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Centralizing information improves market efficiency more than increasing information: Results from experimental asset markets

Listed author(s):
  • Barreda Tarrazona, Iván J.
  • Grimalda, Gianluca
  • Morone, Andrea
  • Nuzzo, Simone
  • Teglio, Andrea

We study the relationship between market efficiency and the distribution of private information in experimental financial asset markets. Traders receive imperfect signals over the real value of an asset. Agents can share their information within a relatively small - compared to market size - group of agents. Both the number of signals and the way these are allocated among agents are manipulated in four experimental treatments. In two treatments signals are evenly distributed among agents. In two other treatments one group of 'quasi-insider' agents receives more signals than all other groups. In the baseline condition no signal is distributed. We show that centralizing information unambiguously achieves higher market efficiency than spreading information evenly. Furthermore, increasing the amount of information has no effect on efficiency either when information is symmetric or when it is asymmetric. We argue that two complementary mechanisms drive these results. First, having more private information ex ante induces traders to rely on their own signals, reducing the expected benefits of sharing information. Second, the presence of quasi-insider being common knowledge prompts agents to extract more information from market prices rather than their own private signals. This leads to swift information aggregation.

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Paper provided by Kiel Institute for the World Economy (IfW) in its series Kiel Working Papers with number 2072.

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Date of creation: 2017
Handle: RePEc:zbw:ifwkwp:2072
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  16. Alfarano, Simone & Camacho, Eva & Morone, Andrea, 2015. "Do investors rely too much on public information to be justified by its accuracy? An experimental study," FinMaP-Working Papers 30, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
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