Date of Award
Doctor of Business
At heart every trader loves volatility; this is where return on investment comes from, this is what drives the proverbial “positive alpha.” As a trader, understanding the probabilities related to the volatility of prices is key, however if you could also predict future prices with reliability the world would be your oyster. To this end, I have achieved three goals with this dissertation, to develop a model to predict future short term prices (direction and magnitude), to effectively test this by generating consistent profits utilizing a trading model developed for this purpose, and to write a paper that anyone with basic knowledge of markets and finance can readily understand. To address my first goal a well-quoted and tradable asset was required. To create a model that traders can use to make money it needed to be volatile with significant short and longer-term price swings. After some analysis, a review of macroeconomic impacts, and drawing in some part on experience, oil emerged as a fitting test, in particular Brent Crude Oil. For simplicity, and to further my third goal, “Oil” as used within this paper will represent Brent Crude Oil unless otherwise specified. While some dissertations and other scholarly works set out to discern theoretical truths this dissertation is much simpler, this is all about the money. Though I am certainly interested, as I am sure many traders are, in discovering the “Holy Grail” of how to trade any type of asset, the scope of my analysis will be confined to Brent Crude Oil prices within a sampling period of January 2002 thru May 2016. The findings indicate that there are factors that are predictive of the price of Oil and the research has allowed several conclusions. These conclusions culminate in a model that consistently generates profitable long and short trading opportunities. The model created spans over 13 years, from January 2003 thru May of 2016, and achieves a trading success ratio (profit generation) exceeding 94% of the trades opened, with the successful per trade average return exceeding 6.5% and the majority of the trades held for less than 60 calendar days (2 months) with an average holding period of 33 days for successful trades, exponentially larger than profits that might be realized by simply holding a single investment in Oil over the period of the analysis. The data contained in the paper will provide the details on the construction of both the prediction model and development of a trading model, variables utilized, and results achieved. The variables in this case are of significant interest and likely not as intuitive as might be expected. Chen, Rogoff, and Rossi (2008) provide some direction, although commodity currencies are utilized, finding that “commodities tend to be less of a barometer of future conditions than are exchange rates.”
Chair and Committee
Radhakrishnan Gopalan, Guofu Zhou, Todd Milbourn
Lenz, Jimmie Harold, "A Traders Guide to the Predictive Universe- A Model for Predicting Oil Price Targets and Trading on them" (2016). Doctor of Business Administration Dissertations. 1.
Analysis Commons, Applied Statistics Commons, Business Administration, Management, and Operations Commons, Categorical Data Analysis Commons, Design of Experiments and Sample Surveys Commons, Longitudinal Data Analysis and Time Series Commons, Multivariate Analysis Commons, Numerical Analysis and Computation Commons, Other Applied Mathematics Commons, Other Mathematics Commons, Other Statistics and Probability Commons, Probability Commons, Risk Analysis Commons, Statistical Models Commons