James Nordlund

Financial Economist
Assistant Professor at Louisiana State University

james@nordlund.ai

Other Ongoing Courses

Theoretical Corporate Finance (Ph.D. level)
Fall 2017, 2018, 2019
This course provides a theoretical overview of the major topics in corporate finance. Students complete replication excercises in SAS to uncover basic, empirical facts about the corporate world that motivate theoretical models discussed in class.

Guest Lectures

Web Scraping and Textual Analysis
For Ph.D. students enrolled in Empirical Corporate Finance.

Introduction to Python for Academic Research
For Ph.D. students in the Accounting and Finance programs attending a research methods workshop.

Previous Courses Taught

Valuation (M.S. level)
Fall 2017
This course provides a rigorous theoretical overview of valuation principles, including discounted cash flow methods, multiples, and options-based pricing.

Investments (B.S. level)
Fall 2014 (at Texas A&M)

Financial Analytics - Fall 2019

(Previously offered: Spring 2018, Fall 2018)

At the completion of this course, students will be able to do the following in the Python 3 programming language:

  1. Organize and manipulate data
  2. Translate raw data into meaningful variables for statistical analysis
  3. Estimate statistical models to drive decisions

Prerequisites

This is a hands-on finance course, suitable to masters students or advanced undergraduates. Students are expected to have taken previous classes in:

where the course numberings reflect codings for undergraduate LSU courses. These courses reflect the bare minimum amount of prerequisite knowledge, and students will benefit from having taken other courses with some exposure to data analysis.

In contrast to the above accounting, finance, math, and statistics prerequisites, no prior programming experience is required.

Computing Environment

This class applies financial concepts to real-world data using Python. Both computation and presentation are emphasized skills in this class, and these two features are joined together in the phenomenal Jupyter notebook system. Basically, a Jupyter notebook is a file that runs within your web browser and is capable of incorporating formatted text, live Python code, and beautifully rendered equations all within one document. This class runs notebooks on cocalc.com, a teaching resource that simplifies distribution of course materials and collection of assignments. Because computation is handled on a web server, there are no specific laptop requirements for the class. However, students are expected to bring a laptop to each class so that they may code along with the lecture.

The computing environment for this course has evolved over time. Virtual machines (VMs) hosted by Azure/AWS had more complicated frameworks for distributing course materials and collecting assignments. A kubernetes implementation (e.g. z2jh) solves some of this, but does not include the chat system or other bells and whistles offered by cocalc.

My Background with Analytics

Language Application Duration of Experience
Python web scraping (BeautifulSoup, Selenium), textual analysis (NLTK, spaCy) 8+ years
Stata applied microeconometric modeling 8+ years
SAS data manipulation and analysis 8+ years
R statistical modeling 3+ years
Matlab data analysis and statistical modeling 2+ years
Machine Learning hobby projects 1+ years