Shell, Eleven Months Later: A Retrospective Check on the June 2025 Valuation

    I. Why this post exists

    In my previous post on Shell I valued the company at 13 June 2025, ran four methods in parallel, and ended with a Kennedy refrain handing the decision over to the reader. The post stopped at a buy-decision built on a 55.9 % probability of underpricing in the base run, with the explicit thesis that the next leg up would come from fundamentals rather than from a higher Brent print. It did not commit to anything beyond that. The honest thing to do – eleven and a half months later – is to come back and check.

The window is unusually clean. On 2 March 2026 a major geopolitical incident around the Strait of Hormuz spliced the observation period into two regimes – a quiet, fundamentals-driven stretch from 13 June 2025 to 27 February 2026, and a shock-driven stretch from 2 March 2026 through (so far) 26 May 2026 – twelve weeks long enough that the shock half is now meaningfully bigger than a single news cycle. That accidental split is the kind of natural experiment a valuation methodology rarely gets in clean form: a calm regime to test the fundamental layer of the model, a shock regime to test the option layer.

    II. What was on the table at 13 June 2025

    A quick recap, for readers who didn’t see the June post.

    At the valuation date Shell traded at $36.25 per share on the article basis. I applied four methods in parallel, each yielding a per-share number on a consistent share count:

    • Fundamentals normalisation – baseline (cycle-median revenue × 8 % operating margin): $26.50. The 8 % was the 2010–2024 cycle-median operating margin; the revenue side was the cycle-median top line over the same window. On its own logic, Shell looked overpriced.

    • Fundamentals normalisation – conservative (cycle-median revenue × 10 % operating margin): $42.16. I called the higher margin conservative precisely because the company was already earning above 11 % and management had given no sign of reversal. On its own logic, underpriced.

    • Oil-price normalisation: $41.68 at the spot of $73, $44.09 at the cycle-median $75.85, with a middle of $42.89. Underpriced.

    • Monte Carlo with correlated Brent and operating-margin draws (100 000 trials): median $40.50; 5th–95th percentile range $25.50–$64.30; 55.9 % of the mass above market. Underpriced on median.

    Four anchors, not one. The fundamentals baseline at $26.50 was deliberately the cheap stress test – it asks what Shell would be worth if its sustainable operating margin reverted from the 11–14 % range it had been printing back to the 2010–2024 cycle-median 8 %, i.e. if recent above-cycle profitability were treated as a temporary tailwind rather than a sustainable level. The other three methods clustered between $40 and $44. I bought at $36.25 with the explicit thesis that the next leg up would come from fundamentals – sustained margin above the cycle median, the buyback, the rising payout ratio – rather than from a higher Brent print.

    III. The window, the regimes, the gap that closed

    Between 13 June 2025 and 26 May 2026 the share price rose 18.3 % on the consistent article basis, from $36.25 to $42.88. Brent ran the opposite way through the calm half of that window – from $76.00 on the valuation date down to $71.32 on the eve of the shock, or –6.2 % and then broke regime on 2 March. The first half of the window is the cleaner test: the re-rating happened despite weak oil, not because of it.

Picture 1. Brent and Shell rebased to 13 Jun 2025 = 100. The dashed line marks 2 Mar 2026 – the Hormuz shock. Brent series ends 18 May 2026 (FRED 5-business-day publication lag).

    Splitting the window at the day before the Hormuz shock sharpens the point. In the pre-shock regime, from 13 June 2025 to 27 February 2026, Shell rose 15.1 % on basis – almost the entire long-run gap to the methods that had said underpriced closed in nine months on a falling Brent. The share price crossed the Monte Carlo median ($40.50) on 25 February 2026, the lower edge of the oil-price normalisation band ($41.68) on 27 February 2026 – literally the eve of the shock – and the conservative fundamental ($42.16) and the oil-price middle ($42.89) on 6 and 11 March 2026 respectively. None of those crossings coincided with Brent above $75. The market re-rated Shell to its intrinsic-value range on the strength of fundamentals, before there was any extra geopolitical premium to allocate.

Picture 2. Monte Carlo distribution of Shell’s intrinsic value (100 000 draws, log-normal calibrated to the June 2025 paper). Markers show where the realised price sat at three reference dates: the valuation date, the eve of the shock, and today.

    Then came 2 March 2026. Brent indexed to the valuation date jumped from 94 to a peak of 182 – close to a doubling off the week before. Shell on the same index moved from 115 to a post-shock peak of 130 on 7 April 2026, then drifted back to 118 by 26 May as Brent receded from its peak. That is a striking asymmetry: an almost doubling in spot Brent delivered, at peak, fifteen index points on top of a share that had already re-rated by fifteen – and most of that incremental fifteen has since unwound while Brent is still trading around 155. The interpretation is straightforward once we sit with the model. The bulk of Shell’s value sits in already-producing assets whose discounted cash flows are not very sensitive to a one-quarter spike in spot Brent. The option layer on undeveloped reserves – Whale, Bonga North, Manatee, the rest – is the only piece of the valuation that should react to a regime change in oil-price volatility, and it did, but in measured size, and only for as long as the volatility regime itself looked changed.

Picture 3. Shell daily close (article basis) against the three near-money valuation anchors – Monte Carlo median, conservative fundamental, oil-price middle – with the Hormuz shock dashed in. The stress-test anchor (Fundamentals 8 % at $26.50) sits below the plotted range.

IV. Reading out each method against eleven months of price tape

    Picture 4 below plots the mean absolute deviation of the realised price from each method’s central anchor, separately for the two regimes. The ranking is informative because it inverts.

Picture 4. Mean absolute deviation of realised price from each method’s central anchor – pre-shock regime (green) versus post-shock regime (orange). Computed from daily Shell article-basis prices, 13 Jun 2025 – 26 May 2026.

    a. Pre-shock regime (the fundamental period). Monte Carlo wins (MAD 9.4 %). The conservative fundamental at 10 % margin is a close second (12.9 %). The oil-price normalisation comes third (14.4 %). The fundamentals baseline at 8 % is last by a wide margin (38.6 %) – which confirms what was already obvious in June: an 8 % anchor is artificially low for a company already earning above 11 %.

    b. Post-shock regime (the geopolitical period). The ranking flips. Oil-price normalisation is now the most accurate (MAD 4.2 %) – intuitively correct, because that method is the one explicitly tied to where oil prices sit. The conservative fundamental holds up unexpectedly well (5.4 %), because the margin anchor turned out to be the right long-run anchor regardless of regime. Monte Carlo is third (9.5 %), barely worse than its pre-shock score; the reason it loses any accuracy at all is that the empirical 2005 – mid-2025 Brent distribution simply did not contain prints above $130, so the realised path strayed outside the bulk of the simulated distribution. The fundamentals baseline at 8 % is again last and by a much wider margin (67.4 %), because the share price kept walking away from a value anchor that was wrong to begin with.

    The cross-regime lesson is the central takeaway of this retrospective. No single method dominates in both states of the world. Monte Carlo is the right tool when the market is weighing fundamentals; oil-price normalisation is the right tool when the market is reacting to a price shock; the conservative fundamental is a useful sanity check across both; and the baseline at the cycle-median margin is mostly a stress test that tells us what Shell would be worth if everything we knew about its recent operating performance were wrong. The right output is the range the four methods jointly span, not any one anchor on its own.

    V. The option layer, an area to keep exploring

    In the June post I priced Shell’s undeveloped reserves as a portfolio of European call options on oil, using Black-Scholes with q = 1/n to penalise time-to-expiry. That layer added a non-trivial premium to the producing-asset DCF. A few fellow financiers reasonably asked whether the option layer was doing real work or just decorating the model with mathematics.

    Let me be precise about what the option-pricing model actually tells us. Black-Scholes is unambiguous on direction: higher realised volatility raises the value of a long-dated call, and lower volatility releases that premium back. Between 2 March and roughly the second week of April 2026 Brent’s realised volatility jumped sharply, then started normalising; over the same window Shell traced a path from $42 to a peak around $47 article basis on 7 April and back toward $43 by 26 May. The option layer must have contributed something to that round trip – the model leaves no room to claim otherwise. What I am not ready to tell you is how much. Disentangling the option-layer contribution from the producing-asset DCF response inside a single shock episode is its own piece of work; I treat it as an open area for further exploration, not a question I have answered here.

    VI. The Graham line, eleven months late

    Benjamin Graham’s observation about the market being a voting machine in the short run and a weighing machine in the long run gets quoted often enough that it has nearly worn itself out. The Shell window is a rare clean example of what he meant. In the nine months before the shock there was no news on oil that should have re-rated Shell by 15 % – Brent went down over that period. What changed was that the market progressively recognised what the four methods had already weighed in June: that Shell’s sustainable operating margin was closer to 14 % than to 8 %, that the buyback was real, that the reinvestment rate was settling at a level consistent with a mature commodity producer returning more cash than it deployed. The market weighed. Then geopolitics arrived and the market voted – briefly, on volatility – in a direction broadly consistent with what an option-pricing layer would predict, before giving most of the incremental premium back as volatility eased. The producing-asset DCF carried the underlying value through both halves.

    VII. And so, dear friends, you just have to carry on

    The cleanest evidence sits in the calm half of the window, not the loud half. By 27 February 2026, the eve of the Hormuz shock, Shell had already crossed the Monte Carlo median ($40.50), touched the lower edge of the oil-price normalisation band ($41.68), and was within striking distance of the conservative fundamental ($42.16) and the oil-price middle ($42.89). On a falling Brent. The market closed the gap between $36.25 and the band the four methods jointly defined while spot oil drifted from $76 to $71. That is the part of the test the Iran shock did not run for me: it had already finished, in my favour, before the first headline out of Hormuz.

    One footnote on hindsight. The post-shock numbers benefit from it; the pre-shock crossings do not – they happened in the calm regime, with Brent working against the trade, and they happened to all three near-money anchors before 2 March.

    Every time I sit down with a valuation I come back to professor Damodaran’s reminder: it is all about fundamentals. And this is another example of professor’s wisdom.

Mind the Multiplier: Calibrating Growth Multiples to Unlock Mispriced Equities

Constructing a robust, fully fledged DCF model is labour‑intensive: it demands a deep grasp of the business, its industry, legal jurisdictions, strategic outlook, and countless other inputs to achieve a reasonably precise estimate of intrinsic value. Because analytical time is scarce, investors must pick their battles—deciding which companies merit that level of scrutiny. What is needed first, therefore, is a quick‑and‑dirty multiplier that can flag the firms most likely to be undervalued and worth the heavier DCF treatment.

This article revisits Benjamin Graham’s intrinsic‑value rule, adapting it to the post‑crisis investment landscape. It replaces the original fixed constants with dynamic anchors linked to prevailing bond yields and market equity‑risk premia, then recalibrates the growth multiplier through contemporary cross‑section data. The resulting formula keeps Graham’s hallmark simplicity, yet reflects today’s low‑rate environment, wider dispersion of corporate growth paths and faster information cycles.

This abridged version has been streamlined for readability. For the full technical exposition, please consult the complete paper at the link below.

https://economyandsociety.in.ua/index.php/journal/article/view/6237/6180

Benjamin Graham’s “intrinsic‑value” formula occupies a peculiar place in investment lore: it appears in virtually every edition of «The Intelligent Investor», yet Graham himself warned that it was provided only «for illustrative purposes». Written in an era of stable 4–5 percent bond yields and modest GDP growth, the formula condensed a stock’s value into three observable variables: earnings per share (EPS), the expected long‑run growth rate (g), and the prevailing yield on high‑grade corporate bonds (Y). More than six decades later, interest rates have traversed the zero lower bound, global equity markets are dominated by high‑growth technology firms, and quantitative easing (QE) has distorted the term structure of risk‑free rates. Unsurprisingly, modern practitioners who apply Graham’s constants mechanically obtain valuations that deviate sharply from market prices. Although the formula is, by definition, a relative-valuation tool rather than an intrinsic one, we still view it as a useful rule of thumb – an acid test for the preliminary valuation stage. The present study revisits the formula’s theoretical underpinnings and demonstrates how a parsimonious ‘rate‑adjusted’ adaptation can restore its usefulness as a first‑pass screening tool. Capital‑market conditions have diverged so radically from the mid‑twentieth‑century environment that any valuation rule baked with static constants risks structural bias. Because structural shifts simultaneously affect the risk‑free rate, growth expectations and market‑required return, any formula that hard‑codes historical constants are prone to systematic mis‑valuation. The research challenge is therefore to retain the heuristic clarity of Graham’s equation while making its key parameters adaptive: updated automatically from observable bond‑market and ERP data, and re‑estimated growth sensitivity that reflects realized corporate performance.

 Since Graham [1] first linked price‑earnings ratios to long‑term earnings growth, a line of inquiry from Cragg and Malkiel [2,3] and Harris and Marston [4] has tested how strongly markets still reward forecast growth. These studies confirm a positive slope yet disagree on its magnitude, largely because they freeze the risk‑free anchor at outdated corporate yields, examine narrow time windows, or neglect cross‑country discount‑rate and currency effects. Building on their insights but correcting those structural limits, this paper recalibrates the growth‑to‑multiple relationship to today’s interest‑rate environment and provides a dynamic, risk‑adjusted heuristic that better bridges Graham’s original intuition with contemporary market behaviour.

Graham’s original value formula is a classic heuristic for valuing (pricing to be precise) growth stocks, originally introduced in the 1960s. Often cited from “Security Analysis” [1], the formula in its original form was (1):

(1)

where:

         V = anticipated value per share

         EPS = trailing-twelve-month earnings per share

         8.5 = P/E for a no-growth firm

         g = expected annual EPS growth rate (%) for the next 7–10 years

         2 = a linear growth premium: every point of sustainable growth adds two points to the acceptable P/E.

The economic logic is straightforward: a stock’s price equals current earnings multiplied by the sum of a growth-neutral P/E and a growth premium. In other words, Graham explicitly folds expected growth into the P/E he applies. That approach aligns with the fundamentals of the P/E ratio itself, where price is ultimately driven by the payout ratio and the expected growth rate (2).

                                                                

    (2)

         In Graham formula (1) he is effectively divide the fundamentals in two sides: first determine the non-growth P/E then add growth and multiple all of it on company’s EPS to get anticipated P/E ratio. 

         But why the Graham used the 8.5 as the growth neutral P/E and divided the growth rate by 2, not by 11? Let’s start with risk neutral P/E. Graham chose it in the late‑1950 s for three related reasons:

         1. Contemporary market evidence. In the two decades after World War II, the average trailing P/E of mature, no‑growth industrial bonds‑rated companies (e.g., utilities and railroads) oscillated between 7- and 10-times earnings, with a rough mid‑point around 8.5. Graham and Dodd had documented those multiples in earlier tables of “Security Analysis” [1].

         2. Yield parity with bonds. At that time AAA corporate bonds yielded about 4.5 %. A P/E of 8.5 equates to an earnings yield of about 11.8 %, giving such equities a risk premium (ERP) of roughly 7 percentage points over the bond yield. Graham saw that spread as adequate compensation for the uncertainty of stock earnings with zero growth.

         3. Didactic clarity or the matching principle. The growth term 2 multiple g needed to lift the P/E sensibly as growth expectations rose; starting from 8.5 meant that a 5 % growth assumption would push the multiple to 8.5 + 2 x 5 = 18.5 – well within the trading range that investors of the era considered plausible.

         To estimate today’s so-called “non-growth” P/E, we first tried to assemble a sample of companies that had shown zero growth over the past decade. That proved impractical – too few firms meet the criterion to yield a meaningful average. We therefore replace Graham’s non-growth concept with a growth-neutral P/E: the multiple appropriate for a hypothetical company whose earnings grow exactly in line with the overall market. In other words, we estimate the P/E for an artificial firm that tracks the market’s average growth rate, using the S&P 500 as our benchmark.

Hence, to derive an up‑to‑date growth‑neutral P / E we begin with the two quantities that underpin any earnings‑yield decomposition: the equity risk premium (ERP) and the risk‑free (or near risk‑free) bond yield. Because the period 2005‑2009 was unusually volatile and because contemporary business cycles are shorter than in Graham’s era – particularly in rapidly scaling sectors such as technology – we shorten Graham’s original 20‑year “look‑back” window to the most recent ten years (June 2015 – June 2025).

1. Estimating the forward (imputed) ERP. We adopt Professor Aswath Damodaran’s monthly implied ERP series [5]. This metric is forward‑looking: it solves for the discount rate that equates the present value of expected S&P 500 dividends, buybacks, and long‑run growth to the index’s current level; the excess of that internal rate of return over the 10‑year Treasury yield is the ERP. The median of these monthly observations over June 2015 – June 2025 is 5.20 %.

2. Selecting the bond yield. Graham treated a high‑grade corporate yield as the practical proxy for the risk‑free rate, even though no corporate bond is literally risk‑free. Today, most analysts distinguish between:

                  - True risk‑free rate: U.S. 10‑year Treasury. Median yield, June 2015 – June 2025 = 2.38 % [6].

                  - Near‑risk‑free rate: Moody’s AAA industrials. Median yield over the same horizon = 3.86 % [7].

3. Converting to a growth‑neutral P / E. The equilibrium earnings yield is simply the chosen bond yield plus the ERP:

- Treasury baseline: 2.38 % +  5.20 % = 7.58 %;

- AAA baseline: 3.86 % + 5.20 %  =  9.06 %.

P / E is the reciprocal of the earnings yield (3):

                                                                                      

  (3)

Using formula (3) we are getting the results:

-       Growth‑neutral P / E with the Treasury rate: 1 ÷ 0.0758 = 13.20. 

-       Growth‑neutral P / E with the AAA rate: 1 ÷ 0.0906 = 11.04. 

These figures represent the market‑consistent multiple for an “average‑growth” firm whose long‑term earnings trajectory merely parallels that of the S&P 500. Any premium over 13 × must therefore be justified by above‑market, persistent growth or by a lower perceived risk; any discount must reflect the opposite. In this way the modernised growth‑neutral P / E preserves Graham’s original intuition while anchoring it to present‑day capital‑market conditions.

The purpose of our revised formula is to derive a growth-neutral P/E – a multiple that would apply to a firm whose earnings expand at precisely the same pace as the overall market. In Graham’s original setup the bond yield adjusted the calculation for equity risk: regular bonds deliver fixed cash flows, so their compensation above the Treasury curve reflects only the issuer’s probability of default. Equity, by contrast, already commands a premium for default (and other) risks through the equity-risk premium (ERP). If we anchored our calculation to a corporate-bond yield, we would be adding that default component twice – once via the bond’s spread over Treasuries and again via the ERP. To avoid such double counting, we discard the AAA-bond anchor and use solely the risk-free rate.

The second parameter in Graham’s formula is the coefficient “2” that multiplies the long-term earnings-growth rate. The intuition is straightforward: for every one-percentage-point change in expected growth, the P/E multiple changes by roughly two points.  Empirical work has long supported this two-for-one rule. Cragg and Malkiel [2] ran one of the first large cross-sectional regressions of P/E on analysts’ long-term growth forecasts and obtained a slope of 1.97. Subsequent studies (Malkiel & Cragg [3]; Harris & Marston [4]) continued to find slopes between 1.8 and 2.2 for U.S. equities from the 1950s through the 1980s. Thus, Graham’s multiplier was not merely heuristic; it matched how the market priced growth at the time.

Replicating or extending any of these studies today demands access to proprietary databases such as I/B/E/S, FactSet and Compustat – resources ordinary scholars cannot freely distribute. State‑of‑the‑art language models from OpenAI can ingest these restricted feeds, perform the calculations and return aggregated statistics, but they are legally barred from releasing raw observations. Consequently, researchers must formulate precise methodological instructions, supply them to the model, and then scrutinise the step‑by‑step results. The present investigation follows exactly that protocol, deploying the most advanced publicly available OpenAI model, “ChatGPT o3‑pro,” to generate an updated growth multiplier that reflects current market conditions while respecting data‑licence constraints. In Table 1 we are reporting the main assumption that the model was using.

Table 1. Fixed assumptions used in developing regression 

Item	Instruction
Sample window	June 2015 - 30 June 2025 (10 complete fiscal years)
Universe	All current S&P 500 members, no sector exclusions
Risk‑free rate	10‑year U.S. Treasury yield (median for the period – 2.38 %)
ERP proxy	Damodaran implied ERP (median for the period – 5.20 %)
Growth‑neutral P/E	13.2 (reciprocal of 7.58 % earnings yield)

         Source: made by author

         The next step is determine the regression methodology. We estimate a pooled OLS regression with year fixed effects and firm-clustered robust standard errors. The regression equation is specified as:                                                      

 (5)

The regression results. After running the pooled OLS regression on the S&P 500 panel (with the data filters noted), we obtain the following key results:

Estimated Growth Multiplier (β) = 1.3. The regression finds a slope coefficient around 1.3 (in units of P/E per 1% growth). This means for each +1 percentage point in annual EPS growth forecast, a stock’s P/E ratio tends to be about 1.3 points higher on average (relative to the growth neutral baseline). 

R-squared. The model explains a substantial portion of the variation in P/E across firms and years. The R² is about 0.25 (25%) for the fixed-effects regression. This indicates that about a quarter of the cross-sectional plus time variation in excess P/Es is captured by differences in growth forecasts (and year dummies). This is reasonably high, given that P/E ratios are also influenced by many other factors (ROE, risk, sector, company size, etc.).

Fixed Effects Impact. The year fixed effects were jointly significant (as a group) – meaning different years had systematically different ΔPE intercepts. This validates using year FE: for instance, 2020 had a positive fixed effect, indicating that even after adjusting for low rates (which raised the baseline P/E) there was still an extra valuation boost that year (perhaps due to stimulus or optimism), whereas 2022–2023 had negative fixed effects (stocks were valued a bit lower than baseline would suggest, perhaps due to higher risk aversion or earnings uncertainty). 

Standard Error: 0.15. Using firm-clustered robust standard errors, β is highly significant. 

As a result, we are now have the new, revisited formula to estimate the right price for stock, which is look:                     

   (6)

         We deliberately use “P” (price) instead of Graham’s original “V” (value) because we are estimating market price, not intrinsic value. The formula is intended to capture market mood and momentum rather than a firm’s fundamentals. In building the growth-multiple regression, we relied on analysts’ forecasts rather than the company’s actual growth. Accordingly, the formula is, by its nature, a relative-valuation tool—not an intrinsic one.

         Later in his life, Graham developed his original formula, adding new assumptions to it [8]. The medicated in 1974 formula looks:

                                                                                   

 (7)

         where:

         4.4 = Yield on AAA corporate bonds in 1962 (Graham’s reference rate)

         Y = Current yield on AAA corporate bonds

         The rationale for this adjustment is straightforward and defensible. When interest rates rise, fixed-income securities become more attractive, prompting investors to shift capital from equities into bonds; this rotation pushes stock prices downward and raises the expected return on stocks. The opposite occurs when rates fall: investors move back into equities, driving prices up and compressing equity yields.

To embed this rate sensitivity in our formula, we use the 10-year median yield on AAA-rated Moody’s bonds (3.86 %) and the most recent yield as of 30 June 2025 (4.24 %) [7]. Accordingly, the updated, rate-adjusted pricing equation for 2025 is:                         

  (8)

         We intend to revisit the fixed inputs – growth-neutral P/E, the growth multiplier, and the AAA bond yield – each year as market conditions evolve. All other variables (e.g., EPS and Y) should always reflect the most current data.

         Conclusion. In re‑examining Graham’s intrinsic‑value heuristics we have shown that the original constants are no longer well‑grounded in today’s market environment and, in some cases, rest on conceptual mis‑specifications, for instance, treating AAA corporate yields as a risk‑free rate. By surveying the modern literature and identifying the gaps in prior tests, we developed a fresh cross‑sectional regression that recalibrates the growth‑to‑multiple relationship and embeds a dynamic adjustment for changes in interest rates. The resulting equation is best viewed as a pricing tool rather than a pure intrinsic‑value model: it captures how the market currently translates expected earnings growth into P/E, thereby offering a disciplined benchmark for relative valuation. Within a value‑investing framework, we treat this benchmark as a triage device rather than a substitute for fundamentals. When a stock screens as undervalued against the updated multiplier, it signals a potential mispricing worth probing through a full fundamental review and discounted‑cash‑flow analysis – reflecting our conviction that markets often err in the short run but tend to correct over time, creating opportunities for patient capital.

 

References

1.  Graham, B., Dodd, D. L., & Cottle, S. (1962). Security analysis: Principles and technique (4th ed.). McGraw-Hill.

2. Cragg, J. G., & Malkiel, B. G. (1968). THE CONSENSUS AND ACCURACY OF SOME PREDICTIONS OF THE GROWTH OF CORPORATE EARNINGS. The Journal of Finance, 23(1), 67–84. https://doi.org/10.1111/j.1540-6261.1968.tb02998.x

3. Malkiel, B. G., & Cragg, J. G. (1970). Expectations and the structure of share prices. American Economic Review, 60(4), 601-617. https://doi.org/10.2307/1883016

4. Harris, R. S., & Marston, F. C. (1992). Estimating Shareholder Risk Premia Using Analysts' Growth Forecasts. Financial Management, 21(2), 63. https://doi.org/10.2307/3665665

5. Implied equity risk premiums—United States (monthly series, September 2008 – present). (Data set). Stern School of Business, New York University. https://pages.stern.nyu.edu/~adamodar/New_Home_Page/datafile/histimpl.html

6. Market Yield on U.S. Treasury Securities at 10-Year Constant Maturity, Quoted on an Investment Basis. (Data set).  Federal Reserve Economic Data | FRED | St. Louis Fed. https://fred.stlouisfed.org/series/DGS10

7. Moody's Seasoned Aaa Corporate Bond Yield. (Data set). Federal Reserve Economic Data | FRED | St. Louis Fed. https://fred.stlouisfed.org/series/DAAA

8. Graham, B. (1974). The future of common stocks. Financial Analysts Journal, 30(5), 20 – 30. https://doi.org/10.2469/faj.v30.n5.20 

9. Damodaran, A. (2024). The implied equity risk premium, 1960‑2024: Lessons from 65 years of capital market history. Working paper, New York University Stern School of Business. https://doi.org/10.2139/ssrn.4758326

10. Fama, E. F., & French, K. R. (2000). Forecasting profitability and earnings. Journal of Business, 73(2), 161–175. https://doi.org/10.1086/209638

 

Commodity Company Valuation: A June 13 Case Study on Shell

  

In my previous post I valued a high‑growth, highly uncertain company. That exercise was fascinating because we had to build a narrative to justify our numbers in a situation of almost pure uncertainty. In this post I turn to valuing a commodity company, which has its own distinct challenges and insights. Some of the tools that worked for the earlier valuation are not appropriate here, while new methods become essential. I will illustrate the process with Shell, an integrated oil major.

The value of a commodity producer is driven primarily by the price of the commodity itself. Accordingly, our key assumptions concern the future path of oil prices and the company’s operating margin (which is tightly linked to those prices). I performed the core valuation on 13 June 2025.  Since then, the oil market has been buffeted by fresh geopolitical shocks: a flare‑up in the Middle East: Israeli strikes, Iranian responses, and temporary U.S. intervention, sent prices on a roller‑coaster: up ~5 % in one day, then down ~7 % a few days later. I will not attempt to forecast day‑to‑day oil prices; if I could do that reliably, I would trade futures, not build DCF models. Instead, I will show how to incorporate such volatility into a valuation framework.

We start with a brief overview of Shell’s business and strategic context, which anchors the narrative that will support our numbers.

I. Shell’s overview.

a. Corporate Identity

• Legal name: Shell plc (formerly Royal Dutch Shell plc; ticker: SHEL, FTSE 100 & NYSE‑listed).
• Headquarters: London, UK; operating in 70‑plus countries.
• Integrated energy major: Up‑ and mid‑stream oil & gas, LNG, chemicals, marketing, renewables and emerging low‑carbon value chains.
• Employees: ~92 000 (FY‑2024).

b. Current Strategic Priorities.

1. More value, less volume in hydrocarbons – keep oil output roughly flat to 2030 but grow gas‑weighted LNG.
2. Capital discipline – group capex $22‑25 bn p.a.; R&ES allocation capped at $4‑5 bn until returns exceed 12 % ROACE.
3. Unit cost & overhead reset – $3 bn structural cost take‑out by 2025 (on track, $2.4 bn delivered).
4. Balanced energy transition – 2030 targets: Scope‑1,2 emissions –50 %; customer‑product intensity ‑20 %; maintain CCS & nature‑based offsets pipeline (4 Mtpa captured equity share 2024).

c. Revenue‑Centred Overview (FY‑2024 & LTM Q1‑2025)

US‑$ billions	FY‑2022	FY‑2023	FY‑2024	LTM Q1‑2025*
Turnover / Sales	386	323	≈ 310	≈ 315
Cash from operations	68	68	62	63
Net debt	48.5	43.5	40.0	38.7
Group ROACE	13.6 %	11.5 %	10.8 %	≈ 11 %

* Figures rounded to nearest $0.5 bn and percentage point from Shell Q4‑24 and Q1‑25 databook.

d. Segment Sales Mix  (FY‑2024)

Segment	Sales, $ bn	Share of group
Integrated Gas	72	23 %
Upstream	59	19 %
Marketing (Mobility + Lubricants)	128	41 %
Chemicals & Products	45	15 %
Renewables & Energy Solutions (R&ES)**	6	2 %

**Shell’s own shorthand inside the segment P&L is R&ES; the public narrative sometimes calls it E&S or E&ES(“Energy & Emerging Solutions”).

As the Master of Marketing I was confused about the biggest Shell’s segment in the 2024, however in the commodity world the term Marketing do not mean the economical discipline. In commodity industries the verb to market means “to sell and physically move a commodity to the best‑paying customer.” Marketing is everything that happens after a molecule leaves the refinery or gas plant and before it reaches the end‑user: wholesale trading, logistics, retail service‑stations, B2B fuel sales, lubricants, bitumen, aviation, marine bunkering, even convenience stores.

 

II. Intrinsic Valuation

When valuing a commodity producer you can follow three broad approaches:

1.     Commodity‑neutral asset DCF + real options

o   Discount the cash flows from the company’s proved‑developed assets at a mid‑cycle commodity price.

o   Value the undeveloped reserves as real options whose pay‑offs depend on future prices, and add those option values to the base DCF.

2.     Cycle‑normalised DCF

o   Normalise the operating fundamentals. Revenue, margins, and the reinvestment rate should be set at mid-cycle levels rather than at last year’s highs or lows.

o   Normalise the commodity price (or use the long‑dated futures strip), since that assumption drives every other line item.

3.     Monte‑Carlo simulation

o   Skip point estimates and let uncertainty speak for itself: model a probability distribution for future commodity prices, run thousands of trials, and read the resulting distribution of per‑share values.

Below, I walk through each method in turn and show how it applies in practice.

 

a. Revenue outlook

For the commodity‑neutral valuation I anchor Shell’s top line to medium‑term demand rather than spot prices. Industry forecasts show global oil demand increasing by roughly 2.5 million barrels a day between 2024 and 2030, topping out near 105.5 mb/d before flattening. That squares with my own view: near‑term consumption is propped up by air‑travel recovery, AI‑driven power needs, and geopolitical stock‑building, even as electric‑vehicle adoption accelerates. Beyond 2030, however, renewables and efficiency gains should progressively trim demand.

Translating this narrative into numbers:

·       2025‑2029: I assume Shell’s consolidated revenue grows 2 % per year, in line with moderate demand expansion and incremental LNG volumes.

·       2030 onward: Growth fades, crosses zero, and settles at ‑2 % in perpetuity, reflecting a gradual, orderly decline in global oil use.

 

b. Operating margin. Although Shell’s margins have traditionally trailed the peer group, management asserts that cost‑discipline, portfolio high‑grading and a shift toward higher‑return projects will close the gap. The plan is to lift the operating margin to about 14 percent once the company reaches its long‑run, sustainable phase.

c. Reinvestment rate. Shell’s sales‑to‑capital ratio is expected to remain close to its current level. Because the upstream oil portfolio will shrink over time, free cash flow should increasingly exceed maintenance needs. As a result, the reinvestment rate is modelled to turn negative in later years—the company returns more cash to investors than it deploys in new projects.

d. Cost of capital. We start with the CAPM to estimate the cost of equity. Because the valuation is in USD, the base is the yield on a U.S. Treasury bond. Since the U.S. sovereign is no longer rated Aaa/AAA by all agencies, I treat Treasuries as quasi-risk-free and deduct the U.S. CDS spread from the bond yield.

·       10-yr T-bond yield: 4.41 %

·       5-yr U.S. sovereign CDS: 0.45 % (45 bp)

·       Adjusted risk-free rate: 4.41 % – 0.45 % = 3.96 %

For the equity-risk premium (ERP) I use the implied U.S. market premium, back-solved from the S&P 500’s current level and dividend/earnings yield.

Because Shell operates globally, we layer on country-risk premium (CRP):

·       Developed markets (e.g., U.S., U.K.) – use the local sovereign CDS as the CRP.

·       Emerging markets – scale the sovereign default spread by the relative volatility of equities to bonds. Equity volatility is measured with the S&P Emerging BMI Index; bond volatility with the iShares J.P. Morgan USD EM Bond ETF. The five-year average of the equity-to-bond volatility ratio (see Table below) gives the uplift factor.

Table 3. Relative equity volatility for the emerging market.

Year	Std Dev (BMI)	Std Dev (JPM Sov Bond)	Relative volatility
2020	23.01%	19.13%	1.20
2021	14.32%	6.90%	2.07
2022	18.67%	15.53%	1.20
2023	11.16%	9.88%	1.13
2024	11.89%	7.21%	1.65
Average Relative Volatility	15.81%	11.73%	1.35

Because oil companies derive most of their value from the reserves they develop, the key risk driver is where the assets sit, not where the barrels are ultimately sold. Accordingly, we weight each region’s equity‑risk premium by the proportion of Shell’s assets located there. Shell discloses exposure only at regional level, so we apply the average ERP for each region. The resulting weights and premium are shown in Table 4.

Table 4. ERP by assets value

Region	Assets	CRP	ERP	Weight	Weighted ERP
United Kingdom	15,822.00	0.27%	4.50%	6.76%	0.3038%
USA	55,245.00	0.45%	4.68%	23.59%	1.1041%
Other Americas	49,372.00	4.07%	8.30%	21.08%	1.7500%
Other Europe	25,149.00	1.60%	5.83%	10.74%	0.6261%
Asia, Oceania, Africa	88,588.00	7.34%	11.57%	37.83%	4.3772%
Total	234,176.00			100.00%	8.1612%

 

The cost of debt was estimated from an imputed credit rating and the corresponding historical default‑spread. Although individual Shell bond issues carry slightly different ratings, the group’s senior unsecured debt has historically centered on A1/A+. Our own rating‐imputation caltucaltion, based on the firm’s interest‑coverage ratio, yields a slightly lower A2/A grade, which we adopt to maintain a conservative stance. The long‑run default‑spread for that rating bucket is 0.85 %. Given that the cost of debt equals the risk‑free rate plus the default premium, the base figure would be 3.96 % + 0.85 % = 4.81 %. However, because our valuation already incorporates regional exposure risk, we take an additional conservative step: we add Shell’s implied CDS spread and a country‑risk premium (CRP). The final cost‑of‑debt inputs are summarised in the table below.

 

Table 5. Pre-tax Cost of debt calculations

Region	Assets	CRP	Imputed Shell CDS	Cost of debt	Weighted cost of debt
United Kingdom	15,822.00	0.27%	0.0085	5%	0.34%
USA	55,245.00	0.45%	0.0085	5%	1.24%
Other Americas	49,372.00	4.07%	0.0085	9%	1.87%
Other Europe	25,149.00	1.60%	0.0085	6%	0.69%
Asia, Oceania, Africa	88,588.00	7.34%	0.0085	12%	4.60%
Total	234,176.00				8.74%

The next input is the equity beta, after which we can finalise Shell’s cost of capital. Using a bottom‑up approach, we compiled a peer set of 300 publicly‑traded oil‑and‑gas companies and took the median levered beta for the group. That beta was then unlevered with the peer‑group’s median debt‑to‑equity ratio and a marginal tax rate of 42 % (representative for mature oil producers).

The calculation yields an unlevered beta of 0.48. Re‑levering this beta with Shell’s own capital structure produces the equity beta used in the final WACC; the full numbers are presented in Table 6. 

Table 6. WACC calculation

Tax Rate =		$                  0.42
Estimating Market Value of Straight Debt =		$          49,409.54
Value of Debt in Operating leases =		$          25,482.99
Levered Beta for equity =		0.58
	Equity	Debt	Capital
Market Value	$215,561.97	$              74,893	$                    290,455
Weight in Cost of Capital	74.22%	25.78%	100.00%
Cost of Component	8.70%	5.07%	7.77%

 

e. Real options. A critical step in valuing an oil company is estimating the worth of its undeveloped reserves. These acreage blocks give the firm the right—but not the obligation—to invest and produce in the future, so they are best viewed as real options to expand. We can price those options with the Black‑Scholes framework, provided we map the input variables to oil‑field fundamentals:

Table 7. Black‑Scholes model inputs

Black‑Scholes input	Oil‑reserve analogue
S  (stock price)	Present value of the cash flows that the undeveloped reserves could generate if developed today.
σ  (volatility of S)	Historical or implied volatility of oil prices. Higher σ makes the option more valuable.
K  (strike price)	Development cost. CAPEX required to bring the field on stream.
r  (risk‑free rate)	Dollar risk‑free yield matching the option’s term.
q  (dividend yield)	Cost of delay: economic rent lost each year the project remains undeveloped. If rights expire evenly over n years, a simple proxy is q = 1 / n.

The Table 8 bellow has the summury of the Shell’s undeveloped reserves. With these inputs we treat each undeveloped tract as a European call option:

Table 8. Shell’s undeveloped reserves (oil price was around 73 USD)

Plot	Primary Resource	Development Lag (years)	Development Cost ($ bn)	Extraction Cost ($/boe) *	Recoverable Barrels (mn boe) *	Years of Use	Gross profit ($ bn)
Whale	Oil	3.00	3.00	35.00	490.00	30.00	13.99
Sparta	Oil	5.00	2.50	35.00	245.00	30.00	5.78
Bonga North	Oil	6.00	5.00	35.00	350.00	15.00	7.51
Gato do Mato	Oil	4.00	3.30	30.00	370.00	30.00	10.87
Jackdaw	Gas‑condensate	3.00	0.61	25.00	185.00	25.00	6.67
Manatee	Gas	3.00	4.00	12.00	480.00	19.00	22.00
Rosmari–Marjoram	Gas	4.00	1.00	15.00	213.00	20.00	8.44
Crux	Gas‑condensate	5.00	2.50	20.00	340.00	12.00	11.19
		4.13	21.91	25.88	2,673.00	22.63	86.44

* All volumes already expressed in million barrels of oil equivalent (mn boe), so totals are comparable across oil and gas projects.

The development lag column in the table shows how many years Shell must spend building out each field. During that period the project generates no cash, so we discount the gross profit back over the development lag at Shell’s weighted‑average cost of capital. With these inputs in place, we can now price Shell’s real‑options portfolio. Picture 1 summarizes the resulting option values.

 

Picture 1. Value of Shell’s real options

 

1. Commodity‑neutral asset DCF + real options. With all inputs in place, we can now build the valuation model and estimate Shell’s intrinsic value. Figure 2 (below) shows the full valuation dashboard—assumptions, calculations, and the narrative links that justify them. The commodity‑neutral valuation indicates that Shell’s shares are currently undervalued.

Picture 2. Shell oil neutral valuation

2. Cycle‑normalised DCF. 

            a. The operating fundamentals. In the fundamentals‑normalisation approach we select a span that is long enough to smooth out the vicissitudes of the oil cycle. For Shell, we use median values for revenue, operating margin, and reinvestment over 2010‑2024 – a period that captures both boom and bust years. Table 9 summarises the results. We run two scenarios:

            •           Baseline: median margin for the period, about 8 %.

            •           Upside (“conservative”) case: a 10 % margin.

Why label the higher margin “conservative”? Because Shell’s current margin already exceeds 11 % and management actions show no sign of reversing the improvement. Using 10 % therefore strikes us as the more defensible long‑term assumption. As one can observe from the results, Shell’s value is highly dependent on its margins.

 

Table 9. The fundamentals‑normalisation approach.

	Baseline	Conservative Estimation
Revenues	$ 344,877.00	$                       344,877.00
Operating Margin	8%	10%
EBIT	$   26,066.43	$                         34,487.70
Tax Rate	0.42	0.42
EBIT (1-t)	$   15,118.53	$                         20,002.87
Reinvestment	$   10,554.00	$                         10,554.00
Reinvestment rate	40%	31%
FCFF	$     8,997.21	$                         13,881.55
Invested Capital (last LTM)	255,324.00	255,324.00
ROC	5.9%	7.8%
Growth rate	2.4%	2.4%
Value of operating assets	$ 171,624.32	$                       264,794.48
- Debt	$   73,505.99	$                         73,505.99
- Minority interests	$     1,856.00	$                           1,856.00
+  Cash	$   35,601.00	$                         35,601.00
+ Non-operating assets	$   25,700.00	$                         25,700.00
Value of equity	$ 157,563.33	$                       250,733.49
- Value of options	$                -	$                                      -
Value of equity in common stock	$ 157,563.33	$                       250,733.49
Number of shares	5,946.54	5,946.54
Estimated value /share	$          26.50	$                                42.16
Stock was trading at	36.25	36.25
Result	overpriced	underpriced

 

 

b. Commodity price normalization. In the commodity‑price normalisation step, the first task is to gauge how closely Shell’s revenue tracks the oil price. To do that we plotted a simple overlay chart (Figure 3) showing Oil prices and Shell’s annual sales. The two lines move largely in tandem, confirming revenue’s heavy dependence on the commodity cycle.

Picture 3. Oil price relative to Shell's revenue

We first confirmed the link between oil prices and Shell’s revenue with a simple overlay chart (Figure 3). To quantify that relationship, we ran a linear regression:

with an R‑squared of 90.8 %, indicating that changes in Oil price explain almost all of the variation in Shell’s top line.

With this regression in hand we estimate “normal” revenue two ways:

            1.         Spot‑price normalisation. Assume today’s Oil price is the long‑run equilibrium. Plugging that price into the regression gives the revenue Shell would earn if the current price persisted indefinitely.

            2.         Cycle‑average normalisation. Use the average Oil price for 2010‑2024, the same period we used to normalise operating margin and reinvestment.

Both approaches yield an intrinsic share value above the market price, implying that Shell is currently undervalued.

 

Table 10. Commodity price normalization approach

	Current oil price	Median Oil price
Oil price	$               73.00	$                       75.85
Revenues	$      342,275.72	$              355,210.91
Operating Margin	10%	10%
EBIT	$        34,227.57	$                35,521.09
Tax Rate	0.42	0.42
EBIT (1-t)	$        19,851.99	$                20,602.23
Reinvestment	$        10,554.00	$                10,554.00
Reinvestment rate	31%	30%
FCFF	$        13,730.67	$                14,480.91
Invested Capital (last LTM)	255,324.00	255,324.00
ROC	7.8%	8.1%
Growth rate	2.4%	2.4%
Value of operating assets	$      261,916.51	$              276,227.57
- Debt	$        73,505.99	$                73,505.99
- Minority interests	$          1,856.00	$                  1,856.00
+  Cash	$        35,601.00	$                35,601.00
+ Non-operating assets	$        25,700.00	$                25,700.00
Value of equity	$      247,855.51	$              262,166.58
- Value of options	$                    -	$                             -
Value of equity in common stock	$      247,855.51	$              262,166.58
Number of shares	5,946.54	5,946.54
Estimated value /share	$               41.68	$                       44.09
Stock was trading at	36.25	36.25
Result	underpriced	underpriced

 

3. Monte‑Carlo simulation. Commodity price and operating margin are the two variables that drive Shell’s valuation – and they are themselves highly correlated. Equity investors should not try to speculate on short‑term oil prices, and even margins are hard to pin down because they move with the commodity. A practical way to handle this uncertainty is to run a Monte‑Carlo simulation.

We start by modelling the distribution of oil prices. Using data from 2005 through June 2025, we fit a beta distribution; Figure 4 shows the resulting curve.

 

Picture 4. Oil price distribution

 

For the operating margin we use a triangular distribution – defined by its minimum (5%, most‑likely (10%), and maximum values (15%), shown in the figure below.

Picture 5. Operating marin distribution 

As noted earlier, oil price and operating margin move together, so we should embed their correlation in the simulation. Using the 2010 – mid‑2025 data set we calculated a Pearson correlation of 0.59 between the two series. This 0.59 correlation is fed into the Monte‑Carlo model, ensuring that higher simulated oil prices are matched with proportionately higher operating‑margin draws and vice‑versa.

Picture 6. Correlation between Oil price and operating margin

After running a 100,000 trials, we have the result which is the frequency distribution of value of Shell stock.

 

Picture 7. The the frequency distribution of value of Shell stock

The simulation yields a median (50th‑percentile) value of $40.50 per share, comfortably above Shell’s market price. This aligns with the conclusions from the earlier valuation methods, reinforcing the view that the stock is undervalued.

III. Pricing game

The pricing dynamics of commodity producers differ sharply from those of the high-growth firms we valued earlier. For a mature commodity company like Shell, a perpetual-growth (terminal-value) approach is inappropriate because its long-run cash flows are tied to cyclical commodity prices, not sustained expansion. Instead, this analysis relies on relative pricing, benchmarking Shell against 12 close peers in the oil sector as of 6 June 2025. Both current and forward multiples are compared, with the forward figures based on consensus five-year growth forecasts for net income, EBIT and revenue.

Table 11 shows Shell trading slightly above peers on the current P/E and markedly above them on the forward P/E. P/E multiples depend mainly on payout policy and expected growth: Shell’s rising payout ratio and declining reinvestment rate, as well as the boost from ongoing share buy-backs, explain the modest premium in the current P/E. The larger premium in the forward P/E reflects analysts’ unusually low growth expectations for Shell over the next five years.

Shell appears undervalued on both current and forward EV/EBIT. Although consensus EBIT growth for Shell is below the peer average, the gap is narrower than for net income or revenue, and improved operating margins drive the multiple lower.

Finally, Shell trades slightly below peers on the current EV/Sales ratio but turns expensive on a forward EV/Sales basis because analysts project a negative five-year revenue CAGR for the company.

Table 11. The ralative pricing 

IV. Conclusion 

We’ve wrapped up our valuation and pricing exercise for Shell, a mature commodity producer. Along the way we tested several oil-company approaches, each with its own quirks.

Personally I favour the commodity-neutral method: value the producing assets first, then layer on real-option value for undeveloped reserves. That keeps the lens on Shell’s future corporate performance rather than on a commodity-price forecast anchored in the past. Even so, it’s still worth stress-testing how different price decks affect margins and cash flow. 

The Monte-Carlo simulation helped on that front. By letting Brent, margins and reinvestment rates fan out across thousands of paths, we could see the distribution of equity values and our risk appetite at a glance. In the base run there’s a 55.9 % probability that Shell is undervalued, which was convincing enough for me to buy shares. 

Of course, any forward-looking model has blind spots, and I’m sure I’ve missed something. Now that you’ve seen the same analysis, the decision like that catchy ’60s refrain – is up to you, it’s up to you, it’s strictly up to you.