FIN 550 — Activity Roster (Box sync)

Canonical source: Video Roster.xlsx (Box file 2228696432632) Last synced: 2026-09-28 (auto, scripts/box-autosync.py) · Box modified 2026-09-27 17:56 CDT · sha1 8e589c978b1a Editing policy: do not edit this file directly; edit the Excel in Box. This is an auto-generated snapshot, regenerated only when the Box content changes.

Lookups key off the Box file ID (stable across renames). Production Status, where the LD team records it, is in Notes: Defined → Scripted (date) → Recorded (date) → Edited → Final.

Naming convention

M[#]L[#].[#] = Module · Lesson · Video, from the LD Video Roster layout. Video IDs keep their original lesson numbers, so a lesson can sit under a different module header; modules below follow the workbook’s headers, not the IDs. Course intro/conclusion use N/A.

Course-level items

Video Title Recording Modality Instructor Final Length Notes
N/A Mathias Kronlund Bio Green Screen — —  
N/A Xing Gao Bio Green Screen — —  
N/A FIN 550 Course Introduction Green Screen — —  
N/A FIN 550 Course Conclusion and Capstone Wrap-Up — — —  

Module 1 — Returns and market models

Lesson M1L1: Stock Returns: Compounding, Beta, and t-Tests

Video Title Recording Modality Instructor Final Length Notes
M1L1.1 Does a 10% Gain Undo a 10% Loss? Green Screen Xing —  
M1L1.2 Does a Bumpier Path Cost You Anything? Green Screen Xing —  
M1L1.3 Does Amazon Move With the Market, More Than One-for-One? Green Screen Xing —  
M1L1.4 When Is a Difference Big Enough to Believe? Green Screen Xing —  

Lesson M1L2: Portfolios: Weights, Benchmarks, and Alpha

Video Title Recording Modality Instructor Final Length Notes
M1L2.1 Should Bigger Companies Count for More? Green Screen Xing —  
M1L2.2 What Counts as Beating the Market? Green Screen Xing —  
M1L2.3 Does a Positive Alpha Prove Skill? Green Screen Xing —  

Module 2 — Event studies: short-run reaction, then long-run performance

Lesson M1L3: Event Studies: Short-Run Reactions

Video Title Recording Modality Instructor Final Length Notes
M1L3.1 Is There Anything Left to Trade On? Green Screen Mathias 10 min  
M1L3.2 What Do You Fix Before You Look? Green Screen Mathias 13 min  
M1L3.3 How Much of the Move Was Exxon? Green Screen Mathias —  
M1L3.4 What Would No Effect Even Look Like? Green Screen Mathias —  
M1L3.5 Is It Really the Dividend? Green Screen Mathias —  

Lesson M1L4: Event Studies: Long-Run Performance

Video Title Recording Modality Instructor Final Length Notes
M1L4.1 Does the Stock Keep Outperforming After the News? Green Screen Mathias —  
M1L4.2 What Was the Portfolio Holding Each Month? Green Screen Mathias —  
M1L4.3 Do the Two Long-Run Methods Agree? Green Screen Mathias —  

Module 3 — Momentum, from construction through evaluation

Lesson M2L1: Momentum: Building the Portfolios

Video Title Recording Modality Instructor Final Length Notes
M2L1.1 Why Hold Portfolios? — Mathias —  
M2L1.2 How Do We Build a Momentum Strategy? — Mathias —  
M2L1.3 What Did the Sorted Portfolios Earn? — Mathias —  

Lesson M2L2: Momentum: Evaluating the Payoff

Video Title Recording Modality Instructor Final Length Notes
M2L2.1 How Much Does Momentum Earn? — Mathias —  
M2L2.2 How Much Risk Comes With the Payoff? — Mathias —  
M2L2.3 How Much Is Compensation for Market Exposure? — Mathias —  
M2L2.4 Momentum’s CAPM Alpha — Mathias —  

Lesson M2L3: Momentum: Pressure-Testing the Payoff

Video Title Recording Modality Instructor Final Length Notes
M2L3.1 How Should We Weight Each Stock? — Mathias —  
M2L3.2 Does Momentum Depend on Weighting and Size? — Mathias —  
M2L3.3 Could We Have Run This Strategy in Real Time? — Mathias —  

Module 4 — Text measurement

Lesson M3L1: Text as Data: Building a Reproducible Measure

Video Title Recording Modality Instructor Final Length Notes
M3L1.1 Should the Result Pick the Rule? — Mathias —  
M3L1.2 When Could Anyone Have Seen This Text? — Mathias —  
M3L1.3 Which Passage Counts as More Negative? — Mathias —  

Lesson M3L2: Text as Data: Measuring Exposure in Context

Video Title Recording Modality Instructor Final Length Notes
M3L2.1 Which Filing Really Talks More About Trade? — Xing —  
M3L2.2 Divided by What, and Judged by Whom? — Xing —  
M3L2.3 Can One Sentence Point Both Ways? — Xing —  

Lesson M3L3: Text as Data: Predicting Forward Returns

Video Title Recording Modality Instructor Final Length Notes
M3L3.1 Where Do the Unmatched Filings Go? — Mathias —  
M3L3.2 Does a Dated Text Score Predict Returns? — Mathias —  
M3L3.3 Can Returns Choose the Word Weights? — Mathias —  

Module 5 — Classification and next-month ranking

Lesson M4L1: Classification: Probability Models for Ranking

Video Title Recording Modality Instructor Final Length Notes
M4L1.1 What Did the Model Know, and When? — Xing —  
M4L1.2 Can a Straight Line Give You a Probability? — Xing —  
M4L1.3 What Scale Does a Logistic Score Live On? — Xing —  
M4L1.4 Is a Bounded Probability Enough to Trade On? — Xing —  

Lesson M4L2: Classification: Out-of-Sample Portfolio Returns

Video Title Recording Modality Instructor Final Length Notes
M4L2.1 Does the Highest Probability Mean the Highest Return? — Xing —  
M4L2.2 Can a Workflow Look Clean and Still Leak? — Xing —  
M4L2.3 Can a Good Ranking Still Lose Money? — Xing —  

Lesson M4L3: Classification: Rare Events, Error Costs, and Timing

Video Title Recording Modality Instructor Final Length Notes
M4L3.1 When Does a Probability Become an Action? — Xing —  
M4L3.2 Do Equally Accurate Rules Cost the Same? — Xing —  
M4L3.3 Was This Filing Ours to Use Yet? — Xing —  

Module 6 — Time-safe validation, regularization, and trees

Lesson M5L1: Validation: Time-Ordered Folds and Data Cleaning

Video Title Recording Modality Instructor Final Length Notes
M5L1.1 How Do You Validate a Model Without Hindsight? — Xing —  
M5L1.2 Which Months Can Train, and Which Can Test? — Xing —  
M5L1.3 Clean Once, or Clean Inside Every Fold? — Xing —  

Lesson M5L2: Lasso: Choosing Among Many Signals

Video Title Recording Modality Instructor Final Length Notes
M5L2.1 Why Not Put Every Signal in the Model? — Xing —  
M5L2.2 How Does Lasso Decide Which Features Stay? — Xing —  
M5L2.3 Does a Sparser Model Actually Forecast Better? — Xing —  

Lesson M5L3: Lasso: Forecasting Returns Out of Sample

Video Title Recording Modality Instructor Final Length Notes
M5L3.1 How Should We Measure a Return Forecast’s Error? — Xing —  
M5L3.2 What Can One Return Forecast Tell Us? — Xing —  

Lesson M6L1: Tree Models: Splits, Bagging, and Random Forests

Video Title Recording Modality Instructor Final Length Notes
M6L1.1 How Does a Tree Split, and When Should It Stop? — Xing —  
M6L1.2 What Does Averaging Do to an Unstable Tree? — Xing —  
M6L1.3 Why Would a Forest Skip the Best Split? — Xing —  

Module 7 — Boosting, neural networks, and why one benchmark is not enough

Lesson M6L2: Tree Models: Boosting and a Fair Comparison

Video Title Recording Modality Instructor Final Length Notes
M6L2.1 How Does Boosting Correct One Forecast? — Xing —  
M6L2.2 Can Weak Level Forecasts Still Rank Returns? — Xing —  
M6L2.3 Do Boosted Trees Beat the Lasso Forecast? — Xing —  

Lesson M6L3: Neural Networks: Building and Comparing Forecasts

Video Title Recording Modality Instructor Final Length Notes
M6L3.1 Can a Shallow Network Learn a Different Shape? — Xing —  
M6L3.2 Does More Information Improve the Forecast? — Xing —  
M6L3.3 Does More Flexibility Improve the Forecast? — Xing —  

Lesson M7L1: Factor Models: Why CAPM Falls Short

Video Title Recording Modality Instructor Final Length Notes
M7L1.1 Why Add Size and Value to the Benchmark? — Mathias —  
M7L1.2 What Can FF3 Tell Us About Momentum? — Mathias —  

Module 8 — Factor models, robustness, and the capstone

Lesson M7L2: Factor Models: Loadings, Alpha, and Benchmarks

Video Title Recording Modality Instructor Final Length Notes
M7L2.1 What Does Alpha Mean in a Factor Regression? — Mathias —  
M7L2.2 Which Factor Is Doing the Work? — Mathias —  

Lesson M8L2: Robustness: Later Data, Delays, and Many Tests

Video Title Recording Modality Instructor Final Length Notes
M8L2.1 Is Later Data Really New Evidence? — Mathias —  
M8L2.2 Why Do Return Patterns Fade? — Mathias —  
M8L2.3 Do Most Published Factors Hold Up? — Mathias —  
M8L2.4 Do Small or Large Firms Carry the Payoff? — Mathias —  

Lesson M8L3: Tail Risk: Drawdowns, Crashes, and a Recommendation

Video Title Recording Modality Instructor Final Length Notes
M8L3.1 How Far Did Wealth Fall, and for How Long? — Mathias —  
M8L3.2 How Bad Are the Worst Months? — Mathias —  
M8L3.3 What Should We Recommend, and What Would Change It? — Mathias —  

For Future (not recorded in 2026; see week_plan.yaml)

Lesson M7L3: Factor Models: Fama-MacBeth Versus Lasso

Video Title Recording Modality Instructor Final Length Notes
M7L3.1 How Do Many Monthly Slopes Become One Estimate? — Mathias —  
M7L3.2 When Can Fama-MacBeth and Lasso Agree? — Mathias —  

Lesson M8L1: Implementation: Crowding, Turnover, and Costs

Video Title Recording Modality Instructor Final Length Notes
M8L1.1 What If Everyone Heads for the Same Exit? — Mathias —  
M8L1.2 How Much of the Portfolio Must Trade? — Mathias —  
M8L1.3 Do Declared Trading Costs Erase the Payoff? — Mathias —  
M8L1.4 Long or Short: Which Side Earns the Payoff? — Mathias —  

Production status (from the Excel Status column)

No production status recorded yet.

TOTAL PLANNED LECTURE VIDEO LENGTH (per Excel total row): 23 minutes.

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