How Much Volatility Is Too Much in a Stock’s Fair Value?

On the March 23 episode of The Morning Filter_, _David Sekeraand Susan Dziubinski answer a viewer question on whether there has been too much volatility in Oracle’s fair value estimate and share lessons on how Morningstar analyzes stocks. Here is an excerpt from the show.

Is Oracle’s Stock Too Volatile?

**Susan Dziubinski: **It’s time for our question of the week. Our question this week is from Carlos. And Carlos asks, “Could you explain how in the course of four to five months, Morningstar’s fair value estimate on Oracle ORCL went from $200 to more than $300, then back to $220. Don’t you think this is too much volatility?”

**David Sekera: **That’s a great question, and I completely understand where the genesis of that question comes from. And to be honest, if I was using Morningstar products, I’d probably be asking the exact same question myself. Before I actually even get specifically to our answer there, I would just say anecdotally, I’ve seen more and larger swings in our fair values over probably the past nine months, more than I think I’ve seen over the past over 16 years that I’ve been here with Morningstar. And I would say the preponderance of those huge swings are all because of artificial intelligence and really trying to understand the long-term growth dynamics of AI and how to be able to incorporate that into your long-term assumptions. So again, what is the intrinsic value of a stock? What’s the fair value of a stock? The fair value of a stock is the present value of all the future free cash flow that a company’s going to generate over its lifetime. As we make any kind of assumption changes, that’s going to change our fair values.

Now, growth stocks in particular will have even greater swings in fair value because the valuation there is going to be really based on what your assumptions are for future growth. So, even just relatively small changes in those future growth assumptions can have pretty big changes in fair value today. To some degree, I think everyone will admit AI is going to have a huge impact, but it’s unknown exactly what that impact is going to look like and when it’s going to be. So then, even just trying to understand what AI does in the next year or two is difficult enough, much less trying to really put that into your model five years out or 10 years out. Getting back to Oracle specifically, as our example, last September, the company announced just major changes to their ongoing business and what they were planning on being over the longer term.

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They’re really specifically going to focus on becoming a hyperscaler, providing cloud infrastructure for artificial intelligence, plans on just building out numbers of massive data centers. And so they provided at that point in time guidance to the marketplace that they thought they could generate $144 billion of revenue for Oracle Cloud by 2030. To put that in perspective, they did $10 billion of revenue there last year, and then they’re forecasting it to be an increase of 77% this year to $18 billion. But either way, I mean, you’re still looking at 14 times growth in just six years. So, when we incorporated that type of growth into our base case, that led our analysts to increase their fair value up to $330 a share from $205, which was really going to be based mostly on their legacy business. Now, as far as then lowering that fair value, subsequently we made some updates to our model a couple of different times, just as we updated and kind of incorporated other changes into our long-term outlook.

When you think about how we calculate the present value of a stock, we use what’s called a three-stage discounted cash flow model. Your stage one is the explicit forecast. That’s when the analyst is actually specifically making kind of individual annual revenue forecasts and margin forecasts and what they think the balance sheet is going to look like, how much the company’s going to spend on capex versus dividends versus whatever else they would spend money on. And then stage two is what we call the fade period. So, essentially what you’re doing is you’re taking the last year of stage one and then you’re going to fade that to what you think kind of a more steady state of that company is going to be. That fade period is usually anywhere from five to 10 years. And then stage three is essentially just a perpetuity formula of that steady state.

So, on Oracle, we recently lowered our economic moat rating to narrow from wide. And it was part of a major economic moat reanalysis that we conducted across, I can’t remember how many companies it was, but it’s pretty much everything that we thought could be substantially impacted by artificial intelligence. So, essentially, lowering that economic moat to narrow from wide means that we’re now modeling in that the company’s going to generate less excess returns over the longer time period than what we had in the model before. So, essentially, we dialed back the amount of excess returns that they would make in stage two and the length of how long they were going to make those changes. And of course, that’s what led to that big significant reduction in fair value.

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I’d also say, too, that in addition to looking at the fair values, I think you should also look at our Uncertainty Ratings. So in this case, we do have it assigned a Very High Uncertainty. And really that’s just to try and help communicate to investors situations like this where you’re just going to have a huge potential range of outcomes over the future, which is of course why we also require a greater margin of safety away from our long-term intrinsic valuation before these stocks would move into 4-star or 5-star territory. Conversely, these are also the stocks that we let run a lot higher above that fair value before they become 2-star or 1-star stocks.

Subscribe to The Morning Filter on Apple Podcasts, or wherever you get your podcasts, and keep up with the latest research from hosts Susan Dziubinski and David Sekera on Morningstar.com.

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			46m 17s
		 Mar 23, 2026

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