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

Background on the Dow Theory Charles Henry Dow Dow indices developed for timing studies William Peter Hamilton Editorialist applied “Dow Theory” 1902-1929 Principles market follows trends Industrial and transportation sectors confirm high volume indicates move

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, 1902 - 1929 vs. 100% stocks Dow: 12% return per year 1/2 DJIA & 1/2 DJTA: 15.5% return per year Conclusion: no timing skill!

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!

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?

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

The Dow Theory 1903 to 1929

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

Hamilton’s Portfolio Vs. S&P

100% S&P vs. Hamilton

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

Bull vs. Bear Calls

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?

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.

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

Stepwise Regression

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

Neural Net Events 1902-1929

Neural Net Events 1930-1996

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