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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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是否成立”。研究者同时拥有数百个公司特征、大量宏观时间序列、文本披露和灵活机器学习模型。候选变量越多，拟合能力越强，越需要无套利约束、样本外评价、复现设计与经济机制来防止把噪声包装成风险溢价。",[1793,1797,1798,1799,1803],{},"本章用三篇同行评议论文与两篇 2026 年工作论文建立一条完整链条：深度网络怎样进入随机贴现因子；“因子动物园”怎样做复现；气候文本怎样转化为风险暴露；机器学习预测怎样进入组合决策；语言模型怎样满足历史信息边界。材料检索截至 ",[1800,1801,1802],"strong",{},"2026 年 8 月 1 日","，属于课程定向更新，不是穷尽式综述。代码使用合成收益率，不构成投资建议或论文复现。",[1805,1806,1807],"h2",{"id":1807},"学习目标",[1793,1809,1810],{},"完成本章后，你应能够：",[1812,1813,1814,1818,1821,1824,1827,1830,1833],"ol",{},[1815,1816,1817],"li",{},"把机器学习资产定价重新写回随机贴现因子与 Euler 方程；",[1815,1819,1820],{},"区分收益预测、定价误差、Sharpe 比率和经济机制；",[1815,1822,1823],{},"解释大量候选因子为何同时带来多重检验与共同主题信息；",[1815,1825,1826],{},"设计发现样本、验证样本与跨市场复现；",[1815,1828,1829],{},"说明文本暴露变量的构造效度、测量误差和定价解释；",[1815,1831,1832],{},"避免把样本内最优模型、文本提及次数或显著 alpha 直接当成可交易因果结论。",[1815,1834,1835],{},"识别工作论文的证据状态，并审计语言模型是否真正 point-in-time。",[1805,1837,1839],{"id":1838},"案例一深度学习不是无套利条件的替代品","案例一：深度学习不是无套利条件的替代品",[1841,1842,1844],"h3",{"id":1843},"_1-统一起点随机贴现因子","1. 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下满足：",[1849,2127,2130],{"className":2128},[2129],"katex-display",[1849,2131,2133,2196],{"className":2132},[1852],[1849,2134,2136],{"className":2135},[1856],[1858,2137,2139],{"xmlns":1860,"display":2138},"block",[1862,2140,2141,2193],{},[1865,2142,2143,2150,2154,2166,2184,2187,2190],{},[2047,2144,2145,2148],{},[1868,2146,2147],{},"E",[1868,2149,1923],{},[1918,2151,2153],{"stretchy":2152},"false","[",[2047,2155,2156,2158],{},[1868,2157,2051],{},[1865,2159,2160,2162,2164],{},[1868,2161,1923],{},[1918,2163,1926],{},[1928,2165,1930],{},[1908,2167,2168,2170,2182],{},[1868,2169,1912],{},[1865,2171,2172,2174,2176,2178,2180],{},[1868,2173,1870],{},[1918,2175,1920],{"separator":1879},[1868,2177,1923],{},[1918,2179,1926],{},[1928,2181,1930],{},[1868,2183,1933],{},[1918,2185,2186],{"stretchy":2152},"]",[1918,2188,2189],{},"=",[1928,2191,2192],{},"0.",[1872,2194,2195],{"encoding":1874},"E_t[m_{t+1}R^e_{i,t+1}]=0.",[1849,2197,2199,2389],{"className":2198,"ariaHidden":1879},[1878],[1849,2200,2202,2206,2250,2254,2303,2373,2377,2382,2386],{"className":2201},[1883],[1849,2203],{"className":2204,"style":2205},[1887],"height:1.1331em;vertical-align:-0.3831em;",[1849,2207,2209,2213],{"className":2208},[1892],[1849,2210,2147],{"className":2211,"style":2212},[1892,1893],"margin-right:0.0576em;",[1849,2214,2216],{"className":2215},[1957],[1849,2217,2219,2241],{"className":2218},[1961,1962],[1849,2220,2222,2238],{"className":2221},[1966],[1849,2223,2226],{"className":2224,"style":2225},[1970],"height:0.2806em;",[1849,2227,2229,2232],{"style":2228},"top:-2.55em;margin-left:-0.0576em;margin-right:0.05em;",[1849,2230],{"className":2231,"style":1979},[1978],[1849,2233,2235],{"className":2234},[1983,1984,1985,1986],[1849,2236,1923],{"className":2237},[1892,1893,1986],[1849,2239,2023],{"className":2240},[2022],[1849,2242,2244],{"className":2243},[1966],[1849,2245,2248],{"className":2246,"style":2247},[1970],"height:0.15em;",[1849,2249],{},[1849,2251,2153],{"className":2252},[2253],"mopen",[1849,2255,2257,2260],{"className":2256},[1892],[1849,2258,2051],{"className":2259},[1892,1893],[1849,2261,2263],{"className":2262},[1957],[1849,2264,2266,2295],{"className":2265},[1961,1962],[1849,2267,2269,2292],{"className":2268},[1966],[1849,2270,2272],{"className":2271,"style":2091},[1970],[1849,2273,2274,2277],{"style":2094},[1849,2275],{"className":2276,"style":1979},[1978],[1849,2278,2280],{"className":2279},[1983,1984,1985,1986],[1849,2281,2283,2286,2289],{"className":2282},[1892,1986],[1849,2284,1923],{"className":2285},[1892,1893,1986],[1849,2287,1926],{"className":2288},[2003,1986],[1849,2290,1930],{"className":2291},[1892,1986],[1849,2293,2023],{"className":2294},[2022],[1849,2296,2298],{"className":2297},[1966],[1849,2299,2301],{"className":2300,"style":2122},[1970],[1849,2302],{},[1849,2304,2306,2309],{"className":2305},[1892],[1849,2307,1912],{"className":2308,"style":1953},[1892,1893],[1849,2310,2312],{"className":2311},[1957],[1849,2313,2315,2364],{"className":2314},[1961,1962],[1849,2316,2318,2361],{"className":2317},[1966],[1849,2319,2322,2349],{"className":2320,"style":2321},[1970],"height:0.7144em;",[1849,2323,2325,2328],{"style":2324},"top:-2.453em;margin-left:-0.0077em;margin-right:0.05em;",[1849,2326],{"className":2327,"style":1979},[1978],[1849,2329,2331],{"className":2330},[1983,1984,1985,1986],[1849,2332,2334,2337,2340,2343,2346],{"className":2333},[1892,1986],[1849,2335,1870],{"className":2336},[1892,1893,1986],[1849,2338,1920],{"className":2339},[1996,1986],[1849,2341,1923],{"className":2342},[1892,1893,1986],[1849,2344,1926],{"className":2345},[2003,1986],[1849,2347,1930],{"className":2348},[1892,1986],[1849,2350,2352,2355],{"style":2351},"top:-3.113em;margin-right:0.05em;",[1849,2353],{"className":2354,"style":1979},[1978],[1849,2356,2358],{"className":2357},[1983,1984,1985,1986],[1849,2359,1933],{"className":2360},[1892,1893,1986],[1849,2362,2023],{"className":2363},[2022],[1849,2365,2367],{"className":2366},[1966],[1849,2368,2371],{"className":2369,"style":2370},[1970],"height:0.3831em;",[1849,2372],{},[1849,2374,2186],{"className":2375},[2376],"mclose",[1849,2378],{"className":2379,"style":2381},[2380],"mspace","margin-right:0.2778em;",[1849,2383,2189],{"className":2384},[2385],"mrel",[1849,2387],{"className":2388,"style":2381},[2380],[1849,2390,2392,2396],{"className":2391},[1883],[1849,2393],{"className":2394,"style":2395},[1887],"height:0.6444em;",[1849,2397,2192],{"className":2398},[1892],[1793,2400,2401],{},"传统线性因子模型令：",[1849,2403,2405],{"className":2404},[2129],[1849,2406,2408,2467],{"className":2407},[1852],[1849,2409,2411],{"className":2410},[1856],[1858,2412,2413],{"xmlns":1860,"display":2138},[1862,2414,2415,2464],{},[1865,2416,2417,2429,2431,2434,2437,2448,2461],{},[2047,2418,2419,2421],{},[1868,2420,2051],{},[1865,2422,2423,2425,2427],{},[1868,2424,1923],{},[1918,2426,1926],{},[1928,2428,1930],{},[1918,2430,2189],{},[1868,2432,2433],{},"a",[1918,2435,2436],{},"−",[2438,2439,2440,2443],"msup",{},[1868,2441,2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SDF 或组合权重：",[1849,2652,2654],{"className":2653},[2129],[1849,2655,2657,2729],{"className":2656},[1852],[1849,2658,2660],{"className":2659},[1856],[1858,2661,2662],{"xmlns":1860,"display":2138},[1862,2663,2664,2726],{},[1865,2665,2666,2678,2680,2688,2691,2698,2700,2712,2714,2721,2724],{},[2047,2667,2668,2670],{},[1868,2669,2051],{},[1865,2671,2672,2674,2676],{},[1868,2673,1923],{},[1918,2675,1926],{},[1928,2677,1930],{},[1918,2679,2189],{},[2047,2681,2682,2685],{},[1868,2683,2684],{},"g",[1868,2686,2687],{},"θ",[1918,2689,2690],{"stretchy":2152},"(",[2047,2692,2693,2696],{},[1868,2694,2695],{},"Z",[1868,2697,1923],{},[1918,2699,1920],{"separator":1879},[2047,2701,2702,2704],{},[1868,2703,1912],{},[1865,2705,2706,2708,2710],{},[1868,2707,1923],{},[1918,2709,1926],{},[1928,2711,1930],{},[1918,2713,1920],{"separator":1879},[2047,2715,2716,2719],{},[1868,2717,2718],{},"X",[1868,2720,1923],{},[1918,2722,2723],{"stretchy":2152},")",[1918,2725,1920],{"separator":1879},[1872,2727,2728],{"encoding":1874},"m_{t+1}=g_\\theta(Z_t,R_{t+1},X_t),",[1849,2730,2732,2796],{"className":2731,"ariaHidden":1879},[1878],[1849,2733,2735,2738,2787,2790,2793],{"className":2734},[1883],[1849,2736],{"className":2737,"style":2072},[1887],[1849,2739,2741,2744],{"className":2740},[1892],[1849,2742,2051],{"className":2743},[1892,1893],[1849,2745,2747],{"className":2746},[1957],[1849,2748,2750,2779],{"className":2749},[1961,1962],[1849,2751,2753,2776],{"className":2752},[1966],[1849,2754,2756],{"className":2755,"style":2091},[1970],[1849,2757,2758,2761],{"style":2094},[1849,2759],{"className":2760,"style":1979},[1978],[1849,2762,2764],{"className":2763},[1983,1984,1985,1986],[1849,2765,2767,2770,2773],{"className":2766},[1892,1986],[1849,2768,1923],{"className":2769},[1892,1893,1986],[1849,2771,1926],{"className":2772},[2003,1986],[1849,2774,1930],{"className":2775},[1892,1986],[1849,2777,2023],{"className":2778},[2022],[1849,2780,2782],{"className":2781},[1966],[1849,2783,2785],{"className":2784,"style":2122},[1970],[1849,2786],{},[1849,2788],{"className":2789,"style":2381},[2380],[1849,2791,2189],{"className":2792},[2385],[1849,2794],{"className":2795,"style":2381},[2380],[1849,2797,2799,2803,2847,2850,2892,2895,2899,2949,2952,2955,2997,3000],{"className":2798},[1883],[1849,2800],{"className":2801,"style":2802},[1887],"height:1em;vertical-align:-0.25em;",[1849,2804,2806,2810],{"className":2805},[1892],[1849,2807,2684],{"className":2808,"style":2809},[1892,1893],"margin-right:0.0359em;",[1849,2811,2813],{"className":2812},[1957],[1849,2814,2816,2839],{"className":2815},[1961,1962],[1849,2817,2819,2836],{"className":2818},[1966],[1849,2820,2823],{"className":2821,"style":2822},[1970],"height:0.3361em;",[1849,2824,2826,2829],{"style":2825},"top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;",[1849,2827],{"className":2828,"style":1979},[1978],[1849,2830,2832],{"className":2831},[1983,1984,1985,1986],[1849,2833,2687],{"className":2834,"style":2835},[1892,1893,1986],"margin-right:0.0278em;",[1849,2837,2023],{"className":2838},[2022],[1849,2840,2842],{"className":2841},[1966],[1849,2843,2845],{"className":2844,"style":2247},[1970],[1849,2846],{},[1849,2848,2690],{"className":2849},[2253],[1849,2851,2853,2857],{"className":2852},[1892],[1849,2854,2695],{"className":2855,"style":2856},[1892,1893],"margin-right:0.0715em;",[1849,2858,2860],{"className":2859},[1957],[1849,2861,2863,2884],{"className":2862},[1961,1962],[1849,2864,2866,2881],{"className":2865},[1966],[1849,2867,2869],{"className":2868,"style":2225},[1970],[1849,2870,2872,2875],{"style":2871},"top:-2.55em;margin-left:-0.0715em;margin-right:0.05em;",[1849,2873],{"className":2874,"style":1979},[1978],[1849,2876,2878],{"className":2877},[1983,1984,1985,1986],[1849,2879,1923],{"className":2880},[1892,1893,1986],[1849,2882,2023],{"className":2883},[2022],[1849,2885,2887],{"className":2886},[1966],[1849,2888,2890],{"className":2889,"style":2247},[1970],[1849,2891],{},[1849,2893,1920],{"className":2894},[1996],[1849,2896],{"className":2897,"style":2898},[2380],"margin-right:0.1667em;",[1849,2900,2902,2905],{"className":2901},[1892],[1849,2903,1912],{"className":2904,"style":1953},[1892,1893],[1849,2906,2908],{"className":2907},[1957],[1849,2909,2911,2941],{"className":2910},[1961,1962],[1849,2912,2914,2938],{"className":2913},[1966],[1849,2915,2917],{"className":2916,"style":2091},[1970],[1849,2918,2920,2923],{"style":2919},"top:-2.55em;margin-left:-0.0077em;margin-right:0.05em;",[1849,2921],{"className":2922,"style":1979},[1978],[1849,2924,2926],{"className":2925},[1983,1984,1985,1986],[1849,2927,2929,2932,2935],{"className":2928},[1892,1986],[1849,2930,1923],{"className":2931},[1892,1893,1986],[1849,2933,1926],{"className":2934},[2003,1986],[1849,2936,1930],{"className":2937},[1892,1986],[1849,2939,2023],{"className":2940},[2022],[1849,2942,2944],{"className":2943},[1966],[1849,2945,2947],{"className":2946,"style":2122},[1970],[1849,2948],{},[1849,2950,1920],{"className":2951},[1996],[1849,2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Chen、Pelger 与 Zhu 的研究设计",[1793,3011,3012,3013,3017],{},"Chen、Pelger 与 Zhu（2023 年在线发表，刊于 2024 年 ",[3014,3015,3016],"em",{},"Management Science"," 70 卷）用深度神经网络估计个股收益的条件资产定价模型。论文的两项关键设计是：",[1812,3019,3020,3023],{},[1815,3021,3022],{},"以无套利条件为准则，并通过对抗式方法构造信息量较高的测试资产；",[1815,3024,3025],{},"从大量宏观时间序列中提取经济状态，使条件关系能够随时间变化。",[1793,3027,3028],{},"论文报告其模型在所研究样本的样本外 Sharpe 比率、解释变异和定价误差上优于基准方法。正确表述是“在论文设定的样本、资产、信息集和评价指标下优于基准”，不是“深度网络已经发现真实 SDF”。",[1841,3030,3032],{"id":3031},"_3-对抗测试资产的直觉","3. 对抗测试资产的直觉",[1793,3034,3035,3036,3123],{},"若只在容易定价的组合上检查模型，错误 SDF 也可能看起来很好。对抗网络可被理解为寻找一个组合 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SDF 的矩条件违背尽可能明显：",[1849,3125,3127],{"className":3126},[2129],[1849,3128,3130,3212],{"className":3129},[1852],[1849,3131,3133],{"className":3132},[1856],[1858,3134,3135],{"xmlns":1860,"display":2138},[1862,3136,3137,3209],{},[1865,3138,3139,3152,3207],{},[3140,3141,3142,3150],"munder",{},[1865,3143,3144,3147],{},[1868,3145,3146],{},"max",[1918,3148,3149],{},"⁡",[1868,3151,3050],{},[1865,3153,3154,3157,3159,3161,3173,3175,3177,3183,3189,3203,3205],{},[1918,3155,3156],{"fence":1879},"∣",[1868,3158,2147],{},[1918,3160,2153],{"stretchy":2152},[2047,3162,3163,3165],{},[1868,3164,2051],{},[1865,3166,3167,3169,3171],{},[1868,3168,1923],{},[1918,3170,1926],{},[1928,3172,1930],{},[1868,3174,3050],{},[1918,3176,2690],{"stretchy":2152},[2047,3178,3179,3181],{},[1868,3180,2718],{},[1868,3182,1923],{},[2438,3184,3185,3187],{},[1918,3186,2723],{"stretchy":2152},[1918,3188,2447],{"mathvariant":2445,"lspace":2446,"rspace":2446},[1908,3190,3191,3193,3201],{},[1868,3192,1912],{},[1865,3194,3195,3197,3199],{},[1868,3196,1923],{},[1918,3198,1926],{},[1928,3200,1930],{},[1868,3202,1933],{},[1918,3204,2186],{"stretchy":2152},[1918,3206,3156],{"fence":1879},[1868,3208,2463],{"mathvariant":2445},[1872,3210,3211],{"encoding":1874},"\\max_w\\left|E[m_{t+1}w(X_t)'R^e_{t+1}]\\right|.",[1849,3213,3215],{"className":3214,"ariaHidden":1879},[1878],[1849,3216,3218,3222,3273,3276,3565,3568],{"className":3217},[1883],[1849,3219],{"className":3220,"style":3221},[1887],"height:1.55em;vertical-align:-0.7em;",[1849,3223,3227],{"className":3224},[3225,3226],"mop","op-limits",[1849,3228,3230,3264],{"className":3229},[1961,1962],[1849,3231,3233,3261],{"className":3232},[1966],[1849,3234,3237,3250],{"className":3235,"style":3236},[1970],"height:0.4306em;",[1849,3238,3240,3244],{"style":3239},"top:-2.4em;margin-left:0em;",[1849,3241],{"className":3242,"style":3243},[1978],"height:3em;",[1849,3245,3247],{"className":3246},[1983,1984,1985,1986],[1849,3248,3050],{"className":3249,"style":3076},[1892,1893,1986],[1849,3251,3253,3256],{"style":3252},"top:-3em;",[1849,3254],{"className":3255,"style":3243},[1978],[1849,3257,3258],{},[1849,3259,3146],{"className":3260},[3225],[1849,3262,2023],{"className":3263},[2022],[1849,3265,3267],{"className":3266},[1966],[1849,3268,3271],{"className":3269,"style":3270},[1970],"height:0.7em;",[1849,3272],{},[1849,3274],{"className":3275,"style":2898},[2380],[1849,3277,3280,3331,3334,3337,3386,3389,3392,3432,3464,3525,3528],{"className":3278},[3279],"minner",[1849,3281,3283],{"className":3282},[2253],[1849,3284,3288],{"className":3285},[3286,3287],"delimsizing","mult",[1849,3289,3291,3322],{"className":3290},[1961,1962],[1849,3292,3294,3319],{"className":3293},[1966],[1849,3295,3298],{"className":3296,"style":3297},[1970],"height:0.85em;",[1849,3299,3301,3305],{"style":3300},"top:-2.85em;",[1849,3302],{"className":3303,"style":3304},[1978],"height:3.2em;",[1849,3306,3308],{"style":3307},"width:0.333em;height:1.2em;",[3309,3310,3315],"svg",{"xmlns":3311,"width":3312,"height":3313,"viewBox":3314},"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg","0.333em","1.2em","0 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网络则努力最小化最难测试资产上的定价误差。这与 GMM 中选择有信息矩条件的思想相连；“对抗”不是脱离经济学的黑盒竞争。",[1841,3575,3577],{"id":3576},"_4-本科生与研究生的不同要求","4. 本科生与研究生的不同要求",[1793,3579,3580],{},"本科生应能解释：模型预测高收益不等于该收益是风险补偿，交易成本和不可实现信息也会降低可交易性。研究生还应检查：",[3582,3583,3584,3587,3590,3593,3596,3599],"ul",{},[1815,3585,3586],{},"测试资产是否用验证数据构造，是否引入数据窥视；",[1815,3588,3589],{},"超参数和早停是否只在训练\u002F验证集选择；",[1815,3591,3592],{},"Sharpe 比率是否经过选择偏误和估计误差校正；",[1815,3594,3595],{},"定价误差的经济量级是否优于简单模型；",[1815,3597,3598],{},"模型在不同时期、市场和交易成本下是否稳定；",[1815,3600,3601],{},"隐状态与风险机制是否具有可解释、可证伪的对应关系。",[1805,3603,3605],{"id":3604},"案例二因子动物园究竟是危机还是信息","案例二：因子动物园究竟是危机还是信息",[1841,3607,3609],{"id":3608},"_1-多重检验的经典担忧","1. 多重检验的经典担忧",[1793,3611,3612,3613,3644],{},"假设研究者检验 ",[1849,3614,3616,3630],{"className":3615},[1852],[1849,3617,3619],{"className":3618},[1856],[1858,3620,3621],{"xmlns":1860},[1862,3622,3623,3628],{},[1865,3624,3625],{},[1868,3626,3627],{},"M",[1872,3629,3627],{"encoding":1874},[1849,3631,3633],{"className":3632,"ariaHidden":1879},[1878],[1849,3634,3636,3640],{"className":3635},[1883],[1849,3637],{"className":3638,"style":3639},[1887],"height:0.6833em;",[1849,3641,3627],{"className":3642,"style":3643},[1892,1893],"margin-right:0.109em;"," 个真实 alpha 均为零的因子，每个检验使用 5% 显著性水平。若检验近似独立，至少出现一个假阳性的概率为：",[1849,3646,3648],{"className":3647},[2129],[1849,3649,3651,3684],{"className":3650},[1852],[1849,3652,3654],{"className":3653},[1856],[1858,3655,3656],{"xmlns":1860,"display":2138},[1862,3657,3658,3681],{},[1865,3659,3660,3662,3664,3666,3668,3670,3673,3679],{},[1928,3661,1930],{},[1918,3663,2436],{},[1918,3665,2690],{"stretchy":2152},[1928,3667,1930],{},[1918,3669,2436],{},[1928,3671,3672],{},"0.05",[2438,3674,3675,3677],{},[1918,3676,2723],{"stretchy":2152},[1868,3678,3627],{},[1868,3680,2463],{"mathvariant":2445},[1872,3682,3683],{"encoding":1874},"1-(1-0.05)^M.",[1849,3685,3687,3706,3727],{"className":3686,"ariaHidden":1879},[1878],[1849,3688,3690,3694,3697,3700,3703],{"className":3689},[1883],[1849,3691],{"className":3692,"style":3693},[1887],"height:0.7278em;vertical-align:-0.0833em;",[1849,3695,1930],{"className":3696},[1892],[1849,3698],{"className":3699,"style":2547},[2380],[1849,3701,2436],{"className":3702},[2003],[1849,3704],{"className":3705,"style":2547},[2380],[1849,3707,3709,3712,3715,3718,3721,3724],{"className":3708},[1883],[1849,3710],{"className":3711,"style":2802},[1887],[1849,3713,2690],{"className":3714},[2253],[1849,3716,1930],{"className":3717},[1892],[1849,3719],{"className":3720,"style":2547},[2380],[1849,3722,2436],{"className":3723},[2003],[1849,3725],{"className":3726,"style":2547},[2380],[1849,3728,3730,3734,3737,3767],{"className":3729},[1883],[1849,3731],{"className":3732,"style":3733},[1887],"height:1.1413em;vertical-align:-0.25em;",[1849,3735,3672],{"className":3736},[1892],[1849,3738,3740,3743],{"className":3739},[2376],[1849,3741,2723],{"className":3742},[2376],[1849,3744,3746],{"className":3745},[1957],[1849,3747,3749],{"className":3748},[1961],[1849,3750,3752],{"className":3751},[1966],[1849,3753,3756],{"className":3754,"style":3755},[1970],"height:0.8913em;",[1849,3757,3758,3761],{"style":2351},[1849,3759],{"className":3760,"style":1979},[1978],[1849,3762,3764],{"className":3763},[1983,1984,1985,1986],[1849,3765,3627],{"className":3766,"style":3643},[1892,1893,1986],[1849,3768,2463],{"className":3769},[1892],[1793,3771,3772,3824],{},[1849,3773,3775,3794],{"className":3774},[1852],[1849,3776,3778],{"className":3777},[1856],[1858,3779,3780],{"xmlns":1860},[1862,3781,3782,3791],{},[1865,3783,3784,3786,3788],{},[1868,3785,3627],{},[1918,3787,2189],{},[1928,3789,3790],{},"100",[1872,3792,3793],{"encoding":1874},"M=100",[1849,3795,3797,3815],{"className":3796,"ariaHidden":1879},[1878],[1849,3798,3800,3803,3806,3809,3812],{"className":3799},[1883],[1849,3801],{"className":3802,"style":3639},[1887],[1849,3804,3627],{"className":3805,"style":3643},[1892,1893],[1849,3807],{"className":3808,"style":2381},[2380],[1849,3810,2189],{"className":3811},[2385],[1849,3813],{"className":3814,"style":2381},[2380],[1849,3816,3818,3821],{"className":3817},[1883],[1849,3819],{"className":3820,"style":2395},[1887],[1849,3822,3790],{"className":3823},[1892]," 时，该概率约为 99.4%。即使因子相关，挑选最大的 t 统计量仍产生严重选择偏误。发现样本中的显著性因此必须与样本外复现、经济先验和联合建模结合。",[1841,3826,3828],{"id":3827},"_2-jensenkelly-与-pedersen-的不同观点","2. Jensen、Kelly 与 Pedersen 的不同观点",[1793,3830,3831],{},"Jensen、Kelly 与 Pedersen（2023）构建了覆盖 93 个国家、153 个因子的全球数据，并用贝叶斯因子复现模型重新评价“复现危机”。论文报告：多数因子可以复现，候选因子可聚为 13 个主题，多数主题进入切点组合，并在新的全球数据中表现出样本外证据。",[1793,3833,3834],{},"他们的重要论点是：大量相似因子不一定只是独立的数据挖掘尝试，也可能是同一潜在经济主题的多种噪声测量。若价值、动量或质量主题有许多相关代理变量，联合层级模型可以从横截面共享信息。",[1841,3836,3838],{"id":3837},"_3-为什么这不等于所有因子都可信","3. 为什么这不等于“所有因子都可信”",[1793,3840,3841],{},"论文的结论来自特定贝叶斯先验、聚类、数据清理和复现定义。频率学多重检验与贝叶斯层级模型回答的问题不同：前者控制重复抽样错误率，后者在给定先验和似然下更新主题与因子可信度。高质量报告应同时给出：",[3582,3843,3844,3847,3850,3853,3856,3859],{},[1815,3845,3846],{},"原始研究定义与可复现实现；",[1815,3848,3849],{},"发现样本、时间外与市场外结果；",[1815,3851,3852],{},"因子之间的相关和主题结构；",[1815,3854,3855],{},"交易成本、换手率、可卖空性和容量；",[1815,3857,3858],{},"多重检验、选择后推断或层级收缩；",[1815,3860,3861],{},"对替代构造、断点日期和微盘股处理的敏感性。",[1841,3863,3865],{"id":3864},"_4-一个实用的三层证据标准","4. 一个实用的三层证据标准",[3867,3868,3869,3885],"table",{},[3870,3871,3872],"thead",{},[3873,3874,3875,3879,3882],"tr",{},[3876,3877,3878],"th",{},"层级",[3876,3880,3881],{},"问题",[3876,3883,3884],{},"最低证据",[3886,3887,3888,3900,3911],"tbody",{},[3873,3889,3890,3894,3897],{},[3891,3892,3893],"td",{},"统计层",[3891,3895,3896],{},"平均收益是否区别于噪声",[3891,3898,3899],{},"置信区间、多重检验、选择后校正",[3873,3901,3902,3905,3908],{},[3891,3903,3904],{},"复现层",[3891,3906,3907],{},"换时期或市场是否仍存在",[3891,3909,3910],{},"预先声明的样本外复现",[3873,3912,3913,3916,3919],{},[3891,3914,3915],{},"经济层",[3891,3917,3918],{},"为什么应获得风险补偿",[3891,3920,3921],{},"风险、摩擦或行为机制及可证伪预测",[1793,3923,3924],{},"某因子可以通过前两层却仍缺乏清楚机制；也可能具有强理论动机但当前估计不精确。两种情况都不应被简化成“有效\u002F无效”二元标签。",[1805,3926,3928],{"id":3927},"案例三把气候讨论转成公司层面风险暴露","案例三：把气候讨论转成公司层面风险暴露",[1841,3930,3932],{"id":3931},"_1-研究问题","1. 研究问题",[1793,3934,3935],{},"行业碳排放或 ESG 评级通常变化慢，难以捕捉一家企业在某个季度面对的具体转型机会、实体风险和监管风险。Sautner、van Lent、Vilkov 与 Zhang（2023）使用上市公司季度业绩电话会议文本，构造随时间变化的公司气候暴露。",[1793,3937,3938],{},"论文将机器学习关键词发现算法用于气候相关词组，分别捕捉：",[3582,3940,3941,3947,3953],{},[1815,3942,3943,3946],{},[1800,3944,3945],{},"机会暴露","：绿色技术、产品需求和转型机会；",[1815,3948,3949,3952],{},[1800,3950,3951],{},"实体风险","：极端天气、资产受损和供应中断；",[1815,3954,3955,3958],{},[1800,3956,3957],{},"监管风险","：碳政策、合规与转型成本。",[1793,3960,3961],{},"最终指标可理解为相关 bigram 在电话会议文本中的相对频率，并对季度序列做适当平滑。",[1841,3963,3965],{"id":3964},"_2-构造效度比回归显著更先","2. 构造效度比回归显著更先",[1793,3967,3968],{},"一个文本指标进入资产定价回归前，至少需要四类验证：",[1812,3970,3971,3977,3983,3989],{},[1815,3972,3973,3976],{},[1800,3974,3975],{},"内容效度","：被算法发现的词组是否真的表达气候暴露？",[1815,3978,3979,3982],{},[1800,3980,3981],{},"收敛效度","：指标是否与已知排放、行业风险或气候事件合理相关？",[1815,3984,3985,3988],{},[1800,3986,3987],{},"区分效度","：它能否区分机会、实体和监管，而不只是一般风险语气？",[1815,3990,3991,3994],{},[1800,3992,3993],{},"预测\u002F结果效度","：它是否预测绿色专利、绿色技术就业或市场定价对象？",[1793,3996,3997],{},"论文的全球样本包含 34 个国家、逾 10,000 家企业，覆盖 2002—2020 年。作者报告指标可预测净零转型相关的实际结果，并包含期权和股票市场定价信息。",[1841,3999,4001],{"id":4000},"_3-被定价不自动等于因果风险溢价","3. “被定价”不自动等于因果风险溢价",[1793,4003,4004],{},"若高暴露公司具有不同收益，可有多种解释：",[3582,4006,4007,4010,4013,4016,4019],{},[1815,4008,4009],{},"投资者要求承担气候风险的补偿；",[1815,4011,4012],{},"暴露预示未来现金流变化；",[1815,4014,4015],{},"文本披露反映管理层注意力或信息环境；",[1815,4017,4018],{},"指标与行业、规模、盈利或政策周期共同变化；",[1815,4020,4021],{},"期权价格反映尾部风险和预期波动，而非平均收益溢价。",[1793,4023,4024],{},"所以需要分别检验现金流渠道、贴现率渠道、事件响应和横截面预期收益，而不能把一个显著系数统称为“气候风险被定价”。",[1841,4026,4028],{"id":4027},"_4-研究生扩展","4. 研究生扩展",[1793,4030,4031,4032,4115,4116,4251],{},"研究生可以先按文本暴露 ",[1849,4033,4035,4058],{"className":4034},[1852],[1849,4036,4038],{"className":4037},[1856],[1858,4039,4040],{"xmlns":1860},[1862,4041,4042,4055],{},[1865,4043,4044],{},[2047,4045,4046,4049],{},[1868,4047,4048],{},"C",[1865,4050,4051,4053],{},[1868,4052,1870],{},[1868,4054,1923],{},[1872,4056,4057],{"encoding":1874},"C_{it}",[1849,4059,4061],{"className":4060,"ariaHidden":1879},[1878],[1849,4062,4064,4068],{"className":4063},[1883],[1849,4065],{"className":4066,"style":4067},[1887],"height:0.8333em;vertical-align:-0.15em;",[1849,4069,4071,4074],{"className":4070},[1892],[1849,4072,4048],{"className":4073,"style":2856},[1892,1893],[1849,4075,4077],{"className":4076},[1957],[1849,4078,4080,4107],{"className":4079},[1961,1962],[1849,4081,4083,4104],{"className":4082},[1966],[1849,4084,4087],{"className":4085,"style":4086},[1970],"height:0.3117em;",[1849,4088,4089,4092],{"style":2871},[1849,4090],{"className":4091,"style":1979},[1978],[1849,4093,4095],{"className":4094},[1983,1984,1985,1986],[1849,4096,4098,4101],{"className":4097},[1892,1986],[1849,4099,1870],{"className":4100},[1892,1893,1986],[1849,4102,1923],{"className":4103},[1892,1893,1986],[1849,4105,2023],{"className":4106},[2022],[1849,4108,4110],{"className":4109},[1966],[1849,4111,4113],{"className":4112,"style":2247},[1970],[1849,4114],{}," 构造可交易或可检验的气候因子收益 ",[1849,4117,4119,4161],{"className":4118},[1852],[1849,4120,4122],{"className":4121},[1856],[1858,4123,4124],{"xmlns":1860},[1862,4125,4126,4158],{},[1865,4127,4128],{},[1908,4129,4130,4132,4140],{},[1868,4131,2452],{},[1865,4133,4134,4136,4138],{},[1868,4135,1923],{},[1918,4137,1926],{},[1928,4139,1930],{},[1865,4141,4142,4145,4148,4150,4152,4154,4156],{},[1868,4143,4144],{},"c",[1868,4146,4147],{},"l",[1868,4149,1870],{},[1868,4151,2051],{},[1868,4153,2433],{},[1868,4155,1923],{},[1868,4157,1933],{},[1872,4159,4160],{"encoding":1874},"f^{climate}_{t+1}",[1849,4162,4164],{"className":4163,"ariaHidden":1879},[1878],[1849,4165,4167,4171],{"className":4166},[1883],[1849,4168],{"className":4169,"style":4170},[1887],"height:1.1555em;vertical-align:-0.3064em;",[1849,4172,4174,4177],{"className":4173},[1892],[1849,4175,2452],{"className":4176,"style":2600},[1892,1893],[1849,4178,4180],{"className":4179},[1957],[1849,4181,4183,4242],{"className":4182},[1961,1962],[1849,4184,4186,4239],{"className":4185},[1966],[1849,4187,4190,4211],{"className":4188,"style":4189},[1970],"height:0.8491em;",[1849,4191,4193,4196],{"style":4192},"top:-2.4519em;margin-left:-0.1076em;margin-right:0.05em;",[1849,4194],{"className":4195,"style":1979},[1978],[1849,4197,4199],{"className":4198},[1983,1984,1985,1986],[1849,4200,4202,4205,4208],{"className":4201},[1892,1986],[1849,4203,1923],{"className":4204},[1892,1893,1986],[1849,4206,1926],{"className":4207},[2003,1986],[1849,4209,1930],{"className":4210},[1892,1986],[1849,4212,4213,4216],{"style":2009},[1849,4214],{"className":4215,"style":1979},[1978],[1849,4217,4219],{"className":4218},[1983,1984,1985,1986],[1849,4220,4222,4225,4229,4233,4236],{"className":4221},[1892,1986],[1849,4223,4144],{"className":4224},[1892,1893,1986],[1849,4226,4147],{"className":4227,"style":4228},[1892,1893,1986],"margin-right:0.0197em;",[1849,4230,4232],{"className":4231},[1892,1893,1986],"ima",[1849,4234,1923],{"className":4235},[1892,1893,1986],[1849,4237,1933],{"className":4238},[1892,1893,1986],[1849,4240,2023],{"className":4241},[2022],[1849,4243,4245],{"className":4244},[1966],[1849,4246,4249],{"className":4247,"style":4248},[1970],"height:0.3064em;",[1849,4250],{},"，再把它放入对所有资产共同的 SDF：",[1849,4253,4255],{"className":4254},[2129],[1849,4256,4258,4351],{"className":4257},[1852],[1849,4259,4261],{"className":4260},[1856],[1858,4262,4263],{"xmlns":1860,"display":2138},[1862,4264,4265,4348],{},[1865,4266,4267,4279,4281,4287,4289,4297,4309,4311,4318,4346],{},[2047,4268,4269,4271],{},[1868,4270,2051],{},[1865,4272,4273,4275,4277],{},[1868,4274,1923],{},[1918,4276,1926],{},[1928,4278,1930],{},[1918,4280,2189],{},[2047,4282,4283,4285],{},[1868,4284,2433],{},[1868,4286,1923],{},[1918,4288,2436],{},[1908,4290,4291,4293,4295],{},[1868,4292,2442],{},[1868,4294,1923],{},[1918,4296,2447],{"mathvariant":2445,"lspace":2446,"rspace":2446},[2047,4298,4299,4301],{},[1868,4300,2452],{},[1865,4302,4303,4305,4307],{},[1868,4304,1923],{},[1918,4306,1926],{},[1928,4308,1930],{},[1918,4310,2436],{},[2047,4312,4313,4316],{},[1868,4314,4315],{},"γ",[1868,4317,1923],{},[1908,4319,4320,4322,4330],{},[1868,4321,2452],{},[1865,4323,4324,4326,4328],{},[1868,4325,1923],{},[1918,4327,1926],{},[1928,4329,1930],{},[1865,4331,4332,4334,4336,4338,4340,4342,4344],{},[1868,4333,4144],{},[1868,4335,4147],{},[1868,4337,1870],{},[1868,4339,2051],{},[1868,4341,2433],{},[1868,4343,1923],{},[1868,4345,1933],{},[1868,4347,2463],{"mathvariant":2445},[1872,4349,4350],{"encoding":1874},"m_{t+1}=a_t-b_t'f_{t+1}-\\gamma_t f^{climate}_{t+1}.\n",[1849,4352,4354,4418,4474,4595],{"className":4353,"ariaHidden":1879},[1878],[1849,4355,4357,4360,4409,4412,4415],{"className":4356},[1883],[1849,4358],{"className":4359,"style":2072},[1887],[1849,4361,4363,4366],{"className":4362},[1892],[1849,4364,2051],{"className":4365},[1892,1893],[1849,4367,4369],{"className":4368},[1957],[1849,4370,4372,4401],{"className":4371},[1961,1962],[1849,4373,4375,4398],{"className":4374},[1966],[1849,4376,4378],{"className":4377,"style":2091},[1970],[1849,4379,4380,4383],{"style":2094},[1849,4381],{"className":4382,"style":1979},[1978],[1849,4384,4386],{"className":4385},[1983,1984,1985,1986],[1849,4387,4389,4392,4395],{"className":4388},[1892,1986],[1849,4390,1923],{"className":4391},[1892,1893,1986],[1849,4393,1926],{"className":4394},[2003,1986],[1849,4396,1930],{"className":4397},[1892,1986],[1849,4399,2023],{"className":4400},[2022],[1849,4402,4404],{"className":4403},[1966],[1849,4405,4407],{"className":4406,"style":2122},[1970],[1849,4408],{},[1849,4410],{"className":4411,"style":2381},[2380],[1849,4413,2189],{"className":4414},[2385],[1849,4416],{"className":4417,"style":2381},[2380],[1849,4419,4421,4425,4465,4468,4471],{"className":4420},[1883],[1849,4422],{"className":4423,"style":4424},[1887],"height:0.7333em;vertical-align:-0.15em;",[1849,4426,4428,4431],{"className":4427},[1892],[1849,4429,2433],{"className":4430},[1892,1893],[1849,4432,4434],{"className":4433},[1957],[1849,4435,4437,4457],{"className":4436},[1961,1962],[1849,4438,4440,4454],{"className":4439},[1966],[1849,4441,4443],{"className":4442,"style":2225},[1970],[1849,4444,4445,4448],{"style":2094},[1849,4446],{"className":4447,"style":1979},[1978],[1849,4449,4451],{"className":4450},[1983,1984,1985,1986],[1849,4452,1923],{"className":4453},[1892,1893,1986],[1849,4455,2023],{"className":4456},[2022],[1849,4458,4460],{"className":4459},[1966],[1849,4461,4463],{"className":4462,"style":2247},[1970],[1849,4464],{},[1849,4466],{"className":4467,"style":2547},[2380],[1849,4469,2436],{"className":4470},[2003],[1849,4472],{"className":4473,"style":2547},[2380],[1849,4475,4477,4481,4537,4586,4589,4592],{"className":4476},[1883],[1849,4478],{"className":4479,"style":4480},[1887],"height:1.0489em;vertical-align:-0.247em;",[1849,4482,4484,4487],{"className":4483},[1892],[1849,4485,2442],{"className":4486},[1892,1893],[1849,4488,4490],{"className":4489},[1957],[1849,4491,4493,4528],{"className":4492},[1961,1962],[1849,4494,4496,4525],{"className":4495},[1966],[1849,4497,4499,4511],{"className":4498,"style":2579},[1970],[1849,4500,4502,4505],{"style":4501},"top:-2.453em;margin-left:0em;margin-right:0.05em;",[1849,4503],{"className":4504,"style":1979},[1978],[1849,4506,4508],{"className":4507},[1983,1984,1985,1986],[1849,4509,1923],{"className":4510},[1892,1893,1986],[1849,4512,4513,4516],{"style":2351},[1849,4514],{"className":4515,"style":1979},[1978],[1849,4517,4519],{"className":4518},[1983,1984,1985,1986],[1849,4520,4522],{"className":4521},[1892,1986],[1849,4523,2447],{"className":4524},[1892,1986],[1849,4526,2023],{"className":4527},[2022],[1849,4529,4531],{"className":4530},[1966],[1849,4532,4535],{"className":4533,"style":4534},[1970],"height:0.247em;",[1849,4536],{},[1849,4538,4540,4543],{"className":4539},[1892],[1849,4541,2452],{"className":4542,"style":2600},[1892,1893],[1849,4544,4546],{"className":4545},[1957],[1849,4547,4549,4578],{"className":4548},[1961,1962],[1849,4550,4552,4575],{"className":4551},[1966],[1849,4553,4555],{"className":4554,"style":2091},[1970],[1849,4556,4557,4560],{"style":2615},[1849,4558],{"className":4559,"style":1979},[1978],[1849,4561,4563],{"className":4562},[1983,1984,1985,1986],[1849,4564,4566,4569,4572],{"className":4565},[1892,1986],[1849,4567,1923],{"className":4568},[1892,1893,1986],[1849,4570,1926],{"className":4571},[2003,1986],[1849,4573,1930],{"className":4574},[1892,1986],[1849,4576,2023],{"className":4577},[2022],[1849,4579,4581],{"className":4580},[1966],[1849,4582,4584],{"className":4583,"style":2122},[1970],[1849,4585],{},[1849,4587],{"className":4588,"style":2547},[2380],[1849,4590,2436],{"className":4591},[2003],[1849,4593],{"className":4594,"style":2547},[2380],[1849,4596,4598,4602,4644,4721],{"className":4597},[1883],[1849,4599],{"className":4600,"style":4601},[1887],"height:1.2044em;vertical-align:-0.3053em;",[1849,4603,4605,4609],{"className":4604},[1892],[1849,4606,4315],{"className":4607,"style":4608},[1892,1893],"margin-right:0.0556em;",[1849,4610,4612],{"className":4611},[1957],[1849,4613,4615,4636],{"className":4614},[1961,1962],[1849,4616,4618,4633],{"className":4617},[1966],[1849,4619,4621],{"className":4620,"style":2225},[1970],[1849,4622,4624,4627],{"style":4623},"top:-2.55em;margin-left:-0.0556em;margin-right:0.05em;",[1849,4625],{"className":4626,"style":1979},[1978],[1849,4628,4630],{"className":4629},[1983,1984,1985,1986],[1849,4631,1923],{"className":4632},[1892,1893,1986],[1849,4634,2023],{"className":4635},[2022],[1849,4637,4639],{"className":4638},[1966],[1849,4640,4642],{"className":4641,"style":2247},[1970],[1849,4643],{},[1849,4645,4647,4650],{"className":4646},[1892],[1849,4648,2452],{"className":4649,"style":2600},[1892,1893],[1849,4651,4653],{"className":4652},[1957],[1849,4654,4656,4713],{"className":4655},[1961,1962],[1849,4657,4659,4710],{"className":4658},[1966],[1849,4660,4663,4684],{"className":4661,"style":4662},[1970],"height:0.8991em;",[1849,4664,4666,4669],{"style":4665},"top:-2.453em;margin-left:-0.1076em;margin-right:0.05em;",[1849,4667],{"className":4668,"style":1979},[1978],[1849,4670,4672],{"className":4671},[1983,1984,1985,1986],[1849,4673,4675,4678,4681],{"className":4674},[1892,1986],[1849,4676,1923],{"className":4677},[1892,1893,1986],[1849,4679,1926],{"className":4680},[2003,1986],[1849,4682,1930],{"className":4683},[1892,1986],[1849,4685,4686,4689],{"style":2351},[1849,4687],{"className":4688,"style":1979},[1978],[1849,4690,4692],{"className":4691},[1983,1984,1985,1986],[1849,4693,4695,4698,4701,4704,4707],{"className":4694},[1892,1986],[1849,4696,4144],{"className":4697},[1892,1893,1986],[1849,4699,4147],{"className":4700,"style":4228},[1892,1893,1986],[1849,4702,4232],{"className":4703},[1892,1893,1986],[1849,4705,1923],{"className":4706},[1892,1893,1986],[1849,4708,1933],{"className":4709},[1892,1893,1986],[1849,4711,2023],{"className":4712},[2022],[1849,4714,4716],{"className":4715},[1966],[1849,4717,4719],{"className":4718,"style":3522},[1970],[1849,4720],{},[1849,4722,2463],{"className":4723},[1892],[1793,4725,4726,4805,4806,4885],{},[1849,4727,4729,4750],{"className":4728},[1852],[1849,4730,4732],{"className":4731},[1856],[1858,4733,4734],{"xmlns":1860},[1862,4735,4736,4748],{},[1865,4737,4738],{},[2047,4739,4740,4742],{},[1868,4741,4048],{},[1865,4743,4744,4746],{},[1868,4745,1870],{},[1868,4747,1923],{},[1872,4749,4057],{"encoding":1874},[1849,4751,4753],{"className":4752,"ariaHidden":1879},[1878],[1849,4754,4756,4759],{"className":4755},[1883],[1849,4757],{"className":4758,"style":4067},[1887],[1849,4760,4762,4765],{"className":4761},[1892],[1849,4763,4048],{"className":4764,"style":2856},[1892,1893],[1849,4766,4768],{"className":4767},[1957],[1849,4769,4771,4797],{"className":4770},[1961,1962],[1849,4772,4774,4794],{"className":4773},[1966],[1849,4775,4777],{"className":4776,"style":4086},[1970],[1849,4778,4779,4782],{"style":2871},[1849,4780],{"className":4781,"style":1979},[1978],[1849,4783,4785],{"className":4784},[1983,1984,1985,1986],[1849,4786,4788,4791],{"className":4787},[1892,1986],[1849,4789,1870],{"className":4790},[1892,1893,1986],[1849,4792,1923],{"className":4793},[1892,1893,1986],[1849,4795,2023],{"className":4796},[2022],[1849,4798,4800],{"className":4799},[1966],[1849,4801,4803],{"className":4802,"style":2247},[1970],[1849,4804],{}," 在这里进入组合形成或测试资产，而不是让每只资产拥有不同的 SDF。研究者必须解决 ",[1849,4807,4809,4830],{"className":4808},[1852],[1849,4810,4812],{"className":4811},[1856],[1858,4813,4814],{"xmlns":1860},[1862,4815,4816,4828],{},[1865,4817,4818],{},[2047,4819,4820,4822],{},[1868,4821,4048],{},[1865,4823,4824,4826],{},[1868,4825,1870],{},[1868,4827,1923],{},[1872,4829,4057],{"encoding":1874},[1849,4831,4833],{"className":4832,"ariaHidden":1879},[1878],[1849,4834,4836,4839],{"className":4835},[1883],[1849,4837],{"className":4838,"style":4067},[1887],[1849,4840,4842,4845],{"className":4841},[1892],[1849,4843,4048],{"className":4844,"style":2856},[1892,1893],[1849,4846,4848],{"className":4847},[1957],[1849,4849,4851,4877],{"className":4850},[1961,1962],[1849,4852,4854,4874],{"className":4853},[1966],[1849,4855,4857],{"className":4856,"style":4086},[1970],[1849,4858,4859,4862],{"style":2871},[1849,4860],{"className":4861,"style":1979},[1978],[1849,4863,4865],{"className":4864},[1983,1984,1985,1986],[1849,4866,4868,4871],{"className":4867},[1892,1986],[1849,4869,1870],{"className":4870},[1892,1893,1986],[1849,4872,1923],{"className":4873},[1892,1893,1986],[1849,4875,2023],{"className":4876},[2022],[1849,4878,4880],{"className":4879},[1966],[1849,4881,4883],{"className":4882,"style":2247},[1970],[1849,4884],{}," 是资产特征还是风险暴露、气候创新如何构造、横截面和时间序列识别怎样分开，以及文本测量误差会如何衰减或扭曲风险价格。",[1805,4887,4889],{"id":4888},"案例四2026-年的两条方法提醒","案例四：2026 年的两条方法提醒",[1841,4891,4893],{"id":4892},"_1-预测误差要按最终决策的代价衡量","1. 预测误差要按最终决策的代价衡量",[1793,4895,4896,4897,4900],{},"Wang、Gao、Harvey、Liu 与 Tao（2026）的 NBER 工作论文 ",[3014,4898,4899],{},"Machine Learning Meets Markowitz"," 质疑常见的“两步法”：先让预测模型最小化所有资产的平均误差，再把预测收益送入组合优化器。若某些资产因风险约束、卖空成本或投资者偏好获得很大权重，它们的预测误差对最终效用更重要；普通预测损失却把各资产误差近似等量看待。",[3867,4902,4903,4913],{},[3870,4904,4905],{},[3873,4906,4907,4910],{},[3876,4908,4909],{},"两步法",[3876,4911,4912],{},"决策一体化思路",[3886,4914,4915,4923,4931],{},[3873,4916,4917,4920],{},[3891,4918,4919],{},"先优化预测指标，再优化组合",[3891,4921,4922],{},"用最终组合目标反向约束预测",[3873,4924,4925,4928],{},[3891,4926,4927],{},"预测误差权重与投资者无关",[3891,4929,4930],{},"误差代价随偏好、约束和摩擦变化",[3873,4932,4933,4936],{},[3891,4934,4935],{},"较低 RMSE 被当作主要胜利",[3891,4937,4938],{},"还要看样本外效用、换手和风险",[1793,4940,4941,4942,4945],{},"这不表示任何端到端网络都优于传统方法。更灵活的目标也更容易过拟合，且该文截至资料日是 ",[1800,4943,4944],{},"NBER 工作论文","，应把结果表述为尚待进一步同行评议与独立复现的研究证据。",[1841,4947,4949],{"id":4948},"_2-只把旧文本放进提示词仍可能穿越","2. “只把旧文本放进提示词”仍可能穿越",[1793,4951,4952,4953,4956],{},"Kelly、Malamud、Schwab 与 Xu（2026）的 NBER 工作论文 ",[3014,4954,4955],{},"Scaling Point-in-Time Language Models"," 指出：使用不受时间限制的互联网语料训练的语言模型，参数中可能已经编码未来信息。研究者即使只向模型输入 2017 年年报，也不能据此断言模型在模拟“2017 年投资者的信息集”。",[1793,4958,4959],{},"他们按日历时间过滤训练语料并构造一系列历史模型检查点，目标是从模型训练阶段消除这种泄漏。对资产定价回测，最小审计表应包括：",[3867,4961,4962,4972],{},[3870,4963,4964],{},[3873,4965,4966,4969],{},[3876,4967,4968],{},"对象",[3876,4970,4971],{},"必须记录",[3886,4973,4974,4982,4990,4998,5006],{},[3873,4975,4976,4979],{},[3891,4977,4978],{},"原始文本",[3891,4980,4981],{},"发布日期、时区、修订与可获得时间",[3873,4983,4984,4987],{},[3891,4985,4986],{},"模型",[3891,4988,4989],{},"训练语料截止日、检查点与后续微调数据",[3873,4991,4992,4995],{},[3891,4993,4994],{},"特征",[3891,4996,4997],{},"第一次可计算日期，而非数据库当前日期",[3873,4999,5000,5003],{},[3891,5001,5002],{},"证券集合",[3891,5004,5005],{},"当时可见的上市、退市和成分信息",[3873,5007,5008,5011],{},[3891,5009,5010],{},"交易",[3891,5012,5013],{},"信号形成、下单、成交与成本假设",[5015,5016,5018],"warning",{"title":5017},"最小反例","用 2026 年通用语言模型重新编码 2017 年公告，可以做“今天回看历史文本”的研究；若没有证明模型参数满足 2017 年信息边界，就不能把它包装成 2017 年可实时实施的策略回测。",[1805,5020,5022],{"id":5021},"浏览器实验200-个候选因子与样本外复现","浏览器实验：200 个候选因子与样本外复现",[1793,5024,5025],{},"下面模拟 200 个候选月度因子，其中前 5 个具有正的真实月均收益，其余为零。我们在前 120 个月选择 t 统计量最大的因子，再在后 120 个月复核。一次运行不能证明一般规律；3 个输出问题分别是：发现样本有多少“显著因子”、赢家是否是真因子、复核是否保持同方向和显著。",[5027,5028],"pyodide",{"code64":5029,"layout":5030,"locale":7,"packages":5031,"title":5032},"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","vertical","numpy","Python：因子筛选、赢家诅咒与样本外复现",[5034,5035],"web-r",{"code64":5036,"layout":5030,"locale":7,"title":5037},"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","R：因子筛选、赢家诅咒与样本外复现",[1841,5039,5040],{"id":5040},"实验审计",[3582,5042,5043,5046,5049,5052],{},[1815,5044,5045],{},"把候选因子从 200 改为 20 和 2,000，观察发现样本显著数量与最大 t 值。",[1815,5047,5048],{},"把发现样本延长而保持总样本 240 个月，说明发现精度与验证精度的权衡。",[1815,5050,5051],{},"让 195 个零因子具有共同噪声，讨论检验相关性为何改变但不消除数据挖掘问题。",[1815,5053,5054,5055,5118],{},"当前复现标准只是同方向且 ",[1849,5056,5058,5082],{"className":5057},[1852],[1849,5059,5061],{"className":5060},[1856],[1858,5062,5063],{"xmlns":1860},[1862,5064,5065,5079],{},[1865,5066,5067,5069,5071,5073,5076],{},[1868,5068,3156],{"mathvariant":2445},[1868,5070,1923],{},[1868,5072,3156],{"mathvariant":2445},[1918,5074,5075],{},">",[1928,5077,5078],{},"1.96",[1872,5080,5081],{"encoding":1874},"|t|>1.96",[1849,5083,5085,5109],{"className":5084,"ariaHidden":1879},[1878],[1849,5086,5088,5091,5094,5097,5100,5103,5106],{"className":5087},[1883],[1849,5089],{"className":5090,"style":2802},[1887],[1849,5092,3156],{"className":5093},[1892],[1849,5095,1923],{"className":5096},[1892,1893],[1849,5098,3156],{"className":5099},[1892],[1849,5101],{"className":5102,"style":2381},[2380],[1849,5104,5075],{"className":5105},[2385],[1849,5107],{"className":5108,"style":2381},[2380],[1849,5110,5112,5115],{"className":5111},[1883],[1849,5113],{"className":5114,"style":2395},[1887],[1849,5116,5078],{"className":5117},[1892],"；真实研究还要核对构造、交易成本、市场覆盖和经济机制。",[1805,5120,5121],{"id":5121},"分层作业",[1841,5123,5124],{"id":5124},"本科生任务",[1793,5126,5127],{},"选择一个公开因子，写一页“证据卡”：因子定义、排序变量、持有期、样本、平均收益、波动、换手率、发现期和至少一个样本外市场。若没有交易成本信息，要明确写“证据缺失”。",[1841,5129,5130],{"id":5130},"研究生任务",[1793,5132,5133],{},"设计一项气候暴露资产定价研究。分别写出：文本指标构造、人工标注验证、气候创新、测试资产、SDF 矩条件、双重聚类或 Fama–MacBeth 推断、行业与规模控制、选择后校正、期权与股票市场的互证。最后列出至少三个无法由显著风险价格排除的替代解释。",[1805,5135,5136],{"id":5136},"一手文献与延伸阅读",[1812,5138,5139,5157,5174,5197,5213],{},[1815,5140,5141,5142,5148,5149,5151,5152,2463],{},"Chen, L., Pelger, M., & Zhu, J. (2023). ",[2433,5143,5147],{"href":5144,"rel":5145},"https:\u002F\u002Fpubsonline.informs.org\u002Fdoi\u002F10.1287\u002Fmnsc.2023.4695",[5146],"nofollow","Deep Learning in Asset Pricing",". ",[3014,5150,3016],{},", 70(2), 714–750. DOI: ",[2433,5153,5156],{"href":5154,"rel":5155},"https:\u002F\u002Fdoi.org\u002F10.1287\u002Fmnsc.2023.4695",[5146],"10.1287\u002Fmnsc.2023.4695",[1815,5158,5159,5160,5148,5165,5168,5169,2463],{},"Jensen, T. I., Kelly, B., & Pedersen, L. H. (2023). ",[2433,5161,5164],{"href":5162,"rel":5163},"https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Ffull\u002F10.1111\u002Fjofi.13249",[5146],"Is There a Replication Crisis in Finance?",[3014,5166,5167],{},"Journal of Finance",", 78(5), 2465–2518. DOI: ",[2433,5170,5173],{"href":5171,"rel":5172},"https:\u002F\u002Fdoi.org\u002F10.1111\u002Fjofi.13249",[5146],"10.1111\u002Fjofi.13249",[1815,5175,5176,5177,5148,5182,5184,5185,5190,5191,5196],{},"Sautner, Z., van Lent, L., Vilkov, G., & Zhang, R. (2023). ",[2433,5178,5181],{"href":5179,"rel":5180},"https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002F10.1111\u002Fjofi.13219",[5146],"Firm-Level Climate Change Exposure",[3014,5183,5167],{},", 78(3), 1449–1498. DOI: ",[2433,5186,5189],{"href":5187,"rel":5188},"https:\u002F\u002Fdoi.org\u002F10.1111\u002Fjofi.13219",[5146],"10.1111\u002Fjofi.13219",". 作者公开的",[2433,5192,5195],{"href":5193,"rel":5194},"https:\u002F\u002Fdoi.org\u002F10.17605\u002FOSF.IO\u002FFD6JQ",[5146],"气候暴露数据","可用于进一步复现训练。",[1815,5198,5199,5200,5204,5205,5148,5210],{},"Wang, Y., Gao, H., Harvey, C. R., Liu, Y., & Tao, X. (2026). ",[2433,5201,4899],{"href":5202,"rel":5203},"https:\u002F\u002Fwww.nber.org\u002Fpapers\u002Fw34861",[5146],". NBER Working Paper 34861. DOI: ",[2433,5206,5209],{"href":5207,"rel":5208},"https:\u002F\u002Fdoi.org\u002F10.3386\u002Fw34861",[5146],"10.3386\u002Fw34861",[1800,5211,5212],{},"工作论文。",[1815,5214,5215,5216,5220,5221,5148,5226],{},"Kelly, B. T., Malamud, S., Schwab, J., & Xu, T. A. (2026). ",[2433,5217,4955],{"href":5218,"rel":5219},"https:\u002F\u002Fwww.nber.org\u002Fpapers\u002Fw35247",[5146],". NBER Working Paper 35247. DOI: ",[2433,5222,5225],{"href":5223,"rel":5224},"https:\u002F\u002Fdoi.org\u002F10.3386\u002Fw35247",[5146],"10.3386\u002Fw35247",[1800,5227,5212],{},[1793,5229,5230,5231,5235,5236,5240,5241,5245],{},"建议与",[2433,5232,5234],{"href":5233},".\u002F05-factor-models\u002F","因子模型","、",[2433,5237,5239],{"href":5238},".\u002F06-intertemporal\u002F","跨期资产定价","和",[2433,5242,5244],{"href":5243},".\u002F11-interactive-labs\u002F","浏览器交互实验","配套学习。",{"title":10,"searchDepth":5247,"depth":5247,"links":5248},2,[5249,5250,5257,5263,5269,5273,5276,5280],{"id":1807,"depth":5247,"text":1807},{"id":1838,"depth":5247,"text":1839,"children":5251},[5252,5254,5255,5256],{"id":1843,"depth":5253,"text":1844},3,{"id":3008,"depth":5253,"text":3009},{"id":3031,"depth":5253,"text":3032},{"id":3576,"depth":5253,"text":3577},{"id":3604,"depth":5247,"text":3605,"children":5258},[5259,5260,5261,5262],{"id":3608,"depth":5253,"text":3609},{"id":3827,"depth":5253,"text":3828},{"id":3837,"depth":5253,"text":3838},{"id":3864,"depth":5253,"text":3865},{"id":3927,"depth":5247,"text":3928,"children":5264},[5265,5266,5267,5268],{"id":3931,"depth":5253,"text":3932},{"id":3964,"depth":5253,"text":3965},{"id":4000,"depth":5253,"text":4001},{"id":4027,"depth":5253,"text":4028},{"id":4888,"depth":5247,"text":4889,"children":5270},[5271,5272],{"id":4892,"depth":5253,"text":4893},{"id":4948,"depth":5253,"text":4949},{"id":5021,"depth":5247,"text":5022,"children":5274},[5275],{"id":5040,"depth":5253,"text":5040},{"id":5121,"depth":5247,"text":5121,"children":5277},[5278,5279],{"id":5124,"depth":5253,"text":5124},{"id":5130,"depth":5253,"text":5130},{"id":5136,"depth":5247,"text":5136},"通过深度资产定价、因子复现与公司气候暴露研究连接无套利、模型选择、文本度量和样本外检验。","md",{"sidebar":5284},{"order":5285},12,true,{"title":1320,"description":5281},"vNEtA9Y-nokZqxFdHoYpm053nrUO-t1o9DfhW5hZ7uU",[5290,5292],{"title":1314,"path":1315,"stem":1316,"description":5291,"children":-1},"使用可点击运行的 Python 与 R 单元完成收益率、组合优化、CAPM、多因子、SDF、期权和债券定价实验。",{"title":1324,"path":1325,"stem":1326,"description":5293,"children":-1},"面向本科生与研究生的计量经济学基础课程，从数据、回归和推断进入内生性、面板、时间序列与可重复实证。",1785754747134]