An agent-based artificial financial market (AFM) is used to study market efficiency and learning in the context of the Neo-Austrian economic paradigm. Efficiency is defined in terms of the “excess” profits associated with different trading strategies, where excess is defined relative to a dynamic buy and hold benchmark in order to make a clean separation between trading gains and market gains. We define an Inefficiency matrix that takes into account the difference in excess profits of one trading strategy versus another (signal) relative to the standard error of those profits (noise) and use this statistical measure to gauge the degree of market efficiency. A one-parameter family of trading strategies is considered, the value of the parameter measuring the relative informational advantage of one strategy versus another. Efficiency is then investigated in terms of the composition of the market defined in terms of the relative proportions of traders using a particular strategy and the parameter values associated with the strategies. We show that markets are more efficient when informational advantages are small (small signal) and when there are many coexisting signals. Learning is introduced by considering “copycat” traders that learn the relative values of the different strategies in the market and copy the most successful one. We show how such learning leads to a more informationally efficient market but can also lead to a less efficient market as measured in terms of excess profits. It is also shown how the presence of exogeneous information shocks that change trader expectations increases efficiency and complicates the inference problem of copycats
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