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China-EU Summer School on Complexity Sciences

Shanghai University for Science and Technology. 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. Outlines. 1. Order-driven markets

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China-EU Summer School on Complexity Sciences

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  1. Shanghai University for Science and Technology China-EU Summer School on Complexity Sciences Universal price impact functions of individual trades in an order-driven market Wei-Xing ZHOUEast China University of Science and Technology14 August 2010

  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. Which events move the price? • 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)

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

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

  7. Volume-price relationship • Volume-volatility relation: vs. • Volume-return relation: vs. Karpoff, J. Fin. Quant. Analysis 22 (1987) 109-126.

  8. New York Stock Exchange • Source • Date sets • Variables Lillo, Farmer & Mantegna, Master curve for price-impact function, Nature 321 (2003) 129-130. TAQ of 1000 largest stocks on NYSE (1995-1998) vs.

  9. 20 Portfolios grouped with Cap

  10. LFM scaling

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

  12. Australian Stock Exchange • Source • Date sets • Variables Lim & Coggins, The immediate price impact of trades on the Australian Stock Exchange, Quantitative Finance (2005) 365-377. 300 constituent stocks of S&P asx 300 index traded on the ASE (2001-2004) vs. Normalized daily-normalized trade size

  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 • Source • Date sets • Variables 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) vs.

  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!

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