Hedge Funds: Analysts Are More Than Their P&L
How do you properly compensate analysts at hedge funds? We walk through a few possible solutions that are fair to both analysts and PMs.
Working with dozens of hedge fund managers and speaking to dozens more, we understand how important it is to have the right compensation structure in place to retain and incentivize talent at a fund, large or small. Whether in a good year with millions to go around, or operating under a tight budget, having a system both understood and agreed upon by the analysts and the PM goes a long way in relieving the stresses associated with compensation conversations. While no system will ever guarantee freedom from friction and almost all systems have discretionary overlays, some managers are able to align the company’s goals with analyst compensation thanks to a thoughtful, agreed-upon system.
Hedge funds are known for paying generous compensation packages – it’s one of the major reasons why top talent from the best schools is attracted to the industry. But with that, the expectations are high and analysts are often hypercompetitive and obsessed with comparison.
If compensation is totally at the discretion of the PM and not backed up by meaningful metrics, many analysts will feel slighted and passed over, no matter what you pay them. Of course, most funds comp packages are backed by metrics, but often it’s simply the P&L that analysts (or their ideas) have generated for the fund.
Therein lies the problem. Consider this scenario: Sunil conducts deeper research and makes higher ROIC trades but has generated less P&L than Steve simply because Steve had a much larger starting capital base. What if Sunil also took on a lot less risk generating that P&L compared to Steve? Steve might have a problem with compensation simply based on one year’s P&L number. Specifically, the issue is that P&L does not always speak to skill and analysts are all too aware of that. Also, P&L might not be the only goal of a fund. Maybe it’s risk-adjusted return, or the ability to engage in very unique trades for uncorrelated alpha, whatever.
Building that level of sophistication into your compensation process requires an understanding of each analyst’s exposure, contribution, and levels of risk taken. In other words, it requires a portfolio intelligence system and a lot of data.
All P&L is Not Created Equal
Running with our prior example, let’s see what PMs can do with a portfolio intelligence system informing their compensation decisions. We will use a hypothetical long / short portfolio that looks like a real hedge fund but has been constructed simply for demonstration purposes. Let’s start with simple P&L generation for the year 2014. In that year, Sunil has generated less dollars than Steve. This is where many managers stop their analysis and use the number in compensation discussions.
Taking this a step further, we see that not all P&L is created equal. The table below breaks down the year’s performance into two buckets by analyst.
We can see from looking at Avg. Exposure (3rd column) that Steve had a much higher allocation through all of 2014, in fact, he controlled over 75% of gross exposure, compared to only 45% for Sunil (on average). Furthermore, Sunil’s stock picks had higher ROIC on average, 9.3% compared to 6.6% for Steve. So if the PM values the ability to select stocks that perform well on an absolute basis, Sunil should be compensated higher than Steve (for selecting higher returning stocks on average) even though Steve generated more dollars for the fund. If the risk that the analysts assumed to generate the P&L is a consideration, the PM would be interested in comparing the % of Risk allocation rather than pure exposure. Risk takes into account the volatility, beta and covariance of each of their securities.
Taking this even further, the PM might value relative performance (or alpha) above absolute P&L. They might have benchmarks that the analysts need to beat in order to achieve their goals. Isolating the alpha generated by these two analysts in the chart below, changes the picture once again. It turns out that even though Sunil had higher ROIC than Steve, it was not high enough compared to a relevant benchmark. Taking out the market movement and adjusting for sector exposure, we can see that Steve actually generated much more alpha given the sectors he was investing in.
This should be a large factor in the compensation decision if the PM values alpha creation.
No matter what the compensation schema is for a manager, our experience shows that being transparent with your analysts goes a long way. If absolute P&L generation is the goal, be clear about that and use facts to inform your compensation discussion. If it is more nuanced than that, having a robust portfolio intelligence system becomes critical in substantiating these difficult decisions and guiding the conversation at year-end.