How do you get a differentiated view?
Starting at a target DACH uni soon, and my goal is to eventually work at a MM HF. I've been investing in PA for a couple of years, mostly with a 2–3 year horizon
My process has generally been to look at stocks where the bull and bear theses are already well known, look at what the market is pricing, and then try to find where I disagree. E.g. if the market is roughly pricing 35% bear / 25% base / 40% bull, I might do the research and conclude that the probabilities are actually closer to 20% / 25% / 55%
I like this approach because I'm not trying to discover some completely unknown thesis, but rather trying to identify where the market is mispricing the probabilities and I don't think I'll realistically be able to come up with consistently more accurate estimates than firms spending millions on data, expert networks, etc.
This makes me wonder how this translates to a MM HF when you're trading around quarterly results
Everyone has access to roughly the same data, speaks to management, follows the same KPIs, reads the same filings, etc
So can you actually have a genuinely differentiated view over a 1–2 quarter horizon? If everyone is looking at the same information, what can you actually know or understand that the market doesn't? Is the edge mainly in identifying non-obvious implications in the data, connecting different pieces of information, forming a different view on what will actually drive the stock, or finding information/signals that others are overlooking? Or is it all about estimating more accurately?
Also, can multiple pods actually be positioned in a very similar way and still make money simply because other market participants (passive funds, retail, less sophisticated active managers, etc.) aren't following the same approach?
Would be interested to hear from anyone actually working at a MM HF
Figuring this out is the job, and to be honest a lot of the time your view isn’t truly differentiated.
It’s usually a function of your research process and feel for the risk/reward. Some teams lean heavily on primary research, some times focus more on trading the narratives.
Not every team follows the same process. We don’t really look at quarterly earnings at all, and focus on macro bets and so have different catalysts. Other teams specifically care about having better FY numbers than the street, some care more about the multiple path and focus on valuation. Many teams sit somewhere between these.
In many sectors the marginal buyers/sellers of stocks are hedge funds as generalist flows tend to be slower and often more defensive. So the job can become front running how you think other teams are positioned and trading the opposite at times.
I think you are onto something in recognizing that it may be about identifying non-obvious implications or making connections.
I'd say you have to be creative and develop niche expertise. The best analysts and PMs I have worked with (I'm an outside consultant) have developed a way of thinking or operating that is proprietary to them hand in hand with elite level pattern recognition that let's them see something in a filing or a situation that is so subtle that no one else is seeing it. I recall for example a two-word, innocuous-looking change to the business description section of a small cap 10-K that (if you happened to understand why it might be an odd turn of phrase at this particular company) was a clear indication that the company was going to pivot from a roll up strategy to putting itself up for sale.
Another one was noticing that a 13G at a small cap was tied to a little known successful entrepreneur in the same space and connecting the dots that this guy was eventually going to grab a board seat and revamp the company strategy.
Or seeing an unusual 10b5-1 plan adoption disclosure where a CEO indicated what price he planned to sell shares at and understanding that that price made no sense unless the company was in play to be acquired.
I see DCF models and classic fundamental analysis as incredibly commoditized and everyone is killing themselves trying to be 0.0001% smarter about it than the next guy, but I think if you can pick at something else in the periphery of what you do that is interesting to you and that you think there could be a bit of edge in, figuring out how to build some reps in that and become and expert in it and develop the pattern recognition. For me (as you might guess from the examples above), it is looking at non-fundamental disclosures through certain lenses, but it could be any number of things that you go after. That's the great thing about this business - endless threads to pull on and learn about (and AI has only supercharged that ability to learn if you use it the right way.)
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