Focused on the Craft of Investing
Joined April 2018
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I’ve been thinking about this a lot lately from the allocator side. Agree most funds blow out at the lows, but to me that’s a failure of process, not proof that duration is dead.
For LOs, a differentiated 5Y view on fundamentals vs. expectations is still the edge. What’s changed is that thematic flows and vol now swing prices much further around that view. The edge is now duration + sizing: adding/trimming as price-implied expectations change.
The market leaves room for this because it’s so incremental. Below is a great poll I saw a while back: same multiple, one company slowing from 20% to 18% growth, the other accelerating from 2% to 5%. Nearly half picked the 5% grower. The second derivative drives a lot, and front-running near-term revisions is a great strategy. LOs struggle to compete with that in the short term, but it also creates longer-dated mispricings for them.
Given today’s market structure, I think LOs are forced to increase turnover to keep their edge, as long as it’s driven by valuation vs. their long-term view, not by price action. LOs running the same static book from 10 years ago are leaving a lot on the table.
here's the poll I keep coming back to nitter.cf/chat_SBC/status/209444…
assuming same multiple on real #s, would you rather buy?
55%20% growth slowing to 18%
45%2% growth accel to 5%
1,006 votes • Final resultsWhat a great post. Much sharper view on what I was trying to articulate.
DURATION 🚫 TURNOVER
"we have duration" is the mantra investors like to say ... it Is this nice marketing pitch slogan... but Its really just a crutch
investors often confuse DURATION & TURNOVER. this is a lesson I learned my first week as an intern close to 15 yrs ago at SAC.... we were prepping for earnings and AMZN had eps coming up, we thought the next 6m would be confusing for investors with topline misses but GM better... I liked it long term espec trading at mkt cap discount to WMT then... I finished my pitch with "we have duration".. I was confident in my work... BIG MIKE (who now runs one of the biggest Long only shops in the world)... stopped and we had a long talk...
Essentially his message was : Duration and TURNOVER are 2 very different things... when people confuse the two they make sloppy mistakes. You can love something long term and have duration... but still sell it and turn the size or whole position over if something is changing. Its your job to figure out when something has "changed"... if something has changed and you rely on some concept of duration you are going to blow up
It took me a while to understand what he meant...people talk about Duration like this "philosophy" ... like a religion or something they physically "have"... its not. Its actually CAN MY STRATEGY WITHSTAND VOLATILITY WITHOUT BEING STOPPED OUT. CAN I MAKE IT THROUGH THE PATH? whether its a risk model/team, LPs, or a boss.... everyone has to answer somewhere
a dogmatic view on "this is how we always did it"... "we dont sell volatility" .... "I dont want to have to explain a change in view"... "duration is a competitive advantage" ... its all philosophy gooblygock and EGO... yes that word that crushes smart people... EGO (more on that later)
duration is how long you can be wrong before the market fires you
turnover is how fast you fire yourself when you indeed wrong
Just remember... DURATION is the only risk management PHRASE that gets louder as things move against you ... thats the tell to move your feet as an active manager
AI + Equity Research
1/ Same line as you. LLM writes assumptions and reasons. Scripts compute every number; gates reject bad inputs. A model never types a number into data.
2/ Routed by job, not measured quality per $. Claude Code and Codex do the modeling, research and reviews, depending on where I have usage. Gemini and Grok scout podcasts. No A/B yet.
3/ Reviewer is a fresh agent that did none of the work, 2 rounds max. It catches real errors (e.g. a growth % calculated across a loss-to-profit switch). No cross-family numbers.
4/ Leakage: I skip backtests for judgment. Models are built from filings only, blind to consensus and prices. Every view is locked (commit + hash) before earnings and scored after. Forward-only sidesteps "the model read the future."
5/ What broke: pass/fail bars. Same scan went 0 of 10 ideas, then 10 of 11, then 2 of 27. Forced ranking worked better. Rules change only on patterns across 3+ names.
6/ Specialists with narrow context, one plan file each. One master chat owns the shared docs. Hand-off loss bit when 23 chats came back in 23 formats. Fixed columns fixed it.
7/ Evals are my weakest part too. Script regression tests, models checked against filings, views scored at each print. No golden set.
8/ Plan, rules version, sources and output hashes saved per run. Not pinning model versions yet.
9/ Plan limits, not API billing, so no $ per report. I log usage per job, pilot 5 before running 18, and reuse prior work.
Honest question for AI engineers and quants actually running this in production:
For analysis workflows like DCF/valuation, bank-style research reports, backtests, and recursive research loops (generate thesis → test → log → new thesis), how are you allocating models?
One frontier model everywhere, or routed by role (cheap model for scouting/extraction, frontier for reasoning, a different model family as adversarial reviewer)?
Not looking for favorites. Looking for what you measured: error rates, cost per run, what broke.
Any resources? Frontier lab Ais are so bias that its not possible for me to get clear answers.
Thread below with the specifics.
And, what's helped most, in order:
10/ Blind first. Models are built from filings with no consensus or prices. The analyst then sizes beliefs against the Street, never against our own model.
11/ Lock it before the print. Every view and lead is committed and hashed with a confirming event and date. Hard to fool yourself after the fact.
12/ Pilot before fanning out. Running 5 groups before 23 caught a missing input and a broken bar that would have wasted every run.
13/ Rank, don't pass/fail. Models hedge every verdict. "If you could model one name in this group, which and why?" got better signal than any threshold.
14/ One master chat, one plan file per job, fixed output formats. Every new chat reads a handoff first. Most of my failures were hand-offs, not reasoning.
For equity research, I'm finding that building a new company model costs about $60-80 in compute at API prices, and a quarterly update about $15-35.
The point being, on a $200/month subscription, I can build roughly 40-50 models a week before hitting the limit.
Holy sh*t, Anthropic’s subscriptions offer so much more value, even compared to the new and efficient GPT-6.1 Sol. It’s not even close.
SemiAnalysis puts Opus 5.5 at over 5x the API-equivalent value of GPT-6.1 Sol for agentic workloads at the same subscription price. It’s not even close on this measure.
And on top: OpenAI just halved the usage limits on its $200 ChatGPT plan.
Comes down to underwriting duration as a near-certainty. Fine until the market questions duration. Valuation/multiple is levered to duration, and their whole book is one duration bet. Problem is their FCF/growth lens can’t tell you if the market’s right until years later.
Also a good reminder that the best investors build an investment process that gets them to #3/#4 but we're human and can block ourselves from getting past #1/#2-
1) never learn from your own mistakes
2) you learn from your own mistakes
3) you learn from other people's mistakes
4) you build a philosophy/process that adapts and does not need to see mistakes to navigate to the market
I had to double check this quote for myself. CEO of $TSM, love it.
But of course, caveat being much of it is timing. Capex hits the balance sheet 12-24 months before being monetized. MSFT looks best partly because more of its buildout is leases, third party capacity, etc.
Any finance folks tried starting an SMB bookkeeping side hustle as a way into fractional CFO/advisory work?
I've mostly ignored SMB Twitter, but lately I've had the itch to be more entrepreneurial. I only work 20-30 hours a week on the allocator side, and comp is very good but the upside is somewhat capped.
A CFA + equity research background is obviously overkill for bookkeeping, but I'm wondering if it could be a good funnel for upselling advisory.
SpaceX $SPCX projected to cross $3T in revenue in a decade and $5T in 15 years. So like 15% of today's US GDP. Got it.
Over the last ~12 months, my podcast feed has outgrown what I can consume in a week. Really like the idea behind the Podcast AI tab by @JaredKubin
