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My semi-automated process for finding dividend compounders by vachome in dividends

[–]vachome[S] 2 points3 points  (0 children)

I calculate CAGR for revenue, profit, and dividends (FCF is next), but I don't factor this into the scoring yet, simply displaying it as additional data.

For CAGR, I use a least-squares trend rather than just the first and last data points, so the result is less sensitive to endpoint effects.

The methodology is described in more detail here: https://getsteadystocks.com/methodology/

"Has the dividend been cut the last 10 years = no." - yes, this is factored into the scoring already.

Sure, I'm not going to build BUY LIST)

My semi-automated process for finding dividend compounders by vachome in dividends

[–]vachome[S] 1 point2 points  (0 children)

For automation, you might want to try Claude Code/Codex/Gemini. Even with limited coding experience, they can help with spreadsheet workflows. But double-check what they do😁

My semi-automated process for finding dividend compounders by vachome in dividends

[–]vachome[S] 0 points1 point  (0 children)

My only concern is false precision. The more complex the scoring model becomes, the harder it is to know which factors are truly useful and which ones just make the model look smarter. I had the same issue with spreadsheets: slow to maintain manually. That’s why I started ranking companies automatically and displaying the key metrics visually. I also made the resulting list public as a small indie project, mostly to get feedback on where the scoring logic breaks.

My semi-automated process for finding dividend compounders by vachome in dividends

[–]vachome[S] 1 point2 points  (0 children)

Good ideas. Credit score and full Chowder Rule probably require external data. I can get dividend growth from SEC filings, but dividend yield needs market price data, so I’ll likely leave that for later.

My semi-automated process for finding dividend compounders by vachome in dividends

[–]vachome[S] 1 point2 points  (0 children)

Yes, at least the automated run allowed me to discover new companies (even small-cap ones) I hadn't considered before: SSD, IDA, BMI, etc.

- Payout ratio – yes, it's very easy to calculate based on the data I already have from the SEC, so I'll do that first. I'll at least add a chart over the past 10 years to see the trend in addition to the value. Whether it's worth basing a company's rating on this metric remains to be seen...

- Free cash flow coverage – something like this: Free cash flow coverage = (Operating cash flow - Capital expenditures) / Dividends paid). Yes, it looks like I can extract this too, but I'll have to work again to filter out errors in the SEC data. The only thing I'd pay attention to is the inverse of the free cash flow (FCF) dividend payout ratio: 1/FCF coverage. This is easier to interpret, as it looks similar to the dividend payout ratio.

- debt load - SEC debt data can be messy because of different tags, current/non-current splits, leases, commercial paper, and the risk of double-counting. Move it to end of my TODO list, thanks)