Abstract

The financial markets have been affected by the recent advancements in social media. Retail traders often use social media as a source of financial information. This thesis provides a mathematical proof of concept for how retail traders on social media can cause a large change in market price and that it is possible to identify the factors that drive the price change before it is seen in the market. An Agent Based Model is used to simulate a self-contained artificial market on a network. Traders gather pricing information from social media posts created by traders on the network and their own pricing techniques. Trader demographics and network structures are varied to study their results. A network with trader demographics of predominately momentum traders has a threshold corresponding to a large change in market price. The threshold, first discovered in this thesis, is denoted as the Herd Threshold. There is a negative relationship between network clustering and the magnitude of the price change. A convolutional neural network is used to predict trader demographics and network structure. The convolutional neural network has greater success when classifying trader demographics than network structure. This thesis is a foundation for multiple avenues of future work.

Library of Congress Subject Headings

Stock exchanges--Mathematical models; Speculation--Mathematical models; Deep learning (Machine learning); Convolutions (Mathematics); Neural networks (Computer science); Social media--Economic aspects

Publication Date

5-11-2022

Document Type

Thesis

Student Type

Graduate

Degree Name

Applied and Computational Mathematics (MS)

Department, Program, or Center

School of Mathematical Sciences (COS)

Advisor

Bernard Brooks

Advisor/Committee Member

Kara Maki

Advisor/Committee Member

Nathan Cahill

Campus

RIT – Main Campus

Plan Codes

ACMTH-MS

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