no code implementations • 27 Nov 2023 • Colin M. Van Oort, Ethan Ratliff-Crain, Brian F. Tivnan, Safwan Wshah
As a baseline, we populate ABMMS with simple trading agents and investigate properties of the generated data.
no code implementations • 13 Nov 2023 • Ethan Ratliff-Crain, Colin M. Van Oort, James Bagrow, Matthew T. K. Koehler, Brian F. Tivnan
In 2001, Rama Cont introduced a now-widely used set of 'stylized facts' to synthesize empirical studies of financial time series, resulting in 11 qualitative properties presumed to be universal to all financial markets.
no code implementations • 13 Feb 2019 • John H. Ring IV, Colin M. Van Oort, David R. Dewhurst, Tyler J. Gray, Christopher M. Danforth, Brian F. Tivnan
Using the most comprehensive, commercially-available dataset of trading activity in U. S. equity markets, we catalog and analyze quote dislocations between the SIP National Best Bid and Offer (NBBO) and a synthetic BBO constructed from direct feeds.
no code implementations • 13 Feb 2019 • Brian F. Tivnan, David Rushing Dewhurst, Colin M. Van Oort, John H. Ring IV, Tyler J. Gray, Brendan F. Tivnan, Matthew T. K. Koehler, Matthew T. McMahon, David Slater, Jason Veneman, Christopher M. Danforth
Using the most comprehensive source of commercially available data on the US National Market System, we analyze all quotes and trades associated with Dow 30 stocks in 2016 from the vantage point of a single and fixed frame of reference.
no code implementations • 15 Jun 2014 • Peter Sheridan Dodds, Eric M. Clark, Suma Desu, Morgan R. Frank, Andrew J. Reagan, Jake Ryland Williams, Lewis Mitchell, Kameron Decker Harris, Isabel M. Kloumann, James P. Bagrow, Karine Megerdoomian, Matthew T. McMahon, Brian F. Tivnan, Christopher M. Danforth
Using human evaluation of 100, 000 words spread across 24 corpora in 10 languages diverse in origin and culture, we present evidence of a deep imprint of human sociality in language, observing that (1) the words of natural human language possess a universal positivity bias; (2) the estimated emotional content of words is consistent between languages under translation; and (3) this positivity bias is strongly independent of frequency of word usage.