capital market efficiency the empirics l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Capital Market Efficiency The Empirics PowerPoint Presentation
Download Presentation
Capital Market Efficiency The Empirics

Loading in 2 Seconds...

play fullscreen
1 / 26

Capital Market Efficiency The Empirics - PowerPoint PPT Presentation


  • 111 Views
  • Uploaded on

Capital Market Efficiency The Empirics. 4 basic traits of efficiency. An efficient market exhibits certain behavioral traits. We can examine the real market to see if it conforms to these traits. If it doesn’t, we can conclude that the market is inefficient.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Capital Market Efficiency The Empirics' - donkor


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
4 basic traits of efficiency
4 basic traits of efficiency
  • An efficient market exhibits certain behavioral traits. We can examine the real market to see if it conforms to these traits. If it doesn’t, we can conclude that the market is inefficient.
    • Act to new information quickly and accurately
    • Price movement is unpredictable (memory-less)
    • No trading strategy consistently beat the market
    • Investment professionals not that professional

What if? Definitions Implications Price Empirics

empirical strategies
Empirical Strategies
  • Look at the historical data. See if they conform with the 4 traits

What if? Definitions Implications Price Empirics

1 st trait reaction to news
1st trait: reaction to news

Early Reaction

Stock price ($)

Delayed Reaction

Days relative to announcement day

-t

0

+t

The timing for a positive news

What if? Definitions Implications Price Empirics

1 st trait reaction to news5
1st trait: reaction to news

Event study“one type of test of the semi-strong form of market efficiency to see if prices reflect all publicly available information or not.”

  • To test this, event studies examine prices and returns over time (particularly around the arrival of new information.)
  • Test for evidence of [1] under reaction, [2] overreaction, [3] early reaction, [4] delayed reaction around the event.
  • If market is “semi-strong-form efficient”, the effects of an event will be reflected immediately in security prices. Thus a measure of the event’s economic impact can be constructed using security prices observed over a relatively short time period.
  • Some examples of events include mergers and acquisitions, earnings announcements, issues of new debt or equity, and announcements of macroeconomic variables such as the trade figures.

What if? Definitions Implications Price Empirics

1 st trait reaction to news6
1st trait: reaction to news

Procedures of a event study:

  • Identify the event of interest. (e.g., Merger Acquistion)
  • Define the event period? (e.g., -10 days and +10 days)
  • Select the sample (e.g., firms which have in common the incidence of the event of interest)
  • Measure the impact by defining abnormal return
  • Estimate the parameters needed to calculate expected returns.
  • Calculate cumulative abnormal returns

What if? Definitions Implications Price Empirics

1 st trait reaction to news7
1st trait: reaction to news
  • The event period should start before you think the event has an effect on the stock price. As an example, for merger announcements, a typical choice is from 25 trading days before the announcement day to 25 trading days after the announcement day
  • The estimation period should be a period right before the event period. For merger announcements, a typical choice is 100 trading days before the start of the event period

-125

-25

0

+25

Estimation Period

Event Period

What if? Definitions Implications Price Empirics

1 st trait reaction to news8
1st trait: reaction to news
  • Abnormal return = Actual realized return – Expected return

E.g., E(rj|rM,t) = a0 + ajrM,t(Return on security j conditional on the return on market)

εj,t = rj,t – E((rj|rM,t)

  • Cumulative abnormal return:CARj,t = ∑-Ttεj,t(Aggregate abnormal returns from –T to t)
  • Average cumulative abnormal return over a sample of securities:Average CARt = (∑j CARj,t)/J(where J = no. of securities in the sample)
  • Plot the graph, examine the pattern. Of course, perform hypothesis testing as well.

What if? Definitions Implications Price Empirics

1 st trait reaction to news9
1st trait: reaction to news

Efficient market response to “bad news”

Source: Szewczyk, Tsetsekos and Santout (1997)

What if? Definitions Implications Price Empirics

1 st trait reaction to news10
1st trait: reaction to news

-29

0

30

How stock splits affect value?Source: Fama, Fisher, Jensen & Roll (1969)

What if? Definitions Implications Price Empirics

1 st trait reaction to news11
1st trait: reaction to news

Announcement Date

What if? Definitions Implications Price Empirics

1 st trait reaction to news12
1st trait: reaction to news

Announcement Date for quarterly earnings reports

Average Cumulative abnormal return

Days relative to Announcement Date

Source: Remdleman, Jones and Latane (1982)

What if? Definitions Implications Price Empirics

1 st trait reaction to news13
1st trait: reaction to news
  • Event study methodology has been applied to a large number of events including:
    • Dividend increases and decreases
    • Earnings announcements
    • Mergers
    • Capital Spending
    • New Issues of Stock
  • The studies generally support the view that the market is semi-strong from efficient.
  • In fact, the studies suggest that markets may even have some foresight into the future—in other words, news tends to leak out in advance of public announcements.

What if? Definitions Implications Price Empirics

2 nd trait random price movements
2nd trait: Random price movements
  • Studies of serial correlation
  • Studies of seasonality
    • Day of the week effect
    • January effect

What if? Definitions Implications Price Empirics

2 nd trait random price movements15
2nd trait: Random price movements

Studies of serial correlation

H0: Cov(ΔPt, ΔPt-i) is significantly different from zero or not, for i ≠ 0

Alternatively,

H0: Cov(Δrt, Δrt-i) is significantly different from zero or not, for i ≠ 0

  • Plot the following types of graph.
  • Note:Statistically significant ≠ Economically significant
    • If you are aware of the correlation, and attempt to trade on the basis of it, brokerage commissions may make your expected profits negative.

What if? Definitions Implications Price Empirics

2 nd trait random price movements16
2nd trait: Random price movements

Return on day t+1 (in %)

Return on day t (in %)

What if? Definitions Implications Price Empirics

2 nd trait random price movements17
2nd trait: Random price movements

FTSE 100 (correlation = -0.08)

Nikkei 500 (correlation = -0.06)

Return on week t+1 (in %)

DAX 30 (Correlation = -0.03)

S & P Composite (correlation = -0.07)

Return on week t (in %)

What if? Definitions Implications Price Empirics

2 nd trait random price movements18
2nd trait: Random price movements

Studies of seasonality

  • Day of the week effect
  • French (1980) and Gibbons & Hess (1981)
  • Using S&P 500 index to proxy returns of stocks for each of the 5 trading days of the week.
  • Found Monday returns are on average lower than returns on other days.
  • If transaction costs are taken into account, however, trading rule based on this pattern fails to generate abnormal returns consistently.
  • But you may consider this effect in timing your own purchases and sales.

What if? Definitions Implications Price Empirics

2 nd trait random price movements19
2nd trait: Random price movements

Studies of seasonality

  • The January effect
  • Keim (1983) and Roll (1983)
  • The most mystifying seasonal effect.
  • Stock returns, especially returns on small stocks, are on average higher in January than in other months.
  • Moreover, much of the higher January return on small stocks comes on the last trading day in December and the first 5 trading days in January.

What if? Definitions Implications Price Empirics

3 rd trait superior trading strategy
3rd trait: Superior trading strategy
  • Caveat - Be careful here!!! It’s in the interest of those who find such rules to hide them rather than publicize them.
  • Price-to-earning ratio. (P/E Ratios)
  • Size effect

What if? Definitions Implications Price Empirics

3 rd trait superior trading strategy21
3rd trait: Superior trading strategy
  • Price-to-earning ratio. (P/E Ratios)
  • The trading rule of “buying stocks that have low price-to-earning ratios , and avoiding stocks with high price-to-earning ratios” seems to consistently outperform the market.
  • Question:
    • 1) what does it mean by low P/E ratio?
    • 2) Survivorship bias?

What if? Definitions Implications Price Empirics

3 rd trait superior trading strategy22
3rd trait: Superior trading strategy
  • Size Effect. (Banz (1981))
  • Small firms tend to have higher returns as compared to larger firms.
  • The trading rule of “buying stocks of smaller firms” seems to consistently outperform the market.
  • Question:
    • 1) Is there any inherent risks of small firms not captured by risk measures?
    • 2) Is it because transaction cost of smaller firms’ stocks are more expensive (due to thinner market)?

What if? Definitions Implications Price Empirics

4 th trait professional investors
4th trait: professional investors?
  • If the market is semi-strong form efficient, then no matter what publicly available information mutual-fund managers rely on to pick stocks, their average returns should be the same as those of the average investor in the market as a whole.
  • We can test efficiency by comparing the performance of professionally managed mutual funds with the performance of a market index.

Evaluating mutual funds performance. (Jensen (1969))

  • Managers of mutual funds are usually highly trained and have access to broad sources of investment information.
  • Thus, if their managed mutual funds consistently outperform the market, then we conclude that such evidence is against the market efficiency hypothesis

What if? Definitions Implications Price Empirics

4 th trait professional investors24
4th trait: professional investors?
  • Using S & P 500 as proxy for the market, estimate the security market line.
  • Estimate the beta for each mutual funds.
  • Plot the mutual funds on the security market line graph (NOTE: net of all expenses)

What if? Definitions Implications Price Empirics

4 th trait professional investors25
4th trait: professional investors?

What if? Definitions Implications Price Empirics

4 th trait professional investors26
4th trait: professional investors?

Average Annual Return on 1493 Mutual Funds and the Market Index

What if? Definitions Implications Price Empirics