1. The Dow Theory William Peter Hamilton’s Track Record Re-Considered
Stephen J. Brown
(NYU Stern School)
William N. Goetzmann
(Yale School of Management)

2. Background on the Dow Theory
Charles Henry Dow
Dow indices developed for timing studies
William Peter Hamilton
Editorialist applied “Dow Theory”
Principles
market follows trends
Industrial and transportation sectors confirm
high volume indicates move

3. Testing the Theory Alfred Cowles III
“Can Stock Market Forecasters Forecast?” E’trica 1934
Coded editorials “Bull” “Bear” or “Neutral”
“Bull” = all stocks “Bear” = short stocks “Neut” = t-bills
Dow Portfolio, vs. 100% stocks
Dow: 12% return per year
1/2 DJIA & 1/2 DJTA: 15.5% return per year
Conclusion: no timing skill!

4. Testing the Theory II Bull & bear forecasts
Sorted the 90 times Hamilton changed his forecast
Half proved profitable, half did not
Conclusion: no timing skill!

5. Problems in Cowles Analysis
100% stocks a correct benchmark?
“Hamilton was long of stocks 55%, short of stocks 16% and out of the market 29% out of the 26 years under review.”
He made 255 forecasts, not 90
Are two successive bear calls informative?

6. Revisiting Hamilton’s Calls
Re-coding
46% bull calls
16% bear calls
38% neutral calls
Created contingency table
call vs. capital appreciation return of DJIA until next editorial

7. The Dow Theory 1903 to 1929

8. Trading Strategy Considered
Back-test of Hamilton portfolio
Assume investment in S&P with dividends & commercial paper as riskless asset.
S&P index created by Cowles as capital weighted measure of stock investment.
Monthly re-balancing

9. Hamilton’s Portfolio Vs. S&P

10. 100% S&P vs. Hamilton

11. Event Study What happened to the DJIA after a call?
Line up returns in event-time
average across call of same direction

12. Bull vs. Bear Calls

13. Recovering The Dow Theory
Hamilton’s calls contain the essence of the Dow Theory.
Can we create a model of the theory?
Does it correspond to the writings about it?

14. Predicting Hamilton’s Signals
Use information available on the editorial date (and to us now)
See if we can forecast Hamilton’s signals
Perform out-of-sample test to see if our recovered Dow Theory worked.

15. Methodology Step-wise regression Neural network
A linear model of Hamilton “bear” signal
Use AIC-like criterion to add and prune variables
Neural network
A non-linear model of Hamilton’s signals
Uses a broad range of variable transformations
No “coefficients” reported

16. Stepwise Regression

17. Neural Network Approach
Feature Vector Analysis
A. Kumar and V.E. McGee “FEVA: Feature vector analysis: explicitly looking for structure and forecastability in time series data,” Economics and Financial Computing, Winter, 1996

18. Neural Net Events

19. Neural Net Events

20. Conclusions The Dow Theory reputation was deserved
Hamilton followed a momentum strategy
The spread between bull and bear calls has continued out of sample, albeit diminished