This project was meant to analyze S&P 500 returns using statistical and machine learning techniques to see if any strategies could be derived that would outperform the market in a five year period between 2012 and 2016.
Currently, all six statistical and machine learning techniques that I applied outperformed the market between 2012 and 2017. For the model choosing S&P 500 components that had outperformed the market and going long those stocks the next year, my model outperformed by 24%. For the KNN machine learning model, my model was up 57% relative to the market return. For the logistic regression machine learning model, my model was up 26% relative to the market return. For my neural network implementation, my model was up 157% relative to the market.
Additionaly, I implemented a glamour and value investment strategy in these same time periods. The value investing strategy was up 92% relative to the market, and the glamour investing strategy was up 77% relative to the market.
Moving forward, I hope to continue refining this project and building it out, by pulling 20 years worth of data to see if it holds up, as well as pulling from a larger group of stocks outside the S&P 500. Due to the data filtering process within Python, as well as the data pulled from Bloomberg, the returns might be skewed. This is something I am actively exploring as I look to further build out this project.