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Title: Liquidity Cost of Market Orders in Taiwan Stock Market: A Study based on An Order-Driven Agent-Based Artificial Stock Market
Authors: 陳樹衡;Huang, Yi-Ping;Min-Chin Hung
Chen, Shu-Heng
Contributors: 政治大學經濟系
Keywords: Order-Driven;Liquidity;Transaction Cost
Date: 2010.11
Issue Date: 2011-07-28 11:33:48 (UTC+8)
Abstract: This thesis construct an order-driven artificial stock market base on Daniels et al.
(2003) model. We also use autoregressive conditional duration (ACD) model initiated
by Engle and Russell (1998) to model duration or order size. We analyzed the
transaction cost of ten securities, including stocks, Exchange-Traded Funds (ETFs)
and Real Estate Investment Trusts (REITs), in Taiwan stock market and compared this
result with the simulated cost of our models. We find that for those frequently traded
securities, for example, TSMC (2330.TW) or China Steel (2002.TW), it is better not
to incorporate ACD model of duration in the model, and for those not frequently
traded securities, for example, President Chain Store (2912.TW) or Gallop No.1 Real
Estate Investment Trust Fund (01008T.TW), it is better to incorporate ACD model of
duration in the model. Our empirical estimates show that the liquidity costs of market
order of these ten securities are generally smaller than 3%, and largely lied between
-1% and 1%. We, however, find that simulation costs of market orders in our model,
with a range from 0% to 10%, are generally larger than those of real data. One
possible reason for this departure is that investors in stock markets generally do not
place their orders blindly. They tend to wait for the appearance of opposite order size,
and then place their orders. They also tend to split up a large order, and then reduce
market impact. These behavior do not exist in our simulation. Regardless of these
differences, our models may still be a simulation tool for transaction cost assessment
when one would like to liquidate their asset in a short span of time.
Relation: Econophysics Colloquium 2010, Academic Sinica
Data Type: conference
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