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 "@id": "https://wulfkaal.github.io/entities/performance",
 "identifier": "kaal:entity:performance",
 "name": "Performance",
 "termCode": "performance",
 "inDefinedTermSet": {
  "@id": "https://wulfkaal.github.io/entities/index.json"
 },
 "author": {
  "@type": "Person",
  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0000-0003-0757-275X"
 },
 "dateModified": "2026-07-29",
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   "name": "status",
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   "value": 5
  },
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   "@type": "PropertyValue",
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   "value": [
    "2017",
    "2024"
   ]
  },
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 "subjectOf": [
  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/2959730-011",
   "identifier": "kaal:claim:2959730-011",
   "text": "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.",
   "abstract": "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%.",
   "citation": "Wulf A. Kaal, Blockchain Applications and Fee Structure Developments in Private Investment Funds (2017). SSRN: https://ssrn.com/abstract=2959730",
   "datePublished": "2017",
   "claim_type": "empirical",
   "confidence": "evidenced",
   "is_failure_mode": true,
   "scope_conditions": [
    "nine years into the ten year 2008 to 2017 wager",
    "returns measured net of fees"
   ],
   "source_pdf_sha256": "ebd86972d8817b0c1c80326ae5564476983cedba3f258ff3b2610f156b93fb50",
   "status": "current"
  },
  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/3409548-005",
   "identifier": "kaal:claim:3409548-005",
   "text": "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.",
   "abstract": "However, according to research done by Preqin, the typical systematic fund doesn't always perform as well as funds operated by human managers.",
   "citation": "Kaal, Financial Technology and Hedge Funds (2019). SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3409548",
   "datePublished": "2019",
   "claim_type": "failure",
   "confidence": "evidenced",
   "is_failure_mode": true,
   "scope_conditions": [
    "typical systematic fund",
    "Preqin research as of 2016"
   ],
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   "status": "current"
  },
  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/3409548-006",
   "identifier": "kaal:claim:3409548-006",
   "text": "Hedge funds that base their strategies on artificial intelligence have delivered better results than the industry average over the preceding five years.",
   "abstract": "Hedge funds that base their strategies on AI have provided better results over the last five years than the average.",
   "citation": "Kaal, Financial Technology and Hedge Funds (2019). SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3409548",
   "datePublished": "2019",
   "claim_type": "empirical",
   "confidence": "evidenced",
   "is_failure_mode": false,
   "scope_conditions": [
    "five year window preceding 2019"
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   "status": "current"
  },
  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/4796714-031",
   "identifier": "kaal:claim:4796714-031",
   "text": "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.",
   "abstract": "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.",
   "citation": "Wulf A. Kaal, AI Governance (2024). SSRN: https://ssrn.com/abstract=4796714",
   "datePublished": "2024",
   "claim_type": "empirical",
   "confidence": "evidenced",
   "is_failure_mode": false,
   "scope_conditions": [
    "requires a smooth and strongly convex loss function",
    "demonstrated across convex and nonconvex losses, time invariant and time varying topologies, and IID and non-IID datasets"
   ],
   "source_pdf_sha256": "59fa63bae179e8f9b6b8efbdf90cee28400276512a1b04f9f579a48641305c93",
   "status": "current"
  },
  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/4941807-026",
   "identifier": "kaal:claim:4941807-026",
   "text": "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.",
   "abstract": "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.",
   "citation": "Wulf A. Kaal, AI Governance Via Web3 Reputation System (2024). SSRN: https://ssrn.com/abstract=4941807",
   "datePublished": "2024",
   "claim_type": "condition",
   "confidence": "evidenced",
   "is_failure_mode": false,
   "scope_conditions": [
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   ],
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   "status": "current"
  }
 ],
 "description": "5 claims in the published works of Wulf A. Kaal carry the concept tag 'performance'. Derived node: a roster, not an adjudicated definition."
}