Financial Analytics Using Python and AI Tools

Overview

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

MBA ACCT-GB-3328 specializations

• Accounting
• Business Analytics
• Financial Systems and Analytics

Undergrad ACCT-GB-6028 concentrations

• Accounting
• New: Computing and Data Science

Takeaways

Use Python and AI agents to access and structure financial data

• Use Python APIs to access market and fundamental data (e.g., equities, factors, fundamentals)
• Use scrapers and eXtensible Business Reporting Language (XBRL) parsers to pull data from SEC filings
• Use LLMs as structured parsers to extract tables, footnotes, and key accounting policies from noisy HTML/PDF text.
• Represent financial data in "analysis-ready" Pandas DataFrames and save in multiple formats (CSV, Excel, Parquet, JSON).

Use Python and AI agents for financial statement analysis

• Compute and visualize key financial metrics (sales growth, margins, ROIC drivers, leverage, liquidity).
• Train and evaluate simple machine-learning models (e.g., regularized regression, tree-based models) to predict financial outcomes (e.g., margins, distress, credit spreads).
• Use LLMs to generate narratives for ratios and trends, and to draft questions for further investigation.

Build Python-based financial statement models with simulation and AI assistance

• Build a simple three-statement model in Python (income statement, balance sheet, cash flow).
• Use Monte Carlo simulations to explore thousands of scenarios for growth, margins, and discount rates.

• Use AI to:

  • auto-generate scenario descriptions (“stress”, “optimistic”, “regime shift”) and translate them into parameter changes,

  • check model outputs for plausibility, and

  • generate human-readable commentary on simulation results.

Use Python and AI agents to run finance simulations

• Learn NumPy matrix operations in the context of portfolio math.
• Analyze statistical properties of stock returns; compute betas, factor loadings, Sharpe ratios.
• Simulate portfolio returns and plot the efficient frontier.
• Build and test CAPM-style and multi-factor models in Python.

• Use AI agents that:

  • design experiments (which portfolios to simulate),

  • call Python functions to compute risk/return metrics, and

  • interpret results in plain English while highlighting limitations.

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

A half-semester Python course will be very useful. However, you can learn what you need for this course using AI.

VS Code and LLMs

The course will use VSCode as an IDE with GitHub Copilot and Claude as AI assistants. Please figure out how to sign up for them and do so before the class begins. You also have free access to NYU Gemini Pro if you log in to Gemini using your NYU (not Stern) account. We will also use it on a standalone basis. Last I checked, the Gemini academic version does not work with VSCode directly.

Exams and Grading

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

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

Assignments

• Online Jupyter assignments.

• Some assignments will explicitly require:

  • using at least one ML model in scikit-learn, and
  • calling an LLM API programmatically from Python.

Penalty for missing classes and unapproved use of devices in class

Paper and pencil

Please bring paper and pen/pencil to take notes in class.

Unapproved device usage

Most of us are now addicted to devices, which impedes learning. You are allowed to use devices only when I ask you to use your computer in class. Otherwise, ALL device use is strictly prohibited, unless you have a qualified disability which allows you to use devices in class. Students using devices when not approved will be asked to leave the classroom. Each time you are asked to leave the class, you will be marked absent. This is very painful, awkward, and disruptive, and I hope I never have to do this, but I will not hesitate to enforce this policy.

Reasons for requiring attendance

Attendance is required 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 the 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.

Attendance policy enforced after the add/drop period

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

Attendance sheet

After entering the class, please mark yourself present on the OneDrive sheet within the first 20 minutes (link posted on Brightspace after the add/drop period). You will be marked absent if you are more than 20 minutes late, unless it is due to factors beyond your control (traffic, subway delays, or 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.

Mark yourself excused on the attendance sheet for excused absences

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 approved. You must update it; I will not mark you excused. 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.

Penalty for missing classes

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.

Seating and name tags

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.

NYU Stern Policies

Please read the NYU Stern Policies for this course.

Help and Office

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

Administrative and System Requirements

Videotaping

Classes are normally not videotaped, except for EMBA classes. If the class is videotaped, the link is posted to Brightspace by NYU Stern IT within a day.

Registered Students Only

Only registered students can attend classes. I cannot override this NYU Stern rule. We do not allow unregistered students to "audit" a class.

Albert and NYU Brightspace

You must be in Albert and NYU Brightspace before starting the first class. If you register late, there might be a delay of a day before you appear in these systems. If you cannot access these systems after a day, please contact the relevant REGISTRAR. I cannot add you to these systems regardless of what someone in IT tells you.

Computer Requirements

You need to bring a computer to every class. If you have any technical questions, please contact Stern IT at (212-998-0180) or servicedesk@stern.nyu.edu, or NYU IT at +1 (212) 998-3333 or askit@nyu.edu. I cannot help you with your computer issues.

Materials

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

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 we transpose financial statement data so that accounts or financial statement items are in columns and dates are in rows.
• How LLMs “see” text (tokens, context windows) and what that implies for how we chunk and store financial text.

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

AI skills

• Designing DataFrames that are convenient input/output formats for ML models and LLM calls (e.g., using dict / JSON columns that can be sent to an API).

Topic 2: Access external financial data and save it in files (APIs + AI extraction)

Analytical concepts

Understanding XBRL

• Structured data and XBRL: taxonomy, current reporting landscape, limits of XBRL.
• Semi-structured data: MD&A, footnotes, earnings call transcripts.

Python skills

Application Programming Interfaces [API]

• Understanding how to access financial data using Python APIs. Handling authentication, rate limits, and pagination.
• External parsers for XBRL; transforming them into clean tables.

File formats

• Reading and writing Excel files, formatting the output of Excel files
• CSV, JSON, Parquet; reading/writing efficiently.

Handling dates

• Parsing dates in Python, Numpy, and Pandas

Data structures

• Dictionaries and JSON

AI skills

• Using LLMs to extract and normalize data that XBRL does not capture cleanly (e.g., non-GAAP metrics, segment disclosures).
• Representing extraction results as JSON and turning them into DataFrames.

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

AI skills

• Preparing feature matrices (X) and targets (y) derived from ROIC and FCF drivers for later supervised learning (e.g., predicting future ROIC or FCF growth).

Topic 4: Plotting ROIC, FCF drivers, and AI-assisted visualization

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

AI skills

• Using LLMs to:

  • auto-draft chart titles and captions based on the underlying data, and
  • flag potential anomalies or data quality issues in plots.

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

AI skills

• Using an LLM to generate:

  • narrative explanations of an amortization schedule (e.g., “explain to a client why interest expense falls over time”), and
  • comparisons of fixed-rate vs floating-rate loans based on your generated 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

Statsmodels

• Using statistical functions in Statsmodels

AI/ML skills

• Introductory machine learning with Sci-Kit Learn
• Comparing classical regressions vs. ML models and interpreting coefficients vs. feature importances.

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

• Time-series packages:

  • Pandas resampling, rolling averages.
  • statsmodels for decomposition, ARIMA/SARIMA.
  • Quick use of prophet or similar for forecasting.

AI/ML skills

• Overview of:

  • PyCaret for automated time-series model comparison,
  • TensorFlow/keras for simple deep learning models (conceptual; possibly in a notebook demo).

• Using an LLM to:

  • interpret model diagnostics and forecast plots,
  • help specify candidate models (“which lags and seasonalities should I try?”).

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

AI skills

• Building a small interactive “agent” in a Jupyter notebook that:

  • lets the user specify a firm and timeframe,
  • fetches the data via Python,
  • computes liquidity, leverage, and ROE metrics, and
  • uses an LLM to produce a short risk commentary.

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

AI skills

• Using LLMs to:

  • convert qualitative scenarios (“mild recession”, “boom”, “regime shift”) into parameter shocks,
  • generate narrative summaries of simulation results for an investment memo,
  • sanity-check whether simulated outcomes align with historical ranges.

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

AI skills

Using LLMs

• summarize earnings call transcripts and 10-K sections related to the event
• perform basic sentiment/tonality analysis (via embeddings or model outputs)
• relate textual changes to abnormal returns

Simple embedding workflows

• Embed document chunks,
• Search for nearest neighbors
• Do RAG-style Q&A about a firm’s disclosures.

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

AI skills

Building a simple portfolio “research assistant” agent

• User asks a question (“Build a market-neutral portfolio of these ten stocks targeting vol X”)
• The agent calls Python tools to compute covariances, run optimizations, and backtests
• The LLM returns a portfolio and an explanation of its risk/return profile and caveats.