SUM: A SURPRISING (UN)REALISTIC MARKET - BUILDING A SIMPLE STOCK MARKET STRUCTURE WITH SWARM
Pietro Terna ()
No 173, Computing in Economics and Finance 2000 from Society for Computational Economics
With SUM, a Surprising (Un)realistic Market, we are dealing with the micro-foundations of a stock market. We avoid any artificially simplified solution about price formation, such as to employ an auctioneer to clear the market; on the contrary, our model produces time series of prices continuously evolving, transaction by transaction. The core of the model is represented by a computational structure that reproduces closely the behavior of the computerized book of a real stock market. The agents send to the book their buy and sell orders, with the related limit prices. The book executes immediately the orders if a counterpart is found in its log; otherwise, it records separately the buy and sell orders, to match them with future orders. The book is cleared at the beginning of each day. Our (un)realistic market emerges from the behavior of myopic agents that: (i) know only the last executed price, (ii) choose randomly the buy or sell side and (iii) fix their limit price by multiplying the previously executed price times a random coefficient. This structure generates increasing and decreasing price sequences with relevant volatility. Also bubbles and crashes appear in this market, generated within the market structure, without the need of exogenous explanations. In this framework, we then relax hypothesis (i) for a small quota of the agents, in order to investigate the consequences of the presence either of subjects using technical trading rules to forecast the future market prices and of cognitive agents capable to learn from their experience. In some way, the last ones can correspond to the artificially intelligent agents behaving as econometricians proposed by Sargent (1993). More generally, within this model we can investigate empirical puzzles that are hard to understand using the traditional representative agent structure. Among these puzzles, the time series predictability and the volatility persistence. Swarm represents for our task the correct developing framework: it provides a multilayer structure and offers the computational power needed to run the experiments for a sufficient number of cycles. Here the multilayer structure contains: (i) the observer layer, that shows the results, and (ii) the model layer, that runs either the time schedule and the environment, with the stock market (realistic) book and the (unrealistic) agents.
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