Mood vs. Money: What Surging Spending and Sinking Sentiment Tell Us About the Late-Cycle Economy
Tariff “buy-aheads,” tech’s $2.5 trillion slide, and an earnings beat built on cost-cuts are reshaping the investment landscape just as confidence cracks and labor finally cools.
Programming Note: I’m late this week after traveling from Seattle to a Pennsylvania for a wedding, but I finished this up on the flight home.
Key Takeaways
In the news: Shoppers keep spending—up 3.1% YoY on BofA cards—even as sentiment slumps, hinting at tariff-driven “buy-ahead” demand that could fade fast.
Chart of the Week: China’s equity ETF (MCHI) swung from worst to first in three months, underscoring how quickly policy headlines can flip geographic leadership.
Beyond Bias: Outcome bias tricks investors into judging decisions by results, not process—keep a decision journal to separate luck from skill.
Building Wealth: Practicing “mental subtraction” (imagining life without a benefit) heightens gratitude and curbs impulse spending.
Historical Perspective: Nixon’s 1971 gold-window closure shows that trust, not metal, underpins fiat money—and that monetary regimes can shift overnight.
Literature Review: A large-scale replication found only 3 of 14 classic experimental-market findings hold up, warning researchers and investors alike to be skeptical of single-study claims.
In the news
Consumers keep swiping even as confidence dives: Bank of America card data show spending up 3.1% YoY in mid-April, with pull-forward buying of cars and phones before 25% import tariffs hit.
Corporate earnings refuse to roll over: after one-third of the S&P 500 has reported, blended Q1 EPS growth ran 10.1% (vs. 7.2% on March 31) despite only 73% of companies beating estimates.
Tech leadership cracks: the “Magnificent 7” have shed $2.5 trillion in value YTD; ex-Mag 7, the S&P is off just 1.2%.
Labor market stays in the middle lane: February JOLTS showed openings flat at 7.6m, quits down to 2%, and layoffs still a rock-bottom 1.1%.
Why it matters
Sentiment–spending gap: historically, confidence that sinks while spending stays lofty is a late-cycle warning. Think 2000 and again in 2007, both episodes flipped from “mood divergence” to demand retrenchment within six–nine months.
Earnings breadth vs. depth: profits are rising, but half the sectors still post negative YoY growth. Investors who only look at the headline growth rate may miss the rotation underway from energy/industrials toward health care and utilities.
Market concentration risk: at last December’s peak the Mag 7 made up 36% of S&P market cap; when a handful of names drive both upside and downside, portfolio volatility can lurch more than the index headline suggests.
Tariff pull-forward distortions: a 1.4% March retail-sales pop looks healthy, but auto dealers report a “beat the duty” rush, sales that may vanish later this summer when sticker prices jump.
Consumers
Top-income households (+3% spending) are cushioning the aggregate data; lower-income spending is flat. If equity markets stay wobbly, the affluent cushion thins fast.
Airline spend –13% YoY hints at substitution: higher fuel surcharges and a 10% tariff on imported aircraft parts already leaking into ticket prices.
Corporate earnings
Average beat size (10%) is the biggest since early 2021, but revenue beats (1%) are the weakest in three years, translation: cost-cutting, not demand, is doing the lifting.
Forward 12-month P/E finally slipped below the 5-year mean (19.8 vs 19.9). Averages mask a huge gap: utilities trade at 16×, Nvidia at 23× after the sell-off.
Tech shake-out
DeepSeek’s January AI model pushed investors to re-price the U.S. AI “moat.” Nvidia’s $5.5 bn China-curb charge adds real earnings drag to the narrative.
Apple’s delayed Siri upgrade is a reminder that AI deployment, not just model creation, drives future revenue. Short-cycle hardware replacement matters when tariffs raise device prices.
Labor tightness lightens, but hasn’t snapped
Quits rate back to 2019 levels means wage growth should cool later this year, giving the Fed room to cut, one reason gold blasted through $3,400/oz on expectations of three 2025 rate cuts.
What we’re watching next week
Big Tech earnings blitz (Meta, Microsoft, Apple, Amazon) for any guidance on tariff pass-through and AI capital-expenditure plans.
ISM manufacturing and Friday payrolls: confirmation that job gains are slowing without turning negative would reinforce the “slow-bleed, not cliff-dive” thesis.
China’s April PMIs (Tuesday). A downside surprise could propel Beijing toward fresh stimulus and extend mainland equity outperformance since January 2024.
One thing DIY investors may overlook
Tariffs hit indexes you don’t expect. Roughly 40% of S&P 500 revenue is foreign-sourced; a global trade war can dent “domestic” ETFs just as much as exporters. Check revenue-by-region for any large holding, most fund fact-sheets bury it in the back pages.
The bottom line
Confidence is sagging, tech darlings look mortal and tariffs are warping near-term data, yet earnings and jobs keep the soft-landing narrative alive. The market’s cross-currents reward investors who track underlying drivers (revenue mix, labor churn, policy dates) rather than headline moves alone.
Chart of the Week: When Leadership Flips Overnight: China’s Sudden Surge vs. U.S. & Europe
The lines on this chart track the total-return scorecard for broad-based equity ETFs in China (MCHI), Europe (VGK) and the United States (VTI) starting 1 Jan 2024. Two things jump out. First, performance leadership has rotated sharply: U.S. stocks dominated for most of 2024, but a wave of Beijing stimulus headlines and index-rebalancing flows catapulted Chinese shares to the top of the table in early 2025, at one point showing a YTD gain of almost 47 percent (19 Mar) before sliding back toward the pack. Europe, by contrast, has delivered a steadier, but far more muted, return profile, reflecting a less policy-driven market and the region’s slower earnings cycle. These diverging paths illustrate how fast capital can pivot when macro narratives evolve: tariff escalations, rate-cut expectations, and National People’s Congress support measures sent money sluicing from one geography to another in a matter of weeks.
For investors the message is two-fold. First, a rising home-market index is not a proxy for economic strength, China’s equity surge materialised while its property sector was still deleveraging, and U.S. shares sagged even as domestic GDP prints remained solid. Understanding which catalysts are truly moving prices (policy pledges, liquidity shifts, index-reweightings) is essential for sizing positions and setting stop-losses. Second, the chart underscores the value of geographic diversification and active rebalancing: had a portfolio remained U.S.-centric after 2024’s rally, it would have missed the first-quarter 2025 pop in Chinese equities; by the same token, a China-heavy stance after March would have handed back a chunk of gains. In short, following cross-regional return dispersion, and the policy or flow events that create it, helps investors avoid anchoring on yesterday’s winners and forces a more dynamic, risk-aware asset-allocation process.
Beyond Bias: Outcome Bias — When Luck Masquerades as Skill
Outcome bias is the tendency to evaluate the quality of a decision based solely on its result, rather than on the decision-making process itself. In investing, this means that a risky trade that happens to pay off is often praised as brilliant, while a well-reasoned strategy that underperforms is dismissed as flawed. Baron and Hershey (1988) were among the first to demonstrate this bias empirically, finding that people rated the same medical decision as more or less ethical depending on whether the patient lived or died, even when the decision logic was identical. In finance, this plays out when investors idolize star managers after a lucky streak or beat themselves up for sticking to a sound thesis that happened to underperform in the short term. Over time, this distorts how investors learn, often reinforcing randomness instead of reason.
To counteract outcome bias, investors must separate process from performance, a core principle in disciplines like poker, aviation, and even military strategy. One effective method is decision journaling: before making an investment, write down the thesis, expected risks, and time horizon. After the outcome is known, revisit your notes not to ask “Did this make money?” but “Was this decision sound given what I knew at the time?” Research by Kahneman and Klein (2009) suggests that structured feedback loops are essential for improving judgment under uncertainty. Another tool is “premortem analysis,” where you assume a decision failed and work backward to ask why, shifting your focus from results to reasoning. The best investors don’t just chase returns, they seek repeatable, rational processes that can survive the randomness of markets.
Building Wealth: The “What If It Never Happened?” Tool — How Mental Subtraction Sharpens Gratitude and Spending
Mental subtraction, also known as counterfactual reflection, is a powerful and underutilized psychological tool that can reshape how we value what we already have. In a seminal study, Koo et al. (2008) demonstrated that participants who imagined the absence of a positive event (e.g., not meeting a spouse, not having a stable job) reported greater happiness than those who simply reflected on the presence of the same event. This flips our intuitive logic: instead of maximizing joy by counting blessings, we actually feel more grateful when we momentarily simulate a world in which those blessings never occurred. The reason lies in contrast: imagining life without something makes its value more salient, restoring emotional vividness dulled by habituation. This insight builds on decades of research in affective forecasting and adaptation, which shows that we quickly normalize gains (Frederick & Loewenstein, 1999) and therefore underestimate how good we already have it.
For DIY investors, the application is clear. Mental subtraction can help counteract lifestyle inflation, FOMO-driven spending, and the constant treadmill of material comparison. Instead of asking, “What should I add?” try asking, “What would life be like without this?” Whether it’s your job, your safety net, your health, or your time, this small mental shift brings clarity to what truly matters. It also refocuses financial decisions on value rather than novelty. By mentally subtracting your current assets or experiences, you’re more likely to hold onto what works and cut what doesn’t. In doing so, you foster a mindset of appreciation, not accumulation, which, as research from Dunn et al. (2011) suggests, leads to better financial well-being and more intentional, fulfilling use of money.
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Historical Perspective: The Day Money Lost Its Anchor — The 1971 Nixon Shock
On August 15, 1971, President Richard Nixon addressed the nation with a decision that would change the global financial system forever. In what became known as the “Nixon Shock,” he announced the suspension of the dollar’s convertibility into gold, effectively ending the Bretton Woods system that had governed international currency exchange since World War II. Until that point, the U.S. dollar was backed by gold at a fixed rate of $35 per ounce, giving it a unique position of global trust. But rising inflation, ballooning war costs, and persistent trade imbalances made the peg unsustainable. Nixon’s unilateral move transformed the dollar from a gold-backed currency to a fiat one, backed only by the full faith and credit of the U.S. government.
The implications were immediate and far-reaching. Freed from the discipline of gold reserves, central banks gained unprecedented flexibility to manage monetary policy, ushering in the modern era of inflation targeting and interest rate manipulation. In the short term, however, the dollar plunged, inflation soared, and trust in the monetary system wavered. Annual inflation in the U.S. reached 11% by 1974 and would peak above 13% by 1980, eroding real wages and upending investor expectations. Meanwhile, the dollar’s dominance persisted, not because of gold, but because of U.S. economic might and the lack of credible alternatives. The fiat system that emerged in the 1970s remains in place today, but the lesson is clear: trust in money is not fixed, it is political, historical, and deeply psychological.
Fast forward to 2025, and echoes of the Nixon Shock are everywhere. The U.S. once again faces questions about debt sustainability, inflation control, and global trust in its currency. While modern institutions like the Federal Reserve are far more sophisticated than their 1970s counterparts, the underlying tension remains: can fiat currency systems maintain credibility when fiscal and monetary authorities are under immense political pressure? Recent interest in Bitcoin, gold, and central bank digital currencies (CBDCs) speaks to a broader unease about fiat systems, and the fear that governments might again shift the rules overnight. The core lesson of 1971 isn’t that gold was lost, it’s that public confidence is the true reserve asset.
For investors, the Nixon Shock is a reminder that financial paradigms can shift suddenly, and without consensus. Those who believed the gold standard was unbreakable were blindsided. The same risk applies today to any belief that interest rates, inflation, or currency regimes will remain stable. Just as the 1970s rewarded those who hedged inflation and penalized those who held cash, today’s uncertain monetary backdrop calls for flexibility, skepticism, and a clear-eyed view of risk. Structural shifts in trust, whether in money, government, or policy, rarely announce themselves.
Supporting Research
Bordo, Michael D. (1993). The Bretton Woods International Monetary System: A Historical Overview. University of Chicago Press.
Eichengreen, Barry (2008). Globalizing Capital: A History of the International Monetary System. Princeton University Press.
Volcker, Paul & Gyohten, Toyoo (1992). Changing Fortunes: The World’s Money and the Threat to American Leadership. Times Books.
Federal Reserve History (2023). “Nixon Ends Convertibility of U.S. Dollars to Gold and Announces Wage/Price Controls.”
Krugman, Paul (1999). The Return of Depression Economics. W.W. Norton & Company.
Literature Review: Do Experimental Markets Replicate? A New Study Tests the Evidence
In financial economics, few tools are as alluring as the experimental asset market, a controlled laboratory setting where researchers simulate a simplified version of a stock market to study how real people behave when trading assets. These experiments offer rare clarity: unlike real-world markets, researchers know the “true” fundamental value of the asset being traded, allowing them to observe mispricing, bubbles, and crashes without the usual noise. First introduced by Smith, Suchanek, and Williams in 1988, this method has been widely used to explore questions about investor psychology, market efficiency, and behavioral biases. But a major question has hovered over the field for years: do the flashy results from these lab markets actually hold up under scrutiny?
A new study by Huber, Holzmeister, Johannesson, and colleagues (2024) delivers a sobering answer. In a massive replication project, the authors tested 17 prominent findings from four well-cited studies in the experimental asset markets literature. Using a sample more than seven times larger than the original studies, 1,544 participants across 166 markets, the team was able to apply high-powered statistical tests to reexamine previous claims. The result? Only 3 of the 14 original findings reported as statistically significant replicated successfully. The average replication effect size was just 2.9% of the original. In other words, the majority of findings that once drew headlines in the field simply didn’t hold up.
What were these findings? The original studies tested hypotheses like: Do emotions, such as excitement, cause investors to drive prices into bubble territory? Does low self-control make markets more prone to mispricing? Do female-dominated markets behave differently than male-dominated ones? And do cognitive traits like fluid intelligence and “Theory of Mind”, the ability to understand others’ mental states, predict trading success? The new replications cast doubt on most of these effects. For example, the researchers found no evidence that excitement or low self-control increased overpricing, despite dramatic claims in earlier papers. Even widely accepted gender-related findings, such as the notion that female traders reduce bubble risk, failed to replicate.
The study doesn’t dismiss the entire literature. One finding did hold up strongly: market experience matters. Participants who had already traded in one experimental session were significantly less prone to overpricing in subsequent rounds. This aligns with a longstanding “stylized fact” in the literature, also supported by earlier work by Hussam, Porter, and Smith (2008), showing that repeated exposure to trading helps individuals internalize the asset’s declining fundamental value. The study also confirmed that cognitive traits like fluid intelligence and Theory of Mind were modestly predictive of trading performance, though the effects were weaker than originally reported.
The implications are significant. Replication crises are nothing new in psychology and economics, but this study suggests that experimental finance, a field often praised for its rigor and control, is not immune. One possible reason for the original overstatements is low statistical power: many of the initial studies had very small sample sizes, often with just six to ten independent markets per treatment. As statisticians like Ioannidis and Gelman have long warned, this leads to inflated effect sizes and a high risk of false positives. The authors also point to poor randomization, limited incentives, and publication bias as contributing factors. In all, the findings underscore the importance of skepticism, especially when bold claims are based on a single lab experiment. For researchers, it’s a call to embrace larger samples, preregistration, and direct replication. For investors and policymakers tempted to over-interpret behavioral experiments, it’s a reminder: even well-designed labs can produce mirages.
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