Who we are

This project includes the efforts of four graduate students who are each seeking masters degrees in either Operations Research or Systems Engineering at George Mason University. This effort serves a Capstone Project, which is chosen in lieu of a thesis, as a requisite for each of the four students to complete their degree. George Mason University is located in Fairfax, Virginia which is immediately outside of Washington, D.C.


Executive Summary

There are many options trading strategies available to investors and fund managers.  Fund managers often use multiple trading strategies because they are profitable in certain market conditions.  This project analyzed a particular option strategy, the Short Strangle strategy with bull put and bear call spreads.  We use this strategy to trade options on the E-Mini S&P 500, a stock market index futures contract on the Chicago Mercantile Exchange.

Fund managers often make investment decisions based on mathematical modeling, experience, and intuition.  Trading models require extensive research to develop and verify useful results.  As a result, they are often proprietary and confidential.  Our investment planning group developed a trading simulation to find optimal trading strategies that consider optimal allocation of investment and other parameters to ensure limited risk investments.

Our work builds on the work of two previous groups by extending their simulation software and models.  In addition to simulation using market data, we developed a prediction model that calculates the expected profit for a particular policy using the Black-Scholes model of options pricing.

Our results show more realistic returns than the previous group’s work.   Optimal policies have returns of 900% over the years 2007 through 2009.  We also determined the optimal values of several parameters, including the best time to trade, which is supported in existing literature.

Problem Statement

Our problem is to determine an optimal options investment strategy, balancing aggressive investment against risk of catastrophic loss, by simulating and comparing all possible policies over a period of time.  Building on the work of previous teams, our tasks were to develop (1) a more realistic simulated trading process and (2) an analytical model to predict the expected profit of an investment strategy and validate the simulation.

Statement of Need

Investors rely on both intuition and mathematical modeling for market prediction and advising trades. However, rigorous models are often the result of extensive resources and are strictly confidential and proprietary.  Operations research techniques can be used to assist decision makers to balance aggressive investment against catastrophic loss by offering scientific justification for decisions.