For the Fourth Annual 2008 Machine Learning for Signal Processing competition entrants were asked to develop a machine learning algorithm that maximizes the rate of return by trading (buying, selling, shorting, or covering) stocks over a six-month time period. Each entrant began with a (fictional) $100,000 USD. Both the training and the test set include the daily price and volume for a total of 2929 stocks that are traded in American stock markets and a total of 41 monthly indices. Stock valuations are based on real (historical) stock prices. This year there were 5 algorithms submitted. The highest annual rate of return of an astonishing 150% was obtained by Peng and Ji of the Rensselaer Polytechnic Institute/Shanghai Maritime University team.