Artificial Neural Networks vs. The Beating the Dow Strategy Dr. John C. Merkel III Morehouse College $

Motivation In 1991 O’Higgins outlined a mechanical strategy of picking stocks to outperform the Dow Jones Industrial Average (DJIA). His method uses only two pieces of information for each stock; its price and dividend yield. We attempt to use a series of artificial neural networks (ANN’s) with price and yield inputs to determine if ANN’s can outperform O’Higgins strategy. Of particular interest: How does the performance of our ANN’s compare to O’Higgins strategy pre- and post-discovery (1991)?

Presentation Outline Part I - Introduction to O’Higgins’ Beating the Dow (BTD) strategy/algorithm. Part II - Applying an ANN to the BTD strategy. Part III - Who won? The Results.

Part I The Beating the Dow Strategy

Michael O’Higgins – Beating the Dow (1991) In his book O’Higgins outlines an investment strategy that outperformed the DJIA. From 1976 – 1991 (16 years) his system returned 18.6% annually vs. 14.2% for the DJIA. Post-discovery (1992 – 2005) his system returned 11.0% vs. 11.2% for the DJIA. The system (algorithm) is simple and involves only the 30 stocks comprising the DJIA with two pieces of data per stock.

The two pieces of data 2. Dividend Yield = Dividend($) / Stock price($) Example: Coke (ticker = KO) pays $1.24/year for each share of their stock you own. So their dividend yield is $1.24/$43.43 = 2.86%. 1.Stock price. For example, Coke (ticker KO) closed at $43.43 per share on Monday, 5 June, 2006.

The Algorithm 1. Sort in descending order by dividend yield 2. Select top 10 from step 1. Sort this in ascending order by price. 3. Select the 5 lowest price stocks from step 2. Purchase and hold for 1 year.

The Algorithm 1. Sort in descending order by dividend yield 2. Select top 10 from step 1. Sort this in ascending order by price. 3. Select the 5 lowest price stocks from step 2. Purchase and hold for 1 year.

The Algorithm 1. Sort in descending order by dividend yield 2. Select top 10 from step 1. Sort this in ascending order by price. 3. Select the 5 lowest price stocks from step 2. Purchase and hold for 1 year.

BTD Performance

Part II Applying a Neural Network

Motivation If O’Higgins is right, then dividend yield and stock price have some predictive power concerning stock performance. Idea: can an ANN use these same 2 pieces of data to make predictions about a stock?

Network Specifics 1. ANN input 1: price input 2: dividend yield ANN target output: 1 year return 2.Topology: feed-forward. 3.Learning algorithm: backprop momentum. 3.Activation functions: hyperbolic tangent. 4. ANN software: SNNS (Stuttgart Neural Network Simulator) priceyield 6 7

Data Processing – training and validation 1.Collect data: opening price of year, dividend yield, 1 year return, for each DJIA stock from Normalize 1966 data so each input/output has μ = 0, σ = 1. Repeat for Combine data (30 x 10 = 300 patterns). 4.Split into training (240 patterns) and validation (60 patterns) files. 5.Calculate μ, σ for each input/output of training file. 6.Scale training data into (-1, 1) using where 7.Similarly scale validation file using μ, σ from training file. 8.Repeat steps 3–7 above for each 10-year window: 67-76, 68-77,…,

Procedure Train 1,000 networks using data. Pick “best” trained network. Run normalized 1976 data through trained network. Output is the network’s 1 year predicted returns for each stock. Rank stocks from highest predicted return to lowest. Purchase the 5 highest ranked stocks, hold for 1 year. Repeat for years 1977–2005.

Part III Results

The Results : ANN beat the Dow 10 out of 16 years ANN beat the BTD 6 out of 16 years ANN had a higher CAGR than Dow ANN had a lower CAGR than BTD

The Results : ANN beat the Dow 10 out of 16 years ANN beat the BTD 6 out of 16 years ANN had a higher CAGR than Dow ANN had a lower CAGR than BTD