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Replication on Morningstar Financial Health Grade

https://www.quantopian.com/help/fundamentals

Morningstar financial health grade is a convenient tool for users to incorporate distance to default to trading strategies. However, it is not easily accessible outside quantopian platform. Under this motivation, I tried to replicate the calculation of financial health grade.

The method that uses standard estimation on equity volatility gives 43% accuracy of grade A, and 67% accuracy of grade A and B. (on 2018-08-01 data)

Lately, it is interesting that even grade D and F can have a low probaility of default (<0.01). That may due to a narrower range in probaility of default while Morningstar ranks the distance to default instead of probability of default.

I did not include dividend in the calculation. You may try to include that and see the difference.

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3 responses

Interesting work!

One thing to note is that the definition of financial_health_grade on Quantopian's fundamental page says:

"To estimate a distance to default, the value of the firm's liabilities is obtained from the firm's latest balance sheet and incorporated into the model. We then rank the calculated distance to default and award 10% of the universe A's, 20% B's, 40% C's, 20% D's, and 10% F's."

However, Morningstar's site shows that they use other variables to calculate financial_health_grade as well. Their definition reads:

To get a good grade in this area, a company should have low financial leverage (assets/equity), high cash-flow coverage (total cash flow/long-term debt), and a high cash position (cash/assets) relative to its sector.

Maybe you could make this custom factor more robust by incorporating these variables?

Thanks for sharing!

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Hey, thanks for sharing, nice implementation of the Merton model !

Here is a comparative when taking dividends into account. It seems more accurate, as it produce the same ranking as financial health grade.

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@Mathieu Meynier

I saw that you minus the dividend after r_f. But, this document put dividend in d_1 and also G(A_0, sigma_A)

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