- author = "Wei Cui",
- title = "An Empirical Investigation of Price Impact: An Agent-based Modelling Approach",
- school = "Michael Smurfit School of Business University College Dublin",
- year = "2012",
- address = "Ireland",
- month = nov,
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://ncra.ucd.ie/papers/wei_thesis.pdf",
- size = "268 pages",
-
abstract = "Understanding price impact is a fundamental task in
finance. Many execution algorithms, used to execute a
large order by dividing and spreading it over time, are
based on price effects and in particular on the way how
volume affects prices. Moreover, the analysis of price
impact is helpful for understanding how financial
markets function as price impact is one of the
mechanisms determining price formation.
The thesis is motivated by the recent emergence of algorithmic trading which requires a good understanding of price impact. This thesis addresses three questions concerning price impact in order to gain a better understanding on the intraday behaviour of price impact, and the factors affecting price impact.
The first study examines the intraday behaviours of price impact and market liquidity. The data is drawn from the NYSE-Euronext TAQ database and the LSE ROB database. Six stocks from the US markets and six stocks from the UK markets are analysed. The intraday patterns on price volatility, bid-ask spread, trading volume and market depth are documented and generally confirm findings in prior studies on intraday phenomena. In particular, a reverse S-shaped intraday pattern on price impact is found for both US and UK stocks for the first time.
The second study investigates whether agent intelligence plays an important role in determining the magnitude of price impact. This chapter constructs an artificial stock market composed of zero-intelligence agents, and calibrates it using the LSE ROB data. The result shows that the price impact in the artificial market is generally larger than that in the real market. This is consistent with the hypothesis that agent intelligence plays an important role in determining the magnitude of price impact. It supports the selective liquidity argument in Farmer et al. (2004) & Hopman (2007).
The third study addresses whether order choice affects the price impact of trading a large order. A typical approach in trading a large order is to devise a strategy which divides it into numerous pieces and spreads it over time (usually one trading day). In this study, several execution strategies with various order types, and a number of simple strategies with one order type as benchmarks are constructed and evaluated by their effects on prices. Novelly, these strategies are evolved and evaluated in simulated artificial markets. The results show that the combined strategies outperform the simple strategies significantly, suggesting that order choice plays an important role in determining the price impact of trading large orders.
The results in this thesis suggest that time-of-the-day, agent intelligence and order choice are important factors affecting price impact, and need to be considered in the theoretical microstructure models and in the design of trading strategies.",
- notes = "Research Supervisor: Prof. Anthony Brabazon",
