Stock Markets and Stock Price Determination

Source: Cecchetti and Redish, Ch. 8

- Common stock:

- Share in the ownership of the firm.

- Gives holder a claim on profits (shareholders: residual claimants) - entitled to dividends paid out.

- Limited liability: shareholder is not liable for firm's losses (beyond the value they have invested in the stock)

- Shareholder has the right to sell the stock; voting rights.

- Issuing stock: a way for businesses to raise money.

- Origins of joint stock companies: pooling savings to finance large capital investments (Dutch sailing ships).

- small denominations aid pooling.

- Investment in stock is equity investment (“equities”).

- Individual investors and stocks:

- can buy individual stocks (small denominations aid in diversification)

- can invest in a mutual fund: fund constructs a portfolio (charges management fee).

- Stock prices determined by what the highest bidder will pay.

- most transactions are of already issued stock (secondary market).

1

The Price (Value) of a Stock: Stocks as Assets

- Valuing assets (see earlier discussion of yields):

- Assets give a claim to a stream of returns: D1, D2, ...Dn

- Price: P

- equilibrium price: the level which gives the same return as on other similar assets (i).

e.g. similar riskiness, liquidity, etc.

- it is the present value of the stream of returns:

P = D1 + D2 + D3 + …+ Dn(1+i) (1+i)2 (1+i)3 (1+i)n

- flows of lending between this asset and substitute assets ensures this holds.

e.g., Price below the level above: - yield is then higher than i- more people buy the asset (demand rises)- P will rise.

- Businesses as assets: what is the value of a business (V) to its owner?

- Present value of the stream of returns the firm generates.

- Returns? Profits

- Define: Profiti = profit in period i.

2

- Then:

V = Profit1 + Profit2 + Profit3 + …+ Profitn

(1+i) (1+i)2 (1+i)3 (1+i)n

where:

- “n” is the last period of the firm’s existence.

- “i” is an appropriate return (discount rate), i.e. return on similar assets.

- future profits are uncertain: equation is based on expected profits.

- “i” will include a risk premium to account for this uncertainty.

- Value of a Share:

- A share gives part ownership in the firm.

- one share means you own: 1/(Total no. of shares) of the firm.

e.g. if 1 million shares, own 1 millionth of the firm.

- Value (price) of a share (S):

S = V/ (Total number of shares)

- Text: focuses on “dividends” as the stream of future payments from the share.

- Dividend: amount of profits paid out per share.

- so value of a share is the present value of expected dividends.

- Let D1, D2…Dn be the expected value of dividends in periods 1…N.

3

- Value of a share is then:

S = D1 + D2 + D3 + …+ Dn(1+i) (1+i)2 (1+i)3(1+i)N

- note: like profits future 'D' are uncertain now. (equation based on expected D)

- The two expressions for the stock price give the same share price if:

- Profits are just paid as dividends.

- Then: D1 = Profit1/(No. of Shares), D2= Profit2/(No. of Shares) etc.

- More generally? if the present value of profits paid equals present value of all future dividends paid.

e.g., profits reinvested in the firm (not paid as dividends) generates additional dividends in the future.

- so time pattern of profits and dividends can differ but their present values may be the same.

- Note: - if a shareholder sells the share at some point in the future

(say period m) then the asset equation becomes:

S = D1 + D2 + D3 + …+ Sm(1+i) (1+i)2 (1+i)3(1+i)m

- where Sm is the share price in period m.

(in text notation: S is Ptoday and Sm is Pm years from now).

- Sm will reflect discounted future profits from period m to n (so get the same result as above).

4

- A convenient special case: “Gordon Growth Model” or "Dividend-Discount Model"

- Say that dividends are expected to grow over time at a rate “g” and dividends are currently Dtoday. Then:

D1 = Dtoday(1+g)D2 = Dtoday (1+g)2

D3 = Dtoday (1+g)3

. .Dn = Dtoday (1+g)n

- So:

S = Dtoday (1+g) + Dtoday (1+g) 2 + …+ Dtoday (1+g) n (1+i) (1+i)2 (1+i)n

= Dtoday { (1+g) + (1+g) 2 + …+ (1+g) n } (1+i) (1+i) 2 (1+i)n

- since the expression in {} is a geometric series it can be simplified to (provided that N→∞ and i>g):

S = Dtoday(1+g) (i-g)

- A simplified case.

- But: perhaps more plausible to project “g” rather than predict individual future Ds.

- All you need is i, Dtoday and an estimate of “g” to predict price.

(text equation (11): i = isafe + risk premium)

5

What determines stock prices according to the asset valuation model?

- Value of a Stock reflects:

- expectations of future profits (dividends)

- discount rate (i): includes a risk premium since future payments uncertain.

- number of stocks issued : how big a share of profits does it entitle the owner of the stock to?

Key factors: Effect on Share Price

Level of the firm’s expected future profits (+)

Number of stocks outstanding (-)

Discount rate or required return (-) (includes risk premium)

- Expected future profits: what lies behind this?

- General business conditions: e.g. recession expected: future profits likely lower.

- Specific business conditions (specific to industry or firm).- determinants of revenues and costs of the firm.

- Discount rate (i):- depends on yields on alternative investments;

- characteristics of the stock vs. other assets:

risk, liquidity premia incorporated (see text equation (11)).

6

- Monetary policy and stock prices?

- Bank of Canada cuts interest rates:- lowers the return on alternatives to stocks;

- equivalently: lowers the discount rate on stocks

- stock prices should rise.

- Monetary policy can also affect future growth and profits: i.e. lower interest rates, more borrowing and spending, boom,

higher profits.

- this will also raise stock prices.

- Central banks often respond to sharp falls in the stock market with interest rate cuts.

e.g. recent crisis, Black Monday (Oct. 19, 1987).

Stock Price Indices

- Measures of the level of the stock market.

- typically based on some average of stock prices.

- Examples:

Dow Jones Industrial Average: - Based on 30 largest US companies. - Mean of their share prices.

i.e. reflects value of a portfolio that includes 1 stock from each of the 30 largest companies.

- Oldest index (since 1880s!).

7

Standard and Poor’s 500: - Largest 500 firms in US. - Value-weighted average of stock prices.

- weight: $ value of outstanding shares. - Reflects value of a portfolio whose composition reflects

relative sizes of largest firms.

(S&P/TSX Composite Index - Cdn. version, NASDAQ index are calculated in a similar way to S&P 500 )

Price-Earnings Ratios:

Price-Earnings Ratios (PE) = (Stock Price / Earnings per share)

- Earnings: typically an annual measure of after-tax profits.

- High PE ratio?- Could reflect high expected profits in the future.- Could reflect low risk i.e. low discount rate (i).- May mean stock is overpriced.

- Main interest in PEs is comparative: - PE vs. other similar stocks: if low – more likely a good buy.

- How is it compared to historical values?- can indicate if over- or under-valued if no good reason

for difference from historical values.

- Some studies: PE has predictive power (see Malakiel, 2011)

- R. Shiller’s data: from Irrational Exuberance website.http://irrationalexuberance.com/

- Used the PE ratio to argue that US stocks were overvalued in 1999 (he was right! – see his graph)

8

- Sharp rise in stock prices and PE ratios 1990s to 2000:

- Some regarded it as reasonable at the time.

- Some stories told (in terms of asset price model):

Glassman and Hassett Dow 36,000: - investor behavior had changed: more sophisticated

- view stocks as less risky (more diversification, mutual funds, etc.): smaller risk premium required.

- result? They argued that- Discount rate on stocks had fallen (lower i).- Stock prices should rise (PE ratios will rise). - Rise would be permanent.

High tech / info technology stories: - a “new economy”- higher future profits due to innovation and technology

e.g. lots of Amazon.com’s out there!

9

Baby-boomers saving for retirement.- bids up stock prices (lowers i on assets generally).

- Other views: a speculative bubble- A. Greenspan US Federal Reserve: “irrational

exuberance”

- no real change in riskiness of stocks.

- high tech boom: competition suggests main benefit will be to customers not higher profits.

10

Speculating on Stocks: Some Views

- Much attention on strategies to make money trading stocks. i.e. by anticipating price changes

- Expect price rise: buy low, sell high !

- Expect price fall: sell now if own it, “short” it if don’t own it.

(short: borrow stock now, sell it at current high price, once price falls buy it and pay back the borrowed stock)

- See: G. Mankiw handout: "What stock to buy? Hey Mom don't ask me?"

(1) Fundamental analysis:

- Determine the true value of a stock:

- true value reflects “fundamentals”

- stock prices are rooted in firm profits.

- focus on predicting and anticipating future profits, risks.

e.g. analyze info on the firm, its markets, its competitors (fundamentals).

- Buy if true value > share price.

- Sell if true value < share price.

i.e. buy before expected rise in profits is priced in.

sell before expected fall in profits is priced in.

- An early statement of this view in 1934: B. Graham and D. Dodd, Security Analysis. Warren Buffet “Oracle of Omaha” was a student of Graham’s.

11

(2) Technical Analysis or “Chartists”:

- A data driven approach.

- Look for patterns in historical stock price movements. - idea that patterns reflect past regularities in investor behavior. - patterned behavior gives rise to predictable trends.

- Monitor current stock prices for such patterns.

- Use historical patterns and current trends to predict future prices.

- See regular feature “What the Charts Say” in The Globe and Mail Report on Business.

- Example: “head and shoulders” – price reaches the neckline (straight, black line) then price will fall

substantially.

- Some claim the approach is linked to “behavioral finance”: - patterns may be rooted in irrational but predictable behavior.

- Newer methods: neural networks, chaotic dynamics- also look for patterns in past data for use in predicting future

prices.

(3) Efficient Markets Hypothesis: (see extended discussion below)

12

Efficient Markets Hypothesis (EMH):

- Stock prices reflect present value of future expected profits.

- How are these expectations formed?

- Efficient Markets view assumes “rational expectations”

i.e. expectations will be the “best guess” or “optimal forecast” and will make use of all relevant available information.

- Why? Profitable to be accurate.

- Investor behavior ensures that stock prices build in relevant current information on future profits.

- Say “good news” becomes known: ↑expected profits.

- investors realize the stock price will rise.

- Investors buy to profit from the expected price rise.

- Extra demand raises the price.

- “Good news” is now built into the stock price.

- If new information is not built in investors are not acting rationally.

- profits that could be made aren’t; or

- (with bad news) losses that could be avoided aren’t.

- All or some investors?- actions of specialists or traders (“smart money”) alone may be

able to make hypothesis hold.

(some disagree: see discussion of “bubbles”)

13

- Some Consequences of the Efficient Markets Hypothesis:

(1) Stock prices only change when new information on future profitability becomes known.

- old and current information is already built into stock prices.

(2) Future changes in stock prices are unpredictable.

- Future new information is unknown now.

- So given point (1): can’t predict future price changes.

Paul Samuelson (1965): “properly anticipated prices fluctuate randomly”

- Stock prices will follow a “random walk”:

- Tomorrow’s stock price (S):

Stomorrow = Stoday+ e

where: e = random error (reflects effect of new info)

- best prediction of tomorrow’s price is today’s price.

- price change is unpredictable (random).

(3) Explaining successful speculation

- Success for those who obtain new information first.

- could be an insider, could be the first realize something.

- can profit from this new information

- Chance /luck: another possible explanation of success.

14

- Apparent success may reflect risk taking (not so successful if adjusted for risk)

- Generally: future price movements are unpredictable and speculation will not be successful.

(4) Patterns in past prices or yields will not be useful in predicting current prices or yields.

- ‘Technical analysis” will be wrong: history is already built into current prices.

(5) EMH is critical of “Fundamental analysis”:

- current knowledge of fundamentals is already built into prices.

- study of existing info on a firm and its prospects will be of little value in determining future stock prices.

- What strategies do advocates of “Efficient Markets” typically suggest:

- can’t beat the market

- speculative strategies will generally be unsuccessful.

- can’t pick winners.

- Favor “index funds” or equivalents: invest in the average stock value, invest long-term.

i.e. don’t pay for “expertise” (avoid high mgt. fees)

See for example: B. Malakiel A Random Walk Down Wall Street.

15

Evidence on the Efficient Markets Hypothesis:

For Efficient Markets:

- Experts vs. the market:

- Wall St. Journal’s Dart board portfolio, San Francisco Chronicle’s Orangutan!

i.e. random picks do as well on average as expert choices.

- Mutual funds: good past performance does not help predict future strong performance.

(see Malakiel (2003) paper on webiste: Exhibit 5-9)(also Malakiel (2011) Exhibits 2,3 – funds vs. benchmarks)

- Many studies suggest technical analysis doesn’t outperform the market.

- Announcements or publicity regarding already known information does not seem to affect stock prices.

- Studies suggesting new information is built in quickly.

- Random walk prediction:

Stomorrow = Stoday+ e

- Testing this? - are changes in S related to historical data: shouldn’t be.

- are changes related to publicly available information: shouldn’t be.

- most studies have supported these predictions.

16

- Some claim support for EMH based on the recent crisis:

- inability of most to predict the end of bubble: consistent with new information as cause.

- Does information technology enhance financial markets ability to process information? If so markets may be closer to EMH than before.

Against efficient markets (see text p. 171, Malakiel, 2011):

- Pricing anomalies:

- Small firm and January effects:- finding of “abnormally high” returns for small firms over long

periods of time.

- stock prices experience abnormal rises in January (may be small firms again);

- why is this not anticipated? - rational investors should bid up the prices of small firm

stocks eliminating the high returns

(see Exhibit 2 from Malakiel (2003) )

- January effect: if anticipated, investors should buy more in December eliminating the effect.

- Several studies suggest there is short-term “momentum” in stock price movements (successive price changes in same direction)

- a predictable pattern inconsistent with EMH

17

- “Return reversals” or “mean-reversion”: some studies find that low (high) returns today are associated with higher (lower) future returns.

- if correct, this should be anticipated.

- How to EMH proponents respond pricing anomalies? - could small firm effect be related to risk?- question how dependable these patterns are over time.- these effects may be too small to be profitably exploited.- evidence that such effects weaken once identified)

Richard Roll (Portfolio Manager and economist), cited in Malakiel, 2011:

“I have tried to invest my client’s money and my own in every single anomaly and predictive device that academics have dreamed up … and I

have yet to make a nickel on any of these … inefficiencies.”

- Excessive volatility:- evidence of overreaction, more fluctuations in prices than justified

by later variations in profits and dividends.

(see handout: Shiller’s data – shouldn’t prices be less volatile than actual profits if prices are optimal forecasts?)

- Stock market crashes:- Great Crash (Oct. 1929: -25%), Black Monday 1987 (20% fall in 1

day, 30% in 1 week); Tech stock collapse 2000-01.

- Can such large changes really reflect new information?

- Successful investors:- Some investors have earned high returns for long periods

e.g. Warren Buffett, David Swensen (Yale U. endowment)

“I’d be a bum on the street with a tin cup if the markets were efficient”Warren Buffett

18

- How would EMH explain this?- luck? (coin flip example) - excessive risk- first to receive new information?

- Maybe they are just better than others at analyzing fundamentals?

- EMH: why don’t others learn or monitor and copy?

- Or are these investors exploiting predictable patterns? (vs. EMH)

------------------------------------------------------------------------------------------

- Efficient markets hypothesis is usually discussed in connection stocks

- it is widely applicable to financial other speculative markets.

- Current prices and yields will reflect all known information affecting borrowing and lending in the market.

- Note on the term "Efficient" in EMH:

- not in the "Pareto efficiency" sense (maximize overall surplus from trading).

- efficient at processing and acting on information.

- welfare implications: efficient prices provide good information

- price signals will tend to do a good job directing finance to the high return projects.

- financial system will work quite well.

- not so if prices are not “efficient”.

19

-Additional information on the still unresolved EMH debate (see website):

B. Malakiel (2011) “The Efficient Markets Hyothesis and the Financial Crisis” see course website (goes through pros and cons)

N. Smith Feb. 8, 2013 (Noahopinion blog): interesting perspective – EMH lots of problems but valuable basic insight for individual investor.)

Behavioural Finance (text pp. 170-171)

- Questions whether usual economic assumption of rational decision-makers is appropriate in financial economics.

- Links to psychology and experimental economics. Kahneman and Tversky -- psychologists. R. Shiller (see bubble model below), R. Thaler another prominent

figure.Experiments important (see bubble example below)

- Aims:- to identify departures from rationality - identify actual patterns of behavior- explain outcomes- some attempts to build models based on patterns discovered.

- Evidence of investor overconfidence, reliance on rules of thumb, loss aversion, herd behavior.

- Proponents: greater realism, ability to explain anomalies.

- Critics: collection of observations, no unifying framework.

are rules of thumb a rational response to costly decision-making?

20

An Older Influential View: J.M. Keynes on Speculation and Asset Pricing

- John Maynard Keynes: - creator of modern macroeconomics: modelling depressions;- academic economist, policy economist and successful speculator.

- Source: J. M. Keynes (1936) General Theory of Employment, Interest and Money

- Ch. 12 discusses long-term expectations and valuation (see website):

- Did not believe that prices will reflect rational projections of future payments.

“Our knowledge of the factors which will govern the yield of an investment some years hence is usually very slight and often negligible. If we speak frankly, we have to admit that our basis of knowledge for estimating the yield ten years hence of a railway, a copper mine, a textile factory, the goodwill of a patent medicine, an Atlantic liner, a building in the City of London amounts to little and sometimes to nothing; or even five years hence. In fact, those who seriously attempt to make any such estimate are often so much in the minority that their behaviour does not govern the market.”

- Problem had become worse over time:

“As a result of the gradual increase in the proportion of the equity in the community’s aggregate capital investment which is owned by persons who do not manage and have no special knowledge of the circumstances, either actual or prospective, of the business in question, the element of real knowledge in the valuation of investments by those who own them or contemplate purchasing them has seriously declined.”

- Expert professionals won’t solve the problem:

“It might have been supposed that competition between expert professionals, possessing judgment and knowledge beyond that of the average private investor, would correct the vagaries of the ignorant individual left to himself. It happens, however, that the energies and skill of the professional investor and speculator are mainly occupied otherwise. For most of these persons are, in fact, largely concerned, not with making superior long-term forecasts of the probable yield of an investment over its whole life, but with foreseeing changes in the conventional basis of valuation a short time ahead of the general public. They are concerned, not with what an investment is really worth to a man who buys it “for keeps”, but

21

with what the market will value it at, under the influence of mass psychology, three months or a year hence.”

- Key role of “mass psychology” anticipating what others will believe:

“Thus the professional investor is forced to concern himself with the anticipation of impending changes, in the news or in the atmosphere, of the kind by which experience shows that the mass psychology of the market is most influenced.”

“For it is, so to speak, a game of Snap, of Old Maid, of Musical Chairs — a pastime in which he is victor who says Snap neither too soon nor too late, who passes the Old Maid to his neighbour before the game is over, who secures a chair for himself when the music stops.”

“…professional investment may be likened to those newspaper competitions in which the competitors have to pick out the six prettiest faces from a hundred photographs, the prize being awarded to the competitor whose choice most nearly corresponds to the average preferences of the competitors as a whole; so that each competitor has to pick, not those faces which he himself finds prettiest, but those which he thinks likeliest to catch the fancy of the other competitors, all of whom are looking at the problem from the same point of view. It is not a case of choosing those 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.”

“Investment based on genuine long-term expectation is so difficult to-day as to be scarcely practicable. He who attempts it must surely lead much more laborious days and run greater risks than he who tries to guess better than the crowd how the crowd will behave; and, given equal intelligence, he may make more disastrous mistakes. There is no clear evidence from experience that the investment policy which is socially advantageous coincides with that which is most profitable. It needs more intelligence to defeat the forces of time and our ignorance of the future than to beat the gun. Moreover, life is not long enough; — human nature desires quick results, there is a peculiar zest in making money quickly, and remoter gains are discounted by the average man at a very high rate.”

22

Speculative Bubbles in Asset Markets

- Handout: K. Lansing “Asset Price Bubbles” Federal Reserve Bank of San Francisco Economic Letters, Dec. 2007 (also on website).

- Readings: Malakiel (2011) see above pp. 32-38 discusses bubbles and the EMH. Garber (1990) “Famous First Bubbles” Journal of Economic Perspectives. R. Shiller (2003) “From Efficient Markets Theory to Behavioral Finance”

Journal of Economic Perspectives.

- What is a “speculative bubble”?

- Assets prices climb well above the present value of expected future payments from the asset.

i.e. prices above levels justified by “fundamentals”.

- Overpricing persists: not just a momentary blip.

- Overpricing rooted in speculation: expect to gain from future price increases.

- Some historical examples of apparent speculative bubbles:

- Dutch tulip mania, 1636

- Mississippi and South Sea Bubbles (France, UK), 1719

- Railway and canal stock speculations (US, Europe) 19th century.

- US stock market (crashes: 1929, 1987), US Dot-com bubble (after 2000).

- Property and housing market bubbles e.g. US-Europe housing prices – a bubble?

(see diagrams from Garber (1990), Shiller (2008) Subprime Solution, Fig. 2 )

23

Bubbles, the Efficient Markets Hypothesis (EMH) and Speculation:

- EMH suggests that bubbles shouldn’t happen.

- Overvalued asset:

- general expectation that price will fall;

- asset holders sell asset now (or “short” it: borrow it and sell it, repay later);

- price falls.

- Does the existence of “bubbles” refute EMH?

- An EMH view of apparent bubbles:

- Perceived bubbles are not really bubbles.

- “Bubble” prices reflect current beliefs about fundamentals.

i.e. buyers have optimistic expectations future payments.

- these optimistic expectations are built into current prices.

- Gene Fama: "I don't even know know what a bubble means. The word is popular. I don't think it has any meaning."

- Garber (1990) “Famous First Bubbles” Journal of Economic Perspectives.

- Considers market fundamental explanations:

- bubble set off by a perception of increased profitability.(not offset by higher risk)

- perception may turn out to be wrong but still rooted in asset pricing fundamentals.

24

- Examples:

- 1920s technological and organizational changes suggest profits will soar (stock bubble late 1920s).

- high tech stocks to 2000: also high profit expectations.

- US housing bubble: income, population growth and fixed quantity of land.

- South Sea and Mississippi bubbles: new business model (government finance)

- Glassman and Hassett Dow 36,000: lower discount rates lead to high stock prices (lower risk premia due to sophistication of investors and innovation)

- Fraud or manipulation misleads investors - convincing (but untrue) story about high future

returns.

- Optimistic expectations may not be sensible – but may only know this after the fact.

- Why do crashes occur in the EMH story?

- optimistic expectations are suddenly revised: new information.

25

- Can a “new information” story explain apparent crashes?

- Often it is difficult to identify any trigger for a crash"

- Great Crash of 1929: J. Wanniski – trade law?!

- Crash in October 1987:- rise in risk-free interest rates, tax/merger and

exchange rate policy uncertainties.

- What changed in 2000? (end of tech bubble) - learning leads to suddenly revised expectations?

- Robert Shiller’s surveys of investors:

- 70% of private and 85% of institutional investors believed the US stock market was overvalued in 1987 but did not sell prior to the October crash.

i.e. investors not acting on fundamentals.

- why? Was it a true speculative bubble?

- not rooted in beliefs about future payments;

- rooted in expectation of capital gains: expect to resell asset at a higher price.

i.e. speculator hopes to gain from a price rise not rooted in “fundamentals”.

26

Experimental Evidence of Bubbles:

- Problem with real world data: can’t see fundamentals or expectations.

- Experimental approach: create an artificial asset market.

- An example: Noussair, Robin and Ruffieiux (2001)

- simple asset: stream of uncertain dividends (4, equally likely, possible values) and fixed final payment.

- 16 trading periods; traders have 10 units of the asset and some cash at the start; traders keep their winnings.

- Expected present value of asset known (fundamental value known).

- Asset prices often above “fundamental value”.

- Bubbles and crashes common (see graphs).

- Environment does not change in the experiment: - no change in expected future payments from the asset. - Bubble can’t be due to EMH reasons.

- Some possible sources of speculation:- different expectations of future trading prices;- different attitudes to risk; - decision errors.

- Are bubbles a natural outcome when humans trade?

- However experimental literature suggests learning: bubbles less likely if with experts?

27

A Feedback Model of Speculative Bubbles: see Shiller (2003)

- Some change (“displacement”):

- Could be in fundamentals: improved expectations of profitability of some type of asset.

- Asset prices rise and those investing in these assets gain.

- Assume some investors base expectations mainly on past trends.

- these now expect similar future price increases

- note: these investors do not have rational expectations.

i.e. expected price increases are not driven by new information.

- These investors buy and so bid up asset prices: expectation fulfilled!

- Other investors start buying these assets: anticipating still more increases: past optimistic expectations proved correct!

i.e. have feedback: past price rises lead investors to expect future prices rises.

- Common to hear justifications for rising prices:

- Talk of: “a new era”, “fundamental changes”.

- See earlier discussion of high tech stock boom to 2000.

- Word-of-mouth, media: spreads stories and news of past gains.

- Spreading expectation of future price rises.

- More demand for asset, price rises: expectations fulfilled!

28

- Plausibility of justifications rises: expectations reinforced. i.e. another type of feedback.

- Role of rational investors (“smart money”)?

- May participate in the bubble.- even if believe assets are overpriced:

- even if expect an eventual crash.

- Act on expectation that other investors will continue to bid up prices.

- rational in that profits are expected.

- Timing problem is key: “smart money” believes it is a bubble

- profits can be made by participating while prices are rising.

- when prices will fall is difficult to predict.

- George Soros (famous investor) – “ride the wave for a while then sell out”.

- Bubble underway, feedback keeps it going.

29

- “Ponzi scheme” aspect of the bubble:

- Expectations of rising prices fulfilled as long as enough new investors enter and buy the asset.

(Aside: Ponzi scheme (after C. Ponzi 1920 New York)- Promise a high return on an investment. - Use $ of new investors to pay the high returns to

original investors: promise fulfilled!- Other investors buy in: use their $ to pay earlier

investors their promised high return etc.

- Collapses when not enough new $ are invested

Albania: mid-1990s – Ponzi schemes valued at 50% GDP!Bernard Madoff – a recent infamous example )

- Bubbles as "natural Ponzi's": similar mechanism, no fraud

- Collapse and crash of the bubble:

- when the bubble is very large new funds run out;

- alternatively: some begin to expect bubble is about to burst

- some begin to sell out; others can’t borrow to buy in.

- may be triggered by some event or news of key figures selling.

- Entry of new investors insufficient: prices don’t rise.

- Expectations revised: prices now expected to fall.

- Race to get out.

- Price collapse. - Feedback now works in reverse.

30

(note: quite possible prices will eventually fall below “fundamentals” price)

Bubbles and Types of Investors:

- Feedback model gives key role to “ordinary investors”

- other names: “noise traders”, “momentum investors”.

- price expectations based on past trends: “chasing trends”.

- expectations: not rational in efficient markets sense.

- Behavioural finance, psychology literature: are people rational regarding decisions with risk and time?

- Evidence of overconfidence, extrapolation, inconsistent

choices.

- Second group of investors: “Smart money”

- Have rational expectations.

- Projection of asset prices and returns based on all available information on future stream of returns.

- Will the “Smart money” offset actions of “other investors”?

- Will rational investors smooth out bubbles?

e.g. bubble starts: - price too high (vs. fundamentals) - smart money expects price to fall eventually - sells, pressure on prices to fall now.

bubble collapses: - price falls below fundamentals - smart money expects price to rise eventually

31

- smart money buys now, keeps price up.

- Problems?- Limits to the ability of “smart money” to obtain finance

to make necessary deals: “limits to arbitrage”

“markets can remain irrational much longer than we can remain solvent”

- Examples of “smart money” losing when betting vs. bubblese.g. G. Soros and dot.com bubble – timing wrong.

- Will the “smart money” play along? (like Soros)

- “Smart money” bases behaviour on available info.

- part of the info is that many investors act on past trends.

- can the “Smart money” profit using this knowledge?

- They know prices are inconsistent with fundamentals.

- May hold or buy assets on the assumption of future capital gains: selling to “ordinary investors” who follow trends.

- Debate over speculative bubbles still unresolved.

32

Real costs of bubbles:

- Is the main effect distributional?

- winners from speculation are richer; losers are poorer.

- does this redistribution matter for the overall economy?

- how should society weigh losses to some vs. gains to others?

- who are the winners and losers? Likely important when answering the questions above.

- Are there effects beyond redistribution?

- Misdirection of real investment if profit expectations overly optimistic.

e.g. high tech stock boom: much investment misplaced.

(if EMH wrong: prices send wrong signals)

- Misdirection of household saving: may have been socially more valuable if consumed.

- Recessions or downturns: can result from collapsing bubbles.

- possible channels:

- fall in household wealth: less demand for goods and services.

(distributional complications: are there offsetting gains in wealth?)

- financial crisis:- collapse of bubble affects financial

33

institutions (FIs) involved;

- FIs deal in many markets and with each other.

- can spread.

- Problems of FIs: reduce access to credit, less spending on goods and services, reduced output and employment.

.

34