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Transcript
Dynamics of Electronic Markets J. Siaw, G. Warnecke, P. Jain, C. Kenney, D. Gershman, R. Riedi, K. Ensor Electronic Markets 1 • Electronic markets (ECNs) are networks that enable users to place orders for stocks via the internet to a system that executes trades automatically (Ex: Island, ArcaBook) • Difference to Traditional markets: • Prices determined by users (no market maker) • Speed Computers used to place orders as well as for Automated Trading • Research needed: the dynamics of ECNs are little studied yet make a large share of trades Intraday dynamics 4 • 3 trading periods per day: – Pre-market (7 AM – 9:30 AM) – Market trading (9:30 AM – 4 PM) – After hours (4 PM – 7 PM) • Pre-market and after hours trading is very sparse Example day: – Pre-market: 79 trades – Market trading: 33961 trades – After hours: 4091 trades • Trading activity correlates with activity on order book • Main period of interest: Market trading • Issue of interest: Stationarity 6 •Survival Time – time between the addition and removal of an order from the queue • Hazard rate - instantaneous probability that the order will be traded or cancelled during the next instant (before t+t) • Survival rate - probability an added order will survive beyond a certain time • Competing risks – orders may be fully or partially traded or cancelled Challenges Large / Complex Data sets: Innovative Data processing required Extremely large files Stocks accumulated in same file Impossible to use traditional software Existing Market Models outdated? Statistical analysis required Ultra – High data frequency Possibility to cancel order -> Intent unclear: actual trade vs influencing the market Not all orders lead to price changes Previously unseen microstructure detail 3 Exploratory Data Analysis • Summary statistics • high volume and liquidity • 125 Million book entries per day • 85% of orders placed are cancelled • orders cancelled within the second • volume steadily increasing over years • Features of interest • spread (recall absence of market maker) • survival times of orders (recall the short life span of the majority) 5 • • • • • Spread price –price of last trade (unique to stock) Limit Orders: queued to be matched or cancelled best bid - the highest buy order in the queue best ask – the lowest sell order in the queue spread = (best ask) – (best bid) • Highly traded stocks (ex. Microsoft): •spread usually $0.01; deviates only shortly • Less traded stocks: spread usually larger • Issues of interest: • absence of market maker shows in 1c-spread • requires new models Heat Plot of Sell Orders Heat Plot of Sell Orders Heat Plot of Buy Orders • Issue of interest: mixture of risk and complexity of data require new models 7 2 Survival of Orders Research questions •What makes the price •Identify orders impacting the price •Effect of order attributes •Volume •Timing •What drives the market • Depending on the “state” of the market, what are the order dynamics • Identify trader “states” (motivations) •Effect of exogenous events •Incorporation of additional microstructure information into existing models 8 Future Work • Survival analysis • Competing risk models • Cox proportional hazard model • Self-excitation (ACD models) • Spread • Correlation • ARIMA time series • Self-excitation • Conditional / Hidden parameter model • Arbitrage • Sensitivity to networks