Statistical Analysis of US Stock Market Tickers - KNZ Score

We conducted a statistical study using over 15 years of historical data from the 30 most traded tickers on the US stock market. The goal of our study was to evaluate, for each trading date, the investment return rate over different time intervals. Figure 1 illustrates a specific example:

Figure 1 - Principle of KNZ Score

• A long-term period "a" with return rate "a"

• A medium-term period "b" with return rate "b"

• A short-term period "c" with return rate "c"

Our working hypothesis is that the higher the return over a certain period, the lower the likelihood of that trend continuing — and vice versa. Based on this assumption, we applied constant multipliers to each return rate in each time interval, as shown in the equation below the chart. The result of this calculation is a value we call delta.

We ran multiple simulations with different sets of constant values and various time intervals to determine the optimal delta values for buy and sell signals for each ticker. Figure 2 shows how the delta indicator fluctuates for a specific ticker, highlighting two complete buy/sell cycles.

Figure 2 - Sample of operation using KNZ Score (delta)

In Figure 2, you can see the points where the delta parameter crosses the "buy" and "sell" lines, suggesting two complete trade cycles.

Figure 3 - All period sample of KNZ Score (delta)

Figure 3 shows the same chart extended over a much longer period.

Figure 4 - All period sample - Normalizing delta to KNZ Score

Figure 4 also covers a long period but introduces a parameter we call KNZ Score. This is a normalized version of the delta value that performed best for each ticker. A knz value of 0 corresponds to a delta at the "sell" threshold, and 1 corresponds to the "buy" threshold. The KNZ Score serves to normalize delta values, making them comparable across different tickers.

Table 1 below presents examples of the highest average annual return obtained after numerous parameter simulations over the period from 2006 to 2025. Some stocks displayed stronger short-term factor characteristics, while others showed stronger long-term ones. Some did not exhibit any consistent behavior pattern that generated significant gains, even after testing a wide range of numerical parameters.

Table 1 – Summary of simulated returns (2006–2025) for selected tickers using the KNZ Score

In summary, the KNZ Score represents a statistical probability of gain based purely on historical data and price behavior. It is 100% based on numerical and statistical analysis. As with any stock market strategy, there is no guarantee of profit, and the method is not based on financial indicators or fundamentals of the companies. Table 1 Below show Sample of company