Quantitative Finance: Design of an Algorithm to Trade the Stock Market

Through the use of knowledge in the field of finance, data analytics, and statistics our group competed in the 2017 Chicago Quantitative Alliance investment challenge. This challenge requires each team to design an investment strategy and use it to compete with some of the most prestigious undergraduate and graduate finance programs. Through extensive research of previous market performance our group designed various investment hypotheses. Once having these general hypotheses, constraints were added to ensure that the populations of data were properly analyzed. Portfolio construction was also analyzed to gain a better understanding of how rebalancing and portfolio allocation should be handled. Then, the hypotheses were statistically modeled so that the qualitative information behind the ideas was transformed into a quantitative analysis. Lastly, each hypothesis was back tested and evaluated using various metrics in order to determine the best hypotheses that we had come up with. These models were then traded for a six-month period.