# Performance

`kaal:entity:performance`

**Status.** derived

This node is assembled mechanically from the 5 claims that carry the concept tag `performance`. It is a roster of what the corpus says under this term. It is **not** an adjudicated definition: no single statement here has been ruled canonical, and no first-appearance call has been made. Read the claims and judge for yourself.

## Every claim under this term

5 claims across 4 works, 2017 to 2024.

**2017**

- [2959730-011](https://wulfkaal.github.io/claims/2959730-011) [empirical/evidenced] *(failure mode)* -- In the Buffett and Seides wager on net of fee returns, the passive S&P 500 index position produced a 7.1% compounded annual return after nine years against 2.2% for the five hedge funds of funds, evidence that industry performance does not justify the 2/20 fee structure.
  > A year before the end of the wager, Buffet's nine-year result is a 7.1 % compounded annual return compared to Seides's 2.2%.
  Wulf A. Kaal, Blockchain Applications and Fee Structure Developments in Private Investment Funds (2017). SSRN: https://ssrn.com/abstract=2959730

**2019**

- [3409548-005](https://wulfkaal.github.io/claims/3409548-005) [failure/evidenced] *(failure mode)* -- Systematic, computer model driven funds do not reliably outperform human managed funds: research finds the typical systematic fund does not always perform as well as funds run by human managers.
  > However, according to research done by Preqin, the typical systematic fund doesn't always perform as well as funds operated by human managers.
  Kaal, Financial Technology and Hedge Funds (2019). SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3409548
- [3409548-006](https://wulfkaal.github.io/claims/3409548-006) [empirical/evidenced] -- Hedge funds that base their strategies on artificial intelligence have delivered better results than the industry average over the preceding five years.
  > Hedge funds that base their strategies on AI have provided better results over the last five years than the average.
  Kaal, Financial Technology and Hedge Funds (2019). SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3409548

**2024**

- [4796714-031](https://wulfkaal.github.io/claims/4796714-031) [empirical/evidenced] -- Decentralized Federated Learning reaches the global minimum with zero performance gap and matches the convergence rate of centralized methods when the loss function is smooth and strongly convex.
  > The DeceFL approach ensures that every client can reach the global minimum with zero performance gap and achieve the same convergence rate as centralized methods when the loss function is smooth and strongly convex.
  Wulf A. Kaal, AI Governance (2024). SSRN: https://ssrn.com/abstract=4796714
- [4941807-026](https://wulfkaal.github.io/claims/4941807-026) [condition/evidenced] -- Decentralized Federated Learning lets every client reach the global minimum with zero performance gap and at the same convergence rate as centralized methods, but only when the loss function is smooth and strongly convex.
  > The DeceFL approach ensures that every client can reach the global minimum with zero performance gap and achieve the same convergence rate as centralized methods when the loss function is smooth and strongly convex.
  Wulf A. Kaal, AI Governance Via Web3 Reputation System (2024). SSRN: https://ssrn.com/abstract=4941807

## Verify

Every claim above resolves to a record carrying a verbatim source quote, the sha256 of the source PDF, and a preformatted citation. Nothing here asks to be taken on trust.

    curl -s https://wulfkaal.github.io/entities/performance.md | sha256sum

**Canonical form.** This markdown file is the canonical hashed representation of this entity node. Its sha256 is the content hash.