Download Report

Transcript SciDAC Poster: INCITE

Dynamics of Electronic Markets
J. Siaw, G. Warnecke, P. Jain, C. Kenney, D. Gershman, R. Riedi, K. Ensor
Electronic Markets
• 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
• 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
• Main period of interest: Market trading
• Issue of interest: Stationarity
•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
 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 Existing Market Models outdated?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
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)
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
Survival of Orders
Research questions
•What makes the price
•Identify orders impacting the price
•Effect of order attributes
•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
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