Financial Data Analysis and Practice
Prof. Luke Stein
Schedule
This course schedule will change during the semester. Ad hoc topic changes (unannounced) may be based on current events or class pace and interest. Announcement of any meeting changes will be distributed via Discord; please ensure that you are monitoring the #announcements channel there.
We may also have special sessions, including where multiple sections may meet simultaneously (typically to accommodate a guest). These will of course be announced in advance, but please aim to maintain availability on class days during all course section times (8:30am–11:15am/12:30pm–3:15pm).
| Meeting | Topics/Modules | Deliverable (typically due 11pm night prior) |
|
|---|---|---|---|
| M 1/15 | No class (MLK Holiday) | — | |
| 1 | W 1/17 | Course introduction | HW0 (due Thursday 1/18) |
| 2 | M 1/22 | S1 | |
| 3 | W 1/24 | P1 | Peer review HW1 |
| 4 | M 1/29 | cont. | Peer review |
| 5 | W 1/31 | cont. | HW2 |
| 6 | M 2/5 | A1 | Peer review |
| 7 | W 2/7 | P2 | HW3 |
| 8 | M 2/12 | A2 | Peer review |
| 9 | W 2/14 | P3, D1 | HW4 |
| M 2/19 | No class (Presidents Day Holiday) | — | |
| 10 | Tu 2/20 | Tuesday class S2, P4 |
Peer review |
| 11 | W 2/21 | A4 Attendance required |
Midterm group project |
| 12 | M 2/26 | A3, D2, S3 | |
| 13 | W 2/28 | P5 | Peer review HW5 |
| 14 | M 3/4 | S4, D3 | Peer review Data/methods demo |
| 15 | W 3/6 | S5 | |
| M 3/11 | No class (Spring Break) | — | |
| W 3/13 | No class (Spring Break) | — | |
| 16 | M 3/18 | S6, A5 | |
| 17 | W 3/20 | A6 | HW6 |
| 18 | M 3/25 | P6 | Peer review |
| 19 | W 3/27 | A4 Attendance required |
Ethics report |
| 20 | M 4/1 | A7 | HW7 |
| 21 | W 4/3 | cont. | Peer review |
| 22 | M 4/8 | D4 | HW8 |
| 23 | W 4/10 | A8, A9 | Peer review |
| M 4/15 | No class (Patriots Day Holiday) | — | |
| 24 | W 4/17 | Group presentations | Final group project |
| 25 | F 4/19 | Friday class Group presentations |
|
| 26 | M 4/22 | Final wrapup | |
| W 4/24 | Final examination | Final exam |
Modules
Python
| Module | Topic | Resources |
|---|---|---|
| P1 | Introduction to Python | TP 1–3, 8, 10–12 WTP 1–7 CfE Getting Started 1, Coding 3, Getting Started 2.1–2.8 PESDA 2, 4, 10 PDA 2.3, 3.1 |
| P2 | Control flow and data structures | TP 5–7 WTP 8–14 CfE Getting Started 2.9–2.16 PESDA 12 PDA 3.2 |
| P3 | Data manipulation | PDSH 3 PESDA 8–9, 16 CfE Data 1, Data 2 PDA 5, 7–8, 10-12 |
| P4 | Visualization | PDSH 4.14 Seaborn Tutorial API overview and Plotting functions |
| P5 | Regression and statistics | PESDA 21 |
| P6 | Numerical Python | PDSH 2 PDA 4 PESDA 3, 11, 19 |
Statistics and Inference
| Module | Topic | Resources |
|---|---|---|
| S1 | Introduction to data | IMS 1–3 |
| S2 | Exploratory data analysis (EDA) | IMS 4–6 |
| S3 | Regression modeling | IMS 7–10 |
| S4 | Foundations of inference | IMS 11–15 |
| S5 | Statistical inference | IMS 16–23 |
| S6 | Inferential modeling | IMS 24–27 |
Financial Data
| Module | Topic | Resources |
|---|---|---|
| D1 | pandas-datareader | datareader documentation |
| D2 | Bloomberg | TBA |
| D3 | WRDS | WRDS Data documentation, Classroom, Research WRDS Python Data Access Library |
| D4 | Alternative data | TBA |
Microsoft Excel
Excel will be used throughout the course, with coverage not divided into explicit modules.
Financial Applications
| Module | Topic | Resources |
|---|---|---|
| A1 | Monte Carlo simulation | TBA |
| A2 | Fixed income | TBA |
| A3 | Equity returns | TBA |
| A4 | Professional ethics | TBA |
| A5 | Foreign exchange | TBA |
| A6 | Factor models | TBA |
| A7 | Capital budgeting | TBA |
| A8 | Derivatives | TBA |
| A9 | Equity portfolios | TBA |
Updated schedule
As promised, the course schedule changes throughout the semester. The following reflects the latest updates:
| Meeting | Financial topics | Technical topics | Deliverable (typically due 11pm night prior) |
||
|---|---|---|---|---|---|
| M 1/15 | No class (MLK Holiday) | ||||
| 1 | W 1/17 | Course introduction | Market returns and risk Implied and realized volatility |
Calculations in Excel | HW0 (due Thursday 1/18) |
| 2 | M 1/22 | Python demo; Introduction to data | Relative performance and hedging Performance attribution (selection/treatment) |
Python demo (Copilot) | |
| 3 | W 1/24 | Questions and analysis; First financial data analysis | Class survey Return skewness and binary returns CRSP |
Bloomberg ABCD Forecasting |
Peer review HW1 |
| 4 | M 1/29 | Python intro | Binomial trees and binomial distribution | Python and notebook ecosystem | Peer review |
| 5 | W 1/31 | Python intro (cont.) | Following and explaining financial news | Python arithmetic booleans functions |
HW2 |
| 6 | M 2/5 | No in-person meeting Python intro (cont.) |
— | Python variables and types (videos) | Peer review |
| 7 | W 2/7 | Python intro (cont.) | Following and explaining financial news Order speed vs. price Monte Carlo simulation |
Initialize/loop/filter Random numbers |
HW3 |
| 8 | M 2/12 | Financial modeling with data | Record highs as binary trees (some sections) Duration and the Gordon Growth model (some sections) Midterm fixed income project |
Non-uniform random variables | Peer review |
| 9 | W 2/14 | Monte Carlo simulation | Brownian motion simulation | Monte Carlo simulation practice | HW4 |
| M 2/19 | No class (Presidents Day Holiday) | ||||
| 10 | Tu 2/20 | Tuesday class Pandas intro |
Prices and returns | Introduction to Pandas | Peer review |
| 11 | W 2/21 | Attendance required Ethics module 1 |
Professional ethics | — | Midterm group project Ethics discussion prep. |
| 12 | M 2/26 | Pandas intro (cont.) | Increasing-coupon bonds (midterm project) | Series and DataFrame, index |
|
| 13 | W 2/28 | Pandas intro (cont.) | Dividends and closing price adjustments Questions ↔ Algorithms ↔ Code |
CSV imports, method chaining | HW5 |
| 14 | M 3/4 | Pandas intro (cont.) | Random variation and sampling error Autocorrelation and return momentum |
Series methods, Visualizing distributions | Peer review |
| 15 | W 3/6 | Applied Pandas analysis | Demand curves and price elasticity Margins/markups and competitive environment Commodities prices (FRED) |
CSV imports, exploratory data analysis | HW6 |
| M 3/11 | No class (Spring Break) | Peer review | |||
| W 3/13 | No class (Spring Break) | ||||
| 16 | M 3/18 | Applied Pandas analysis (cont.) | Mortgage data (FRED) Term structures |
FRED API EDA and visualization |
|
| 17 | W 3/20 | Data/methods demonstration presentations | Various | Various | Data/methods demo |
| 18 | M 3/25 | Data/methods demonstration presentations (cont.) | Various | Various | |
| 19 | W 3/27 | Attendance required Ethics module 2 |
Professional ethics | — | Ethics report |
| 20 | M 4/1 | Data visualization and CAPM | The Capital Asset Pricing Model (CAPM) Ticker re-use |
Data cleaning and EDA Visualizing relationships |
|
| 21 | W 4/3 | Introduction to linear regression | Total risk, systematic risk, idiosyncratic risk | Seaborn and Pandas EDA Covariance, correlation, and regression slopes |
|
| 22 | M 4/8 | Introduction to linear regression (cont.) | CAPM alpha and beta House price prediction |
OLS in statsmodelsInterpreting regression outputs Visualizing non-linear/heterogeneous effects |
HW7 (optional) |
| 23 | W 4/10 | Peer review (optional/make-up) | |||
| M 4/15 | No class (Patriots Day Holiday) | ||||
| 24 | W 4/17 | Group presentations | Various | — | Final group project |
| 25 | F 4/19 | Friday class Group presentations |
Various | — | |
| 26 | M 4/22 | Final wrapup | |||
| W 4/24 | Final examination | Final exam |