s where communication comes in. Good performance communication connects returns to:
- The investment philosophy
- The decision-making process
- The risks taken (and avoided)
- The type of prospect the strategy is designed for
This is exactly why performance evaluation doesn’t stop at returns in the CFA Institute’s CIPM curriculum. Measurement, attribution, and appraisal are distinct steps fora reason—each adds context that raw performance alone cannot provide. Without that context, returns become just numbers on a page.
The Role of Standards: Necessary, Not Narrative
The GIPS Standards exist to ensure performance is fairly represented and fully disclosed. They do an excellent job of standardizing how performance is calculated and what must be presented. But GIPS compliance doesn’t automatically make performance meaningful to the reader.
A GIPS Report answers questions like:
- What was the annual return of the composite?
- What was the annual return of the composite’s benchmark?
- How volatile was the strategy compared to the benchmark?
It does not answer:
- Why did this strategy struggle in down markets?
- What risks did the manager consciously take?
- How should an allocator think about using this strategy in a broader portfolio?
That’s not a flaw in the standards, it’s a reminder that communication sits on top of compliance, not inside it.
Risk Statistics: Where Stories Start (or Die)
One of the most common communication missteps is overloading clients with risk statistics without explaining what they actually mean or how they can be used to assess the active decisions made in your investment process.
Sharpe ratios, capture ratios, alpha, beta—they’re powerful information. But without interpretation, they’re just numbers.
For example:
- A downside capture ratio below 100% isn’t impressive on its own.
- It becomes compelling when you explain how intentionally implemented downside protection was achieved and what trade-offs were accepted in strong up-markets.
This is where performance communication turns data into insight—connecting risk statistics back to portfolio construction and decision-making. Too often, managers select statistics because they look good or because they’ve seen them used elsewhere, rather than because they align with their investment process and demonstrate how their active decisions add value. The most effective communicators use risk statistics intentionally, in the context of what they are trying to deliver to the investor.
We often see firms change the statistics show Your most powerful story may come from when your statistics show you’ve missed the mark. Explaining why and how you are correcting course demonstrates discipline, self-awareness and control.
Know Your Audience Before You Tell the Story
Before you dive into risk statistics, every manager should be asking themselves about their audience. This is where performance communication becomes strategic. Who are you actually talking to? The right performance story depends entirely on your target audience.
Institutional Prospects
Institutional clients and consultants often expect:
- Detailed risk statistics
- Benchmark-relative analysis
- Attribution and metrics that demonstrate consistency
- Clear articulation of where the strategy fits in a portfolio
They want to understand process, discipline, and risk control. Performance data must be presented with precision and context –grounded in methodology, repeatability and portfolio role. Often, GIPS compliance is a must. Speaking their language builds credibility and demonstrates that you respect the rigor of their decision-making process. It shows that you understand how they evaluate managers and that you are prepared to stand behind your process.
Retail or High-Net-Worth Individuals
Many individual investors don’t care about alpha or capture ratios in isolation. What they really want to know is:
- Will this help me retire comfortably?
- Can I afford that second home?
- How confident should I feel during market downturns?
For this audience, the same performance data must be framed differently—around goals, outcomes, and peace of mind. Sharing how you track and report on these goals in your communication goes a long way in building trust. It signals that you are committed to their goals and will hold yourself accountable to them. It reassures them that you are not just managing money, you’re protecting the lifestyle they are building.
Keep in mind that cultural differences also shape expectations. For example, US-based investors are primarily results oriented, while investors in Japan often expect deeper transparency into the process and inputs, wanting to understand and validate how those results were achieved.
Same Numbers. Different Story.
The mistake many firms make is assuming one performance narrative works for everyone. It doesn’t. Effective communication adapts:
- The statistics you emphasize
- The language you use
- The level of detail you provide
- The context you wrap around the results
The goal isn’t to simplify the truth, it’s to translate it to ensure it resonates with the person on the other side of the table.
The Best Performance Reports Tell a Coherent Story
Strong performance communication does three things well:
- It sets expectations
Before showing numbers, it reminds the reader what the strategy is designed to do—and just as importantly, what it’s not designed to do. - It explains outcomes
Attribution, risk metrics, and market context are used selectively to explain results, not overwhelm the reader. - It reinforces discipline
Good communication shows consistency between philosophy, process, and performance—especially during periods of underperformance.
This doesn’t mean dumbing anything down. It means respecting the audience enough to guide them through the data.
Calculation Builds Credibility. Communication Builds Confidence.
Performance calculation earns you a seat at the table.
Performance communication earns trust.
Firms that master both don’t just report results—they help clients understand them, evaluate them, and believe in them.
In an industry where numbers are everywhere, clarity is often the true differentiator.
Recommended Post
Most managers assume that losing an allocation comes down to returns. Underperform the benchmark, underperform peers, and the mandate goes elsewhere. That happens, but it's not usually the reason a manager gets cut from a search after the numbers already looked competitive.
More often, it's something in how the performance was presented that made an allocator hesitate. A number that didn't match across two documents. A risk statistic nobody could explain. A question in due diligence that the manager couldn't answer cleanly. None of these are calculation errors. They're trust problems, and trust is what allocators are ultimately seeking when they write a check.
Here are the performance problems we see that cost managers allocations most often, and none of them start with the returns themselves.
The Numbers Don't Match Across Documents
An allocator pulls up your factsheet, your pitchbook, and your GIPS® Composite Report, and the composite's five-year return isn't quite the same in all three. Maybe it's a rounding difference, or the factsheet reflects a different "as of" date. The allocator doesn't know that, and they aren't going to assume the best. Inconsistency reads as carelessness, and carelessness in performance reporting raises an obvious question: what else isn't being checked?
This is why we push firms to treat marketing and GIPS compliance as one coordinated process rather than two departments working from different source files. Every document that leaves the building should trace back to the same underlying data.
This matters even more now that due diligence itself is being automated. Operational due diligence teams and consultants are increasingly running AI tools that cross-check pitchbooks, factsheets, DDQs, and regulatory filings against each other, flagging contradictions that used to slip through manual review. A rounding difference or a stale figure that a person might have missed a few years ago is exactly the kind of inconsistency these tools are built to catch instantly. Clean, consistent marketing materials aren't just good practice anymore — they're what it takes to pass a review that may happen before a person ever looks at your numbers.
Performance That Looks Selected, Not Reported
Showing your best-performing account, your best-performing period, or a composite with an unusually small number of accounts invites the question every allocator is trained to ask: what am I not being shown? Due diligence teams know that everyone can't be top quartile. The SEC Marketing Rule's anti-cherry-picking provisions exist because this pattern is common enough that regulators built rules around it, and sophisticated allocators are watching for it. If your performance can be read as overly flattering rather than representative, assume a diligence team will read it that way.
Wanting to lead with your best numbers is an understandable impulse. But diligence teams are trained specifically to spot it, and selective disclosure, even when every number in it is accurate, tends to read as a bigger warning sign than an honest, complete track record would. The stronger story is discipline: the periods where you held to your stated mandate and didn't deviate even while returns lagged. That's a harder story to tell than "we outperformed," but it's the one that actually holds up, because it shows you didn't drift toward whatever was working elsewhere just to keep pace. Chasing returns outside your stated process isn't skill, it's strategy drift, and allocators are trained to spot that just as readily as cherry-picked out performance.
Our advice: resist the instinct to lead with your best examples, and show the scenarios that build trust instead. We recommend showing the ones that demonstrate you stuck to your stated mandate, policies, and procedures, especially when the outcome wasn't your best quarter. Discipline under pressure is a more durable credential than a strong one-off time period, and it's the kind of evidence that holds up long after that number is forgotten.
Statistics You Show But Can't Explain
A page full of risk statistics doesn't build confidence on its own. It invites a follow-up question, and if the manager can't explain what a downside capture ratio of 85% says about the decisions actually made in the portfolio, the statistic becomes a liability instead of an asset. Allocators aren't just checking whether the numbers are favorable. They're checking whether the manager understands their own portfolio well enough to explain it. Statistics presented without interpretation signal that the second answer is “no.”
Likewise, a page of portfolio characteristics that have nothing to do with how the strategy is actually run are not doing you any favors. If you're not making decisions at the sector level, a sector breakdown doesn't tell an allocator anything about your process. If you don't manage individual position sizing, a top-ten holdings list is not adding value.
Your factsheet should be a roadmap for the conversation you want to have, not a checklist of everything other managers include. Every number on it should be something you can explain: how it got there, what decision it reflects, and what it says about how you manage money. A statistic that's only there because everyone else shows it likely isn't helping you if it doesn’t demonstrate active decision making. It's inviting a question you may not have a good answer to.
It's the same logic as a good resume. One padded with every certification, hobby, and unrelated past role doesn't read as impressive, it reads as overwhelming and maybe irrelevant, and it makes the reader work harder to find what actually matters to the job at hand. A factsheet works the same way. The strongest ones include only what's relevant to the case being made and make it easy to connect every line back to it.
No One Can Explain Why a Decision Was Made
This is the one that costs managers the most, and it's rarely about the numbers at all. An allocator asks why a composite was redefined, why a benchmark changed, or why a particular account was excluded, and the answer is a shrug or "that's how we've always done it." Undocumented decisions create the impression that performance is being managed reactively rather than governed intentionally. Firms that can point to a clear, contemporaneous record of why a judgment call was made close that conversation quickly. Firms that can't do this will leave the allocator wondering what other judgment calls haven't been documented either.
The Common Thread
None of these problems are really about whether the strategy performed well. It comes down to whether the story behind the numbers holds up consistently under scrutiny. Allocators aren't just buying returns. They're also buying confidence that what they're being shown today will still be true, and still explainable, a year from now.
The fix isn't more disclosure for its own sake. It's making sure everything across your performance reporting tells the same, well-documented story before an allocator ever has the chance to ask why it doesn't.
GIPS® is a registered trademark owned by CFA Institute. CFA Institute does not endorse or promote this organization, nor does it warrant the accuracy or quality of the content contained herein.
There is a common assumption among boutique investment managers that the Global Investment Performance Standards (GIPS®) are built for the largest firms in the industry — that compliance is something you pursue once you've reached a certain scale, a certain client type, or a certain level of institutional credibility.
That assumption is understandable. And it is costing firms real opportunities.
The GIPS standards have no AUM threshold to get started. There is no minimum number of clients or composites required before a firm can claim compliance. And increasingly, the institutional marketplace is not waiting for firms to reach some undefined moment of readiness before asking for it. If you are newer to the GIPS standards and want a foundation for what they are and why firms pursue them, start with our post What Are the GIPS Standards?
The Market Has Already Decided
The gatekeepers of institutional capital such as consultants, outsourced CIO platforms, model delivery networks, and institutional allocators, have been quietly raising the bar on performance reporting standards for years. GIPS compliance has shifted from a differentiator to a baseline expectation in many of these channels.
According to eVestment, two out of three manager searches conducted by investors and consultants on their platform exclude firms that are not GIPS compliant. That means boutique managers without a compliance claim are not being passed over, they are simply not being seen. As we explored in From Compliance to Growth, GIPS compliance has effectively become the price of admission for firms seeking to expand into institutional channels.
The question is not whether your firm will eventually need it. For most managers with institutional ambitions, the answer to that question is already yes. The real question is when you choose to pursue it, and whether you make that choice on your own terms or in response to a mandate you cannot afford to lose.
What Compliance Actually Builds Inside Your Firm
The benefits most managers focus on are external. Things like the credibility signal, the access to channels, the due diligence box that gets checked. Those benefits are real. But some of the most meaningful returns from GIPS compliance are internal.
Implementing the GIPS standards requires firms to formalize processes that often exist informally. Composite definitions. Discretion criteria. Benchmark selection rationale. Fee policies. Error correction procedures. For many boutique managers, the implementation process is the first time these decisions have been documented and applied consistently across the firm.
That discipline matters beyond GIPS compliance itself. A firm with clean, documented performance infrastructure is better positioned for regulatory examinations, investor due diligence, and operational due diligence reviews. It demonstrates to sophisticated allocators that the firm is run with the same rigor they apply to their own oversight responsibilities. And for firms that are not primarily focused on institutional distribution, this operational foundation has standalone value, the kind of infrastructure that supports sound governance regardless of who is asking. For more on what a well-governed GIPS compliance program looks like once it is in place, see What Good GIPS Compliance Governance Looks Like in Practice.
The Single Best Argument for Starting Now
Here is the point that does not get made often enough: the smaller your firm and the shorter your track record, the easier it is to become compliant. That ratio flips quickly as you grow.
Retroactively constructing composites across a large number of separate accounts is genuinely difficult work, particularly when no framework existed at the time to assign accounts to composites at inception, or to move accounts between composites as investment objectives changed, client restrictions were added or removed, or mandates evolved. Working through that history portfolio by portfolio, period by period, requires both detailed documentation and sound judgment. It is one of the most time-consuming phases of any GIPS compliance implementation, and the complexity compounds with every account and every year of history added.
A firm with 30 separate accounts and a two-year track record faces a very different implementation project than the same firm a few years later with 500 accounts and a five-year track record. The strategy, the clients, and the investment process may be nearly identical, but the administrative burden of reconstructing historical composite membership correctly is not.
The firms that find implementation most manageable are the ones that started before the project grew into something unwieldy. The firms that find it most painful are the ones that waited until an institutional prospect made it urgent.
What if you are not ready to commit to full compliance yet?
That is a legitimate position. But there is a practical middle path worth considering: even if a firm does not want to claim compliance with the GIPS standards today, building out the composite structure and creating policies and procedures for managing those composites now is a worthwhile investment. That framework does not require a formal compliance claim to be useful. Additionally, it can be carried directly into a full GIPS compliance program when the time is right, dramatically reducing the effort required at that stage.
The Real Costs
Becoming GIPS compliant requires real work, and it is worth being direct about what that entails. At a high level, implementation comes down to four phases: defining the firm, building a GIPS standards policies and procedures manual, constructing composites and calculating performance, and creating GIPS Reports with ongoing monitoring controls. We walk through each phase in detail in A Practical Framework for Implementing the GIPS Standards.
In terms of ongoing commitment, firms should expect monthly composite management, annual GIPS Report updates, periodic policies and procedures reviews, and distribution tracking. For a lean team, owning all of this internally is often not realistic. The good news is that outsourcing to a GIPS compliance consultant is a well-established path for boutique managers and one that many firms in our client base have taken successfully. The total cost of compliance for a focused, well-organized firm is frequently lower than managers expect, particularly when implementation is approached while the firm's history and account universe are still manageable.
Is This the Right Time for Your Firm?
Not every firm is at the same point in this decision. Managers with the strongest case for pursuing GIPS compliance now include:
- Firms actively pursuing institutional mandates or seeking coverage from investment consultants
- Managers on model delivery platforms or building toward that distribution channel
- Firms planning meaningful growth over the next two to three years
- Any manager whose clients or prospects have already raised the question
- Firms that simply want to build a best-in-class performance reporting foundation, regardless of where their distribution strategy stands today
The case is lower urgency for firms focused exclusively on high-net-worth or retail clients with no near-term institutional ambitions; however, there is still value in building a sound performance reporting structure, and the sooner it is established, the easier the work will be.
On Verification: You Can Wait
Verification is independent, voluntary, and valuable. It is also not required to claim compliance with the GIPS standards, and for cost-conscious boutiques, it is a reasonable place to exercise flexibility.
A firm can become GIPS compliant today and gain all the operational benefits and the ability to make the compliance claim and defer pursuing verification until there is specific demand for it. When an institutional prospect or consultant asks whether the firm is verified, that is the right moment to add it. The compliance foundation built now makes that future engagement faster and less disruptive. For a detailed walkthrough of what the verification process involves, see our series How to Survive a GIPS Verification.
Verification is worth having. It just does not need to happen on day one.
The Longer You Wait, The Heavier the Lift
GIPS compliance is not an initiative that gets easier with time. Every year a firm grows its account base, extends its track record, and adds complexity to its operations without a compliance framework in place is another year of history that will eventually need to be organized, documented, and reconstructed.
The managers who find implementation most straight forward are not the ones with the most resources. They are the ones who started early enough that the project was still proportionate to the size of the task.
If your firm is headed toward institutional distribution (most boutique managers we work with are), the best time to build this infrastructure is before you need it. The second best time is now.
Longs Peak Advisory Services specializes in GIPS compliance and investment performance consulting for investment managers and asset owners. We have helped over 250 firms implement and maintain compliance with the GIPS standards. If you are evaluating whether now is the right time for your firm, we would be glad to talk through it. Reach out athello@longspeakadvisory.com.
GIPS® is a registered trademark owned by CFA Institute. CFA Institute does not endorse or promote this organization, nor does it warrant the accuracy or quality of the content contained herein.
Every Spring, the performance measurement community gathers for PMAR: The Performance Measurement, Attribution & Risk Conference, hosted by TSG. This year marked the twenty-fourth annual, and I left thinking about it differently than I have in years past.
Most years, the themes evolve gradually. This year, I felt like the ground was moving.
The theme nobody put on the agenda but ran underneath nearly every session was the pace of change. Specifically, what artificial intelligence is about to do to our work. And while I came away energized, I also came away with a healthy dose of " we (as in everyone) are not ready for how fast this is coming."
Here's what stayed with me.
AI Was the Undercurrent of the Whole Event
The session titled "AI, Anxiety, and Opportunity: What Performance Professionals Need to Know" was, predictably, one of the most sought-after sessions of the conference. The panel, which included practitioners from across the industry, did a nice job naming both sides of the coin: the anxiety of not knowing what your job looks like in five years, and the opportunity sitting right in front of us if we lean in.
Here's my honest read of the room, though. The mood was optimistic. Maybe a little too optimistic. There was a comfortable assumption that AI will mostly handle the tedious parts and leave the interesting work to us. Or that AI won’t take your job, someone that knows AI will. I'm not sure it'll be that tidy.
From what we're already seeing in our own work and across the firms we serve, the capabilities are advancing faster than most people can comprehend. The days where “our industry is just slower to adapt” are gone. Just last week, anthropic released Fable 5 and before it was shut down (temporarily?), we played around with it a little and its capabilities are dumbfounding. I don't think it will be long before these conferences look drastically different. Different sessions, different vendors, maybe a different sense of what the job even is. That's not a doom prediction. It's just a reason to pay closer attention than feels comfortable.
Separating Skill From Luck Just Got Harder and More Important
One of my favorite sessions was Michael Ervolini's "You Can't Find Skill in Returns: Distinguishing Performance From the Decisions That Generate Them." It's a deceptively simple premise: returns tell you what happened, not whether the manager was actually good. A great number can come from a great decision, or from luck. A bad number can hide genuine skill.
What I appreciate about PMAR is that the community keeps bringing fresh perspectives to this old, hard problem: how do we actually evaluate skill versus luck? It's a question that never fully resolves, and every year someone pushes the thinking forward.
It struck me that this question gets more important in an AI world, not less. As machines take over more of the calculation and even some of the decision-making, our value shifts toward judgment – knowing which decisions deserved credit, which results were noise, and what a number actually means in context. That's the kind of discernment a model can assist with but can't own. For more from Mr. Ervolini, here's a link to his latest book Skill vs. Luck.
The GIPS Challenges That Keep Coming Back
I'm biased here, but the "Common GIPS Challenges and How to Avoid Them" session was a highlight for us, in part because our own Matthew Deatherage, CFA, CIPM, was on the panel alongside peers from TSG, MassPRIM, and Strategic Investment Group.
What I always find striking about this topic is how consistent the challenges are. Firms pursuing compliance with the Global Investment Performance Standards (GIPS®)* tend to stumble on the same handful of issues year after year, and almost all of them are avoidable with the right foundation in place. That's a big part of why we do what we do at Longs Peak: helping firms get ahead of those pitfalls instead of discovering them during verification or, worse, during a regulatory exam.
Matt is a familiar face on these panels, and it's great to have our perspective in the mix. But the takeaway that stuck with me tied right back to the AI thread running through the whole conference.
Across several different panels, presenters talked about feeding the GIPS standards into their own AI models to churn out GIPS reports. And here's the thing, anyone can do that. You can drop the standards into a model in minutes. What a model can't do is provide critical judgment about how a principles-based framework should be applied to your specific facts and circumstances and whether those GIPS reports and statistics were calculated correctly. The GIPS standards aren't a checklist; they're a set of principles that require interpretation, and interpretation is exactly where experience earns its keep.
I'm not saying don't use AI to help build a framework. Use it. But like any model, if you don't really know what you're asking it to do, the output won't save you. Simply asking a model to "make my firm GIPS compliant" isn't going to make it so. At least not yet!
And there's one problem every performance professional already knows AI hasn't solved: data. As they say, garbage in, garbage out. Meaningful performance lives and dies on clean, well-organized data, and no software tool or AI model fixes messy inputs alone. At Longs Peak, we have spent the last 10 years working with clients to improve data quality through data integrity testing. For us, these AI models have only expanded what’s possible. We know one thing for sure: setting these tools up with the proper context (i.e., knowing what to look for) and then evaluating that context on an ongoing basis may turn out to be the most crucial piece of it all.
CFA Institute Is Listening on the CIPM
A session I didn't expect to find as interesting as I did was "CIPM Through the Practitioner Lens," facilitated by Rob Langrick of CFA Institute. Rather than simply presenting at the room, CFA Institute came to listen and gather candid feedback on the CIPM designation: where it's delivering value, where it's falling short, and how it should evolve to stay relevant to the work we actually do day to day.
The audience didn't hold back, and there were some genuinely thoughtful suggestions including how the code of ethics will evolve in this new AI era, some recommendations on reformatting the exam to break it into smaller chunks (going into greater detail on each) as well as adding a CIPM group within the CFA societies to encourage further connection. It was refreshing to see CFA Institute putting real energy behind a credential that so many of us have invested in and want to see grow in value. Given the pace of change in our field, willingness to adapt feels necessary. For anyone interested in contributing ideas to the CIPM, you can use this link to provide feedback.
A Quick Word on the Trivia
I'd be remiss not to mention that Performance Trivia got a much-needed upgrade this year. In past years, only a handful of contestants got to play while the rest of us watched (though in fairness, not all of us were clamoring for the spotlight). The new format this time allowed everyone to participate (without taking center stage), and it was a lot more fun for it. A small change, but it captured something I value about this community: it's competitive, but it's also genuinely collegial and prides itself on memorizing quirky names and vintage formulas.
Before PMAR Even Started: Women in Performance Measurement
For me, the week actually started the day before the conference, at the Women in Performance Measurement (WiPM) gathering. An event created just for the women in our industry. It's one of my favorite parts of this trip every year, and not only because the conversation is good. There's something energizing about being in a room full of women who do this work, comparing notes and reconnecting.
Fittingly, AI came up here too, though in a much more hands-on way than it would on the main stage. Practitioners shared real use cases, both personal and professional: the small ways AI is already saving them time day to day, and the bigger experiments they're running at their firms. It was practical, curious, and refreshingly free of hype.
We were also lucky to have a guest speaker, Lidia Arshavsky, who spoke on executive presence. She broke down how executive presence actually gets evaluated inside organizations (the signals people pick up on, often without realizing it) and offered practical recommendations for strengthening your own. It was the kind of talk that's useful no matter where you are in your career.
It was a great way to kick off PMAR, and an even better way to reconnect with women I only get to see a few times a year. Sometimes the most valuable part of a conference happens in these opportunities to network and reconnect within our niche performance community. A big thank you to TSG who donated the space for this event to take place and have done so for many years.
What AI Can't Take From Us
The conference's forward-looking sessions, including "Innovative Ways to Present Performance: Dashboards & Analytics," got me thinking. The tools are evolving so quickly and so much of the analysis, presentation, and reporting can now be automated. I am left wondering how long the traditional use of software in our space will last in its current form.
When the capabilities advancing fastest don’t always come from the established vendors, who benefits? My hope is that everyone does. That these tools level a playing field that used to tilt heavily toward the largest institutions, give smaller firms the ability to deliver high-caliber analytics previously out of reach, and push the whole field toward better solutions. That makes for a more competitive space and ultimately a clearer picture for investors to evaluate their options.
That's the optimistic case, and I believe it. But it only holds if we stay clear-eyed about where our own value comes from and that's the note I want to leave you on. The pace of change is a reason to focus, not to panic. The things that make us valuable are the things AI can't take: consciousness, judgment, and the human-in-the-loop accountability that clients ultimately trust. Machines will calculate faster and present prettier. They won't sit across the table from a client and take responsibility for what a number actually means.
So, by all means, get curious about the tools (Claude seemed to be most people’s favorite – mine as well). Experiment. Don't be the individual or firm that gets left behind. But anchor yourself in the part of this work that's irreplaceably human, because that's the part that was always the point.
See you at PMAR 2027. I suspect it'll look a little different.
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