Economics & Trading: Unified Study Brief

A single-sitting overview distilled from the six-volume encyclopedia in this directory. Target read time: 60–90 minutes. Designed so you can come away with a strong working mental model and know where to dive deeper.

When something is interesting, jump to the full report — section pointers are inline as → 02§4. Glossary terms are bolded on first use and defined either inline or in §9 below.

1. How the field is organized

Economics is the study of how people, firms, and societies allocate scarce resources. The field splits into two halves with very different methods and questions, plus a third pursuit — financial markets — that sits between them.

• Microeconomics asks how individual agents (consumers, workers, firms) make choices and how those choices interact in specific markets. It's bottom-up. Its modern method is empirical: identify a causal effect from data and let theory follow.
• Macroeconomics asks how the aggregate economy behaves — output, employment, inflation, growth, the business cycle. It's top-down. Its modern method is model-based: write down a system of equations describing the whole economy, fit it to data, and use it for policy experiments.
• Finance and trading sit on top: how assets are priced, how markets clear, how investors and traders extract returns or manage risk. The theory is microeconomic (individual decisions under uncertainty), the macro environment determines the inputs (rates, growth, inflation), and the practice is increasingly engineering and machine learning.

The intellectual history matters because today's debates are layered on top of older ones. Understanding why macroeconomics splintered into schools in the 1970s tells you why the Fed talks the way it does today. Understanding why microeconomics went through a "credibility revolution" in the 1990s–2010s tells you why "experiment" and "natural experiment" are the highest-status words in modern empirical work.

The deep encyclopedia split this into six volumes:

2. Schools of thought, in one page

Economics has gone through several major paradigm shifts. Each was driven by an external crisis or by an internal methodological breakthrough.

Classical (1776 – mid-1800s) — Smith, Ricardo, Mill. The big ideas: division of labor and specialization create wealth; markets self-organize via prices ("the invisible hand"); comparative advantage means countries gain from trade even when one is better at everything. Labor theory of value: a good's worth derives from the labor required to produce it. Limitation: couldn't explain why diamonds cost more than water if water is more useful.

Marxian (mid-1800s) — Marx, Engels. Adopted labor theory of value, focused on class conflict, capital accumulation, and inherent crisis tendencies. Largely outside mainstream economics today but periodically revived (e.g., Piketty 2014's data on long-run capital returns echoed Marxian themes).

Marginalist revolution (1870s) — Jevons, Menger, Walras. Killed the value problem: a good's worth derives from the marginal utility of one more unit to the buyer, not the labor in it. This subjective, calculus-based view became the foundation for all subsequent mainstream micro. Walras gave us general equilibrium: prices in all markets clear simultaneously.

Neoclassical synthesis (early 1900s) — Marshall consolidated supply-and-demand into the apparatus students still learn. Partial equilibrium (study one market at a time, hold others constant) became the workhorse method.

Austrian school (1900s onward) — Mises, Hayek, Kirzner. Emphasized subjective value, the impossibility of socialist economic calculation, the knowledge problem (prices aggregate dispersed local information), and entrepreneurship as discovery. Influential in finance and libertarian policy circles, less so in academic mainstream.

Keynesian (1936) — Keynes' General Theory in the Depression's wake. Key insight: aggregate demand can be deficient; markets don't necessarily self-correct at full employment in the short run; government spending can stabilize. Hicks's IS-LM model turned Keynes into a tractable two-equation system.

Monetarism (1950s–70s) — Friedman. Inflation is "always and everywhere a monetary phenomenon." Money supply growth drives prices long-run; rational monetary policy means a stable money rule. Set up the 1980s Volcker disinflation playbook.

New Classical / Rational Expectations (1970s) — Lucas, Sargent, Wallace. Hit Keynesian econometrics with the Lucas critique: agents who anticipate policy will change their behavior, breaking estimated relationships. Demanded that macro models be built up from optimizing agents with rational expectations. Birthed Real Business Cycle (RBC) theory: business cycles are efficient responses to technology shocks, not failures requiring intervention.

New Keynesian (1980s–today) — Mankiw, Romer, Woodford, Galí. Kept Lucas's microfoundations but added sticky prices and wages so monetary policy can have real effects. Produced the workhorse DSGE (Dynamic Stochastic General Equilibrium) models central banks use.

Heterogeneous-Agent New Keynesian (HANK), late 2010s–today — Kaplan, Moll, Violante, Auclert. Replaced the "representative agent" (one average household) with a distribution of households differing in income, wealth, and marginal propensity to consume (MPC). Transformed our understanding of how monetary policy actually works (more on this in §5).

Behavioral (1970s onward) — Kahneman, Tversky, Thaler, Akerlof. Documented systematic deviations from rational choice: loss aversion, present bias, default effects, mental accounting. Influence is huge in policy ("nudge units") but the 2010s replication crisis hit some claims hard. Core findings survive; many flashier nudge results don't.

Credibility revolution (1990s–today) — Card, Angrist, Imbens, Krueger (Nobel 2021). Reoriented empirical economics around clean identification of causal effects: natural experiments, difference-in-differences, regression discontinuity, instrumental variables, randomized controlled trials. This is now the dominant empirical method across micro and increasingly macro.

Modern Monetary Theory (MMT) — Wray, Kelton. A heterodox view that monetary-sovereign governments can spend without revenue constraint (constrained only by inflation). Influential in some policy circles, sharply criticized by most mainstream macroeconomists. Made the post-COVID inflation debate spicier.

→ For the full lineage with key papers and intellectual genealogies: 01-economic-theory-and-history.md.

3. Microeconomics: what to know

Core machinery (everyone uses these)

• Supply and demand as equilibrium: market clears at the price where quantity demanded equals quantity supplied. Shifts vs movements along the curve. Elasticity (how much quantity changes per 1% price change).
• Utility maximization: consumers pick the bundle they prefer subject to a budget constraint. Producers similarly maximize profit. The whole calculus-laden apparatus reduces, for our purposes, to "people pick the best option in their feasible set."
• Marginal thinking: decisions are made at the margin — "one more unit." Marginal cost, marginal revenue, marginal product. A firm produces until marginal revenue = marginal cost.
• Surplus: consumer surplus = what you'd pay minus what you do pay; producer surplus = what you receive minus cost. Together they measure welfare. Taxes, monopoly, and other "wedges" reduce surplus (deadweight loss).
• Externalities and public goods: things markets undersupply (clean air, basic research) or oversupply (pollution). Justify Pigouvian taxes and subsidies.
• Asymmetric information: when one party knows more than the other. Akerlof's used-car "lemons" market. Solutions: signaling (the informed party reveals via costly action — getting a degree), screening (the uninformed party sorts via contract design — insurance with different deductibles).
• Game theory: strategic interaction. Nash equilibrium = each player's strategy is best given the others'. Prisoner's dilemma, coordination games, repeated games (cooperation becomes possible).

Current frontier (2021–2026)

Market design. Roth, Milgrom, Wilson — applying mechanism design to real allocation problems. Kidney exchange (deferred acceptance algorithms), school choice (Boston vs. deferred acceptance vs. top trading cycles), FCC spectrum auctions, refugee placement. This is one of economics' most successful applied subfields.

Auction theory frontier. Google's 2019–2021 migration from second-price to first-price ad auctions broke a piece of textbook revenue equivalence. Combinatorial auctions, interdependent values, online ad markets.

Behavioral econ after the replication crisis. What survived: loss aversion (mostly in the loss domain), default effects, present bias, mental accounting. What didn't: ego depletion, power posing, much "priming" work. Chater-Loewenstein (2023) "i-frame vs. s-frame" — a serious internal critique that behavioral econ over-individualized problems that needed systemic solutions.

Industrial organization (IO) and antitrust. Platforms and two-sided markets (Rochet-Tirole). The 2024–2026 antitrust trilogy split 2-1 against Big Tech: Google search-monopoly liability (Mehta, Aug 2024), Amazon case proceeding, Meta winning on broad market definition that included TikTok/YouTube (Boasberg, Nov 2025). Live frontier: kill zones, the economics of attention, recommender system distortions.

Causal inference / credibility revolution maturation. Modern DiD has been rebuilt: Callaway-Sant'Anna, Borusyak-Jaravel-Spiess, de Chaisemartin-D'Haultfœuille all addressed problems with the classic two-way fixed-effects estimator under staggered treatment timing. Synthetic control (Abadie), regression discontinuity (Calonico-Cattaneo-Titiunik), instrumental variables (Imbens-Angrist LATE) all have modernized best-practice toolkits.

Labor: monopsony renaissance. Card-Krueger started it, Azar-Marinescu-Steinbaum and Manning have entrenched it. Labor markets have substantial employer power; wage markdowns of 15–50% are now consensus estimates. Minimum-wage research has consolidated around modest disemployment effects (Cengiz et al., Dube). Noncompetes: the FTC's April 2024 ban (enjoined August 2024) drew on the strong empirical case even though the federal rule itself failed.

Inequality measurement debate. Piketty-Saez-Zucman vs. Auten-Splinter — same tax data, opposite stories on top-1% income share trends. Hinges on how to impute underreported income. Genuinely unresolved.

Cash transfers: 2021 American Rescue Plan Child Tax Credit cut child poverty to 5.2% (macro-level effect). Baby's First Years RCT showed null cognitive effects at age 4 (micro-level test). Both are real; reconciling them is the open question.

Causal ML / structural estimation revival. Double machine learning (Chernozhukov), conditional factor models (Kelly), autoencoder asset pricing — applying modern ML to causal estimation problems with discipline.

→ Full treatment with citations: 03-microeconomics-sota.md.

4. Macroeconomics: what to know

Core machinery

• GDP and price indices. Nominal vs real (inflation-adjusted). CPI, PCE, core PCE, supercore (services ex-housing — the Fed watches this closely). Atlanta Fed's GDPNow is the most-cited nowcast.
• Inflation, unemployment, and the Phillips curve. The negative relationship between unemployment and inflation (Phillips 1958). Friedman 1968 added expectations: only unexpected inflation moves unemployment short-run. NAIRU: the unemployment rate consistent with stable inflation.
• Monetary policy. Central banks (Fed, ECB, BoE, BoJ) set short-term interest rates and manage the balance sheet to hit an inflation target (usually 2%) and, for the Fed, also maximum employment ("dual mandate"). Taylor rule: a simple algebraic rule describing what central banks roughly do.
• Fiscal policy. Government spending and taxes. Fiscal multiplier: how much GDP rises per dollar of spending. Estimates span 0.3 to over 2.0 depending on regime, instrument, and whether at the zero lower bound.
• Growth. The Solow model: output grows due to capital accumulation (which has diminishing returns and converges), and total factor productivity (TFP) growth, which is the residual and the actual driver of long-run living standards.
• The business cycle. Recessions and expansions. NBER's Business Cycle Dating Committee dates them officially. Modern macro debates whether cycles are driven by demand shocks (Keynesian), technology shocks (RBC), credit cycles (Minsky), or all of the above.

Current frontier (2021–2026)

Frameworks. The current workhorse is New Keynesian DSGE with HANK extensions (more in §5). Fiscal Theory of the Price Level (FTPL) — Cochrane, Sims — has moved from fringe to respectable. The idea: inflation is ultimately set by the path of government surpluses, not just monetary policy. Bianchi-Faccini-Melosi (QJE 2023) gave it strong post-COVID empirical support.

The 2021–2024 inflation episode. Massive and unexpected. Decomposition is contested:

• Supply-side camp (Baqaee-Farhi, Shapiro, BIS work): COVID supply disruptions + sectoral mismatch did most of the work. Disinflation happened "immaculately" as supply normalized.
• Demand-side camp (Domash-Summers, much of mainstream NK): excess fiscal stimulus + tight labor markets drove it; standard Phillips-curve mechanics still work.
• Profit/margin camp (Bivens, Weber): firm pricing power amplified the shock; a piece often called "greedflation" though academics use more neutral terms.
• Modern view: probably 50/50 supply and demand, plus second-round wage-price dynamics that didn't fully spiral.

Phillips curve debate. Not dead — nonlinear. Hazell-Herreño-Nakamura-Steinsson argued it's mostly flat; Benigno-Eggertsson argued it has a "slanted L" shape that steepens dramatically when vacancies are very high relative to unemployment. The Beveridge curve shifts of 2020–2023 became the empirical battleground; soft-landing camp (Figura-Waller) won the post-mortem.

Monetary policy frameworks. Big regime changes during the writing window:

• Fed: adopted Flexible Average Inflation Targeting (FAIT) in August 2020, abandoned it in 2025 framework review.
• ECB: completed 2021 strategy review, refined again 2025.
• BoJ: exited Yield Curve Control in March 2024, ending decades of unconventional policy.
• r-star (the neutral real interest rate): heavily contested. NY Fed estimates ~0.84% real, Richmond Fed ~2%+, market-implied somewhere between. The disagreement determines whether current policy is restrictive.

Balance sheet policy. Post-2008 the Fed entered an "ample reserves" regime — paying interest on reserves (IORB), running an overnight reverse repo facility (RRP), and using QE/QT to manage long-rate term premia. The 2025 era has Fed remittances to Treasury at zero or negative due to balance-sheet losses, an under-appreciated political issue.

Fiscal sustainability. Blanchard (2019, 2023) revived the question with R-G dynamics: when interest rates are below growth rates (r < g), debt can be rolled over indefinitely without raising taxes. Sustained low r-g in 2010s made this look benign; 2022's rate spike made it scary again. IMF DSA framework is the standard.

Secular stagnation vs. AI productivity boom. Larry Summers (2013 onward): a structural shortfall of demand depresses r-star. Brynjolfsson-Rock-Syverson: AI productivity gains follow a J-curve — big up-front investment, lag, then a surge. Whether the post-2024 AI capex wave produces real TFP growth or capital misallocation is the big-picture macro question.

Climate and demographic macro. Optimal carbon price: $51 vs $190/ton depending on discount rate (Nordhaus vs Stern style assumptions). Aging: Eggertsson-Mehrotra-Robbins and Auclert-Malmberg-Martenet-Rognlie find aging is r-suppressing in standard models, but Goodhart-Pradhan argue we're flipping into a regime where aging is inflationary instead.

International. Dollar dominance persists despite predictions of decline (Gopinath). US-China decoupling is reshaping supply chains. Trilemma (fixed exchange rate, free capital flows, independent monetary policy — pick two) still binds.

→ Full treatment: 02-macroeconomics-sota.md.

5. How micro and macro fit together

This is the most intellectually interesting development of the last 20 years. The bridge volume (04) covers it in depth; here's the spine.

The Lucas critique (1976)

Macro models that just fit historical correlations break when policy changes, because people's expectations and behavior change. Therefore macro models must be built from optimizing individual agents with rational expectations. This kicked off the "microfoundations" program. Pros: discipline, internal consistency. Cons: forced unrealistic assumptions (rational expectations, representative agents) to keep things tractable.

The representative-agent shortcut, and its undoing

For 30 years macro assumed one "average" household. This made models solvable but hid two crucial things: (1) MPCs vary enormously across households (a low-income household spends ~80 cents of every extra dollar in the same quarter; a wealthy one spends maybe 10 cents over years); (2) policy redistributes, and the redistribution itself matters for aggregate effects.

HANK: heterogeneous-agent New Keynesian (2018 onward)

Kaplan-Moll-Violante (2018) built a tractable model with a full distribution of household wealth and income. The shocking result, when you decompose monetary policy transmission:

• In representative-agent models, the direct intertemporal channel (interest rates change → people consume now vs later) does ~95% of the work.
• In HANK, the indirect labor-demand channel does ~80%: a rate cut boosts firm investment and hiring, which raises wages for high-MPC households, who spend it, which drives the recovery.

This is a quantitative reversal that changes how central banks should think about what they're actually doing.

iMPCs and the intertemporal Keynesian cross

Auclert-Rognlie-Straub (2024) showed that the dynamic fiscal multiplier is summarized by the vector of intertemporal MPCs (iMPCs) — how much extra spending you get in each future quarter from a dollar today — interacted with the deficit path. This is a sufficient-statistic result: you don't need to commit to a full model to forecast fiscal effects, just measure iMPCs from micro data.

Granularity (Gabaix 2011)

Macro silently assumed the law of large numbers: firm-level shocks average out in GDP. Gabaix showed that when firm sizes are fat-tailed (a few enormous firms, many small ones — true everywhere), the diversification rate is $1/\ln N$ instead of $1/\sqrt{N}$. The implication: Apple, Walmart, Exxon, and TSMC are macroeconomic events. A bad earnings report at a giant firm shows up in GDP.

Production network propagation

Acemoglu-Carvalho-Ozdaglar-Tahbaz-Salehi (2012) mapped how shocks travel through supply chains. Carvalho-Nirei-Saito-Tahbaz-Salehi documented a 0.47pp GDP drop from the 2011 Tōhoku earthquake propagating through Japanese supply chains. Baqaee-Farhi showed that in disaggregated production networks, "small" sectoral shocks can have outsized aggregate effects — this framework drove the credible interpretation of COVID inflation as supply-driven.

Identified moments (the empirical bridge)

Nakamura-Steinsson, Chodorow-Reich, Mian-Sufi — use clean micro causal estimates (cross-region variation, IV, RD) to discipline macro models. Mian-Sufi's housing-debt thesis is the canonical example: micro data on household leverage explains macro consumption collapse in 2008–2010.

Behavioral macro

What if rational expectations is wrong? Gabaix (sparse cognition), García-Schmidt-Woodford (reflective equilibrium), level-k thinking. These resolve the "forward guidance puzzle" (rational-expectations NK models predict implausibly large effects from far-future rate promises) by making distant commitments cognitively discounted.

So what?

The pure micro-only and pure macro-only views are both incomplete. The 2020–2022 inflation needed both halves to explain: supply-side disaggregated networks (Baqaee-Farhi) plus high-MPC households spending stimulus checks (HANK). The modern synthesis is empirically rich and computationally challenging — modern frontier methods like sequence-space Jacobians (Auclert-Bardóczy-Rognlie-Straub) let you compute macro responses from micro building blocks without solving the full stochastic equilibrium.

→ Full treatment: 04-micro-macro-intermingling.md.

6. Trading and markets: what to know

Foundational theory

Efficient Market Hypothesis (EMH) — Fama 1970. Three strengths:

• Weak: prices reflect past price history (so charting can't earn excess returns)
• Semi-strong: prices reflect all public information (so fundamental analysis of public data can't either)
• Strong: prices reflect even inside information

Reality: weak form is mostly true; semi-strong is mostly true but with documented anomalies; strong form is false. The joint hypothesis problem: you can never test EMH alone — you're always testing it jointly with whatever asset-pricing model you used. Grossman-Stiglitz (1980): markets can't be perfectly efficient or no one would pay to do research. The right question isn't "are markets efficient" but "how inefficient must they be to compensate the people who make them efficient?"

Modern Portfolio Theory (Markowitz 1952). Investors should care about portfolio mean and variance, not individual asset returns. Diversification reduces variance for free. The efficient frontier: the set of portfolios with the highest return for each level of risk.

Capital Asset Pricing Model (CAPM). Sharpe-Lintner. Every asset's expected return is the risk-free rate plus the asset's beta times the market risk premium:

$$ E[R_i] = R_f + \beta_i \cdot (E[R_m] - R_f) $$

Beta is just the asset's covariance with the market, normalized. CAPM is mostly empirically wrong, but the framework — decomposing returns into systematic and idiosyncratic components — is foundational.

Factor models. Fama-French extended CAPM with size (small minus big, SMB) and value (high minus low book-to-market, HML). Carhart added momentum (MOM). Modern versions add profitability, investment, low volatility, quality. The "factor zoo" problem: hundreds of factors have been published, most don't replicate. Harvey-Liu-Zhu showed most published anomalies fail an adjusted t-stat hurdle of 3.0. Lopez de Prado's deflated Sharpe and purged k-fold CV are now table-stakes safeguards against false discovery.

Black-Scholes-Merton (1973). Closed-form pricing formula for European options:

$$ C = S \cdot N(d_1) - K e^{-rT} \cdot N(d_2) $$

where $N(\cdot)$ is the standard normal CDF, $d_1, d_2$ are defined in terms of $S, K, r, T, \sigma$. The derivation requires Itô's lemma and PDE methods (study: stochastic calculus, PDEs, measure-theoretic probability). The intuition: under risk-neutral pricing, the option's price is the discounted expected payoff. Crucial insight: only volatility $\sigma$ is unobservable. Hence implied volatility, vol surfaces, smiles, skew.

The asset class quick map

• Equities: stocks. Liquid, well-studied, factor models dominant. Market microstructure complex (NMS regulation, dark pools, PFOF debate, IEX speed bump).
• Fixed income: bonds. Yield curve construction, duration, convexity. Crucial for everything else (discount rates, expected returns, central-bank policy transmission).
• FX: currencies. Carry trades dominant. Triangulated arbitrage keeps cross-rates aligned.
• Commodities: physical goods. Futures-based trading, contango/backwardation, roll yield. Inflation hedge debate.
• Options/derivatives: pricing via BSM extended (local vol, stochastic vol, jumps). Greeks (delta, gamma, vega, theta, rho) for risk management. 2024 explosion in 0DTE options (zero-days-to-expiry SPX options) crossed 50% of index volume — restructured dealer-gamma dynamics, now a baseline macro variable.
• Credit: corporate bonds, CDS, structured products. Spread products. CLOs and private credit are the post-2008 growth area.
• Crypto/DeFi: 24/7, fragmented, on-chain and off-chain venues. AMM mechanics now mature.

Quantitative methods

Statistical arbitrage: pairs trading, cointegration, mean reversion. Worked spectacularly in the 1990s–2000s, alpha has decayed.

Market making: profit from the bid-ask spread, manage inventory. Foundational models: Glosten-Milgrom, Kyle (1985), Avellaneda-Stoikov. Modern HFT is the industrialization of market making.

Microstructure: how prices form at sub-second timescales. Order book dynamics, market vs limit orders, queue position, price impact, Almgren-Chriss optimal execution (VWAP/TWAP/Implementation Shortfall).

HFT: latency arbitrage, market making, statistical arbitrage at microsecond scale. Flash Boys hype mostly wrong — modern equity microstructure has tight spreads and the IEX speed bump was modest. Retail PFOF debate is alive but internalization generally gives retail price improvement.

Time-series momentum / trend following. Moskowitz-Ooi-Pedersen showed momentum at the asset-class level works across centuries and markets. Underlies the entire managed-futures / CTA industry.

Carry trades across asset classes. Koijen-Moskowitz-Pedersen-Vrugt generalized carry as a global factor.

Volatility. Vol risk premium (implied > realized on average), variance swaps, dispersion trades. Short-vol strategies blew up in Feb 2018 ("Volmageddon"). Long-vol strategies (Spitznagel) are insurance with a known drag.

Machine learning and the SOTA frontier

Marcos Lopez de Prado's Advances in Financial Machine Learning (2018) is foundational reading. Key contributions:

• Sample uniqueness: financial returns are autocorrelated; naive CV overstates performance
• Purged k-fold CV and combinatorial purged CV: prevent label leakage
• Meta-labeling: separate the "should I trade" decision from "what size"
• Triple-barrier method: define labels by profit-take, stop-loss, time-out
• Deflated Sharpe ratio (with Bailey): adjusts for multiple-testing

Gu-Kelly-Xiu (2020) showed ML (random forests, neural nets) roughly doubles linear-model Sharpe in equity return prediction — but mostly by capturing interactions among the same canonical predictors (momentum, liquidity, volatility), not by discovering new alpha.

Deep hedging (Buehler et al.): RL learns to hedge options under realistic frictions, replacing analytic Greek-based hedging.

LLMs in trading: news sentiment classically (RavenPack, FinBERT); modern (Lopez Lira "Can ChatGPT forecast stock returns" papers). Earnings-call analysis. Limited evidence of stable alpha after costs.

Alternative data: satellite imagery, credit card panels, web scraping, app usage data. Mature market with significant alpha decay since 2020.

Causal ML for finance: double ML applied to alpha estimation, treatment-effects framing for event studies.

Crypto/DeFi

AMMs: constant-product (Uniswap V2 — x · y = k), stableswap (Curve), concentrated liquidity (Uniswap V3). Impermanent loss (really divergence loss) for liquidity providers when prices move.

Perpetual swaps: futures with no expiry. Funding rate mechanism keeps the perp price anchored to spot. Hyperliquid, GMX, dYdX dominate.

MEV (maximal extractable value): the on-chain version of latency arbitrage. Searchers find arbitrage and sandwich opportunities, builders assemble blocks, proposers (validators) choose blocks. PBS (proposer-builder separation) and MEV-Boost are the institutional plumbing. Sandwich attacks on retail DEX trades are the canonical extractive case.

Stablecoins: USDC, USDT (fiat-backed), DAI (crypto-collateralized), formerly UST (algorithmic, blew up May 2022). Now systemically important; GENIUS Act (US) and MiCA (EU) are the regulatory frameworks.

Live 2024–2026 frontier

• 0DTE options restructuring intraday SPX dynamics
• Treasury basis trade — hedge funds long Treasuries, short futures, financed in repo — became large enough to be a systemic concern (Fed papers 2023–2024)
• Passive investing market-structure effects (Bond-Garcia, Coles-Heath-Ringgenberg)
• Private credit boom — banking system disintermediation
• AI in trading firms — what's hype vs real
• Retail trading post-Robinhood, gamification consequences
• Crypto-equity correlation evolution since 2020

Practitioner archetypes (for vocabulary)

• Renaissance Technologies, Two Sigma, DE Shaw, Citadel, Jane Street, Jump, Hudson River, XTX, Optiver, Susquehanna: quant/HFT
• AQR, Bridgewater: systematic factor / risk-parity
• Millennium, Point72, Balyasny, Citadel: multi-strategy "pod shops"
• Brevan Howard, Tudor: discretionary macro
• Buffett, Marks, Klarman, Ackman: discretionary value/special situations
• Soros, Druckenmiller, Dalio: legendary discretionary macro

→ Full treatment: 05-trading-theory-and-practice.md.

7. Cross-cutting big ideas

If you remember nothing else, remember these twelve.

1. Marginal thinking is everything. Decisions happen at the margin — the next unit, the next dollar, the next hour. This single idea underpins consumer theory, producer theory, monetary policy reaction functions, and most of finance.

2. General equilibrium beats partial-equilibrium intuition surprisingly often. When you change one thing in an interconnected system, the second-order effects often dominate. This is why economists keep saying "yes, but" to common-sense policy ideas.

3. The credibility revolution changed what counts as evidence. "Correlated with" used to be enough; now you need a research design that identifies a causal effect. This is methodologically as important as anything theoretical in the field.

4. The Lucas critique never goes away. Any model fit to past data that assumes behavior is fixed will mislead about counterfactual policy. Modern macro is permanently constrained by this.

5. Heterogeneity matters more than you'd think. Aggregating over a representative agent hides distributional effects that often dominate. HANK reshaped monetary policy thinking; granularity reshaped business-cycle thinking.

6. Markets aggregate dispersed information. Hayek's knowledge problem is real and important. Prices encode what nobody knows individually. This is the deep case for markets — not that participants are rational, but that the system is informationally efficient in a way central planning isn't.

7. No-arbitrage is the engine of asset pricing. If two portfolios have the same future cash flows, they must have the same price today. Almost all of derivatives pricing is built on this single principle.

8. EMH is roughly true, but the right question is Grossman-Stiglitz: how inefficient must markets be to pay the researchers who make them efficient? The existence of profitable systematic trading is consistent with EMH being "mostly true."

9. Most published anomalies don't replicate. In econ generally and in trading-strategy research specifically, multiple-testing and p-hacking are pervasive. Lopez de Prado's deflated Sharpe and modern DiD's robustness checks are the response. Trust replication, not single papers.

10. Macroeconomic policy is fundamentally about expectations. Whether monetary or fiscal, modern policy works partly (often mostly) through influencing what households, firms, and markets expect to happen. Forward guidance, inflation targeting credibility, fiscal rules — all are expectation-management.

11. Distributional politics is now central to macro. Inequality measurement, MPC heterogeneity, the geography of opportunity, deaths of despair — what used to be sociologists' territory is now mainstream macro.

12. The frontier of every applied field is computational. Modern macro (sequence-space Jacobians), modern micro (causal ML), modern finance (deep hedging, autoencoder asset pricing) — the gating constraint is increasingly compute and software, not theory.

8. The essential math toolkit

These are the algebra-level formulas that recur across the field. Memorize these and you can read most professional discussion.

Time value of money

$$ PV = \frac{FV}{(1+r)^t} $$ A dollar now is worth more than a dollar later. r is the discount rate.

Fisher equation

$$ (1 + i) = (1 + r)(1 + \pi) \approx 1 + r + \pi $$ Nominal interest = real interest + inflation. Underpins everything monetary.

Quantity equation

$$ M \cdot V = P \cdot Y $$ Money supply × velocity = price level × real output. Foundational identity (not a theory by itself; how the variables co-move is the theory).

Taylor rule

$$ i_t = r^* + \pi_t + 0.5 (\pi_t - \pi^) + 0.5 (y_t - y^) $$ What the Fed roughly does: raise rates when inflation is above target and output is above potential.

Phillips curve (expectations-augmented)

$$ \pi_t = \pi_t^e - \beta (u_t - u^*) + v_t $$ Inflation = expected inflation − slope × unemployment gap + cost-push shock.

Solow steady state condition

$$ s \cdot f(k) = (n + \delta) k $$ Saving equals capital widening. Capital per worker is constant when investment offsets depreciation and population growth.

CAPM

$$ E[R_i] = R_f + \beta_i (E[R_m] - R_f) $$ Expected return = risk-free rate + beta × market risk premium.

Sharpe ratio

$$ S = \frac{\bar{R} - R_f}{\sigma_R} $$ Excess return per unit of standard deviation. The most-cited risk-adjusted performance metric.

Portfolio variance (2 assets)

$$ \sigma_p^2 = w_1^2 \sigma_1^2 + w_2^2 \sigma_2^2 + 2 w_1 w_2 \rho_{12} \sigma_1 \sigma_2 $$ Diversification: portfolio variance < weighted average of individual variances when correlation < 1.

Put-call parity

$$ C - P = S - K e^{-rT} $$ Call minus put = stock minus discounted strike. Pure no-arbitrage — if violated you can lock in risk-free profit.

Constant-product AMM (Uniswap)

$$ x \cdot y = k $$ Reserves of two tokens multiply to a constant. Price = $y/x$ (token Y per token X).

Impermanent loss (rough)

$$ IL = \frac{2\sqrt{p}}{1+p} - 1 \quad \text{where } p = \text{price ratio} $$ LP underperforms holding when price diverges. Symmetric in $p$.

Fiscal-theory identity

$$ \frac{B_t}{P_t} = \sum_{s=0}^{\infty} \frac{E_t [\text{primary surplus}_{t+s}]}{(1+r)^s} $$ Real debt = present value of future primary surpluses. If markets believe future surpluses are too low, the price level must rise. (FTPL)

Where you need to go beyond algebra

• Calculus: marginal analysis, optimization, growth models (the workhorse Solow steady state above is the algebraic shortcut). Study: any standard calculus textbook, then the math chapter of Romer's Advanced Macroeconomics.
• Linear algebra: input-output models, principal components, factor analysis, modern portfolio theory in n dimensions. Study: Strang or Axler.
• Probability and statistics: literally everywhere. Study: Casella-Berger, then Wasserman.
• Stochastic calculus: Black-Scholes derivation, continuous-time finance, modern DSGE. Study: Shreve volumes 1 and 2.
• Dynamic programming: optimal monetary policy, Aiyagari/Bewley models, HANK steady states. Study: Stokey-Lucas-Prescott (hard) or Adda-Cooper (gentler).
• Measure theory: rigorous probability and continuous-type mechanism design. Study: Billingsley or Folland.
• Numerical methods: DSGE solution, sequence-space Jacobians, ML estimation. Study: Judd, then language-specific (Python/Julia/MATLAB libraries).

9. The compact glossary (50 highest-leverage terms)

For the full ~570-entry reference, see 06-unified-glossary.md. These are the ones you'll see most.

10. Where to dig deeper

When a topic from this brief sparks interest, here's where to go.

For a longer suggested reading order across the encyclopedia: 00-index.md.

For independent textbook study (in rough order of difficulty):

1. Mankiw, Principles of Economics — gentle on-ramp covering both halves
2. Acemoglu-Laibson-List, Economics — modern intermediate principles
3. Hull, Options, Futures, and Other Derivatives — the trading bible
4. Tirole, The Theory of Industrial Organization — micro classic
5. Romer, Advanced Macroeconomics — graduate macro, accessible
6. Bodie-Kane-Marcus, Investments — comprehensive finance reference
7. Acemoglu, Introduction to Modern Economic Growth — graduate growth
8. Lopez de Prado, Advances in Financial Machine Learning — quant finance frontier
9. Auclert-Rognlie, working papers on HANK — modern macro frontier (technical)
10. Mas-Colell-Whinston-Green, Microeconomic Theory — the canonical PhD micro text (hard)

Notes on this brief

• This brief is a synthesis of the six-volume encyclopedia in output/. It is not itself sourced — when a claim matters to you, follow the pointer to the underlying full report where the citations live.
• Time-sensitive facts (specific FOMC actions, court rulings, mid-2026 market data) should be verified against primary sources before relying on them — the underlying reports were finalized May 2026.
• The compact glossary in §9 deliberately covers high-frequency terms only. The full glossary (06) has the disambiguation entries for terms whose meaning differs across micro/macro/trading.