Market Efficiency and an Introduction to Behavioral Finance

1
Lecture 10

• Market Efficiency and an Introduction to
  Behavioral Finance

2
Efficient Market Hypothesis

• Efficient Market Hypothesis Prices reflect all
  available information.
• Three different levels
• Investors are rational. They can process
  information fully and promptly.
• To the extent that some investors are not
  rational, their trades are uncorrelated and
  therefore cancel each other out without affecting
  prices.
• To the extent that investors are irrational in
  similar ways, they are met by rational
  arbitrageurs who eliminate their influence on
  prices.

3
How do markets become efficient?

• Price reflect information as a result of trading.
  Even a small of profit is enough to induce
  large institutional traders.
• Costly information efficient market
• Active vs. passive investors
• Complications
• Noise traders vs. smart money
• Beauty contest
• Difficulties to implement arbitrage strategy

4
Evidence about Efficiency

• Active funds on average do not beat the stock
  market index. But there are SOME exceptions.
• In the short term, the top performers are
  typically active funds
• In the long period, Berkshire Hatheway managed by
  Warren Buffet is a famous example.
• There are still many anomalies discovered which
  are inconsistent with market efficiency, as we
  mentioned before.
• Excessive Trading and Volatility
• Size Effect Book-to-Market ratio
• Mean Reversion and Momentum.

5
Recent Researches

• In order to explain apparent anomalies in the
  market, recent researches in the market
  efficiency try to incorporate findings in
  psychology
• overconfidence
• loss aversion and the prospect theory
• bounded rationality.

6
Overconfidence

• Examples of overconfidence are plenty in everyday
  life and financial markets.
• How overconfidence biases decisions
• People place too much weight on information they
  collect themselves because they tend to
  overestimate the precision of that information.
• People tend to ignore, or at least under-weigh,
  information that lowers their self-esteem.

7
O/C and B/M Ratio

• Overconfident investors tend to under-weigh
  information provided by accountants and other
  professionals.
• It is possible that they bid up the prices of
  hot stocks too much. Therefore stocks with low
  B/M ratio could have lower average rates of
  return.

8
O/C and Momentum

• Investors over-weigh their prior beliefs and
  thereby under-react to new information.
• Or, investors over-weigh information that confirm
  their original valuation and under-weigh
  information that is inconsistent with their
  views.
• Therefore, it could produce momentum effect.

9
O/C and Growth Uncertainty

• Overconfidence affects difficult-to-value
  companies more than stable companies.
• Implications
• Lower B/M companies have more growth options, the
  prices of their stocks should exhibit stronger
  overconfidence effect
• Momentum effects should be stronger for
  hard-to-value growth stocks than for stable ones.

10
Tests of O/C Effects

• Hypothesis If overconfidence affects stock
  prices, then we should observe a relationship
  between stock returns and both B/M and momentum.
• We then examine the performance of 125 portfolios
  sorted on size, B/M and momentum.

11
Time-Series Behavior

• Long-short strategy bought the
  high-B/M-high-momentum (HH) portfolio and short
  low-B/M-low-momentum (LL) portfolio from 1964-97.
• This strategy realized an average annual profit
  of 12.64. Moreover, the beta of this portfolio
  is -0.258 and the alpha is 1.17 per month with a
  t-statistic 6.62.

12
Applying the HH-LL Strategy

• Mutual funds could have increased their Sharpe
  ratios by tilting toward the HH-LL portfolio. See
  Panel B of Table 3 (Daniel-Titman).
• Table 4 shows how much the Sharpe ratios of the
  average growth fund would have improved by
  various amount.

13
O/C and Excessive Trading

• Overconfidence increases trading activity because
  it causes investors to be too certain about their
  own opinions and to not consider sufficiently the
  opinion of others.
• Overconfident investors also perceive their
  actions to be less risky than generally proved to
  be the case.
• Excessive trading affects the volume of trading,
  but it may not affect the price.
• That is the reason why investors (consumers)
  behavior is more important in marketing than
  finance!

14
Profitability of Day Traders Jordan Diltz
(FAJ, Nov/Dec 2003)

• Using data from Feb. 1998 through Oct 1999,
• Among 316 day traders, about twice as many day
  traders (64.2) lose money as make money (35.8).
• About one trader in five is more than marginally
  profitable (19.4 made more than 5,000, and
  14.2 made more than 10,000. The most profitable
  made 197,000).
• The least profitable lost more than 748,000.
• The average gross profit for all traders was more
  than 8,000, whereas net profit was about -750.

15
Testing for Regular Share Investors

• Hypothesis whether the securities bought by the
  investors outperformed those they sold by enough
  to cover the costs of trading.
• Results No. See Figure 3 (Barber Odean).
• These investors did not make profitable trades.

16
Turnover and Performance

• Those who trade most actively will most reduce
  their returns through trading.
• Households in the high-turnover quintile earned a
  net annualized mean return of 11.4 households
  in the low-turnover quintile earned a net
  annualized mean return of 18.5.
• Trading is hazardous to your wealth!

17
Gender and Performance

• Overall, men claim more ability than do women.
  Men are more overconfident about their stock
  picking ability.
• In the study, men traded 45 more actively than
  women (76.9 vs. 52.8 turnover annually), and
  men reduced their net annual returns through
  trading by 0.94 more than women.
• Men underperformed their buy-and-hold portfolios
  by 2.652 annually Women underperformed their
  buy-and-hold portfolios by 1.716 annually

18
Samuelsons Question (1963)

• Samuelson asked a colleague this question Would
  you be willing to accept the following bet a 50
  chance to win 200 and a 50 chance to lose 100?
• His colleague turned this bet down but announced
  that he was happy to accept 100 such bets. This
  answer provoked Samuelson into proving a theorem
  showing his colleague was irrational.

19
Asymmetric Utility

• His colleague offered his rationale I wont bet
  because I would feel the 100 loss more than the
  200 gain.
• One simple utility function that would capture
  this notion is the following

20
Prospect Theory

• Investors utility function is defined on the
  basis of gains and losses rather than levels of
  wealth.
• People behave as if maximising an S-shaped
  utility function, as shown in Figure 1 (Barber
  and Odean 2000).
• Difficulties Choosing the reference point and
  the time horizon to measure gains or losses.

21
Investors Reference Point

• Investors reference points are assumed to be
  their purchase prices in the study.
• However, people could set reference points with
  regard to the expected returns, and transaction
  costs involved.

22
The Disposition Effect

• Because of the S-shaped utility function,
  investors tend to sell winners (risk averse on
  the gains side) and keep losers (risk seeking
  on the losses side).
• In summary, because people dislike incurring
  losses much more than they enjoy making gains,
  and people are willing to gamble in the domain of
  losses, investors will hold onto stocks that have
  lost value (relative to the reference point of
  their purchase) and will be eager to sell stocks
  that have risen in value. Financial economists
  called this the disposition effect.

23
Taxes

• Investors reluctance to realise losses is
  inconsistent with optimal tax-loss selling for
  taxable investments.
• For tax purposes, investors should postpone
  taxable gains by continuing to hold their
  profitable investments.
• They should capture tax losses by selling their
  losing investments.

24
Empirical Testing

• Hypothesis people sell gains more readily than
  losses, and the reference point is the average
  purchase price.
• The results in Table 2 (Barber and Odean) show
  that investors indeed sold a higher proportion of
  their winners than of their losers. Also people
  did sell more losers in December for tax reasons.

25
Desire to Rebalance

• Investors might sell winners and hold losers in
  an effort to rebalance their portfolio.
• In that case, the investors who sell winners
  would be likely to make new purchases.
• The data in the study eliminates such a factor.

26
Loss Reversal

• Investors might hold losers because they expect
  the losers may outperform the winners in the
  future.
• Their belief was, on average, mistaken because
  the data show that the momentum exists for up to
  2 years for the winners sold.

27
Summary

• Indeed, investors may not be rational in their
  trading of securities all the time.
• This may definitely affect the trading volumes
  it may or may not affect prices.
• However, there is little evidence that investors
  can consistently beat the market by predicting
  other peoples behavioral biases. Understanding
  our potential behavioral biases could help us
  avoiding excessive trading and reducing mistakes.