1 China-EU Summer School on Complexity Sciences Universal price impact functions of individual trades in an order-driven market Wei-Xing ZHOU East China.

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Presentation transcript:

1 China-EU Summer School on Complexity Sciences Universal price impact functions of individual trades in an order-driven market Wei-Xing ZHOU East China University of Science and Technology 14 August 2010 Shanghai University for Science and Technology

2 Outlines 1. Order-driven markets 2. LFM scaling with NYSE data 3. LC scaling with ASE data 4. New scaling with Chinese data 5. Summary

3 Order-driven market

4  Cancelation of all orders at the best ask or bid  Submission of an order inside the spread  All partially filled orders (market orders)  Some filled orders (market orders) Which events move the price?

5 Market orders vs. Limit orders Buy orders vs. Sell orders Filled orders vs. Partially filled order Classification of orders

6 Mid-price at time t:Mid-price at time t: Immediate price impact is defined as the relative change of mid-price right before and after the transaction:Immediate price impact is defined as the relative change of mid-price right before and after the transaction: Immediate price impact

7 Volume-volatility relation:Volume-volatility relation: vs. vs. Volume-return relation:Volume-return relation: vs. vs. Volume-price relationship Karpoff, J. Fin. Quant. Analysis 22 (1987)

8 New York Stock Exchange Lillo, Farmer & Mantegna, Master curve for price- impact function, Nature 321 (2003) Lillo, Farmer & Mantegna, Master curve for price- impact function, Nature 321 (2003) TAQ of 1000 largest stocks on NYSE ( ) TAQ of 1000 largest stocks on NYSE ( ) vs. vs.  Source  Date sets  Variables

9 20 Portfolios grouped with Cap

10 LFM scaling

11 LFM scaling in Chinese data? NOT satisfactory!!!

12 Australian Stock Exchange Lim & Coggins, The immediate price impact of trades on the Australian Stock Exchange, Quantitative Finance (2005) Lim & Coggins, The immediate price impact of trades on the Australian Stock Exchange, Quantitative Finance (2005) constituent stocks of S&P asx 300 index traded on the ASE ( ) 300 constituent stocks of S&P asx 300 index traded on the ASE ( ) vs. vs. Normalized daily-normalized trade size Normalized daily-normalized trade size  Source  Date sets  Variables

13 10 Portfolios grouped with Cap

14 LC scaling

15 LC scaling

16 LC scaling

17 LC scaling

18 LC scaling in Chinese data? NOT satisfactory!!!

19 Shenzhen Stock Exchange Zhou, Universal price impact functions of individual trades in an order-driven market, Quantitative Finance (2010) to appear. Zhou, Universal price impact functions of individual trades in an order-driven market, Quantitative Finance (2010) to appear. 23 constituent stocks of SZSE component index traded on the SZSE (2003) 23 constituent stocks of SZSE component index traded on the SZSE (2003) vs. vs.  Source  Date sets  Variables

20 23 SZSE stocks

21 LFM scaling in Chinese data? NOT satisfactory!!!

22 LC scaling in Chinese data? NOT satisfactory!!!

23 Simple scaling for buy orders

24 Simple scaling for sell orders

25 No buy-sell asymmetry Slope = 2/3

26 Anomalous hook explained

27 Summary  Simpler scaling form without additional variable  Partially filled orders have greater price impact  No buy-sell asymmetry at the transaction level  Anomalous volume-return relation explained

28 Thank you for your attention!