Meta

To do

• decide between having isolated Concept Dictionaries (i.e., one for Behavioral Finance, one for Cloud Computing concepts, etc.) or having one unified Dictionary with tags for each relevant subject — multi-tagging makes more sense
  • transition concepts out of: https://docs.google.com/spreadsheets/d/1ICjRp0lgXVDwAmxi9yKaEplWO1zwS_6Bj3Uo8X96UoU/edit?usp=sharing
    • figure out how to link "related concepts" to primary concepts
    • populate each term with tags
  • delete Google Sheet when finished
• is there a better way than WebVOWL to spatially map concepts? — want to do concept grouping and explore semantic/logical/etc. relationships; different concepts have different "levels" (i.e., L1: "commoditizing the complement" → L2: [ex 1, ex 2, ex 3] + lateral relationships in L1)
• create image gallery for abstractions that are best expressed visually + collect existing image-based concepts from various folders

Concept Dictionary

See Also:

Dictionary

Lists

List of "core concept" candidates (ongoing core dump)

Platforms

Turn below into their own entries & then add info within the pages

• zero-billion dollar markets
• model drift
• strategy decay
  • related: https://dcfstate.substack.com/p/angle-shots-and-meta-games, https://www.strangeloopcanon.com/p/strategy-decay-as-an-institutional, [excerpt from Intelligent Investor about January effect], [excerpt from OSAM youtube video on factor decay]
  • from The Diff:
    • "It's practically an iron law in finance that time-weighted returns are higher than dollar-weighted returns: a fund that performs well will raise more money, and that subsequent performance won't necessarily be as high as the performance that got them all that attention in the first place. (There are investors who have a good lifetime track record but generated negative lifetime returns in dollars because they peaked in assets under management at the worst possible time.) ARK, for example, generated 5.24% annual returns since inception weighted by dollars ($, WSJ), underperforming the S&P. It's not quite a fair comparison, since the people who didn't put money in ARK when it was low, but added money when it was high, wouldn't necessarily have had any better luck timing the S&P. But it does illustrate the challenging math of long-term returns. They revert to some kind of mean, but the easiest time to raise money is after a run of good luck."
• overfitting