How do the successful pod PMs QUANTIFY positioning?
I’ve worked as an analyst at a few SM hedge funds and my pitch is generally:
- Earnings will be X% higher due to x or y or z
- Multiple will re-rate from X to Y which is justified by a or b or c
- The timing/catalyst for this to happen is …
I spend a lot of time talking to management or expert calls or sell-side to find datapoints to support or deny the thesis and to look for things I may have missed outside my core thesis.
But positioning is a dark art. All I have is the sell-side sales desk taking polls and reporting back expected numbers. And the sell-side analyst telling me what’s owned or not by long only, hedge funds, pods. This doesn’t feel robust, and often times maybe made up.
What are better ways to quantify positioning?
And importantly then how do quantify that into a target price?
Bump. But frankly I don't think you'll ever get an in-depth answer to this here.
Great question - unfortunately I'm not smart enough to answer but I'll bump in hopes that someone will chime in.
Brett caughran had a thread on the buyside whisper... some of which comes into play I imagine.
Also, this is where technicals actually serve a purpose. They will show you sentiment on a stock
In terms of deeper quantification - idk!
Positioning is an art not a science. Spec sales are often directionally right in most cases, but they sometimes miss.
Some MMs tell you internal crowding (i.e. within this fund and in this sector, these are the most crowded longs/shorts).
Now, your question of how to translate crowding into target price... you are asking for the impossible. As said, it's an art not a science. Crowding is meant to give you a sense of the risk of adverse moves particularly around a short-term event (earnings, investor days, etc.).
UBS, MS, GS all have positioning tools for institutional investors to use. The source is prime brokerage data which serve as proxies for how their clients have bough / sold names. Generally we tend to aggregate several brokers long positioning and short positioning data. We check if the data point to the same direction and if it stacks up versus the verbal overlay from the specs. If not -> investigate.
Whilst the absolute number in however way the brokers presents it tends to differ from broker to broker, what truly matters is how positioning has changed (i.e. direction) since the last time the company communicated with the market and how that compares to the revisions since that date.
We self-index positioning data from various brokers and third party sources through a time series analyses, and create more analyses on top to understand why something might have moved in a certain direction (like abs and rel re/de-rating since x date, DTC change since x date, SI change since x date, broker score changes in Bloomie since x date) to get a feel for how relative positioning of both sellside and buyside has changed versus market and versus peers since a specific date.
Some stocks are driven by sellside (esp names with poor buyside following). For other sectors the sellside matters less, and buyside views are a more solid proxy of direction (e.g. gaming). Hence why you'd discriminate positioning data between various sectors.
There will be noise in the data, so the point is not to be exact, but to get a steer in how perception has changed on the sector (vs history and rel vs market) and the name (again, vs history and rel vs peers) over a specific time period. Also, the data we receive is time delayed because the PBs are not allowed to disclose live data. We don't care as we wish to triangulate direction. We can overlay this with high touch flow insights from traders.
Be aware that placings, stock issuance, ABBs etc. These dilute the positioning data as marginal buyers could top off their existing positions in other ways then buying in the open market, which in theory could impact incremental buyer/seller appetite.
Hey,
I just read your comment and was wondering, if it is common to do statistical analysis like time series analysis in a mm l/s team. And how „fluent“ do you have to be in statistics? Is it enough to study finance and have the basic courses or did you have some STEM degree which is why you use more quantitative methods?
thanks in advance.
So when I say time series I mean a quick output in excel as a line graph to gauge the pattern. It's nothing advanced.
Generally, comprehension of elementary stats is great (probabilities, standard deviations, regressions) esp as you progress to doing more risk management, but it doesn't require a STEM degree by any means. Knowhow of elementary stats is readily available for free online and generally you don't even need to grasp the mathematical proof to utilize it, just the definition. The real life application is easier then you think.
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