Collective intelligence: a new approach to stock price forecasting

A group that makes better decisions than its individual members is considered to exhibit collective intelligence (CI). This paper describes the design and testing of a prototype Cl system that makes stock trading decisions based on group input. We hypothesized that the CI system would outperform the major stock indices, and that the performance of the system would improve as the size of the group increased. During an eleven trading-day test period, the system outperformed the NASDAQ, S&P 500, and DJIA stock indices by margins of 12.40%, 5.68%, and 2.25% respectively Statistical analysis showed that it was highly unlikely that a random sample of NASDAQ stock picks would have performed as well as the system (p<.02). We also found that the system performed better when trading decisions were based on input from larger groups, suggesting that CI rather than individual intelligence was responsible for the system´s overall good performance. Further testing is needed to see if these results will hold up over a longer period of time and with more participants