Financial Exchanges. Today’s Lecture Background on financial exchanges The role of financial...

Financial Exchanges

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.

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?

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?

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.

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.

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.

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).

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).

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

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?

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.

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.

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

Specialist market

0 1b If d>b, smart trader will not sell

If d>b, smart trader will sell

If dumb trader arrives, sells for b, specialist gets E[d]=1/2If smart trader arrives, only sells if d<b, I.e. with pr=b

E[Profit] = (1/2)b - b = - (1/2)b

E[Profit] = 0

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 conditionE[Profit] = (1/2) * [ 1/2 - b ] + (1/2) * b * (- 1/2* b) = 0

Solving for the competitive bid price1/2 - b = 1/2* b2

1-2b-b2 =0b = 0.414

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?

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

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

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.

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”.

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?

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.

Example

110

120

130

100

80

90

There is currently no trade to execute b/c best sell offer is 110 and best buy offer is 100.

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

Example

110

120

130

100

80

90

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

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!

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.

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.

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!

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…

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…

Market Design and Toxic Assets

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

Increase in consumer lending

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.

Traditional Lending (in pictures)

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“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.

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!)

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.

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!

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).

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.

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.

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.

Housing Bubbles & Crashes

Many financial crises triggered by real estate US Savings and Loan collapse (early 90s) Japanese bank failures in (early 90s) Sweden and Finland bank failures (early 90s) US and other recent problems

Why real estate (rather than, say, business failures) Owners of real estate tend to be highly levered, as

compared to, say, owners of businesses. Maybe why stock market crashes in 1987 and 2001 didn’t

trigger broader financial crisis … although these events themselves may have been exacerbated by investor leverage.

Investor Leverage Buyers of loans in the secondary market also used

leverage… sometimes lot of it! In January 2007, it was possible to buy a AAA mortgage-backed

security, and borrow 98% of the purchase price. These rates were available, however, only if the investor rolled

the loan over every night (in the “repo” market) Feedback and the leverage cycle, again…

If a small number of optimistic investors can borrow 50:1, they can have large buying power, and drive up asset prices --- particularly if it is hard to “short” the asset.

Once prices start to fall, however, the optimists will be wiped out & credit will tighten, so prices fall a lot --- it’s harder to borrow & optimists are out of the market.

This can lead to a crash in the secondary market.

Feedback & Panics

Bank runs - the old-fashioned kind Consumers have deposited money at bank and can come

at any time to withdraw. A deposit is like a loan that is rolled over every second.

Bank has lent the money out, keeping only a small reserve, so if more than a few investors withdraw, bank can’t pay.

If consumers believe the bank has made some bad loans, there can be a race to get money out.

Government solution: deposit “insurance” Many bank runs in the 19th century and Great Depression,

following which US Gov’t decided to insure deposits. If investors demand their money, and bank can’t pay, govt

will. So long as investors are confident in FDIC, no runs.

Feedback & Panics

Bank runs - the modern kind Suppose a financial institutions is borrowing short term

(e.g. buying mortgage securities with overnight financing). If investors get nervous, they pull their money, knowing that

the gov’t may not be there to “insure” repo loans. Plus, there are some additional twists..

If institution is a broker (Lehman, Bear, Morgan Stanley), its customers (e.g. hedge funds), may also run, so franchise value of the institution disappears.

Small number of “core” institutions are interdependent: e.g. they may have sold each other insurance, so if bank A fails, and owes money to bank B, bank B may fail.

Stepping back (macro picture)

From 2000-2007, there was a lot of money chasing investment opportunities. Savings glut in China, Middle East, etc. Fed had interest rates low so investors wanted better

investments than treasuries. Lots of this money found its way into housing.

Home ownership increased from 65% to 69%. House prices increased by XX%. Leverage increased a lot: many new purchases at zero

percent down, and home equity loans let homeowners increase size of mortgage relative to house value.

Didn’t everyone see problems coming? Why not?

The panic of 2007… Housing market started to turn:

bad loans (esp. subprime from 2005-06) began to default, even tranches viewed as “safe” become risky. Everyone realized that investors holding securities based on

bad loans were going to take losses. Loss of confidence in shadow banks

many of which were holding these securities, and many of which were reliant on repo financing

Triggering feedback effect and panic Slow-motion run/collapse of the repo market Prices being offered for mortgage-backed securities dropped

sharply, making it hard for investors to liquidate position without taking big losses.

… continuing into 2008

Positions of finacial institutions deteriorated Failure of Bear Stearns in Feb 2008. Takeover of Fannie Mae, Freddie Mac, IndyMac. Collapse of WaMu, Lehmann, AIG, Merrill, Wachovia, etc.

Government interventions Initially, offered loans to replace lost repo financing, and

responded in ad hoc way to individual failures. Eventually proposed TARP

Idea: buy “bad assets” from the banks, restoring confidence Actual: buy equity shares in the banks, restoring solvency.

Plus Fed eventually buys $1+ trillion of mortgage-backed securities essentially taking on the role of lender to prevent further economic collapse, especially in housing.

Market Design Problems

Market for “toxic assets” Once housing prices started to fall, seemingly

“safe” securities became risky, market shut down. Why did the market shut down?

Asymmetric information? Anticipation of bailout? Could it have been re-started?

TARP proposal was for Treasury to buy large quantities of toxic assets.

Initially considered a SAA or treasury-style auction. Later considered setting up funds managed by

professional investors who would buy in various ways.


Market for Credit Default Swaps CDS are insurance contracts that pay off if a given loan (or

bond) defaults. Example. I buy a Lehmann bond that promises $1, and pay

AIG to insure it. I pay AIG premiums and if the Lehmann defaults, AIG makes me whole.

Markets for CDS settlements? When Lehmann goes under, it takes a while to sort our who

gets what, but ideally want to “settle” the CDS contracts right away based on value of the bonds. But what is it?

Should CDS markets be bilateral or exchange-based? CDS contracts are bilateral - concern is spillover effects and

non-transparency. New proposals call for exchanges where parties would trade against the exchange.


Funding markets for financial institutions Part of the problem in the crisis was the heavy

reliance on short-term repo financing. Is it possible to have better markets/models for

financing financial institutions? Insured deposits? Loans that can be re-paid with

stock certificates? Or maybe just limits on leverage? Difficult regulatory problem because gov’t

ultimately bears a lot of the risk, but doesn’t want to overly constrain financial institutions.

Macroeconomics

How does the recession link to the crisis? Starting in 2007-08 and accelerating in fall 2008, consumer

spending, employment, and investment dropped. Various theories to explain this

Harder for consumers and firms to get credit Loss of consumer confidence and wealth Loss of business confidence, and uncertainty Government creating additional uncertainty…

Of course, in 2009, the survivors in the financial markets actually did great … Partly because of gov’t lending them lots of money at cheap

rates, and partly because market dislocations created a lot of opportunities…

Financial Exchanges. Today’s Lecture Background on financial exchanges The role of financial...

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