Corey Hoffstein 🏴☠️ ✓
@choffstein · 🎙 www.flirtingwithmodels.com · joined 14 Oct 2009
CEO & CIO, Newfound Research | 🥞 Return Stacked® ETFs | 🌊 Liquidity Cascades | 📆 Rebalance Timing Luck | ⚡️ Risk cannot be destroyed, only transformed.
85 560Followers
1 248Following
42 019Posts total
1.2MViews on collected posts
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State Street w/ largest seeded ETF launch *ever* today…
State Street SPDR UC Investments 90/10 Endowment Strategy Index ETF (UCBG)
Launched w/ *$2.5bil*
UC Investments manages University of California’s retirement, endowment, etc. https://t.co/NnAt44EcxM
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Taking "buy, borrow, die" to a new level.
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I've got a podcast with @variational_lvs coming out soon about Variational's on-chain RWA swaps.
It's a really interesting model. The OLP can segregate flow, aggregate risk, and then hedge via tradfi dealer relationships (over $1bn in capacity at the moment).
Right now, long h
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@choffstein Congrats & great thread Corey.
Also, don’t think I’ve ever seen a bookmark/like ratio > 1
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@MebFaber @CliffordAsness If you made it this far, thank you. I appreciate the generosity of your time. I hope some of these ideas or lessons resonated with you.
It will be fun for me to look back in another 15 and see how many of these stood the test of time.
Until then,
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@MebFaber @CliffordAsness 16. The Market is Usually Right
Did I say 15 ideas and lessons? Here’s a bonus lesson that’s taken me far longer to learn than I’d care to admit.
The market is, for the most part, usually right. It took me applying Lesson #11 – “What’s the Trade” –
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@MebFaber @CliffordAsness 15. A backtest is just a single draw of a stochastic process.
As the saying goes, nobody has ever seen a bad backtest.
And our industry, as a whole, has every right to be skeptical about backtests. Just about every seasoned quant can tell you a stor
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@MebFaber @CliffordAsness 14. Jensen’s Inequality
Jensen’s inequality basically says, “a function applied to a mean does not necessarily equal the mean applied after the function.”
What does that mean and how is it useful? Consider this example.
You’re building a simple mo
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@MebFaber 13. Behavioral Time is decades longer than Statistical Time
I recently stole this one from @CliffordAsness. This point has less to do with any practical portfolio construction thoughts or useful mental models. It’s just simply acknowledging that managing money in
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@MebFaber 12. The trade-off between Type I and Type II errors is asymmetric
Academic finance is obsessed with Type I errors. The literature is littered with strategies exhibiting alphas significant at a 5% level. The literature wants to avoid reporting false positives.
In pra
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@MebFaber 11. Always ask: “What’s the trade?”
In July 2019, Greek 10-Year Bonds were trading with a yield that was nearly identical to US 10-Year Bonds.
By December, the yield on Greek 10-year bonds was 40 basis points under US 10-year bonds. How could that make any sense?
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@MebFaber 10. Under strong uncertainty, “halvsies” can be an optimal decision.
During the factor wars of the mid-2010s, a war raged between firms as to what the best portfolio construction approach was: mixed or integrated.
The mixed approach said that each factor should be c
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@MebFaber 9. Certain signals are only valuable at extremes.
I was sent a chart recently with a plot of valuations for U.S. large-cap, mid-cap, and small-cap stocks. The valuations were represented as an average composite of price-to-earnings, price-to-book, and price-to-sale
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@MebFaber 8. The more diversified a portfolio is, the higher the hurdle rate for market timing.
Market timing is probably finance’s most alluring siren’s song. It sounds so simple. Whether it’s market beta or some investment strategy, we all want to say: “just don’t do the
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@MebFaber 7. It’s long/short portfolios all the way down.
I don’t remember when this one came to me, but it’s one of my favorite mental models. The phrase is a play off of the “Turtles all the way down” expression.
Every portfolio, and every portfolio decision, can be decompos
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@MebFaber 6. It’s usually the unintended bets that blow you up.
I once read a comic – I think it was Farside, but I haven’t been able to find it – that joked that the end of the world would come right after a bunch of scientists in a lab said, “Neat, it worked!”
It’s very ra
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@MebFaber 5. The philosophical limits of diversification: if you diversify away all the risk, you shouldn’t expect any reward.
One of the most common due diligence questions is, “when doesn’t this strategy work?” It’s an important question to ask for making sure you understa
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4. Diversification has multiple forms.
In 2007, @MebFaber published his paper A Quantitative Approach to Tactical Asset Allocation where he explored the application of a 10-month moving average as a timing model on a variety of asset classes.
It will likely go down in
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3. Diversifying, cheap beta is worth just as much as equally diversifying, expensive alpha.
I’ll put this lesson in the category of, “things that are obvious but might need to be said anyway.”
Our industry is obsessed with finding alpha. But, for the most part, a portfolio
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2. No Pain, No Premium
The philosophy of “no pain, no premium” is just a reminder that over the long run, we get paid to bear risk. And, eventually, risk is likely going to manifest and create losses in our portfolio. After all, if there were no risk of losses, then why
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1. "Risk cannot be destroyed, only transformed."
For graduate school, I pursued my MS in Computational Finance at Carnegie Mellon University. This financial engineering program is a cross-disciplinary collaboration between the finance, mathematics, statistics, and
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This is going to be an obnoxiously long thread, with obnoxiously long posts, so if you'd rather read it as a blog or listen to an audio version, here are the links:
📝 https://t.co/UDkJl4WnjW
🎙️ Spotify: https://t.co/RGo9SK3Xqo
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My company, Newfound Research, turned 15 today.
Coming up on this anniversary, I reflected quite a bit on my career. I’m not sure why, but this milestone feels larger than I would've expected.
So I decided to write something.
15 Ideas, Frameworks, and Lessons from 15 Years
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Im Vergleich zu Konten gleicher Größe
4 Beiträge aus den letzten 90 Tagen, verglichen mit der Größenklasse 10K–100K Follower. wird weit gezeigt, aber nur wenige dieser Zuschauer reagieren.
Medianaufrufe14 186dieses Konto924Median für 10K–100K
Reichweite, %16.58%dieses Konto3.62%Median für 10K–100K
Interaktion, %0.37%dieses Konto1.52%Median für 10K–100K
| Kennzahl | Dieses Konto | Median für 10K–100K | Verhältnis |
|---|---|---|---|
| Medianaufrufe pro Beitrag | 14 186 | 924 | 15.3× |
| Reichweite (Aufrufe ÷ Follower) | 16.58% | 3.62% | 4.58× |
| Interaktionsrate | 0.37% | 1.52% | 0.25× |
Weitere Konten dieser Größe → Mit einem anderen Konto vergleichen → Wie diese Vergleichswerte entstehen →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
22.9K28 Aug
31.3K
25.2K
15.9K
14.8K
14.2K
41.2K
14.4K
13.2K
1.1K29 Aug
9.7K1 Sep
14.6K2 Sep
54.5K
13.7K
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.28%28 Aug
0.30%
0.44%
0.50%
0.61%
0.50%
0.31%
0.70%
1.77%
0.37%29 Aug
0.94%1 Sep
0.40%2 Sep
0.14%
0.35%
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.
What the audience does
Likes52.4%3 804 in total
Reposts5.4%394 in total
Replies2.8%200 in total
Quotes1.8%132 in total
Bookmarks37.6%2 731 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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