Nevertheless, the narrative hasn't exactly been smooth. Perhaps most glaringly, HPE stock has gone practically nowhere in the past 52 weeks. In the trailing six months — a timeframe where several tech peers delivered strong returns after shaking off the cobwebs from early 2025 — HPE has gained less than 6%. Due to the underperformance, though, contrarians have targeted Hewlett Packard stock as a potential discount.
In assessing the various publications in the blogosphere, the belief that the tech enterprise will capture a rising share of AI infrastructure demand is doing most of the analytical work. Additionally, these reports are working off the assumption that because HPE stock is undervalued relative to sector peers, the security will eventually re-rate higher.
While it may seem like a logical premise, the issue is that no one really knows whether Hewlett Packard can truly make a dent in the AI industry. Further, we should also be aware that there is no gravitational principle that pulls undervalued stocks into fairly valued status. Indeed, the concept of valuation is problematic as it only has meaning relative to a model.
Change the model, change the label — that’s the hidden premise that all investors should be cognizant of.
From my perspective, I don’t know if HPE stock is genuinely undervalued because I don’t know how the underlying company will perform. What I do know is that HPE stock is under heavy selling pressure. Statistically, extended pessimism tends to spark a reflexive move higher. Therefore, using quantitative analysis, we’re going to attempt to get ahead of this move through a controlled options trade.
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What is Quantitative Analysis for the Financial Markets?
At its foundation, quantitative analysis examines historical price behavior to derive probabilistic insight, with the end goal of identifying trades that offer a measurable edge. At first glance, this approach may look similar to technical analysis. The distinction, however, lies in falsifiability. Quantitative work is built on logic-driven systems that allow other analysts to replicate results precisely and test whether the conclusions hold up under scrutiny.
By contrast, technical analysis is often subject to interpretation. There's a long-standing joke that two technicians can study the same chart and walk away with five different conclusions. While exaggerated, the point is valid: technical signals lack a formal mechanism for verification. If a trader believes a stock is forming a head-and-shoulders pattern, there's no objective process to confirm whether that pattern truly exists or whether it's simply in the eye of the beholder.
A related limitation is that technical analysis frequently operates outside standard statistical frameworks. Even if we accept that a particular chart formation is legitimate, it is typically treated as a single observation. More troubling, the supposed meaning of that pattern is often inferred from how similar formations behaved in other securities, rather than from rigorous testing on the asset being traded.
In effect, technicians project historical analogs from unrelated stocks onto a new situation. Quantitative analysis avoids this mismatch. Unless clearly stated otherwise, empirical work focuses strictly on the security under examination, ensuring that conclusions are grounded in relevant data rather than loose comparisons.
This discipline becomes especially powerful in options trading because it emphasizes causality rather than mere correlation. Many technical indicators rely on moving averages and momentum measures — useful tools but fundamentally descriptive. A quantitative framework, on the other hand, searches for structural conditions that can influence what happens next.
To use a football analogy, a technician may watch a running back's pre-snap movement and guess the next play. A quant studies the formation itself — the alignment, the spacing, the tendencies — to understand the likely outcome. Some technicians may have an intuitive gift for reading markets, but from a scientific standpoint, quantitative analysis provides a repeatable, testable process that delivers greater consistency and reliability.
Calculating the Potential Next Move for HPE Stock
Using data going back to January 2019, any given 10-week period will likely see HPE stock land between $22 and $22.35 (assuming a spot price of $22.02, Thursday's close). Over many 10-week trials, peak probability density would likely materialize between $22.10 and $22.15. Therefore, under a fixed-time distribution, HPE enjoys an upward bias.
However, we're not interested in trading the security's aggregate behavior. Instead, as options traders, we're interested in anticipating what the next play is going to be. In that sense, HPE stock gives us a clue thanks to its quantitative structure.
In the trailing 10 weeks, HPE printed only four up weeks, leading to an overall downward slope. Under this setup, the forward 10-week returns would be expected to range between $21.40 and $23.50, with peak probability density potentially materializing around $22.20. In terms of comparing the two projected peak prices, not much variance exists. However, the reward tail extends further north than is otherwise observed under aggregate conditions.
Additionally, risk topography — a three-dimensional view of market demand structure — provides interesting insights. In addition to the expected (terminal) price and probability density, risk topography includes a third axis, population occurrence or frequency.
Using the topological tool, HPE stock would be expected to see transitory activity between $22 and $23 over the course of the next 10 weeks before terminating at $22.20. However, this projected forecast isn't set in stone. It's possible that, due to reflexivity, HPE could swing higher than would normally be expected.
Given the demand structure of HPE stock, I'm looking at the 22/23 bull call spread expiring Feb. 20, 2026. This wager involves two simultaneous transactions: buy the $22 call and sell the $23 call, for a net debit paid of $50 (the most that can be lost).
Should HPE stock rise through the second-leg strike ($23) at expiration, the maximum profit would also be $50, a 100% payout. Breakeven comes in at $22.50, which is relatively close to peak probability density. Therefore, the 22/23 call spread balances cost exposure while also allowing traders to stretch for a decent payout.
To be sure, call spreads with loftier second-leg strikes — such as at $25 or $26 — offer significant payouts. However, the problem here is that such moves, while not impossible, are not realistic given the data at hand.
Sure, gen AI has been the clarion call for Hewlett Packard Enterprise and similar entities. However, from an options trading perspective under the current sentiment regime, a more controlled wager based on demand structure could be the more prudent move.
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