Rich Falk-Wallace ✓

@richfalkwallace · New York, NY · joined 25 Nov 2021

Arcana | Idio, crowding, risk, & performance

13 593Followers

850Following

614Posts total

54.9KViews on collected posts

Latest posts

Rich Falk-Wallace ✓@richfalkwallace19 Aug

@FundamentEdge is hosting an “AI Investing Forum” on 9/17 at 5PM in NYC, with great content and an exciting line up. Sign up here if you’d like to attend: https://t.co/jGYms0zvXB https://t.co/kjtIw5O49l 897 views · 1 likes · 1 reposts · 0 replies Open on X →

Rich Falk-Wallace ✓@richfalkwallace18 Aug

Excited for this one. Join us! 1.4K views · 4 likes · 0 reposts · 0 replies Open on X →

Rich Falk-Wallace ✓@richfalkwallace18 Aug

NEW EVENT: AI Investing Forum, NYC Sep 17th Since our first AI webinar nearly two years ago, I have had the opportunity to have many conversations with builders at the intersection of investing & AI. It has been a fascinating project and a fun endeavor for me personally (I ht 14.3K views · 65 likes · 6 reposts · 10 replies Open on X →

Rich Falk-Wallace ✓@richfalkwallace12 Jul

What risk models do is "predict future correlation" in your portfolio That's important because all drawdowns (or benchmark underperformances if you are long) are correlational (1) -- -- and in the limit all correlation in your book drags your Sharpe ratio towards the market ht 5.3K views · 39 likes · 4 reposts · 2 replies Open on X →

Rich Falk-Wallace ✓@richfalkwallace22 Mar 2025

'Performance Decomposition' intro slides. Outline: 1. The "fundamental law" & its adaptations 2. Sharpes for long term investors 3. Decomposing the information ratio 4. Decomposing hit rate 5. From information -> to Sharpe ratio 6. Summary This is part of the third week of ht 10.3K views · 78 likes · 9 reposts · 19 replies Open on X →

Rich Falk-Wallace ✓@richfalkwallace28 Jan 2025

@richfalkwallace @__paleologo The world is sparse because it's regular, not because it's heavy-tailed. Regularity, smoothness, repetition of patterns, etc. are the reason many datasets of the world are of way lower intrinsic dimension than they seem. Sparsity is a way to articula 453 views · 4 likes · 1 reposts · 0 replies Open on X →

Rich Falk-Wallace ✓@richfalkwallace28 Jan 2025

Good illustration of actually my favorite framing of factors, also from @__paleologo : https://t.co/97ga0F0FtD 22.1K views · 92 likes · 12 reposts · 3 replies Open on X →

Against accounts of the same size

4 posts from the last 90 days, next to the 10K–100K follower range. shown widely, but few of those viewers react.

Median views3 382this account924median for 10K–100K

Reach, %24.88%this account3.62%median for 10K–100K

Engagement, %0.45%this account1.52%median for 10K–100K

Metric	This account	Median for 10K–100K	Ratio
Median views per post	3 382	924	3.66×
Reach (views ÷ followers)	24.88%	3.62%	6.87×
Engagement rate	0.45%	1.52%	0.30×

Others in this range → Compare with another account → How these benchmarks are built →

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

22.1K28 Jan

453

10.3K22 Mar

5.3K12 Jul

14.3K18 Aug

1.4K

89719 Aug

Last 7 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.49%28 Jan

1.10%

1.04%22 Mar

0.84%12 Jul

0.57%18 Aug

0.28%

0.33%19 Aug

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.

What the audience does

Likes42.6%283 in total

Reposts5.0%33 in total

Replies5.1%34 in total

Quotes0.6%4 in total

Bookmarks46.7%310 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

Followers by day

5 Sep

Daily snapshots since 05 Sep 2026; the dashed line is the starting count.