Smoothing Financial Extremes with AI Regression

 Using AI to Help Compute a Regression-Based Approach When Dealing With Volatile Numbers, like EPS

Introduction:

Finance is full of ups and downs, and metrics like EPS can swing wildly yearly, making it hard to spot real trends. (Especially with smaller, younger companies ) . That’s where a regression-based approach comes in; it helps cut through the noise by smoothing out outliers and revealing more reliable patterns. The best part? AI makes this easier than ever. With the right tools, you can quickly make sense of messy data and uncover insights that actually help you make smarter decisions.

More On Regression-Based Approach:

Smoothing Out Volatility: When EPS swings sharply, or turns negative, year-over-year growth rates can be misleading or even meaningless. A regression trend line helps by capturing the broader trajectory, not just isolated spikes.

Using All Data Points: Rather than cherry-picking strong years, regression looks at the full set of earnings data. This approach smooths out the noise and reveals a clearer trend in EPS over time.

Extracting a Consistent Growth Measure: Regression gives you a slope (average yearly EPS change) and an intercept. Comparing the slope to a chosen EPS point, like the final year, helps estimate a more reliable CAGR, minimizing early-year distortions.

Economic Intuition: Damodaran’s approach links growth to actual business performance. By smoothing earnings into a clear trend, it helps you better model future growth and build more grounded valuation forecasts.

How To Ask An AI For These Calculations To Help You Out !! 

 We'll take EPS or Earnings Per Share as an example for this. Let's assume these to be the numbers.

Type this into an AI bot. (ChatGpt, Deepseek, Copilot, etc):

 

Using the following EPS data: Year 1: -0.50, Year 2: -1.55, Year 3: 1.10, Year 4: 2.20, and Year 5: 2.00, please perform a regression-based analysis. 

1. Compute the regression line in the form EPS = a + b*t (where t represents the year).

2. Show the calculations for the slope (b) and intercept (a).

3. Use the regression line to predict the EPS at Year 5.

4. Then, calculate the smoothed CAGR by dividing the slope by the predicted EPS at Year 5.

Please provide detailed steps and the final estimated growth rate.

For A More straightforward approach, TYPE THIS:

 So, let's say, Year 1, the EPS is -0.50, Year 2 EPS is -1.55, Year 3 EPS is 1.1, Year 4 the EPS is 2.2 and year 5 the EPS is 2 could u calculate this using the regression-based approached and then tell me the CAGR to smooth the volality and smoth out extreem changes?

The AI bot would do the calculation and tell you the compounded growth rate over the period, even though the data started with negatives. For the example above, the CAGR is 36.5%

Using the regression-based approach on the given EPS data, we found a regression line:

EPS = –1.975 + 0.875·t

This yields a predicted EPS of 2.4 in Year 5. Approximating the CAGR as the slope divided by the predicted Year‑5 EPS gives us about 36.5% per year as the smoothed growth rate.

Final Thoughts:

Why This Helps: Regression smooths out wild swings, especially early losses, giving you a steady trend for forecasting. It avoids the erratic or undefined results that come from year-over-year changes on negative EPS.

Caveats: This method is a simplification. Since negative EPS rules out a log-scale model, we use a linear one instead. Always sanity-check the result against business realities and industry context before using it in valuation.