HOW-TO assess the fundamental strength of the company?

26 thg 5, 2023

A new series of my publications will be devoted to the guides for the indicators that I use in my work. I'll start with the Fundamental Strength Indicator.

To understand what it is for, let's imagine that you are the manager of a long-distance running team, and you need to recruit a team of excellent athletes. However, you don’t even know the names of these athletes or their contract amounts. You only have information about their health and athletic performance: hemoglobin and iron content in the blood, maximal oxygen consumption, steps-per-minute rate, speed, age, etc. Each player has their own large table with different parameters. And you have, let’s say, a thousand tables like that.

If you spend 3 minutes studying one table, it will take you 50 hours to analyze all the tables, which is just over 2 days of continuous work. And how long would it take to compare each athlete with the rest? Approximately 2 years of continuous work.

This is obviously no good, that is why you take a computer, enter all the data from the tables and start thinking about how you can reduce the time to compare one athlete with another. As a result of your brainstorming, you come to the following conclusions:

• Each parameter has its own range of values, which can give you an idea of whether an athlete is suitable or not suitable for a marathon.
  
• The parameter may have its own dynamics: it may increase from month to month, stay the same, or decrease.
  
• Each parameter can be assigned a score.
  

For example, the step-per-minute rate can be:

• 175 and above (+1 point)
  
• 165 - 174 (0 points)
  
• 164 and below (-1 point)
  

And you do that with each parameter.

What are these points for? To convert indicators that use different units into one measurement system. Thanks to this method, you can now compare apples to oranges.

Then, you sum up all the points per month and get one single number - let's call it athletic strength.

You like your thought process, and you apply this algorithm to every athlete’s table.



Now, instead of dozens of parameters per month, you have one number (athletic strength) for each athlete. It looks like your task has been dramatically simplified.

Next, to study the dynamics of athletic strength from month to month, you "ask" your computer to create a plot for each of the athletes.



This chart shows that Athlete #1's athletic strength has fluctuated chaotically in the first three quarters of 2022, possibly due to the lack of regular training. But then you observe a positive trend, where athletic strength has grown from month to month. It seems like the athlete has taken up training.

Then, to compare one athlete with another, you “ask” your computer to add the average value of athletic strength over the past six months (average pre-competition training period) to the existing plot. Now, you can use the most recent average value as a weighted score of athletic strength and compare athletes with each other based on this value.



Thanks to this solution, you speed up the analysis process by a magnitude: one athlete – one number.

It seems that you can then simply sort the table by the highest athletic strength weighted score and consider the best athletes. However, not wanting to sort the table every time the data is updated or when you get new athletes, you make a better decision.

The logic behind the points system implies that there is a maximum and a minimum possible number of points that one athlete can get. This allows you to create ranges of scores for athletes with excellent, mediocre, and poor training.

For example, let’s say the maximum is 15 and the minimum is -15. Athletes with a score of 8 to 15 will be considered excellent, 1 to 7 – mediocre, and 0 to -15 – poor.

That’s it! Now, thanks to this gradation, you can simply check which range the weighted athletic strength falls within, and decide whether each athlete will be admitted to the team.



I believe that now your primary selection will take no more than one working day (including a lunch break).

Now let's mentally replace athletes with public companies. Instead of data on health and athletic performance, we will have data from the companies’ financial statements and financial ratios.

Applying a similar algorithm, we will get the fundamental strength of the company instead of athletic strength.

I think it's time to show the Fundamental Strength Indicator. Let's launch! What do we see?

- First, it is a Histogram with bars of three colors: green, orange, and red. The width of the histogram depends on the depth of data from the company statements. The more historical data, the wider the histogram over time.



The green color of the bars means that the company has been showing excellent financial results by the sum of the factors in that time period. According to my terminology, the company has a "strong foundation" during this period. Green corresponds to values between 8 and 15 (where 15 is the maximum possible positive value on the sum of the factors).

The orange color of the bars means that according to the sum of factors during this period the company demonstrated mediocre financial results, i.e. it has a "mediocre foundation". Orange color corresponds to values from 1 to 7.

The red color of the bars means that according to the sum of factors in this period of time, the company demonstrated weak financial results, i.e. it has a "weak foundation". The red color corresponds to values from -15 to 0 (where -15 is the maximum possible negative value on the sum of factors).

- Second, this is the Blue Line, which is the moving average of the Histogram bars over the last year (*). Averaging over the year is necessary in order to obtain a weighted estimate that is not subject to medium-term fluctuations. It is by the last value of the blue line that the actual Fundamental Strength of the company is determined.
(*) The last year means the last 252 trading days, including the current trading day.





- Third, these are operating, investing, and financing Cash Flows expressed in Diluted net income. These flows look like thick green, orange, and red lines, respectively.



- Fourth, this is the Table on the left, which shows the latest actual value of the Fundamental Strength and Cash Flows.



Indicator settings:

In the indicator settings, I can disable the visibility of the Histogram, Blue Line, Cash Flows (each separately), and Table. It helps to study each of the parameters separately. It is also possible to change the color, transparency, and thickness of lines.



That's all for today, but in my next post, I will share another guide on how to use the Fundamental Strength Indicator. See you!

P. S. The movie Moneyball was released in 2011, where Brad Pitt plays the role of Billy Bean, the sports manager of the Oakland Athletics baseball team. With a small budget, he managed to assemble a high-scoring team based on the analysis of player performance. As a result, this approach was applied by other teams in the league, and Billy Bean received massive recognition from the professional community.