On Deep-Fake Stock Prices and Why Investor Behavior Might Not Matter

We propose an agent-based model of financial markets with only one asset. Thirty-two agents follow very simple rules inspired by Wolfram's Rule 110. They engage in buying, selling, and/or holding. Each agent is endowed with a starting balance sheet marked-to-market in each iteration. The simulation allows for margin calls for both buying and selling. During each iteration, the number of buy, hold, and sell positions is aggregated into a market price with the help of a simple, linear formula. The formula generates a price depending on the number of buy and sell positions. Various results are obtained by altering the pricing formula, the trading algorithm, and the initial conditions. When applying commonly used statistical tools, we find processes that are essentially indistinguishable from the price of real assets. They even display bubbles and crashes, just like real market data. Our model is remarkable because it can apparently generate a process of equivalent complexity to that of a real asset price, but it starts from a handful of initial conditions and a small number of very simple linear algorithms in which randomness plays no part. We contend our results have far-reaching implications for the debate around investor behavior and the regulation of financial markets.