Financial Exchanges

1. Financial Exchanges

2. Today’s Lecture • Background on financial exchanges • The role of financial exchanges • Desirable attributes of an exchange • History of these markets • Specialist markets, OTC markets, exchanges • The move to electronic exchanges • Market design issues • Information aggregration, large orders • Competing platforms, transparency, dark pools.

3. Public equity markets • Focus on markets for public equity • Companies issue publicly traded stock. • Historically traded on a few large exchanges • Recently competition between exchanges and a great deal of innovation in exchange design. • Questions to consider • What are the objectives for a successful market? • What designs help achieve these objectives? • What is the role of competition between markets?

4. Market objectives • Objectives for the public equities market • Price discovery (prices reflect current information) • Fair competition (open access, nondiscrimination) • Investor protection and confidence • US regulates financial markets to achieve these objectives, looking at things such as • How fast are orders executed? How large are spreads? How large is systemic risk (e.g. risk of a complete market shut-down)? Are certain investors being advantaged or disadvantaged? Is there cheating or fraud?

5. Desirable market properties • Liquidity • In liquid markets, traders can buy or sell large quantities of shares without a large price impact. • Transparency • Participants have information available to them before making a trade (receive a quote, see open offers) and after a trade (see prices, quantities). • Price discovery • Prices incorporate and track available information in the market - and do so in a reasonable and efficient way.

6. Organization of Markets • Historically, equities in US were mainly traded on the floor of the NYSE. • NYSE as a “specialist” market • Each stock managed by a specialist • Specialist quotes “bid” and “ask” prices • Investors, who are physically on the trading floor, trade with the specialist at these prices • Specialist holds some stock to keep market functioning, but not very large positions.

7. Organization of Markets • Nasdaq competes with NYSE and was historically an “over the counter” market. • Organization of OTC markets • Small number of “brokers” quote bids/ask to prospective traders, who can trade with any of the brokers. • In some OTC markets, executed trades are posted publicly creating a degree of transparency. • OTC organization is typical for less “liquid” securities: corporate and municipal bonds, derivatives, etc.

8. Organization of markets • Equity trading has increasingly moved to electronic order books, including at NYSE. • Organization of electronic exchanges • Traders submit orders to buy or sell • Orders are posted in an electronic “book” • If a buy order comes in above a current sell order, the orders are “crossed” and a trade is executed. • Different exchanges allow different types of orders (more on this in a minute).

9. Organization of markets • Many large trades take place “upstairs” - not on the NYSE floor or in a public exchange • Organization of large trades • Often a bilateral negotiation or by private placement. • Example: investor approaches Goldman Sachs to sell a large position. GS either finds a buyer, or buyers, or buys the position itself and then dribbles it out over time. • Recently, many electronic exchanges are trying to automate large trades by allowing for more sophisticated types of orders (more later).

10. Recent events: 2005-2009 • Location of trades • In Jan 2005: NYSE accounted for 80% of trading volume in NYSE-listed stocks; by Oct 2009, down to 25% • Execution speeds for trades • Falls from 10.1 seconds in 2005, to 0.7 seconds in 2009. • Trading volume • From 2.1 bn shares/day in 2005 to 5.9 bn in 2009. • Average trade size • Falls from 724 shares in 2005 to 268 shares in 2009

11. Current market • Trading mostly done on electronic platforms • Five large exchanges (transparent) • Multiple smaller electronic exchanges • Internal or “dark” trading pools (not transparent) • Questions • Does this fragmentation matter? (Offer to buy and sell must be posted publicly to all exchanges.) • Why the proliferation of markets? Should different types of trades be executed in different markets?

12. Applying economic theory • Price formation and price evolution • “Efficient” markets with asymmetric information • Model bid/ask spreads in specialist markets • Search costs and market frictions • Bid/Ask spreads in OTC markets • Large orders and price impacts • Design of exchanges, and exchange competition.

13. Modeling price formation • Consider a specialist market • Specialists offer bid price b (offer to buy) and ask price a. • Traders arrive and can buy or sell at these prices. • After trading, world ends, stock pays d ~ U[0,1]. • First consider traders coming to sell… • Two types of traders, equally likely to arrive • Smart trader: knows d, sells only if b>d • Dumb trader: doesn’t know d, sells at any b. • Specialists don’t know d, but they understand the environment and quote a price that ensure they will just break even on average.

14. Bid prices in market • Specialist quotes a price b • With probability 1/2, dumb trader shows up • Trader sells the stock for b • Specialist makes a profit d-b • With probability 1/2, smart trader shows up • Trader sells the stock if b>d • Specialist makes a profit (really a loss) d-b

15. Specialist market If dumb trader arrives, sells for b, specialist gets E[d]=1/2 If smart trader arrives, only sells if d<b, I.e. with pr=b If d>b, smart trader will not sell b 0 If d>b, smart trader will sell 1 E[Profit] = (1/2)b - b = - (1/2)b E[Profit] = 0

16. Bid Prices in Market • What is the expected profit for specialist • If dumb trader: expected profit is 1/2 - b • If smart trader: expected profit is b * [ -(1/2)b ] • Specialist break-even condition E[Profit] = (1/2) * [ 1/2 - b ] + (1/2) * b * (- 1/2* b) = 0 • Solving for the competitive bid price 1/2 - b = 1/2* b2 1-2b-b2 =0 b = 0.414

17. Ask prices in the market • Now suppose traders may also show up to buy, and specialist quotes an “ask” price. • Two types of buyers, equally likely to arrive • Smart traders: know d and buy if d>a • Dumb traders: don’t know d and buy at any a • Specialists quote an ask price that ensures they will just break even in expectation • Will the ask be above or below the bid?

18. Ask prices in the market • Specialist quotes an ask a • With probability 1/2, dumb trader arrives • Buys at a • Specialist profit is a-d • With probability 1/2, smart trader arrives • Buys if a<d • Specialist profit is a-d

19. Solving for ask prices • Specialist expected profit • If dumb trader: profit is a-1/2 • If smart trader: profit is -(1-a)*(1-a)/2. • Solving for the break-even ask price 2a - 1 = (1-a)2 a = 0.586 • Compare to the bid b = 0.414 • The “spread” is a-b, here 0.172

20. Price Formation & Dynamics • Efficient market theory • Current prices equal E[Value | Current Info]. • True in this theory, except an offer to buy or sell convey NEW information. • So each “buy” trade raises the price and each “sell” trade lowers the price. • Specialists charge a “spread” a>b to protect themselves from private information of the traders. • More liquid market means lower spreads, and probably less movement of prices with each trade.

21. Competition and Spreads • What makes spreads larger or smaller? • More informed traders => larger spreads • Less specialist competition => larger spreads • Competition and spreads? • If specialist has no competition, can set a=1,b=0. • Trades with probability 1/2, but makes an expected profit of 1/2 on each trade. • Extreme example, but can more generally specialist can increase spread and trade only with the dumb money. • Competition prevents this by forcing spreads to be narrower - but requires traders to be able to “shop”.

22. Theory of Exchanges • View exchange as a “double auction” • Buyers put in demand curves • Sellers put in supply curves • In “one-shot” case, would collect all offers • Compute aggregate demand and supply. • Find market clearing price, execute trades. • In practice, trading takes places in real time • This means that orders don’t all come in at once. • And trades get executed as the opportunity arises • Why trade in real time, rather than an auction every hour or day or week?

23. Dynamics of the order book • Traders put in buy and sell orders • Limit order: offer to buy or sell at some price p • Market order: buy or sell at best offered price. • Example of the “order book” w/ limit orders • Orders to buy at 80, 90, 100 • Order to sell at 110, 120, 130.

24. Example 130 120 There is currently no trade to execute b/c best sell offer is 110 and best buy offer is 100. 110 100 90 80

25. Dynamics of the order book • Current order book • Orders to buy at 80, 90, 100 • Order to sell at 110, 120, 130. • Buy order comes in at 120. • “Crossed” with the best sell order (110). • Updated order book • Orders to buy at 80, 90, 100 • Orders to sell at 120, 130

26. Example 130 120 110 100 90 80

27. Example 130 120 110 100 90 80

28. Example 130 120 110 100 90 80

29. Example 130 120 110 100 90 80

30. Price impact of large trades • What happens if there is a large trade • Large buy order can “eat up” the supply curve • If there is little “liquidity”, maybe big price impact. • Example of order book • Buy orders 80,85,90,95,100 • Sell orders 105,110,115,120,125,130,135 • Average of best buy/sell offers: 102.5 • Buy order for four units at best sell offers => • Buy orders 80, 85, 90, 95, 100 • Sell orders 125, 130, 135 • Average of best buy/sell offers: 112.5

31. Efficient markets? • Theory of efficient markets assumes roughly that p = E[Value| Current Info]. • If a seller has to liquidate a large holding of stock for reasons that aren’t informative about the value of the company, shouldn’t matter for the price. • But in practice, doesn’t always work this way. • Example: de-listing of stock from S&P • Index funds all sell on a given day. • This is understood well in advance, so no “news” • But stock price generally falls and often takes a substantial amount of time to recover!

32. Problems for Large Traders • Limit orders make it difficult for large traders to get a good price… • Example: • Buyer and seller each willing to hold two units, but have decreasing marginal values. • Buyer has values 35 and 32 • Seller has values 30 and 25 • Efficient to trade both units • Buyer ideally offers 55 for two units.

33. Large trades, cont. • Example, cont. • Buyer values 35 and 32 • Seller values 30 and 25 • If buyer offers 30 and 25, seller can trade one unit at 30 and won’t want to trade a second unit. • To get the seller to sell both units, buyer must offer 30 and 30, and spend 60 for 2 units. • More profitable to offer 25 for one unit, and make profit 35-25=10, than pay 60 for two units and make profit 35+32-60=7.

34. More problems • Large traders suffer from “front-running” • Example of order book • Buy orders 80,85,90,95,100 • Sell orders 105,110,115,120,125,130,135 • Large trader submits buy order for four units • Should pay 105, 110, 115 and 120 for these units. • Front-running strategy • Front-runner jumps ahead and submits buy order for 3 units, then offers to sell 3 units at 120. • Front-runner buys units at 105, 110, 115, sells at 120 • Large trader pays 120 for all units • It pays to be fast in a financial market!

35. Strategies for large trades • Large traders try to avoid this • Execute trades slowly, e.g. order one unit (pay 105), then one more unit (pay 110), and so forth. • But this slows things down, and seems inefficient. • Alternative to take the trade “upstairs”: find a large seller, or pay an intermediary to assemble or liquidate the position slowly. • New exchanges try to improve the market design to facilitate large trades…

36. Innovations in exchanges • “Icebergs” and hidden orders • Allows traders to submit offers that are entered in the order book, but “hidden” from view. • Makes it harder for predatory traders to front-run, and can allow large traders flexibility. • “Dark pools” • Orders submitted to broker (e.g. Goldman Sachs) are “crossed” before being submitted publicly to the exchange. • Traders cannot see what is going on in this “dark” exchange, which benefits from seeing the prices and being able to access the liquidity in the public exchanges. • Many interesting market design questions around the design of public and dark exchanges…

37. Market Design and Toxic Assets

38. Today’s Lecture • Securitization and markets for loans • How credit markets and securitization work • Why securitization: informational theories • Economics of secured credit markets • Leverage theory & Feedback effects • Application to real estate and secondary markets • The panic of 2007-08 & market failure • How the market failed, explanations and implications • Attempts to “restart” and “redesign” these markets

39. Increase in consumer lending

40. Consumer Lending • Traditional consumer lending (by banks) • Bank takes deposits from consumers • Bank lends the money out to borrowers • Bank collects payments on loans • As payments come in, can originate new loans. • Modern consumer lending (by banks & others) • Lender raises money (maybe deposits, but maybe not) • Lender originates loans to borrowers • Lender resells loans to secondary market • Cash from loan sales can be used to originate new loans.

41. Traditional Lending (in pictures) Borrowers Step 1 Step 2 $ $ $ Step 0 Lender Depositors

42. Securitization (in pictures) Borrowers Step 3: $ (via a loan “servicer”) Step 1 IOU IOU $ Step 2 $ Trust $ Step 0 $ Lender Depositors $: Step 3 Step 2 Investors Insurers Insurance (CDS) $

43. “Securitization” • Securitization process • Lender sells a “pool” of loans to the trust. • Trust sells financial claims on the loan pool. • Sale of the claims used to pay the lender. • Trust collects loan payments & pays claim holders. • Key features of the market • Pooling of many loans (rather than resale of single loans) • Tranching of pool payments to create securities • Why these features? Risk-sharing & information.

44. Pooling • Pooling can diversify risk • Suppose each loan promises $1 but defaults with prob = 0.1 • So E[Payment]= $0.90, but Pr[Payment = 0] = 0.1. • Consider 1% claim on 100 loans with independent default • E[Payment] = 0.90, but Pr[Payment = 0] = (0.1)100. • As the pool gets large, if defaults are really independent, then each fractional investor is likely to get close to $0.90 • Pooling can lower transaction costs • If each loan sold separately, investors want to inspect each one to cherry-pick the pool => sale process very costly. • If loans are pooled, everyone gets a representative claim on the pool & harder to cherry-pick. (Analogy to de Beers!)

45. Tranching • Consider pool of loans made by bank • Loans promise $100, but may deliver as little as $80. • Investors think all outcomes btwn 80 and 100 equally likely. • Suppose the bank knows what the outcome will be (v). • Suppose bank tries to sell the whole pool • If investors offer 100, bank will sell, but E[Ret. to Inv]=90. • If investors offer 90, bank will sell if v<90, E[Ret. to Inv]=85 • In equilibrium investors cannot offer p>80 and break even. • So, investors offer 80, and bank only sells if v=80. • The market doesn’t work to allow resale.

46. Tranching • Alternative “tranching” structure • Bank sells claim on the first $80 in loan payments, and keeps all additional loan payments. • Investors will pay $80 b/c claim is a sure thing. • So finance the pool with a combination of debt (sold to investors) and equity (held by the bank). • From theory to practice • Typically several tranches, which take losses in order: “junior” tranche is like equity, “senior” more like debt. • Key feature: junior tranches are “information sensitive”, but senior tranches are less so --- doesn’t matter if pool will return 80, 85, 90, 95, 100 - investors get paid regardless!

47. Ratings Agencies • Rating agencies (Moody’s, S&P, Fitch) • Usually grade corporate debt: AAA,AA,A, etc. • Higher grades “safe”, lower more likely to default. • Also grade “securitization” proposals: most senior tranches might be AAA, equity tranche maybe B. • Concerns about ratings agencies • Investors relied “blindly” on high ratings, but maybe… • Gaming: tranches designed to “just” make the grade. • Bad models: underestimated default from falling house prices, or the possibility of mass (correlated) defaults. • Focus on probability of default rather than whether defaults would occur in “bad” states of the world (subtle).

48. Secured Lending & Collateral • What happens when borrowers default? • If a loan is unsecured (like a credit card) • Lender can try to harass the borrower, but not much else the lender can do to recover value. • If a loan is secured (like a mortgage) • Lender gets the underlying collateral. So default is less costly for the lender, who can charge a lower interest rate. • If collateral is sufficiently valuable, default won’t even occur because if the borrower can’t make payments, she can sell the asset and use the proceeds to pay off the loan.

49. Collateral & Feedback effects • With secured lending, there can be feedback effects between the credit market and the asset market. • Example with housing market • When prices are rising, lenders expect that even if they lend a large fraction of the purchase price, the house will become more valuable and so default won’t happen. • And if home buyers can borrow a lot and at low rates, they can pay more, so prices go up => positive feedback. • When prices are falling, the reverse can happen…. • Lenders will lend a smaller fraction of the purchase price, buyers have a harder time getting cash, prices fall more…. • Downward spiral can be exacerbated because borrowers who can’t make payments default and houses are sold at auction increasing supply of houses for sale.

50. The Leverage Cycle • On the way up… • Asset prices expected to rise • Lenders offer generous credit • Buyers can spend more • Prices do rise, and so loans get repaid… • On the way down • Asset prices expected to fall • Lenders tighten credit • Buyers can’t spend as much • Prices do fall, and so loans don’t get repaid, and so.. • Additional “forced sales” bring prices down further.