This course is listed as SPECIAL TOPICS IN ASSET PRICING FRE-GY 9713 in Albert.
Fall 2021: M 2-4:30 PM, Finance and Risk Engineering Program at NYU Tandon Engineering School.
Please attend the highly recommended Excel bootcamp.
I will assume that you already know Python and will teach actual financial statement analytics in the course.
Modeling financial statements is vital to finance The course teaches two sets of skills: modeling financial statements and financial statement analytics. The first part establishes the framework needed to link financial statements to valuation, including identifying key metrics. The second part shows how to use modern tools (Python) to extract these metrics from historical financial statement data. These parts are summarized below and described in detail in the course outline below.
1. Revenues and operating expenses
2. Revenue-related accruals and deferrals
3. Operating expense-related accruals and deferrals
4. Productive capacity, capex, and depreciation, and taxes
5. Unlevered free cash flows and financing needs
6. Borrowing capacity, liquidity, debt financing, and interest
7. Equity financing and linking to valuation
8. Working with XBRL (Extensible Business Reporting Language)
9. Analyzing historical sales
10. Analyzing historical expenses
11. Identifying abnormal accruals and divergence of earnings and cash flows
12. Understanding credit rating changes and defaults [We will not try the almost impossible task of predicting future stock and bond returns.]
13. Identifying peer companies
14. Identifying LBO and acquisition targets
The NYU Tandon School values an inclusive and equitable environment for all our students. I hope to foster a sense of community in this class and consider it a place where individuals of all backgrounds, beliefs, ethnicities, national origins, gender identities, sexual orientations, religious and political affiliations, and abilities will be treated respectfully. I intend that all students’ learning needs be addressed both in and out of class and view diverse students as a resource, strength, and benefit. If this standard is not being upheld, please feel free to speak with me.
Knowledge of financial accounting will be a big plus. However, it is not required per se. Students without this background will need to work hard to keep up with the course. I will provide extensive materials on Financial Accounting, including prework, before the course starts. If you have the aptitude for it, you can pick it up quickly. My undergraduate is in Electronics Engineering. I picked up accounting on my own, so can you. The course will not teach Python per se. Most people can pick it up on their own.
You will be building models using Excel. I am assuming you know basic Excel and can pick up the rest as the course moves along.
If you expect to build valuation/credit risk models using financial statement data or write code to manipulate or analyze financial data, you will benefit from this course. This course will teach you how to code in Python to process accounting and financial market data based on financial analysis and statistical concepts. This course is unsuitable for those who want a managerial overview of data analytics techniques without hands-on coding.
Requiring attendance is necessary for several reasons. First, many of you misjudge how much you miss out on learning when you miss classes. It is difficult to catch up once you miss a class. Watching a video (if available) is inadequate as it is cognitively far inferior to paying attention in a classroom. Second, less than 25% of the students who miss a class watch the video (if available). As a result, they are lost in subsequent classes, which provides wrong signals to me as an instructor. Third, there is diminished classroom interaction and poorer quality of class discussion if you are absent. Fourth, you do not get enough feedback if you do not work through the questions I pose in class. Fifth, I lose the feedback on how much you are learning with fewer questions in class.
The policy below will be in effect only after the add/drop period.
Without mandatory attendance, as much as half the class can be absent. Therefore, though I dislike doing this, I penalize absences. I understand that there are valid reasons for absences. If you anticipate being absent for good reasons, please email me well in advance. You can 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 shown below.
For sections meeting in 150-190 minute sessions, you would 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 would lose two grades, and so on.
For sections meeting in 75-80 minute sessions, you would 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 would lose two grades, and so on.
Please sit in the same seat in every class and display your name tags. After entering the class, please mark yourself present in the first 20 minutes in the OneDrive sheet (link posted on OneDrive after the add/drop period is over.) 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.
Potential market size
Market share and pricing power
Cost structure and competitive advantage
Fixed costs versus variable costs
A generalized model of the timing differences between income flows and cash flows
Accruals: When income flows precede cash flows
Deferrals: When income flows follow cash flows
Understanding lead/lag functions as an efficient and powerful way to model accruals/deferrals
When revenues precede receipts
Long-term receivables and interest earned
Accruing contra-revenues in anticipation of returns
Accruing bad debt expenses in anticipation of write-offs
Contra-assets: Allowance for returns and bad debts
Deliverables: When revenues follow receipts
Subscription-based models: Receipts drive future revenues
Event-based models: Future expected revenues drive current receipts
When expenses precede payments
Periodic payments and lumpy payments for bonus plans
Long-term accruals and judgments
When expenses follow payments
Days of prepayments, prepaid rent, insurance, advertising
When future expected expenses drive current payments
Inventories: Future expected cost of goods sold drive current purchases, days of inventory
Distinguishing between costs, expenses, and payments
Future expected sales drive demand for current capacity, which drives capex
Useful lives, salvage values, and depreciation patterns
Taxes payable: Current tax expense or tax bill versus tax paid
Deferred taxes: Total tax expense versus current tax expense
Net operating profit after tax
Growth in net operating assets
Operating working capital
Sources of liquidity
Common mistakes in modeling liquidity: Why current ratio, quick ratio, and working capital are often useless measures of liquidity
Repayment ability and debt/EBITDA multiples
Interest coverage ratio
Debt to value ratio
Growth beyond the forecast horizon
Language syntax: Dictionaries and Tuples
Interfaces: Understanding application programming interfaces [API]
Interacting with web-based data
Understanding growth drivers
Business cycles: Opex versus capex commodities
Seasonal growth: Identifying seasonal patterns
Using Pandas for time series analysis
Challenges of time series analysis vis-à-vis cross-sectional analysis
Operating leverage, financial leverage, and variances
Using the difference between sales variance and the variance of various earnings measures to infer the extent of fixed costs
Macroeconomic effects: Quantifying systematic business risk; Behavior of sales and earnings in recessions
Using NumPy: NumPy and scientific computing
Using Statmodels: Using basic statistical functions in Statmodels
Using Sci-Kit Learn: Running regressions with Sci-Kit Learn
Unexplained increase in receivables
Unexplained decrease in deferred revenues
Unexplained increase in prepayments and deferred expenses
Unexplained decrease in payables and accrued expenses
The “good” and “bad” causes of divergence of earnings and cash flows
Identifying outliers using Sci-Kit learn
Reducing the number of independent variables using Sci-Kit learn
Understanding the causes of distress
Understanding which financial metrics could be leading indicators of distress
Understanding the determinants of credit ratings
Logit regression: Using Sci-Kit Learn for logit regressions
Cluster analysis: Using Sci-Kit Learn for cluster analysis
Unsupervised learning and cluster analysis
What is unsupervised learning? SIC codes versus FAMA-FRENCH Classification versus machine learning
Comparing the traditional methods of clustering that are based on intuition with the modern machine-learning-based methods Making sense of clustering based on machine learning
Using Sci-Kit Learn for cluster analysis
Which financial metrics distinguish companies that are the target of acquisitions from those that are not acquired?
Which financial metrics distinguish companies that are the target of LBOs from those that are not taken private?
What is the typical premium paid for targets?
What are the determinants of premium paid?
Using Sci-Kit Learn for logit regressions
Using Sci-Kit Learn for cluster analysis
Using Sci-Kit Learn for regression analysis