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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
I taught this course for the first time in spring 2019. I have made one major change for fall 2020. I am going to assume that the students already know Python and will teach actual financial statement analytics in the course.
Understanding and modeling financial statements is vital to corporate finance, valuation and investments, both fundamental and quant, and is foundational to anyone considering a career in asset management, investment banking or corporate 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 show 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 are not going to 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 with respect. It is my intent that all students’ learning needs be addressed both in and out of class and that the diversity that students bring to this class be viewed 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 markets data based on financial analysis and statistical concepts. This course is not suitable for those who want a managerial overview of data analytics techniques without the hands-on coding.
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