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42 & the ultimate question(s) about αI

AI may level the playing field, but only if you know the right questions

Picking up where we left off…

As noted last week, I participated in an LSEG panel on the complex intersection between artificial intelligence (AI), geopolitics and investment management. Last week I wrote about how AI and geopolitics are complexly co-determined by a quest for ultimate power. As promised, this week I’m writing about how AI, and particularly large language models (LLMs), affect investment management. The answer, paradoxically, relates to the ultimate questions on life, the universe and everything, and ends up being a natural advertisement for my research.

The Ultimate Question of Life, the Universe and Everything

Readers of Douglas Adams’ The Hitchhiker’s Guide to the Galaxy “trilogy” will surely have recognized the references above, but for others let me explain. In Adams’ book, a race of hyper-intelligent, pan-dimensional mice create a supercomputer to find “the Answer to the Ultimate Question of Life, the Universe, and Everything.” After 7.5 million years the computer, “Deep Thought,” spits out the answer: “42.” The mice, puzzled and exasperated, then ask Deep Thought, “what is the Ultimate question?” Although Deep Thought cannot tell them, it designs an organic computer – the earth – that can (though it is sadly destroyed seconds before revealing the Ultimate question).

“It is better to know some of the questions than all of the answers.”

– James Thurber (American humorist)

The question matters

Adams’ amusing plot line amusingly illustrates James Thurber’s point above. An answer’s meaning may change depending on the question being asked. For instance, “Peanuts” has very different implications in response to the questions “What are your favorite nuts?” and “What are you allergic to?” Similarly, slight differences in a question can create radically different answers. E.g. “What is the meaning of life?” versus “What is the meaning of mylife?” Or, “Is it legal?” versus “Is it just?”

Answer first, as questions later

This is a serious problem for AI users: AI will provide an answer, it may even provide a good answer, but the answer it yields is only useful if you asked it the right question. If you ask your favorite LLM “What should I wear to dinner tonight?” and it suggests “smart casual trousers with a polo shirt,” you have no one to blame but yourself for your embarrassment if you fail to add the critical detail “at Buckingham Palace.” AI’s “question problem” can be acutely dangerous in investment management, especially when widely available to investors. To understand why, it is helpful to understand the effects of the quantitative revolution that has taken place in finance over the last few decades and how AI, particularly LLMs, extend them.

From Liar’s Poker to Flashboys

Finance generates immense amounts of numeric data – prices, volumes, et cetera – but until computing and digitization allowed for its rapid analysis, statistical methods were of limited use in trading. As desktop computing spread in the 1980s and began to uncover “hidden” profitable relationships in these data, banks and asset managers started to replace the quick-calculating but relatively uneducated “city boys” from Brooklyn and Bow that Michael Lewis famously caricatured in Liar’s Poker with PhDs in Mathematics, Statistics, Economics, and Physics. Even fundamental investors became “quantimental,” i.e. their subjective views are increasingly based in statistical analyses. But the “alpha” (α) – excess returns – these methods produced proved elusive. Academia churned out hordes of new quantitative PhDs to exploit every conceivable source of digital alpha across markets. Like the last mastodons, they hunted it to the furthest reaches of digitization such that three decades later Mr. Lewis wrote Flash Boys about ultra-high-frequency traders slaying alpha hiding in the nano seconds between others’ trades.

LLMs’ terra nova

AI encompasses a broad array of models that include some of the models in the “digital” revolution described above, including high-frequency algorithms. But LLMs and their early predecessors, natural language processing, opened a “land bridge” to an immense terra nova full of new alpha-dons to be hunted both by quants and their quantimental cousins. As vast as the numeric data in finance are, they represent a small fraction of the information LLMs can exploit in “unstructured” written, spoken and visual data. Previously, a corporate earnings call would yield just dozens of numbers – revenue, costs, earnings, et cetera – but now the entire call can be digitized for sentiment, nuance, hints about future sales, industry trends, et cetera. In a matter of hours, a single analyst using an LLM can scour the earnings calls of every Russell 3000 company for trends in consumer spending, margins, or passthrough of tariffs.

The herd chasing herd

The vast new information landscape opened by LLMs, in theory, offer infinite new herds of alpha. But only if you know where to look! That brings us back to the problem of Douglas Adams’ mice: what is the question?! Or, in this case questions? The very vastness of the potential space and the limits of (most) human imagination encourages the hunters themselves to herd, chasing the plentiful alpha-dons across plains of the visible horizon rather than into dark forests or up craggy mountain valleys. But LLMs make the problem of crowding potentially worse both by greatly expanding the number of hunters, and through reinforcement of biases and errors.

New hunters join the chase

In the age of computer-distilled digital data, the number of hunters were at least constrained by the throughput of mathematically oriented new graduates. But LLMs allow every Art History major (like Michael Lewis) to code in Python and query SEC databases just as did the quants of old, but now with access to far more data than before. Now everyone can be a quantimentalist alpha hunter! This is a blessing, as I describe below, but it does increase the crowds of alpha hunters.

All models have problems

But perhaps a greater problem with AI – that likely amplifies the harms caused by crowding – is error reinforcement. All optimization models are subject to error for at least four reasons: (1) misspecification, i.e. the hypothesized model may be wrong; (2) bias in the data collection – or, especially in the case of AI, data creation – that biases the result; (3) natural randomness in the data that distort results; or (4) infrequent outlier events that may be either over or underrepresented in the data, overstating (understating) the model’s fit, and understating (overstating) its risk. AI, and particularly LLMs, may be especially subject to these risks in non-obvious ways.

To error is human, AI perfects

This creates potentially serious reinforcement biases. LLMs draw their data not from the “truth” but from the available pool of human knowledge. If human understanding is wrong – or even just the understanding of a consensus of humans – the pool of knowledge will be biased and LLMs will reflect that bias. But LLM users are not independent to this process: as generators of the biased data, they are themselves biased and thus predisposed to ask questions that align with their bias. As any pollster knows, both the questions one asks and their wording greatly affect the answer returned.11 Hence, where human bias exists, the questions LLM users ask are likely to reflect “misspecification” that amplifies LLMs inherent bias (model errors (1) and (2)).

Self-reinforcing feedback loops

Consider the following feedback loop: academic economists came up with models to rationalize the low growth and inflation of the last decade; central bankers internalized those models in their speeches; Wall Street analysts mimicked them, and the press regurgitated their implications. Along came Covid and in its wake inflation, growth, interest rates, and earnings, all greatly exceeded expectations. The same academics, central bankers, analysts, and press have subsequently refined a long chain of post hoc rationalizations that either were known when the forecasts were made (e.g. fiscal policy) or were originally cited as reasons why the jump in all these variables would be “transitory” (e.g. supply constraints and demand shocks). Ask your favorite LLM why growth, inflation, interest rates, and earnings all persistently exceeded expectations in 2021-’25; does its answer reflect the “truth” or a biased pool of knowledge?

No one saw that coming

More so than traditional, structured-data models, reliance on LLMs is also likely to miss outliers and encourage herding. While both Classical and Bayesian statistics offer probabilistic measures of model fit, LLMs typically are evaluated versus pre-determined benchmarks for accuracy where the objective “truth” against which they are judged can be ambiguous (e.g. what is the correct answer to “What is the meaning of life?”). This makes the questions asked even more important. LLM optimization will “regress to the mean,” i.e. give you the most commonly accepted answers, without regard to the magnitude of variation in the actual data (model error (3)). That may be fine for “What’s the average elevation of Kansas?”; but not for “What’s the average elevation of Colorado?” It becomes especially troublesome when large outliers are possible but not prevalent in the data, e.g. “What’s the probability that Donald Trump wins the US presidency?” asked in 2016; or overrepresented in the data, e.g. “What’s the probability that Marine Le Pen wins the French presidency?” asked in 2016 (model error (4)).

Falling alpha and rising sigmas

These characteristics of AI and LLMs make their increasing use in investment management a cause of some concern. Despite the immense potential for alpha that LLMs offer, human tendencies combined with these model’s question dependency suggest they are likely to increase herding behavior in markets. Indeed, there is evidence that even in our short history with them, LLMs are impairing people’s ability to ask the right questions!22 There are two unfortunate consequences of this. The first is that just as the original digital revolution in quantitative finance bid away reliable alpha from markets, LLMs are likely to do the same, especially in areas of finance that were previously dominated by fundamental analysis (by humans) of nonstructured data. But the flip side is that herding generates significant potential for volatility, especially when the crowd is wrong. Thus, the likelihood of “six sigma” risk events is increasing, even as alpha falls.33

Something wicked this way comes

These problems are likely to be intensified by the change in the global risk environment. Technological change and – especially – political and geopolitical realignment are creating far greater Uncertainty, i.e. nonquantifiable risks. Since these risks are non-quantifiable, they are not well suited to traditional statistical analysis, and since they are infrequent or even absent from the historical record, they are less likely to be identified by LLMs.

The good news about AI

Yet, there are good reasons to cheer AI’s development and be hopeful about the opportunities it opens in investment management. For most of my career in finance the industry has consolidated as digitization created economies of scale and encouraged asset accumulation by large firms that could afford either to develop increasingly complex quantitative models or large teams of fundamental analysts (or both). As alluded above, AI levels the field immensely. Now small teams and even individual investors (or Substackers) can feasibly analyze thousands of companies with sophisticated AI-enabled screening algorithms cheaply and quickly like this one from YWR. Further, these new “hunters” are more likely to generate questions that allow them to explore the forests and hidden valleys of the pool of human knowledge.

Give alpha a chance

Indeed, LLMs with the right questions give individual investors a great advantage over large investors, who by definition inhabit the consensus. Using Howard Marks’ 2×2 risk framework (Figure 1), the ideal place for an investor to be is out of consensus.44 If you’re wrong, there’s little downside because there’s no crowd trying to get out with you. If you’re right, the upside can be immense as the crowd runs to join you. Being in consensus gives the exact opposite payoff profile: little upside with big risks.

The truth is out there

But being both right and out of consensus requires asking the right questions. One of the econometricians that taught me, the late Nobel laureate Clive Granger, was fond of saying “if your model doesn’t forecast, it’s misspecified; if it does, it might be correct.” My “model,” which is largely based on asking the right questions, correctly forecast Donald Trump’s election (and Brexit) in 2016, Marine Le Pen’s loss a few months later, the “Missingflation” and low interest rates of the 2010s, and high post-Covid inflation, growth, earnings, and interest rates. More importantly, because I asked the right questions initially, the reasoning behind my forecasts didn’t pivot ex post like so many who got all or most of those wrong. That’s one of the main reasons why I never worry about AI taking my job (even if I do worry that Skynet – i.e. AGI – ends humanity). It’s also a good reason why – sorry for the shameless plug – you should subscribe to Seriously, Marvin?! and Thematic Markets.

Comment

1

“Experiments in Wording Opinion Questions,” Graham Kalton, Martin Collins & Lindsay Brook, Journal of the Royal Statistical Society Series C: Applied Statistics, vol. 27, no. 2, June 1978, pp. 149–161.

2

“The Philosophic Turn for AI Agents: Replacing centralized digital rhetoric with decentralized truth-seeking,” Philipp Koralus, working paper, HAI Lab, Institute for Ethics in AI, University of Oxford, 24 April 2025.

3

In statistics, the standard deviation, a measure of the average variability in a random variable is usually denoted with the Greek letter sigma. An extremely unlikely event, the proverbial “one-in-10,000-years” occurrence, is often called a “six-sigma” event, indicating that it is extremely unlikely.

4

“Dare to Be Great,” Howard Marks, Memo to: Oaktree Clients, Oaktree Capital Management, 7 September 2006.

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Economic and market forecasts:

Economic & market forecasts
  • Sustained higher US rates, vulnerability of backend rates and term premia to steepening in H2 2023: Are we there yet? 

  • No recession, continued strength of US economy throughout 2023: Solved: Drivers of the dollar cycle; Clash of the Themes; Are we there yet?; Opportunity knocks: Are you listening?; Götterdämmerung.

  • No backtracking or “pivot” by the Fed in 2023: Solved: Drivers of the dollar cycle; Clash of the Themes; Are we there yet?; Opportunity knocks: Are you listening?; Götterdämmerung.

  • No banking crisis or reversal by the Fed following the failures of Silicon Valley Bank: Did QE cause bank failures? Opportunity knocks: Are you listening? 

  • No new “Plaza Accord” resulting from US dollar strength and Fed rate hikes in 2022: Plaza 2.0 bid, not offered. 

  • No default by Nigeria before or in the aftermath of the 2023 national elections: Debt reality versus perceptions. 

  • The continued fall in US real rates through the 2000s and early 2010s. Themes & framework: Mercantilism (with Chinese characteristics) and the associated $Bloc/Chinese co-prosperity sphere undermined the marginal product of capital in the US while simultaneously increasing non-US demand for US Treasuries.

  • Emerging market outperformance in the 2000s. Themes & framework: Mercantilism (with Chinese characteristics) drove both the development of the $Bloc/Chinese co-prosperity sphere and the commodity supercycle, while Apex neoliberalism supported institutional reforms that lowered EM risk premia, all of which encouraged foreign direct investment that raised productivity and led to rapid economic growth.

  • The end of emerging markets’ “original sin” and growth of EM local bond markets, a development supported by a G7 initiative that I led at the US Treasury. Themes & framework: The $Bloc/Chinese co-prosperity sphere provided a new stability in many EM exchange rates while institutional reforms undertaken by many as part of Apex neoliberalism lowered EM risk premia. 

  • The Global Financial Crisis (albeit see admission of errors below). Themes & framework: Mercantilism (with Chinese characteristics) and $Bloc/Chinese co-prosperity sphere simultaneously increased incentives for debt finance in the US (as demand for “safe” USD bonds rose globally) while undermining US means of repayment as the US marginal product of capital in traded goods fell.  Combined with poorly designed bank regulation that allowed banks to leverage themselves well beyond regulators’ intent (Apex neoliberalism), “complexity cascaded”.

  • The failure of QE to generate post-GFC inflation. Themes & framework: Believing is being: inflation expectations were stable to falling amid deleveraging and associated lethargic income growth, lowering real interest rates as nominal rates were pinned at the zero lower bound, and implying sustained weak money demand.  Stuffing banks with more reserves changed none of those variables.  As Keynes described seven decades earlier – in the absence of negative nominal rates – monetary policy at the zero lower bound is equivalent to “pushing on a string”.

  • The lack of effect of balance sheet runoff on interest rates. Themes & framework: Believing is being and portfolio theory rejected the then dominant “portfolio balance” theory of QE.  QE was a credible commitment by central banks to keep rates on hold, suppressing expectations for future rates and thus the yield curve.  This is why the “Taper Tantrum” had its largest effects on 3-5 year forward rates as expectations for rates hiked rose, while long-dated forward rates fell.  Measures of long-dated term premia continued to fall as the Fed reduced its balance sheet.

  • Peak Chinese growth in 2011. Themes & framework: Mercantilism (with Chinese characteristics) led to unsustainable contributions of investment to GDP and a collapse in China’s marginal product of capital amid historically large debt to GDP, a phenomenon that peaked with China’s 50% surge in domestic credit in response to the GFC.  Domestic funding of its debt trapped China within its own financial repression scheme, frustrating its efforts to unwind its $Bloc/Chinese co-prosperity sphere and internationalization the renminbi as a closed capital account is required to avoid savings flight and a collapse of the debt bubble. This self-funded debt bubble implies that losses from consequent overinvestment are “amortized” in the form of slower growth.

  • Emerging Market underperformance of the last decade (and likely future decade). Themes & framework: Institutional reform in EM peaked with US policy credibility before the GFC under Apex neoliberalism; China’s peak within Mercantilism (with Chinese characteristics) in 2011 and the associated end of the commodity supercycle ended the “pack” economic benefits of the $Bloc/Chinese co-prosperity sphere, while simultaneously the advent of Localization began to shift production back to advanced economies.  Amid stagnating growth and backsliding reforms, EM FX and asset prices looked (and continue to look) overvalued and risk spreads remain too thin.

  • Falling USD reserve share in the 2000s and a rising share since peak China. Themes & framework: The massive reserve accumulation required to sustain undervalution of the $Bloc/Chinese co-prosperity sphere came to be seen as “sovereign wealth” that required diversification, rather than a liquidity store for crises, and led to a consequent fall in the USD share of reserves.  Yet the greater financial openness and cross-border claims that accompanied the Apex neoliberalism necessarily implied a proportional increase in capital outflows in periods of risk aversion; i.e. historically large reserves were not as large as perceived in reality.  As China and the $Bloc/Chinese co-prosperity sphere slowed after 2011, and Global entropy increased Uncertainty with attendant effects on risk aversion, emerging markets learned painfully in 2014 that reserves were neither excess nor sovereign wealth, but instead necessary liquidity, and that the USD’s safe haven properties were unparalleled.

  • Low and falling inflation throughout the 2010s. Themes & framework: Missingflation, the unexplained trend component of global inflation that had led to two decades of inflation overforecasting by economists, showed no signs of abating and seemed to be caused by a continued slide in inflation expectations (Believing is being) as central banks struggled with the zero lower bound.  The end of the trend would require both for central banks to make more credible commitment to raise inflation in conjunction with a sustained positive inflation shock.

  • The (trend) bottoming of long-term US real rates, higher-than-expected peak in Fed funds rates and US equity outperformance in the last decade. Themes & framework:  China’s peak in 2011 under Mercantilism (with Chinese characteristics) and the dissolution of the $Bloc/Chinese co-prosperity sphere ended the trend of falling US real rates they had created.  But it wasn’t until Localization gathered sufficient steam – and the US private sector had deleveraged – that US openness and technological leadership sufficiently raised US returns to capital to support a rise in US real rates.  The Trump administration’s political support (Politics of Rage) for Localization gave another nudge to US relative returns to capital, and Covid again accelerated these phenomena further (and likely more sustainably).

  • The capitulation of Saudi Arabia’s price leadership/management in 2014 in the face of surging US tight oil production leading to lower and more volatile crude oil prices. Themes & framework: The end of the commodity supercycle brought about by peak China under Mercantilism (with Chinese characteristics) paused relentless crude oil demand growth, allowing innovation and a business-friendly US regime to undermined Saudi Arabia’s price leadership with tight oil production.  Cartel dynamics combined with Saudi Arabia’s long-run price maximization led to a collapse in Saudi-enforced OPEC discipline, and lower, more volatile crude oil prices.

  • Financial volatility’s shift to a lower median level with more frequent, shorter explosions during the last decade. Themes & framework: Rising Uncertainty in politics (Politics of Rage), geopolitics (Global entropy), technology (Localization), and policy (Missingflation) amid Complexity cascades shifted the relative shares of total risk away from quantifiable sources towards unquantifiable sources; the counter-intuitive implication of rising uncertainty is lower median volatility as active risk taking retreats in information lulls, with violent explosions of price activity when new information is revealed.

  • Persistence of post-Covid supply constraints and sustainable inflation supported by rearview-mirror central bank policies leading to a flip in the direction of Missingflation. Themes & framework: Covid simultaneously accelerated both the economic and political motivations for Localization, caused a permanent shift in the structure of global demand, and disrupted existing global supply chains. Short-run aggregate supply could not adjust to the jump in investment and shift in demand quickly enough, creating prolonged shortages and the need for prices to curtail demand. The associated cost-push inflation was all the spark needed to ignite Believing is being changes in inflation expectation driven by central banks’ backward-looking policies based on a lack of understanding of Missingflation.

  • The Fed’s post-Covid rate cycle would be more like 1994 than the post-2000 gradualist cycles (a call I made in early 2021). Themes & framework: The intent of the Fed’s FAIT policy always was to boost long-run inflation expectations by falling “behind the curve” on inflation.  Yet their lack of understanding of the causes of Missingflation and insufficient faith in their own ability to generate Believing is being kept them focused on “fighting the last war” too long, allowing inflation to run too hot, too quickly.  But contrary to market conventional wisdom, inflation is deeply politically unpopular in an aging society and no central banker wants to be remembered as failing to control inflation.  Accordingly, the Fed (eventually) will react forcefully to contain the Believing is being genie they underestimated.

  • Consistent underperformance of European economy, assets and the euro since the Global Financial Crisis. Themes & framework: Relative to trend growth, Europe was more highly indebted than the US, yet European policymakers too long considered the GFC an “American problem”.  Combined with Europe’s greater institutional rigidities and a reluctance to write down bad assets, it would take proportionately longer to achieve necessary deleveraging.  China’s 2011 peak within Mercantilism (with Chinese characteristics) and the rise of Localization undermined all parts of Europe: the globalization-dependent South and China-dependent North, while inflexibility and low levels of technological innovation inhibit Europe’s ability to adjust to the new global economic order.

  • UK outperformance of consensus Brexit forecasts. Themes & framework: Consensus forecast for post-Brexit UK were based on three flawed assumptions (due to underappreciation of themes!). First, by ignoring emergent Localization, growth forecasts grounded in Apex neoliberalism wrongly assumed globalization would continue to be a major driver of economic growth.  Second, the consensus failed to acknowledge the UK’s long-run structural competitiveness: world-leading universities providing a technological edge in Localization; and strong, enduring institutions offering safety and stability amid Global entropy and rising Uncertainty.  Third, the consensus, ironically, ignored the largest driver of trade (by a wide margin) in their own models: “gravity”, or proximity to trading partners.  Network effects are extraordinarily powerful in trade and difficult to overcome.

  • Importance and implications for markets of Scottish independence referendum in 2014. Themes & framework: As one of the earliest manifestations of the Politics of Rage and its demands for greater political representation, the Scottish referendum was an unanticipated shock to markets and one of the first signs of Global entropy and the Uncertainty to come.

Global entropy

Manifest and growing disorder

By ignoring the endogeneity of complex systems and Rodrick’s globalization trilemma – that democracy, national self-determination, and economic globalization cannot enduringly coexist – Apex neoliberalism sowed the seeds of its own demise, leading to today’s manifest and growing global disorder: the end of Post-World War II international rules, rising ethnonationalism, multipolarity, the Politics of Rage, and the unwinding of globalized supply chains.  In short, Huntington’s Clash of Civilizations trumped Fukuyama’s End of History.  Apex neoliberalism facilitated rising trade-to-income ratios, cross-border capital flows, intergovernmental cooperation, and intra-economy income inequality; while simultaneously lowering financial spreads, inter-economy income inequality, and inter-state warfare.  Global entropy likely will reverse many of these effects.

Missingflation

Economists don’t understand inflation

What are economists missing about inflation?  In the two decades before Covid, market analysts, academic economists and central banks consistently overforecast inflation; in the last two years they have persistently underforecast it.  Enduring one-way errors are not “white noise”; they demonstrate bias and strongly suggest that economists’ current understanding of inflation is flawed.  Demographics, globalization and technology help to explain some of the forecast miss, but significant omitted variable bias remains, most likely due to failure to explicitly incorporate Believing is being.

Believing is being

Self-fulfilling beliefs are real

Beliefs drive everything from asset bubbles, to debt dynamics, to crypto currencies’ values, to inflation and hyperinflations (probably Missingflation, too).  Good economists understand this but often omit beliefs from models to simplify because of the difficulty in measuring beliefs.  Unfortunately, too many bad economists copy those models without understanding the potential for omitted variable bias.  The rapid social, technological, political, and geopolitical changes behind Global entropy and Uncertainty are swiftly shifting beliefs, driving a feedback loop of economic and political outcomes.  Yet the infrequency of these deviations and difficulty in measuring them make statistical modeling nearly impossible.  Only through economic theory and full-spectrum information collection can we infer when and how beliefs are adjusting and their likely effects.

Complexity cascades

Complex systems fail unpredictably

Human societies, nation states and (especially) economies are examples of complex systems. Complex systems always operate in “broken” mode and ironically are more structurally stable when they have lots of small failures.  But when they are subjected to massive or cascading shocks, complex systems can fail unpredictably and totally.  Covid and manifest Global entropy represent self-reinforcing mammoth shockwaves that imply systemic collapses – in all spheres, socio-political, geopolitical, economic, and financial – are more likely than the consensus admits.

Uncertainty

Not all risks can be quantified

All risks are not the same.  Some are quantifiable, like the chance of being dealt an ace in a game of cards. Others are not but can be subjectively guessed, like the chance you leave a casino a winner. Then there is uncertainty, the most dangerous of all risks because it is by definition, non-quantifiable: what is the chance the casino gets hit by a meteor?  Apex neoliberalism created a façade of quantifiable risks; Global entropy and Complexity cascades are illustrating that the world is far more uncertain.  The quantitative models in finance, business, economics, and politics that gained dominance during Apex neoliberalism generally have performed poorly as Global entropy has become more pronounced, a trend that is likely to sustain as uncertainty rises further.  Scenario analysis and “satisficing” are the only proven frameworks for dealing with uncertainty.

Politics of Rage

The proletariat want their franchise back

Four decades ago, globalization and increasing economic returns to intellectual capital opened a fissure between elites and everyone else, especially in more developed economies.  The economic and political consequences of Apex neoliberalism widened this fissure into a chasm of mistrust that has resulted in the political turmoil that engulfed most advanced economies in the last decade.  Contrary to conventional wisdom, its causes derive more from perceived and actual political disenfranchisement than economic distress and inequality.  Trends in the former suggest the wave is not near cresting, implying sustained socio-political, geopolitical and economic disruptions.

$Bloc/Chinese co-prosperity sphere

FX herding cures “fear of floating”

The “co-prosperity sphere” of bloc managed exchange rates centered around Chinese trade and the US financial system, alternatively known as Bretton Woods II or Chimerica, dramatically reoriented global supply chains, supported emerging markets’ financial development and economic boom of the 2000s, drove much of the dollar’s 2002-’11 depreciation, and ultimately likely caused the Global Financial Crisis.  Emerging market crises of the late 1990s marked the final chapter in the Bretton Woods exchange rate system.  Yet “Fear of floating” persisted until China’s Mercantilism and contemporaneous accession to WTO provided an alternative: exchange rates managed by “herd” or by “pack”.  Hiding within the herd provided financial stability for China’s EM trading partners, while simultaneously allowing them hunt as a pack for foreign direct investment and supply-chain dominance.  The size and rapid growth of the co-prosperity sphere distorted the global economy like a massive stellar object warps space-time.  Collective suppression of exchange rates and domestic cost of capital diverted supply chain growth into the bloc, while attendant reserve accumulation led to a surge in demand for “safe” core economy bonds.  The former undermined returns to capital in traded goods production outside the bloc and the latter depressed interest rates on “safe” US debt, encouraging overinvestment in non-traded goods like housing.  (Note: I labeled this phenomenon “the dollar bloc” when I first wrote about it in 2003-04, but later referred to it as “the co-prosperity sphere”.)

Mercantilism (with Chinese Characteristics)

State capitalism’s unintended costs

China’s 1994-2012 “miracle” that lifted nearly a billion people out of poverty and its current growth problems both originate in its extreme application of the mercantilist “Asian growth model” originated by Japan and later copied by Asia’s “Tigers”.  A combination of capital controls, protectionism, domestic financial repression, and industrial policy direct underpriced capital to favored industries that promote rapid capital accumulation and development by leveraging external demand (and technology) from advanced economies. Rapid development and convergence comes at cost, however.  Underpriced capital and exchange rates lead to distortive overinvestment.  Those losses are realized either abruptly and painfully through write downs – like those enforced on late-‘90s Asian Crisis economies by the IMF – or, if the economy can self fund, are “amortized” as lost future growth.  Japan’s lost decade and China’s current funk are examples of the amortization path of economic loss.

Apex neoliberalism

Liberal capital democracy’s pyrrhic victory

Rapid global growth, particularly in the less developed world, “hyperglobalized” production and the growth of inter-governmental coordination derive substantively from the triumph of neoliberalism that followed the collapse of its ideological competitors with the Soviet Union’s fall and emerging market crises of the 1990s. But so too did the seeds of its undoing: The Politics of Rage, Mercantilism (with Chinese characteristics), Missingflation, and ultimately Global entropy.  Rapid adoption of Western economic institutions and trade mechanisms followed from neoliberalism’s victory, promoting a world of hyperglobalization: ever-more dispersed but integrated global supply chains, just-in-time industrial processes with reduced redundancy, unfettered cross-border capital flows, and uniform rules that increased the influence of international institutions, non-governmental organizations and multinationals at the expense of local political control and less-skilled citizenry.  Resultant uniformity and coincident digitization created a façade of certainty and quantification, promoting an overreliance on quantitative methods in decision processes, risk control and forecasting.

Economic and market phenomena:

Economic & market forecasts
  • The continued fall in US real rates through the 2000s and early 2010s.  Themes & framework: Mercantilism (with Chinese characteristics) and the associated $Bloc/Chinese co-prosperity sphere undermined the marginal product of capital in the US while simultaneously increasing non-US demand for US Treasuries.
     
  • Emerging market outperformance in the 2000s.  Themes & framework: Mercantilism (with Chinese characteristics) drove both the development of the $Bloc/Chinese co-prosperity sphere and the commodity supercycle, while Apex neoliberalism supported institutional reforms that lowered EM risk premia, all of which encouraged foreign direct investment that raised productivity and led to rapid economic growth.
     
  • The end of emerging markets’ “original sin” and growth of EM local bond markets, a development supported by a G7 initiative that I led at the US Treasury.  Themes & framework: The $Bloc/Chinese co-prosperity sphere provided a new stability in many EM exchange rates while institutional reforms undertaken by many as part of Apex neoliberalism lowered EM risk premia.
     
  • The Global Financial Crisis (albeit see admission of errors below).  Themes & framework: Mercantilism (with Chinese characteristics) and $Bloc/Chinese co-prosperity sphere simultaneously increased incentives for debt finance in the US (as demand for “safe” USD bonds rose globally) while undermining US means of repayment as the US marginal product of capital in traded goods fell.  Combined with poorly designed bank regulation that allowed banks to leverage themselves well beyond regulators’ intent (Apex neoliberalism), “complexity cascaded”.
     
  • The failure of QE to generate post-GFC inflation.  Themes & framework: Believing is being: inflation expectations were stable to falling amid deleveraging and associated lethargic income growth, lowering real interest rates as nominal rates were pinned at the zero lower bound, and implying sustained weak money demand.  Stuffing banks with more reserves changed none of those variables.  As Keynes described seven decades earlier – in the absence of negative nominal rates – monetary policy at the zero lower bound is equivalent to “pushing on a string”.
     
  • The lack of effect of balance sheet runoff on interest rates.  Themes & framework: Believing is being and portfolio theory rejected the then dominant “portfolio balance” theory of QE.  QE was a credible commitment by central banks to keep rates on hold, suppressing expectations for future rates and thus the yield curve.  This is why the “Taper Tantrum” had its largest effects on 3-5 year forward rates as expectations for rates hiked rose, while long-dated forward rates fell.  Measures of long-dated term premia continued to fall as the Fed reduced its balance sheet.
     
  • Peak Chinese growth in 2011.  Themes & framework: Mercantilism (with Chinese characteristics) led to unsustainable contributions of investment to GDP and a collapse in China’s marginal product of capital amid historically large debt to GDP, a phenomenon that peaked with China’s 50% surge in domestic credit in response to the GFC.  Domestic funding of its debt trapped China within its own financial repression scheme, frustrating its efforts to unwind its $Bloc/Chinese co-prosperity sphere and internationalization the renminbi as a closed capital account is required to avoid savings flight and a collapse of the debt bubble. This self-funded debt bubble implies that losses from consequent overinvestment are “amortized” in the form of slower growth.
     
  • Emerging Market underperformance of the last decade (and likely future decade).  Themes & framework: Institutional reform in EM peaked with US policy credibility before the GFC under Apex neoliberalism; China’s peak within Mercantilism (with Chinese characteristics) in 2011 and the associated end of the commodity supercycle ended the “pack” economic benefits of the $Bloc/Chinese co-prosperity sphere, while simultaneously the advent of Localization began to shift production back to advanced economies.  Amid stagnating growth and backsliding reforms, EM FX and asset prices looked (and continue to look) overvalued and risk spreads remain too thin.
     
  • Falling USD reserve share in the 2000s and a rising share since peak China.  Themes & framework: The massive reserve accumulation required to sustain undervalution of the $Bloc/Chinese co-prosperity sphere came to be seen as “sovereign wealth” that required diversification, rather than a liquidity store for crises, and led to a consequent fall in the USD share of reserves.  Yet the greater financial openness and cross-border claims that accompanied the Apex neoliberalism necessarily implied a proportional increase in capital outflows in periods of risk aversion; i.e. historically large reserves were not as large as perceived in reality.  As China and the $Bloc/Chinese co-prosperity sphere slowed after 2011, and Global entropy increased Uncertainty with attendant effects on risk aversion, emerging markets learned painfully in 2014 that reserves were neither excess nor sovereign wealth, but instead necessary liquidity, and that the USD’s safe haven properties were unparalleled.
     
  • Low and falling inflation throughout the 2010s.  Themes & framework: Missingflation, the unexplained trend component of global inflation that had led to two decades of inflation overforecasting by economists, showed no signs of abating and seemed to be caused by a continued slide in inflation expectations (Believing is being) as central banks struggled with the zero lower bound.  The end of the trend would require both for central banks to make more credible commitment to raise inflation in conjunction with a sustained positive inflation shock.
     
  • The (trend) bottoming of long-term US real rates, higher-than-expected peak in Fed funds rates and US equity outperformance in the last decade.  Themes & framework:  China’s peak in 2011 under Mercantilism (with Chinese characteristics) and the dissolution of the $Bloc/Chinese co-prosperity sphere ended the trend of falling US real rates they had created.  But it wasn’t until Localization gathered sufficient steam – and the US private sector had deleveraged – that US openness and technological leadership sufficiently raised US returns to capital to support a rise in US real rates.  The Trump administration’s political support (Politics of Rage) for Localization gave another nudge to US relative returns to capital, and Covid again accelerated these phenomena further (and likely more sustainably).
     
  • The capitulation of Saudi Arabia’s price leadership/management in 2014 in the face of surging US tight oil production leading to lower and more volatile crude oil prices.  Themes & framework: The end of the commodity supercycle brought about by peak China under Mercantilism (with Chinese characteristics) paused relentless crude oil demand growth, allowing innovation and a business-friendly US regime to undermined Saudi Arabia’s price leadership with tight oil production.  Cartel dynamics combined with Saudi Arabia’s long-run price maximization led to a collapse in Saudi-enforced OPEC discipline, and lower, more volatile crude oil prices.
     
  • Financial volatility’s shift to a lower median level with more frequent, shorter explosions during the last decade.  Themes & framework: Rising Uncertainty in politics (Politics of Rage), geopolitics (Global entropy), technology (Localization), and policy (Missingflation) amid Complexity cascades shifted the relative shares of total risk away from quantifiable sources towards unquantifiable sources; the counter-intuitive implication of rising uncertainty is lower median volatility as active risk taking retreats in information lulls, with violent explosions of price activity when new information is revealed.
     
  • Persistence of post-Covid supply constraints and sustainable inflation supported by rearview-mirror central bank policies leading to a flip in the direction of Missingflation.  Themes & framework: Covid simultaneously accelerated both the economic and political motivations for Localization, caused a permanent shift in the structure of global demand, and disrupted existing global supply chains. Short-run aggregate supply could not adjust to the jump in investment and shift in demand quickly enough, creating prolonged shortages and the need for prices to curtail demand. The associated cost-push inflation was all the spark needed to ignite Believing is being changes in inflation expectation driven by central banks’ backward-looking policies based on a lack of understanding of Missingflation.
     
  • The Fed’s post-Covid rate cycle would be more like 1994 than the post-2000 gradualist cycles (a call I made in early 2021).  Themes & framework: The intent of the Fed’s FAIT policy always was to boost long-run inflation expectations by falling “behind the curve” on inflation.  Yet their lack of understanding of the causes of Missingflation and insufficient faith in their own ability to generate Believing is being kept them focused on “fighting the last war” too long, allowing inflation to run too hot, too quickly.  But contrary to market conventional wisdom, inflation is deeply politically unpopular in an aging society and no central banker wants to be remembered as failing to control inflation.  Accordingly, the Fed (eventually) will react forcefully to contain the Believing is being genie they underestimated.
     
  • Consistent underperformance of European economy, assets and the euro since the Global Financial Crisis.  Themes & framework: Relative to trend growth, Europe was more highly indebted than the US, yet European policymakers too long considered the GFC an “American problem”.  Combined with Europe’s greater institutional rigidities and a reluctance to write down bad assets, it would take proportionately longer to achieve necessary deleveraging.  China’s 2011 peak within Mercantilism (with Chinese characteristics) and the rise of Localization undermined all parts of Europe: the globalization-dependent South and China-dependent North, while inflexibility and low levels of technological innovation inhibit Europe’s ability to adjust to the new global economic order.
     
  • UK outperformance of consensus Brexit forecasts.  Themes & framework: Consensus forecast for post-Brexit UK were based on three flawed assumptions (due to underappreciation of themes!). First, by ignoring emergent Localization, growth forecasts grounded in Apex neoliberalism wrongly assumed globalization would continue to be a major driver of economic growth.  Second, the consensus failed to acknowledge the UK’s long-run structural competitiveness: world-leading universities providing a technological edge in Localization; and strong, enduring institutions offering safety and stability amid Global entropy and rising Uncertainty.  Third, the consensus, ironically, ignored the largest driver of trade (by a wide margin) in their own models: “gravity”, or proximity to trading partners.  Network effects are extraordinarily powerful in trade and difficult to overcome.
     
  • Importance and implications for markets of Scottish independence referendum in 2014.  Themes & framework: As one of the earliest manifestations of the Politics of Rage and its demands for greater political representation, the Scottish referendum was an unanticipated shock to markets and one of the first signs of Global entropy and the Uncertainty to come.

Foreign exchange forecasts:

Foreign exchange forecasts
  • The dollar’s trend fall 2002-’11. Themes & framework: The effects of the $Bloc/Chinese co-prosperity sphere on relative returns to capital, balance of payments and the effects of “diversification” as sovereign reserves evolved into sovereign wealth.

  • The dollar’s trend turn in 2011, surge 2014-2016, and counter-consensus strength in 2018 and 2021 (I was the only sell-side analyst to forecast USD strength in 2021). Themes & framework: The same forces driving the US real rates higher in the last several years – the end of Mercantilism (with Chinese characteristics) and its associated $Bloc/Chinese co-prosperity sphere, disproportionate benefit to the US from accelerating Localization, Global entropy and Complexity cascades, all played out in the USD, too, with added support from increased safe haven demand for the greenback due to rising Uncertainty brought about by Global entropy and Complexity cascades.

  • The euro’s plunge from $1.36 in mid 2014 to $1.05 in early 2015. Themes & framework: Reluctant deleveraging from the Global Financial Crisis combined with the sharp deterioration in European returns to capital following China’s peak within Mercantilism (with Chinese characteristics) and the shift to Localization from globalization implied a sharply lower real value of the euro. When ECB President Draghi ruled out the deflationary (1990s Japan) path to devaluation with his commitment to “whatever it takes”, Believing is being implied an immediate and massive change in the nominal value of the euro was required as the expected path for domestic prices flipped.

  • The yen’s surge from above 120 per dollar to below 105 in H1 2016. Themes & framework: Abenomics’ biggest success was its Believing is being commitment to reflate Japan’s economy, leading to a sharp depreciation of the nominal yen as expected future deflation was unwound. But yen depreciation included an “overshoot” to compensate for the risks that inflation might overshoot. The Bank of Japan’s tacit admission that “Quantitative & Qualitative Easing” could not create sufficient inflation with its December 2015 adoption of negative interest rates implied a rapid unwind of the overshoot given the yen’s deep undervaluation as beliefs shifted again.

  • The pound sterling’s pre-EU referendum fall to $1.40, post-referendum floor near $1.20, and its post-Brexit rebound above $1.30. Themes & framework: A steep risk premium in sterling was required to compensate for the Uncertainty induced by the Politics of Rage driven jolt.  Yet, 1.20 represented a 60+ year low in purchasing power parity and seemed to undervalue the UK’s long-run structural assets – strong institutions, top global universities, leading tech industry – all of which were appreciating in value in a world of Localization, Global entropy, broader Uncertainty, and rising potential for Complexity cascades.

Political forecasts:

  • Political instability in Russia and China in 2023: Clash of the Themes.

  • Brexit 2016. Themes & framework: The chasm between elites (including those in markets) and ordinary citizens over the latter’s sense of disenfranchisement, the fundamental cause of the Politics of Rage, was clear well before the referendum and strongly suggested that bias in polling turnout models could fully account for the polls’ projected margin of defeat.

  • Trump 2016. Themes & framework: Record pre-election postal votes from registered independents (for whom no party seeks to “get out the vote”) suggested that, as with Brexit, the Politics of Rage’s disenfranchised and unaccounted for voters would be sufficient to overcome the (narrow) projected margin of loss.

  • Marine Le Pen’s 2017 success in reaching the run-off but ultimate failure to win French presidency. Themes & framework: The Politics of Rage framework suggested an undercounting of both Le Pen and left-wing populists’ support, giving her a clear path to the second round. But her projected margin of loss in the general election was far too large to be due solely to turnout bias, implying no chance of second-round success, particularly with left-wing populist voters dropping out or shifting support to Macron.

  • Trump narrow loss, post-election conflict 2020. Themes & framework: Pollsters’ mistaken focus on education levels as the source of their 2016 turnout errors rather than on (mis)trust driven by the Politics of Rage implied polls still were biased. Large shifts in minority voters towards Trump and unusually high “undecided” voters late in a highly polarized election also suggested the magnitude of Trump’s outperformance would be large (indeed, it was larger than 2016).  But the margin to overcome also was much wider in 2020, suggesting a close loss by Trump. Increasing polarization and mistrust on both sides implied a violent reaction by a minority, whichever side lost.

Admission of errors:

I get things wrong, too, but hopefully am the wiser for it.  This list is far from complete, but represents some of the ones that both stung and taught me the most.

  • Failing to specify financial institutions as at risk from credit trauma in 2023. While I did highlight that the most predictable source of for 2023 would be credit events following the massive rise in interest rates in 2022 (Debt reality versus perceptions), I didn’t specifically identify banks and other financial institutions as especially vulnerable, which proved to be the case with the failure of a few regional US banks in March 2023. Lessons learnt: Sometimes one’s focus on the underlying causes blinds to the obvious consequences, and levered entities with broad exposure will always be at risk from any traumas within an economy, even if sector or region specific.

  • Not seeing the Global Financial Crisis sooner. I saw the GFC earlier than many, but the extent of the financial system’s capital shortfall eluded me far longer than it should have.  At the US Treasury from 2006 through early 2008 I was responsible for assessing foreign financial risks (sadly, institutional territorialism prevented an integrated approach with my domestic-side colleagues that may have focused more attention on off-balance-sheet financing of US housing).  I chaired meeting after meeting in 2006-‘07 with market professionals, academics, regulators, and other policymakers where a noisy minority of participants argued that historic levels of debt to GDP implied an impending crisis.  Yet when I challenged them to explain a channel of transmission, what would be the tipping point, why it had not occurred already, or to present evidence that bank capital was insufficient to absorb even an historic drop in US housing prices, none – including some who have become very famous for “calling” the GFC – could do so.  Ultimately, it was the behavior of banks in funding markets in mid-to-late 2007 that clued me in that banks’ capital bases might not be what they purported (or, equivalently, contingent liabilities off balance sheet were far larger than people understood).  What I had not done – nor, apparently had any of the “experts” I consulted in those years – was the detailed micro-level analysis that the protagonists of The Big Short had done (kudos to them).  Lessons learnt: 1. “Macro” analysis often requires “micro foundations”; 2. many people claim expertise, don’t rely on it without evidence; and 3. notwithstanding (2), even if they can’t explain it, pay attention when a gathering minority claim to smell smoke. (Implicit lesson 4: don’t assume that the left hand is talking to the right hand in any organization.)

  • Losing my nerve at the bottom of markets in 2009. My framework helped me to correctly call the bottom in credit markets in January 2009 and recommend to the distressed debt fund I then worked for that we aggressively buy leveraged loans.  But on market research mission in late February, I got spooked by US Treasury and Fed officials I met in New York and Washington.  Throwing my framework out the window, on 6 March 2009, the exact day the S&P 500 hit its 666 low, I wrote a memo urging the fund to sell SPX futures as a hedge on its market exposure.  Lessons learnt: 1. Never abandon your framework; and 2. don’t assume someone’s position or pedigree alone gives them an informational or analytical advantage: demand reasons and evidence.

  • Missing the euro’s partial rebound in 2017; indeed, I forecast it lower! I failed to acknowledge the extent of the uptick in economic activity, and importantly, the credit growth that the ECB’s “anything it takes” policy was generating.  This one stung as I was a strong believer in then-President Mario Draghi’s approach and had previously highlighted credit expansion as a key metric of success.  Lessons learnt: Keep your eyes on the ball and regularly check to see if any of the ex ante conditions established for changing your mind have been met.

  • Dollar weakness in summer 2020. Another one that really stings.  Having correctly forecast that Covid-induced Uncertainty would lead to a surge in the USD as everyone scrambled for high-quality assets and liquidity in March-April 2020, I then failed to incorporate the unwind of that flight to quality as risk tolerance returned once the panic subsided.  Lesson learnt: Sadly, the same as 2017 dollar lesson: Keep your eyes on the ball and mind your pre-established conditions for turning points.

  • I completely missed the Fed’s mini easing cycle in 2019. While we will never know and Covid eviscerated my chance at redemption, I still believe the Fed’s easing was not merited and likely would have necessitated more aggressive hiking later had the Covid crisis not intervened.  The US economy continued to grow strongly in 2019, investment held up, and while headline inflation moderated somewhat, bottlenecks were generating rapid acceleration in a number of CPI subcomponents.  But even if I was right on the economy, the FOMC sets policy and I failed to listen to them, particularly the increasing support for average inflation targeting.  Lessons learnt: Policymakers set policy, listen to them even if you think they’re wrong.

  • Underestimating Jeremy Corbyn in the 2017 UK general election. I expected a small Conservative victory based on then Prime Minister Theresa May’s outreach to the working class and the Labour Party’s anti-Brexit tone.  What I failed to notice was that Mr. Corbyn’s grass-roots campaign cleverly focused on local bread-and-butter issues and greater political devolution, directly addressing one of the primary drivers of the Politics of Rage: a widening sense of political disenfranchisement among the average citizenry.  Corbyn’s tangible policies and outreach – which he largely abandoned in his losing 2019 campaign – easily trumped Ms. May’s intangible rhetoric as “hidden” Brexit voters didn’t trust her.  Lessons learnt: Again, “macro” analysis needs “micro foundations”, particularly in politics; ignore at your own peril.

  • Texas Governor Rick Perry to win the 2012 Republican nomination and beat President Barack Obama in the general election. The foundations of the Politics of Rage were already well apparent by the 2012 election and Rick Perry’s campaign was well tuned to court the rising sense of disenfranchisement of working-class voters, particularly in Appalachia, and increasing distrust of institutional expertise across voters.  Many of those voters were Democrats who were disappointed with the lack of “Hope and Change” promised by President Obama, making him vulnerable to any Republican who could attract a significant number of Democratic voters.  Governor Perry’s bigger challenge appeared to be winning the Republican nomination, but his solid conservative credentials and popularity in the second-largest state suggested he would eek it out.  Who knew he would self-immolate in a nationally televised debate?  Lessons learnt: Themes are important – Trump proved the Politics of Rage four years later – but idiosyncratic risks always are present.