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'Zero intelligence' trading closely mimics stock market

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  • Ian Pitchford
    Zero intelligence trading closely mimics stock market 01 February 2005 A model that assumes stock market traders have zero intelligence has been found to
    Message 1 of 1 , Feb 2, 2005
      'Zero intelligence' trading closely mimics stock market
      01 February 2005

      A model that assumes stock market traders have zero intelligence has been
      found to mimic the behaviour of the London Stock Exchange very closely.

      However, the surprising result does not mean traders are actually just
      buying and selling at random, say researchers. Instead, it suggests that the
      movement of markets depend less on the strategic behaviour of traders and
      more on the structure and constraints of the trading system itself.

      The research, led by J Doyne Farmer and his colleagues at the Santa Fe
      Institute, New Mexico, US, say the finding could be used to identify ways to
      lower volatility in the stock markets and reduce transaction costs, both of
      which would benefit small investors and perhaps bigger investors too.

      A spokesperson for the London Stock Exchange says: "It's an interesting bit
      of work that mirrors things we're looking at ourselves."

      Most models of financial markets start with the assumption that traders act
      rationally and have access to all the information they need. The models are
      then tweaked to take into account that these assumptions are not always
      entirely true.

      But Farmer and his colleagues took a different approach. "We begin with
      random agents," he says. "The model was idealised, but nonetheless we still
      thought it might match some of the properties of real markets."

      Buying and selling
      In the model, agents with zero intelligence place random orders to buy and
      sell stocks at a given price. If an order to sell is lower than the highest
      buy price in the system, the transaction will take place and the order will
      be removed - a market order. If the sell order is higher than the highest
      buy price, it will stay in the system until a matching buy order is found -
      a limit order. For example, if the highest order to buy a stock is $10,
      limit orders to sell will be above $10 and market orders to sell will be
      below $10.

      The team used the model to examine two important characteristics of
      financial markets. These were the spread - the price difference between the
      best buy and sell limit orders - and the price diffusion rate - a standard
      measure of risk that looks at how quickly the price changes and by how much.

      The model was tested against London Stock Exchange data on 11 real stocks
      collected over 21 months - 6 million buy and sell orders. It predicted 96%
      of the spread variance and 76% of the variance in the price diffusion rate.
      The model also showed that increasing the number of market orders increased
      price volatility because there are then fewer limit orders to match up with
      each other.

      Incentives and charges
      The observation could be useful in the real financial markets. "If it is
      considered socially desirable to lower volatility, this can be done by
      giving incentives for people who place limit orders, and charging the people
      who place market orders," Farmer says.

      Some amount of volatility is important, because prices should reflect any
      new information, but many observers believe there is more volatility than
      there should be. "On one day the prices of US stock dropped 20% on no
      apparent news," says Farmer. "High volatility makes people jittery and sours
      the investment climate." It also creates a high spread, which can make it
      more expensive to trade in shares.

      The London Stock Exchange already has a charging structure in place that
      encourages limit orders. "Limit orders are a good way for smaller investors
      to trade on the order book," says a spokesperson.

      Journal reference: Proceedings of the National Academy of Sciences (DOI:
      10.1073/pnas.0409157102)
      http://www.newscientist.com/article.ns?id=dn6948
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