Good Afternoon – 10/18/05. Review from last time Behavioral finance and technical analysis A yahoo example Begin Chapters 14 – 18 – Fed stuff.

Good Afternoon – 10/18/05Good Afternoon – 10/18/05

Review from last timeBehavioral finance and technical analysisA yahoo exampleBegin Chapters 14 – 18 – Fed stuff

Chapter 7 – rational expectations Chapter 7 – rational expectations

Rational ExpectationsRational 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 termProperties of error term

Mean of zeroUnpredictable with current information setUncorrelated 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 How can you make money in the stock market?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 Discuss why that was a waste of time! time!

A formal look at the A formal look at the forecasting model and forecasting model and

properties of error term (on properties of error term (on overhead)! A test of the overhead)! A test of the

efficient market hypothesis efficient market hypothesis

Properties of error termProperties of error term

Mean of zeroUnpredictable with current information setUncorrelated with itself

A little about regression A little about regression analysis analysis

What do economists do?Theory vs applied workRecall 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 1837.302 457.6706 4.014465 0.0001 PINC(-1) 1.015550 0.004001 253.8458 0.0000

REAL_FF(-1) -0.078855 0.016039 -4.916470 0.0000

R-squared 0.998747 Mean dependent var 5481.939 Adjusted R-squared 0.998735 S.D. dependent var 1631.541 S.E. of regression 58.01901 Akaike info criterion 10.97278 Sum squared resid 740565.2 Schwarz criterion 11.01862 Log likelihood -1220.465 F-statistic 87666.56 Durbin-Watson stat 1.061403 Prob(F-statistic) 0.000000

Data – Coke – closing price – Data – Coke – closing price – daily data; 1/2/70 – 1/25/99 – daily data; 1/2/70 – 1/25/99 –

source, yahoosource, yahoo

20

40

60

80

100

120

140

160

1/02/70 9/02/77 5/03/85 1/01/93

CLOSE - Coke

So what does our forecasting So what does our forecasting model of coke look like? 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 0.113314 0.047963 2.362514 0.0182 CLOSE(-1) 0.998174 0.000689 1449.142 0.0000

R-squared 0.996404 Mean dependent var 63.79229 Adjusted R-squared 0.996403 S.D. dependent var 27.90979 S.E. of regression 1.673779 Akaike info criterion 3.868308 Sum squared resid 21232.84 Schwarz criterion 3.870138 Log likelihood -14660.82 F-statistic 2100012. Durbin-Watson stat 2.008938 Prob(F-statistic) 0.000000

-100

-80

-60

-40

-20

0

20

0

50

100

150

200

1/05/70 9/05/77 5/06/85 1/04/93

Residual Actual Fitted

0

1000

2000

3000

4000

5000

-75.0 -62.5 -50.0 -37.5 -25.0 -12.5 0.0

Series: Close (t) - Close (t-1)Sample 1/05/1970 1/25/1999Observations 7581

Mean -0.003166Median 0.000000Maximum 6.000000Minimum -82.75000Std. Dev. 1.674444Skewness -23.14830Kurtosis 962.4173

Jarque-Bera 2.91E+08Probability 0.000000

And finally, a look at trying to And finally, a look at trying to predict the error term with past predict the error term with past

informationinformation

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 -0.003859 0.019250 -0.200447 0.8411 DCLOSE(-1) -0.005667 0.011499 -0.492847 0.6221 DCLOSE(-2) -0.008836 0.011499 -0.768422 0.4423 DCLOSE(-3) -0.014633 0.011499 -1.272572 0.2032 DCLOSE(-4) -0.002607 0.011498 -0.226782 0.8206 DCLOSE(-5) -0.026925 0.011497 -2.341823 0.0192 DCLOSE(-6) 0.004066 0.011498 0.353615 0.7236 DCLOSE(-7) -0.019810 0.011498 -1.723006 0.0849 DCLOSE(-8) -0.004913 0.011499 -0.427293 0.6692 DCLOSE(-9) -0.003631 0.011498 -0.315763 0.7522

DCLOSE(-10) -0.021146 0.011498 -1.839080 0.0659

R-squared 0.001850 Mean dependent var -0.003533 Adjusted R-squared 0.000529 S.D. dependent var 1.675370 S.E. of regression 1.674927 Akaike info criterion 3.870868 Sum squared resid 21208.67 Schwarz criterion 3.880940 Log likelihood -14642.17 F-statistic 1.401002 Durbin-Watson stat 2.000118 Prob(F-statistic) 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 -0.002946 0.048054 -0.061314 0.9511 CLOSE(-1) -0.004620 0.011493 -0.401970 0.6877 CLOSE(-2) -0.002241 0.016200 -0.138322 0.8900 CLOSE(-3) -0.006108 0.016200 -0.377004 0.7062 CLOSE(-4) 0.011725 0.016200 0.723806 0.4692 CLOSE(-5) 0.001287 0.011492 0.112013 0.9108

R-squared 0.000237 Mean dependent var -3.49E-05 Adjusted R-squared -0.000423 S.D. dependent var 1.674016 S.E. of regression 1.674370 Akaike info criterion 3.869542 Sum squared resid 21225.40 Schwarz criterion 3.875032 Log likelihood -14653.76 F-statistic 0.359482 Durbin-Watson stat 2.000054 Prob(F-statistic) 0.876388

Picking up some loose endsPicking up some loose ends

Terms from Finance Portion of Terms from Finance Portion of course – we discussed all but the course – we discussed all but the

terms in black fontterms in black font 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.

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 FinanceDiscuss Behavioral Finance 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_finan

ce

Behavioral FinanceBehavioral Finance

Discuss day trading and Google, Amazon.com, etc

See article that is now posted

Since efficient market theory Since efficient market theory suggests that it is impossible to suggests that it is impossible to beat the market – let’s move on beat the market – let’s move on

to technical analysisto technical analysis

Example – Bollinger BandsExample – Bollinger Bands

Go to new posting on Bollinger bandsThen go to Yahoo finance -

Begin Fed stuff – open market Begin Fed stuff – open market operationsoperations