The leading performer by far since the crisis started on Feb. 28 is bank loans. The Invesco Senior Loan ETF (BKLN) is up 2.8% during this period, well ahead of the rest of the field.

The rest of the winners since Feb. 28: floating-rate securities (FLRN), a cash proxy (SHV), standard “junk” bonds (SJNK and JNK), and short-term inflation-indexed Treasuries (STIP). The remainder of the market is nursing losses, led by long Treasuries (TLT), which are currently posting a loss in excess of 5%.

What explains the performance divide? Treasuries are under pressure as inflation concerns lurk due to the run-up in the cost of energy. This spike has raised headline measures of prices and prompted forecasts that the Federal Reserve will be forced to raise interest rates later this year.

That’s hardly a bullish backdrop for fixed-income securities, yet bank loans and junk bonds have managed to post gains. One reason: private credit fundamentals remain strong despite recent market stress, according to Goldman Sachs. Defaults have been low and borrower performance solid. “The fundamentals of private credit still appear strong,” says Vivek Bantwal, global co-head of private credit at Goldman Sachs Asset Management.

Add in the higher yields and the package has been too good to ignore for investors. BKLN’s distribution yield is 6.61% (as of June 9), or nearly two percentage points above the long-bond’s current yield.

But the easy gains may be in the rearview mirror as the lingering inflationary effects of the Iran conflict continue to resonate. With no easy solutions on the horizon for a crisis that continues to keep Gulf energy exports low, the odds still look slim for a return to pre-war pricing pressure in the near term.

BKLN appears to be pricing in the shifting sentiment, driven by fading optimism for a quick end to the conflict and the macro blowback. The ETF is still comfortably ahead of the field since Feb. 28, but the recent peak looks like a ceiling for the foreseeable future without a material change in the outlook for a resumption in shipping through the Strait of Hormuz.

While markets are currently betting against a prolonged conflict, the latest news flow continues to challenge this forecast for the near term. Despite former President Trump’s calls for restraint, Israel and Iran’s recent strikes suggest that a resolution is still nowhere on the horizon.

The latest sign that ending the conflict will be messy and take longer than expected came on Sunday, when Iran and Israel resumed fighting—exchanging missile strikes for the first time since the April cease-fire. President Trump said he would demand that Israel not retaliate, but that effort has failed as renewed fighting continues into Monday. As the Times of Israel reports: “The Israeli military says it is prepared for at least a few more days of fighting against Iran, and potentially a full resumption of the war.”

Unsurprisingly, oil prices spiked, rising 4% in early Monday trading. Crude remains below its peak since the war began on Feb. 28, but a return to pre-war prices looks unlikely anytime soon.

The renewed conflict between Iran and Israel may not be shocking, nor is it likely to radically shift expectations relative to recent history. But this hydra-headed conflict has momentum on multiple fronts, suggesting that the crisis, even if it doesn’t deepen, will endure in one form or another. The macro risk, as a result, is becoming chronic rather than actute.

Depending on one’s view, markets have developed either a degree of acceptance or complacency about the conflict and its macroeconomic implications. Christopher Smart, a former trade adviser and Treasury official in the Obama administration, noted: last week: “With every passing day, the world is learning to live without the Gulf’s seaborne exports.”

True—but that tolerance has always been precarious, built on the assumption that normalcy in the Middle East would soon return. As the crisis drags on, the logic behind that assumption weakens, and the fallout is increasingly spilling into the U.S. economy.

Friday’s upbeat payrolls report is a case in point. In ordinary times, news of solid hiring for a third consecutive month would be celebrated on Wall Street. But in the current climate, good economic news is bad news for the bond market: a robust labor market suggests the Federal Reserve will face growing pressure to raise interest rates to offset the supply‑side energy shock pushing headline inflation higher.

Fed funds futures still price in no change at the next several policy meetings, including the June 17 FOMC gathering, when new Fed Chair Kevin Warsh makes his public debut at the post‑meeting press conference. But the Treasury market is becoming increasingly anxious—the policy‑sensitive two‑year yield continues to climb well above the median Fed funds rate, underscoring the bond market’s expectation that a rate hike is near.

The conflict is becoming harder to end because violence is spreading across multiple fronts, major powers’ goals are diverging, and the political conditions needed for de‑escalation are eroding rather than improving. A key factor that will be difficult to minimize: Iran has discovered that controlling the world’s most important energy chokepoint gives it strategic leverage that even great‑power military pressure cannot fully neutralize. This has emboldened Tehran and reshaped regional deterrence dynamics.

Markets have only partially priced in this risk, assuming that a return to normal was close at hand. Facts on the ground suggest otherwise—a reality that has yet to be fully reflected in asset prices or monetary policy.

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Inflation worries weighed on markets last week. Not exactly news at this late date, but the better‑than‑expected US payrolls data for May highlighted that the world’s biggest economy remains resilient in the face of an energy crisis. Treasury yields, unsurprisingly, rose as investors sharpened their focus on the possibility that inflation risk may linger longer than recently expected, supported by a relatively robust economy, which in turn lifts the odds that the Federal Reserve may soon start raising interest rates.

No one should dismiss these concerns, but it’s still early for strategic‑minded investors to assume the worst‑case scenario is baked in. By some measures, a pullback was overdue. The S&P 500 Index had rallied for nine straight weeks, a relatively rare event with only ten prior occurrences to the latest run‑up, according to The Motley Fool. The odds for a pause were high even before Friday’s surprisingly strong jobs report.

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Global asset‑allocation strategies suffered on Friday as well. All of our proxy ETFs fell sharply last week. The aggressive strategy (AOA) was especially hard hit, slumping 2.3%.

continue reading at The ETF Portfolio Strategist

● 1873: The Rothschilds, the First Great Depression, and the Making of the Modern World
Liaquat Ahamed
Review via The Wall Street Journal
In one single unforgettable day—Friday, May 9, 1873—prices on the Vienna Stock Exchange plunged by 45%. All at once the world was changed.
“1873,” by Liaquat Ahamed, author of “Lords of Finance: The Bankers Who Broke the World” (2009), is the story of the trans-Atlantic depression that followed the crash. It is the story, too, of a generation-long, befuddling decline in the prices of all kinds of things. In 1878, confronting the lowest prices for pig iron since colonial times, American ironmasters wondered if the smokestacks on their idled blast furnaces might serve a higher use as astronomical observatories.

● Lightning Beneath the Sea: The Race to Wire the World and the Dawn of the Information Age
James M. Tabor
Review via The Wall Street Journal
You’ll rarely know for certain, but when you send an email, check your social-media feed or read this newspaper online, you may be sending pulses of light through a conduit the size of a garden hose resting on the floor of the sea. Around 500 fiber-optic cables, not counting those owned by governments, stretch for more than a million total miles beneath the oceans. They provide the physical backbone for the weightless world of the internet. Life without them would be hard to imagine.

● Contingent Expectations: Uncertainty, Risk, and Economic Behavior in Historical Perspective
Alexander Nützenadel and Jochen Streb
Summary via publisher (Princeton U. Press)
Expectations play a crucial role in shaping economic behavior. But how are expectations actually formed, and how has this changed over time? The financial crisis of 2007–08 cast doubt on traditional theories of expectation formation, particularly the rational expectations framework. In Contingent Expectations, Alexander Nützenadel and Jochen Streb examine the ways that past experiences influence the economic expectations and decision-making of households, investors, and policymakers through history, and offer an alternative perspective. Combining a comprehensive empirical analysis of expectation formation from the eighteenth century to the present day with an assessment of the relevant economic theory, Nützenadel and Streb present a new theoretical framework, contingent expectations, for understanding economic expectation.

Please note that the links to books above are affiliate links with Amazon.com and James Picerno (a.k.a. The Capital Spectator) earns money if you buy one of the titles listed. Also note that you will not pay extra for a book even though it generates revenue for The Capital Spectator. By purchasing books through this site, you provide support for The Capital Spectator’s free content. Thank you!

How Fear Beats Greed: The Impact of Positive and Negative Sentiment on Global Stock Markets
Jiye Ryu (Hongik University), et al.
February 2026
This paper investigates the impact of positive and negative sentiment on stock returns and volatilities across developed and emerging markets using the consumer confidence index as a proxy for sentiment. We conduct a comparative study of developed and emerging markets to assess whether sentiment-driven mispricing is due to overpricing or underpricing and to examine the effect of sentiment on return and risk dynamics.

Diversification Under Stress: Empirical Evidence of Correlation Breakdown Across Sectors and International Markets
Fabio Trachsler (ETH Zürich)
May 2026
We investigate whether portfolio diversification across U.S. sectors and international equity markets retains its risk-reducing properties under stress. Using daily return data from 1999 to 2025 spanning ten sector ETFs and eight international equity indices, we document a systematic correlation breakdown: mean pairwise correlations rise significantly during crisis episodes, precisely when diversification would be most valuable. Across nine crisis periods and 59 systematically identified stress events, correlations increase in 71.2 % of all cases, with a mean ∆ρ = 0.094 that is statistically highly significant (p < 0.0001). We show that the nature of a shock matters more than its magnitude: idiosyncratic events leave correlations intact, while systemic shocks produce strong co-movement across all sectors simultaneously. Sector and international diversification fail together in systemic crises but diverge in idiosyncratic ones, a distinction that, to our knowledge, has not been systematically documented across this breadth of episodes. We introduce the Trachsler Resilience Score, a formal criterion for selecting sectors that are robust to correlation breakdown under stress, and validate it out-of-sample: the Resilience Portfolio reduces maximum drawdown in 83 % of independent stress events (p = 0.003). Our findings suggest that naive diversification offers substantially less protection than classical portfolio theory implies, and that crisis-conditional correlation structure should be an explicit input to portfolio construction.

Industry Rotation Using Market-State Similarity
Valeriy Zakamulin (University of Agde)
May 2026
This paper studies whether lagged market returns contain useful information about subsequent industry returns and whether this information can be used for active industry rotation. Using monthly Kenneth French industry portfolios, we first show that conventional mean predictability is weak. Quantile predictive regressions, however, reveal that market-to-industry predictability is concentrated mainly in the tails of the conditional return distribution, especially in downside states. Motivated by this evidence, we propose a non-parametric strategy based on market-state similarity. At each portfolio-formation date, the strategy identifies historical months in which the standardized market excess return was closest to its current value, measures subsequent industry performance after those similar states, and goes long industries with positive historical subsequent returns and short industries with negative historical subsequent returns. Out-of-sample simulations show that the active strategy delivers a higher Sharpe ratio, lower volatility, and substantially smaller drawdowns than the passive equal-weighted industry benchmark. The results are robust to industry classification and model-parameter choices.

When Sector ETFs Pull Apart
Jackson Wang (independent)
May 2026
The SPDR Select-Sector ETFs are usually treated as positively correlated slices of one underlying market. Across the full 1999-2026 sample of daily returns the average pairwise correlation among the 11 sectors is well above 0.5, and almost no two sectors have ever been unconditionally negatively correlated. Conditioning on a rolling 60-day window, however, surfaces narrow but recurring divergence regimes: episodes in which the cross-sectional spread between the best-and worst-performing sector explodes, and pairs that are usually mildly positive flip to outright negative correlation. The XLK-XLE pair, for example, has a long-run average rolling correlation of 0.41 but reached-0.43 on 2000-10-17 during the dot-com unwind. We build a long history of these regimes and find that the most recent example-the post-ChatGPT AI boom from 2022-11-30 through 2026-05-08-is one of the largest in the sample: SMH returned 410.1% and XLK 164.9% while XLE managed 37.6% and XLP only 19.8%. During this window the average rolling 60-day correlation between XLK and XLE collapsed to 0.15. A naive cross-sectional sector momentum long/short (top-2 long, bottom-2 short, monthly rebalanced, three-month lookback) does not earn a positive risk premium over the full sample (Sharpe-0.14), but a long-only top-2 momentum sleeve captures most of the AI-boom rotation, returning 68.8% over the window vs SPY’s 95.0%. The lesson is that sector ETF divergence is real, identifiable, and useful for tilting equity exposure, but generic mean-reversion or momentum strategies do not naturally monetize it.

Regime-Based Portfolio Allocation Using Hidden Markov Models and Reinforcement Learning
Ajay Kumar Verma (independent), et al.
November 2025
This study develops a regime-aware portfolio allocation framework that integrates Markov switching models with Reinforcement Learning (RL) to dynamically allocate across equities (SPY), long-term Treasuries (TLT), and gold (GLD). Using daily ETF data from 2004-2025, we first characterize market behavior through a discrete Markov chain and then estimate a three-state Gaussian Hidden Markov Model (HMM) selected by the Bayesian Information Criterion (BIC). The estimated regimes-low-volatility, transitional, and high-volatility-exhibit strong persistence and state-dependent return dynamics consistent with recent findings on nonlinear market states (Ardia et al., 2024; Gupta & Pierdzioch, 2023). State-conditional analysis shows that SPY dominates in stable regimes, while TLT and GLD provide protection during stressed periods, motivating regime-conditioned allocation rules.

Learn To Use R For Portfolio Analysis
Quantitative Investment Portfolio Analytics In R:
An Introduction To R For Modeling Portfolio Risk and Return
By James Picerno

The 6.59% trailing yield for the SPDR High Yield Bond ETF (JNK) remains the payout leader. On the opposite end of the spectrum, US stocks (VTI) are posting the lowest yield at 1.06%.

The highest‑yielding asset classes offer trailing yields above Treasuries, which top out at 4.99% for the 30‑year maturity.

For comparison, consumer inflation is running at 3.80% on an annual basis through April. Using that benchmark, about half of the major asset classes are generating positive real yields. The average trailing yield across all asset classes is 3.95%, slightly above the current inflation rate.

For readers eyeing these yields as a basis for asset allocation, the usual caveats apply. Trailing payout rates may or may not persist. Unlike the ability to lock in current yields on government bonds through a buy‑and‑hold strategy, historical payout rates for risk assets—such as those delivered via ETFs—can be misleading in real time because both payout amounts and share prices fluctuate. The table above is presented as a first step for comparing yields and considering how to structure a portfolio when the goal focuses on generating income.

One reason to be cautious when reviewing trailing yield is the ever‑present risk that whatever you earn in payouts from ETFs could be offset—or more than offset—by declining share prices. That’s why it’s essential to consider total‑return expectations when evaluating yield opportunities. For perspective on forward‑looking performance, you can start with the monthly updates of CapitalSpectator.com’s long‑term outlook for major asset classes.

The opportunity to earn yields above the “risk‑free” payout rates on US Treasuries may look appealing, but relatively high yields generally signal higher risks. That doesn’t mean it’s misguided to build a portfolio designed to maximize yield, but it’s rarely, if ever, a free lunch.

Learn To Use R For Portfolio Analysis
Quantitative Investment Portfolio Analytics In R:
An Introduction To R For Modeling Portfolio Risk and Return
By James Picerno

Output is projected to rise 2.5% in Q2, based on the median nowcast from a set of estimates compiled by CapitalSpectator.com. If correct, the Q2 report (scheduled for July 30) will mark a moderately stronger gain than the 1.6% increase in Q1.

Recent Q2 nowcasts have been stable, holding in the 2%-plus range. Today’s 2.5% estimate is up slightly from the previous 2.4% nowcast on May 21.

The concern is that deeper economic pain for the US has only been delayed rather than avoided. A prominent economic bear—Moody’s chief economist Mark Zandi—says the spike in oil prices has raised recession risk. Without a deal with Iran to reopen energy exports through the Strait of Hormuz, gas prices could soon top $5 a gallon, which he predicts would lead to lower consumer spending and an economic downturn. Writing on social media last week, he said:

Consumers are running out of financial resources to maintain their spending, which stalled out last month. And then, of course, there is the surge in inflation, which is closing in on 4%, double the Federal Reserve’s target. And all of this comes after massive deficit‑financed tax cuts, which are now fading fast. The Iran war needs to end, and the Strait of Hormuz needs to be reopened soon, or recession will become more likely than not.

HFI Research, an energy research firm, predicts: “By the end of June, if the Strait of Hormuz is still closed, global oil inventory operational minimum is guaranteed.”

The latest news from the Gulf isn’t encouraging. The US and Iran have launched new strikes, raising fresh uncertainty about the prospects for peace talks.

When and how the ongoing conflict and disruption to energy supplies will affect the US economy remains unclear. For now, at least, the effects on GDP nowcasts appear minimal. US growth is probably slower than it otherwise would have been absent the war, but recession risk remains low for the moment. The next move—up or down— in the nowcasts may depend on how long the energy bottleneck lasts.

GMI is a market-value-weighted mix of the major asset classes (excluding cash) via ETF proxies. Today’s long-run outlook is calculated as the average of three models (defined below). The current 7.6% annualized estimate for GMI ticked up from last month’s estimate, but is still substantially below the trailing 10.1% annualized return that GMI has generated over the past decade.

In line with recent history, about a third of GMI’s components are projected to generate returns below their respective results over the past ten years (indicated by the red boxes in the far-right column below). GMI expected performance is also subpar vs. its trailing ten-year history through May: 7.6% vs. 10.1%.

GMI represents a theoretical benchmark for the “optimal” portfolio that’s suited for the average investor with an infinite time horizon. Accordingly, GMI is useful as a starting point for customizing asset allocation and portfolio design to match a particular investor’s expectations, objectives, risk tolerance, etc. GMI’s history suggests that this passive benchmark’s performance will be competitive with most active asset-allocation strategies, especially after adjusting for risk, trading costs and taxes.

It’s likely that some, most or possibly all of the forecasts above will be wide of the mark in some degree. GMI’s projections, however, are expected to be somewhat more reliable vs. the estimates for its  components. Predictions for the specific markets (US stocks, commodities, etc.) are subject to greater variability compared with aggregating the forecasts into the GMI estimate, a process that may reduce some of the errors through time.

Another way to view the projections above is to use the estimates as a baseline for refining expectations. For instance, the point forecasts above can be adjusted with additional modeling that accounts for other factors and assumptions not used here. Customizing portfolios for a specfic investor, to reflect risk tolerance, time horizon, and so on, is also recommended.

For perspective on how GMI’s realized total return has evolved through time, consider the benchmark’s track record on a rolling 10-year annualized basis. The chart below compares GMI’s performance vs. ETFs tracking US stocks and US bonds through last month. GMI’s current return for the past ten years is a robust annualized 10.1%.

Here’s a brief summary of how the forecasts are generated and definitions of the other metrics in the table above:

BB: The Building Block model uses historical returns as a proxy for estimating the future. The sample period used starts in January 1998 (the earliest available date for all the asset classes listed above). The procedure is to calculate the risk premium for each asset class, compute the annualized return and then add an expected risk-free rate to generate a total return forecast. For the expected risk-free rate, we’re using the latest yield on the 10-year Treasury Inflation Protected Security (TIPS). This yield is considered a market estimate of a risk-free, real (inflation-adjusted) return for a “safe” asset — this “risk-free” rate is also used for all the models outlined below. Note that the BB model used here is (loosely) based on a methodology originally outlined by Ibbotson Associates (a division of Morningstar).

EQ: The Equilibrium model reverse engineers expected return by way of risk. Rather than trying to predict return directly, this model relies on the somewhat more reliable framework of using risk metrics to estimate future performance. The process is relatively robust in the sense that forecasting risk is slightly easier than projecting return. The three inputs:

* An estimate of the overall portfolio’s expected market price of risk, defined as the Sharpe ratio, which is the ratio of risk premia to volatility (standard deviation). Note: the “portfolio” here and throughout is defined as GMI

* The expected volatility (standard deviation) of each asset (GMI’s market components)

* The expected correlation for each asset relative to the portfolio (GMI)

This model for estimating equilibrium returns was initially outlined in a 1974 paper by Professor Bill Sharpe. For a summary, see Gary Brinson’s explanation in Chapter 3 of The Portable MBA in Investment. I also review the model in my book Dynamic Asset Allocation. Note that this methodology initially estimates a risk premium and then adds an expected risk-free rate to arrive at total return forecasts. The expected risk-free rate is outlined in BB above.

ADJ: This methodology is identical to the Equilibrium model (EQ) outlined above with one exception: the forecasts are adjusted based on short-term momentum and longer-term mean reversion factors. Momentum is defined as the current price relative to the trailing 12-month moving average. The mean reversion factor is estimated as the current price relative to the trailing 60-month (5-year) moving average. The equilibrium forecasts are adjusted based on current prices relative to the 12-month and 60-month moving averages. If current prices are above (below) the moving averages, the unadjusted risk premia estimates are decreased (increased). The formula for adjustment is simply taking the inverse of the average of the current price to the two moving averages. For example: if an asset class’s current price is 10% above its 12-month moving average and 20% over its 60-month moving average, the unadjusted forecast is reduced by 15% (the average of 10% and 20%). The logic here is that when prices are relatively high vs. recent history, the equilibrium forecasts are reduced. On the flip side, when prices are relatively low vs. recent history, the equilibrium forecasts are increased.

Avg: This column is a simple average of the three forecasts for each row (asset class)

10yr Ret: For perspective on actual returns, this column shows the trailing 10-year annualized total return for the asset classes through the current target month.

Spread: Average-model forecast less trailing 10-year return.

US stocks led the rally in May: Vanguard Total US Stock Market ETF (VTI) rose 5.2%, the fund’s strongest monthly gain in a year. Developed-market equities ex-US (VEA) posted a solid second-place performance, advancing 4.3% and marking a second-straight monthly increase.

US bonds (BND) edged higher for a second month. Inflation-indexed Treasuries (TIP) also moved higher again in May. Notably, TIP is outperforming the broad US fixed-income benchmark (BND) so far in 2026 by more than a percentage point: 1.7% vs. 0.5%.

The main loser last month: a broad measure of commodities (GSG), which fell 7.5%. Despite the setback, commodities are still the leading performer for the major asset classes, clocking in with a near-38% rally in 2026.

All but one of the major asset classes are posting year-to-date gains. The only red ink on the ledger for 2026: governments bonds in developed markets ex-US (BWX) are off 0.9% for the year.

The Global Market Index (GMI) rallied again in May, rising 4.1% and extending its winning streak to 13 of the past 14 months.  GMI is an unmanaged benchmark (maintained by The Capital Spectator) that holds all the major asset classes (except cash) in market-value weights via ETFs and serves as a competitive benchmark for globally diversified, multi-asset-class portfolio strategies.