Understanding Factor models and Cone programming

This was a very interesting project we undertook. Not because its result was any enormous value addition but because it helped in grasping concepts and procedures critical to function in practice. It helped in going through the rigorous process of data cleaning and implementing factor models and checking their significance to predict stock returns. Further on we explored optimization using cone programming, which is a special case of interior point methods, to maximize the sharpe ratio and obtain a market beating portfolio.

Here is the Abstract:

The goal of our project is to utilize factor models to explain returns and optimize the Sharpe ratio to create a portfolio that outperforms the S&P 500. After re fining our data we have a universe of 335 stock in which we can invest. We re-balance our portfolio quarterly and incorporate factor models and Sharpe ratio optimization through cone programming to form the portfolio. The rest of the paper is organized as follows: Section 1 is a short introduction of our paper, Section 2 gives a brief idea of the data available and what kind of choices we made to reach the final universe of stocks, Section 3 gives an idea of the general methodology used in the paper, Section 4 describes the results that we have reached, Section 5 presents the significance test we performed, Section 6 presents the results of different sensitivity analysis and Section 7 summarizes the project and gives suggestions for further research.

Investment Allocation using Factor models and Cone programming optimization