Transcript Good Morning! - Pennsylvania State University

Good Afternoon – 10/18/05

Review from last time
 Behavioral finance and technical analysis
 A yahoo example
 Begin Chapters 14 – 18 – Fed stuff
Chapter 7 – rational expectations
Rational Expectations

Trying to predict how long it takes to get to
work (textbook, CH. 7)
 Are you always right?
 What are the properties of the forecast
errors?
Properties of error term

Mean of zero
 Unpredictable with current information set
 Uncorrelated with itself

So what are rational expectations? You do
the best with the information set that is
available to you.
 A soon as new (relevant) information
arrives, you change you expectations (how
quickly?)
 Football example again!
How can you make money in
the stock market?

On overhead, be sure to discuss all the
variables needed to be successful in terms
of making the best forecast possible!
Discuss why that was a waste of
time!
A formal look at the
forecasting model and
properties of error term (on
overhead)! A test of the
efficient market hypothesis
Properties of error term

Mean of zero
 Unpredictable with current information set
 Uncorrelated with itself
A little about regression
analysis

What do economists do?
 Theory vs applied work
 Recall Consumption Function (go to
overhead)
Dependent Variable: CONEXP
Method: Least Squares
Date: 10/13/05 Time: 12:15
Sample (adjusted): 1987M02 2005M08
Included observations: 223 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
PINC(-1)
REAL_FF(-1)
1837.302
1.015550
-0.078855
457.6706
0.004001
0.016039
4.014465
253.8458
-4.916470
0.0001
0.0000
0.0000
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
0.998747
0.998735
58.01901
740565.2
-1220.465
1.061403
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
F-statistic
Prob(F-statistic)
5481.939
1631.541
10.97278
11.01862
87666.56
0.000000
Data – Coke – closing price –
daily data; 1/2/70 – 1/25/99 –
source, yahoo
160
140
120
100
80
60
40
20
1/02/70
9/02/77
5/03/85
CLOSE - Cok e
1/01/93
So what does our forecasting
model of coke look like?
Dependent Variable: CLOSE
Method: Least Squares
Date: 10/24/04 Time: 11:20
Sample(adjusted): 1/05/1970 1/25/1999
Included observations: 7581 after adjusting endpoints
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
CLOSE(-1)
0.113314
0.998174
0.047963
0.000689
2.362514
1449.142
0.0182
0.0000
0.996404
0.996403
1.673779
21232.84
-14660.82
2.008938
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
F-statistic
Prob(F-statistic)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
63.79229
27.90979
3.868308
3.870138
2100012.
0.000000
200
150
100
50
20
0
0
-20
-40
-60
-80
-100
1/05/70
9/05/77
Res idual
5/06/85
Ac tual
1/04/93
Fitted
5000
Series: Close (t) - Close (t-1)
Sample 1/05/1970 1/25/1999
Observations 7581
4000
3000
2000
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
-0.003166
0.000000
6.000000
-82.75000
1.674444
-23.14830
962.4173
Jarque-Bera
Probability
2.91E+08
0.000000
1000
0
-75.0 -62.5 -50.0 -37.5 -25.0 -12.5 0.0
And finally, a look at trying to
predict the error term with past
information
Dependent Variable: DCLOSE
Method: Least Squares
Date: 10/25/04 Time: 11:11
Sample(adjusted): 1/19/1970 1/25/1999
Included observations: 7571 after adjusting endpoints
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
DCLOSE(-1)
DCLOSE(-2)
DCLOSE(-3)
DCLOSE(-4)
DCLOSE(-5)
DCLOSE(-6)
DCLOSE(-7)
DCLOSE(-8)
DCLOSE(-9)
DCLOSE(-10)
-0.003859
-0.005667
-0.008836
-0.014633
-0.002607
-0.026925
0.004066
-0.019810
-0.004913
-0.003631
-0.021146
0.019250
0.011499
0.011499
0.011499
0.011498
0.011497
0.011498
0.011498
0.011499
0.011498
0.011498
-0.200447
-0.492847
-0.768422
-1.272572
-0.226782
-2.341823
0.353615
-1.723006
-0.427293
-0.315763
-1.839080
0.8411
0.6221
0.4423
0.2032
0.8206
0.0192
0.7236
0.0849
0.6692
0.7522
0.0659
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
0.001850
0.000529
1.674927
21208.67
-14642.17
2.000118
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
F-statistic
Prob(F-statistic)
-0.003533
1.675370
3.870868
3.880940
1.401002
0.172788
Dependent Variable: COKEERRORS
Method: Least Squares
Date: 03/14/05 Time: 14:08
Sample (adjusted): 1/09/1970 1/25/1999
Included observations: 7577 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
CLOSE(-1)
CLOSE(-2)
CLOSE(-3)
CLOSE(-4)
CLOSE(-5)
-0.002946
-0.004620
-0.002241
-0.006108
0.011725
0.001287
0.048054
0.011493
0.016200
0.016200
0.016200
0.011492
-0.061314
-0.401970
-0.138322
-0.377004
0.723806
0.112013
0.9511
0.6877
0.8900
0.7062
0.4692
0.9108
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
0.000237
-0.000423
1.674370
21225.40
-14653.76
2.000054
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
F-statistic
Prob(F-statistic)
-3.49E-05
1.674016
3.869542
3.875032
0.359482
0.876388
Picking up some loose ends
Terms from Finance Portion of
course – we discussed all but the
terms in black font
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1. Options – calls, puts, strike price, writing
options.
2. Derivatives – what does this term mean?
3. Stock price determination – formula
4. The efficient market theory.
5. Autoregressive properties.
6. Technical analysis.
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7. Shorting stocks and short covering.
8. NEWS.
9. Jawboning.
10. Price to earnings ratio.
11. Earnings per share.
12. Futures.
13. Closing position.
14. Hedging.
15. Inside information.
16. Random walk.
17. Bulls vs. Bears.
18. Exercise.
19. In the money.
Discuss Behavioral Finance
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Definition : Behavioral Finance (BF) is the
application of psychology to finance.
It is based on the study of behavioral biases and
their effects on financial markets, such as
anomalies & inefficiencies on prices and
returns.
BF tries to detect and understand those biases /
anomalies, and if possible to use them in
investment strategies.
Here is another link
http://www.wordiq.com/definition/Behavioral_fin
ance
Behavioral Finance

Discuss day trading and Google,
Amazon.com, etc
 See article that is now posted
Since efficient market theory
suggests that it is impossible to
beat the market – let’s move on
to technical analysis
Example – Bollinger Bands

Go to new posting on Bollinger bands
 Then go to Yahoo finance -
Begin Fed stuff – open market
operations