Schedule

This course schedule will change during the semester. Ad hoc topic changes (unannounced) will 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.

To see a constantly updated version of the schedule, please just scroll down on this page to the “Updated schedule” section, which I will use to track what we actually do.

Midterm and final examinations are scheduled for Friday, February 27 and Monday, May 4, respectively.

	Date	Potential topics	Deliverable
	M 1/19	No class (MLK Holiday)
1	W 1/21	Course introduction Market returns and risk Intraday and overnight risk and returns	HW0
2	M 1/26	Python demo; Introduction to data Relative performance and hedging Python demo (Copilot)
3	W 1/28	Financial questions and financial analysis Class survey Return skewness and binary returns CRSP / Bloomberg	HW1
4	M 2/2	Python introduction Binomial trees and binomial distribution Python and the notebook ecosystem
5	W 2/4	Following and explaining financial news Python arithmetic and booleans Intro. to functions	HW2
6	M 2/9	Python variables and types Two-asset portfolios and correlation
7	W 2/11	Conditional programming and looping “Biggest number game” if, Initialize/loop/filter, Random numbers	HW3
	M 2/16	No class (Presidents Day Holiday)
8	Tu 2/17	Monday classes meet on Tuesday (Babson Monday) Financial modeling with data Record highs as binary trees
9	W 2/18	Midterm project presentations	Midterm group project (due Tuesday 2/17)
10	M 2/23	Pandas introduction From Monte Carlo to backtesting
11	W 2/25	Prices and returns Series and DataFrame, index	HW4
	F 2/27 9a–12p	Midterm examination	Midterm exam
12	M 3/2	Dividends and closing price adjustments Questions ↔ Algorithms ↔ Code CSV imports
13	W 3/4	Attendance required: Professional ethics	Ethics discussion prep.
14	M 3/9	Random variation and sampling error Autocorrelation and return momentum Series methods
15	W 3/11	Demand curves and price elasticity Margins/markups and the competitive environment FRED data	HW5
	M 3/16	No class (Spring Break)
	W 3/18	No class (Spring Break)
	M 3/23	No class (Luke traveling)
16	W 3/25	Mortgage data (FRED) Term structures EDA and visualization	HW6
17	M 3/30	Working with data Data cleaning, filtering, loc and iloc
18	W 4/1	Attendance required: Professional ethics	Ethics report (due Tuesday 3/31)
19	M 4/6	Data/methods demonstration presentations	Data/methods demo (due Sunday 4/5)
20	W 4/8	Data/methods demonstration presentations
21	M 4/13	The Capital Asset Pricing Model (CAPM) Visualizing relationships
22	W 4/15	Introduction to regression Total, systematic, and idiosyncratic risk Covariance, correlation, and regression	HW7
	M 4/20	No class (Patriots Day Holiday)
23	W 4/22	Group final project presentations	Final group project (due Tuesday 4/21)
24	F 4/24	Monday classes meet on Friday (Babson Monday) Group final project presentations
25	M 4/27	House price prediction OLS in statsmodels Interpreting regression outputs
26	W 4/29	Final wrap-up Review and catch-up
	M 5/4 2–5p	Final examination	Final exam

Updated schedule

As promised, the course schedule changes throughout the semester. “*” indicates topics covered only in some sections. The following reflects the latest updates:

	Meeting	Financial topics	Technical topics	Deliverable
1	W 1/21 Course introduction	Intraday risk and returns	Calculations in Excel	HW0
2	M 1/26 Python demo (Online)	Equity price data	Python demo Google Colab
3	W 1/28 Intro. to Python	Cash flow modeling (bonds)	Actions and returns List and string arithmetic Initialize/Loop/Append	HW1
4	M 2/2 Intro. to Monte Carlo simulation	Geometric and arithmetic average returns Performance attribution* Approximating exponential processes (Rule of 72, doublings-per-thousand)	Binary tree Monte Carlo simulation Line graphs (time-series)
5	W 2/4 Building and exploring datasets	Bloomberg Indices and tracking assets	Time-series and cross-sectional correlation ABCD and undocumented datasets	HW2
6	M 2/9 Continuous Monte Carlo simulation	Order books and market microstructure IPO underpricing	Continuous random variables Conditional programming Histograms (cross-section)* The Law of Large Numbers
7	W 2/11 Assessing historical returns	Brownian motion and time aggregation	Methods and method chaining Pivot tables	HW3
8	Tu 2/17 Continuous data modeling	All-time highs, drawdowns, investor sentiment Parametric modeling (returns)	Intro to Pandas methods Python package management
9	W 2/18 Midterm project presentations	Fixed income analysis	Functional programming Integrating across analytical tools Time-sensitive analysis	Midterm group project
10	M 2/23 No class (snow/illness)
11	W 2/25
	F 2/27 Midterm examination			Midterm exam