Nathan Dong, PhD, CFA 

Department of Finance, Carroll School of Management, Boston College 

Data Analytics in Finance (MFIN8870) 

Open to graudate (MBA/MSF) students only

Course Description 

This course is an introduction to data analytics for financial applications using Python programming language and its ecosystem of packages including Matplotlib, Numpy, Pandas, SciPy, and StatsModels). Students will investigate a variety of empirical questions in asset management, corporate finance, derivatives pricing, and trading strategy. The course will highlight how big data and automation of analysis using Python can help shape the way finance is practiced by focusing on problems currently confronting finance professionals. 

Prerequisites 

• Corporate Finance

• Investment

• Some knowledge in Derivatives and Risk Analytics is helpful.

Course Objectives 

Upon successful completion of this course, students will be able to

• use Python to analyze financial data and automate data processing

• understand how data analytics can improve financial decision-making

• perform data analytics in finance-related roles in the financial sector

Requirements 

• Attendance and participation

• In-class quizzes

• Homework assignments

• Individual coding project

• Mid-term and final exams

Optional Textbooks 

For background readings in finance:

• Principles of Corporate Finance (Brealey, Myers and Allen)

• Investments (Bodie, Kane and Marcus)

• Fundamentals of Futures and Options Markets (Hull)

For programming in Python:

• Learn Python 3 the Hard Way (Shaw)

• Learning Python (Lutz)

More advanced Python topics and applications in data analytics:

• Python Data Science Handbook (VanderPlas)

• Python for Data Analysis (McKinney)

Software Installation 

Download and install Anaconda Individual Edition ⇗ 

To start IDLE in MacBook's Terminal: 

conda activate

python3 -m idlelib

To start IDLE in PC's Anaconda Prompt: 

python -m idlelib

Course Schedule 

1. Introduction to Python and software setup

2. Data type and flow control

3. File input and output

4. Data visualization

5. Financial time series

6. Building a MA-based trading system

7. Mid-term exam

8. Statistics in trading strategies

9. Regressions in stock price prediction

10. Equity valuation and simulation

11. Credit risk analysis and VaR

12. Derivatives price

13. Arbitrage using options (box spread)

14. Final exam