Struggling with “edge”
What are legit examples of edge? Alt data isn't really an edge anymore. I personally think the level of biz analysis done by certain LOs (specifically RCG does) is a true edge, but that's all I can think of (not to mention differentiated time horizon from other funds). In activism there simply aren't "a whole lot of scalable activist funds" so differentiation isn't a huge issue.
But regular SM L/S - what are real, non bullshit examples of edge here?
Is it sector focus? Idk. How many L/S SMs and focus on TMT + pods — Probably hundreds — realistically speaking they’re all probably experts and have similar views on names / themes
You’d be surprised how little work most people do. It baffles me more every year
Unpopular view, but most of the time I don't think hours of work results in edge.
following
what's RCF stand for?
Sorry RCG — Ruane cunniff goldfarb (the sequoia bros). Have spoken to these guys and while the whole compounder / levered long strat may be basic, I must say the level of work these guys do is shocking. Know the biz inside out better than mgmt
i see what you're saying, but IMO it's hard to call what they have "edge" when they've underperformed their benchmark so drastically over every timeframe - see below for sequoia returns. pretty damning IMO. not familiar with them and their analyses - but generally speaking, a lot of groups can rattle off facts all day long and sound smart but not have an edge at all (in this case, negative edge).
Lol just no. They do not know the biz 'better than mgmt,' I've worked at a high-quality AM for many years now -- this is the height of naivete. You as an analyst need to know the core moving parts -- mgmt knows that plus everything else
Knowing a company in depth can lead to insight but does not automatically mean alpha generation -- which explains why RCG's returns are in the shitter. Underperforming by 400+bps (before fees!) over 5yrs / 500+bps over 10yrs is literally awful. Hard to do worse than this as a pro investor
ruane cunniff goldfarb
What are ur thoughts on edge in l/s (alt data aside if ur at a pod). Specifically asking on SM side, so maybe not portfolio construction like in a pod. Like what separates a Viking LP Soroban etc from xyz other shop
I like how the intern is brushing off altdata as no edge but referring to the spectacularly underperforming edge of Ruane as a true edge.
You do realize edge is meant to be tangibly visible via outperformance right?
Like alt data, high quality biz analysis is an edge, but having 1 basic edge does not = outperformance / alpha. For ex: I think RCG level biz analysis + pod level portfolio construction would be killer, but those 2 can't really coexist / go outside the mandate of a pod and LO.
But it does / can fit L/S SM - viking is a great example here
Just saying RCG are great business analysts — doesn’t mean they’re the best investors ever. Trying to find other, real edges
What does RCG do that makes you think they are great business analysts, especially taking into account their shit returns
Timeframe is important. Pods all run different strategies with different horizons, even if within the same space (TMT). Larger pods act as multi-strat sub-divisions where jr. and partner PMs are running a different strategy.
There are three broad categories of edge: informational, analytical, and behavioural.
1) Only bet when you have an informational or analytical edge and
2) Where the market payout is attractive from a risk/reward standpoint (risk management can be a behavioural edge)
The transmission mechanism of how this works is such:
INFO --> getting info before others is an informational edge --> assessing whether the market has priced in the information is an analytical edge.
Much of the dispersion in analyst estimates comes from the second step. But edge is ultimately about triangulating a gap in the market arguments for or against a stock. Do not bet vs. consensus, understand where the clusters of views are and their rationale, and then get laser focused on what's driving the difference in expectations.
It may be that below consensus cluster is seeing the industry weakening and are cutting estimates across the board, while above consensus cluster sees margins as resilient. You need to have good judgement in order to determine which of those camps you think is more accurate (or if the market is pricing one camp too aggressively despite the lack of visibility).
This.
Good comment. Michael Mauboussin wrote an awesome paper where he defines the 4 types of edges. Behavioural, Analytical, Informational & Technical.
Would highly recommend that you read it.
https://macro-ops.com/wp-content/uploads/2019/02/Who-Is-On-the-Other-Side.pdf
Agree - the research process (especially in-and-out pods) is barely dissimilar but sizing / positioning / perception of R/R is where people vary drastically and there is no universal answer of success in here - do you lean on your longs outperforming given multiples re-rating / business going better or do you fade it for the opposite reasons? The research process is the same, but depending on your portfolio tilt your conclusion / how your brain process it could be different as well.
Could you expand on the first point "PM and sr analyst w/ sleeve (both in same pod) run different strategies"? How does this look? If say $3bn TMT pod, PM runs $2bn, and then 2 sr analysts run like 500 each (analysts get 5-7% of the pnl on their $500m books, then PM gets 10-13% of the pnl on the analyst books + 15-20% cut on his $2bn book?). What is the difference between their strategies? Names covered? Different events / styles (i.e. in healthcare, one guy trades around trial data, another focuses on q/q, maybe another a bit more LT?)?
Are analysts allowed to take a tilt / have some exposure, and then PM nets that out in the broader book?
It varies paid by pod. I’ve never seen “analysts” actually hold the risk, they’re normally promoted to a “Jr PM” role if they start doing so. Normally it’s a coverage decision (“I want to take our strategy and apply it to this new set of names”). No I’m really wants to take factor / style tilts because that’s weaksauce. Coverage, as you know, is grind-y and labor intensive, but you can find lots of trades to deploy capital on, even at small size, due to market extending beyond a normal range. If you’re a PM and want to develop future PM talent, giving one of your analysts a grind-y and decent hunting group (call it the c book) is a good place to for them to learn.
Why do you think activist funds aren't scalable?
Didn’t say that, said there aren’t a whole lot of scalable (large) activist funds who r able to launch huge campaigns / create mass value. So bc there’s only a handful of them (maybe less than 10 funds) vs 1000s of l/s SMs and pods, way less competition and the activist edge hasn’t really been arbed out and activism isn’t a commoditized product
recently had an interesting chat w. a proprietary data/research analyst at one of the large NY-based HFs. I think it's really important to note that if you are a company with huge resources (like the firm we were discussing), you have access to really interesting data that is a) expensive and b) extremely messy. If you have trained professionals that understand how to make sense of this messy data (think sattelite data, credit card data, etc.), you can develop a pretty strong alpha when investing in market trends.
totally agree...sometimes you are just amazed by how little work people have done before they deploy capital. Having specialists to process niche data is more than enough to beat 75% of the other institutional investors in the market
Every large hedge fund analyzes these data sets...especially credit card data.
It's not an edge anymore. I've worked for 3 shops so far and all of them have done this.
insider information
Steve Cohen checks out
"dark edge"
Someone told me a story about a LO analyst that had been around for 20+ years--long enough that he saw most companies in his industry go through multiple management changes. Apparently new CEO's would reach out to him for advice given his experience. Whenever management would announce long-term goals, he had the pattern recognition to say if they were achievable or not.
Idk if that story is true but that seems like it would be an edge.
Exactly
Damn that's sick
I think process and discipline gives real "edge" too, doesn't have to be on thesis only. If anything most funds/people blow up because they're lacking the former, not the latter.
At the multimanager level, this is it. Forget about satellite data, anyone can access that these days. But having a robust process and sticking with it is surprisingly uncommon.
I hate getting this question in interviews.
My most profitable ideas have usually been things that are pretty obvious but that I was just a bit early to. I don’t think I had any profound insight in these instances, but I suppose the “edge” was just knowing and staying on top of my coverage space.
I saw another post above pointing out that edge comes from have a good process which I agree with as well.
1. Game selection - play in markets that are inherently less efficient due to less sophisticated competition and access to information. Tilt the field itself so you don't have to bet on yourself being a genius to win. No style points for winning with max difficulty mode in investing. Exit and move on to other such markets if rest of market catches up and supply of capital flows in.
2. Generalist mindset with a broad reference set - this might be a bit contrarian to the consensus superiority of sector specialist model. Tetlock and other "decision science" academics have shown that people make worse forecasts as they are given more detailed info about the specific issue. People that do the best have broad knowledge base and life experiences to analogize from and set more appropriate base rates/benchmarks for forecasting and make greater use of common sense. I personally believe in this as I have gone through the experience of working in a specialist model where I was internally rewarded/incentivized to study every detail/facts about a business (RCG model). Often times, I would squint really hard to find reasons to rationalize being contrarian on some hated/"overlooked" names by clinging to random contrarian factoids or management/channel comments I've sourced through dogged diligence work. Later I realized that I was getting really emotionally invested in taking risk on names that I had sunk the most hours/mental energy into, and willfully ignored the big picture flow of why a business was doomed in the first place (that I likely identified as a risk factor on the first day of working on it but later did mental gymnastics to "mitigate").
I have since switched to being a generalist and draw analogies to other situations I've seen in not just the markets but also just product/business/human stories. My results have been stronger since adopting this mindset. For example, I've avoided a lot of blow-ups in trying to spot sector inflection points by thinking in terms of parallels from what could happen if this played out like [insert diverse analogies - e.g. energy supply/demand cycle, healthcare consolidation, SaaS transition, retail winners/losers and reason why this situation is like or unlike the above, or mix of many]. I am less in the weeds on what the company said in this footnote or that, but I can take a step back and think about the broad patterns at play.
_
Excellent comment. What sort of fund/team/ mandate do you work for?
Can you give any examples of this type of market?
I think the last one on the generalist model/mindset is great. It is somewhat equivalent to Charlie Munger's mental models.
If we don't learn from Charlie Munger, I don't know who else should we learn from. Maybe Warren Buffett.
Extremely well said/articulated
Nobody has a real edge that’s why you’re struggling with it
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