The Quest to Justify Active Fees

Every professional investor with a discretionary investment approach in public securities knows that getting away with active management fees is not as easy as it used to be, especially if you’re a hedge fund. While the reasons may seem obvious to all, thus not worthy of a blog post, the path that brought us here is less obvious, and I'll even suggest, illuminating. In this article, we walk through a timeline of developing challenges for the traditional 2 and 20, starting 30 years ago. While doing so, we offer managers a few actionable opportunities to defend their value-add (and fees).

1980-1989: Solid Track Records

When hedge funds came to the market in sizable flocks in the mid-80s, they were an exclusive club where performance was a guarantee, the opportunities for alpha plentiful, and investor terms very selective—in fact, prohibitive for the masses. No one questioned multi-million dollars minimums or multi-year lockups, even for investments in very liquid public equities.

After a while, the industry’s lucrative terms attracted new entrants, many of whom did not manage to replicate the returns posted by the industry pioneers. As a protection against mediocre managers portraying themselves as the new Soros, investors started to demand track record information. Back then, believe it or not, the famous disclaimer, “Past returns are not indicative of future performance,” was neither a statistically proven fact nor a compliance requirement. We now know that past returns not only do not have predictive power, but they are frankly a dangerous tool for inferring anything. Back in the jungle of new entrants though, they allowed investors to set a reasonable hurdle to clear.  

The obsession with track record information did not stop there; it gained momentum through the years and is still alive today, in somewhat perverse ways. Investors started to calculate dozens of derived metrics (% Positive / Negative Months, Average Up / Down Month Ratio, Maximum Run-Ups vs. Drawdowns, and duration thereof) and started pontificating about the relative superiority of one versus the other. We have bad news for you: none of these actually have any predictive power. But by all means, you can certainly impress your friends by reciting such figures at industry panels.  

First hurdle: From that time onwards, and continuing through today, managers have been burdened with publishing track records with increasing frequency; it started with quarterly, moved down to monthly, then to weekly estimates.  

‍

1990-1999: Outperforming Benchmarks

The 90s came along, and roaring equity markets led investors to push hedge fund managers to compare their performance (which on average lagged the S&P or the Nasdaq) to equity benchmarks. While these requests became the seed that later enriched benchmark providers—who created a monopoly out of simple arithmetic definitions (Euclides would have been so jealous)—it led to the creation of additional, but equally senseless, return metrics. In addition to track record as an input, firms included its comparison to the relevant (or most relevant) benchmarks.  

Around that time, things like Information Ratio and Up-Capture / Down-Capture were born, along with many others. Despite investor demands, it took hedge fund managers a minute to embrace benchmarks. You see, these demands were at odds with a favorite talking point of managers: “Well we’re an absolute return fund, we do not really benchmark ourselves to anything.” You still hear this occasionally today.

Over time though, managers had to concede, especially because (even if you’re pursuing an absolute return objective) benchmarks serve the excellent purpose of defining the selection universe: the jar of marbles out of which skilled managers will pull out their best ideas. Figure 1 depicts this comparison between a fund performance and benchmark, showing how the Novus Platform is used to ingest and visualize benchmark metrics.

‍

Second hurdle: Now managers had to buy benchmark licenses and report their track record in absolute and relative terms. While this did not require a significantly higher effort (compared to reporting on track records in the absolute), costs went up notably, as benchmark providers charge dearly.  

‍

2000-2009: Adjusting for Beta

The Capital Asset Pricing Model (or CAPM for brevity) was introduced in the 1960s as a mathematical model to explain the riskiness of an asset as a function of the riskiness of the underlying market to which it belongs. While the mathematically curious reader is invited to consult the many available sources on the matter, it is sufficient for the purposes of this article to recall the concept of β, a key variable in the CAPM model. Figure 2 provides a graphical representation of the CAPM model which will be useful in the ensuing discussion.

‍

A security with β > 1 (where β is estimated by regressing security returns versus the market returns over a given period) signifies that a security tends to move more than the market does, and in an equal direction. Consider a security with a β = 1.2. If markets moved by +1% in the day, the security on average would post 1.2% on that same day. Conversely, if the market fell by 1%, one would expect the security to fall by 1.2%.

Armed with this insight, investors started asking managers to report on the embedded β of their portfolio, as though returns generating from high β securities were inherently a bad thing. I still do not understand the almost dogmatic aversion to β professed by some investors. After all, over time, more volatility offers more opportunities for outsized returns. In Figure 3 below, the Novus Platform uses beta to provide a more detailed backstory for performance through illustrating it alongside volatility.

‍

‍

Third hurdle: Managers now had to report the embedded β in their portfolio, either by regressing top line returns vs. benchmarks, or by calculating it bottom-up from the β of their underlying positions. Given two sets of equal returns, those generated with a lower β were deemed to be higher quality. It did not matter that for managers β is an ex-post metric; meaning, managers never look at β before constructing a portfolio, and only occasionally inspect after. β became another item you report on but rarely use.

‍

2010-2015: Adjusting for Risk

Investors then realized that not all leverage is created equal. A $100 long position in a mega cap stock vs. a $100 short position in another mega cap stock is different than a similar pair trade where both stocks are micro-caps. Both pairs have the same $200 gross exposure and $0 net exposure. However, the micro-cap pair is clearly riskier.

This is when ‘risk’ came into play, the quantitative discipline of assessing how much capital is potentially at loss based on the underlying securities in a portfolio. The events of 2008, unexpected by most, spurred the growth of the risk discipline.

‍

‍

Fourth hurdle: Due to this shift, managers were asked to report on the embedded riskiness of their portfolio. Managers needed to report market risk, liquidity risk, counterparty risk, unencumbered cash position vs. total cash, and expected shortfalls under stress scenarios; most of which required, likely for leverage, a bottom-up aggregation from metrics calculated at the individual security level.

Figure 4 above displays instances in which a portfolio’s returns exceeded the value at risk, representing periods of higher-than-expected volatility. Most fundamental managers rarely think of VaR to measure the potential loss of capital in a position they’re taking. For them, concepts such as margin of safety are more suitable. And yet, if managers wanted to have a fair shot at competing for institutional investor allocations, they had to report on VaR and other VaR-related metrics. Another performance hurdle was added: managers worthy of their active fees had to deliver outsized returns relative to the risk embedded in the portfolio.  

‍

2016-2020: Adjusting for Leverage

As investors started to get more curious about how exactly managers were generating returns, they discovered that—occasionally—superior performance is achieved as a function of levered bets applied to passive investments undergoing a bull market. Leverage has to do with risk, but is not the same. Think of leverage like the volume level applied to a piece of music. If the underlying music is good, leverage may be a good thing. If the opposite is true, you’re just amplifying cacophony. The same holds true for portfolios.  

‍

‍

Fifth hurdle: Managers were now asked to report net and long exposure, which in turn required managers to define the very notion of an exposure for a given asset class or investment instrument. For example, for a long equity position, the exposure is simply the market value, but for an equity option, it’s the delta adjusted notional. Also, the whole exercise requires one to define netting policies. If I am long $100 of gold and short $50 of silver and their correlation is 0.9, does it mean that I am net long $55 of gold ($100 – 0.9 * $50= $55)? Tricky.  

In Figure 5 above, the Novus Platform compares net adjusted return with a net adjusted benchmark return, with the purpose of adjusting for market risk. Unlike benchmarks, which are top-level metrics in the order of a few dozen, leverage calculations require a bottom-up exercise which increases analytical complexity by a few orders of magnitude. The use of leverage also requires a transformation exercise, whereby returns produced via the use of leverage must be adjusted to provide a fair comparison with the reference benchmark. This is when concepts such as leverage-adjusted performance, or net-adjusted benchmark returns, came into play.  

‍

2021-2025: Adjusting for Factors

Fama-French took the CAPM model from the 1960s to the next level, and demonstrated that security returns could be explained (in addition to market returns) by two additional factors (or regressors if you want to speak mathematically): the value vs. growth factor and the small vs. large cap factor.  

Bluntly speaking, Fama-French discovered that value stocks tend to outperform growth stocks and small caps tend to outperform large caps (speaking as a crude average across multiple periods). Hence, if you compared two managers (A and B), and A had more exposure to value and small cap stocks, one would have to ‘discount’ this inherent advantage when comparing returns.  

In other words, investing in a security whose β to the small vs. large cap factor was positive meant that the investor was betting on the security going up because of the inherent exposure to the factor rather than its intrinsic properties. In plain terms, investors did not want to pay managers active fees to generate returns via investments in small cap stocks, because, as we said, those tend to structurally outperform the broader market.  

Just as the concept of return led to a senseless proliferation of return-based metrics, the Fama-French model led to a proliferation of factors, some of which, like their equivalent return-based metrics, have little explanatory or predictive power. Regressions were run versus the Chinese Consumer Index factor, the Inflation Adjusted New Economy factor, and many others. In hedge fund land, an exposure to growth is considered an anathema, as if value was a better choice by definition.‍

(Value managers keep referencing literature stating that over time value outperforms growth in the long term, but ‘long’ may mean several decades, as growth has beaten value for quite some time now.)

‍

‍

Sixth hurdle: Managers now had to report on their factor exposures and demonstrate that their returns can’t be explained by factor bets. If such an explanation existed, investors could simply buy cheap exposures to those factors and exploit the inherent premium at lower fees.

Calculating factor exposure is not terribly difficult; even Excel has a regression calculator embedded in its basic version. Interpreting results though, requires more care—the kind that we rarely see applied.

Figure 6 measures the contribution of different factors in the US Factor model to a portfolio’s return. The Novus Platform makes it possible to dissect performance and attribute it to different factors using various models.

One will always get βs because of a regression, but just because you can calculate them does not mean that they bear significance. Any statistical regression package gives practitioners an indication of the statistical significance of the βs coming out of a regression calculation. So in theory it would be very easy to see if the results bore any meaning at all. Most often though, this information is ignored, resulting in statistically insignificant βs being debated as though they had meaning.  

‍

Stacked Hurdles

To summarize, if you’re a manager demanding active fees, not only do you have to outperform your reference benchmark, but you need to do so after adjusting for leverage and risk in a way that is not explained by bets in high beta securities or factor exposures. This ever-rising bar from the last three decades results in a challenging hurdle to clear.

Is it over?

Afraid not, as we think there are more hurdles to come.

‍

2025-2030: Adjusting for ESG and The Rise of the Machines

The growing ESG sensitivity may lead investors to demand that managers generate returns within a more constrained, ESG friendly universe, which may limit degrees of freedom to generate alpha. Investors may not want to pay active fees for returns generated by investing outside of certain ESG constraints. Figure 7 shows a manager’s exposure to different ESG classes, allowing the manager to quickly and easily access their portfolio’s ESG friendliness.

‍

‍

‍

Additionally, the growing usage of artificial intelligence and machine learning may give birth to algorithms which can reproduce talented investors’ way of choosing and managing securities. Can a manager beat its artificial clone? Can we even recognize the difference? Will investors want to pay active fees to a manager that can be codified by an algorithm? We get into speculative arguments that are so fascinating, one could almost come up with a Blade Runner equivalent to investment management.  

‍

What to Do

Active fees are challenged nowadays, and investors seem to be ready to pay them only if performance clears all hurdles discussed above. While it may be rational for managers to conclude that they should start reporting whether they clear all these hurdles, it’s not what we recommend.  

To be clear, we strongly advise investors to endow themselves with a system (such as the Novus Platform) which allows you to calculate everything we discussed above, and of course, to periodically peruse results; but we also advise against changing a manager investment process to engineer outperformance vs. hurdles. None of the managers we work with came into the office one day saying: “We have a β of 1.35 to the Chinese Consumer Factor, let’s buy more South American stocks.” The most important element here (it will be the subject of a separate discussion) is to measure intentions; mangers must use metrics that define whether the degrees of freedom used to generate returns really work in their favor and are optimized for long term success.  

‍