The value of information in a multi-agent market model
- Bence Toth, Enrico Scalas, Jurgen Huber and Michael Kirchler
- A version of the paper can be found here.
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We present an experimental and simulated model of a multi-agent stock market driven by a double auction order matching mechanism. Studying the effect of cumulative information on the performance of traders, we find a non monotonic relationship of net returns of traders as a function of information levels, both in the experiments and in the simulations. Particularly, averagely informed traders perform worse than the non informed and only traders with high levels of information (insiders) are able to beat the market. The simulations and the experiments reproduce many stylized facts of stock markets, such as fast decay of autocorrelation of returns, volatility clustering and fat-tailed distribution of returns. These results have an important message for everyday life. They can give a possible explanation why, on average, professional fund managers perform worse than the market index.
The experiments are set up in such a way that certain traders receive different levels of information in a simulated trading environment. The question the authors address is whether or not more information translates into better performance. If agents are perfectly rational we should see a monotonic relationship between information and returns (on average).
Of course, readers of Turnkey Analyst already understand that humans aren’t perfectly rational. And of course, the results bear this truth out once again…
From the authors:
The results suggest that only those traders with near perfect information can beat the market. Of course, attaining perfect information in real-world markets is equivalent to attaining insider information that is generally unavailable to the marketplace (unless you like dressing up in chains).
Time for some more channel checks to confirm the discounted cash flow model is correct?
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