All class emails are sent to the email address per the NYU Brightspace roster. The Almaris system (if used for your class) also uses the same email. NYU Brightspace lists @nyu.edu email for some of you and @stern.nyu.edu email for others. I don’t control these matters, nor can I change them. Please contact NYU IT (212-998-3333) or NYU Stern IT (212-998-0180) to learn how to read/forward/check spam for @nyu.edu as well as @stern.nyu.edu accounts. Contact them for all ZOOM/email/NYU Brightspace/Admin/CapitalIQ issues. If NYU/Stern support cannot help you, please escalate these matters to Student Affairs/Dean's office without involving me. I will not respond to support requests. Almaris support cannot change emails. You can do so by clicking on the link "Change email" on their main login page.

Overview

Spring 2022: As a cross-listed grad/undergrad class.

MBA ACCT-GB-3328 specializations:

Undergrad ACCT-UB-0028 concentration: Accounting

The course teaches you how to manipulate and analyze financial data in Python using professional tools. While no prior programming/Python experience is assumed, it does involve coding and is not a managerial overview of data analytics.

The course covers the following skills:

  1. Structured thinking about financial analysis tasks so that you can automate them using organized and maintainable code.
  2. Automating financial data input and output by interacting with financial statement data in Excel, SQL, and XBRL formats.
  3. Financial data analytics for exposure to data analytics packages.

Takeaways

Structured thinking

Automating financial data input and output

Financial data analytics for exposure to data analytics packages

Prerequisites

Materials

Exams and Grading

There are no in-class quizzes, midterms, or final exams.

System Requirements

Help and Office

Assignments

Topics

Topic 1: Computing basic financial metrics from data stored in files

Analytical tasks

Compute key financial statement metrics for companies

Python skills learned

Access data in external files

Use control structures such as loops

Understand data types

Topic 2: Working with XBRL (Extensible Business Reporting Language)

Analytical tasks

Understanding XBRL

Python skills learned

Language syntax

Interfaces

Topic 3: Reading data stored in databases using SQL

Analytical tasks

Relational databases

Python skills learned

Topic 4: Computing key statistical metrics for financial data

Analytical tasks

Industry effects

Macroeconomic effects: Quantifying systematic business risk

Python skills learned

Using NumPy

Using Statmodels

Using Sci-Kit Learn

Working with large data sets

Topic 5: Plotting key financial metrics

Analytical tasks

Cognitive factors

Key financial metrics for analysis and valuation

Python skills learned

Using MatPlotLib

Using Sci-Kit Learn

Topic 6: Identifying peer companies

Analytical tasks

Python skills learned

Cluster analysis

Topic 7: Forecasting sales and earnings

Analytical tasks

Understanding growth drivers

Python skills learned

Pandas for time series analysis

Topic 8: Identifying abnormal accruals and deferrals

Analytical tasks

Accruals and deferrals relating to revenues

Accruals and deferrals relating to expenses

Understanding the divergence of earnings and cash flows

Python skills learned

Regression analysis and outliers

Topic 9: Interacting with Valuation Models in Excel

Analytical tasks

Excel financial models

Python skills learned

Using OpenPyXL

Topic 10: Parsing Management Discussion and Analysis and news

Analytical tasks

Python skills learned

Topic 11: Credit ratings and distress

Analytical tasks

Leading indicators of distress

Python skills learned

Logit regression

Cluster analysis

Topic 12: Acquisitions and leveraged buyouts

Analytical tasks

Identifying potential acquisition and LBO targets

Relative valuation of targets

Python skills learned

Logit regression

Cluster analysis

Multi-variate regression