{
  "docId": "019dd923-5e88-73ef-bd5a-16f06e21d85d",
  "docSlug": "859fd9d3a5bec613",
  "documentTitle": "Goldman Sachs Ayco Outlook 2024 Webinar Materials",
  "authorId": "GoldmanSachs",
  "authorName": "Goldman Sachs",
  "documentKindSlug": "consulting-deck",
  "documentKindLabel": "Consulting deck",
  "sourceTypeSlug": "equity_research",
  "sourceTypeLabel": "Equity research",
  "presentationDate": null,
  "orientation": "portrait",
  "aspectRatio": 0.773,
  "pageNumber": 8,
  "pageCount": 12,
  "prevPage": 7,
  "nextPage": 9,
  "slideType": "appendix_methodology",
  "function": "present_framework",
  "density": "dense",
  "nDataPoints": 0,
  "notes": "Contains mathematical formulas for portfolio optimization and visual comparisons of asset allocation pie charts.",
  "elementsJson": [
    "pie_chart",
    "paragraph",
    "other"
  ],
  "metadataConfidence": 1,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5e88-73ef-bd5a-16f06e21d85d/8",
  "deckHref": "/decks/019dd923-5e88-73ef-bd5a-16f06e21d85d",
  "deckJsonHref": "/decks/019dd923-5e88-73ef-bd5a-16f06e21d85d.json",
  "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5a-16f06e21d85d#slide-8",
  "components": [
    {
      "bbox": null,
      "kind": "callout",
      "text": "Robust optimization lets us construct portfolios with fewer subjective inputs.",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd952-1d6d-7403-aea0-8324d68d3a59",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.15,
        "w": 0.15,
        "x": 0.75,
        "y": 0.75
      },
      "kind": "chart",
      "text": "Robust Optimization",
      "attrs": null,
      "subkind": "pie",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "12437dcc-d079-4303-875c-7cffaaea83d5",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.15,
        "w": 0.15,
        "x": 0.51,
        "y": 0.75
      },
      "kind": "chart",
      "text": "Traditional Optimization",
      "attrs": null,
      "subkind": "pie",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "1de563f6-2ea0-4b73-b679-5f17c8e29808",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.15,
        "w": 0.15,
        "x": 0.118,
        "y": 0.165
      },
      "kind": "chart",
      "text": "Undiversified Factor Allocation",
      "attrs": null,
      "subkind": "pie",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "c1edf659-21a1-4d0d-b727-e0164249dc4a",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.15,
        "w": 0.15,
        "x": 0.435,
        "y": 0.165
      },
      "kind": "chart",
      "text": "Diversified Factor Allocation",
      "attrs": null,
      "subkind": "pie",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "e43f617b-dbcc-4f3d-95c0-01cd03004109",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.08,
        "w": 0.383,
        "x": 0.51,
        "y": 0.121
      },
      "kind": "paragraph",
      "text": "Our robust optimization technique seeks to address this shortcoming by explicitly accounting for standard errors of expected returns in portfolio construction.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "5c5585eb-e733-4cf6-817b-d942f20a85fc",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.1,
        "w": 0.383,
        "x": 0.103,
        "y": 0.418
      },
      "kind": "paragraph",
      "text": "Many approaches have tried to address this well-known shortcoming, including the Bayesian solution of Black and Litterman [1992], the re-sampling method of Michaud [1998], and the risk parity approach.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "90c78a3d-c282-4c2b-bc51-82ad4620b09c",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.015,
        "w": 0.383,
        "x": 0.103,
        "y": 0.121
      },
      "kind": "title",
      "text": "EXHIBIT 3 Diversification from a New Perspective",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "6c0a835f-f10a-4f26-98ec-b32e62ad2380",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.03,
        "w": 0.383,
        "x": 0.51,
        "y": 0.688
      },
      "kind": "title",
      "text": "EXHIBIT 4 Robust Optimization Acknowledges that Expected Returns are Uncertain",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "a746a8b9-7887-45a5-a737-9a1caadc0c80",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [
    {
      "name": "2x2 matrix",
      "slug": "matrix-2x2",
      "agent": null,
      "layer": "slide",
      "matchId": "30b44efe-5ad2-470c-9c1f-68d7f16febf2",
      "evidence": "chart/pie: Undiversified Factor Allocation chart/pie: Diversified Factor Allocation",
      "confidence": 0.5
    }
  ],
  "frameworks": [
    {
      "name": "Mean-Variance Optimization",
      "slug": null,
      "matchId": "42c184a3-eb72-4a80-9a81-44c675d1b500",
      "evidence": "Mentions Markowitz [1952] and portfolio weights formulas",
      "confidence": 1
    }
  ],
  "arcBeats": [
    {
      "to": 9,
      "from": 7,
      "beatId": "bdd15ddd-65aa-42c8-84ea-aed5da8041d9",
      "arcName": "The Consultant's Gambit",
      "arcSlug": "consultants-gambit",
      "beatName": "Evidence & Proof",
      "beatSlug": "consultants-gambit-evidence-proof",
      "evidence": "Exhibits providing data and examples of risk premium profiles and portfolio optimization",
      "position": 3,
      "confidence": 0.8,
      "parentBeatName": "Evidence",
      "parentBeatSlug": "evidence"
    }
  ],
  "loops": [
    {
      "to": 9,
      "from": 6,
      "name": "Zoom In",
      "slug": "06-zoom-in",
      "bestFor": "Technical deep-dives, case studies, detailed analysis",
      "matchId": "bb377d98-9315-4535-83e4-250b5d69a933",
      "evidence": "Exhibits providing detailed data and analysis",
      "position": 0,
      "objective": "What are the benefits of a multi-factor approach?",
      "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication",
      "confidence": 0.7,
      "description": "Start broad, then progressively focus on specific details that prove your point"
    },
    {
      "to": 10,
      "from": 6,
      "name": "Mece Breakdown",
      "slug": "40-mece-breakdown",
      "bestFor": "Problem structuring, ensuring completeness, strategic analysis",
      "matchId": "b0923f47-e87b-4109-b1cb-650ffea08aa8",
      "evidence": "Exhibits breaking down the methodology and application",
      "position": 1,
      "objective": "How does the multi-factor approach work?",
      "structure": "The Whole -> Category A (distinct) -> Category B (distinct) -> Category C (distinct) -> Complete Coverage",
      "confidence": 0.6,
      "description": "Divide a complex topic into mutually exclusive, collectively exhaustive categories"
    }
  ],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}