Decomposing 60/40

With interest rates on the rise from who-can-be-sure-of-what (we have our thoughts…), bond prices are generally on the downtrend. But bond income (from the now generally higher yields) is on the rise. We therefore have once again entered the “60/40 Portfolio is Dead!” phase of the interest rate cycle. These generally lazy takes often fail to provide sufficiently broad context either as to why investors might want to incorporate bonds in their portfolios or to the various ways one can choose to do so. So, we’d like to remind folks that neither all bonds nor all bond investing methodologies are created equal. As is always the case in investing, your results will depend greatly on how you choose to invest in the asset class.

Secular shifts in interest rates create variability across fixed income investment outcomes
Over most any meaningful time frame, bond prices have proved rather reliably less volatile than stocks
While it’s true that directional changes in bond prices periodically align with those of stocks, such positive correlation is not a reliable characteristic (a statement of the opposite is generally more accurate)
Just as one can invest in stocks in a manner well different than all stocks (i.e., the “market”), one can invest in bonds in a manner well different than the “bond market”

202609 SAM Commentary Download

Bonds as Ballast

“60/40 is Dead!” memes come in various forms, often focusing on stock/bond correlations and flare up conversationally when that number turns meaningfully positive or when bond prices are falling by their lonesome, such as they may have been more recently as longer-term bond yields were reaching multi-decade highs. The notion implicit to many of these takes is that investors should look to other asset classes to fill portfolio role(s) for which bonds otherwise may have been utilized. Time and again, the notes fail to present such periods as occasionally expected behavior, while also generally failing to revisit the core reason many invest in bonds: their relative price stability, even when the direction of price changes is near-term negative and/or directionally aligned with stock price moves. In Figure 1 we present the historical range of three “markets”: the U.S. stock market (SP500, represented by the S&P 500 Index), the U.S. Investment Grade bond market (AGG, represented by the Bloomberg U.S. Aggregate Bond Index, which tracks the U.S. investment grade taxable bond market), and our preferred bond benchmark (ST-IG), the Bloomberg 1-5 Yr Government/Credit Index, which comprises a short-duration subset of the Aggregate. Readily apparent from the data is the fact that stocks are more volatile (showing wider ranges with markedly higher highs and lower lows) than the broader bond market over each of the rolling periods reviewed, while the historical ranges of returns for the short-duration index are narrower still. This despite the fact that bond returns are not altogether reliably positive on a month-to-month basis (bonds carry investment risk too!!!) and that, fair to the core arguments of the T6040PID crowd, stocks and bonds rather often, actually, move down at the same time. See Figure 2 for more.

The data in Figure 1 support our preference for an approach that generally emphasizes shorter duration bonds in the portfolio. Bonds carry two primary risks: interest rate and credit. The former reflects a bond’s sensitivity to changes in interest rates, with bonds that have a longer time until maturity tending to carry more interest rate risk. Credit risk is the potential for the issuer to default. While we have sought across the portfolios we manage to modulate exposure to interest rate risk over time, both higher and lower, we generally have maintained less-than-market sensitivity to changes in interest rates (measured by “duration”; duration tends to increase with time to maturity, though the math will depend on additional bond characteristics).

Investors generally seek additional compensation for that interest rate risk, such that interest rates tend to be increasingly higher for bonds of increasingly longer duration . In normal times, therefore, it may seem attractive to add duration to a portfolio in order to pick up additional income. But as is the case with all investing, a higher expected return carries higher expected risk. These facts inform our twin preferences of 1) preferring equity exposure when seeking higher returns while 2) seeking to use bond holdings as a damper against stock market volatility. Since we mostly seek to use bonds as a stock-risk offset, we tend toward the more cautious side of the spectrum when seeking incremental income through an extension in portfolio duration.

Softening the Blow

Our preference for seeking to add portfolio stability with fixed income is not merely a personal predilection. While the range-out-outcome view of Figure 1 helps to demonstrate the relative stability of bond returns, Figure 3 conveys a characteristic of bonds perhaps even more meaningful to client investment experience. Decades of work have shown us that clients have tended to appreciate the generally more stable performance of bonds during larger equity market meltdowns, even though bonds may have limited their absolute returns during more favorable equity market climes. That is, bonds can help limit the depth, duration and/or emotional tax of market declines. In this context, history is relatively less ambiguous. In Figure 3 we show twelve of the largest U.S. equity market drawdowns (decline from prior peak back to breakeven) along with the corresponding performance of our fixed income benchmark over the same timeframe. Due to a limitation of the bond data, we are using monthly data up to September 30, 1997. From then onward we show daily data. The monthly periodicity certainly flatters the stock drawdowns over that earlier period, in that we are missing intra-month market troughs, and may well flatter the bond performance for the same reason, but we cannot know this as the data are not available. That qualification noted, this relative stability during times of market duress is among the more defensible reasons, in our view, to maintain exposure to fixed income in portfolios that, for whatever reason, require some manner of potential damper against equity market risk (e.g., intention for the funds, owner tolerance for investment risk).

There are numerous ways to demonstrate this potential dampening effect, and we present another in Figure 4, which shows the relative drawdowns of the various benchmarks that we use to compare performance of the accounts that we manage. Because we tend to target equity exposure in 10% increments, we maintain benchmarks at eleven risk levels: 0% equity/100% fixed income, 10/90, 20/80 and so on through 100% equity. The implication of the chart is that portfolio drawdowns tend to be lighter in more or less direct proportion to the amount of fixed income owned relative to equity in the portfolio. Even so, the lighter expected drawdown that tends to come along with higher ratios of fixed income to equity in a portfolio may come at the cost of lower long-term returns. In Figure 5 we chart those same data we used to create Figure 4, only this time without resetting the value to zero at each new peak. Starkly outlined by the final values is the potential benefit that may come from assuming additional investment risk, in the case of this chart by taking on additional equity exposure.

There is No 60/40

There really isn’t any “one” 60/40, which means that “the” 60/40 can’t be dead. Indeed, potential implementations of a 60/40 approach (or 20/80 split; 70/30 split, etc.) likely are as varied as there are managers who build them. We believe the construction of the 40% fixed income slice of the 60/40 portfolio can be every bit as impactful to long-term returns and client comfort on the way to realizing them as can be the composition of the 60% equity portion. As we have sought to show on these pages, for example, investor experiences likely differed widely during the Fed’s 2022-23 hiking cycle depending on how their managers had positioned the fixed income side of the ledger vis-à-vis interest rate risk. Importantly, we look for our approach within fixed income to neatly align with our overarching belief that striking an appropriate balance of expected returns against tolerance for investment risk is central to success in the pursuit of long-term financial goals.

Important Information

Signature Resources Capital Management, LLC (SRCM) is a registered investment adviser. Registration does not imply a certain level of skill or training. This material is provided for informational purposes only and should not be construed as investment advice or as an offer to buy or sell any security or implement any investment strategy. A decision to engage SRCM should be made only after carefully reviewing the applicable investment management agreement and SRCM’s Form ADV Part 2A and Part 2B, conducting any due diligence you consider appropriate, and consulting your legal, tax, and accounting advisors. All investing involves risk, including the possible loss of principal. Additional information regarding SRCM’s services, fees, risks, and conflicts of interest is available in the applicable investment management agreement and Form ADV. Information presented here is unaudited, subject to change, and intended only as a general guide to current views.

The S&P 500 Index measures the performance of the large-cap segment of the U.S. equity market.

The Bloomberg U.S. Aggregate Index is a broad-based flagship benchmark that measures the investment grade, U.S. dollar-denominated, fixed-rate taxable bond market. The index includes Treasuries, government-related and corporate securities, mortgage-backed securities (MSB; agency fixed-rate pass-throughs), asset-backed securities (ABS) and commercial mortgage-backed securities (CMBS; agency and non-agency).

The Bloomberg 1-5 Yr Government/Credit Index is a broad-based benchmark that measures the non-securitized component of the U.S. Aggregate Index. It includes investment grade, US dollar-denominated, fixed-rate Treasuries, government-related and corporate securities with 1 to 5 years to maturity.

Past performance is not a guarantee of future results. The views expressed reflect SRCM’s opinions as of the date of writing, are subject to change, and may not reflect current thinking. This material is based on proprietary research and analysis, together with information believed to be reliable; however, SRCM does not represent that such information is accurate or complete and accepts no liability for any loss arising from its use. Certain content may be theoretical in nature and subject to inherent limitations. Any references to market exposures or specific investments are provided for illustrative purposes only and may or may not be reflected in client portfolios. No reader should assume that any investment or strategy discussed was or will be profitable. This material may also contain projections, targets, or other forward-looking statements based on current expectations and assumptions. These statements are not guarantees of future outcomes, and actual results may differ materially. You should consult your financial advisor to determine whether any investment or strategy is appropriate for your individual circumstances.

Challenging Bias

The past decade has not been particularly kind to diversifiers; a handful of large U.S. growth stocks generally outperformed international stocks, smaller companies and Value-oriented investments. As the gap widened, diversification may have looked less like prudent risk management and more like an unnecessary constraint. But recent strength across formerly lagging market segments offers a useful reminder that diversification’s greatest challenge remains squaring the intuition (more should be better) with the fact of narrow nearer-term outperformance:

Diversification is not designed to maximize returns in every period but, rather, to reduce dependence on any single market outcome
A properly diversified portfolio near always will contain holdings that appear disappointing in hindsight; the strongest arguments against diversification often emerge after a lengthy period of narrow market leadership
Market leadership has historically changed over time, though the timing of those changes is unpredictable
Not to identify tomorrow’s winner, among the goals of diversification is to improve the probability of achieving long-term financial objectives across a wide range of possible futures

202607 SAM Commentary Download

Diversification’s Drawbacks

The past decade has been kind to equity investors. It has been kinder to investors with more concentrated portfolios—most notably those focused on the U.S. large-cap stocks represented by the S&P 500 Index—than to investors holding more diversified combinations of domestic and international stocks. In hindsight, there nearly always will have been a “better” portfolio to own. For U.S. investors in particular, who already enjoy efficient access to a large and widely diversified home market, international stocks can be especially difficult to defend when they lag for an extended period. We continue to emphasize that broader approach, even though the empirical case for diversification may appear less compelling after a decade of unusually narrow market leadership.
Since 2009, with relatively few exceptions, U.S. large-cap growth stocks have outpaced developed international markets on an annual return basis. Smaller companies also generally failed to keep pace. Value-oriented investments remained largely out of favor as investors paid increasingly high prices for realized growth and the promise of a continuation of recent trends. Against that backdrop, many investors found themselves asking a reasonable question: why own anything other than what has been working?

Hindsight Is No Guarantee

Investment decisions often are evaluated with the benefit of hindsight. Looking backward, it can seem obvious that a concentrated investment in the strongest-performing market segment would have been preferable to a more diversified allocation. But in 2015, few investors could have precisely identified the companies that would dominate market returns over the following decade. At the time, sentiment toward Nvidia (NVDA), in particular, was far less enthusiastic than later results would appear to justify. Coverage was concentrated on gaming and personal computer exposure, while artificial intelligence was mostly discounted and cryptocurrency mining was not yet a key component of the investment narrative. Although Nvidia eventually delivered the strongest performance among the group, a review of contemporaneous expectations suggests it may have been among the least appreciated of the companies that later became known as the Magnificent 7 [Alphabet (GOOG/GOOGL), Amazon (AMZN), Apple (AAPL), Meta (META), Microsoft (MSFT), Nvidia and Tesla (TSLA)].

The difficulty, of course, is that investors do not make decisions with the benefit of hindsight. They make them before the future unfolds, and the future often develops in ways that differ meaningfully from prevailing expectations. Recent performance across the Magnificent 7 offers a useful reminder. Several members of the group have roughly kept pace with, or even trailed, the broader market this year. Apple has been a notable exception. Once scolded for its lack of aggressive AI investment, the stock now seems to have found relative strength largely because the company took a slower, more thoughtful and defensible approach to investment in AI.

Even Nvidia has trailed the market as investors seemingly have begun to realize that there must be profit to come from all this investment in capacity for AI compute capacity, while they also may be glomming onto the idea that many (most?) of us might be better off investing in our own personal AI infrastructure (side note: we are editing this doc on a MacBook Pro, which we purchased seeking to test and eventually ensure we can safely take advantage of the remarkable capabilities that genAI tech can bring without compromising privacy and blowing up our tech budget).

So, the challenge is identifying market-beaters before they become winners, maintaining conviction long enough to benefit from being correct and getting out before sentiment and fundamentals shift. Diversification acknowledges that this task is extraordinarily difficult. Rather than attempting to identify a narrow set of future chart toppers, diversification seeks exposure to a broad range of potential outcomes. The tradeoff is obvious: a diversified portfolio will almost certainly own some investments that disappoint. It may be just as likely to own some that surprise positively. The objective is not to maximize returns in every period. The objective is to pursue attractive long-term returns while reducing the risk that a single error meaningfully derails a financial plan.

Cycles Cycle

The strongest arguments against diversification often emerge near the peak of a particularly dominant trend. By then, investors have become increasingly convinced that recent winners deserve larger allocations and that underperforming areas are no longer worth holding. History suggests such conclusions may be unwarranted. One of the recurring lessons of investment history is that leadership changes. The individual stocks, sectors, countries and investment styles that dominate one decade often look quite different from those that dominate the next. That does not mean market leadership changes on a predictable timetable. It does mean that investors should be cautious about assuming recent trends will continue indefinitely. The challenge is that timing those transitions is exceptionally difficult. Investors frequently abandon lagging investments shortly before performance improves and enthusiastically add to recent winners after much of the gain has already occurred. The result can be a cycle of buying high and selling low that gradually undermines long-term outcomes.

Balancing Breadth

A diversified approach will always be judged against the best-performing investment(s) over some trailing period. That comparison virtually guarantees disappointment for diversifiers. But while concentration can be rewarding, it can also dramatically increase the consequences of being wrong. One need not look too far into the past to see when shares in Nvidia lost two-thirds of their value on account of the reversal of the cryptocurrency mining trade.

Our preference for portfolios diversified across a range of exposures—asset classes, regions, currencies and individual security characteristics—accepts the possibility that not every holding will shine simultaneously. In exchange, it seeks to avoid excessive dependence on a single market segment, economic outcome or investment narrative. As U.S.-domiciled investors, we maintain relatively unique home-market characteristics that push against the more-can-be-better narrative. This fact generally supports our broader tilts toward U.S. stocks and bonds across all but the most aggressive portfolios we manage. Even so, our preference for broad diversification nods to the fact that equity market records represent a moment in the course of human history. The broader historical context shows that global, regional, macroeconomic and industrial supremacy ebbs and flows in manners hard to predict, in ways generally obtuse to the preferences and predictions of the contemporary hegemon.

Less dramatically, instead of attempting to identify the single best-performing asset class over the next ten years, investors should be seeking to fund retirements, support future spending needs, preserve purchasing power and maintain confidence through a wide range of market conditions. These objectives rarely require perfection. Rather, they require discipline, patience and a portfolio construction approach that acknowledges the limits of our ability to correctly predict the future.

Important Information

Statera Asset Management is a dba of Signature Resources Capital Management, LLC (SRCM). Signature Resources Capital Management, LLC (SRCM) is a registered investment adviser. Registration does not imply a certain level of skill or training. This material is provided for informational purposes only and should not be construed as investment advice or as an offer to buy or sell any security or implement any investment strategy. A decision to engage SRCM should be made only after carefully reviewing the applicable investment management agreement and SRCM’s Form ADV Part 2A and Part 2B, conducting any due diligence you consider appropriate, and consulting your legal, tax, and accounting advisors. All investing involves risk, including the possible loss of principal. Additional information regarding SRCM’s services, fees, risks, and conflicts of interest is available in the applicable investment management agreement and Form ADV. Information presented here is unaudited, subject to change, and intended only as a general guide to current views.

What Am I Missing?

When you invest in a specific stock, sector, theme (or meme), you’re basically saying you think it’s a better bet than all the other options out there. That is, any choice to invest in one thing is at the same time a choice not to invest in all other things. How sure can you be that such a narrow decision is the better one? Not at all (unless you’re breaking the law). Hard to fault the desire for excess return. But in theme-dominant market such as these, the shoulda-woulda-coulda thoughts that can drive one to chase stories can lead to poor choices:

In the end, timing themes/trends/stocks is virtually impossible on a consistent basis. Resulting portfolio overweights can lead to disappointment when trends flag, fads stale, momentum shifts
So, we seek broad diversification in an attempt participate…even if only partially…in any trend before it takes off. To the extent novel themes catch an unexpected bid, we may already own the names that might participate
Will we generally “fully” participate in the latest market trends? Very likely not. But that works both ways: we might only partially participate in a trend after other investors have given it wings; but we also are very unlikely to unduly participate in trends adverse to the greater objective of success in achieving financial goals

202605 SAM Commentary Download

What’s Your Angle?

Might be that the most important lesson an investor can learn is that, barring actions using the sorts of privileged information that constitute inside information, one can have no special insight into any stock’s, any sector’s or even the market’s near-, medium- or long-term future. No manner of “analysis” is going to result in your having more knowledge about tomorrow. So, all you have is an opinion. With that in mind, it is quite likely that your opinion is shared by some non-trivial portion of the investment universe. You and those folks have made that opinion known via your trades (or lack thereof…not having an opinion and not trading on an opinion is still a trade). Since those trades will have had some manner of impact on market prices, the market already reflects your opinion, along with the opinions of every other investor. When we say “reflects,” we mean to suggest that, so long as we require that the basis for owning any stock is the claim of future income from the issuer that ownership bestows on the owner of the stock, the current price represents the current value of all that future potential income (which need not be distributed to the investor, mind you; the income can be retained by the company for further investment). But that’s not all. Implicit in that price is the potential variability of the eventual outcomes versus what is presently expected, and not just in the context of that individual stock, but in the context of all securities (e.g., government bonds, corporate bonds, etc.). In easier terms, a stock’s price reflects every investor’s present assessment of the amount and likelihood of future income and capital gain relative to similar evaluations of every other stock (and potential other investments). These assessments will be different for each security across many axes, with relative value primarily relating to the price paid for expected growth and the expected variability of that growth across all stocks.

Why Relative Value Matters

We’ve homed in on relative value because that characteristic is core to how we should think about investing when it comes to our opinions regarding future developments. By relative value, we mean how attractive a stock’s current price is relative to its expected fundamentals and risk, compared with other investable alternatives. Stocks with low relative value reflect more aggressive expectations regarding future fundamentals. Dubbed “Growth” stocks for that reason, these stocks have captured investor interest in such a way as to raise their current prices relative to their current fundamentals to a level that “discounts” that future growth. In other words, Growth is already priced into these stocks and already incorporates consensus views regarding relative growth outcomes.

Of course, that investors believe the growth will come to pass does not mean that it will. More importantly, future returns depend not simply on whether growth occurs, but on whether the outcome is better or worse than what is already expected. Since prices of Growth stocks already reflect greater expected growth, those stocks are likely to underperform on a going-forward basis if expectations were too high. The historical tendency is such that actual growth can disappoint relative to what was priced in. So, we tend to think of Growth stocks as having low relative value. Naturally, the converse may be said of stocks with high relative value, for which investor expectations can be read as relatively low. These “Value” stocks reflect, variously, relatively weak investor confidence in the future relative improvement of their fundamentals.

Another way to look at Growth, versus Value, is that investors have assessed a lower risk that fundamentals will improve for Growth stocks, whereas they’ve adopted a “wait-and-see” approach for Value stocks. That is, they’re not called Growth stocks because they are better, they’re called Growth stocks because investors expect that they will be better. Might be that the “are better” has influenced the “will be better” bit, but it’s the latter that’ll matter most for future returns.

Importantly, theory suggests that investors aren’t making these assessments in a siloed manner. That is, one should believe that investors take all information and potential investment opportunities into consideration when making any decision. But we have the sense that theory is well removed from practice. The recent not altogether random, but at the same time mostly in our view irrational gyrations in individual stocks on account of memetic AI-related developments quickly come to mind. Even so, theory further suggests that, even if one or many individuals blindly consider one investment without regard to any other investment, other investors will trade in consideration of any resulting valuation mismatch (again, based on opinions regarding the future, which may or may not come to light).

Which brings us back to the opening of this commentary. When we understand that the current price of any market-traded security reflects all information—everyone’s observations and expectations for any individual investment, relative to all potential investments—we should be humble about assuming no one else shares our view. Otherwise, if you believe your opinion is that much different from everyone else’s, then you must also believe your expected outcome is relatively unlikely. If it were more likely, more people would share that view and the market price would more closely reflect it. In that context, you must also believe your opinion points to additional gain beyond what is already priced in. More importantly, it must also lead you to want to overweight that perceived opportunity relative to all the other opportunities you might consider.

So, for any specific bet on the future to be “correct”, it must be sufficiently distinct from what’s already priced in, while also being optimal relative to all other potential uses of capital. We find all those hurdles far too high to clear on a regular basis.

Stocks Gonna Stock

Since markets already reflect the consensus view of future fundamentals and risk, outperformance requires being right where the market is wrong—and right enough to justify the portfolio tradeoff. That is why we generally believe investors are better off letting stocks do what stocks will do. Prices will rise and fall based on factors we cannot control. So, we prefer to own at least some portion of many, many stocks, allowing us to possibly participate in future developments that are not yet priced in. That does not mean we believe investors should always take a purely passive approach, though that can be entirely appropriate. As noted in the Growth-versus-Value discussion above, we favor an approach that recognizes how differences in investor opinion can cause stocks to exhibit characteristics associated with higher expected returns. Because it can take time for those expected returns to emerge, we generally tilt portfolios toward those characteristics in a systematic, diversified way. This allows us to pursue expected return differences without relying on concentrated predictions. In our view, that design preserves more market-like return behavior while still giving the portfolio a chance to benefit from what we believe are more thoughtful equity allocations.

As we have learned first-hand over the many years we have been investing portfolios, this approach requires massive and expensive datasets, extensive analytics, thoughtful portfolio design and careful implementation. And in order to achieve the additional expected return an overweight to these observably more attractive characteristics presents, the approach must remain lean from a cost perspective. Hence, our preference for a fund-based methodology. Most importantly, the funds now available to us provide exactly the manners of investment we might otherwise directly implement ourselves. Further, the fund-based approach in practice allows for much broader (i.e., more individual stock holdings expressing favored characteristics) and much more adaptable portfolio implementation, potentially providing greater diversification, lower turnover costs, operational efficiency and tax-management advantages depending on vehicle/account type. By most metrics, then, the funds-based approach is the better one from both the mechanics and efficiency perspectives. By the way, we implement a necessarily different but methodologically consistent approach within fixed income.

More May Not Be Better

The modern finance industry is never short of shiny new things to sell. Most rely on some manner of belief that the simpler approach is suboptimal, perhaps even wrong. Even without the help of industrial finance , investors can find themselves constantly drawn to new opportunities and regularly tempted by the possibility of excess gain relative to how their portfolios are currently positioned. We admit that the instinct to seek greater gain is natural, if only because it seems possible. But that instinct can be checked with a combination of intuition and math. When we review most of these pitches and do the work required before implementation, we usually find that the shiny option compares poorly with the tried and true across the most probable range of outcomes. In the end, we have learned through such work and direct experience that there generally are rather few reasons to layer additional complexity on a portfolio over our present method (even as that method has evolved over time) in the pursuit of a more optimal path to reach client financial goals. More often than not, we end up circling back to our existing strategies. That does not mean that our approach never changes. And even within our approach, there’s a fluidity intentionally embedded in the style that’s meant to adapt to changing opportunities. Ultimately, the work that tends to curb most adventures is the same that led us to the financial planning and investment methodologies we implement today.

Most importantly, we have learned that clients have historically found greater comfort in understanding that, while there is little we can know about the future, there is much we can learn from the past. If we accept a reasonably identifiable level of uncertainty, we may find that the reward over time has been sufficient compensation for bearing it. In the meantime, we should seek to balance comfort with investment risk and the desire for investment reward in the most efficient, understandable, and practical way available.

Important Information

Statera Asset Management is a dba of Signature Resources Capital Management, LLC (SRCM), which is a Registered Investment Advisor. Registration of an investment adviser does not imply any specific level of skill or training. The information contained herein has been prepared solely for informational purposes and is not an offer to buy or sell any security or to participate in any trading strategy. Any decision to utilize the services described herein should be made after reviewing such definitive investment management agreement and SRCM’s Form ADV Part 2A and 2Bs and conducting such due diligence as the client deems necessary and consulting the client’s own legal, accounting and tax advisors in order to make an independent determination of the suitability and consequences of SRCM services. Any portfolio with SRCM involves significant risk, including a complete loss of capital. The applicable definitive investment management agreement and Form ADV Part 2 contains a more thorough discussion of risk and conflict, which should be carefully reviewed prior to making any investment decision. Please contact your investment adviser representative to obtain a copy of Form ADV Part 2. All data presented herein is unaudited, subject to revision by SRCM, and is provided solely as a guide to current expectations.

Past performance is not a guarantee or reliable indicator of future results. The opinions expressed herein are those of SRCM as of the date of writing and are subject to change. The material is based on SRCM proprietary research and analysis of global markets and investing. The information and/or analysis contained in this material have been compiled, or arrived at, from sources believed to be reliable; however, SRCM does not make any representation as to their accuracy or completeness and does not accept liability for any loss arising from the use hereof. Some internally generated information may be considered theoretical in nature and is subject to inherent limitations associated thereby. Any market exposures referenced may or may not be represented in portfolios of clients of SRCM or its affiliates, and do not represent all securities purchased, sold or recommended for client accounts. The reader should not assume that any investments in market exposures identified or described were or will be profitable. The information in this material may contain projections or other forward-looking statements regarding future events, targets or expectations, and are current as of the date indicated. There is no assurance that such events or targets will be achieved. Thus, potential outcomes may be significantly different. This material is not intended as and should not be used to provide investment advice and is not an offer to sell a security or a solicitation or an offer, or a recommendation, to buy a security. Investors should consult with an advisor to determine the appropriate investment vehicle.

This Is Risk

Global equity markets have seen increased volatility as the war in Iran has prompted regional contagion. A consequent surge in oil prices has lifted concerns that cost-of-energy flow-through will lift going-forward inflation; the rise in interest rates that has come as a result has pressured bond markets. But the Iran war is only the latest source of market worry:

Investors have reacted (in our opinion far too injudiciously) in knee-jerk fashion to each revelation on the artificial intelligence (AI) front, dumping potential losers, tenuously as that connection has been made
That AI fear-trade has expanded to private credit, where AI infrastructure overspend and a heavy software-focus has prompted rushes to withdraw funds from generally illiquid products that have struggled to meet demand
Each case is a reminder that investment risk comes in many forms, arises at any moment, may compound with contemporaneous risk and generally scales along with expected return (higher expected return = higher risk)
Investors who accept these characteristics of investment markets as core to their natures may find such periods of turbulence easier to understand and endure

202603 SAM Commentary Download

All Eyes on Oil

For all the geopolitical angst, we’d argue the equity market might be seen as yet lacking in comparable drama. This may be because investors have looked to history to understand that tense geopolitical moments tend to resolve with little enduring macroeconomic consequence. But increasing strains in oil markets from the expanding Iran war may well add to market jitters. Assuming the perceived timing of the end of the war is the primary driver of recent changes in assumptions, oil traders now are thinking the conflict will last a good bit longer than they did in the middle of last week. So, while oil futures markets still suggest the price of oil is expected to fall in reasonably short order once the war concludes, present expectations now foresee oil prices at higher levels for longer. Price expectations could quickly reverse (further inflate), were the war to end (escalate even further).

Where to from Here?

Perhaps of greatest potential risk to equity markets might be an Iran left without firm leadership, a region broadly unstable, and therefore an oil shipping market left markedly riskier as a result. Supplies thus could remain strained, with the price of oil maintaining a multi-year high. As those costs flow through to other goods, concerns of persistent inflation will grow. Bond markets will struggle to balance rising prices with a still modestly growing economy that has barely lifted employment over the last year, with interest rates potentially rising on account of the need to clear a higher inflation hurdle and potentially weaker growth (which might, then, boost rates of corporate debt defaults). Stories with “stagflation” in the title referencing the 1970s will become more numerous.

Meantime, markets are likely to continue to gyrate, given the present tension and lack of clarity as to its release. As statements by President Trump and his administration remain near wholly vacant of specifics regarding the intentions of the war, methods to model its denouement remain elusive. And talk of boots on the ground over the weekend lifted the likelihood of a war measured in months.

The war with Iran comes as investors could be read as struggling (and increasingly so) to balance the promise of artificial intelligence (AI) with its budding potential boost to productivity against the actual costs of the technologies, including the radically escalating spend on AI computing infrastructure and still broadly elusive profits, at least given what one can glean from company statements.

At the same time, the AI story has cast a shadow on private credit markets, which already were suffering rising concerns that the relatively opaque debt market has grown too quickly and without commensurate safeguards/oversight and that some manner of dislocation might soon be forthcoming.

Optimists…for the Long Term

Readers will know that we generally maintain a long-term optimistic outlook. We hold that stance, generally, regardless the present market situation. And that’s because we can honestly argue that we’ve been here before. If not specifically so, then contextually so. There have been other wars, many in the Middle East, with particularly negative impacts on energy markets. And such wars have sometimes arisen amidst a relatively strong market, some participants in which may have been looking for a reason to sell.

It would be pollyannish of us to believe that the war in Iran might end with no lasting global macro consequence. We already have learned that the regime is more intractable than this administration might have thought. Until oil shipments can proceed through the Strait of Hormuz unmolested—and until such time as shippers and their insurers firmly believe that’s the case—oil prices are likely to remain well above the $55-to-$65 range of the past year. Equity investors are likely to continue to fret increased aggression as they incorporate higher inflation into their expectations (which, all other things equal, should depress current valuations). And as bond investors do the same, yields are likely to maintain an upward drift. The Federal Reserve now will be even more challenged to substantiate shifts in monetary policy, given the potentially diverging trends working against its dual mandate of stable prices and full employment. Lower rates may support employment, but potentially at the expense of higher inflation. And higher inflation may force the Fed to leave rates where they are, perhaps even lift them to provide a countervailing force on prices.

The upshot is that increased market volatility is to be expected for the near term. We hope not over the medium term, too, but we should not be surprised if that proves the case. Even so, we believe investors who have properly understood that investment markets routinely see periods of increased tumult on account of the realization of such risks, and who had already aligned their risk exposures according to their expected levels of comfort during such periods, may find some manner of relief knowing that markets generally have recovered in the fullness of time.

Important Information

The Risk of Being Wrong

This month, I push back against the extremes.

Tilting Against the Tide

We’ve written often of our preference for “factor” investing in equities, an approach that leads us to variously “tilt” portfolios toward characteristics that reflect observable “risk premia” in that they can be seen to have led to outperformance over time. There’s no guarantee that outperformance will come, though; there’s a risk that the premium won’t be realized. Such has been the case for some time now as the Magnificent 7 and other larger, growthier stocks have powered ahead of the rest of the market. Still, that converse trend has not crimped our preference for the approach:

Maintaining a factor-based approach can prove challenging at times. And latching onto a growth train may seem the easier path in periods such as the past few years
Market history shows relative performance tends to cluster in cycles, but the beginnings and ends of those cycles are unknowable in advance. Factor exposures may be indicative of variation in longer-term outcomes, but they are weak to useless as market timing tools
As the growth side of the ledger [opinion alert!] grows more and more irrational, though, we find the historical evidence increasingly relevant

202601 SAM Commentary Download

Matter of Factors

So long as one takes a longer-term view, data backing core emphases on relative size, valuation and profitability remain cogent. In Figure 1 we show the cumulative total return of small-cap value stocks, versus large-cap value stocks. Where the area is green, the top represents the index of small-cap value, while the bottom represents the index of large-cap value (and vice versa, where grey shows large-cap value outperformance). Though both series ended the period with about the same annualized return, there were meaningful interim returns gaps between the two groups of stocks. The grand relative gains in small-cap value stocks after the Tech Bubble (2000-01), the Great Financial Crisis (2007-09) and the COVID pandemic came only after the substantial relative declines prior to each crisis. The cyclicality of the difference in relative returns reflects both the promise of factor-based investing and the risks generally to be borne in pursuing any such methodology.

The cost of the potential near- and medium-term underperformance of a factor-based equity investment approach is the risk one must assume to achieve the potential premium that the approach may present. In Figure 2, we again use the two narrow extremes of the factor mix to support this observation. Over the past thirty years, small-cap value stocks (value stocks are those that measure less-expensive based on some ratio of price to company “fundamentals”, such as assets, earnings or revenue) have shown a tendency to underperform large-cap growth (growth is the inverse of value) stocks over shorter periods of time. But stretch the review period and a picture of more consistent—though still not pervasive—outperformance comes into focus. Where we consolidated the data in Figure 2, we show the entire detail in Figure 3 in order to convey the cyclicality of relative performance. The rolling periods expand from 1 year (indigo) to 20 years (yellow). The higher peaks and lower troughs of the 1-year rolling periods express not only the heightened short-term volatility of markets in general, but also the potentially substantial difference between the short-term performance of small-cap value stocks, versus large-cap growth stocks.

Discipline Trumps Timing

To achieve the potential gains presumably inherent to a factor-based investment strategy, though, we believe one must be systematic. That is, the definitions one uses to establish the relative weights of stocks in a portfolio must be reasonably stable and consistent. As important, we think, is to implement a multifactor approach in a manner that seeks to optimize the portfolio’s potential benefit from the targeted exposures. In the portfolios where we utilize multifactor approaches—multifactor equity investment methodologies being just one of the various investment approaches we deploy in client portfolios—we generally “tilt” exposures toward our preferred characteristics, aiming to boost expected returns without adding undue risk of underperformance relative to the market and without unnecessarily over-complicating the approach. The tilt aspect of our method reflects the notion that, while we tend to own some proportion of most stocks in a given investment universe (which in our portfolios tend to align with three quasi-regional exposures: U.S. stocks, international developed-market stocks and emerging-market stocks), we tend to maintain greater-than-market weights in stocks that express our preferred characteristics and lower-than-market weights in those that do not.

As part of that focus on efficiency, we generally seek to achieve our equity factor exposures exclusively via exchange traded funds (ETFs, and to a lesser extent these days mutual funds). The primary reason we take this stance is that ETFs allow the portfolio to evolve over time to optimize factor exposures without the undue realization of gains, either short- or long-term. The funds we choose also generally consider all factor exposures for each underlying holding (hence the multifactor moniker). One could instead choose single-factor funds (e.g., a small-cap fund or a value fund), but such a methodology generally is considered less optimal from an expected-return standpoint. The approach also adds unnecessary friction to the portfolio in that the exposures must be modulated via a rebalancing of the individual funds, a process that is largely more efficient when done within the fund itself.

Further, for most portfolios (we’d argue probably for all portfolios from a practical perspective), a fund-based approach trumps one that invests in individual stocks as well, given the far greater breadth of holdings that can be achieved in a fund—thousands of stocks, versus hundreds, perhaps even tens in an average account or household of accounts—and the potential greater efficiencies achieved by the single-portfolio scenario that a fund presents to all investors in the fund. Holding a larger number of stocks generally enables more optimal achievement of factor exposures, while the ETF construct generally enables more tax- and cost-efficient tailoring of those exposures as markets evolve over time. The costs of those funds in terms of expense ratios obviously matter, which is why we narrow our universe of multifactor funds to those at the less-expensive end of the spectrum. Present preferred multifactor funds carry expense ratios ranging from 0.12% to 0.17% for U.S.-focused funds, 0.18% to 0.23% for international developed stock funds and 0.33% to 0.39% for emerging market stock funds. These expenses ratios have changed over time, to date only becoming less expensive as the funds have matured.

Finally, we generally scale multifactor tilts in line with a portfolio’s overall risk tolerance so that higher-risk models carry more pronounced emphases on these factors. The approach aligns with observation that stronger tilts widen the potential for the performance of the portfolio to diverge from that of the broader market (in industry parlance, stronger tilts increase potential tracking error). That potential performance divergence can be in both directions (higher and lower potential returns), with the expected bias that the longer-term relative performance will be higher. The graduated tilts also acknowledge the fact that, in order to achieve the premium bit of the risk premium, one must endure the risk of near- and medium-term underperformance. As noted earlier, we seek to reduce the potential downside performance gap through tilts, rather than via an exclusive focus on stocks expressing more favorable characteristics.

FAIr Value

In summary, we think this disciplined, long-term, evidence-based approach seeks to enhance expected returns while acknowledging the inherent cyclicality and potential for periods of underperformance. As shown in Figure 3, we’re in the midst of substantial long-term small-cap value underperformance relative to large-cap growth stocks. Given the narrowness of the comparison, the gap overemphasizes the relative underperformance of multifactor-tilted portfolios, as a tilted portfolio would have maintained some exposure to the larger and growthier side of the spectrum. In recent times, actually, this underperformance mostly has been a U.S.-based phenomenon; multifactor approaches have tended to outperform in international stock markets over the past several years. The underperformance of small/value is notable, nonetheless, for its depth and duration.

So, what does that underperformance tell us about the future? Perhaps nothing. Potentially something. We’ve many reasons to be concerned about exposure to U.S. equity markets. But, then again, when ain’t that been true? We’ll leave aside the “if” for now, as any concerns about the magnitude of stock exposure may be far more easily handled via a change in allocation between equity and fixed income (feel free to reach out to an advisor to discuss). This month’s commentary discusses the “how” of equity exposure. And in that context, we continue to find that investor expectations for going-forward fundamental growth related to generative artificial intelligence (genAI)—expectations clearly reflected in the relative performance and present valuations of a host of large-cap growth stocks—greatly outpace practical medium- and perhaps even long-term potential for end-use revenue to sufficiently compensate for the rather outlandish monies being dedicated to genAI infrastructure. Said more concretely, there’s a valuation disconnect, in our minds, that may contract before a more realistic view of the potential profitability of genAI broadly takes hold.

Again, research has shown little reason to believe factor investments can be timed to market opportunities. And in our experience, the shifts in investor sentiment that have reversed trends in factor underperformance have come quickly and without immediately obvious triggers, as we show in Figure 4. Seems investor opinions may be consolidating toward a more rational view here, as small-cap value stocks are on track for their third month of outperformance relative to large-cap growth stocks. But time will tell. Meantime, we continue to find a strategy that tilts against the rising tide of genAI exuberance a sensible stance for those similarly concerned about the potential for eventual investor disappointment.

Important Information

Signature Resources Capital Management, LLC (SRCM) is a Registered Investment Advisor. Registration of an investment adviser does not imply any specific level of skill or training. The information contained herein has been prepared solely for informational purposes. It is not intended as and should not be used to provide investment advice and is not an offer to buy or sell any security or to participate in any trading strategy. Any decision to utilize the services described herein should be made after reviewing such definitive investment management agreement and SRCM’s Form ADV Part 2A and 2Bs and conducting such due diligence as the client deems necessary and consulting the client’s own legal, accounting and tax advisors in order to make an independent determination of the suitability and consequences of SRCM services. Any portfolio with SRCM involves significant risk, including a complete loss of capital. The applicable definitive investment management agreement and Form ADV Part 2 contains a more thorough discussion of risk and conflict, which should be carefully reviewed prior to making any investment decision. All data presented herein is unaudited, subject to revision by SRCM, and is provided solely as a guide to current expectations.

“Small-cap value stocks” are represented by the Bloomberg U.S. 2500 Value Total Return Index, which comprises the smaller 2,500 of the largest 3,000 U.S. companies with superior value factor scores based on their earnings yield, valuation, dividend yield and growth.

“Large-cap growth stocks” are represented by the Bloomberg 500 Growth Total Return Index, which comprises the largest 500 of the largest 3,000 U.S. companies with superior growth factor scores based on their earnings yield, valuation, dividend yield, and growth.

Wade or Jump?

Regularly investing over time may allow one to take advantage of market volatility, rather than simply suffer it. As stocks bounce up and down, investors may find that market drawdowns present opportunities to invest at lower prices. Perhaps easy to default, then, to a dollar cost averaging plan (DCAP)—the practice of investing a particular sum of money into the stock market over time, versus all at once—thinking that DCAPs always make for better outcomes. History suggests, however, that when it comes to investing a specific pile of excess cash, one may find that investing it all at once may make more sense, even if it feels less comfortable in the moment:

We think it wise to regularly invest freshly earned excess cash (e.g., a portion of each paycheck)
But when it comes to splitting up a single sum of excess cash (e.g., a large year-end bonus or annual contribution to a retirement plan), lump-sum investing has often—but not always—come out ahead
Even so, cautious investors may find any opportunity cost of wading into the market a small potential price for the comfort a DCAP may provide, as any missed gains may fade into the background over time
Either way, we believe that maintaining any disciplined investment plan trumps having none at all

202512 SAM Commentary Download

Generally, But Not Always Up

Obviously, investing excess cash all at once on the best day possible (meaning the lowest level of the market over any given period) would likely result in the highest gain. But such perfect timing is not possible. Investors may instead turn to a dollar cost averaging plan (DCAP)_1 as means to potentially avoid investing at relatively inopportune times. As we show in Figure 1, however, spreading out the investment of some sum of excess funds, rather than investing the entire amount all at once, historically has not led to higher returns. Of course, there is always the possibility that a meaningful market decline is just around the corner, such that a DCAP might lead to a better return. And in that sense, a DCAP minimally may provide an extra layer of comfort to investors who might otherwise be fearful of increasing an allocation to stocks at any given point in time.

But Maybe Don’t Just Sit There

Meantime, we find it important to invest the cash portion of a DCAP in very short-term, liquid cash-like securities (e.g., a money market fund) as this may help avoid missing out on potential, relatively lower-risk returns. And that’s part of a broader, more foundational message that individuals should consider being invested in some manner, as avoiding investing altogether almost guarantees a suboptimal financial outcome.

As part of finding comfort with being invested, we believe it is important that investors determine a level of exposure to equity market risk—meaning a level of potential decline—that they believe they will find manageable in the event of an actual market decline. Hypothetical declines are nothing like the actual sort, though, which is why we tend to focus so much on investment risk in our commentaries and discussions with clients. After an appropriate level of equity exposure is defined, a DCAP may provide an additional layer of comfort with potential near-term market swings. And investors may find that any gains missed through the implementation of a DCAP are unlikely to have proved meaningfully detrimental in the fullness of time.

That long-term mindset is critical, though, as stocks don’t always go up. In Figure 2 we show the percentage of time the U.S. equity market_2 saw gains over a range of rolling time periods_3. And in Figure 3 we show the data underlying the averages from Figure 2. Both charts show that the propensity to have seen a gain from equity investing has grown as one increased the hypothetical “time in the market”. But they also make clear that losses over reasonably longer periods of time, while not historically common, are not improbable.

As we look forward to the new year, we wish everyone a safe and festive close to 2025 and a grand launch into 2026!

Important Information

“U.S. stocks” are represented by the S&P 500 Index measures the performance of the large-cap segment of the U.S. equity market.

1_A dollar cost averaging plan involves continuous investment in securities regardless in fluctuation in price levels of such securities. Investors should consider their ability to continue purchasing through fluctuating price levels. Such a plan does not assure a profit or protect against loss in declining markets.

2_“U.S. stocks” are represented by the S&P 500 Index measures the performance of the large-cap segment of the U.S. equity market.

3_A rolling time period refers to a specific duration—such as one year, three years, or five years—that shifts forward incrementally over time. Instead of evaluating results only at fixed intervals (like calendar years), rolling periods analyze performance or data across overlapping intervals. For example, a rolling five-year period on monthly data like we show at the top of Figure 2 would look at January 2015 to January 2020, then February 2015 to February 2020, and so on.).

“AI” Already Means Nothing

Seems not since the Tech Bubble of 2000-01 have we experienced such a rapid shift from “meaningful advance in technology” to rampant hype bordering on hysteria. Now ubiquitous, “AI” dominates the empty boilerplate phraseology of executives and pundits—however hollowly—seeking to convey techno-sophistication and endless promise. Finding the memetic expansion unprecedented and becoming ever more lacking in reasonable basis, we wouldn’t care so much were generative artificial intelligence (genAI, or just AI) not such a powerful market and macroeconomic force. But we find such boosterism is…slowly…losing ground to reality:

Growing actual costs for building and operating AI infrastructure vastly outpace downstream AI revenue
Parabolic ramps in expected revenue ignore the tech’s inherent flaws and are becoming more heavily dependent on presumed capabilities unlikely to materialize in the medium term from present technologies
Enthusiasm for AI’s yet-unrealized potential arguably has boosted portions of the market well beyond reasonable valuations
Concerned about excess investor enthusiasm for the AI theme, we think an immediate reckoning is not near

202511 SAM Commentary Download

Search for Meaning

We’re kind of shocked how quickly “AI” has become a meaningless, throwaway concept, one particularly fond of which seem to be public company executives, investment strategist sorts and your average Reddit poster. Everyone’s gonna “do AI” and make loads of dough! Given time-in-the-market and technological maturity (much debate happens here, as we will discuss in as bit), though, we wonder why we’ve so little detail regarding actual services-related revenue being derived from AI technologies (excluding hardware sales and those from the folks renting out servers to do the required math). The relevance of course is that there’s no sense for the hardware to exist without those end-use dollars. For the half-trillion or more invested so far in infrastructure and considering plans for trillions more through decade end, we should have better insight into realistic top-line growth.

All About Tomorrow

The typical response is that we should just wait and see. Thing is, we have been waiting and seeing. And for as many times as we’ve been impressed by AI tech—from the near-magical AI-generated “podcast” that discussed a commentary I wrote to the far more practical application of AI tech for searches across internal documentation—we have yet to be impressed with actual sales data.

“But wait!”, you might say, “OpenAI has secured more than a trillion dollars in deals!” That can’t be for nothing! True. But those deals—increasingly circular (seller invests in buyer of goods/services from seller) as so many of them and others like them in the AI space are—relate mostly to securing the capacity to do AI math. They mostly do not relate to any sources of reasons to do the AI math. But the latter is all we really should care about.
And here we’re certainly not the only ones with growing concerns that the tendency to provide dead-end results is an unavoidable defect of large language model-based technologies. It’s now well understood (among those who care to pay attention) that AI technologies: 1) do not “understand” the world in any manner a human might conceptualize that word, 2) do not contextualize the world in any other sense than by consolidating descriptions thereof given some manner of relevant historical example in text, picture, illustration or video, 3) provide responses to prompts that one should never presume have any factual basis beyond a potentially irrelevant contextual basis given the underlying computational method.

The phrase “stochastic parrot” is overused and probably, at this point, too trite when considering more recent advances in overlay technologies used to accommodate underlying flaws, but it’s nonetheless indicative of the core problem. I asked CoPilot to explain why, which I summarize as: stochastic means “based on probability” and refers to the fact that LLMs use statistics to predict responses. They therefore don’t in any manner have any “understanding” of their responses—much as a parrot likely doesn’t when mimicking human speech—which as a result may have no factual basis in reality.
Note the emphasis on “may”. Perhaps responses to prompts are most of the time completely accurate and on point. But we think it matters greatly that the expected hit rate is south of 100. And that’s because even in non-mission-critical situations, it matters, given the seemingly growing tendency to offload work to an LLM.
For example, I asked Microsoft’s CoPilot app to review this document for, “clarity, comprehensiveness and accuracy.” After otherwise generally praising the text, excepting sarcasm, editorialization and structure, it noted that, “The document occasionally repeats entire paragraphs, which may confuse readers or suggest editing oversights.” When asked to what it was referring, it highlighted the introduction above, stating that it both came at the beginning of the document and, “again in the early section before the ‘Tight Ropes’”. Hrm. After I noted that the paragraph is not repeated, just exists in a text box at the beginning of the document, it thanked me for the clarification and agreed.

Such clinical text reviews represent in our view the lowest bar to clear for AI tech. And, yet, in our experience it often comically fails to clear that hurdle. From queries on docs we’re creating to AI results from Internet searches, we regularly find very obvious mistakes in the results. Cure cancer? AI tech can’t even reliably cure Word files.

Seems the answer, at present at least, is to throw more money around. We would presume the zillion-dollar pay packages being reported for AI scientists/engineers/etc. are meant to solve the parrot problem. Not sure what to think about the monies being allocated to infrastructure under the presumption either that the flaw will be solved or that users won’t care to notice. Shown in Figure 1, the “hyperscalers” have spent an estimated $330 billion on capex over the 12 months ended 09.30.25 (representing ~21% of revenue and ~52% of operating cash flow) a figure expected to exceed $550 billion by the end of 2027 (~27% of revenue and ~67% of CF). Much of that spend—Perhaps half? Depends on the source of the anecdote and the location of the AI complex…but a large portion—represents revenue for Nvidia (NVDA). The chipmaker has seen its trailing revenue jump nearly 7-fold to an estimated $185 billion for the four fiscal quarters ended 10.31.2025 since 04.30.23 after revenue trailed off post crypto-mining craze, over which time its market cap soared ~14x to more than $5 trillion, now accounting for more than 7% of U.S. market cap.

Cost/Profit Chasm

Importantly, Nvidia revenue is mostly not from AI. Rather, the chipmaker generates revenue mostly from the means to “do” AI. Genuine genAI-driven revenue from user-focused products and services remains broadly under-reported, with guesstimates based on anecdotes still coming in below $100 billion on an annualized basis [a least by means of our searches the data covering those services/products that are enabled solely via the deployment of AI technology and excluding AI overlays of existing services/products and excluding revenue generate for providing the means (servers, etc.) for AI training and inference]. To be fair, though, it’s tough to gauge AI-specific revenue and revenue that’s enhanced through the addition of AI technology. For example, how does one consider Microsoft’s addition of CoPilot AI technology to its Office 365 suite? The company upped the annual fee for Microsoft 365 Personal and Microsoft 365 Family users by $3 per month to $99.99 and $129,99, respectively, but does not otherwise charge them specifically for “basic” CoPilot, use of which is limited. A Microsoft 365 Premium license for $199.99 ups CoPilot limits and users can add an unlimited version of CoPilot for $20 per month. Corporate users can add Microsoft 365 CoPilot for $30 per month, but an existing Microsoft 365 plan is required. Importantly, Microsoft is not yet breaking out revenue for CoPilot.

This quarter’s earnings season so far has brought little in the way of additional detail, so we have the sense that the mismatch between capex and revenue to pay back that investment is unlikely to improve materially over the short term. And that’s before we consider the expected ongoing capex ramp (see Figure 1), which more and more seems to us a grand exercise in blind faith. The reference to past bubbles is easy here. We still use some train tracks laid down more than a century ago. But without trains full of sold or sellable cargo, train tracks are useless. And we’ve still yet to fill the optical fiber strung a quarter-century ago. Without the paid-for data traffic to fill that fiber and busy the connectors and switches that move them all around, the whole of the optical space infrastructure otherwise would be useless.

Worse in the current context, those analogies fail to reflect the relatively delicate nature of AI infrastructure. Unlike rails and fiber optic cable, the chips used for AI “training” (building the models) and “inference” (using the models) don’t last anywhere near as long. Estimates suggest that the latest chips used in training will be useful at their current tasks for little more than three years. So, at least the semiconductor portion of the AI infrastructure cycle must be assumed to be set on a repeat cycle. Not only that, but each increment in AI capability so far has proved vastly more expensive from the required-infrastructure and time-to-completion standpoints, further exacerbating the mismatch between development/operational costs and revenue.

Meta (META) CEO Mark Zuckerberg remains unfazed by such concerns, having commented earlier in the year that his company wants to make sure it’s not underinvesting. Hard to see that happening at present. Capex for 2025 is now expected to range between $70 billion and $72 billion. On next year’s budgets, Meta CFO Susan Li stated on the call, “our current expectation is that capex dollar growth will be notably larger in 2026 than 2025 [our emphasis]. We also anticipate total expenses will grow at a significantly faster percentage rate in 2026 than 2025, with growth primarily driven by infrastructure costs, including incremental cloud expenses and depreciation.”

META shares have sunk 13.7% since the call, perhaps a hint that investors may be turning a more skeptical eye toward “more equals more” spending on AI. Part of that may be a growing realization that AI technology may be of a radically different sort when it comes to profitability dynamics. While investors have grown to expect that costs for the provision of Internet-based services tend to fall, often dramatically, as use scales, such may not be the case with AI tools. As one means to make them more accurate and/or useful, inference providers have developed “reasoning” models, which (crudely speaking) iterate over a series of answers to steer the eventual result toward factual and contextual precision. But those iterations require more compute, effectively reducing the potential margin on the request (leaving the potential revenue question aside for the moment). Same for pictures and video. For each additional complication, compute time rises, often dramatically. While we have learned from company reports that such costs are coming down via efficiencies gained from dynamic model choice and otherwise, comparison to, for example, the provision of classic search results is not a favorable one for the delivery of responses generated from inference. That is, a simple “search” request given to a classic search engine is handled in a radically different fashion when that request is entered into an AI chat, with costs (again, depending on an evolving mix of requested output and model choice) still strikingly higher for the latter.

It remains an open question as to what long-term-profitable AI product/service models will look like. And here is among the places where, for us, the “AI means nothing” truly presents. AI “agents” were meant to have been the next big thing already this year, paving the path toward expansive profits. The idea is that an agent could operate in some manner of autonomous fashion to produce desired outcomes in various situations. Often cited is an agent that could book an entire vacation for a family, not that anyone in their right mind would allow such a thing to occur. We tend to think that such agents must ultimately be more rules-based than AI and therefore are likely to prove little more than extensions of long-available algorithms. An exploration of analyst reports suggests many companies are reporting reasonably large revenue streams from “agentic” platforms. But it seems even this term is being diluted to include AI chat overlays onto existing customer service-oriented platforms (zzzzz) and, well, folks just calling it “agentic” because they know they’re supposed to.

Too Critical Too Soon?

To be clear though, while we believe we might maintain a better understanding of the inherent limitations of large language model-based artificial intelligence platforms than the average AI booster, as we said earlier, we continue to be amazed (if not as much anymore) by the inherent magic in the tech. Haven’t mentioned AI in the audio-visual space yet. Here, too, the journey from prompt to output can seem close to mystical.

And while you will never see such output on these pages or in our podcasts unless it is obviously identified as such, we admittedly are utilizing AI tools more often for querying our own content and external resources. Outside of otherwise normal (ehem…acknowledging that we remain in a relatively high inflation regime) year-on-year cost increases, though, we are not paying more for the AI add-ons. That “costs up, revenue flat” scenario we think ultimately will prevail across a wide swath of the AI space, likely leaving broad provision of such services only to the largest, most profitable of the players (who also will gobble up any minnows that manage to break the surface). But we don’t think that the provision of AI tools will radically alter tech-space profitability except for, perhaps, to the downside. Important in that consideration is our present understanding that there is little demonstrably and defensibly unique about any of the large language models or any individual company’s ability to utilize the output of LLMs for some manner of product/service provision. We suppose patents could end up allowing service providers to develop moats, but there’s little hint yet of efforts to lay sole claim to the AI tool space. So, we expect rising costs and competitive pressures to limit profitability for the near term across the AI space.

Of Course They Will

Those pressures may lead to more creative application of AI. Which brings us, of course, to the now seemingly inevitable dip-into-the-dumpster for any once fresh Internet-tangent tech: porn and ads. Just a few weeks ago, OpenAI CEO Sam Altman indirectly highlighted the intention to open its ChatGPT service to more adult-oriented fare, though he remains cagey on the topic of in-chat advertising. Meta’s Zuckerberg, on the other hand, provided a glimpse of a glorious AI future on the earnings call: “I think that these models will also improve modernization and all of the different ways that we’ve talked about so far in terms of improving engagement, improving advertising, helping advertisers engage. I mean, there’s the one opportunity that we just, we usually talk about on these calls, but hasn’t, hasn’t come up as much here is just the ability to make it so that advertisers are increasingly just going to be able to give us a business objective and give us a credit card or bank account and like have the AI system basically figure out everything else that’s necessary, including video or different types of creative that might resonate with different people that are personalized in different ways, finding who the right customers are all of the capabilities that we’re building, I think go towards improving all of these different things. So, I’m quite optimistic about that.”

Going to editorialize to the extreme here, but we can’t say we share that optimism. Indeed, much of the early hype around agents relates to helping customers buy things (read: steer them toward the highest ad bidder), an impact that remains well removed, to put it mildly, from using AI to cure cancer and nowhere close to the far dreamier “artificial general intelligence” or AGI. That terminology once meant a level of autonomous capability meeting or exceeding that of a human but has since become yet another moniker without meaning.

Case in point, productivity software and cloud services vendor Microsoft (MSFT) just reached a deal with OpenAI to restructure an earlier ownership/tech-sharing agreement. A sticking point in the deal was the definition of AGI, the achievement of which—as determined by the board of OpenAI—was meant to have enabled OpenAI to limit/terminate Microsoft’s exclusive access to OpenAI’s technology. Per reporting on the matter, OpenAI had suggested a financial benchmark—$100 million in profits—to represent the trigger. Arbitrary and irrelevant we presume Microsoft countered, wanting the restriction removed instead. The two now have agreed to have a yet-unnamed panel of experts define AGI and determine when it’s been reached. No one knows what AGI is. They think they will know when they see it. But we remain very far from the metaphorical corner around which AGI exists.

A Bubble to Burst?

In our view, the gaps between “what AI could be” and “what AI is” and between “what AI costs” and “what AI makes” are chasms unlikely to be spanned by funding alone and over a timeline approaching that which we see embedded in the AI investment theme. Placing those observations against the weight of the AI trade in the U.S. stock market and concerns that we are in some manner of bubble are both warranted and worrying.

Countering those fears, many don’t find the could-be/is now gap as stark as we do. And, of course, AI boosters have a retort. For example, further down the line, LLMs will give way to “world models”, which will (by their conjectures) greatly expand the capabilities and applications of AI tech. World models are variously thought of as digital representations of real-world scenarios that should enable AI to “think” in an environment based on “rules” rather than, as LLMs presently do, simply predicting text outputs. Seems the basis for world model projections are the great successes of mostly game-playing supercomputers that have mastered Go and chess. And meaningful progress is likely to be made in world models that incorporate physics for use in autonomous piloting in all its forms. But those “worlds” are incredibly narrow relative to the actual world—which humans navigate within a vastly wider set of set physical rules (e.g., gravity) and using ever-expanding and -evolving knowledge and experience gain through recognition, cognition, interaction and repetition. Relevant rules and contextualizations within both sets are likely too incomprehensively—and therefore potentially incompatibly—numerous, to resolve into a computer model. Given precedent, then, we won’t be surprised to find the concept of world models diluted into something vacuously narrow at some point in the future.

So, we expect that the cost/benefit chasm will remain wide for some time to come. Investor reaction to Meta’s and Microsoft’s Q3 results leave us thinking investor suspicion and scrutiny are on the rise. But a look at shares in Alphabet (GOOG/GOOGL) and Amazon (AMZN) since those two provided their calendar Q3 results leaves us skeptical that a crash is nigh. But we do expect one to come, left wondering now at the onset of and eventual pace at which an alternative reality sets in. It may be that the present opacity regarding potential revenue from AI products/services is the very thing that keeps hopes high for AI companies and their shares and that will continue to provide support for stocks over the near and even medium term. As actual revenue figures become more widely available, however, more realistic long-term outlooks will follow. And as growth expectations potentially wane, so too may the now high-flying shares in AI-adjacent companies.

And that may include shares in Nvidia (NVDA). While we might think that the chipmaker will retain its technological superiority in the dedicated chip space, perhaps forever, we remain convinced that infrastructure budgets will stabilize, perhaps even begin to drift lower next year and beyond as the pressure to show paybacks on the spending ramps. And the denouement of this chip cycle we think will have an effect on NVDA shares similar to that of the bust of the crypto mining craze. At a mere 29 times fiscal 2028 (ending 01/31/2028) full-year revenue, according to Bloomberg, the stock seems cheap, no? But that multiple presumes a 55 percent year-over-year jump in current quarter revenue, a 34 percent gain next fiscal year and another 20 percent or so the next to $322 billion. Meantime, profitability is set to at worst be flat, as earnings soar 45 percent, 43 percent and 23 percent in that same period order, to $7.04 per share. It would seem announced plans for AI infrastructure might just support those estimates. We expect, however, that many of those plans won’t come to fruition.

Still, while we may be worried about excess investor enthusiasm for AI-adjacent names we think an immediate reckoning is not upon us. And to be clear, one may never arrive. Perhaps we are totally wrong in our expectations for eventual remedy to LLM imperfections. Maybe AI-related revenue will soar from new services, existing product overlays and otherwise. AI infrastructure budgets might well grow even from here. AI-space valuations presently expect such soon to prove the case, however, and it’s not unlikely the world will turn out otherwise.

A financial crisis of any sort we yet don’t expect to see though. Given that the majority of potential excess capex so far has been bought using mega-cap Tech cash flow (not debt), we continue to believe that any negative stock reactions will come as a result of disappointment, rather than contagion. To the extent that AI infrastructure investors (including the Magnificent 7) shift further into debt (with the help of private credit providers), the potential for contagion may grow. But we think we remain well removed from any manner of financial crisis.

Whether from declining infrastructure spend, the collapse of a small player (or players) in the infrastructure space, disappointing actual AI revenue trends or a mix of the above (likely to be the case), however, a crisis of confidence may be in the cards. And that might mean a substantial decline in the U.S. equity market, given the present weight of Nvidia and the hyperscalers of just under 30% by market cap. While our strategies generally maintain exposure to the broader market, including many of the more extremely valued AI-related names, we generally are underweight those names relative to a passive exposure based on market capitalization. Any flight from AI stocks, therefore, is likely to negatively impact our portfolios, but we imagine to a lesser extent, given our generally lighter exposure to the trend. Just as was the case after the Tech Bubble, we tend to believe that funds that had been invested on-trend may shift to rather more unloved sections of the U.S. market and abroad. And that shift may find those stocks sporting characteristics we tend to appreciate gaining favor from investors increasingly weary and wary of the AI theme.

Important Information

“U.S. stocks” are represented by the S&P 500 Index measures the performance of the large-cap segment of the U.S. equity market.

The opinions expressed herein are those of SRCM as of the date of writing and are subject to change. The material is based on SRCM proprietary research and analysis of global markets and investing. The information and/or analysis contained in this material have been compiled, or arrived at, from sources believed to be reliable; however, SRCM does not make any representation as to their accuracy or completeness and does not accept liability for any loss arising from the use hereof. Some internally generated information may be considered theoretical in nature and is subject to inherent limitations associated thereby. Any market exposures referenced may or may not be represented in portfolios of clients of SRCM or its affiliates, and do not represent all securities purchased, sold or recommended for client accounts. The reader should not assume that any investments in market exposures identified or described were or will be profitable. The information in this material may contain projections or other forward-looking statements regarding future events, targets or expectations, and are current as of the date indicated. There is no assurance that such events or targets will be achieved. Thus, potential outcomes may be significantly different. This material is not intended as and should not be used to provide investment advice and is not an offer to sell a security or a solicitation or an offer, or a recommendation, to buy a security. Investors should consult with an advisor to determine the appropriate investment vehicle.

“No Risk-Free Path”

Responding to a reporter’s question regarding the Federal Reserve’s going-forward efforts to balance the risk of weaker employment against that of growing inflation, Chairman Powell stated, “We have a situation where we have two-sided risk, and that means there’s no risk-free path.” That two-sided risk scenario always exists, of course, but managing against both of the Fed’s “dual mandates” seems more challenging at present:

Neither the rate of unemployment nor the rate of inflation is immediately worrisome, though we’d agree with many that view the persistence of the latter well above the Fed’s 2% target as the greater threat
The directions of change are rather more concerning. Even so, the presumed drivers of upticks on both fronts include factors both abnormal and potentially ephemeral so the Fed’s caution—both in executing a quarter-point cut and in not making a larger cut—seems warranted
The Fed has said it will remain vigilant to incoming data, the bias of which prompted an “insurance cut” against an even weaker job market, a cut that otherwise may be too small to stoke inflation further

202509 SAM Commentary Download

Tight Ropes

Operating under a mandate to achieve “full employment” and to maintain “price stability”, the U.S. Federal Reserve is tasked with fostering a steady environment for macroeconomic growth. Critically, for neither of those quoted items is there a law-bound reference. Maximum employment, for the Fed, is that level of employment/unemployment, “that the economy can sustain while maintaining a stable inflation rate.” Leaving aside the reference to inflation, a wide range of factors variously over time will influence domestic employment. That leaves the “maximum” bit a TBD “we’ll know it when we see it” objective through time. The theory is that tighter labor markets are reflective of a robust economy, one that might be prone to experience heightened inflation pressures. One such pressure may stem from the theory that increased demand for workers relative to the labor force may spark greater demand for higher wages. As that incremental compensation chases an unchanged supply of goods, higher prices (inflation) may result. The reverse is thought to be true as well: an oversupply of potential workers leads to static or even falling compensation, dollars for which retailers must concede pricing in order to capture. The specific levels at which a theoretical equilibrium of just-enough labor supply/demand are thought to change through time as due to shifts in demographics, required skills, the pace at which jobs become available and are found, regional availability of jobs, regulation and otherwise.

Similarly, “price stability” has proved a moveable target over the years. Left without a specific aim prior to that time, the Fed has maintained a 2%-ish inflation target since January 2012. After realizing that target (plus or minus 0.10%) in only 17 of the 103 months thereafter, or about 17% of the time, the Fed in August 2020 shifted its stance toward its inflation target from a constant value to an average value, thereby allowing for excess inflation after periods of less-than-desired increases in prices and vice-versa. That shift came, rather ironically, just prior to the COVID-response- and Ukraine war-induced inflation spike in 2021-22. Since then, the 2% goal has remained well elusive, in this post-COVID era on account of annual price increases running far hotter than the Fed would like.

Where We are Now

As it stands, the level of unemployment remains on the tighter side of recent history, while inflation remains on the hotter side of the Fed’s target. By levels alone, one would think that the Federal Reserve should retain a reasonably restrictive stance. However, as we’ve noted so often in the past, in economics as in finance and investment, the direction and rate of change in metrics often matter more to decision makers and investors. And on those counts, the job market seems to be weakening somewhat, but the drivers of that weaking are curious and the pace of unemployment growth has been modest. Questions surround the potential impact of the reduction of the workforce based on falling immigration and the exodus of prior immigrants. Given stable demand for workers, labor markets should tighten. But hiring has slowed, with the younger generation of workers bearing the brunt of the burden. Perhaps the shift in tariff policy has dampened employer demand for labor, offsetting the loss of potential workers. Domestic manufacturers nonetheless continue to rank the building and retention of a reliable workforce among their greater challenges.

So it’s perhaps not surprising that wage growth remains robust (near 4% per year) for the average worker. As we suggested earlier, those extra funds may be putting upward pressure on prices. Changes in supply on account of tariffs could further these forces. And the tariffs themselves likely are showing up across the spectrum of goods and services. Services prices are also rising, perhaps as a result of higher wage demands as well. The upshot is that we no longer are in a state of disinflation. Even so, the change in the upward pace of inflation (the acceleration of inflation) remains tepid.

So, which is the greater concern? Which should be the greater concern? By cutting rates a quarter point earlier this month, the Fed confirmed that trends in employment were top of mind, even as the labor market remains historically tight (in the aggregate). The cut was small, however, qualified by Chairman Powell as “insurance” against greater weakness in the labor market. And by retaining what the Open Market Committee continues to believe is a modestly restrictive stance, the current rate backdrop should help to keep a lid on prices.

The decision continues to leave doors open from the remainder of the Fed meetings this year and into next. And on that front the Committee for good reasons we think can be seen as remaining more vigilant against inflation. The broader economy remains energetic: Gross Domestic Product (GDP) for the second quarter of this year was revised up to an annualized rate of 3.8% from a prior estimate of 3.3%, after a modest decline in Q1 (as suppliers bolstered inventories in advance of the revised tariff regime). Both GDP and employment are “lagging” indicators, in that they describe the past and tell us little of the future. But “coincident” and “leading” measures of the economy remain broadly healthy, if mixed. Retail sales, industrial production and personal income sit well on positive/growth side of the ledger. Manufacturing continues to struggle, but not greatly. Service providers, on the other hand, are showing broad strength, even as stubborn (and rising) prices may be crimping margins. Meantime, concerns about inflation are weighing down consumer sentiment, with trends there remaining on the negative side of recent history.

Taken together, the data express an economy that remains well on the growth side of the ledger, and not in any manner excessively so (leaving aside the bonkers growth in spending on generative artificial intelligence infrastructure, which we will address in next month’s commentary). But that strength may be based on narrow footing. Barring any dramatic break from present trend, then, we should not be surprised to find the Federal Reserve open to decisions presently divergent from current market expectations.

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I’ve Heard This One Before

In this podcast I speak to concerns regarding the AI land grab.

Decomposing 60/40

Bonds as Ballast

Softening the Blow

There is No 60/40

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Challenging Bias

Diversification’s Drawbacks

Hindsight Is No Guarantee

Cycles Cycle

Balancing Breadth

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What Am I Missing?

What’s Your Angle?

Why Relative Value Matters

Stocks Gonna Stock

More May Not Be Better

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This Is Risk

All Eyes on Oil

Where to from Here?

Optimists…for the Long Term

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The Risk of Being Wrong

Tilting Against the Tide

Matter of Factors

Discipline Trumps Timing

FAIr Value

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Wade or Jump?

Generally, But Not Always Up

But Maybe Don’t Just Sit There

As we look forward to the new year, we wish everyone a safe and festive close to 2025 and a grand launch into 2026!

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“AI” Already Means Nothing

Search for Meaning

All About Tomorrow

Cost/Profit Chasm

Too Critical Too Soon?

Of Course They Will

A Bubble to Burst?

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“No Risk-Free Path”

Tight Ropes

Where We are Now

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I’ve Heard This One Before

Clarity. Discipline. Consistency.