Hank Baxter – Sector-Adaptive Fundamental Engine (S.A.F.E.)

I have created a sector-adjusted quantitative scoring model which evaluates companies across five main pillars: Profitability, growth, cash flow quality, solvency, and valuation. With my model, I’ve solved the problem seen in many of the basic models in the financial world that use basic benchmarks when scoring companies (e.x. “A 20% margin is good for every company”).

In what I’ve created, I use a dynamic normalization engine which acts as the “back-end brain table” which takes the specific company data and shifts it around based on the sector that the business is in. This means that it knows that an 8% margin is incredibly strong for a company in retail but weak for software companies.

What You’ll Learn In Sector-Adaptive Fundamental Engine (S.A.F.E.)

• Data Integrity: I’ve built my engine to scrub various data using the ‘LET’ and ‘SUBSTITUTE’ functions which take inconsistent data taken from online, and automatically converts percentage strings into easy to read mathematical values used in calculations.
• Earnings Quality: Instead of focusing solely on the net income of a company and scoring it based on what they’ve made, I integrated an accrual quality filter (A.K.A. Sloan Ratio Logic). What this does is it penalizes companies that have reported high profits but their cash flows are reported to be in the negatives; This acts as a “red flag” detector for companies with low-quality earnings.
• Solvency Lifeline: When going from mature companies to early stage IPOs, I built a cash runway metric which can more accurately rate a new company’s performance. This works by calculating the IPOs monthly burn rate against its current liquidity to measure its survivability/ when it may “expire”. I have found this to be an incredibly important metric when evaluating non-profitable growth stocks.
• Valuation Discipline: My model’s designed to filter out popular opinions seen online. It scores the quality and finances of the company that is being evaluated and gives the raw “Is this company’s financials factually strong compared to what people believe”. As an example: The model identified the company Walmart to be fundamentally strong but to be quantitatively overvalued seeing that its PE ratio moved two standard deviations from the sector-specific target set for it. This allows for the prevention of “missing out” on an investment by giving it a fair price for its quality.

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