Order Splitting Strategy to Reduce Market Impact in Tehran Stock Exchange

Document Type : Research Paper

Authors

1 Assistant Prof., Department of Financial Engineering, Faculty of Industrial Engineering & Systems, Tarbiat Modares University, Tehran, Iran.

2 M.Sc., Department of Financial Engineering, Faculty of Financial Science, Kharazmi University, Tehran, Iran

Abstract

Objective: This research is aimed at offering an order splitting strategy to divide a large order into a number of smaller orders to reduce Market Impact cost and imbalances created by Large orders in the market.
Methods: Due to the limited access to data and high volume of calculations, for some shares of the Tehran Stock Exchange (TSE), market impact cost function of bought trades were calculated using the I-star model. Then, by using the MI function and based on the investor's trading horizon, we split a large order into a series of small orders to place them at intervals rather than ordering all at once. The goal is to reduce the market impact cost, and avoid creating an imbalance of large orders in the market.
Results: According to the intraday patterns of the average trading volumes and the market impact cost, it is observed that the trading volume of shares is low and market impacts cost are high at the beginning of the day, then at the end of the day, as the trading volume and market liquidity increase, the market impact cost incurred by traders reduces. This is mainly because investors will not need to increase trading prices to complete their orders when the stock market experiences an increase in liquidity and trading volumes, and this is also seen in the Tehran Stock Exchange. The market impact cost function for the shares in Tehran Stock Exchange is also concave and investors behave much more aggressively when buying compared to selling.
Conclusion: The results show that by examining the intraday patterns of liquidity parameters such as trading volumes and market impact then designing a trading strategy for the splitting of large orders can reduce the additional costs incurred by traders and result in orders being traded in relatively more reasonable prices.

Keywords

Main Subjects


Admati, A., & Pfleiderer, P. (1988). A theory of intraday patterns: volume and price variability. Review of financial studies, 1(1), 3-40.
Alfonsi, A., Fruth, A., Schied, A. (2010). Optimal execution strategies in limit order books with general shape functions. Quantitative finance, 10(2), 143-157.
Almgren, R. (2003). Optimal executions with non-linear impact functions and trading enhanced risk. Applied mathematical Finance, 10, 1-18.
Almgren, R., Chriss, N. )2003(. Bidding principles. Risk, 97-102
Bertsimas, D., & Lo, A. W. (1998). Optimal control of execution costs. Journal of Financial Markets, 1(1), 1-50.
Biais, B. (1995). An Empirical Analysis of the Limit Order Book and the Order Flow in the Paris Bourse. The Journal of Finance, 50(5), 1655–1689.
Bloomfield, R., O’Hara, M., & Saar, G. (2013). Hidden Liquidity: Some New Light on Dark Trading. Working Paper.
Bouchaud, J. P. (2009). Price impact. Capital Fund Management, 13(1), 397-419.
Chan, L.K., Lakonishok, J. (1995). The behavior of stock prices around institutional trades. The Journal of Finance, 50 (4), 1147-1174.
Chan, L.K., Lakonishok, J., 1997. Institutional equity trading costs: NYSE versus Nasdaq. Journal of Finance, 52 (2), 713-735.
Chen, Y., Li, D., & Gao, X. (2017). Optimal Order Exposure in a Limit Order Market. Available at SSRN: https://ssrn.com/abstract=2938377
Cont, R., & Kukanov, A. (2017). Optimal order placement in limit order markets. Quantitative Finance17(1), 21-39.
Curato, G., Gatheral, J., & Lillo, F. (2017). Optimal execution with non-linear transient market impact. Quantitative Finance, 17(1), 41-54.
Easley, D., O’Hara, M. (1987). Price, trade size, and information in securities markets. Journal of Financial Economics,19(1), 69-90.
Engle, R.F. (1982). Autoregressive conditional heteroscadisticity with estimates of the variance of United Kingdom inflation. Econometrica, 50, 987-1008.
Farmer, D., J., Gillemot, L., Lillo, F., Mike, S., & Sen, A. (2004). What really causes large price changes? Quantitative Finance, 4(4), 383-397.
Gatheral, J. (2010). No-dynamic-arbitrage and market impact. Quantitative Finance,10 (7), 749-759.
Glosten, L., Milgrom, P. (1985). Bid, ask and transaction prices in a specialist market with heterogeneously informed traders. Journal of Financial Economics,14(1), 71–100.
Haghighi, A., Fallahpour, S., & Eyvazlu, R. (2016). Modelling order arrivals at price limits using Hawkes processes. Financial research letters, 19, 267-272.
Hasbrouck, J. (1991). Measuring the Information Content of Stock Trades. The Journal of Finance, 46(1), 179–207.
Hopman, C. (2007). Do supply and demand drive stock prices? Quantitative Finance, 7(1), 37-53.
Huberman, G., Stanzl, W. (2001). Optimal liquidity trading. Yale School of Management, Working Papers ysm165, Yale School of Management, revised 01 Aug 2001.
Junka, M. (2012). Optimal execution strategy in the presence of permanent price impact and fixed transaction cost. Optimal Control Applications and Methods, 33(6), 713-738.
Kervel, V., Kwan, A., & Westerholm, J. (2018). Order splitting and searching for a Counterparty. Available in: http://finance.uc.cl/docs/conferences/15th/KervelKwan Westerholm_2018_Vincent_van_Kervel.pdf.
Kissell, R., Glantz, M., & Malamut, R. (2004). A practical framework for estimating transaction costs and developing optimal trading strategies to achieve best execution. Finance Research Letters, 1(1), 35-46.
Kyle, A. S. (1985). Continous auctions and insider trading. Econometrica, 53, 1315-1335.
Lee, C., Ready, M. (1991). Inferring trade direction from intraday data. Journal of Finance, 46, 733-747.
Lillo, F., Farmer, J., & Mantegna, R. (2002). Single curve collapse of the price impact function for the New York stock exchange. Available at: https://arxiv.org/pdf/cond-mat/ 0207428.pdf.
Lillo, F., Farmer, J., & Mantegna, R. (2003). Econophysics: Master curve for Price impact function. Nature, 421, 129-130.
Obizhaeva, A. A., & Wang, J. (2013). Optimal trading strategy and supply/demand dynamics. Journal of Financial Markets, 16(1), 1-32.
Ohnishi, M., & Shimoshimizu, M. (2019). Optimal and equilibrium execution strategies with generalized price impact. Available at SSRN: https://ssrn.com/abstract=3323335.
Patzelt, F., & Bouchaud, J.F. (2017). Universal scaling and nonlinearity of aggregate price impact in financial markets. Physical Review E, 97(1).
Pham, M.M.H. & Vath, V.L. (2005). A model of optimal portfolio selection under liquidity risk and price impact. Finance and Stochastics, 11(1), 51-90.
Rastegar, M. A., & Saedifar, Kh. (2017). Optimal Execution Strategy: An Agent-based Approach, Financial Research Journal, 19(2), 239-262. (in Persian)
Rastegar, M. A., Teimouri, F., & Bagherian, B. (2018). Order Placement Strategy: Trade-off between Market Impact and Non-Execution Risk. Financial Research Journal, 20(2), 151-172. (in Persian)
Stoll, H. (1997). The Components of the Bid-Ask Spread: A General Approach. The Review of Financial Studies, 10(4), 995-1034.
Torre, F. (1997). Market Impact model, Handbook. Available in: https://pdfs.semanticscholar.org/06e9/7a3e189ed21ee51c857fc735f589a4e57bcd.pdf.
Vayanos, D. (1999). Strategic Trading and Welfare in a Dynamic Market. Review of Economic Studies, 66(2), 219–254. 
Wagner, E. M. (1993). Best Execution.  Financial Analysts Journal, 49(1), 65-71.
Wagner, W. (Ed.) (1991). The Complete Guide to Security Transactions. John Wiley.