Timing under individual evolutionary learning in a continuous double auction
Authored by Mikhail Anufriev, de Leur Michiel van
Date Published: 2018
DOI: 10.1007/s00191-017-0530-8
Sponsors:
Australian Research Council (ARC)
Platforms:
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Model Documentation:
Other Narrative
Mathematical description
Model Code URLs:
Model code not found
Abstract
The moment of order submission plays an important role for the trading
outcome in a Continuous Double Auction; submitting an offer at the
beginning of the trading period may yield a lower profit, as the trade
is likely to be settled at the own offered price, whereas late offers
result in a lower probability of trading. This timing problem makes the
order submission strategy more difficult. We extend the behavioral model
of Individual Evolutionary Learning to incorporate the timing problem
and study the limiting distribution of submission moments and the
resulting offer function that maps submission moments to offers. We find
that traders submit different offers at different submission moments the
distribution of which uni-modal with a peak moving from late to early as
the market size increases. This behavior exacerbates efficiency loss
from learning. If traders evaluate profitability of their strategies
over longer history, orders are submitted later with the same effect of
market size.
Tags
models
Bounded rationality
Efficiency
rationality
games
Agent-based
models
Financial-markets
Traders
Limit order book
Order-driven market
Individual evolutionary learning
Moment of order submission