Finding Undervalued Stocks with Machine Learning

Abstract

1.1 Past Work There has been a great amount of studies with Machine Learning on financial market. However, a majority of the studies focus on short-term market performances and high frequency trading strategies. Nevertheless, there are studies that focus on long term strategies. One study shows that nonlinear support vector machines can systematically identify stocks with high and low future returns[1]. Other studies also suggest that SVM would a preferable methodology. This will be also confirmed through our analysis.

Cite this paper

@inproceedings{Rekhi2014FindingUS, title={Finding Undervalued Stocks with Machine Learning}, author={Ramneet Singh Rekhi and Tucker L. Ward and Huan Wei and Michael Vincent Downs}, year={2014} }