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摘要:Lecture Notes,Capital Markets,Investment,Finance,提供英国lecture Notes.......

Lecture Notes,Capital Markets,Investment,Finance, 提供英国lecture Notes.

Lecture 4 – Capital Markets, Investment and Finance

Market Efficiency - Anomalies   Capital Markets, Investment and Finance  

• Impossible to consistently beat the market!
• Cannot 'predict' prices from past prices.
• Anomalies, seasonalities, regularities, calendar effect

(i) Weekend effect - lower returns between Friday's close and Monday's close.

(ii)  January effect - returns much higher during January.

(iii) Trading month effect - mean returns positive only for days in first half of trading month.

(iv)  Many others - 'Friday 13' - data miningThe Essay is provided by UK Assignment


Implications for market efficiency and asset pricing model:

• Mainly US, UK, Spain, Japan, Australia, etc
 Imperfections in trading process; regularities of more general concern.

• Require support for different data sets, over different time periods;
 'conflicting' evidence!!

 Need inter-country comparisons
 Little work done for UK data
 Not interested in explanations - only their existence
 Authentic, Identifiable, Material, Persistent

Mills & Coutts (1995)

• October1992 FT-SE Mid 250, FT-SE 350, FT-SE 100
• Associated industry baskets - range of indices
      broad industrial base - no 'thin' trading
• 01/86 - 16/10/92 - 1719 observations - events for index P1
daily return = 100 log (Pt/Pt-1)
• returns not adjusted for dividends - implications?!

Methodology

Vector of returns P = (P1,.....,PT)
Matrix of dummy variables for anomaly = X
Vector of coefficients (means) = β

Deg model  p  = X β + u  u = (u1 ,.....,uT)
Much evidence to suggest that returns are not normally distributed and display serial correlation and heteroskedasticity.

Strong mixing allows for both the above but still allows the OLS estimator of β,
 β = (X1X)-1 X1 p to be consistent and asymptotically normally distributed.


Statistical EvidenceThe Essay is provided by UK Assignment

Sample means, sample distributions and t-stats.

No Friday 13 effect (if anything higher returns!)

Weekend Effect

• Table 1 - means returns significant negative for 250 and 350 on Monday
• Consistent with Board and Sutcliffe  (1988) but note that effect had died out by mid 80's.

• Mean returns positive for all days being largest on Wednesday and Friday - variance largest for Monday for all indices.

• Bank Holiday return positive but insignificant from zero

• Incorporation of S.E. Account Effects.

 Account begins on Monday for two (3) weeks.

 Payment for shares bought during account is not due until second Monday following end of account.

 If purchase delayed from last Friday of account to following Monday, buyer has an extra 11 days of interest free credit between purchase and payment.


M T W T F
                  e
                 e
M T W T F
 

M T W T F 
e              e
M T W T F  

M T W T F


Buy   
pay

pay 

Buy   
    
    
  Buy  The Essay is provided by UK Assignment

Implications of S.E account:

• Above average return on Monday not first day of account

• If share purchase delayed from Friday to following Monday not first day of account - period of interest free credit reduced  lower than average weekend return.

• Dividends usually paid on Mondays - excluded  expected on average negative Monday return - shares 'ex dividend'

• Sometimes first day of account not a Monday and do not follow a bank holiday - delaying purchase gives six additional days of free credit  therefore higher return.

Splitting Monday into account and non-account days leads to these results:

• Non account Mondays have significant negative returns for all in

• Account Monday positive returns for 100 but zero for 250 account days not Mondays large positive mean
• Monday effect is in part a settlement effect; similar to Condanyanni et al (1987) for US data.
• High positive returns on account Mondays indicate settlement effect is dominant factor and may mask strength of the underlying negative weekend effect.
When does negative return occur - 'close to close' data, need 'real time' data.
• Yadav and Pope (1992) using hourly data on the FT - SE 100 suggest that 'abnormal' Monday returns accrue during trading on the Monday, not over weekend.

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