Looking at two bank reports on the same topic can often yield interesting results. There are times when bank research is best viewed from the standpoint of how their analysis does or does not correlate with one another. Regarding hedge fund allocation decisions, one bank appears to have a nuanced if probing understanding of algorithmic market environments while other research is more nuanced on the noncorrelated topic. Looking at current hedge fund asset flow patterns this is apparent.

Also see hedge fund letters

MS HF analysis performance 3 Hedge Funds, Hedge Fund Allocations, US Banks

Institutional investors have a duty to recognize independent from less independent research

The best bank research indicator is on non-political issues or when the banks do not have a financial stake in the outcome. Examples of bad bank research included analysis of the Petrobras 100-year bond – a deal that ended in court amid charges of fraud – and the immediate aftermath of the Brexit vote. In both these cases major bank research all drew the same conclusions and avoided analysis of the same issues.

Overall there is much more positive and accurate research than is negative.

JPMorgan’s Marko Kolanovic, the first major derivatives analyst to predict a flash crash to a degree, is one such example of analysis that provides investors the ability to model their probability paths. Bank analysts are full of insight when they are allowed to freely communicate. For an institutional investor the key is to recognize the truly insightful analysis.

Consider a recent Goldman Sachs Prime Services report on “Allocator Insights,” detailing the current state of the hedge fund world, and compare it to Morgan Stanley’s version of the same, titled “Recent Hedge Fund Trends,” to illustrate the point. It is solid research work from both banks, but the way they look at the world of hedge funds is very different – and this frames their analysis.

MS HF analysis performance

Bank research can provide significant insight — when it is independent

Both Goldman Sachs and Morgan Stanley have produced outstanding analysis. Goldman’s volatility research is particularly noteworthy. Morgan Stanley, for its part, has expressed a desire not to be sheep while leading the herd. They, too, provide outstanding insight in many of their reports.

But there are nuanced differences in how they consider the world of hedge funds. One bank takes more of a “systematic CTA / derivatives industry / quant” mathematical logic look at hedge funds while the other seems to be stuck in an old school Wall Street framing for hedge funds.

The first difference is in how they categorize hedge funds. Goldman appears to group hedge funds based on core strategy performance drivers rather than more common approaches. In Goldman’s hedge fund world there are categories such as Quant / Relative Value, CTAs and Systematic Macro. To an allocator these nuanced differences should matter because they speak to how a fund correlates to a beta market environment. These are the core building blocks for building an algorithmically-driven, market environment diversified program where performance and risk management can be modeled.

While the Morgan Stanley Prime Brokerage report uses conformity with many of the top level category descriptors with Goldman – Quant Equity, Discretionary Macro, Equity L/S, for instance – the analysis doesn’t support a beta market environment outlook to the same degree. In short, they appear to favor the more traditional equity, long / only view of the world. The term “Quant Relative Value” is not in the Morgan Stanley report, for instance.

MS HF analysis performance 2

Morgan Stanley notes where the hedge fund industry leadership has gone

The Morgan Stanley piece noted the obvious: Systematic Macro and CTA strategies have been leading hedge funds. With gains of near 5% year to date, the one-time generally overlooked investment category once again delivered during periods of crisis but suffered at logically predictable times as well. It is not perfect, but during crisis when volatility leads to price persistence and relative value patterns divert from their norm only to later mean revert, it has a documented history of performance. Once current oddity, not mentioned in either report, is that managed futures is performing positively along with the equity market — a rare positive correlation.

Morgan Stanley noted that it is the Equity L/S and Event Driven strategies – most of whom have a significant long-only tilt – that are suffering hedge fund withdrawals. While two-thirds of investors are interested in maintaining or increasing their HF exposure, the money is flowing into the strategies that can be modeled and perform during crisis.

The Morgan Stanley report didn’t touch on it, but it is important for investors to recognize the core performance drivers in each strategy and under which situations they might find challenging market environments — even in crisis.

Certain strategies, for instance, are known by quantitative analysts to have a weakness in a market environment of mean reversion. Many hedge funds are said not to have modeled risk of these strategies during beta market environments. When asked to review their forward looking modeling on their Risk Parity strategy, Prosek, the media management firm representing Bridgewater Associates, declined to comment.

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Goldman Sachs noting Quant / Relative Value is one example of beta market environment sophistication

The Goldman Sachs piece, for its part, providing sub-category analysis on allocator searches, noted they still preferred Discretionary Macro – human analysis – as opposed to interest in CTAs and Systematic Macros.

A quant category of particular interest to those who follow beta market environment analysis is the Quant / Relative Value category. Goldman noted that in this space, typically correlated to an algorithmic market environment of relative price divergence and convergence to a statistical mean, grew 18% in the first quarter and 20% in the second quarter.

To a reasonably sophisticate hedge fund investor, this checks an important box in the beta market environment check list – and allocators appear to be recognizing this in increasing numbers.