Stock investing: understanding the SDR stock algorithm

What is SDR? This is not a term that shows up on Investopedia or Wikinvest. That’s right. I invented it because I wanted to have a better understanding of risk analysis. As I look at hundreds of stocks, it is impossible for me to determine risk. I don’t know the industries well, or the people running these companies. I have only common sense, articles on the internet, and numbers to crunch. How much a company makes per share matters a lot to me as a metric for “return” on investment as well as dividends and growth averages over the life of the company. However, risk is also another important criteria.

SDR means:
(stability in income) : debt ratio.
If you look at a company’s financial records, you can see what their earnings have been year after year after year. It is generally good to buy a stock that is growing in income. If every year that goes by, the company makes more and more money, your stock will probably go up in value as it will affect the stock’s price per earnings ratio which is one of the most critical metrics in stock analysis.

Earnings growth & stability
What I do is to look at the company’s earnings over the last twenty years and calculate an average percentage growth. Unfortunately, in the real world growth is compounded, so you will be forced to use “the rule of 72” which you can look up on the internet. Putting 72’s aside, I look for income stability, particularly in recessions. I am terribly afraid that my stock investments will go bust if there is a bad depression similar to 1929. So, if I see a stock that survived the great depression, and didn’t do too badly in the recession of 2008 & 2009, then it has some stability to it. I analyze how stable or unstable the consistency in their income per share is, and add that to my stock algorithm.

Is unstable earnings bad?
However, after long and careful thought, I decided that a company with unstable income is not the worst thing. If you hold that company for twenty years, the fact that they had a bad year in 2021 and 2027 won’t phase you assuming they don’t go out of business completely and assuming you don’t need to sell the shares suddenly during a bad year. If a company is deep in debt, that is not necessarily a horrible thing assuming its income is relatively stable at all times including economic downturns. I learned that companies like Coke, Pepsi and Kraft stay steady in bad times while Mastercard, Starbucks, and Sam Adams did not do as well.

The basic idea is that a company that is deep in debt that also has unstable income is a likely candidate for bankruptcy in the next decade or so. It is unclear when a company would go under, but it happens all the time. If income is not stable, but the company’s equity ratio is high — you might be safe. If the income is stable but the debt is high, you also might be safe. Ideally you would have low debt and stable income, but that is only the care with very few companies. The point of this article is that I no longer analyze debt ratio as a separate issue as its relevancy only matters when combined with income stability stats.

In short, in my algorithm gives stocks up to 20 points. 3 of those points are earned from the SDR portion of the algorithm. Perhaps I should give more as two of the stocks I just bought just fell about 10% the day after I purchased them — gulp! If you have low debt and stable income you get all three points. If you have unstable income with high equity or vice versa, you might get two out of three points. But, if you are lacking in both departments, I might assign you a negative rating since the risk would be quite severe! I created a table to go over all possible scenarios including moderate debt and moderate income stability, moderate to low, and various gradations. If you like my idea for this component of a stock buying algorithm, you can create your own table and fool around with it until you get it how you like it. Algorithms are not for everyone, but if you do things like buying stocks or hiring outsourcing companies — you need some type of an algorithm combined with a good sense of people!

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