WITHDRAWN New Mathematical Framework and Novel Machine-learning Based Computational Methodology to Determine the Influence of Variables in a Time Dependent System

This research project proposes a methodology for predicting rare, unforeseeable events, typically referred to as "black swan" events, by introducing a novel, quantitative "influence score" metric. Where traditional predictive models usually fall short, the proposed influence score provides an analytical, computationally feasible measure for identifying key influencing variables that shape an entire system. With the ability to quantify these key variables, this approach to determining influence also provides insights to the stability and interconnectedness of a system.

Our results show that in a major stock index, the Dow Jones Transportation Average, there are specific companies within the stock network that disproportionately have more influence than other companies. While this project focuses primarily on its applications in this setting, this influence score has implications in various fields beyond economics. Especially during times of uncertainty, the influence score can serve as a metric for determining significance, leading to improved decision-making through targeted responses. With this influence measure providing insight to complicated, stochastic systems, policymakers, researchers, and industry leaders are able to use this tool as a way to navigate and mitigate the impact of unpredictable events.

Econometrics

Recursive Neural Networks

LSTM Model

Economics 

Software & Data Science Technologies

Symposium on Data Science and Statistics (SDSS) 2025