Financial Analytics Using Python and AI Tools

Overview

This course has been thoroughly revised and updated for Spring 2025.

MBA ACCT-GB-3328 specializations

• Accounting
• Business Analytics
• Financial Systems and Analytics

Undergrad ACCT-UB-6028 concentrations

• Accounting
• New: Computing and Data Science

Takeaways

Use Python to access data needed for financial analysis

• Learn Python APIs to access financial data
• Reading and writing files in various formats
• Understanding eXtensible Business Reporting Language (XBRL)
• Standardizing financial statements with different line items

Use Python for financial statement analysis

• Compute and plot frequently used financial statement metrics such as sales growth, margins, invested capital turnover, earnings volatility, leverage, and liquidity
• Compute basic valuation metrics: price-to-earnings ratios, price-to-book ratios
• Use statistical tools to identify trends, correlations, and outliers

Use Python to build simple financial statement models in Python

• Building a simple three-statement financial statement model
• Monte Carlo simulations to plot outcomes for a large number of scenarios

Use Python to run finance simulations

• Learn Numpy matrix operations
• Analyze properties of stock returns
• Simulate portfolio returns and plot the efficient frontier
• Simulate capital asset pricing model (CAPM)

Required Prerequisites

All ACCT courses have the core courses in Financial Accounting as a prerequisite.

• Master's students
  • COR1-GB 1306: Financial Accounting and Reporting
  • COR1-GB2206: Accounting (Tech & Luxury)
  • ACCT-GB-2103: Financial Statement Analysis (MS in Accounting)
• Undergraduate students
  • ACCT-UB.0001: Principles of Financial Accounting

Recommended Background

The course assumes you have taken a statistics course in your graduate or undergraduate program. It will also be extremely helpful if you have taken a half-semester Python course.

Materials

I write and distribute my materials. Therefore, no textbook is required, and you need not purchase anything.

ChatGPT

The course will use ChatGPT extensively. If you use VSCode, you can sign up for GitHub Copilot for free. Otherwise, I strongly recommend that you subscribe to ChatGPT 4.0.

Attendance and penalty for missing classes

Requiring attendance is necessary for several reasons. First, you incorrectly assume you can catch up on a missed class by watching a recording (if available). Videos do not engage your brain as much as a live class. Second, less than 20% of you watch the recording (if available). You are then lost in class, which provides wrong signals to me as an instructor. Third, your absence hurts class discussions. Fourth, you miss out on feedback if you do not work through the questions I pose in class. Fifth, I lose the feedback since there are fewer questions.

The policy below will be in effect only after the add/drop period.

Without mandatory attendance, attendance is often below 50%. Therefore, though I dislike doing this, I penalize absences. If you anticipate being absent for good reasons, please email me well in advance. Please enter "Excused" on the attendance sheet described below to avoid the penalty if I approve. If you miss a class due to emergencies and cannot tell me in advance, do not panic. Take care of the emergency first, and then email me. I will permit you to change the "Absent" to "Excused." But, if you miss a class without a valid reason, there is a penalty, as stated below.

For sections meeting in 150-190 minute sessions, you will lose one grade (A to A-, A- to B+, B+ to B, B to B-, and so on) for EVERY missed session unless you were explicitly excused via email. Thus, if you miss two class sessions, you will lose two grades, and so on.

For sections meeting in 75-80 minute sessions, you will lose one grade (A to A-, A- to B+, B+ to B, B to B-, and so on) for EVERY TWO missed sessions unless you were explicitly excused via email. Thus, if you miss four class sessions, you will lose two grades, and so on.

Please sit in the same seat in every class and display your name tags. For Zoom classes, you must keep your video on AT ALL TIMES. You must also have a good working headset or mic, as it is extremely rude to be inaudible and force me to ask you to repeat yourself. After entering the class, please mark yourself present in the first 20 minutes on the OneDrive sheet (link posted on Brightspace). You will be marked absent if you are more than 20 minutes late unless it is because of factors beyond your control (traffic, subway, interviews running late). You will also be marked absent if you leave the class early unless you have my permission or get it afterward. You will get an F in the course if you are caught cheating on the attendance sheet.

Exams and Grading

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

• Please read about the penalty for missing classes above.
• Assignments: 50%
• Final project: 50%

System Requirements

• You need to be in the following systems before the start of the first class:
  • Albert
  • NYU Brightspace
  • If you are a non-Stern student, Stern automatically creates a Stern account for you when registering for a Stern course. All class emails are sent to your Stern email, not NYU email. Please forward your Stern email to your NYU email.
• Only registered students can attend. I cannot override this NYU rule.

Help and Office

• Me: dgode@stern.nyu.edu, 212-998-0021, Office: KMC 10-86.
• Teaching assistant: Please check NYU Brightspace.

Assignments

• Online Jupyter assignments.

Topic 1: Data structures used to represent financial data

Analytical concepts

Organization of financial data: Row versus column orientation

• The typical format of financial data: Accounts or financial statement items are row headings, while column headings are dates
• Optimal organization of Pandas data frames: Why do we transpose financial statement data so that accounts or financial statement items are in columns and dates are in rows?

Python skills

Numpy versus Pandas

• Named rows and columns
• Inhomogeneous data and missing data
• Input and output
• Merging and grouping
• Speed and memory

Pandas essentials

• Series versus data frames
• Rows, Columns, Size, Size in memory
• Head, tail, and random sampling

Understand data types and simple operators

• Numbers, strings, and dates
• Broadcast operators
• Simple vectorized operations

Manipulate rows and columns in Pandas

• Select rows and columns via slices: brackets, loc, and iloc
• Add and delete rows and columns

Topic 2: Access external financial data and save it in files

Analytical concepts

Understanding XBRL

• What is structured data?
• What is the XBRL taxonomy?
• Current financial reporting landscape and the limits of XBRL

Python skills

Application Programming Interfaces [API]

• Understanding how to access financial data using Python APIs.
• How to use external parsers for XBRL

File formats

• Reading and writing Excel files, formatting the output of Excel files
• CSV files
• Binary files

Handling dates

• Parsing dates in Python, Numpy, and Pandas

Data structures

• Dictionaries and JSON

Topic 3: ROIC and free cash flow drivers: Size, growth, margins, and NOA turnover

Analytical concepts

Sales growth

• Sequential growth
• Year-over-year growth
• Compounded annual growth rate

ROIC drivers

• Operating margin after tax and its components: Various expense ratios
• Net operating assets intensity and balance sheet subtotals such as current and non-current operating assets and liabilities, operating working capital, fixed capital, total capital, and invested capital
• Computing ROIC as net operating profit after tax divided by invested capital

Unlevered free cash flows

• Computing unlevered free cash flows
• Understanding how ROIC and growth affect unlevered free cash flows

Python skills

Loops versus vectorized and broadcast operations

• Simple row and column operations
• Python loops versus vectorized and broadcast operations in Pandas
• Why you should avoid writing loops in Pandas
• How to avoid loops using lead-lag differences

Topic 4: Plotting ROIC and free cash flow drivers

Analytical concepts

Cognitive factors

• What are the design principles for displaying quantitative information? We will use the guidelines in Edward Tufte's book “Visual Display of Quantitative Information.”

Peer company analysis

• Comparing and plotting sales, sales growth, expense ratios, and net operating asset ratios for a selected company and its peers

Python skills

Types of charts

• Lines, bars, scatter charts, histograms, area charts
• Dual axis charts

Pandas plotting

• Concise Pandas plotting commands
• State-machine approach

Full power of Matplotlib plotting

• Object-oriented approach for Matplotlib plots
• Customizing charts

Topic 5: Discount rates, time value of money, loans, and bonds

Analytical concepts

Time value functions

• Compute present value and future value
• Infer internal rate of return
• Compute installment payments

Simple financial instruments

• Bonds
• Loan amortization tables

Python skills

Numpy

• Limitations of numpy_financial
• Bonds
• Loan amortization tables

Date manipulation in Python

• Bonds
• Loan amortization tables

XLSXWriter

• Bonds
• Loan amortization tables

scipy.optimize

• Bonds
• Loan amortization tables

Topic 6: How business risk raises discount rate

Analytical concepts

Operating leverage and business risk

• Business cycles and sales variability
• Operating leverage and earnings variability
• Opex versus capex commodities

Identifying time series patterns

• Seasonality
• Cyclicality

Identifying discrete events

• Restructurings
• Acquisitions and dispositions

Python skills

Matplotlib

• Visualizing trends and outliers

Statmodels

• Using statistical functions in Statmodels

Sci-Kit Learn

• Machine learning with Sci-Kit Learn

Topic 7: Business risk drivers: Cyclicality and seasonality

Analytical concepts

Statistical techniques

• A simple and brief introduction to time series analysis of financial statement data

Python skills

Introduction to statistical packages for time series analysis

• A brief overview of the following packages:

Topic 8: Liquidity, leverage, and ROE

Analytical concepts

Liquidity

• Financial assets to sales

Leverage

• Debt/EBITA, Debt/EBIT
• Debt/Equity

Return on equity

• Net income/Equity
• Sharpe ratio

Python skills

Advanced plotting with Matplotlib and Plotly

• Visualizing the higher volatility of ROE vis-a-vis ROIC due to leverage
• Making interactive plots with Plotly

Statmodels

• Visualizing the higher volatility of ROE vis-a-vis ROIC due to leverage
• Making interactive plots with Plotly

Topic 9: Three-statement model of growth and ROIC

Analytical concepts

Three-statement financial model

• Income statement inputs: Size, growth, and margins
• Balance sheet operating inputs: Net operating asset intensity
  • Operating working capital intensity
  • Fixed capital intensity
• Balance sheet financial inputs: Liquidity and leverage
• Business risk, unlevered and levered cost of capital

Monte Carlo simulations

• Monte Carlo simulations to plot outcomes for a large number of scenarios
• Demonstrating the advantage of Python over Excel

Python skills

Comparing Numpy, Pandas data series, and Pandas data frames

• Implementing financial statement models using Numpy, Pandas data series, and Pandas data frames

Challenges of developing iterative models in Python

• Ease of developing iterative models in Excel
• Difficulty in developing iterative models in Python

Topic 10: Valuation multiples and stock prices

Analytical concepts

Stock returns

• Dividend yield versus capital gain
• Using adjusted stock prices to measure total return
• Arithmetic returns versus geometric returns
• Cumulative returns

Macroeconomic effects: Quantifying systematic business risk

• Correlations among stock returns
• Measuring beta

Identifying discrete events

• Identifying days or weeks with high volatility

Key valuation multiples

• Price-to-book and price-to-earnings ratios

Python skills

Using Python for regressions

• scipy.stats
• statsmodels.api
• statsmodels.formula

Topic 11: Simulating portfolio returns

Analytical concepts

Simulating correlated stock returns

• Variance-covariance matrix
• Portfolio returns with correlated securities
• Possible returns and the efficient frontier

Optimal portfolios and capital asset pricing model (CAPM)

• Quadratic utility functions
• Covariance
• Beta
• Verifying the CAPM relationships

Python skills

Numpy matrices

• Numpy matrix manipulations