Artificial swarm intelligence amplifies accuracy when predicting financial markets

Louis Rosenberg, Niccolo Pescetelli, Gregg Willcox

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publication2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2017
EditorsSatyajit Chakrabarti, Himadri Nath Saha
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages58-62
Number of pages5
ISBN (Electronic)9781538611043
DOIs
StatePublished - Jul 1 2017
Externally publishedYes
Event8th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2017 - New York City, United States
Duration: Oct 19 2017Oct 21 2017

Publication series

Name2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2017
Volume2018-January

Other

Other8th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2017
Country/TerritoryUnited States
CityNew York City
Period10/19/1710/21/17

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Hardware and Architecture
  • Computer Networks and Communications
  • Biotechnology

Keywords

  • Artificial intelligence
  • Artificial swarm intelligence
  • Collective intelligence
  • Human swarming
  • Swarm intelligence

Fingerprint

Dive into the research topics of 'Artificial swarm intelligence amplifies accuracy when predicting financial markets'. Together they form a unique fingerprint.

Cite this