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Search, Evaluate, and Read Evidence","\u002Fen\u002Facademic-writing\u002F02-reading-and-literature-matrix","en\u002Facademic-writing\u002F02-reading-and-literature-matrix",{"title":27,"path":28,"stem":29},"3. From Sources to Synthesis and Argument","\u002Fen\u002Facademic-writing\u002F03-arguments-outlines-and-paragraphs","en\u002Facademic-writing\u002F03-arguments-outlines-and-paragraphs",{"title":31,"path":32,"stem":33},"4. Literature Review and a Defensible Gap","\u002Fen\u002Facademic-writing\u002F04-writing-the-literature-review","en\u002Facademic-writing\u002F04-writing-the-literature-review",{"title":35,"path":36,"stem":37},"5. Research Design, Evidence, and Core Sections","\u002Fen\u002Facademic-writing\u002F05-core-sections-and-evidence","en\u002Facademic-writing\u002F05-core-sections-and-evidence",{"title":39,"path":40,"stem":41},"6. Citation, Paraphrasing, Integrity, and AI","\u002Fen\u002Facademic-writing\u002F06-citation-paraphrasing-and-integrity","en\u002Facademic-writing\u002F06-citation-paraphrasing-and-integrity",{"title":43,"path":44,"stem":45},"7. Revision, Review, and Submission","\u002Fen\u002Facademic-writing\u002F07-revision-style-and-submission","en\u002Facademic-writing\u002F07-revision-style-and-submission",{"title":47,"path":48,"stem":49},"8. Research Workbook","\u002Fen\u002Facademic-writing\u002F08-literature-review-checklist-and-template","en\u002Facademic-writing\u002F08-literature-review-checklist-and-template",{"title":51,"path":52,"stem":53},"9. Research Workflow and Tools","\u002Fen\u002Facademic-writing\u002F09-tools-for-academic-writing-and-literature-management","en\u002Facademic-writing\u002F09-tools-for-academic-writing-and-literature-management",{"title":55,"path":56,"stem":57},"10. Worked Project — From Question to Defensible Conclusion","\u002Fen\u002Facademic-writing\u002F10-worked-example-from-block-structure-to-question-chain","en\u002Facademic-writing\u002F10-worked-example-from-block-structure-to-question-chain",{"title":59,"path":60,"stem":61,"children":62,"page":249},"Accounting","\u002Fen\u002Faccounting","en\u002Faccounting",[63,69,87,93,193],{"title":64,"path":65,"stem":66,"children":67},"Accounting — From Evidence to Decisions","\u002Fen\u002Faccounting\u002F00-index","en\u002Faccounting\u002F00-index",[68],{"title":64,"path":65,"stem":66},{"title":70,"path":71,"stem":72,"children":73},"Accounting Appendix","\u002Fen\u002Faccounting\u002Fappendix","en\u002Faccounting\u002Fappendix\u002Findex",[74,75,79,83],{"title":70,"path":71,"stem":72},{"title":76,"path":77,"stem":78},"Worked Examples and Error Diagnosis","\u002Fen\u002Faccounting\u002Fappendix\u002F24-examples-pitfalls","en\u002Faccounting\u002Fappendix\u002F24-examples-pitfalls",{"title":80,"path":81,"stem":82},"Accounting Glossary","\u002Fen\u002Faccounting\u002Fappendix\u002F26-glossary","en\u002Faccounting\u002Fappendix\u002F26-glossary",{"title":84,"path":85,"stem":86},"Reading and Evidence Map","\u002Fen\u002Faccounting\u002Fappendix\u002F27-reading-map","en\u002Faccounting\u002Fappendix\u002F27-reading-map",{"title":88,"path":89,"stem":90,"children":91},"Northstar Record-to-Decision Capstone","\u002Fen\u002Faccounting\u002Fcapstone","en\u002Faccounting\u002Fcapstone\u002Findex",[92],{"title":88,"path":89,"stem":90},{"title":94,"path":95,"stem":96,"children":97},"Financial Accounting","\u002Fen\u002Faccounting\u002Ffinancial-accounting","en\u002Faccounting\u002Ffinancial-accounting\u002Findex",[98,99,117,131,165,179],{"title":94,"path":95,"stem":96},{"title":100,"path":101,"stem":102,"children":103},"1. Foundations","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations","en\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002Findex",[104,105,109,113],{"title":100,"path":101,"stem":102},{"title":106,"path":107,"stem":108},"Objectives and Qualitative Characteristics","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F01-objectives-qualitative-characteristics","en\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F01-objectives-qualitative-characteristics",{"title":110,"path":111,"stem":112},"Equation and Elements","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F02-equation-elements","en\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F02-equation-elements",{"title":114,"path":115,"stem":116},"Accrual Basis, Estimates and Periods","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F03-accounting-bases-principles","en\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F03-accounting-bases-principles",{"title":118,"path":119,"stem":120,"children":121},"2. Recording Transactions","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions","en\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002Findex",[122,123,127],{"title":118,"path":119,"stem":120},{"title":124,"path":125,"stem":126},"Double-Entry and Debit\u002FCredit","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002F04-double-entry-dr-cr","en\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002F04-double-entry-dr-cr",{"title":128,"path":129,"stem":130},"Accounting Cycle","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002F05-accounting-cycle","en\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002F05-accounting-cycle",{"title":132,"path":133,"stem":134,"children":135},"3. Measurement and Adjustments","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002Findex",[136,137,141,145,149,153,157,161],{"title":132,"path":133,"stem":134},{"title":138,"path":139,"stem":140},"Revenue Recognition","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F07-revenue-recognition","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F07-revenue-recognition",{"title":142,"path":143,"stem":144},"Inventory","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F08-inventory","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F08-inventory",{"title":146,"path":147,"stem":148},"Receivables and Expected Credit Losses","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F09-receivables","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F09-receivables",{"title":150,"path":151,"stem":152},"Property, Plant and Equipment","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F10-ppe","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F10-ppe",{"title":154,"path":155,"stem":156},"Intangible Assets","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F11-intangibles","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F11-intangibles",{"title":158,"path":159,"stem":160},"Leases","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F12-leases","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F12-leases",{"title":162,"path":163,"stem":164},"Current and Deferred Income Tax","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F13-income-tax","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F13-income-tax",{"title":166,"path":167,"stem":168,"children":169},"4. Reporting and Cash","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash","en\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002Findex",[170,171,175],{"title":166,"path":167,"stem":168},{"title":172,"path":173,"stem":174},"Linked Financial Statements","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002F06-financial-statements","en\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002F06-financial-statements",{"title":176,"path":177,"stem":178},"Cash, Reconciliation and Internal Control","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002F14-cash-control","en\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002F14-cash-control",{"title":180,"path":181,"stem":182,"children":183},"5. Analysis and Comparison","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison","en\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002Findex",[184,185,189],{"title":180,"path":181,"stem":182},{"title":186,"path":187,"stem":188},"Ratio Analysis","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F15-ratio-analysis","en\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F15-ratio-analysis",{"title":190,"path":191,"stem":192},"IFRS versus US GAAP","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F25-ifrs-gaap","en\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F25-ifrs-gaap",{"title":194,"path":195,"stem":196,"children":197},"Management Accounting","\u002Fen\u002Faccounting\u002Fmanagement-accounting","en\u002Faccounting\u002Fmanagement-accounting\u002Findex",[198,199,217,235],{"title":194,"path":195,"stem":196},{"title":200,"path":201,"stem":202,"children":203},"1. Cost Foundations","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations","en\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002Findex",[204,205,209,213],{"title":200,"path":201,"stem":202},{"title":206,"path":207,"stem":208},"Cost Concepts and Behaviour","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F16-cost-concepts","en\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F16-cost-concepts",{"title":210,"path":211,"stem":212},"Costing Systems","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F17-costing-systems","en\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F17-costing-systems",{"title":214,"path":215,"stem":216},"Variable and Absorption Costing","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F19-variable-vs-absorption","en\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F19-variable-vs-absorption",{"title":218,"path":219,"stem":220,"children":221},"2. Planning and Control","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control","en\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002Findex",[222,223,227,231],{"title":218,"path":219,"stem":220},{"title":224,"path":225,"stem":226},"Cost–Volume–Profit Analysis","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F18-cvp-analysis","en\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F18-cvp-analysis",{"title":228,"path":229,"stem":230},"Budgeting and Variance Analysis","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F20-budgeting-variances","en\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F20-budgeting-variances",{"title":232,"path":233,"stem":234},"Performance Measurement","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F21-performance-measurement","en\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F21-performance-measurement",{"title":236,"path":237,"stem":238,"children":239},"3. Decisions and Investment","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment","en\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002Findex",[240,241,245],{"title":236,"path":237,"stem":238},{"title":242,"path":243,"stem":244},"Short-Term Decisions","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002F22-short-term-decisions","en\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002F22-short-term-decisions",{"title":246,"path":247,"stem":248},"Capital Budgeting","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002F23-capital-budgeting","en\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002F23-capital-budgeting",false,{"title":251,"path":252,"stem":253,"children":254,"page":249},"Business Analytics","\u002Fen\u002Fbusiness-analytics","en\u002Fbusiness-analytics",[255,261,279,305,335,365,383,400],{"title":256,"path":257,"stem":258,"children":259},"Business Analytics — From Data to Defensible Action","\u002Fen\u002Fbusiness-analytics\u002F00-index","en\u002Fbusiness-analytics\u002F00-index",[260],{"title":256,"path":257,"stem":258},{"title":262,"path":263,"stem":264,"children":265},"0. Decision and Data Foundations","\u002Fen\u002Fbusiness-analytics\u002F00-intro","en\u002Fbusiness-analytics\u002F00-intro\u002Findex",[266,267,271,275],{"title":262,"path":263,"stem":264},{"title":268,"path":269,"stem":270},"Decision Framing","\u002Fen\u002Fbusiness-analytics\u002F00-intro\u002F01-decision-framing","en\u002Fbusiness-analytics\u002F00-intro\u002F01-decision-framing",{"title":272,"path":273,"stem":274},"Data Contracts","\u002Fen\u002Fbusiness-analytics\u002F00-intro\u002F02-data-contracts","en\u002Fbusiness-analytics\u002F00-intro\u002F02-data-contracts",{"title":276,"path":277,"stem":278},"Reproducible Analytics Workflow","\u002Fen\u002Fbusiness-analytics\u002F00-intro\u002F03-reproducible-workflow","en\u002Fbusiness-analytics\u002F00-intro\u002F03-reproducible-workflow",{"title":280,"path":281,"stem":282,"children":283},"1. Descriptive Analytics","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive","en\u002Fbusiness-analytics\u002F01-descriptive\u002Findex",[284,285,289,293,297,301],{"title":280,"path":281,"stem":282},{"title":286,"path":287,"stem":288},"Distributions and Exploratory Data Analysis","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive\u002F04-distributions-eda","en\u002Fbusiness-analytics\u002F01-descriptive\u002F04-distributions-eda",{"title":290,"path":291,"stem":292},"KPIs and Denominators","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive\u002F05-kpis-denominators","en\u002Fbusiness-analytics\u002F01-descriptive\u002F05-kpis-denominators",{"title":294,"path":295,"stem":296},"Segments, Cohorts and Funnels","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive\u002F06-segmentation-cohorts-funnels","en\u002Fbusiness-analytics\u002F01-descriptive\u002F06-segmentation-cohorts-funnels",{"title":298,"path":299,"stem":300},"Visual Evidence and Data Stories","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive\u002F07-visualisation-story","en\u002Fbusiness-analytics\u002F01-descriptive\u002F07-visualisation-story",{"title":302,"path":303,"stem":304},"Experiments and Causal Boundaries","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive\u002F08-experiments-causal-boundary","en\u002Fbusiness-analytics\u002F01-descriptive\u002F08-experiments-causal-boundary",{"title":306,"path":307,"stem":308,"children":309},"2. Predictive Analytics","\u002Fen\u002Fbusiness-analytics\u002F02-predictive","en\u002Fbusiness-analytics\u002F02-predictive\u002Findex",[310,311,315,319,323,327,331],{"title":306,"path":307,"stem":308},{"title":312,"path":313,"stem":314},"Validation, Baselines and Leakage","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F09-validation-leakage","en\u002Fbusiness-analytics\u002F02-predictive\u002F09-validation-leakage",{"title":316,"path":317,"stem":318},"Regression and Forecasting","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F10-regression-forecasting","en\u002Fbusiness-analytics\u002F02-predictive\u002F10-regression-forecasting",{"title":320,"path":321,"stem":322},"Classification and Calibration","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F11-classification-calibration","en\u002Fbusiness-analytics\u002F02-predictive\u002F11-classification-calibration",{"title":324,"path":325,"stem":326},"Trees and Ensembles","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F12-trees-ensembles","en\u002Fbusiness-analytics\u002F02-predictive\u002F12-trees-ensembles",{"title":328,"path":329,"stem":330},"Decision Metrics and Thresholds","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F13-decision-metrics-thresholds","en\u002Fbusiness-analytics\u002F02-predictive\u002F13-decision-metrics-thresholds",{"title":332,"path":333,"stem":334},"Explainability, Monitoring and Drift","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F14-explainability-drift","en\u002Fbusiness-analytics\u002F02-predictive\u002F14-explainability-drift",{"title":336,"path":337,"stem":338,"children":339},"3. Prescriptive Analytics","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive","en\u002Fbusiness-analytics\u002F03-prescriptive\u002Findex",[340,341,345,349,353,357,361],{"title":336,"path":337,"stem":338},{"title":342,"path":343,"stem":344},"Decision-Making under Uncertainty","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F15-decision-uncertainty","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F15-decision-uncertainty",{"title":346,"path":347,"stem":348},"Linear Optimisation","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F16-linear-optimization","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F16-linear-optimization",{"title":350,"path":351,"stem":352},"Inventory and Allocation","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F17-inventory-allocation","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F17-inventory-allocation",{"title":354,"path":355,"stem":356},"Queueing and Simulation","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F18-queueing-simulation","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F18-queueing-simulation",{"title":358,"path":359,"stem":360},"Pricing and Revenue Management","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F19-pricing-experimentation","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F19-pricing-experimentation",{"title":362,"path":363,"stem":364},"Causal Targeting and Policy Learning","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F20-causal-targeting","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F20-causal-targeting",{"title":366,"path":367,"stem":368,"children":369},"4. Deployment and Governance","\u002Fen\u002Fbusiness-analytics\u002F04-deployment","en\u002Fbusiness-analytics\u002F04-deployment\u002Findex",[370,371,375,379],{"title":366,"path":367,"stem":368},{"title":372,"path":373,"stem":374},"Data Products and Monitoring","\u002Fen\u002Fbusiness-analytics\u002F04-deployment\u002F21-data-products-monitoring","en\u002Fbusiness-analytics\u002F04-deployment\u002F21-data-products-monitoring",{"title":376,"path":377,"stem":378},"Governance, Fairness and Privacy","\u002Fen\u002Fbusiness-analytics\u002F04-deployment\u002F22-governance-fairness-privacy","en\u002Fbusiness-analytics\u002F04-deployment\u002F22-governance-fairness-privacy",{"title":380,"path":381,"stem":382},"Adoption and Business Value","\u002Fen\u002Fbusiness-analytics\u002F04-deployment\u002F23-adoption-value","en\u002Fbusiness-analytics\u002F04-deployment\u002F23-adoption-value",{"title":384,"path":385,"stem":386,"children":387},"Appendix and Revision Tools","\u002Fen\u002Fbusiness-analytics\u002Fappendix","en\u002Fbusiness-analytics\u002Fappendix\u002Findex",[388,389,393,397],{"title":384,"path":385,"stem":386},{"title":390,"path":391,"stem":392},"Formula and Decision Map","\u002Fen\u002Fbusiness-analytics\u002Fappendix\u002F24-formula-map","en\u002Fbusiness-analytics\u002Fappendix\u002F24-formula-map",{"title":394,"path":395,"stem":396},"Worked Examples and Pitfalls","\u002Fen\u002Fbusiness-analytics\u002Fappendix\u002F25-worked-examples-pitfalls","en\u002Fbusiness-analytics\u002Fappendix\u002F25-worked-examples-pitfalls",{"title":84,"path":398,"stem":399},"\u002Fen\u002Fbusiness-analytics\u002Fappendix\u002F26-reading-map","en\u002Fbusiness-analytics\u002Fappendix\u002F26-reading-map",{"title":401,"path":402,"stem":403,"children":404},"Capstone — The Evening Delivery Promise","\u002Fen\u002Fbusiness-analytics\u002Fcapstone","en\u002Fbusiness-analytics\u002Fcapstone\u002Findex",[405],{"title":401,"path":402,"stem":403},{"title":407,"path":408,"stem":409,"children":410,"page":249},"Financial Economic Time Series","\u002Fen\u002Ffinancial-economic-time-series","en\u002Ffinancial-economic-time-series",[411,417,423,429,435,441,447,453,459,465,471],{"title":412,"path":413,"stem":414,"children":415},"Financial and Economic Time Series — Course Guide","\u002Fen\u002Ffinancial-economic-time-series\u002F00-intro","en\u002Ffinancial-economic-time-series\u002F00-intro\u002Findex",[416],{"title":412,"path":413,"stem":414},{"title":418,"path":419,"stem":420,"children":421},"Three Versions of Time Series","\u002Fen\u002Ffinancial-economic-time-series\u002F01-bridge","en\u002Ffinancial-economic-time-series\u002F01-bridge\u002Findex",[422],{"title":418,"path":419,"stem":420},{"title":424,"path":425,"stem":426,"children":427},"Data, Clocks, and Transformations","\u002Fen\u002Ffinancial-economic-time-series\u002F02-data-transformations","en\u002Ffinancial-economic-time-series\u002F02-data-transformations\u002Findex",[428],{"title":424,"path":425,"stem":426},{"title":430,"path":431,"stem":432,"children":433},"Predictive Regressions and Persistent Predictors","\u002Fen\u002Ffinancial-economic-time-series\u002F03-predictive-regressions","en\u002Ffinancial-economic-time-series\u002F03-predictive-regressions\u002Findex",[434],{"title":430,"path":431,"stem":432},{"title":436,"path":437,"stem":438,"children":439},"Volatility, Tails, and Financial Risk","\u002Fen\u002Ffinancial-economic-time-series\u002F04-volatility-risk","en\u002Ffinancial-economic-time-series\u002F04-volatility-risk\u002Findex",[440],{"title":436,"path":437,"stem":438},{"title":442,"path":443,"stem":444,"children":445},"Unit Roots, Cointegration, and Error Correction","\u002Fen\u002Ffinancial-economic-time-series\u002F05-unit-roots-cointegration","en\u002Ffinancial-economic-time-series\u002F05-unit-roots-cointegration\u002Findex",[446],{"title":442,"path":443,"stem":444},{"title":448,"path":449,"stem":450,"children":451},"VAR, Structural Identification, and Local Projections","\u002Fen\u002Ffinancial-economic-time-series\u002F06-var-identification","en\u002Ffinancial-economic-time-series\u002F06-var-identification\u002Findex",[452],{"title":448,"path":449,"stem":450},{"title":454,"path":455,"stem":456,"children":457},"State Space, Mixed Frequency, and Nowcasting","\u002Fen\u002Ffinancial-economic-time-series\u002F07-state-space-nowcasting","en\u002Ffinancial-economic-time-series\u002F07-state-space-nowcasting\u002Findex",[458],{"title":454,"path":455,"stem":456},{"title":460,"path":461,"stem":462,"children":463},"Forecast Evaluation for Decisions","\u002Fen\u002Ffinancial-economic-time-series\u002F08-forecast-evaluation","en\u002Ffinancial-economic-time-series\u002F08-forecast-evaluation\u002Findex",[464],{"title":460,"path":461,"stem":462},{"title":466,"path":467,"stem":468,"children":469},"Integrated R Laboratory","\u002Fen\u002Ffinancial-economic-time-series\u002F09-r-laboratory","en\u002Ffinancial-economic-time-series\u002F09-r-laboratory\u002Findex",[470],{"title":466,"path":467,"stem":468},{"title":472,"path":473,"stem":474,"children":475},"Capstones, Data, and Reading Ladder","\u002Fen\u002Ffinancial-economic-time-series\u002F10-capstone-readings","en\u002Ffinancial-economic-time-series\u002F10-capstone-readings\u002Findex",[476],{"title":472,"path":473,"stem":474},{"title":478,"path":479,"stem":480,"children":481,"page":249},"Intro To Economics","\u002Fen\u002Fintro-to-economics","en\u002Fintro-to-economics",[482,486,490,494,498,502,506,510,514,518,522,526,530],{"title":483,"path":484,"stem":485},"Introduction to Economics — Decisions, Markets, and the Macroeconomy","\u002Fen\u002Fintro-to-economics\u002F00-intro","en\u002Fintro-to-economics\u002F00-intro",{"title":487,"path":488,"stem":489},"Chapter 1 — Choice, Opportunity Cost, and Trade","\u002Fen\u002Fintro-to-economics\u002F01-foundations","en\u002Fintro-to-economics\u002F01-foundations",{"title":491,"path":492,"stem":493},"Chapter 2 — Demand, Supply, Equilibrium, and Welfare","\u002Fen\u002Fintro-to-economics\u002F02-demand-and-supply","en\u002Fintro-to-economics\u002F02-demand-and-supply",{"title":495,"path":496,"stem":497},"Chapter 3 — Elasticity, Revenue, and Tax Incidence","\u002Fen\u002Fintro-to-economics\u002F03-elasticity","en\u002Fintro-to-economics\u002F03-elasticity",{"title":499,"path":500,"stem":501},"Chapter 4 — Firms, Market Power, and Market Failure","\u002Fen\u002Fintro-to-economics\u002F04-market-structures","en\u002Fintro-to-economics\u002F04-market-structures",{"title":503,"path":504,"stem":505},"Chapter 5 — GDP, Income, Wealth, and Welfare","\u002Fen\u002Fintro-to-economics\u002F05-gdp-and-wealth","en\u002Fintro-to-economics\u002F05-gdp-and-wealth",{"title":507,"path":508,"stem":509},"Chapter 6 — Inflation, Purchasing Power, and Labour Markets","\u002Fen\u002Fintro-to-economics\u002F06-inflation-and-unemployment","en\u002Fintro-to-economics\u002F06-inflation-and-unemployment",{"title":511,"path":512,"stem":513},"Chapter 7 — Productivity, Technology, and Economic Growth","\u002Fen\u002Fintro-to-economics\u002F07-economic-growth","en\u002Fintro-to-economics\u002F07-economic-growth",{"title":515,"path":516,"stem":517},"Chapter 8 — Money, Credit, and Banking","\u002Fen\u002Fintro-to-economics\u002F08-money-and-banking","en\u002Fintro-to-economics\u002F08-money-and-banking",{"title":519,"path":520,"stem":521},"Chapter 9 — Business Cycles, AD–AS, and Monetary Policy","\u002Fen\u002Fintro-to-economics\u002F09-monetary-policy-and-ad-as","en\u002Fintro-to-economics\u002F09-monetary-policy-and-ad-as",{"title":523,"path":524,"stem":525},"Chapter 10 — Fiscal Policy, Distribution, and Public Debt","\u002Fen\u002Fintro-to-economics\u002F10-fiscal-policy","en\u002Fintro-to-economics\u002F10-fiscal-policy",{"title":527,"path":528,"stem":529},"Chapter 11 — Trade, Capital Flows, and Exchange Rates","\u002Fen\u002Fintro-to-economics\u002F11-open-economy-and-exchange-rates","en\u002Fintro-to-economics\u002F11-open-economy-and-exchange-rates",{"title":531,"path":532,"stem":533},"Chapter 12 — Integrated Economic Analysis Studio","\u002Fen\u002Fintro-to-economics\u002F12-review-and-case-studies","en\u002Fintro-to-economics\u002F12-review-and-case-studies",{"title":535,"path":536,"stem":537,"children":538,"page":249},"Microeconometrics","\u002Fen\u002Fmicroeconometrics","en\u002Fmicroeconometrics",[539,557,563,581,603,629,651,673,691,709],{"title":540,"path":541,"stem":542,"children":543},"0. Causal Questions and Designs","\u002Fen\u002Fmicroeconometrics\u002F00-foundations","en\u002Fmicroeconometrics\u002F00-foundations\u002Findex",[544,545,549,553],{"title":540,"path":541,"stem":542},{"title":546,"path":547,"stem":548},"Causal Questions and Estimands","\u002Fen\u002Fmicroeconometrics\u002F00-foundations\u002F01-causal-question-estimands","en\u002Fmicroeconometrics\u002F00-foundations\u002F01-causal-question-estimands",{"title":550,"path":551,"stem":552},"Potential Outcomes and Experiments","\u002Fen\u002Fmicroeconometrics\u002F00-foundations\u002F02-potential-outcomes-experiments","en\u002Fmicroeconometrics\u002F00-foundations\u002F02-potential-outcomes-experiments",{"title":554,"path":555,"stem":556},"Causal Diagrams and Controls","\u002Fen\u002Fmicroeconometrics\u002F00-foundations\u002F03-dags-controls","en\u002Fmicroeconometrics\u002F00-foundations\u002F03-dags-controls",{"title":558,"path":559,"stem":560,"children":561},"Microeconometrics — Designing Credible Counterfactuals","\u002Fen\u002Fmicroeconometrics\u002F00-index","en\u002Fmicroeconometrics\u002F00-index",[562],{"title":558,"path":559,"stem":560},{"title":564,"path":565,"stem":566,"children":567},"1. Regression and Inference","\u002Fen\u002Fmicroeconometrics\u002F01-regression","en\u002Fmicroeconometrics\u002F01-regression\u002Findex",[568,569,573,577],{"title":564,"path":565,"stem":566},{"title":570,"path":571,"stem":572},"OLS as a Projection","\u002Fen\u002Fmicroeconometrics\u002F01-regression\u002F04-ols-projection","en\u002Fmicroeconometrics\u002F01-regression\u002F04-ols-projection",{"title":574,"path":575,"stem":576},"FWL, Selection and Controls","\u002Fen\u002Fmicroeconometrics\u002F01-regression\u002F05-fwl-selection-controls","en\u002Fmicroeconometrics\u002F01-regression\u002F05-fwl-selection-controls",{"title":578,"path":579,"stem":580},"Inference and Clustering","\u002Fen\u002Fmicroeconometrics\u002F01-regression\u002F06-inference-clustering","en\u002Fmicroeconometrics\u002F01-regression\u002F06-inference-clustering",{"title":582,"path":583,"stem":584,"children":585},"2. Instruments and Panel Data","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel","en\u002Fmicroeconometrics\u002F02-iv-panel\u002Findex",[586,587,591,595,599],{"title":582,"path":583,"stem":584},{"title":588,"path":589,"stem":590},"Instrumental Variables","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel\u002F07-iv-identification","en\u002Fmicroeconometrics\u002F02-iv-panel\u002F07-iv-identification",{"title":592,"path":593,"stem":594},"Weak Instruments and LATE","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel\u002F08-weak-iv-late","en\u002Fmicroeconometrics\u002F02-iv-panel\u002F08-weak-iv-late",{"title":596,"path":597,"stem":598},"Panel Fixed Effects","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel\u002F09-panel-fixed-effects","en\u002Fmicroeconometrics\u002F02-iv-panel\u002F09-panel-fixed-effects",{"title":600,"path":601,"stem":602},"Dynamic Panels and Limits","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel\u002F10-dynamic-panel","en\u002Fmicroeconometrics\u002F02-iv-panel\u002F10-dynamic-panel",{"title":604,"path":605,"stem":606,"children":607},"3. Policy Evaluation Designs","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs","en\u002Fmicroeconometrics\u002F03-policy-designs\u002Findex",[608,609,613,617,621,625],{"title":604,"path":605,"stem":606},{"title":610,"path":611,"stem":612},"Difference-in-Differences","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F11-did-core","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F11-did-core",{"title":614,"path":615,"stem":616},"Staggered DID and Event Studies","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F12-staggered-event-studies","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F12-staggered-event-studies",{"title":618,"path":619,"stem":620},"Regression Discontinuity","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F13-rdd","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F13-rdd",{"title":622,"path":623,"stem":624},"Matching, Weighting and Overlap","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F14-matching-weighting","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F14-matching-weighting",{"title":626,"path":627,"stem":628},"Synthetic Control","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F15-synthetic-control","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F15-synthetic-control",{"title":630,"path":631,"stem":632,"children":633},"Module 4 — Choice and Limited Outcomes","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002Findex",[634,635,639,643,647],{"title":630,"path":631,"stem":632},{"title":636,"path":637,"stem":638},"Binary Choice","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F16-binary-choice","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F16-binary-choice",{"title":640,"path":641,"stem":642},"Multinomial and Ordered Choice","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F17-multinomial-choice","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F17-multinomial-choice",{"title":644,"path":645,"stem":646},"Count Outcomes","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F18-count-outcomes","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F18-count-outcomes",{"title":648,"path":649,"stem":650},"Censoring, Truncation and Selection","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F19-censoring-selection","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F19-censoring-selection",{"title":652,"path":653,"stem":654,"children":655},"Module 5 — Modern Causal Analysis","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal","en\u002Fmicroeconometrics\u002F05-modern-causal\u002Findex",[656,657,661,665,669],{"title":652,"path":653,"stem":654},{"title":658,"path":659,"stem":660},"Double Machine Learning","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal\u002F20-dml","en\u002Fmicroeconometrics\u002F05-modern-causal\u002F20-dml",{"title":662,"path":663,"stem":664},"Heterogeneity and Policy Learning","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal\u002F21-heterogeneity-policy","en\u002Fmicroeconometrics\u002F05-modern-causal\u002F21-heterogeneity-policy",{"title":666,"path":667,"stem":668},"Sensitivity and Partial Identification","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal\u002F22-sensitivity-partial-id","en\u002Fmicroeconometrics\u002F05-modern-causal\u002F22-sensitivity-partial-id",{"title":670,"path":671,"stem":672},"External Validity and Transport","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal\u002F23-external-validity","en\u002Fmicroeconometrics\u002F05-modern-causal\u002F23-external-validity",{"title":674,"path":675,"stem":676,"children":677},"Module 6 — From Data to Auditable Evidence","\u002Fen\u002Fmicroeconometrics\u002F06-workflow","en\u002Fmicroeconometrics\u002F06-workflow\u002Findex",[678,679,683,687],{"title":674,"path":675,"stem":676},{"title":680,"path":681,"stem":682},"Data Provenance","\u002Fen\u002Fmicroeconometrics\u002F06-workflow\u002F24-data-provenance","en\u002Fmicroeconometrics\u002F06-workflow\u002F24-data-provenance",{"title":684,"path":685,"stem":686},"Reproducibility and Reporting","\u002Fen\u002Fmicroeconometrics\u002F06-workflow\u002F25-reproducibility-reporting","en\u002Fmicroeconometrics\u002F06-workflow\u002F25-reproducibility-reporting",{"title":688,"path":689,"stem":690},"Paper and Evidence Audit","\u002Fen\u002Fmicroeconometrics\u002F06-workflow\u002F26-paper-audit","en\u002Fmicroeconometrics\u002F06-workflow\u002F26-paper-audit",{"title":692,"path":693,"stem":694,"children":695},"Appendix — Revision and Evidence Map","\u002Fen\u002Fmicroeconometrics\u002Fappendix","en\u002Fmicroeconometrics\u002Fappendix\u002Findex",[696,697,701,705],{"title":692,"path":693,"stem":694},{"title":698,"path":699,"stem":700},"Formula and Design Map","\u002Fen\u002Fmicroeconometrics\u002Fappendix\u002F27-formula-design-map","en\u002Fmicroeconometrics\u002Fappendix\u002F27-formula-design-map",{"title":702,"path":703,"stem":704},"Ten Worked Microeconometric Pitfalls","\u002Fen\u002Fmicroeconometrics\u002Fappendix\u002F28-worked-pitfalls","en\u002Fmicroeconometrics\u002Fappendix\u002F28-worked-pitfalls",{"title":706,"path":707,"stem":708},"Reading and Software Map","\u002Fen\u002Fmicroeconometrics\u002Fappendix\u002F29-reading-software-map","en\u002Fmicroeconometrics\u002Fappendix\u002F29-reading-software-map",{"title":710,"path":711,"stem":712,"children":713},"Capstone — Should Northbridge Expand Pathways?","\u002Fen\u002Fmicroeconometrics\u002Fcapstone","en\u002Fmicroeconometrics\u002Fcapstone\u002Findex",[714],{"title":710,"path":711,"stem":712},{"title":716,"path":717,"stem":718,"children":719,"page":249},"Microeconomics","\u002Fen\u002Fmicroeconomics","en\u002Fmicroeconomics",[720,726,732,738,744,750,756,762,768,774,780,786,792],{"title":721,"path":722,"stem":723,"children":724},"Advanced Microeconomics — Course Guide","\u002Fen\u002Fmicroeconomics\u002F00-intro","en\u002Fmicroeconomics\u002F00-intro\u002Findex",[725],{"title":721,"path":722,"stem":723},{"title":727,"path":728,"stem":729,"children":730},"Module 1 — Choice, Duality, and Revealed Preference","\u002Fen\u002Fmicroeconomics\u002F01-fundations","en\u002Fmicroeconomics\u002F01-fundations\u002Findex",[731],{"title":727,"path":728,"stem":729},{"title":733,"path":734,"stem":735,"children":736},"Module 2 — Comparative Statics and Welfare Measurement","\u002Fen\u002Fmicroeconomics\u002F02-comparative-statics","en\u002Fmicroeconomics\u002F02-comparative-statics\u002Findex",[737],{"title":733,"path":734,"stem":735},{"title":739,"path":740,"stem":741,"children":742},"Module 3 — Choice under Risk and Insurance","\u002Fen\u002Fmicroeconomics\u002F03-uncertainty","en\u002Fmicroeconomics\u002F03-uncertainty\u002Findex",[743],{"title":739,"path":740,"stem":741},{"title":745,"path":746,"stem":747,"children":748},"Module 4 — General Equilibrium and Welfare","\u002Fen\u002Fmicroeconomics\u002F04-general-equilibrium","en\u002Fmicroeconomics\u002F04-general-equilibrium\u002Findex",[749],{"title":745,"path":746,"stem":747},{"title":751,"path":752,"stem":753,"children":754},"Module 5 — Static, Dynamic, and Repeated Games","\u002Fen\u002Fmicroeconomics\u002F05-game-theory","en\u002Fmicroeconomics\u002F05-game-theory\u002Findex",[755],{"title":751,"path":752,"stem":753},{"title":757,"path":758,"stem":759,"children":760},"Module 6 — Oligopoly, Entry, and Algorithmic Pricing","\u002Fen\u002Fmicroeconomics\u002F06-oligopoly","en\u002Fmicroeconomics\u002F06-oligopoly\u002Findex",[761],{"title":757,"path":758,"stem":759},{"title":763,"path":764,"stem":765,"children":766},"Module 7 — Information Economics and Contracts","\u002Fen\u002Fmicroeconomics\u002F07-information-economics","en\u002Fmicroeconomics\u002F07-information-economics\u002Findex",[767],{"title":763,"path":764,"stem":765},{"title":769,"path":770,"stem":771,"children":772},"Module 8 — Mechanism Design and Auctions","\u002Fen\u002Fmicroeconomics\u002F08-mechanism-design","en\u002Fmicroeconomics\u002F08-mechanism-design\u002Findex",[773],{"title":769,"path":770,"stem":771},{"title":775,"path":776,"stem":777,"children":778},"Module 9 — Behavioural and Experimental Microeconomics","\u002Fen\u002Fmicroeconomics\u002F09-behavioural-economics","en\u002Fmicroeconomics\u002F09-behavioural-economics\u002Findex",[779],{"title":775,"path":776,"stem":777},{"title":781,"path":782,"stem":783,"children":784},"Module 10 — Externalities, Public Goods, and Collective Action","\u002Fen\u002Fmicroeconomics\u002F10-externalities-public-goods","en\u002Fmicroeconomics\u002F10-externalities-public-goods\u002Findex",[785],{"title":781,"path":782,"stem":783},{"title":787,"path":788,"stem":789,"children":790},"Module 11 — Matching and Market Design","\u002Fen\u002Fmicroeconomics\u002F11-market-design","en\u002Fmicroeconomics\u002F11-market-design\u002Findex",[791],{"title":787,"path":788,"stem":789},{"title":793,"path":794,"stem":795,"children":796},"Module 12 — Integrated Microeconomic Design Studio","\u002Fen\u002Fmicroeconomics\u002F12-review","en\u002Fmicroeconomics\u002F12-review\u002Findex",[797],{"title":793,"path":794,"stem":795},{"title":799,"path":800,"stem":801,"children":802},"Playground","\u002Fen\u002Fplayground","en\u002Fplayground\u002Findex",[803,804,808,812,816,820],{"title":799,"path":800,"stem":801},{"title":805,"path":806,"stem":807},"Typst Plugin Playground","\u002Fen\u002Fplayground\u002F01-typstex","en\u002Fplayground\u002F01-typstex",{"title":809,"path":810,"stem":811},"Citation Plugin Test","\u002Fen\u002Fplayground\u002F02-citation","en\u002Fplayground\u002F02-citation",{"title":813,"path":814,"stem":815},"Pyodide Playground","\u002Fen\u002Fplayground\u002F03-pyodide","en\u002Fplayground\u002F03-pyodide",{"title":817,"path":818,"stem":819},"WebR Playground","\u002Fen\u002Fplayground\u002F04-webr","en\u002Fplayground\u002F04-webr",{"title":821,"path":822,"stem":823},"Chart.js 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文献综述的构建与写作","\u002Fzh\u002Facademic-writing\u002F04-writing-the-literature-review","zh\u002Facademic-writing\u002F04-writing-the-literature-review",{"title":1056,"path":1057,"stem":1058},"8. 文献综述清单与模板","\u002Fzh\u002Facademic-writing\u002F08-literature-review-checklist-and-template","zh\u002Facademic-writing\u002F08-literature-review-checklist-and-template",{"title":1060,"path":1061,"stem":1062},"10. 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Alpha 策略全流程","\u002Fzh\u002Fquant\u002F09-case-study","zh\u002Fquant\u002F09-case-study",{"title":1779,"path":1780,"stem":1781},"量化投资文献与软件图谱","\u002Fzh\u002Fquant\u002F10-reading-software-map","zh\u002Fquant\u002F10-reading-software-map",null,{"id":1784,"title":1278,"body":1785,"description":3775,"extension":3776,"features":1782,"hero":1782,"layout":1782,"locale":1782,"meta":3777,"navigation":1782,"path":1279,"published":3780,"seo":3781,"stem":1280,"__hash__":3782},"docs\u002Fzh\u002Fasset-pricing\u002F05-factor-models\u002Findex.md",{"type":1786,"value":1787,"toc":3753},"minimark",[1788,1792,1796,1799,1803,1863,1866,1869,1888,1891,1909,1912,1915,1969,1972,1975,2584,2919,2922,2933,2936,2940,2943,2947,2950,2953,2957,2960,2963,2966,2969,2972,2976,2979,2982,2985,2988,2991,2995,2998,3263,3266,3461,3464,3468,3471,3478,3481,3484,3574,3577,3610,3613,3627,3630,3747,3750],[1789,1790,1278],"h1",{"id":1791},"第五章因子模型",[1793,1794,1795],"p",{},"CAPM 用一个市场因子解释所有资产的预期收益，但大量实证结果显示，单一 beta 很难解释横截面收益差异。因子模型把问题扩展为：资产收益是否由多个共同风险源或系统性特征驱动？如果一个资产在某些共同因子上暴露更高，它是否应该获得更高预期收益？",[1793,1797,1798],{},"本章是从“理论资产定价”走向“实证资产定价”和“量化因子投资”的桥梁。读这一章时，要同时保持两种视角：因子可能代表经济风险，也可能只是统计上有预测力的特征；两者在投资实践中都重要，但解释力度和可持续性不同。",[1800,1801,1802],"h2",{"id":1802},"本章路线",[1804,1805,1806,1819],"table",{},[1807,1808,1809],"thead",{},[1810,1811,1812,1816],"tr",{},[1813,1814,1815],"th",{},"课次",[1813,1817,1818],{},"核心问题",[1820,1821,1822,1831,1839,1847,1855],"tbody",{},[1810,1823,1824,1828],{},[1825,1826,1827],"td",{},"5.1 APT",[1825,1829,1830],{},"少数系统冲击怎样约束横截面预期收益？",[1810,1832,1833,1836],{},[1825,1834,1835],{},"5.2 统计基础",[1825,1837,1838],{},"暴露、溢价与 alpha 分别由哪一维数据识别？",[1810,1840,1841,1844],{},[1825,1842,1843],{},"5.3—5.5 经典模型",[1825,1845,1846],{},"规模、价值、动量、盈利与投资因子解释什么？",[1810,1848,1849,1852],{},[1825,1850,1851],{},"5.6 因子动物园",[1825,1853,1854],{},"多重检验和模型选择怎样制造偶然因子？",[1810,1856,1857,1860],{},[1825,1858,1859],{},"5.7 因子投资",[1825,1861,1862],{},"统计溢价扣除换手、容量和成本后还剩什么？",[1800,1864,1865],{"id":1865},"学习成果",[1793,1867,1868],{},"通过本章学习，你将掌握：",[1870,1871,1872,1876,1879,1882,1885],"ol",{},[1873,1874,1875],"li",{},"APT的理论基础和与CAPM的关系",[1873,1877,1878],{},"如何构建和估计因子模型",[1873,1880,1881],{},"主要的经典因子模型及其经济含义",[1873,1883,1884],{},"因子动物园问题和因子筛选",[1873,1886,1887],{},"因子投资的实践策略",[1793,1889,1890],{},"读完本章后，你应能完成以下任务：",[1892,1893,1894,1897,1900,1903,1906],"ul",{},[1873,1895,1896],{},"写出线性因子模型，并解释 alpha、beta、因子收益和残差的含义。",[1873,1898,1899],{},"区分时间序列回归和横截面回归在资产定价检验中的作用。",[1873,1901,1902],{},"解释 APT 与 CAPM 的共同点和差异。",[1873,1904,1905],{},"说明 Fama-French、Carhart 和五因子模型各自增加了什么经济信息。",[1873,1907,1908],{},"识别因子研究中的数据挖掘、拥挤交易和交易成本问题。",[1800,1910,1911],{"id":1911},"金融与经济动机",[1793,1913,1914],{},"如果两个股票市场 beta 都接近 1，但一个是小盘价值股，一个是大盘成长股，它们长期平均收益可能明显不同。CAPM 把这种差异放进 alpha，但因子模型会进一步追问：这是不是因为它们暴露于不同的系统性风险？还是因为投资者行为、机构约束或市场摩擦造成了可持续异象？",[1804,1916,1917,1927],{},[1807,1918,1919],{},[1810,1920,1921,1924],{},[1813,1922,1923],{},"现象",[1813,1925,1926],{},"因子模型的处理",[1820,1928,1929,1937,1945,1953,1961],{},[1810,1930,1931,1934],{},[1825,1932,1933],{},"小盘股平均收益更高",[1825,1935,1936],{},"SMB 因子或规模特征",[1810,1938,1939,1942],{},[1825,1940,1941],{},"价值股平均收益更高",[1825,1943,1944],{},"HML 因子或账面市值比",[1810,1946,1947,1950],{},[1825,1948,1949],{},"赢家股票短期继续上涨",[1825,1951,1952],{},"动量因子",[1810,1954,1955,1958],{},[1825,1956,1957],{},"盈利能力高的公司表现更好",[1825,1959,1960],{},"盈利能力因子",[1810,1962,1963,1966],{},[1825,1964,1965],{},"因子数量越来越多",[1825,1967,1968],{},"因子动物园与多重检验问题",[1800,1970,1971],{"id":1971},"模型设置与直觉",[1793,1973,1974],{},"线性因子模型通常写成：",[1976,1977,1980],"span",{"className":1978},[1979],"katex-display",[1976,1981,1984,2112],{"className":1982},[1983],"katex",[1976,1985,1988],{"className":1986},[1987],"katex-mathml",[1989,1990,1993],"math",{"xmlns":1991,"display":1992},"http:\u002F\u002Fwww.w3.org\u002F1998\u002FMath\u002FMathML","block",[1994,1995,1996,2107],"semantics",{},[1997,1998,1999,2022,2025,2033,2036,2049,2062,2064,2067,2069,2080,2092,2094],"mrow",{},[2000,2001,2002,2006,2019],"msubsup",{},[2003,2004,2005],"mi",{},"R",[1997,2007,2008,2011,2016],{},[2003,2009,2010],{},"i",[2012,2013,2015],"mo",{"separator":2014},"true",",",[2003,2017,2018],{},"t",[2003,2020,2021],{},"e",[2012,2023,2024],{},"=",[2026,2027,2028,2031],"msub",{},[2003,2029,2030],{},"α",[2003,2032,2010],{},[2012,2034,2035],{},"+",[2026,2037,2038,2041],{},[2003,2039,2040],{},"β",[1997,2042,2043,2045],{},[2003,2044,2010],{},[2046,2047,2048],"mn",{},"1",[2026,2050,2051,2054],{},[2003,2052,2053],{},"F",[1997,2055,2056,2058,2060],{},[2046,2057,2048],{},[2012,2059,2015],{"separator":2014},[2003,2061,2018],{},[2012,2063,2035],{},[2012,2065,2066],{},"⋯",[2012,2068,2035],{},[2026,2070,2071,2073],{},[2003,2072,2040],{},[1997,2074,2075,2077],{},[2003,2076,2010],{},[2003,2078,2079],{},"k",[2026,2081,2082,2084],{},[2003,2083,2053],{},[1997,2085,2086,2088,2090],{},[2003,2087,2079],{},[2012,2089,2015],{"separator":2014},[2003,2091,2018],{},[2012,2093,2035],{},[2026,2095,2096,2099],{},[2003,2097,2098],{},"ε",[1997,2100,2101,2103,2105],{},[2003,2102,2010],{},[2012,2104,2015],{"separator":2014},[2003,2106,2018],{},[2108,2109,2111],"annotation",{"encoding":2110},"application\u002Fx-tex","R_{i,t}^e=\\alpha_i+\\beta_{i1}F_{1,t}+\\cdots+\\beta_{ik}F_{k,t}+\\varepsilon_{i,t}",[1976,2113,2116,2218,2280,2397,2417,2527],{"className":2114,"ariaHidden":2014},[2115],"katex-html",[1976,2117,2120,2125,2206,2211,2215],{"className":2118},[2119],"base",[1976,2121],{"className":2122,"style":2124},[2123],"strut","height:1.0975em;vertical-align:-0.3831em;",[1976,2126,2129,2134],{"className":2127},[2128],"mord",[1976,2130,2005],{"className":2131,"style":2133},[2128,2132],"mathnormal","margin-right:0.0077em;",[1976,2135,2138],{"className":2136},[2137],"msupsub",[1976,2139,2143,2197],{"className":2140},[2141,2142],"vlist-t","vlist-t2",[1976,2144,2147,2192],{"className":2145},[2146],"vlist-r",[1976,2148,2152,2180],{"className":2149,"style":2151},[2150],"vlist","height:0.7144em;",[1976,2153,2155,2160],{"style":2154},"top:-2.453em;margin-left:-0.0077em;margin-right:0.05em;",[1976,2156],{"className":2157,"style":2159},[2158],"pstrut","height:2.7em;",[1976,2161,2167],{"className":2162},[2163,2164,2165,2166],"sizing","reset-size6","size3","mtight",[1976,2168,2170,2173,2177],{"className":2169},[2128,2166],[1976,2171,2010],{"className":2172},[2128,2132,2166],[1976,2174,2015],{"className":2175},[2176,2166],"mpunct",[1976,2178,2018],{"className":2179},[2128,2132,2166],[1976,2181,2183,2186],{"style":2182},"top:-3.113em;margin-right:0.05em;",[1976,2184],{"className":2185,"style":2159},[2158],[1976,2187,2189],{"className":2188},[2163,2164,2165,2166],[1976,2190,2021],{"className":2191},[2128,2132,2166],[1976,2193,2196],{"className":2194},[2195],"vlist-s","​",[1976,2198,2200],{"className":2199},[2146],[1976,2201,2204],{"className":2202,"style":2203},[2150],"height:0.3831em;",[1976,2205],{},[1976,2207],{"className":2208,"style":2210},[2209],"mspace","margin-right:0.2778em;",[1976,2212,2024],{"className":2213},[2214],"mrel",[1976,2216],{"className":2217,"style":2210},[2209],[1976,2219,2221,2225,2269,2273,2277],{"className":2220},[2119],[1976,2222],{"className":2223,"style":2224},[2123],"height:0.7333em;vertical-align:-0.15em;",[1976,2226,2228,2232],{"className":2227},[2128],[1976,2229,2030],{"className":2230,"style":2231},[2128,2132],"margin-right:0.0037em;",[1976,2233,2235],{"className":2234},[2137],[1976,2236,2238,2260],{"className":2237},[2141,2142],[1976,2239,2241,2257],{"className":2240},[2146],[1976,2242,2245],{"className":2243,"style":2244},[2150],"height:0.3117em;",[1976,2246,2248,2251],{"style":2247},"top:-2.55em;margin-left:-0.0037em;margin-right:0.05em;",[1976,2249],{"className":2250,"style":2159},[2158],[1976,2252,2254],{"className":2253},[2163,2164,2165,2166],[1976,2255,2010],{"className":2256},[2128,2132,2166],[1976,2258,2196],{"className":2259},[2195],[1976,2261,2263],{"className":2262},[2146],[1976,2264,2267],{"className":2265,"style":2266},[2150],"height:0.15em;",[1976,2268],{},[1976,2270],{"className":2271,"style":2272},[2209],"margin-right:0.2222em;",[1976,2274,2035],{"className":2275},[2276],"mbin",[1976,2278],{"className":2279,"style":2272},[2209],[1976,2281,2283,2287,2335,2388,2391,2394],{"className":2282},[2119],[1976,2284],{"className":2285,"style":2286},[2123],"height:0.9805em;vertical-align:-0.2861em;",[1976,2288,2290,2294],{"className":2289},[2128],[1976,2291,2040],{"className":2292,"style":2293},[2128,2132],"margin-right:0.0528em;",[1976,2295,2297],{"className":2296},[2137],[1976,2298,2300,2327],{"className":2299},[2141,2142],[1976,2301,2303,2324],{"className":2302},[2146],[1976,2304,2306],{"className":2305,"style":2244},[2150],[1976,2307,2309,2312],{"style":2308},"top:-2.55em;margin-left:-0.0528em;margin-right:0.05em;",[1976,2310],{"className":2311,"style":2159},[2158],[1976,2313,2315],{"className":2314},[2163,2164,2165,2166],[1976,2316,2318,2321],{"className":2317},[2128,2166],[1976,2319,2010],{"className":2320},[2128,2132,2166],[1976,2322,2048],{"className":2323},[2128,2166],[1976,2325,2196],{"className":2326},[2195],[1976,2328,2330],{"className":2329},[2146],[1976,2331,2333],{"className":2332,"style":2266},[2150],[1976,2334],{},[1976,2336,2338,2342],{"className":2337},[2128],[1976,2339,2053],{"className":2340,"style":2341},[2128,2132],"margin-right:0.1389em;",[1976,2343,2345],{"className":2344},[2137],[1976,2346,2348,2379],{"className":2347},[2141,2142],[1976,2349,2351,2376],{"className":2350},[2146],[1976,2352,2355],{"className":2353,"style":2354},[2150],"height:0.3011em;",[1976,2356,2358,2361],{"style":2357},"top:-2.55em;margin-left:-0.1389em;margin-right:0.05em;",[1976,2359],{"className":2360,"style":2159},[2158],[1976,2362,2364],{"className":2363},[2163,2164,2165,2166],[1976,2365,2367,2370,2373],{"className":2366},[2128,2166],[1976,2368,2048],{"className":2369},[2128,2166],[1976,2371,2015],{"className":2372},[2176,2166],[1976,2374,2018],{"className":2375},[2128,2132,2166],[1976,2377,2196],{"className":2378},[2195],[1976,2380,2382],{"className":2381},[2146],[1976,2383,2386],{"className":2384,"style":2385},[2150],"height:0.2861em;",[1976,2387],{},[1976,2389],{"className":2390,"style":2272},[2209],[1976,2392,2035],{"className":2393},[2276],[1976,2395],{"className":2396,"style":2272},[2209],[1976,2398,2400,2404,2408,2411,2414],{"className":2399},[2119],[1976,2401],{"className":2402,"style":2403},[2123],"height:0.6667em;vertical-align:-0.0833em;",[1976,2405,2066],{"className":2406},[2407],"minner",[1976,2409],{"className":2410,"style":2272},[2209],[1976,2412,2035],{"className":2413},[2276],[1976,2415],{"className":2416,"style":2272},[2209],[1976,2418,2420,2423,2469,2518,2521,2524],{"className":2419},[2119],[1976,2421],{"className":2422,"style":2286},[2123],[1976,2424,2426,2429],{"className":2425},[2128],[1976,2427,2040],{"className":2428,"style":2293},[2128,2132],[1976,2430,2432],{"className":2431},[2137],[1976,2433,2435,2461],{"className":2434},[2141,2142],[1976,2436,2438,2458],{"className":2437},[2146],[1976,2439,2442],{"className":2440,"style":2441},[2150],"height:0.3361em;",[1976,2443,2444,2447],{"style":2308},[1976,2445],{"className":2446,"style":2159},[2158],[1976,2448,2450],{"className":2449},[2163,2164,2165,2166],[1976,2451,2453],{"className":2452},[2128,2166],[1976,2454,2457],{"className":2455,"style":2456},[2128,2132,2166],"margin-right:0.0315em;","ik",[1976,2459,2196],{"className":2460},[2195],[1976,2462,2464],{"className":2463},[2146],[1976,2465,2467],{"className":2466,"style":2266},[2150],[1976,2468],{},[1976,2470,2472,2475],{"className":2471},[2128],[1976,2473,2053],{"className":2474,"style":2341},[2128,2132],[1976,2476,2478],{"className":2477},[2137],[1976,2479,2481,2510],{"className":2480},[2141,2142],[1976,2482,2484,2507],{"className":2483},[2146],[1976,2485,2487],{"className":2486,"style":2441},[2150],[1976,2488,2489,2492],{"style":2357},[1976,2490],{"className":2491,"style":2159},[2158],[1976,2493,2495],{"className":2494},[2163,2164,2165,2166],[1976,2496,2498,2501,2504],{"className":2497},[2128,2166],[1976,2499,2079],{"className":2500,"style":2456},[2128,2132,2166],[1976,2502,2015],{"className":2503},[2176,2166],[1976,2505,2018],{"className":2506},[2128,2132,2166],[1976,2508,2196],{"className":2509},[2195],[1976,2511,2513],{"className":2512},[2146],[1976,2514,2516],{"className":2515,"style":2385},[2150],[1976,2517],{},[1976,2519],{"className":2520,"style":2272},[2209],[1976,2522,2035],{"className":2523},[2276],[1976,2525],{"className":2526,"style":2272},[2209],[1976,2528,2530,2534],{"className":2529},[2119],[1976,2531],{"className":2532,"style":2533},[2123],"height:0.7167em;vertical-align:-0.2861em;",[1976,2535,2537,2540],{"className":2536},[2128],[1976,2538,2098],{"className":2539},[2128,2132],[1976,2541,2543],{"className":2542},[2137],[1976,2544,2546,2576],{"className":2545},[2141,2142],[1976,2547,2549,2573],{"className":2548},[2146],[1976,2550,2552],{"className":2551,"style":2244},[2150],[1976,2553,2555,2558],{"style":2554},"top:-2.55em;margin-left:0em;margin-right:0.05em;",[1976,2556],{"className":2557,"style":2159},[2158],[1976,2559,2561],{"className":2560},[2163,2164,2165,2166],[1976,2562,2564,2567,2570],{"className":2563},[2128,2166],[1976,2565,2010],{"className":2566},[2128,2132,2166],[1976,2568,2015],{"className":2569},[2176,2166],[1976,2571,2018],{"className":2572},[2128,2132,2166],[1976,2574,2196],{"className":2575},[2195],[1976,2577,2579],{"className":2578},[2146],[1976,2580,2582],{"className":2581,"style":2385},[2150],[1976,2583],{},[1793,2585,2586,2587,2690,2691,2720,2721,2809,2810,2839,2840,2918],{},"其中 ",[1976,2588,2590,2616],{"className":2589},[1983],[1976,2591,2593],{"className":2592},[1987],[1989,2594,2595],{"xmlns":1991},[1994,2596,2597,2613],{},[1997,2598,2599],{},[2000,2600,2601,2603,2611],{},[2003,2602,2005],{},[1997,2604,2605,2607,2609],{},[2003,2606,2010],{},[2012,2608,2015],{"separator":2014},[2003,2610,2018],{},[2003,2612,2021],{},[2108,2614,2615],{"encoding":2110},"R_{i,t}^e",[1976,2617,2619],{"className":2618,"ariaHidden":2014},[2115],[1976,2620,2622,2626],{"className":2621},[2119],[1976,2623],{"className":2624,"style":2625},[2123],"height:1.0781em;vertical-align:-0.3948em;",[1976,2627,2629,2632],{"className":2628},[2128],[1976,2630,2005],{"className":2631,"style":2133},[2128,2132],[1976,2633,2635],{"className":2634},[2137],[1976,2636,2638,2681],{"className":2637},[2141,2142],[1976,2639,2641,2678],{"className":2640},[2146],[1976,2642,2645,2666],{"className":2643,"style":2644},[2150],"height:0.6644em;",[1976,2646,2648,2651],{"style":2647},"top:-2.4413em;margin-left:-0.0077em;margin-right:0.05em;",[1976,2649],{"className":2650,"style":2159},[2158],[1976,2652,2654],{"className":2653},[2163,2164,2165,2166],[1976,2655,2657,2660,2663],{"className":2656},[2128,2166],[1976,2658,2010],{"className":2659},[2128,2132,2166],[1976,2661,2015],{"className":2662},[2176,2166],[1976,2664,2018],{"className":2665},[2128,2132,2166],[1976,2667,2669,2672],{"style":2668},"top:-3.063em;margin-right:0.05em;",[1976,2670],{"className":2671,"style":2159},[2158],[1976,2673,2675],{"className":2674},[2163,2164,2165,2166],[1976,2676,2021],{"className":2677},[2128,2132,2166],[1976,2679,2196],{"className":2680},[2195],[1976,2682,2684],{"className":2683},[2146],[1976,2685,2688],{"className":2686,"style":2687},[2150],"height:0.3948em;",[1976,2689],{}," 是资产 ",[1976,2692,2694,2707],{"className":2693},[1983],[1976,2695,2697],{"className":2696},[1987],[1989,2698,2699],{"xmlns":1991},[1994,2700,2701,2705],{},[1997,2702,2703],{},[2003,2704,2010],{},[2108,2706,2010],{"encoding":2110},[1976,2708,2710],{"className":2709,"ariaHidden":2014},[2115],[1976,2711,2713,2717],{"className":2712},[2119],[1976,2714],{"className":2715,"style":2716},[2123],"height:0.6595em;",[1976,2718,2010],{"className":2719},[2128,2132]," 的超额收益，",[1976,2722,2724,2749],{"className":2723},[1983],[1976,2725,2727],{"className":2726},[1987],[1989,2728,2729],{"xmlns":1991},[1994,2730,2731,2746],{},[1997,2732,2733],{},[2026,2734,2735,2737],{},[2003,2736,2053],{},[1997,2738,2739,2742,2744],{},[2003,2740,2741],{},"j",[2012,2743,2015],{"separator":2014},[2003,2745,2018],{},[2108,2747,2748],{"encoding":2110},"F_{j,t}",[1976,2750,2752],{"className":2751,"ariaHidden":2014},[2115],[1976,2753,2755,2759],{"className":2754},[2119],[1976,2756],{"className":2757,"style":2758},[2123],"height:0.9694em;vertical-align:-0.2861em;",[1976,2760,2762,2765],{"className":2761},[2128],[1976,2763,2053],{"className":2764,"style":2341},[2128,2132],[1976,2766,2768],{"className":2767},[2137],[1976,2769,2771,2801],{"className":2770},[2141,2142],[1976,2772,2774,2798],{"className":2773},[2146],[1976,2775,2777],{"className":2776,"style":2244},[2150],[1976,2778,2779,2782],{"style":2357},[1976,2780],{"className":2781,"style":2159},[2158],[1976,2783,2785],{"className":2784},[2163,2164,2165,2166],[1976,2786,2788,2792,2795],{"className":2787},[2128,2166],[1976,2789,2741],{"className":2790,"style":2791},[2128,2132,2166],"margin-right:0.0572em;",[1976,2793,2015],{"className":2794},[2176,2166],[1976,2796,2018],{"className":2797},[2128,2132,2166],[1976,2799,2196],{"className":2800},[2195],[1976,2802,2804],{"className":2803},[2146],[1976,2805,2807],{"className":2806,"style":2385},[2150],[1976,2808],{}," 是第 ",[1976,2811,2813,2826],{"className":2812},[1983],[1976,2814,2816],{"className":2815},[1987],[1989,2817,2818],{"xmlns":1991},[1994,2819,2820,2824],{},[1997,2821,2822],{},[2003,2823,2741],{},[2108,2825,2741],{"encoding":2110},[1976,2827,2829],{"className":2828,"ariaHidden":2014},[2115],[1976,2830,2832,2836],{"className":2831},[2119],[1976,2833],{"className":2834,"style":2835},[2123],"height:0.854em;vertical-align:-0.1944em;",[1976,2837,2741],{"className":2838,"style":2791},[2128,2132]," 个因子收益，",[1976,2841,2843,2865],{"className":2842},[1983],[1976,2844,2846],{"className":2845},[1987],[1989,2847,2848],{"xmlns":1991},[1994,2849,2850,2862],{},[1997,2851,2852],{},[2026,2853,2854,2856],{},[2003,2855,2040],{},[1997,2857,2858,2860],{},[2003,2859,2010],{},[2003,2861,2741],{},[2108,2863,2864],{"encoding":2110},"\\beta_{ij}",[1976,2866,2868],{"className":2867,"ariaHidden":2014},[2115],[1976,2869,2871,2874],{"className":2870},[2119],[1976,2872],{"className":2873,"style":2286},[2123],[1976,2875,2877,2880],{"className":2876},[2128],[1976,2878,2040],{"className":2879,"style":2293},[2128,2132],[1976,2881,2883],{"className":2882},[2137],[1976,2884,2886,2910],{"className":2885},[2141,2142],[1976,2887,2889,2907],{"className":2888},[2146],[1976,2890,2892],{"className":2891,"style":2244},[2150],[1976,2893,2894,2897],{"style":2308},[1976,2895],{"className":2896,"style":2159},[2158],[1976,2898,2900],{"className":2899},[2163,2164,2165,2166],[1976,2901,2903],{"className":2902},[2128,2166],[1976,2904,2906],{"className":2905,"style":2791},[2128,2132,2166],"ij",[1976,2908,2196],{"className":2909},[2195],[1976,2911,2913],{"className":2912},[2146],[1976,2914,2916],{"className":2915,"style":2385},[2150],[1976,2917],{}," 是资产对该因子的暴露。",[1793,2920,2921],{},"直觉上，因子模型把资产收益拆成三部分：",[1870,2923,2924,2927,2930],{},[1873,2925,2926],{},"因共同风险或特征获得的收益；",[1873,2928,2929],{},"模型无法解释的平均收益 alpha；",[1873,2931,2932],{},"个体资产自己的噪声。",[1793,2934,2935],{},"如果模型足够好，alpha 应该接近 0；如果大量资产仍有显著 alpha，说明模型遗漏了重要风险、定价错误或检验设计有问题。",[2937,2938],"mermaid-diagram",{"code64":2939,"locale":7},"Zmxvd2NoYXJ0IExSCiAgQVsi6LWE5Lqn6LaF6aKd5pS255uKIl0gLS0+IEJbIuWFseWQjOWboOWtkOaatOmcsiJdCiAgQSAtLT4gQ1siYWxwaGEiXQogIEEgLS0+IERbIueJuei0qOaui+W3riJdCiAgQiAtLT4gRVsi6aOO6Zmp6KGl5YG\u002F5oiW54m55b6B5rqi5Lu3Il0KICBDIC0tPiBGWyLmqKHlnovpgZfmvI\u002FmiJblvILluLjmlLbnm4oiXQ==",[1800,2941,2942],{"id":2942},"关键定义与定理",[2944,2945,2946],"h3",{"id":2946},"线性因子模型",[1793,2948,2949],{},"资产收益由一组共同因子线性解释的模型。",[1793,2951,2952],{},"中文解释：它假设很多资产一起涨跌，不是巧合，而是因为它们共同暴露于同一些风险源或特征。",[2944,2954,2956],{"id":2955},"因子暴露-beta","因子暴露 beta",[1793,2958,2959],{},"资产对某个因子的敏感度。",[1793,2961,2962],{},"中文解释：价值因子 beta 高的资产，通常在价值股组合相对成长股表现好时也表现好。",[2944,2964,2965],{"id":2965},"因子风险溢价",[1793,2967,2968],{},"承担某个因子暴露所获得的预期补偿。",[1793,2970,2971],{},"中文解释：如果某因子代表坏状态风险，那么投资者要求正溢价；如果只是统计特征，它的溢价是否可持续需要额外验证。",[2944,2973,2975],{"id":2974},"apt","APT",[1793,2977,2978],{},"套利定价理论说明：若资产收益由少数共同因子驱动，且特质风险可被充分分散，则不存在套利时预期收益应近似为因子 beta 的线性函数。",[1793,2980,2981],{},"中文解释：APT 不要求所有人持有市场组合，但要求不能存在大规模、低风险、正收益的套利组合。",[2944,2983,2984],{"id":2984},"因子动物园",[1793,2986,2987],{},"文献中被提出的大量收益预测因子。",[1793,2989,2990],{},"中文解释：因子太多时，显著性可能来自反复试验。真正有用的因子应有经济逻辑、稳健样本外表现和可交易性。",[1800,2992,2994],{"id":2993},"例子三因子收益归因","例子：三因子收益归因",[1793,2996,2997],{},"某基金月度超额收益回归结果为：",[1976,2999,3001],{"className":3000},[1979],[1976,3002,3004,3077],{"className":3003},[1983],[1976,3005,3007],{"className":3006},[1987],[1989,3008,3009],{"xmlns":1991,"display":1992},[1994,3010,3011,3074],{},[1997,3012,3013,3021,3023,3026,3030,3032,3035,3038,3041,3044,3046,3049,3052,3054,3057,3059,3062,3065,3067,3070,3072],{},[2000,3014,3015,3017,3019],{},[2003,3016,2005],{},[2003,3018,1793],{},[2003,3020,2021],{},[2012,3022,2024],{},[2046,3024,3025],{},"0.20",[2003,3027,3029],{"mathvariant":3028},"normal","%",[2012,3031,2035],{},[2046,3033,3034],{},"1.05",[2003,3036,3037],{},"M",[2003,3039,3040],{},"K",[2003,3042,3043],{},"T",[2012,3045,2035],{},[2046,3047,3048],{},"0.30",[2003,3050,3051],{},"S",[2003,3053,3037],{},[2003,3055,3056],{},"B",[2012,3058,2035],{},[2046,3060,3061],{},"0.45",[2003,3063,3064],{},"H",[2003,3066,3037],{},[2003,3068,3069],{},"L",[2012,3071,2035],{},[2003,3073,2098],{},[2108,3075,3076],{"encoding":2110},"R_p^e=0.20\\%+1.05MKT+0.30SMB+0.45HML+\\varepsilon",[1976,3078,3080,3146,3166,3196,3225,3253],{"className":3079,"ariaHidden":2014},[2115],[1976,3081,3083,3086,3137,3140,3143],{"className":3082},[2119],[1976,3084],{"className":3085,"style":2124},[2123],[1976,3087,3089,3092],{"className":3088},[2128],[1976,3090,2005],{"className":3091,"style":2133},[2128,2132],[1976,3093,3095],{"className":3094},[2137],[1976,3096,3098,3129],{"className":3097},[2141,2142],[1976,3099,3101,3126],{"className":3100},[2146],[1976,3102,3104,3115],{"className":3103,"style":2151},[2150],[1976,3105,3106,3109],{"style":2154},[1976,3107],{"className":3108,"style":2159},[2158],[1976,3110,3112],{"className":3111},[2163,2164,2165,2166],[1976,3113,1793],{"className":3114},[2128,2132,2166],[1976,3116,3117,3120],{"style":2182},[1976,3118],{"className":3119,"style":2159},[2158],[1976,3121,3123],{"className":3122},[2163,2164,2165,2166],[1976,3124,2021],{"className":3125},[2128,2132,2166],[1976,3127,2196],{"className":3128},[2195],[1976,3130,3132],{"className":3131},[2146],[1976,3133,3135],{"className":3134,"style":2203},[2150],[1976,3136],{},[1976,3138],{"className":3139,"style":2210},[2209],[1976,3141,2024],{"className":3142},[2214],[1976,3144],{"className":3145,"style":2210},[2209],[1976,3147,3149,3153,3157,3160,3163],{"className":3148},[2119],[1976,3150],{"className":3151,"style":3152},[2123],"height:0.8333em;vertical-align:-0.0833em;",[1976,3154,3156],{"className":3155},[2128],"0.20%",[1976,3158],{"className":3159,"style":2272},[2209],[1976,3161,2035],{"className":3162},[2276],[1976,3164],{"className":3165,"style":2272},[2209],[1976,3167,3169,3173,3176,3180,3184,3187,3190,3193],{"className":3168},[2119],[1976,3170],{"className":3171,"style":3172},[2123],"height:0.7667em;vertical-align:-0.0833em;",[1976,3174,3034],{"className":3175},[2128],[1976,3177,3037],{"className":3178,"style":3179},[2128,2132],"margin-right:0.109em;",[1976,3181,3040],{"className":3182,"style":3183},[2128,2132],"margin-right:0.0715em;",[1976,3185,3043],{"className":3186,"style":2341},[2128,2132],[1976,3188],{"className":3189,"style":2272},[2209],[1976,3191,2035],{"className":3192},[2276],[1976,3194],{"className":3195,"style":2272},[2209],[1976,3197,3199,3202,3205,3209,3212,3216,3219,3222],{"className":3198},[2119],[1976,3200],{"className":3201,"style":3172},[2123],[1976,3203,3048],{"className":3204},[2128],[1976,3206,3051],{"className":3207,"style":3208},[2128,2132],"margin-right:0.0576em;",[1976,3210,3037],{"className":3211,"style":3179},[2128,2132],[1976,3213,3056],{"className":3214,"style":3215},[2128,2132],"margin-right:0.0502em;",[1976,3217],{"className":3218,"style":2272},[2209],[1976,3220,2035],{"className":3221},[2276],[1976,3223],{"className":3224,"style":2272},[2209],[1976,3226,3228,3231,3234,3238,3241,3244,3247,3250],{"className":3227},[2119],[1976,3229],{"className":3230,"style":3172},[2123],[1976,3232,3061],{"className":3233},[2128],[1976,3235,3064],{"className":3236,"style":3237},[2128,2132],"margin-right:0.0813em;",[1976,3239,3037],{"className":3240,"style":3179},[2128,2132],[1976,3242,3069],{"className":3243},[2128,2132],[1976,3245],{"className":3246,"style":2272},[2209],[1976,3248,2035],{"className":3249},[2276],[1976,3251],{"className":3252,"style":2272},[2209],[1976,3254,3256,3260],{"className":3255},[2119],[1976,3257],{"className":3258,"style":3259},[2123],"height:0.4306em;",[1976,3261,2098],{"className":3262},[2128,2132],[1793,3264,3265],{},"当月市场因子收益 2%，SMB 为 -1%，HML 为 1.5%。模型解释的超额收益为：",[1976,3267,3269],{"className":3268},[1979],[1976,3270,3272,3339],{"className":3271},[1983],[1976,3273,3275],{"className":3274},[1987],[1989,3276,3277],{"xmlns":1991,"display":1992},[1994,3278,3279,3336],{},[1997,3280,3281,3283,3285,3287,3289,3293,3296,3298,3301,3303,3305,3307,3310,3312,3314,3316,3318,3320,3322,3325,3327,3329,3331,3334],{},[2046,3282,3025],{},[2003,3284,3029],{"mathvariant":3028},[2012,3286,2035],{},[2046,3288,3034],{},[2012,3290,3292],{"stretchy":3291},"false","(",[2046,3294,3295],{},"2",[2003,3297,3029],{"mathvariant":3028},[2012,3299,3300],{"stretchy":3291},")",[2012,3302,2035],{},[2046,3304,3048],{},[2012,3306,3292],{"stretchy":3291},[2012,3308,3309],{},"−",[2046,3311,2048],{},[2003,3313,3029],{"mathvariant":3028},[2012,3315,3300],{"stretchy":3291},[2012,3317,2035],{},[2046,3319,3061],{},[2012,3321,3292],{"stretchy":3291},[2046,3323,3324],{},"1.5",[2003,3326,3029],{"mathvariant":3028},[2012,3328,3300],{"stretchy":3291},[2012,3330,2024],{},[2046,3332,3333],{},"2.675",[2003,3335,3029],{"mathvariant":3028},[2108,3337,3338],{"encoding":2110},"0.20\\%+1.05(2\\%)+0.30(-1\\%)+0.45(1.5\\%)=2.675\\%",[1976,3340,3342,3360,3391,3422,3450],{"className":3341,"ariaHidden":2014},[2115],[1976,3343,3345,3348,3351,3354,3357],{"className":3344},[2119],[1976,3346],{"className":3347,"style":3152},[2123],[1976,3349,3156],{"className":3350},[2128],[1976,3352],{"className":3353,"style":2272},[2209],[1976,3355,2035],{"className":3356},[2276],[1976,3358],{"className":3359,"style":2272},[2209],[1976,3361,3363,3367,3370,3374,3378,3382,3385,3388],{"className":3362},[2119],[1976,3364],{"className":3365,"style":3366},[2123],"height:1em;vertical-align:-0.25em;",[1976,3368,3034],{"className":3369},[2128],[1976,3371,3292],{"className":3372},[3373],"mopen",[1976,3375,3377],{"className":3376},[2128],"2%",[1976,3379,3300],{"className":3380},[3381],"mclose",[1976,3383],{"className":3384,"style":2272},[2209],[1976,3386,2035],{"className":3387},[2276],[1976,3389],{"className":3390,"style":2272},[2209],[1976,3392,3394,3397,3400,3403,3406,3410,3413,3416,3419],{"className":3393},[2119],[1976,3395],{"className":3396,"style":3366},[2123],[1976,3398,3048],{"className":3399},[2128],[1976,3401,3292],{"className":3402},[3373],[1976,3404,3309],{"className":3405},[2128],[1976,3407,3409],{"className":3408},[2128],"1%",[1976,3411,3300],{"className":3412},[3381],[1976,3414],{"className":3415,"style":2272},[2209],[1976,3417,2035],{"className":3418},[2276],[1976,3420],{"className":3421,"style":2272},[2209],[1976,3423,3425,3428,3431,3434,3438,3441,3444,3447],{"className":3424},[2119],[1976,3426],{"className":3427,"style":3366},[2123],[1976,3429,3061],{"className":3430},[2128],[1976,3432,3292],{"className":3433},[3373],[1976,3435,3437],{"className":3436},[2128],"1.5%",[1976,3439,3300],{"className":3440},[3381],[1976,3442],{"className":3443,"style":2210},[2209],[1976,3445,2024],{"className":3446},[2214],[1976,3448],{"className":3449,"style":2210},[2209],[1976,3451,3453,3457],{"className":3452},[2119],[1976,3454],{"className":3455,"style":3456},[2123],"height:0.8056em;vertical-align:-0.0556em;",[1976,3458,3460],{"className":3459},[2128],"2.675%",[1793,3462,3463],{},"若基金实际超额收益为 3.0%，当月残差为 0.325%。解释时不要直接说经理创造了 3.0% alpha，因为其中大部分来自市场、规模和价值因子暴露。",[1800,3465,3467],{"id":3466},"可运行例题暴露平均-alpha-与单月残差要分开","可运行例题：暴露、平均 alpha 与单月残差要分开",[1793,3469,3470],{},"回归截距 0.20% 是样本期平均的条件外异常收益估计；某个月实际收益与拟合值的差是残差，不是“当月 alpha”。",[3472,3473],"pyodide",{"code64":3474,"layout":3475,"locale":7,"packages":3476,"title":3477},"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\u002FlkIjmlLbnm4o6IHtmaXR0ZWRfcmV0dXJuOi4zJX0iKQpwcmludChmIuWNleaciOaui+W3rjoge3Jlc2lkdWFsOi4zJX0iKQ==","vertical","numpy","Python：三因子收益归因",[1793,3479,3480],{},"若要判断经理是否持续创造 alpha，需要整段样本、推断方法和可交易成本；单月残差只说明该月还有未被模型解释的部分。",[1800,3482,3483],{"id":3483},"学习顺序建议",[1804,3485,3486,3499],{},[1807,3487,3488],{},[1810,3489,3490,3493,3496],{},[1813,3491,3492],{},"小节",[1813,3494,3495],{},"先抓住的问题",[1813,3497,3498],{},"需要特别留意",[1820,3500,3501,3511,3521,3532,3543,3554,3564],{},[1810,3502,3503,3505,3508],{},[1825,3504,1827],{},[1825,3506,3507],{},"无套利如何支持多因子定价",[1825,3509,3510],{},"因子结构、充分分散、近似定价",[1810,3512,3513,3515,3518],{},[1825,3514,1835],{},[1825,3516,3517],{},"因子模型如何估计和检验",[1825,3519,3520],{},"回归、标准误、多重共线性",[1810,3522,3523,3526,3529],{},[1825,3524,3525],{},"5.3 FF3",[1825,3527,3528],{},"市场、规模、价值如何解释收益",[1825,3530,3531],{},"SMB、HML 构造逻辑",[1810,3533,3534,3537,3540],{},[1825,3535,3536],{},"5.4 Carhart",[1825,3538,3539],{},"动量为什么需要单独建模",[1825,3541,3542],{},"动量收益与换手成本",[1810,3544,3545,3548,3551],{},[1825,3546,3547],{},"5.5 FF5",[1825,3549,3550],{},"盈利能力和投资因子的加入",[1825,3552,3553],{},"与 HML 的解释重叠",[1810,3555,3556,3558,3561],{},[1825,3557,1851],{},[1825,3559,3560],{},"如何筛选可信因子",[1825,3562,3563],{},"数据挖掘、样本外、经济机制",[1810,3565,3566,3568,3571],{},[1825,3567,1859],{},[1825,3569,3570],{},"因子如何进入组合",[1825,3572,3573],{},"暴露控制、交易成本、拥挤",[1800,3575,3576],{"id":3576},"常见错误与考试陷阱",[1870,3578,3579,3586,3592,3598,3604],{},[1873,3580,3581,3585],{},[3582,3583,3584],"strong",{},"把因子收益当成资产收益","：因子通常是多空组合或特征排序组合，不是单只资产。",[1873,3587,3588,3591],{},[3582,3589,3590],{},"把显著 alpha 当成投资机会","：alpha 可能来自遗漏因子、幸存者偏差或交易成本未计入。",[1873,3593,3594,3597],{},[3582,3595,3596],{},"忽略因子相关性","：多个因子高度相关时，单个 beta 和 t 值可能不稳定。",[1873,3599,3600,3603],{},[3582,3601,3602],{},"把特征和风险混为一谈","：账面市值比等特征可以预测收益，但是否代表风险需要额外论证。",[1873,3605,3606,3609],{},[3582,3607,3608],{},"用样本内表现选择因子","：这会高估因子有效性，必须看样本外和跨市场稳健性。",[1800,3611,3612],{"id":3612},"自测题",[1870,3614,3615,3618,3621,3624],{},[1873,3616,3617],{},"APT 与 CAPM 都是线性预期收益模型，它们的核心差异是什么？",[1873,3619,3620],{},"为什么一个基金有正收益不代表有正 alpha？",[1873,3622,3623],{},"若某股票对 HML 的 beta 为 0.8，HML 当月收益为 -2%，这个因子对股票当月收益贡献多少？",[1873,3625,3626],{},"判断一个新因子是否可信，应检查哪些证据？",[1800,3628,3629],{"id":3629},"答案指引",[1870,3631,3632,3635,3638,3744],{},[1873,3633,3634],{},"CAPM 依赖市场组合和均衡假设，只有一个市场因子；APT 基于因子结构和无套利，可以有多个共同因子。",[1873,3636,3637],{},"正收益可能来自市场上涨或已知因子暴露。alpha 是扣除模型解释部分之后的平均超额收益。",[1873,3639,3640,3641,3743],{},"贡献为 ",[1976,3642,3644,3681],{"className":3643},[1983],[1976,3645,3647],{"className":3646},[1987],[1989,3648,3649],{"xmlns":1991},[1994,3650,3651,3678],{},[1997,3652,3653,3656,3659,3661,3663,3665,3667,3669,3671,3673,3676],{},[2046,3654,3655],{},"0.8",[2012,3657,3658],{},"×",[2012,3660,3292],{"stretchy":3291},[2012,3662,3309],{},[2046,3664,3295],{},[2003,3666,3029],{"mathvariant":3028},[2012,3668,3300],{"stretchy":3291},[2012,3670,2024],{},[2012,3672,3309],{},[2046,3674,3675],{},"1.6",[2003,3677,3029],{"mathvariant":3028},[2108,3679,3680],{"encoding":2110},"0.8\\times(-2\\%)=-1.6\\%",[1976,3682,3684,3703,3730],{"className":3683,"ariaHidden":2014},[2115],[1976,3685,3687,3691,3694,3697,3700],{"className":3686},[2119],[1976,3688],{"className":3689,"style":3690},[2123],"height:0.7278em;vertical-align:-0.0833em;",[1976,3692,3655],{"className":3693},[2128],[1976,3695],{"className":3696,"style":2272},[2209],[1976,3698,3658],{"className":3699},[2276],[1976,3701],{"className":3702,"style":2272},[2209],[1976,3704,3706,3709,3712,3715,3718,3721,3724,3727],{"className":3705},[2119],[1976,3707],{"className":3708,"style":3366},[2123],[1976,3710,3292],{"className":3711},[3373],[1976,3713,3309],{"className":3714},[2128],[1976,3716,3377],{"className":3717},[2128],[1976,3719,3300],{"className":3720},[3381],[1976,3722],{"className":3723,"style":2210},[2209],[1976,3725,2024],{"className":3726},[2214],[1976,3728],{"className":3729,"style":2210},[2209],[1976,3731,3733,3736,3739],{"className":3732},[2119],[1976,3734],{"className":3735,"style":3152},[2123],[1976,3737,3309],{"className":3738},[2128],[1976,3740,3742],{"className":3741},[2128],"1.6%","。",[1873,3745,3746],{},"至少检查经济机制、样本外表现、跨市场稳健性、是否经受多重检验调整、交易成本和容量约束。",[1800,3748,3749],{"id":3749},"小结与过渡",[1793,3751,3752],{},"因子模型把资产定价从单一市场 beta 扩展到多维风险与特征暴露。下一章进入跨期资产定价，用消费、状态价格和随机贴现因子把 CAPM、因子模型与更一般的动态定价框架连接起来。",{"title":10,"searchDepth":3754,"depth":3754,"links":3755},2,[3756,3757,3758,3759,3760,3768,3769,3770,3771,3772,3773,3774],{"id":1802,"depth":3754,"text":1802},{"id":1865,"depth":3754,"text":1865},{"id":1911,"depth":3754,"text":1911},{"id":1971,"depth":3754,"text":1971},{"id":2942,"depth":3754,"text":2942,"children":3761},[3762,3764,3765,3766,3767],{"id":2946,"depth":3763,"text":2946},3,{"id":2955,"depth":3763,"text":2956},{"id":2965,"depth":3763,"text":2965},{"id":2974,"depth":3763,"text":2975},{"id":2984,"depth":3763,"text":2984},{"id":2993,"depth":3754,"text":2994},{"id":3466,"depth":3754,"text":3467},{"id":3483,"depth":3754,"text":3483},{"id":3576,"depth":3754,"text":3576},{"id":3612,"depth":3754,"text":3612},{"id":3629,"depth":3754,"text":3629},{"id":3749,"depth":3754,"text":3749},"多因子定价模型理论与实践","md",{"sidebar":3778},{"order":3779},5,true,{"title":1278,"description":3775},"OREezJ-dVeiD9n2RCqSHMla6CQMUz1oTVBhwfFxzM1s",[3784,3786],{"title":1272,"path":1273,"stem":1274,"description":3785,"children":-1},"CAPM理论、推导与实证检验",{"title":1284,"path":1285,"stem":1286,"description":3787,"children":-1},"跨期选择、消费资本资产定价与随机贴现因子",1785754746322]