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.

To see a constantly updated version of the schedule, please just scroll down on this page to the “Updated schedule” section.

Midterm and final examinations are tentatively scheduled for Friday 4/4 1–3pm, and Wednesday 5/7, 9am–12pm, respectively, in Olin Hall room 120.

	M/W	Tu/Th	Potential topics	Deliverable
	M 1/20		No class (MLK Holiday)	—
1	W 1/22	Tu 1/21	Course introduction Market returns and risk Implied and realized volatility Calculations in Excel	HW0 (due Thursday 1/23)
2	M 1/27	Th 1/23	Python demo; Introduction to data Relative performance and hedging Performance attribution Python demo (Copilot)
3	W 1/29	Tu 1/28	Financial questions and financial analysis Class survey Return skewness and binary returns CRSP Bloomberg Intro. to forecasting	HW1
4	M 2/3	Th 1/30	Python introduction Binomial trees and binomial distribution Python and the notebook ecosystem
5	W 2/5	Tu 2/4	Following and explaining financial news Python arithmetic Boolean math Intro. to functions	HW2
6	M 2/10	Th 2/6	Python variables and types
7	W 2/12	Tu 2/11	Order speed and execution quality Monte Carlo simulation Initialize/loop/filter Random numbers	HW3
	M 2/17		No class (Presidents Day Holiday)	—
8	Tu 2/18	Th 2/13	Financial modeling with data Record highs as binary trees Duration and the Gordon Growth model Non-uniform random variables
		Tu 2/18	Monday classes meet on Tuesday (Babson Monday)	—
9	W 2/19	Th 2/20	Monte Carlo simulation Brownian motion	HW4
10	M 2/24	Tu 2/25	Pandas introduction Prices and returns
11	W 2/26	Th 2/27	Midterm project presentations Series and DataFrame, index	Midterm group project (due Tuesday 2/25)
12	M 3/3	Tu 3/4	Dividends and closing price adjustments Questions ↔ Algorithms ↔ Code CSV imports Method chaining	HW5
13	W 3/5	Th 3/6	Attendance required: Professional ethics	Ethics discussion prep.
14	M 3/10	Tu 3/11	Random variation and sampling error Autocorrelation and return momentum Series methods Visualizing distributions
15	W 3/12	Th 3/13	Demand curves and price elasticity Margins/markups and the competitive environment Commodities prices (FRED) CSV imports Exploratory data analysis (EDA)	HW6
	M 3/17	Tu 3/18	No class (Spring Break)	—
	W 3/19	Th 3/20	No class (Spring Break)	—
16	M 3/24	Tu 3/25	Mortgage data (FRED) Term structures FRED API EDA and visualization
17	W 3/26	Th 3/27	Data/methods demonstration presentations	Data/methods demo (due Tuesday 3/25)
18	M 3/31	Tu 4/1	Data/methods demonstration presentations
19	W 4/2	Th 4/3	Attendance required: Professional ethics	Ethics report (due Tuesday 4/1)
	F 4/4	F 4/4	Midterm examination	Midterm exam (1–3p in Olin 120; subject to change)
20	M 4/7	Tu 4/8	The Capital Asset Pricing Model (CAPM) Data cleaning and EDA Visualizing relationships
21	W 4/9	Th 4/10	Introduction to regression Total risk, systematic risk, idiosyncratic risk Seaborn and Pandas EDA Covariance, correlation, and regression slopes	HW7
22	M 4/14	Tu 4/15	House price prediction OLS in statsmodels Interpreting regression outputs Visualizing non-linear/heterogeneous effects
23	W 4/16	Th 4/17	TBA
	M 4/21		No class (Patriots Day Holiday)	—
24	W 4/23	Tu 4/22	Group presentations	Final group project (due Monday 4/21)
25	F 4/25	Th 4/24	Group presentations
26	M 4/28	Tu 4/29	Final wrapup
	W 5/7	W 5/7	Final examination	Final exam (9a–12p in Olin 120; subject to change)

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	1/21–2 Course introduction	Intraday and overnight risk and returns* Market making*	Calculations in Excel	HW0 (due Thursday 1/23)
2	1/23–27 Python demo	Market making* Record highs and binary trees*	Python demo
3	1/28–29 Introduction to data	Volatility and return extremes (NVDA) Core data issues and student survey* Bloomberg	ABCD	HW1
4	1/30–2/3 Describing time series	Event studies in a binary model (tarrifs)* Introduction to Monte Carlo	Mean, Standard deviation Actions and returns*
5	2/5–6 Statistical models and fit	Binary trees and binomial models Over-dispersion and persistence/reversals	Actions and returns* Bayesian updating Binomial distributions	HW2
6	2/10–11 Two-asset portfolios; Python intro	Two-asset portfolios Correlation and idiosyncratic risk Leverage constraints Sharpe ratios	Bloomberg data exports Python arithmetic Boolean math Intro. to functions
7	2/12–13 Conditional programming and looping	Midterm project introduction “Biggest number game”*	if Initialize/loop/filter Random numbers List comprehension*	HW3
8	2/18 (Catch-up; see MW class 9)
9	2/19–20 Valuing liquidity	Order speed and execution quality Monte Carlo simulation	Binomial simulation*
10	2/24–25 Working with Python packages	From Monte Carlo to backtesting (pairwise comparisons of mean, min, max daily returns)	Python’s package ecosystem
11	2/26–27 Attendance required Midterm project presentations	Fixed income analysis		Midterm group project (due Tuesday 2/25)
12	3/3–4 Pandas Series; momentum and reversals	Momentum, reversals, and autocorrelation Binomial modeling and the cross-section	Pandas Series	HW4
13	3/5–6 Attendance required Professional ethics	Professional ethics		Ethics discussion prep.
14	3/10–11 Pandas Dataframes; commodities prices	Commodities prices FRED	CSV imports Python time-series methods	Midterm project peer reviews
15	3/12–13 Applied Pandas analysis; Mortgage market data	Mortgage pricing and term structure Binary options and refinancing	Manipulating and creating columns	HW5
16	3/24–25 Applied Pandas analysis; Drawdowns and “corrections”	Drawdowns and “corrections” Technical analysis	Filtering data loc and iloc
17	3/26–27 Attendance required Data/methods demonstrations	Student-chosen topics	Student-chosen topics	Data/methods demo (due Tuesday 3/25)
18	3/31–4/1 Data/methods demonstrations; Exploratory data analysis	Data/methods demonstrations* WRDS/CRSP Ticker re-use*	Data cleaning and EDA
19	4/2–3 Attendance required Professional ethics	Professional ethics	Data on web pages (e.g., Wikipedia)	Ethics report (due Tuesday 4/1)
	F 4/4 Midterm examination	All topics covered so far	All topics covered so far	Midterm exam (1–3p in Olin 120)
20	4/7–8 Assessing tariff effects; Introduce final project	After-hours trading and index futures Final project introduction	Data cleaning and EDA*
21	4/9–10 Visualization and regression	Systematic and idiosyncratic risk* Dividend policy* Ticker re-use*	Data cleaning and EDA* Graphing distributions (displot) and relationships (relplot)
22	4/14–15 Visualizing and measuring risk	Systematic and idiosyncratic risk* Sharpe ratios* CAPM alpha and beta*	Calculations within groups (groupby) Covariance, correlation, and regression slopes	HW6
23	4/16–17 Applied regression analysis	House price prediction	OLS in statsmodels Interpreting regression outputs Visualizing non-linear/heterogeneous effects*
24	4/22–23 Attendance required Final project presentations	Cross-sectional asset pricing and interest-rate sensitivity		Final group project (due Monday 4/21)
25	4/24–25 Attendance required Final project presentations	(cont.)
26	M 4/28–29 Final wrap-up	Drawdown likelihood Final wrap-up		HW7
	W 5/7 Final examination	All topics	All topics	Final exam (9a–12p in Olin 120; subject to change)