The Price Is Right… or Is It? Measuring Valuation in Context

There isn’t a single, universally successful approach when it comes to investing. For example, while some investors prefer to jump onboard high-momentum stocks to ride short-term waves, others follow the Buffett approach — entering solid companies they understand when those are trading at a discount. Both strategies can deliver outstanding returns when executed properly, and most investors don’t exclusively stick to one style.

Our Advanced Dashboard reflects this duality. It’s why we built both the Retail Momentum Screener — to capture energetic stocks enjoying their shining moment — and the Stock Health Dashboard, which helps assess how robust a company’s fundamentals are and how its current price stands from a valuation standpoint.

We designed these products around our own investing needs, consolidating in one place all the metrics we personally monitor when deciding whether to invest in a stock. When we launched, several features were still marked as “coming soon.” One of them was the Z-score of the revenue multiple on the Stock Health Dashboard.

The idea behind this feature is that some high-growth companies tend to trade at elevated revenue multiples for extended periods. Take Shopify, for example — it stayed above 30× for a long time. Normally, such a high multiple is a red flag, yet in Shopify’s case, it’s hard to argue that buying at that valuation in 2019 wasn’t a good move, as it kept compounding for years afterward. The Z-score puts this kind of metric in historical context — helping investors see when a company is cheap or expensive by its own standards.

The reason we didn’t implement it at launch wasn’t the complexity of the equation, but rather the data structure: calculating this requires calling a long historical series (in this case, revenue), not just price — something our initial database setup wasn’t designed for.

After spending part of the summer working on other projects like our Bitcoin Dashboard, we returned to this feature — and realized we could do much better. We decided to build an entire Valuation Section within the Stock Health Dashboard to visualize a company’s current valuation across multiple metrics.

Valuation has always been one of the first things I look at before jumping into a position — and in the current market, where smaller individual stocks are experiencing incredible runs, that insight is more crucial than ever. It helps not only in assessing the level of risk when entering a new stock, but also in recognizing when it might be time to step off the train.

We already had most of the key valuation metrics displayed in the Stock Health Dashboard, but what was really missing was the ability to visualize these metrics over the stock’s history. I realized this because, until now, I often relied on bits of personal code and even used a scientific graphing software (Datagraph) to assess how expensive a stock — or the market as a whole — had become.

So, we turned those personal scripts into a powerful new visualization tool, now fully integrated into both the Stock Health Dashboard and the Advanced Market section. This addition allows users to easily track and contextualize valuation trends over time.

These new features are straightforward to use, so some of you may want to skip this post — but I still think it’s worth explaining why they matter and how they work.

(Note: you might experience issues loading the updated dashboard the first time. If that happens, simply clear your browser’s cache and reload the page.)

Valuation metrics

The “Valuation in Context” section that we’ve added to our Advanced Datahub and Stock Health dashboards allows you to visualize four different valuation metrics across a stock’s (or the index’s) history. Three of them use the same visualization style, while the last one is a bit different.

The first three are: the revenue multiple, the normalized distance of a stock’s price from its 200-day SMA, and its RSI.

The revenue multiple is a way to evaluate a company’s price — particularly useful when the company has negative earnings because it’s heavily investing in growth. This metric is actually king in the world of venture capital, as it gives a good sense of how risky an investment might be. For instance, if a company is bought for 10× its revenue and that revenue is growing at 80% per year, the high multiple quickly normalizes. High-growth companies tend to trade at high multiples, while slower-growth companies trade at lower ones. Ultimately, it’s all about how fast future growth can justify the price you paid today.

The distance from the 200-day SMA — the simple moving average most often used to gauge a stock’s trend — is an excellent measure of momentum. When a stock rallies strongly, this metric tends to inflate. We normalize this distance, since the raw difference (Price − SMA200) would naturally increase over time. Expressing it as a percentage makes it more meaningful: for example, a 20% distance means the stock’s price is 20% above its 200-day SMA.

The RSI (Relative Strength Index) is a momentum oscillator that measures the speed and magnitude of recent price changes to determine if a stock is overbought or oversold. It oscillates between 0 and 100, with values above 70 typically indicating overbought conditions and below 30 indicating oversold conditions. In the dashboard, we plot its historical Z-score, allowing you to see how extreme current momentum is compared with the stock’s own past behavior rather than using arbitrary fixed levels. This helps identify when momentum is unusually strong or weak in a historical context.

The last valuation metrics, which have it’s own visualization interface is the Price to earning ratio. The price-to-earnings (P/E) ratio measures how much investors are willing to pay for each dollar of a company’s earnings.

For example, if a stock trades at $100 and earns $5 per share annually, its P/E ratio is 20 — meaning investors pay $20 for each $1 of profit the company generates each year.

It’s important because it gives a quick sense of how the market values a company’s profitability. A high P/E may suggest optimism about future growth (or simply overvaluation), while a low P/E may signal undervaluation — or doubts about the company’s prospects.

In the long run, the P/E ratio ties back to a fundamental investing principle: owning a stock is ultimately owning a piece of a company’s future earnings. Dividends, share buybacks, and long-term appreciation all come from those earnings. So, if a company never produces (or grows) profits, the logic for holding its stock indefinitely weakens — regardless of hype or market momentum.

Visualization

One way I’ve always loved to visualize data through time is by using statistical bin distributions. For me, they provide a clear and intuitive way to see how “outstanding” or unusual a current data point really is. A recent experience reminded me that this approach isn’t just visually convincing for me — it resonates with others, too.

A friend of mine asked if I wanted to buy a beach house in Spain with him. For him, it’s a retirement project; for me, it would be more like a place to work from a few weeks each year (and I do love Spain). We went to visit a few places last summer and discussed making an offer on one of them. One of my arguments for waiting a bit was that the Euro-to-Canadian-dollar exchange rate was quite unfavorable at the time. His answer was, “The Euro is always expensive.”

So, right there on his kitchen island (I had my laptop with me), I quickly plotted this graph:

He was instantly stunned. He realized that what had felt normally expensive to him was actually a level we’d seen only a few times in Euro-CAD history. He then agreed that, since we weren’t in a rush to buy (especially with the Spanish government cracking down on Airbnb and foreign buyers, stalling the market), it made sense to wait for a more favorable moment to convert our dollars into euros (and maybe a better price).

The key in this story is that seeing the historical distribution of a value gives us a true sense of how likely or exceptional that data point is. And when it comes to valuation, that context really matters.

With that in mind, our new “Valuation in Context” addition to the dashboard lets you visualize the histogram of each of the first three valuation metrics discussed earlier. This allows us, for example, to see that AMD’s current distance from its 200-day SMA is at a level rarely seen in the past 10 years.

Similarly, Lululemon is now far more “out of trend” than usual.

Instead of blending the last 10 years into a single set of bins like in my Euro–CAD example, we color-coded the values by year. In some cases, like RSI, the year doesn’t matter much — but for valuation metrics like the revenue multiple, it often does. Market environments evolve: there are eras when stocks tend to trade at higher multiples and others when they’re generally cheaper. Taking that into account provides valuable context.

Take Apple, for example:

Looking across the last 10 years, Apple’s current revenue multiple appears extremely high. But when you look more closely, you see that this is mostly because Apple traded in a very different valuation range between 2016 and 2020. If you filter the view to only the last five years, by deselecting the earlier ones, the picture changes:

Apple still looks expensive, but not absurdly so.

This feature is also useful for removing years when a stock behaved unusually. Take NIO, one of the big post-COVID winners — its current distance from its 200-day SMA doesn’t look euphoric at all:

But if we remove the extraordinary years of 2020 and 2021, we get a very different picture:

yes, from that point of view, NIO is currently trending very strongly.

In addition to the valuation histograms, you’ll see a chart on the right showing how the current value (expressed in standard deviations) evolved over time, with price action plotted above. This helps visualize how the stock behaved when reaching certain thresholds. For instance, it’s clear from this graph that buying Apple when it reached near -4 standard deviations from its 200-day SMA has historically been a great entry point.

You can also activate the market-neutral price trend. If you remember, the market-neutral price trend shows the stock’s price after removing the effect of the overall market, weighted by its beta. This highlights when a stock truly outperforms or underperforms its expected behavior. Visualizing that adjusted price relative to its 200-day SMA can be quite revealing.

For example, during COVID, when it became clear that we were all forced to stay home watching TV, Netflix massively outperformed the market on a market-neutral basis — a trend that was immediately obvious in this visualization.

Similarly, look at how NVDA outperformed its expected trajectory during the tariff correction in April. Seeing this made it clear that it was a “buy-the-dip” opportunity.

Price-to-Earnings Ratio

Visualizing the price-to-earnings (P/E) ratio is a bit trickier than the other valuation metrics. As a company’s earnings improve, it can transition from negative EPS (earnings per share) to positive EPS — a milestone that usually drives strong stock price appreciation. This is exactly what Tesla experienced around 2021.

The issue lies in the math: since the P/E ratio is calculated by dividing the price by the EPS, as earnings move from negative toward zero, the ratio tends to infinity (we still haven’t solved dividing by zero!). This makes it impossible to plot a meaningful histogram for the P/E ratio across time.

Most professional quantitative platforms address this by displaying the P/E ratio only when a company has positive earnings — arguing that this valuation metric only makes sense in that context. But we see things differently.

First, many companies experience negative earnings at different points in their history — especially during periods of rapid growth, restructuring, or recession — which would hinder our ability to generate a consistent histogram plot, even for companies that are currently profitable. More importantly, spotting a company transitioning from negative to positive earnings is one of the most powerful signals in the market — and we don’t want to miss that, as investors usually reward such turning points heavily.

That said, interpreting negative P/E ratios is tricky. The same negative P/E can represent either a bullish or bearish situation, depending on the context. But I won’t go too far down that rabbit hole here — it gets complicated quickly (even OpenAI got confused at one point). For those curious to dig deeper into this nuance, we’ve included a detailed explanation with numerical examples in the appendix after this post.

Since the math behind the P/E ratio doesn’t lend itself well to histogram plotting, we decided instead to break it down into its two natural components and visualize it on a 2D chart — with EPS on the x-axis and Price on the y-axis.

In this setup, a point located toward the lower-right of the graph represents a discounted valuation, while a point toward the upper-left indicates a very expensive company. Because multiple combinations of price and earnings can produce the same P/E ratio, we also added contour lines representing specific P/E levels as reference guides.

For example, here’s Apple’s P/E ratio plotted this way.

As you can see, Apple’s EPS currently stands at record highs — but the stock price is also quite elevated, keeping it around a P/E ratio of roughly 37.5, a level it hasn’t exceeded for long in its history.

Also note that the color intensity of each dot reflects how long the stock spent in that range. A darker or more saturated dot means Apple traded at that specific price–earnings combination for many days, giving a quick visual sense of where the stock tends to “live” over time.

Like we mentioned above, this way of visualizing the P/E ratio allows for a continuous view — even for companies that have experienced periods of negative earnings. This makes it particularly valuable for observing transitions from losses to profitability (or vice versa for a declining company).

Like I mentioned above, take Tesla for example. Around 2020–2021, it transitioned from a cash-burning company to one generating positive cash flow.

On the chart, you can clearly see earnings turning positive and the stock price beginning to appreciate around that same period. Interestingly, the visualization also highlights Tesla’s more recent dip in earnings — a period that coincides with the company falling slightly out of favor among some investors as Elon Musk became increasingly vocal in politics.

For the time-series graph of the P/E ratio’s Z-score, the same singularity issue around zero earnings prevented us from directly applying the Z-score equation. Instead, we built a derived signal based on the two variables that make up the P/E ratio — taking the average of their respective Z-scores. In other words, we used Z-score(-EPS) + Z-score(Price).

This approach can create some minor discontinuities since EPS often jumps sharply during earnings reports, but overall, it effectively highlights both opportunity zones and periods of overheating according to this metric.

Advanced Market Signal Datahub

We also added a similar section in the Advanced Market Signals page of our DataHub, with only a few differences. This new feature lets you analyze how the current market is priced relative to its historical context.

However, because the market has a much longer history and tends to follow recurring valuation cycles, we extended the time range to cover the last 30 years.

For RSI and the distance from the 200-day SMA, valuations are calculated using daily candles — just as we do for individual stocks. These indicators are most useful for identifying short-term overbought or oversold conditions.

For the P/E ratio, values are calculated on a monthly basis, except for the current reading, which updates daily with price movements.

We also added the CAPE (Shiller P/E) ratio, which offers a more normalized long-term perspective on valuation. This replaces the revenue multiple — a metric that isn’t particularly meaningful for the S&P 500, since the index is composed mostly of mature, profitable companies typically valued on earnings rather than revenue.

Because the S&P 500 companies are profitable, the market P/E ratio remains positive, avoiding the issue of ratios exploding toward infinity when earnings turn negative. For this reason, we opted for a classic histogram visualization to put it in historical context — the most straightforward statistical representation.

To improve clarity, we removed the few bins corresponding to P/E ratios above 50, which only appeared during the 2008 financial crisis when earnings temporarily collapsed. We believe such extreme readings are only attainable during deep recessions or market freefalls. If we ever see such levels but in an uptrend… well, that would mean we’re living through the biggest bubble in history — and I’d be perfectly fine being wrong here, since by then we’ll all be brushing our teeth with champagne and eating caviar for breakfast.

Conclusion

When considering whether to invest in a company or decide if it’s time to exit, looking at its current valuation from multiple perspectives can be incredibly useful—especially when viewed in its own historical context. Over the long run, this is what truly matters. Metrics like revenue and earnings trajectories are the foundation of long-term performance. We built these new tools to help you visualize these metrics in a way we couldn’t easily find anywhere else on the web.

That said, valuation isn’t the only force at play—particularly in the short term. The stock market is, in many ways, a popularity contest. Trends, narratives, economic context, and charismatic CEOs can all play a much larger role in short-term price movements. Take Zoom, for example:

It’s not a bad company when you look at its earnings or at how low it trades now relative to its own history. But two things are clear: it completely lost the trend and is still viewed as a “COVID bubble” play. And perhaps the real issue isn’t how cheap it looks now—but rather how insanely expensive it was in 2020, when it traded at a revenue multiple of 112x and a P/E ratio above 600.

Now, contrast that with Palantir, which checks every box for today’s market hype: backed by a legendary investor (Peter Thiel), led by a charismatic CEO, and positioned as a leader in AI software. Yet despite its accelerating growth, its valuation metrics are stretched beyond reason.

Do these numbers remind you of anything? They’re strikingly similar to Zoom’s in 2020. It’s already been over a year that this stock has defied gravity — proof that, in the short term, valuation can remain largely irrelevant. But if you believe in mean reversion, and considering that Palantir’s growth isn’t following an exponential trajectory, it’s hard to imagine these valuation metrics won’t eventually normalize.

Ultimately, it all depends on your risk tolerance and investment horizon. But fundamentally, beyond all the trends that temporarily lift stocks, the investing game might be as simple as buying great companies when they’re cheap and selling them when they become expensive. That’s essentially what Buffett taught us.

Take a look at these graphs and notice where those valuation metrics stood when he entered Apple—and when he began to sell. You can do the same exercise for almost any company you admire, and you’ll find that unusually low valuations across these indicators typically marked outstanding entry points. Personally, I’m looking forward to the next correction—it might be the perfect time to go shopping with this new tool.

Annexe: The Core Problem of negative PE ratio

When earnings (E) are negative, P/E (or E/P) can move in the same numeric direction for completely opposite fundamental reasons — one bullish, one bearish.

Example 1 – Price falls, earnings stable (potentially bullish)

• EPS = –1

• Price = $2 → $0.5

→ E/P goes from –0.5 → –2

The ratio became more negative, even though the stock got cheaper.

If losses are expected to recover soon, this drop in price might actually signal an opportunity (valuation reset).

Example 2 – Price stable, losses deepen (bearish)

• Price = $2 (unchanged)

• EPS = –1 → –4

→ E/P goes from –0.5 → –2

Same numeric outcome (–2) and trajectory (–0.5 → –2), but now it’s a deterioration in fundamentals — the company is losing four times more money per share for the same valuation.

The Key Insight

In both examples, E/P = –2, yet the stories are diametrically opposed:

• In the first, price weakness makes valuation potentially attractive.

• In the second, worsening earnings make the investment riskier.

This is why analysts say:

“When earnings are negative, P/E or E/P lose economic meaning — the same number can describe both opportunity and distress.”

The Takeaway

Once EPS < 0, interpreting P/E or E/P requires context:

• Is the price adjusting faster than fundamentals?

• Are losses deepening or stabilizing?

Without that context, a more negative number can mean either recovery potential or accelerating decline.