[Paleopsych] New Scientist: 'Zero intelligence' trading closely mimics stock market

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'Zero intelligence' trading closely mimics stock market
      * 11:59 01 February 2005
      * Katharine Davis

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

    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

    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)

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      * [15]J Doyne Farmer, Santa Fe Institute
      * [16]London Stock Exchange
      * [17]Proceedings of the National Academy of Sciences


   12. http://www.newscientist.com/article.ns?id=mg18324662.100
   13. http://www.newscientist.com/article.ns?id=mg18224425.200
   14. http://www.newscientist.com/article.ns?id=mg17623721.100
   15. http://www.santafe.edu/~jdf/
   16. http://www.londonstockexchange.com/en-gb/
   17. http://www.pnas.org/

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