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Many social species amplify their decision-making accuracy by deliberating in real-time closed-loop systems. Known as Swarm Intelligence (SI), this natural process has been studied extensively in schools of fish, flocks of birds, and swarms of bees. The present research looks at human groups and tests their ability to make financial forecasts by working together in systems modeled after natural swarms. Specifically, groups of financial traders were tasked with forecasting the weekly trends of four common market indices (SPX, GLD, GDX, and Crude Oil) over a period of 19 consecutive weeks. Results showed that individual forecasters, who averaged 56.6% accuracy when predicting weekly trends on their own, amplified their accuracy to 77.0% when predicting together as real-time swarms. This reflects a 36% increase in forecasting accuracy and shows high statistical significance (p<0.001). Further, if investments had been made according to these swarm-based forecasts, the group would have netted a 13.3% return on investment (ROI) over the 19 weeks, compared to the individual’s 0.7% ROI. This suggests that enabling groups of traders to form real-time systems online, governed by swarm intelligence algorithms, has the potential to significantly increase the accuracy and ROI of financial forecasts. 

Sales forecasts are critical to businesses of all sizes, enabling teams to project revenue, prioritize marketing, plan distribution, and scale inventory levels. To date, however, sales forecasts of new products have been shown to be highly inaccurate, due in large part to the lack of data about each new product and the subjective judgements required to compensate for this lack of data. The present study explores product sales forecasting performed by human groups and compares the accuracy of group forecasts generated by traditional polls to those made using Artificial Swarm Intelligence (ASI), a technique which has been shown to amplify the forecasting accuracy of groups in a wide range of fields. In collaboration with a major fashion retailer and a major fashion publisher, groups of fashion-conscious millennial women predicted the relative sales volumes of eight sweaters promoted during the 2018 holiday season, first by ranking each sweater’s sales in an online poll, and then using Swarm software to form an ASI system. The Swarm-based forecast was significantly more accurate than the poll. In fact, the top four sweaters ranked by swarm sold 23.7% more units, or $600,000 worth of sweaters during the target period, as compared to the top four sweaters as ranked by survey, (p = 0.0497), indicating that swarms of small consumer groups can be used to forecast sales with significantly higher accuracy than a traditional poll.