LONG-TERM STUDY WITH OXFORD SHOWS TRADERS USING SWARM OUTPERFORM ACROSS FOUR MAJOR INDICES
The financial traders met every Tuesday in the Swarm platform to register their collective forecast for the movement, both in direction and amplitude, of each of the four indices over the next 72 hours. The results were scored against the closing price on Friday afternoon. Because the traders also registered their own individual forecasts, researchers were able determine how the Swarm AI system performed against the average financial trader, as well as the crowd-based vote. In this way, researchers compared three distinct methodologies for eliciting optimal forecasting strategy from a group of experienced financial forecasters.
The Swarm platform enables groups to generate accurate forecasts by combine their knowledge, experience, and intuition with real-time Swarm Intelligence algorithms. An example of a group converging upon an answer in the Swarm platform is shown below. Each magnet represents the time-varying input from a single financial trader as the group deliberates and converges on a solution. In this instance, the 3-day forecast was that Crude Oil increase in value over the three day period by an amount less than 4%.
While the above question is posed very simply with just four discrete options, the Swarm platform processes the complex interactive behaviors that produce the final answer. The image below is a Support Density Plot showing the real-time behaviors processed by the AI engine as the system converges upon an optimized solution.
So, how did the system perform? The chart below compares the accuracy of (i) individual participants, (ii) the “crowd” deciding by majority vote, and (iii) the interactive “swarm” moderated by AI algorithms. As shown, when using Swarm the financial traders generated accurate forecasts 77% of the time. This was a significant improvement over the crowd at 66% and the individuals at 61%. It’s important to note that the results from the individual participants were bootstrapped 10,000 times in order to determine whether or not this amplification of intelligence for the Swarm AI system could be explained away by random chance. This analysis a highly significant result (p=0.002%).
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