Asset Pricing and Earnings Fluctuations in a Dynamic Corporate Economy

TL;DR

This paper introduces a new predictive mathematical model for macroeconomics that incorporates asset prices, earnings fluctuations, and complex merger probabilities, utilizing AI and statistical physics techniques to generate practical predictions.

Contribution

It develops a novel class of cluster-size models that account for complex dependencies in corporate mergers and provide predictive insights after training on market data.

Findings

Model accurately predicts asset prices and earnings fluctuations.

Incorporates complex merger probabilities based on economic factors.

Generates usable predictions for market analysis.

Abstract

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 $C_1, C_2, ..., C_n$ of different sizes. We ascribe a degree of randomness to the event that any particular pair of corporations $C_i, C_j$ might undergo a merger, with probability matrix $p_{ij}$. Previous random-graph models set $p_{ij}$ equal to a constant, while in a real-world economy, $p_{ij}$ 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 $p_{ij}$ on economic factors, and generate usable predictions.