2004/04/07 by William Gordon Ritter, Ritter, William Gordon
Economics, Econometrics and Finance · Physics and Astronomy · #Complex Systems and Time Series Analysis #FOS: Physical sciences #Opinion Dynamics and Social Influence #Other Condensed Matter (cond-mat.other) #Theoretical and Computational Physics #cond-mat.other
paper · pdf · doi:10.48550/arxiv.cond-mat/0404189
arxiv created 2004/04/07 · openalex publication_date 2004/04/07 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We give a new predictive mathematical model for macroeconomics, which deals specifically with asset prices and earnings fluctuations, in the presence of a dynamic economy involving mergers, acquisitions, and hostile takeovers. Consider a model economy with a large number of corporations C1, C2, ..., Cn of different sizes. We ascribe a degree of randomness to the event that any particular pair of corporations Ci, Cj might undergo a merger, with probability matrix pij. Previous random-graph models set pij equal to a constant, while in a real-world economy, pij is a complicated function of a large number of variables. We combine techniques of artificial intelligence and statistical physics to define a general class of mathematical models which, after being trained with past market data, give numerical predictions for certain quantities of interest including asset prices, earnings fluctuations, and merger/acquisition likelihood. These new models might reasonably be called ``cluster-size models.'' They partially capture the complicated dependence of pij on economic factors, and generate usable predictions.