You need at least one scoring model up and running to take full advantage of our systems. The only question is whether you will use one of our preconfigured models or build your own unique scoring model. This article explains the procedure for creating a scoring model from scratch.
Setting up a new scoring model
To set up a new model, click on the Scoring top menu and choose +New Scoring Model. You should see this:
You can use one of our predefined templates or create the model from scratch. Let's do the latter. Type the name for your new model and click Create button.
The next step is to constitute some categories (i.e., value, growth, momentum, etc.). Your model can have only one or many different categories. You can name them whatever you want. These settings do not affect final scoring but are recommended for bundling together many single indicators from the same field (i.e., P/S, P/E, and P/BV ratios) into one group (i.e., valuation). This will help you avoid chaos and better understand how the algorithms calculate the final scoring of a company.
Let's add our first category and name it Fundamentals (we'll be able to add more categories later):
After clicking the Add New Category button, we should see this screen:
Now it's time to add our first rule to the model. We have five different types of rules to choose from, each unique and serving a different purpose. Please read their short descriptions below.
What type of rules can we use?
All of the rules are built by comparing the selected metric to some other factor. By "metric," we mean any financial indicator you want to see, use, or compare. P/S ratio, Price Target, Upside, or EPS Estimates for the Next Fiscal Year are all considered "metrics".
Let's dig in to see how we can utilize the different types of rules.
Metric Value in Static Range
Choose this one when you want to assess whether the particular metric's reading is high or low compared to the static range you're going to define.
Use it with metrics like Debt-to-Assets, Current Ratio, or Return on Invested Capital because desired levels of these are more or less static for most companies.
Metric Value in Historical Range
The same as above, except that you want the range not to be static but rather unique for every stock, where highs and lows (the range) are calculated automatically based on historical data.
Use it with metrics like P/S, EV/EBITDA, or Upside because the attractiveness of these readings depends hugely on the particular company's situation, its growth prospects, and the whole industry.
Difference Between Two Metrics
Approach it when you want to assess the desired metric, not within static nor dynamic range, but rather comparing it to another metric.
Use it when you want to contrast, for example, current P/S with its median from the last five years (is current reading higher or lower than the median, and by how much?).
Metric Change Over Time
This one helps to score a company by determining how much the single particular metric has changed over time (how much the metric has risen or declined over time in the percentage values).
Use it when assessing the dynamic of Revenue, EPS, or Cash growth over time, or the magnitude of revisions for metrics like EPS or Revenue Estimates, or even the Price Target.
Metric Decline Over Time
The same as above, except that the starting point to gauge the decline is not defined by the single past point in time but by its last peak (highest reading).
Use it when you want to gauge the percentage magnitude of the decline of such metrics as Stock Price, Price Target, EPS Estimates, Total Debt, etc.
If you want to look at the full description of the rules with more examples, please refer to this article: Different types of rules you can use.
Alternatively, you can also use one of our pre-configured rules (the sixths option in the Configure tab) we've created as an example. These are dozens of real rules used by our users or us. You can use them as they are or just as a blueprint for further modification.
Adding new rules to the model
Let's choose the first type of rule, which is Metric Value in the Static Range. Click on the purple button below the metric, and you should see the place to enter all the parameters:
Now it's time to fill in the details.
Choosing the Name is up to you, as well as the Category, but we suggest using relevant names/categories so that it would be easier to read and understand the results at a later stage.
This is the upper limit of how high the best company should be scored (rewarded with how many points?). The maximum score threshold doesn't matter much to the single metric, but it is a good way of overweighting or underweighting the significance of the particular metric in comparison to the other metric.
Suppose you want to have the following metrics in the Fundamental category: the Debt-to-Assets and the Current Ratio. But at the same time, you believe that short-term liquidity is twice as significant as longer-term solvency. Thus, you might want to set the Maximum Score for the Debt-to-Assets at 5 points and the Current Ratio at 10 points since it's twice as important, in your opinion.
The pure numbers you use are relative and don't mean that much since our algorithms use rankings with two decimal places anyway, but the number (maximum score) starts to mean a lot when compared to other metrics' maximum scores.
This is your definition of what does it mean to be a good or a bad reading of the metric. In other words this is the place to define the "Worst" and "Best" thresholds for the particular metric. When the metric reading crosses the "Best" threshold, the company receives maximum scoring; on the other hand, it receives zero points when the "Worst" threshold is crossed.
Everything between receives some points accordingly to the place of the reading on a grading scale (i.e., the closer the reading is to the "Worst" threshold, the fewer points the company receives; closer to "Best", the more points it's rewarded with up until crossing the "Best" barrier, in which case it receives the maximum points available).
In our example, we try to add the Debt-to-Assets ratio, which means that the lower the debt level is compared to assets, the better situation of the company, hence we need to reverse the threshold (as on the screen) and tell the algorithm that the "Best" situation is when the company has 5% of debt to assets or less; and the "Worst" situation is when the company has 40% or more of debt.
If we were using a metric like Return on Invested Capital or the Operating Margin, we would oppositely set the threshold since we want the ROIC or Margin to be as high as possible. Hence the Grading Scale for the Operating Margin would look like this: "Worst = 5%, Best = 40%".
This might be checked if you want the just-added rule to be treated as an extremely important one, meaning that if the company doesn't score better by least 0.01 points than the minimum threshold, it would be put at the bottom of any list of the companies that you add it to later. Think of it as of the forced downgrade no matter how good the company scored in other categories.
Basically, it's better not to use this option unless you really want to definitely get rid of the companies that do not meet your criteria (and rewarding them with zero points is not a sufficient punishment).
That being said, you might want to tick the Must-have Rule checkbox in metrics like Market Cap or stock Price to eliminate the companies with low capitalization or penny stocks traded for less the 5$ per share.
If all the fields are filled, click the Add New Rule button to save it.
Filling your model with companies
With at least one rule added to our scoring model, you can finally add some companies and let our algorithms score them according to the rules (in this case – only one rule).
To do that, click on the My Companies tab and start adding some tickers:
Now you have one rule configured and three companies added to your scoring model. Thanks to that, our algorithms will monitor all the metric changes (Debt-to-Assets in that case) and update the model daily with new data. Every time you return to the My Companies tab, you will see a fresh, updated, and sorted list of your stocks according to their current ranking.
Of course, you'll need more companies and rules to take the most from the automated scoring model. We encourage you to read the other articles from this section and experiment a bit on your own with adding and configuring more rules.
To add more companies all at once, you can use the Screener tool, read about it in the Screener section
To add more rules designed by yourself, read the rest of the manuals in the Scoring Model section
To use one of our predefined ready-to-go scoring models, please refer to this article