Asset Market Experiments Econ 333 November 20, 2014.

Asset Market Experiments

Econ 333

November 20, 2014

“Don’t confuse brains with a bull market”– Seen on the jacket of a “runner” on the floor of the

Chicago Mercantile Exchange (1980’s).

Markets can rise and fall rapidly

Can you spot the largest one day drop in stock prices in U.S. history?

1987 closeup: 22% single day decline

So what did you think?

What influenced your decisions to buy or sell?

How did you determine what the asset was worth?

What is link to “real world”?

Keynes

Keynes famously compared the stock market to a newspaper’s beauty contest– The London newspaper would print photos of beautiful

women. – You had to write in with the six women you liked the most.– Everyone that picked the most popular woman could win a

prize. Incentive problem:

– If you simply picked the women YOU thought were pretty, you wouldn’t win, because your entries have to match the popular vote.

– You had to pick those women whom you thought that everyone else thought that everyone else liked!

Keynes

“It is not a case of choosing those [faces] which, to the best of one’s judgment, are really the prettiest, nor even those which average opinion genuinely thinks the prettiest. We have reached the third degree where we devote our intelligences to anticipating what average opinion expects the average opinion to be…”

Keynes

Keynes’ point was simply that some investors may be less interested in a stock’s fundamentals, and more so in what it will sell for in a few weeks.– Herding behavior, people flock to particular stocks

creating upward price pressure– Could help explain market volatility not justified by

market fundamentals

You played the “Beauty Contest Game”

Pick a number that is 2/3 of the average of what you and your classmates pick.– Average 33.7– 2/3 of the average 22.5

– “kamel” wins $5 20

Dutch tulip mania

Early 17th century Tulip bulbs traded at nearly 10x average

annual income In 1635, 40 bulbs traded for 100,000 florins

– 1 ton of butter cost 100 florins– 8 “fat swine” cost 240 florins

In 1637 markets crashed– Many traders were financially ruined– Much of the trading took place in taverns

NASDAQ: Tech “Bubble”

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Are these rapid price changes bubbles?

Nobel Laureate Eugene Fama (2013) talks to John Cassidy– http://www.newyorker.com/news/john-cassidy/interview-with-eugene-fama

Fama and Shiller both win in 2013– http://noahpinionblog.blogspot.fr/2014/01/the-econ-nobel-prize-is-really-

weird.html

Fama: – “I don’t even know what a bubble means. These

words have become popular. I don’t think they have any meaning.”

Shiller:

“A half-century ago, there was a lively discussion among economists about the dynamics of price expectations. For example, Alain C. Enthoven, then of the Massachusetts Institute of Technology, and Kenneth J. Arrow of Stanford wrote in 1956 that expectations that extrapolate past price increases can produce economic instability. But that thinking was largely cast aside in the 1960s, when my profession embraced the theory that efficient markets formed by people holding rational expectations could explain virtually all economic activity.

As a result, economists in recent decades have not developed expectations theory much further. That needs to be corrected in coming years. In the meantime, this failing helps explain why the current crisis was generally unpredicted, and why its future course is so poorly understood.”

Lab helps us study asset markets

Deviations from fundamentals can be observed– In the field, the fundamental is not observed

(ambiguity)

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Two Kinds of Markets in the Lab and Field1. Consumer product supply and demand markets:

Most final goods and services in the economy are not re-traded; costs/benefits realized then repeat over time, in a flow.

* Lab performance better than economists expected. * Price discovery process very efficient

* We all benefit from exchange, specialization.

2. Asset markets: Items like houses, stocks and bonds are re-traded.* Lab outcomes far worse than economists expected.

* Price bubbles are common.* Observe winners and losers

Asset Bubbles in the lab0

100

200

300

400

500

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Round

Progenitor OwnFund

First Gen., Progenitors and Own Experience Sessions

01

002

003

004

005

00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Round

Advice ExperienceFund

Third Gen., Advice and Own Experience Sessions

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How much is an asset worth?

Rational expectations model:– An asset’s current market value tends to converge

to the risk-adjusted discounted present value of the rationally expected dividend stream

– OK, what does that mean??? Implications

– In equilibrium, the price of a stock will change only if there is new information about the expected dividend stream

Asset markets

All participants are traders Trading an asset that generates dividends in

each period Dividends are drawn randomly Value of asset depends on number of periods

remaining Two ways to earn money: dividends and

capital gains

Your experiment 2014

Possible dividends: 0, 8, 28, 60 cents– Equally likely expected value of dividend is 24

15 rounds Asset has no value at end of exp Expected value of asset (aka fundamental

value or intrinsic value)– during round 1 is 360 = 15 rounds x 24 cent

expected dividend– Declines by 24 cents every round

Econport

http://www.econport.org/econport/request?page=web_home

Objectives of asset mkt experiment

Will agents trade an asset whose dividend distribution is common knowledge?– Why are we asking this question?

If so, can we characterize the price adjustment process?

Will we observe bubbles and crashes?

In a rational expectations equilibrium:– What is an asset worth? – How many trades would you predict?

Some key results

Bubble in early periods, followed by crash in later periods

Bubble-crash diminishes with experience – but does not disappear entirely

Presence of bubbles are robust to other institutional variations:– Short selling, margin buying, broker fees– Business professionals– See Palan (2013) and Porter and Smith (2008)

Alternative paradigms

Ambiguity & Information aggregation