TY - GEN
T1 - Artificial swarm intelligence amplifies accuracy when predicting financial markets
AU - Rosenberg, Louis
AU - Pescetelli, Niccolo
AU - Willcox, Gregg
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - Across the natural world, many species have evolved methods for amplifying their decision-making accuracy by thinking together in real-time closed-loop systems. Known as Swarm Intelligence (SI) in the field of biology, the process has been deeply studied in schools of fish, flocks of bird, and swarms of bees. The present research looks at human groups and tests their ability to make financial predictions by forming online systems modeled after natural swarms. Specifically, groups of financial traders were tasked with predicting the weekly trends of four common market indices (SPX, GLD, GDX, and Crude Oil) over a period of 14 consecutive weeks. Results showed that individual participants, who averaged 61% accuracy when predicting weekly trends on their own, amplified their accuracy to 77% when predicting together as real-time swarms. These results reflect a 26% increase in financial prediction accuracy and show high statistical significance (p=0.001). 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 of financial forecasts.
AB - Across the natural world, many species have evolved methods for amplifying their decision-making accuracy by thinking together in real-time closed-loop systems. Known as Swarm Intelligence (SI) in the field of biology, the process has been deeply studied in schools of fish, flocks of bird, and swarms of bees. The present research looks at human groups and tests their ability to make financial predictions by forming online systems modeled after natural swarms. Specifically, groups of financial traders were tasked with predicting the weekly trends of four common market indices (SPX, GLD, GDX, and Crude Oil) over a period of 14 consecutive weeks. Results showed that individual participants, who averaged 61% accuracy when predicting weekly trends on their own, amplified their accuracy to 77% when predicting together as real-time swarms. These results reflect a 26% increase in financial prediction accuracy and show high statistical significance (p=0.001). 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 of financial forecasts.
KW - Artificial intelligence
KW - Artificial swarm intelligence
KW - Collective intelligence
KW - Human swarming
KW - Swarm intelligence
UR - http://www.scopus.com/inward/record.url?scp=85046366078&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046366078&partnerID=8YFLogxK
U2 - 10.1109/UEMCON.2017.8248984
DO - 10.1109/UEMCON.2017.8248984
M3 - Conference contribution
AN - SCOPUS:85046366078
T3 - 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2017
SP - 58
EP - 62
BT - 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2017
A2 - Chakrabarti, Satyajit
A2 - Saha, Himadri Nath
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2017
Y2 - 19 October 2017 through 21 October 2017
ER -