1. Overview This model takes advantage of the correlation between price movements in commodities future contracts and price movements in equities’ whose core operations involve the production of commodities. The model focuses on five commodities – gold, silver, aluminum, copper, and crude oil – and a total of 16 equities. The model is entirely maintained in an Excel file. Multiple components of the model have been automated with the use of the programming language VBA. Security Scoring Process Each indicator is applied to each security in the model. The result of each indicator (a “buy” signal is a 1 and “sell” signal is a -1) is scaled by its 60-day moving average probability of success. The seven indicators scores are summed for each security and then scaled by the security’s trading volume relative to its 30- day moving average trading volume. Decision Making Process The five commodities are ranked by security score. The commodity with the highest score is selected, and its corresponding equities are ranked. The equity with the highest score is picked for a long position. The commodity with the lowest score is selected, and its corresponding equities are ranked. The equity with the lowest score is picked for a short position. Portfolio Creation Theory regarding portfolio management has identified that the idiosyncratic risk posed by individual investments can be eliminated by diversification. However, systematic risk cannot be eliminated. β measures a stock’s correlation to the overall market and is therefore an estimate of the systematic risk of an investment. The long and short positions are weighted such that the portfolio β is 0 for every day of trading. By constructing a portfolio with β = 0, systematic risk is removed from the portfolio making it a relatively safe investment strategy and only requiring a return equal to the risk free rate. Data Used in the Model The model was designed and tested using 11 years of historical data regarding the trading prices of five commodities and 16 equities. The commodities have been chosen based on the accessibility of data and the existence of viable companies that are fundamentally exposed to the fluctuations of commodity prices. In addition, the companies that were chosen are raw materials producers and have a majority of their operations exposed to a single commodity. The companies used are publicly traded and relatively liquid. A Pairs Trading Strategy for Trading Commodity-Exposed Equities Authors : Jack Chen, Matthew Gatto, Thomas Lumpki n Adviser : Dr. Iraj Zandi Abstract The equity markets offer an enormous opportunity for the creation of wealth. Two main schools of thought exist with regards to investment decisions: fundamental analysis and technical analysis. The former bases investment decisions on variables that will affect a firm’s value, such as operations and economic trends. The latter believes that future price movements can be predicted using past price movement data. By combining these two methods, this project attempts to utilize technical analysis to predict changes in the fundamental value drivers of companies. The model attempts to capitalize on market inefficiencies to capture statistically significant, risk-adjusted excess returns. The model utilizes momentum focused financial indicators to predict the movements of commodity prices. Because commodities are a key value driver for companies that are producers of commodities, the price movements change the fundamental profitability outlook for such companies. Once a prediction of commodity price movements is attained, financial indicators are used to determine the predicted price movements of equities with exposure to those commodities. After the most attractive equities have been chosen, a pairs trading strategy is implemented to create a portfolio with zero systematic risk. The model produced a return of 18.44% over a nearly six-year period. This is equivalent to a 2.9% annually compounded return. Over the same period of time, the S&P 500 returned -10.2%, a -1.8% annually compounded return. The Beta of the strategy, which is the measure of the systematic risk, was -0.2375. The 95% confidence interval of this estimate is bounded by -0.37 and -0.01797, which is slightly statistically different than zero. Demonstration Day and Times: Thursday, April 23 rd, 2009 10:30AM, 11AM, 11:30AM, 12PM Acknowledgements : Dr. Ken Laker, Dr. Vukan Vuchic, and Dr. Philip Farnum for their support and encouragement. Indicators Indicators in technical analysis use past price data in order to make predictions on future price movements. There are varying types of indicators; two main categories are broad market indicators and security -specific indicators. Since our model focuses specifically on commodities and commodity-exposed equities and because our model is always long one stock and short another, we have not concerned ourselves with broad market indicators. Instead. We have focused on security-specific indicators in order to generate investment decisions. Results Between the 1rst of January 2003 and the 1rst of December 2008, the model produced a cumulative return equal to 18.44%, which is equivalent to a compounded annual return of 2.9%. Over the same period, the S&P 500 returned - 10.2%, a -1.8% cumulative return. An index of commodities, the Goldman Sachs Commodity Price Index, returned 51.87% cumulative return and a 7.3% annually compounded return. Although benchmarks are useful for determining relative performance over a period of time, they are incomplete because they do not convey any sense of risk, systematic risk in particular. To take systematic risk of the different options into account, the Betas of each of the strategies should be considered. The Beta of the strategy was -0.2375 and had a 95% confidence interval with a lower bound equal to -0.37 and an upper bound equal to -0.01797. This demonstrates that the beta is slightly statistically different than zero, which was one of the design goals of the project. Considering that the beta is approximately zero, the strategy should produce the risk free rate, which can be deduced from the 10-year treasury bond yield in 2003, 4.07%. This shows that the annual return of the model is slightly low relative to alternatives with comparable risk characteristics. Figure 1: Model Flow Chart This diagram shows the individual components and processes that constitute our model. Table 1: Sample Calculation This table is an example of how a security’s final score is calculated. Figure 2: Weekly Strategy Returns vs. Weekly S&P500 Returns The strategy’s results showed very little linear relationship with those of the S&P500. Thus, we essentially eliminated systematic market risk from our strategy. Figure 3: Portfolio Value Over Time Our portfolio remained relatively stable at its original value until 2008. Our model successfully navigated 2008’s volatile markets and yielded a positive return.