From Alpha to Algorithms: The Rise of “Scientific” Hedge Funds and the Squeeze on Investors

Multimanager shops promise uncorrelated returns, but are investors getting squeezed by fee hikes and automation?

I initially described Bill Ackman’s Pershing Square LP as the quintessential hedge fund, embodying what most people imagine when they hear the term. However, I recognized the subjectivity of that statement — after all, everyone’s mental image of a hedge fund differs.

Reflecting further, I believe two prevalent models dominate the industry. The first aligns with my initial impression of Pershing Square: the old-school, classic hedge fund. Picture a charismatic, high-profile manager, like Ackman, who makes a limited number of concentrated bets with unshakeable conviction. These decisions stem from their natural talent, relentless hard work, years of market experience, and, let’s not forget, a healthy dose of gut instinct.

This interpretation might resonate with many, but it’s crucial to acknowledge the evolving landscape. Enter the second model: the modern, data-driven hedge fund. Here, complex algorithms and quantitative analysis take center stage, meticulously dissecting markets and identifying opportunities. While individual brilliance still plays a role, it’s often part of a collaborative, team-oriented approach.

In contrast to the charismatic lone wolves of old-school hedge funds, a newer model has emerged: the multimanager, multistrategy powerhouses. Think Citadel, Millennium, or Point72. These behemoths house numerous specialized “pod” teams, each led by a portfolio manager responsible for a specific sector or strategy. Unlike their media-savvy counterparts, these managers typically operate under the radar, quietly churning out their individual bets.

This decentralized approach offers several advantages. By pooling talents and strategies, multimanager funds can diversify their risk and potentially capture broader market opportunities. Additionally, smaller pods foster a nimble and entrepreneurial culture, encouraging innovation and adaptation.

However, this structure also presents challenges. Coordinating numerous strategies can be complex, and information silos within the fund can hinder performance. Furthermore, the lack of a single, identifiable frontman may diminish investor confidence in the overall vision.

Ultimately, both the old-school, high-conviction model and the newer, collaborative approach have their merits and drawbacks. Understanding these differences is crucial for investors navigating the diverse landscape of the hedge fund industry.

Beyond contrasting personalities, a key distinction between classic and “pod shop” hedge funds lies in their operational philosophies.

Classic funds often hinge on the intuition and charisma of their individual managers. While gut feeling may still inform investment decisions, pod shops operate with a more “scientific” approach. This manifests in several ways:

1. Incentives Aligned with Skillful Alpha: Managers aren’t solely rewarded for following the herd. Instead, their bonus hinges on generating uncorrelated alpha — outperforming their assigned sector or the broader market by selecting superior and inferior stocks within it.
2. Factor-Neutral Portfolios: To isolate manager skill, portfolios are constructed to be largely market-neutral. This means exposure to broad market factors like rising or falling tides is minimized, leaving their alpha generation on center stage.
3. Sophisticated Performance Measurement: Gone are the days of gut-check evaluations. Advanced quantitative techniques dissect manager performance, neutralizing market influences and isolating “true skill” reflected in alpha generation.

In summary, pod shops prioritize data-driven analysis and risk-adjusted returns, while classic funds often lean on individual experience and market intuition. Both approaches have their merits and risks, ultimately catering to different investor preferences and risk profiles.

The allure of multimanager funds for institutional investors lies in their promise of genuine alpha, or uncorrelated returns, distinct from traditional asset classes. This makes them a compelling diversification tool. These funds essentially offer:

• Exposure to diverse strategies: Unlike traditional passive investments, multimanager funds provide access to a pool of specialized managers employing various strategies across different sectors or asset classes.
• Reduced volatility: By blending uncorrelated strategies, the overall portfolio volatility tends to be lower, offering stability alongside potential for additional returns.
• Attractive risk-adjusted returns: The “12% with very little volatility” pitch highlights the potentially superior risk-adjusted return profile compared to individual asset classes.

However, this approach wouldn’t resonate with everyone. Some investors seek the thrill and potential for outsized returns offered by high-conviction, single-manager funds. But for institutional investors prioritizing stability and diversification, multimanager funds present a sophisticated and potentially rewarding option.

One intriguing theory behind Bill Ackman’s public closed-end fund for retail investors boils down to changing investor preferences. Institutional investors are increasingly drawn to the data-driven, multimanager approach offered by “pod shop” hedge funds. This signifies a potential shift away from the traditional charismatic single-manager model, which, while still captivating audiences on Twitter and other social media, might be losing some of its traditional allure among larger, professional investors.

However, retail investors often differ significantly from their institutional counterparts. The excitement and potential for high returns associated with the single-manager, gut-instinct approach still hold strong appeal for many individual investors. Ackman’s fund could be capitalizing on this enduring interest, offering retail investors a chance to participate in a classic hedge fund strategy that might be evolving within the institutional sphere.

While “scientific” management in multimanager funds offers potential benefits, a potential downside lurks: overly precise skill attribution. This refers to the practice of meticulously dissecting fund performance and assigning credit to specific individuals or strategies. While aiming for transparency, it can create unintended consequences.

Imagine a hypothetical scenario where a fund claims: “Of the $47 million earned last year, $13 million stems from analyst research, $14 million from portfolio manager selection, $11 million from risk management, and $9 million from your initial investment.” In this case, investors receive only the $9 million, while the rest is distributed based on attributed skill.

The concern here is that overly granular skill attribution might lead to excessive fees for managers, analysts, and other staff. If all “skill-generated” returns are distributed, investors might see a diminished share of the overall gain. This begs the question: at what point does attributing performance to specific individuals become detrimental to investor returns?

It’s crucial to strike a balance between recognizing individual contributions and ensuring fair compensation for investors. Transparency is key, but overly precise skill attribution shouldn’t come at the expense of investor value.

Hedge Fund Clients See Shrinking Share of Profits, Despite Strong Performance

A recent survey by BNP Paribas SA reveals a concerning trend for investors in multistrategy hedge funds. While these funds delivered superior returns compared to their peers and the broader market, clients are receiving a decreasing share of the profits.

The survey, conducted among 238 allocators managing $1.2 trillion in hedge fund assets, found that:

• Clients of multistrategy funds that passed on all their costs received only 41 cents of every $1 made by the fund in 2022.
• This represents a significant drop from 54 cents in 2021, highlighting a shift in the fee structure.
• Despite the strong performance, these funds have the lowest after-fee returns among multistrategy funds.

This trend suggests that popular multistrategy hedge funds are increasingly keeping a larger portion of the profits for themselves, leaving investors with a smaller share, even when the funds outperform. This raises concerns about the alignment of interests between fund managers and their clients.

It’s important to note that this is just one study, and further investigation is needed to understand the wider implications and potential causes of this trend. However, it highlights an important consideration for investors seeking to allocate capital to multistrategy hedge funds.

The classic hedge fund model often featured “star manager” charisma and bold bets, with success seemingly attributed to a mix of personal talent and market movements. While the exact source of performance remained opaque, a “crude compromise” existed: managers took a fixed fee and a percentage of profits, leaving the rest for investors.

Fast forward to modern multimanager funds, and transparency takes center stage. These funds strive to meticulously dissect performance, attributing specific percentages of return to individual contributors or strategies. While this offers greater insight, it also raises potential issues.

Imagine a hypothetical scenario where a fund declares: “59% of our gains stemmed directly from our team’s skill, justifying our keeping that portion.” This shift raises concerns about excessive fees based on overly specific skill attribution. If every “skill-generated” return goes to the fund, what remains for investors?

The key lies in striking a balance:

• Recognizing individual contributions fosters accountability and motivation.
• Ensuring fair compensation for investors is paramount.

Transparency is crucial, but overly granular skill attribution shouldn’t come at the expense of investor value. Finding the right equilibrium remains a crucial challenge within the evolving landscape of hedge funds.

The Rise of Automation in Multimanager Funds: A Challenge for Portfolio Managers?

A recent report by Business Insider highlights a key trend within the multimanager fund landscape: increased reliance on automation. Former Point72 President Doug Haynes’ new fund intends to utilize a “quantamental” strategy, combining fundamental analysis with data-driven algorithms. This raises questions about the potential impact on portfolio managers’ role.

Under the “Idea Lab” and later “Latitude” strategies at Point72, analysts’ insights are filtered through algorithms for portfolio construction. This highlights a shift towards technology-driven decision-making, potentially streamlining portfolio management. While the new fund claims “substantial investor interest,” it’s crucial to consider the implications:

• Will this reliance on automation diminish the need for traditional portfolio manager expertise?
• What are the potential benefits and drawbacks of this approach for investors?
• Does it ensure a healthy balance between human judgment and quantitative analysis?

It’s important to note that while automation can enhance efficiency and risk management, effective investment decisions often require nuanced understanding and judgement, areas where human experience still holds value. Finding the optimal blend between technology and human expertise remains a key challenge for multimanager funds.