https://fleckenstein-m.github.io/FINC672-SP2026/index.html Course Description This course provides an overview of empirical research in finance. The course is organized into two parts. In the first part, students build foundational programming and data science skills in Python, with a practical focus on collecting, cleaning, transforming, and analyzing financial datasets. These skills are increasingly essential in the era of big data and FinTech. In the second part, students study widely used, state-of-the-art statistical methods in empirical asset pricing and machine learning. Throughout the course, students apply programming tools and statistical techniques to real-world research questions in equity, derivatives, and fixed-income markets. Hugo en-us https://fleckenstein-m.github.io/FINC672-SP2026/calendar/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://fleckenstein-m.github.io/FINC672-SP2026/calendar/index.html Course Calendar Date Day Lecture Assignment Due 2026-02-03 Tuesday Lecture 01 (Intro to Colab) 2026-02-05 Thursday Lecture 02 (Objects) 2026-02-10 Tuesday Lecture 03 (Functions) Assignments 01 to 06 2026-02-12 Thursday Lecture 04 (Control Flow) 2026-02-17 Tuesday Lecture 05 (Lists and Dictionaries) 2026-02-19 Thursday Lecture 06 (Libraries) Assignments 7-14 2026-02-24 Tuesday Lecture 07 (Intro to Pandas) 2026-02-26 Thursday Lecture 07b (Intro to Pandas) 2026-03-03 Thursday Lecture 08 (More Pandas) 2026-03-05 Thursday Lecture 09 (Returns) Assignments 15-18 2026-03-10 Tuesday Midterm I 2026-03-12 Thursday Lecture 10 (Pandas Time Series) 2026-03-17 Tuesday Lecture 10b (Pandas Time Series) 2026-03-19 Thursday Lecture 11 (Visualization) 2026-03-24 Tuesday Spring Break 2026-03-26 Thursday Spring Break 2026-03-31 Tuesday Lecture 12 (Data APIs) 2026-04-02 Thursday Lecture 13 (Portfolio Mathematics) Assignments 19-25 2026-04-07 Tuesday Lecture 13b (Portfolio Mathematics) 2026-04-09 Thursday Lecture 14 (Portfolio Optimization I) Assignments 26-30 2026-04-14 Tuesday Lecture 15 (Portfolio Optimization II) 2026-04-16 Thursday Midterm II 2026-04-21 Tuesday Midterm II Review & Lecture 16 (CAPM) 2026-04-23 Thursday Lecture 17 (Multifactor Models) 2026-04-28 Tuesday Lecture 17b (Multifactor Models) Assignments 31-36 2026-04-30 Thursday Project Proposals Due & Lecture 18 (Option Pricing) 2026-05-05 Tuesday Lecture 19 (Machine Learning I) 2026-05-07 Thursday Lecture 20 (Machine Learning II) 2026-05-12 Tuesday Lecture 21 (Machine Learning III) 2026-05-14 Thursday Lecture 20 (Machine Learning IV) & Last Day of Classes Assignments 37-39 2026-05-16 Saturday Final Exam: 2.00pm - 4.00pm; Purnell Hall 114 2026-05-21 Thursday Project Due https://fleckenstein-m.github.io/FINC672-SP2026/lectures/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://fleckenstein-m.github.io/FINC672-SP2026/lectures/index.html https://fleckenstein-m.github.io/FINC672-SP2026/assignments/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://fleckenstein-m.github.io/FINC672-SP2026/assignments/index.html Problem Sets Assignment Description Link to Google Colab Assignment 01 Variables Notebook Assignment 02 Numbers and Booleans Notebook Assignment 03 Strings Notebook Assignment 04 Arithmetic Operators Notebook Assignment 05 Comparisons Notebook Assignment 06 (Re-)assignments Notebook Assignment 07 Indexing Notebook Assignment 08 Comparisons II Notebook Assignment 09 Logical and Identity Operators Notebook Assignment 10 Functions Notebook Assignment 11 If-Else Notebook Assignment 15 For Loops Notebook Assignment 12 Lists Notebook Assignment 13 Tuples Notebook Assignment 14 Dictionaries Notebook Assignment 16 Conditional Statements, Functions, Loops Notebook Assignment 17 Arrays Notebook Assignment 18 DataFrames Notebook Assignment 19 Arithmetic Returns Notebook Assignment 20 Arithmetic Returns II Notebook Assignment 21 Log Returns Notebook Assignment 22 Risk/Return Visualization Notebook Assignment 23 Return Volatility Notebook Assignment 24 Return Volatility II Notebook Assignment 25 Portfolio Returns Notebook Assignment 26 Index Returns Notebook Assignment 27 Return Covariance Notebook Assignment 28 Portfolio Mathematics Notebook Assignment 29 Portfolio Optimization Notebook Assignment 30 Portfolio Optimization II Notebook Assignment 31 Performance Evaluation Notebook Assignment 32 CAPM Notebook Assignment 33 CAPM Regressions Notebook Assignment 34 Multivariate Regressions Notebook Assignment 35 Systematic/Idiosyncratic Risk Notebook Assignment 36 Multifactor Models Notebook Assignment 37 Options and Black Scholes Notebook Assignment 38 Machine Learning I Notebook Assignment 39 Machine Learning II Notebook Solutions Assignment Description Link to Google Colab Assignment 01 Variables Notebook Assignment 02 Numbers and Booleans Notebook Assignment 03 Strings Notebook Assignment 04 Arithmetic Operators Notebook Assignment 05 Comparisons Notebook Assignment 06 (Re-)assignments Notebook Assignment 07 Indexing Notebook Assignment 08 Comparisons II Notebook Assignment 09 Logical and Identity Operators Notebook Assignment 10 Functions Notebook Assignment 11 If-Else Notebook Assignment 15 For Loops Notebook Assignment 12 Lists Notebook Assignment 13 Tuples Notebook Assignment 14 Dictionaries Notebook Assignment 16 Conditional Statements, Functions, Loops Notebook Assignment 17 Arrays Notebook Assignment 18 DataFrames Notebook Assignment 19 Arithmetic Returns Notebook Assignment 20 Arithmetic Returns II Notebook Assignment 21 Log Returns Notebook Assignment 22 Risk/Return Visualization Notebook Assignment 23 Return Volatility Notebook Assignment 24 Return Volatility II Notebook Assignment 25 Portfolio Returns Notebook Assignment 26 Index Returns Notebook Assignment 27 Return Covariance Notebook Assignment 28 Portfolio Mathematics Notebook Assignment 29 Portfolio Optimization Notebook Assignment 30 Portfolio Optimization II Notebook Assignment 31 Performance Evaluation Notebook Assignment 32 CAPM Notebook Assignment 33 CAPM Regressions Notebook Assignment 34 Multivariate Regressions Notebook Assignment 35 Systematic/Idiosyncratic Risk Notebook Assignment 36 Multifactor Models Notebook Assignment 37 Options and Black Scholes Notebook Assignment 38 Machine Learning I Notebook Assignment 39 Machine Learning II Notebook