Market Fundamentals
Recommended reading order
See study-guide for the full two-track learning path. This note is Track A, Step 1 (Phase 1a: Market Fundamentals).
- market-fundamentals — you are here
- settlement-and-clearing → trade lifecycle, CCPs, DvP
- trading-fundamentals → spreads, market makers, adverse selection
- volatility → realized and implied vol
- order-books → CLOB structure, matching engines
- glosten-milgrom-model → the formal spread model
Sources
This note draws on Harris, Trading and Exchanges (2003) for participant taxonomy and market structure; Hull, Options, Futures, and Other Derivatives for derivatives and risk transfer; Fabozzi, Handbook of Fixed Income Securities for bond markets; and Kyle (1985) for the dimensions of liquidity. See the source table at the bottom for the full list and when to use each.
What Is a Financial Market?
A financial market is an institutional mechanism that performs four economic functions simultaneously. Understanding which function is being served at any moment is the key to understanding everything that follows in microstructure.
Price Discovery
Price discovery is the process by which the market aggregates dispersed private information into a single public signal: the price.
At any given moment, thousands of actors hold fragments of information about an asset’s value: a fundamental analyst has modeled a company’s cash flows, an insider knows about an upcoming earnings surprise, a macro trader has a view on interest rates, a quantitative fund has detected a statistical pattern. No single actor has the full picture. The market synthesizes all of these fragments through the continuous interaction of buy and sell orders into a price that reflects the collective assessment of value.
This is Hayek’s insight from his 1945 paper “The Use of Knowledge in Society,” applied to financial markets: prices are information aggregation devices.
Harris frames this precisely: “Price discovery is the process of finding market clearing prices… Prices are informative because informed traders cause them to reflect information about fundamental values.” (Harris, Trading and Exchanges, Ch. 6)
The quality of price discovery is measurable. A price that accurately reflects all available information is said to be informationally efficient (the foundation of the Efficient Market Hypothesis). Metrics include: the speed at which prices incorporate news, the variance of pricing errors, and the bid-ask spread (the gap between the best buy and sell prices) as a proxy for information asymmetry.
Why it matters practically: Quoted prices on exchanges are simultaneously a product of price discovery (reflecting information) and an input to it (other participants observe those quotes and update their own estimates). This reflexivity is central to microstructure.
Risk Transfer
Markets allow participants to separate the bearing of risk from the activities that generate it. This is arguably the most economically important function.
A wheat farmer faces price risk. An airline faces fuel price risk. A bank faces interest rate risk. A company going public faces the risk of concentrated ownership. Without markets, each actor would be forced to bear the full risk of their position. Markets allow:
- The farmer to hedge via futures — risk transfers to a speculator or a flour mill with opposite exposure
- The airline to buy fuel swaps — risk transfers to an energy trader or producer
- The bank to trade interest rate swaps — risk transfers to another institution with complementary exposure
- The entrepreneur to sell equity — risk transfers (partially) to diversified investors
This is not zero-sum in welfare terms. Risk transfer is value-creating because risk can be moved to those who are most willing and able to bear it (due to diversification, different time horizons, or different risk appetites). This is a core insight of Arrow-Debreu general equilibrium theory — a mathematical framework (Kenneth Arrow and Gérard Debreu, 1954) that proves, under idealized conditions, that a complete set of markets for every possible future state of the world leads to an efficient allocation of resources. The theory is unrealistic as stated (we don’t have a market for every possible outcome), but it gives the conceptual foundation: the more risk-transfer instruments (futures, options, insurance, swaps) a market offers, the closer the economy gets to efficient allocation. Every new derivatives contract is, in this sense, a step toward the Arrow-Debreu ideal.
Hull opens with exactly this framing: derivatives markets exist primarily to allow hedging — the transfer of risk from those who have it and don’t want it to those who are willing to take it for a price. (Hull, Options, Futures, and Other Derivatives, Ch. 1)
Capital Allocation
Markets channel savings to their highest-valued use. When an investor buys a newly issued bond from a company, they are directly providing capital. When they buy shares in an IPO (Initial Public Offering — a company’s first sale of stock to the public), same thing. Even secondary market trading serves capital allocation indirectly: the existence of liquid secondary markets makes investors willing to provide capital in primary markets because they know they can exit.
This is the economy-wide function: financial markets are the mechanism by which society decides which projects get funded. A startup that the market values highly can raise capital cheaply (high stock price → can issue fewer shares for the same capital). A struggling firm faces a high cost of capital (low bond prices → high yields → expensive to borrow).
The efficiency of this process directly affects economic growth — this is why financial development is correlated with GDP growth across countries (Levine, “Finance and Growth,” Handbook of Economic Growth, 2005).
Liquidity Provision
Liquidity is the ability to buy or sell an asset quickly, in size, without significantly moving the price. It has multiple dimensions (Kyle, 1985):
| Dimension | What It Means |
|---|---|
| Tightness | How small is the bid-ask spread? |
| Depth | How much volume can be traded at or near the current price? |
| Resiliency | How quickly does the price recover after a large trade? |
| Immediacy | How fast can you execute? |
Liquidity is not a natural state — it is produced by market participants (market makers, in particular) who stand ready to trade even when there is no natural counterparty. This is a service, and they are compensated for it via the spread. The mechanics of liquidity provision are covered in depth starting from trading-fundamentals.
What Would Happen Without Markets?
Without organized markets:
- Price discovery collapses. Prices would be set by bilateral negotiation, each party operating with incomplete information. Massive information asymmetry → adverse selection → the “market for lemons” problem. George Akerlof’s 1970 paper showed that when buyers can’t distinguish good products from bad ones (he used the used-car market as his example — “lemons” is American slang for defective cars), rational buyers assume the worst and offer low prices. Sellers of good products refuse to sell at those low prices and exit the market. The market unravels: only the worst products remain. Applied to finance: if a bond seller knows more about the bond’s quality than the buyer, the buyer rationally discounts the price, which drives away good issuers, leaving only risky ones. Organized markets with disclosure rules, credit ratings, and transparent pricing exist precisely to break this death spiral. Imagine trying to sell a bond when the buyer has no idea what comparable bonds are trading at — that’s the lemons problem in action.
- Risk is trapped. Every actor bears the full risk of their position. The farmer eats the price drop, the airline eats the fuel spike, the entrepreneur can never diversify away from their company. The economy is more fragile.
- Capital allocation is arbitrary. Without price signals, which projects get funded? Political connections? Guesswork? This is the calculation problem that Mises and Hayek identified in socialist economies.
- Illiquidity premium dominates. Without the ability to exit, investors demand enormous compensation for committing capital. Cost of capital skyrockets. Fewer projects are viable. Growth slows.
SMBs (small and medium businesses) that can’t issue bonds and rely on bank lending or alternative lenders illustrate this directly — the cost of capital for an unrated SMB vs. a publicly traded investment-grade company reflects this market access gap.
Asset Classes
Each asset class represents a fundamentally different claim structure on cash flows, assets, or outcomes.
Equity
A residual ownership claim on a corporation’s assets and earnings. “Residual” is the key word: equityholders are paid last, after all obligations (debt, taxes, operating costs). Upside is theoretically unlimited; downside is limited to capital invested (limited liability — a legal innovation from the 19th century that made modern capitalism possible).
The fact that equity is residual has profound implications. Equity prices are extremely sensitive to small changes in expectations about future cash flows, because all the senior claims (debt, fixed costs) are subtracted first. This is leverage at the corporate structure level. A 10% decline in a company’s asset value might wipe out 50% of equity value if the company is 80% debt-funded.
Key variants: common stock (voting + residual claim), preferred stock (senior to common, fixed dividend, often no voting — economically a hybrid between debt and equity).
Reference: Brealey, Myers & Allen, Principles of Corporate Finance. Damodaran, Investment Valuation for the practitioner side.
Fixed Income (Debt)
A contractual claim to a defined stream of cash flows (coupons + principal). The key distinction from equity: cash flows are specified in advance (barring default). Senior to equity in the capital structure. Upside is capped (you get your coupons and principal, nothing more). Downside is default and loss of principal.
Fixed income is the largest asset class in the world by notional outstanding — over $130 trillion globally. Far larger than equity markets. Yet it gets less attention because much of it trades OTC (over the counter — bilateral transactions between dealers, not on a public exchange).
Microstructure implication: Fixed income markets are overwhelmingly dealer markets, not exchange-traded order book markets. Most bonds trade via RFQ (Request for Quote — a buyer messages several dealers, asks for a price, and trades bilaterally). This is a completely different microstructure from equity markets. See trading-venues for the venue landscape.
Key variants: Treasury/sovereign bonds, corporate bonds (investment grade and high yield), municipal bonds, floating-rate notes, inflation-linked bonds (TIPS), convertible bonds (hybrid), money market instruments (T-bills, commercial paper, repo).
Reference: Fabozzi, Handbook of Fixed Income Securities. Tuckman & Serrano, Fixed Income Securities for the analytical side.
Derivatives
A contingent claim whose value derives from (is contingent on) the price of an underlying asset, rate, index, or event. Not ownership, not debt — a contractual right or obligation whose payoff depends on a future state of the world.
Derivatives are zero net supply — every long position has a corresponding short. The total notional outstanding ($600+ trillion for OTC derivatives) doesn’t represent “money invested”; it represents the scale of risk transfer.
For listed options, the OCC (Options Clearing Corporation) becomes the counterparty via novation (interposing itself between buyer and seller). For OTC derivatives, two counterparties enter a contract governed by ISDA Master Agreements.
Key variants: forwards, futures, options (calls and puts), swaps (interest rate, credit default, equity, FX), exotics (barriers, Asians, lookbacks), structured products (combinations of the above).
Reference: Hull, Options, Futures, and Other Derivatives. Shreve, Stochastic Calculus for Finance I & II for the mathematical foundations.
Commodities
Claims on physical goods: energy (crude oil, natural gas), metals (gold, copper), agriculture (wheat, soybeans, coffee). Most financial participation is via futures, not physical delivery.
The microstructure of commodity markets is distinctive because of the physical delivery constraint. Unlike financial assets, commodities have storage costs, transportation costs, and seasonality. These physical realities create pricing dynamics unique to commodities:
Cost of carry is the total cost of holding an asset over a period: storage, insurance, and financing (the interest cost of tying up capital), minus any income the asset produces (dividends, coupons, or a convenience yield — the implicit benefit of having the physical commodity in hand). In finance, “carry” by itself refers to the net income or cost from holding a position — a bond’s carry is its coupon minus its financing cost; a commodity’s carry is typically negative (storage costs money). Futures prices are determined by carry: the futures price equals the spot price plus the cost of carry.
Contango (futures price > spot price): The normal state when storage costs dominate. If crude oil spot is $70/barrel and it costs $2/month to store a barrel (tank rental, insurance, financing), then the 3-month futures contract should trade near $76 — the spot price plus the cost of carry. A buyer willing to take delivery in 3 months rather than today should pay for the storage someone else is doing. Contango is the market saying: “holding physical inventory costs money; future delivery is priced to reflect that cost.”
Backwardation (futures price < spot price): Occurs when there is a convenience yield — an implicit benefit from holding the physical commodity right now. Example: if a cold snap hits and natural gas inventories are low, gas-in-hand today is worth more than a promise of gas next month, because you can sell it now to freezing customers. Refineries, power plants, and food processors will pay a premium for immediate physical supply. The futures curve inverts: the prompt month (nearest contract) trades above later months. Backwardation is the market saying: “having this stuff physically available right now is worth paying extra for.”
Neither state is an anomaly or a mispricing — both reflect real economic forces (storage costs vs. convenience yield). Contango and backwardation can alternate in the same commodity as supply/demand conditions shift.
Reference: Geman, Commodities and Commodity Derivatives. Hull covers futures pricing and cost-of-carry in Ch. 5.
Crypto / Digital Assets
A heterogeneous class including: L1 tokens (BTC, ETH — claims on block space and network security), stablecoins (USDC, USDT — claims on reserves or algorithmic pegs), governance tokens (UNI, AAVE — voting rights in protocols), utility tokens, NFTs, and tokenized real-world assets (on-chain representations of bonds, equities, real estate).
BTC is neither debt nor equity — it’s closer to a digital commodity (CFTC classification). ETH post-merge has quasi-equity-like properties (staking yield, burn mechanism that acts like a buyback). Governance tokens are equity-like but usually without legal rights. Stablecoins are debt instruments in practice. Tokenized securities inherit the claim structure of the underlying.
DeFi protocols replicate market infrastructure (exchanges, lending, market making) in smart contracts. The DeFi path explores how — see constant-product-amm and the DeFi index.
Summary: Claim Structures
| Asset Class | Claim Type | Priority in Default | Upside | Downside |
|---|---|---|---|---|
| Equity | Residual ownership | Last | Unlimited | Limited to investment |
| Fixed Income | Contractual debt | Senior (varies by tranche) | Capped (coupons + par) | Loss of principal |
| Derivatives | Contingent | N/A (bilateral contract) | Varies | Varies |
| Commodities | Physical / derivative | N/A | Theoretically unlimited | Spot can’t go below zero (usually) |
| Crypto | Heterogeneous | Usually none (no legal framework) | Unlimited | Total loss |
Market Participants
The taxonomy of who is in the market and why they trade determines the information content of order flow, the structure of bid-ask spreads, and ultimately the quality of prices.
Harris organizes participants by their trading motive — more useful than the usual buy-side/sell-side split:
| Motive | Description |
|---|---|
| Informed traders | Trade because they have information about fundamental value |
| Noise / liquidity traders | Trade for reasons unrelated to information (rebalancing, redemptions, taxes) |
| Parasitic traders | Front-runners, order anticipators (see mev-fundamentals) |
| Futile traders | Believe they have information but don’t (overconfident retail, etc.) |
(Harris, Trading and Exchanges, Ch. 4)
Buy-Side (Capital Allocators)
These entities deploy capital — they decide what to buy and sell.
| Participant | What They Do | Time Horizon |
|---|---|---|
| Asset managers (BlackRock, Vanguard, Fidelity) | Manage pooled capital (mutual funds, ETFs). Mostly benchmark-relative. | Medium–long |
| Pension funds (CalPERS, Norway’s GPFG) | Manage retirement assets. ALM (asset-liability matching) is their core problem. | Very long |
| Hedge funds (Citadel, Millennium, Bridgewater) | Absolute return strategies. Unconstrained. Use leverage, shorting, derivatives. | Short–medium |
| Sovereign wealth funds (GIC, ADIA) | Manage national savings from commodity revenues, trade surpluses. | Very long |
| Insurance companies (Allianz, MetLife) | Invest premium float. Must match long-dated liabilities. Huge fixed-income buyers. | Long |
| Endowments (Yale, Harvard) | Manage institutional wealth. Heavy alternatives allocation (PE, VC, real assets) because they can afford illiquidity. | Perpetual |
| Retail investors | Individuals trading directly. In aggregate, a massive pool of capital. | Varies |
Microstructure implication: Buy-side participants are overwhelmingly uninformed in Harris’s sense — they trade for portfolio reasons, not because they have private information about a specific stock’s value. But they create adverse selection risk for market makers, because the market maker can’t always distinguish a pension fund rebalancing (uninformed — safe to trade with) from a hedge fund with a view (informed — dangerous to trade with). This is the Glosten-Milgrom problem (1985), and it is the theoretical foundation of the bid-ask spread.
Sell-Side (Intermediaries)
These entities facilitate the buy-side’s trading. They don’t (in theory) take directional positions — they provide access, execution, and liquidity.
| Participant | What They Do |
|---|---|
| Broker-dealers (Goldman Sachs, Morgan Stanley, JP Morgan) | Provide execution, research, prime brokerage, capital introduction, lending. Act as both agent (broker) and principal (dealer) — see below. |
| Market makers (Citadel Securities, Virtu, Jane Street, Optiver) | Continuously quote bid and ask prices. Earn the spread. Manage inventory risk. Provide immediacy — the ability for someone to trade right now without waiting for a natural counterparty. |
| Investment banks (same entities, different hat) | Underwrite securities issuance (IPOs, bond offerings). Advise on M&A. Structure products. Bridge issuers who need capital and investors who have it. |
| Inter-dealer brokers (TP ICAP, BGC Partners, Tradition) | Facilitate trading between dealers in OTC markets (rates, FX, credit). Provide anonymity and price discovery in the dealer-to-dealer market. |
Broker vs. dealer — the two hats. When a firm acts as broker (agent), it finds a counterparty for the client’s trade and charges a commission. The firm never owns the asset; it earns a fee for matchmaking. When the same firm acts as dealer (principal), it trades from its own inventory — if you want to sell 10,000 shares of AAPL and no buyer is immediately available, the dealer buys them from you using its own capital, warehouses the risk, and sells them later. The dealer earns the bid-ask spread but bears the risk that the price moves against them before they can offload. Most large firms (Goldman Sachs, Morgan Stanley) do both, which is why the legal entity is called a “broker-dealer.” The distinction matters because the firm’s incentives differ: as broker, the incentive is best execution for the client; as dealer, the incentive is managing its own P&L. For the full treatment — including riskless principal, block trades, algorithmic execution, and retail order flow — see trade-types.
Exchange obligations for designated market makers require firms to maintain continuous two-sided quotes — bid and offer — within certain spread and size parameters. The exchange grants benefits in return (fee rebates, informational advantages). The firm profits from the spread but bears inventory risk: if everyone sells to you, you accumulate an unwanted position. Managing that inventory risk — hedging, offloading, adjusting quotes — is the core skill. See trading-fundamentals and ho-stoll-inventory-model for the formal models.
Infrastructure (The Plumbing)
Every trade involves a chain of infrastructure:
Order → Exchange/Venue → Match → Clearing → Settlement → Custody
| Entity | What It Does | Engineering Analogy |
|---|---|---|
| Exchanges (NYSE, Nasdaq, CME, ICE) | Operate the matching engine. Provide price transparency. Enforce rules. | The message broker — a deterministic state machine processing an ordered log of events. See matching-engine-system-design. |
| ATSs / MTFs (IEX, Turquoise, Liquidnet) | ATSs (Alternative Trading Systems — US regulatory category for non-exchange venues) and MTFs (Multilateral Trading Facilities — the EU equivalent under MiFID II). Includes dark pools (venues that do not display orders publicly before execution) and other off-exchange venues. Less pre-trade transparency. | Alternative message brokers with different matching rules (some designed to protect against latency arbitrage). See trading-venues. |
| Clearinghouses / CCPs (DTCC, CME Clearing, LCH, OCC) | CCPs (Central Counterparties) become the counterparty to every trade (novation). Manage margin. Mutualize default risk. | The transaction coordinator — ensures atomicity. If one side defaults, the CCP steps in. A financial two-phase commit. See settlement-and-clearing. |
| CSDs (DTC, Euroclear, Clearstream) | Hold the definitive record of who owns what. Process settlement (delivery vs. payment). | The system of record — the authoritative database. Settlement is the final write. |
| Custodians (BNY Mellon, State Street) | Safekeep assets on behalf of investors. Handle corporate actions, dividends, proxy voting. | The managed storage layer. |
| Payment systems (Fedwire, CHIPS, TARGET2, SWIFT) | Move cash between parties. | The settlement bus for the cash leg. |
DeFi smart contracts vertically integrate these roles — a single contract can act as exchange, clearinghouse, and depository. The tradeoffs of this architecture are explored in blockchain-transaction-lifecycle.
Regulators
Regulation exists because of market failures: information asymmetry, externalities, systemic risk, and the tendency of unregulated financial systems to produce fraud, manipulation, and crises.
| Regulator | Jurisdiction | Oversees |
|---|---|---|
| SEC (Securities and Exchange Commission) | US | Securities: equities, bonds, funds, security-based swaps. Created 1934, post–Depression. |
| CFTC (Commodity Futures Trading Commission) | US | Futures, options on futures, commodity swaps. Separate from SEC because futures were historically agricultural — a jurisdictional accident with real consequences for crypto regulation. |
| FINRA | US | Self-regulatory organization for broker-dealers. |
| Federal Reserve | US | Bank supervision, monetary policy, systemic risk (post-Dodd-Frank). |
| ESMA (European Securities and Markets Authority) | EU | Securities regulation across the EU. MiFID II (2018) reshaped European microstructure: dark pool caps, best execution requirements, unbundling of research. |
| FCA (Financial Conduct Authority) | UK | Post-Brexit UK equivalent. |
| BIS / Basel Committee | Global | Capital adequacy standards for banks (Basel I/II/III/IV). The PD/LGD/EAD parameters in credit risk are Basel concepts. |
The core regulatory tension in market microstructure: transparency vs. liquidity. More transparency (public order books, trade reporting) improves price discovery but can harm liquidity providers who don’t want their positions exposed. MiFID II pushed hard toward transparency; the US has Reg NMS (2005). Both have complex, debated consequences — a recurring theme in this learning path.
Primary vs. Secondary Markets
Primary Markets: Asset Creation
Every tradeable asset has a lifecycle: it is created (primary market), then traded (secondary market), and eventually matures, expires, or is retired.
Equity — IPO:
- Company hires investment bank(s) as underwriter(s) (bookrunners)
- Due diligence, SEC registration (S-1 filing), roadshow
- Book building: underwriter solicits indications of interest from institutional investors, builds a demand curve, sets the offering price
- Shares are allocated (usually favoring large institutional clients)
- Stock begins trading on an exchange (the “listing”)
The underwriter typically receives a greenshoe option (formally: over-allotment option, named after Green Shoe Manufacturing Co., which first used it in 1963). The mechanism: the underwriter initially sells 115% of the planned shares — 15% more than the offering size, creating a short position. If demand is strong and the price rises, the underwriter exercises the option to buy the extra 15% from the issuer at the offering price, covering the short. If the price drops, the underwriter buys shares in the open market to cover the short, which supports the price. Either way, the mechanism stabilizes the aftermarket price during the first days of trading. See ipo-process for the complete lifecycle including Regulation M, direct listings, and SPACs.
Fixed Income — Bond issuance:
- Issuer hires a lead manager (bookrunner) bank
- Credit rating obtained from agencies (Moody’s, S&P, Fitch)
- Syndication: the lead manager assembles a group of banks (the syndicate) who collectively place the bonds directly with institutional investors — pension funds, insurance companies, hedge funds, asset managers. This is not an exchange listing. Most corporate bonds are placed OTC: the syndicate banks call their institutional clients, gauge demand, allocate bonds, and settle bilaterally. Some bonds are later admitted to an exchange (like Eurex or the Luxembourg Stock Exchange) for regulatory purposes or to allow secondary trading, but the primary distribution is always through this private placement process.
- Pricing set as a spread over a benchmark (e.g., “T+150 bps” means 150 basis points — where 1 bp = 0.01% — over the equivalent-maturity Treasury)
- Settlement and admission to trading (usually OTC — see trading-venues for how bonds trade in the secondary market)
Derivatives — Created, not issued:
Derivatives don’t have an “issuer.” A futures contract is created when a buyer and seller agree to trade — the open interest increases by one contract. Listed options are created by the writer (seller) and cleared through the OCC. OTC derivatives are created when two counterparties sign an ISDA confirmation.
Crypto — Token launches:
The primary market for crypto has taken several forms: ICOs (Initial Coin Offerings — 2017 era, mostly fraudulent, SEC cracked down), IEOs (Initial Exchange Offerings — a centralized exchange like Binance vets and hosts the token sale, providing due diligence missing from ICOs), IDOs (Initial DEX Offerings — tokens launch directly on a decentralized exchange, with liquidity bootstrapped via smart contracts), fair launches (no pre-mine — Bitcoin is the canonical example), airdrops (tokens distributed freely to early users — Uniswap’s UNI airdrop in 2020 is the famous case), and bonding curve launches (deterministic primary markets — see the Pump.fun lifecycle).
Secondary Markets: Continuous Trading
Once an asset exists, the secondary market provides continuous price discovery and liquidity. This is where microstructure lives.
Two fundamental structures:
Order-driven markets (order books):
Buyers and sellers submit limit orders (price + quantity) to a central order book. An exchange matching engine matches incoming orders against resting orders using price-time priority. Transparent: anyone can see the book. Examples: equity markets (NYSE, Nasdaq), futures (CME), crypto spot (Binance, Coinbase).
Quote-driven markets (dealer markets):
Dealers quote bid/ask prices to clients. Clients trade against dealer quotes via RFQ. The dealer uses their balance sheet to warehouse risk. Less transparent: prices are often bilateral. Examples: most fixed income, FX, OTC derivatives, large block equity trades. See trading-venues for the full venue landscape.
DeFi AMMs: A third structure. Neither order-driven nor quote-driven — a smart contract prices assets according to a mathematical formula. See constant-product-amm for the theory.
Why Secondary Markets Enable Primary Markets
The secondary market’s quality directly determines the primary market’s efficiency.
If an investor buys a bond in the primary market and knows they can sell it easily in a liquid secondary market, they’ll accept a lower yield. If the secondary market is illiquid, they demand a liquidity premium. This directly increases the issuer’s cost of capital.
This is why governments invest heavily in maintaining liquid sovereign bond markets (regular issuance schedules, primary dealer systems, repo markets), companies care about stock exchange listing, and regulators obsess over market structure — it affects the real economy via cost of capital.
Harris, Ch. 3: “Markets exist because they reduce the costs of trading… The cost of trading is ultimately a cost of capital.” This single sentence connects microstructure to the real economy.
Source Summary
| Source | Covers | When to Use |
|---|---|---|
| Harris, Trading and Exchanges (2003) | Participant taxonomy, order types, market structure, regulation | Primary microstructure textbook. Start with Ch. 1–6. |
| Hull, Options, Futures, and Other Derivatives | Derivatives pricing, mechanics, hedging, risk management | Derivatives pricing and Greeks |
| Fabozzi, Handbook of Fixed Income Securities | Instruments, markets, analytics | Bonds, yield curves, duration, structured products |
| Tuckman & Serrano, Fixed Income Securities | Analytical fixed income: term structure, risk metrics, models | Yield curve work, duration, convexity |
| Shreve, Stochastic Calculus for Finance I & II | Binomial model, Black-Scholes, martingales | Derivatives pricing theory |
| Kyle, “Continuous Auctions and Insider Trading” (1985) | Informed trading, price impact, liquidity | Core microstructure paper — see kyle-lambda |
| Glosten & Milgrom, “Bid, Ask and Transaction Prices” (1985) | Adverse selection model of the spread | Core microstructure paper — see glosten-milgrom-model |
| Harvey et al., DeFi and the Future of Finance (2021) | AMMs, lending, stablecoins, composability | Starting point for the DeFi portion |
Questions to sit with:
- I claimed markets aggregate dispersed information into prices. But how? Through what mechanism does a hedge fund’s private view about a stock get incorporated into the price? Walk through the causal chain: from the fund’s decision to the price moving. (Connecting informed trading → order flow → market maker updating quotes.)
- Equity is a “residual” claim. A company with €100M in assets, €80M in senior debt, and €20M in equity sees asset value drop to €75M. What happens to the debt and equity holders? What does this remind you of, in options terms? (Merton, 1974, formalized this connection.)
- TradFi separates infrastructure (exchange → CCP → CSD). What are the advantages and disadvantages of this separation vs. vertical integration? Think about failure modes from a distributed systems perspective.
- Where does a convertible bond sit in the claim structure taxonomy? It’s not purely debt, not purely equity, and it has a derivative embedded. How would you decompose it?
See also
- settlement-and-clearing — the next note: trade lifecycle, CCPs, DvP, and CLS Bank
- trading-fundamentals — spreads, market makers, and the timing problem
- trade-types — agency vs principal, block trades, algorithmic execution, retail PFOF
- ipo-process — the full IPO lifecycle, greenshoe, direct listings, SPACs
- trading-venues — dark pools, PFOF, RFQ, and the venue landscape
- order-books — CLOB structure and matching engine mechanics
- constant-product-amm — how DeFi replaces the order book with a formula
- bonding-curves — deterministic primary markets for token launches