Even the most relevant enterprises can suffer from shock downturns — a harsh lesson that cybersecurity specialist SentinelOne Inc (NYSE: S) learned the hard way. While the company's latest financial results technically beat expectations on both the top and bottom lines, guidance disappointed investors, leading to a severe drop in S stock. Nevertheless, the security's expected risk geometry suggests that there could be an opportunity for upside for bullish speculators.
At first glance, circumstances appeared to be auspicious. For the third quarter, SentinelOne reported adjusted earnings of 7 cents per share, beating out the consensus view of 5 cents per share. On the top line, the company generated $258.91 million, exceeding analysts' consensus target of $257.7 million. Further, total revenue jumped by 23% against the year-ago level, with customers with annualized recurring revenue (ARR) of $100,000 or more growing 20% to 1,572 in the most recently concluded quarter.
Despite encouraging talking points from management, which emphasized growing demand for the company's AI-native security platform — a holistic solution that combines data, intelligence and defense — investors were fixated on guidance. Unfortunately, SentinelOne projected fourth-quarter sales to reach about $271 million, disappointing analysts who were expecting $273.09 million.
Not surprisingly, investors read between the lines. During yesterday’s afterhours session, S stock fell more than 7%. On Friday afternoon in the open-market session, the equity dipped about 13%.
Despite the pain of the earnings miss, there's a science lesson here. What we're witnessing is heteroskedasticity in action. With SentinelOne delivering disappointing guidance, the event trigged a volatility shock against S stock: price gets noisy, ranges expand and the distribution of outcomes widens. In other words, volatility isn't constant but clusters around impactful events.
Later, as the market digests the news, uncertainty bleeds out, with volatility eventually collapsing back toward its baseline. This eventual transition from high volatility to low volatility is also heteroskedasticity in action.
What we're going to do is to measure this phenomenon to potentially make better trading decisions.
Calculating The Geometry of Risk
To be sure, we don't want to turn financial articles into hardcore science experiments. Without going too deep into the weeds, understanding the basics of heteroskedasticity is vital to advanced trading tactics because this phenomenon changes the curvature of risk. When system variance expands or contracts, the density of probable outcomes also redistributes.
What we've been noting in the past few months for Options Corner articles is that this redistribution manifests asymmetrically — and often very strangely. That's one of the limitations of the standard Black-Scholes-Merton model. It assumes a reality or distribution that is elegantly symmetrical. While such models are aesthetically pleasing, they don't accurately reflect the contours of risk as a "physical" object.
Granted, because a public security represents a singular journey across time, the format doesn't lend itself to probabilistic analysis. As such, we can segment the data into hundreds (or thousands) of trials or sequences. For mathematical purposes, the trials can be considered as projectiles like cannonballs.
If it really were the case that the market is random, then the dispersion of these cannonballs would have no rhyme or reason. However, the market isn't random, with the projectiles grouping together at certain places more so than others. Further, a specific subset of cannonballs may also yield a different grouping, which can potentially serve as the basis for structural arbitrage.
Using the above probabilistic logic, we can calculate the forward 10-week distribution of S stock to likely range between $13.20 and $15.45 (assuming an anchor price or starting point of $14.73). Further, price clustering would likely be predominant at around $14.60.
The above assessment aggregates all data since SentinelOne's initial public offering. However, we're interested in the current signal, which is the 3-7-D formation; that is, in the past 10 weeks, S stock printed three up weeks and seven down weeks, with an overall downward slope.
Under this setup, the stock's forward 10-week returns may range between $12.35 and $17.25, with price clustering likely to be predominant at $14.25. Obviously, that's unfavorable. However, the risk curvature is relatively flat up to $15.20. From there, the probabilistic curve gradually and almost gently descends to the end of the distribution (again, around $17.25).
Taking Advantage Of Some Serious Intel
At this point, you might be wondering why the geometry of probabilistic mass is so important? The answer comes down to how aggressive we can be with our options strategy. If the risk curvature suddenly plunges to zero, that means there's very little chance of the security reaching the price where the curvature zeroed out.
In contrast, a gradual descent of probabilistic mass means that there's a fighting chance for the stock to reach the affected prices. Because of this empirical backdrop, it may behoove us to be more aggressive than usual to avoid incurring an opportunity cost.
With all that being said, arguably the most aggressive idea to consider is the 14/16 bull call spread expiring Jan. 16, 2026. This trade requires S stock to rise through the second-leg strike ($16) at expiration, which is ambitious. However, the maximum payout for doing so clocks in at over 122%.
More enticingly, the breakeven price sits at $14.90. Therefore, with the bull spread, we're paying for the portion of the premium that is realistically likely to materialize. At the same time, we're selling the premium that is less likely to materialize, thereby discounting our net long position.
Since we know the geometry of risk and that the $17 strike price is empirically unrealistic, we're not focusing on that. Instead, we're targeting the area of probabilistic mass that is rational. However, we would never know this information if we didn't calculate risk curvature.
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