Metaorder limit prices in evaluating expected market impact and assessing execution service quality
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DOIhttp://dx.doi.org/10.21511/imfi.16(2).2019.30
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Article InfoVolume 16 2019, Issue #2, pp. 355-369
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The paper examines the bias introduced by metaorder limit prices when measuring quality of execution services on financial market. While evaluating the quality of execution services, observed execution costs should be adjusted for metaorder participation rate, size and duration to ensure that they are comparable across execution service providers. One of the exogenous factors which may bias measured execution costs are the different metaorder limit prices in the sample. Currently, there are no proposed methods to normalize for this bias. In the research, the difference in execution costs for metaorders with different limit prices was examined by implementing a limit order book simulation model. It was discovered that the difference in metaorder limit prices is a source of significant heterogeneity in the execution cost distribution. However, we were able to prove that when market agents trade with constant intensities, the difference in execution costs for metaorders with different limit prices is fully explained by their realized participation rate. As a result, financial institution may assess quality of execution services for metaorders without any reservations about differences in metaorders limit prices as long as execution costs are adjusted for different participation rates.
- Keywords
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JEL Classification (Paper profile tab)G12, G17, C53
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References23
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Tables3
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Figures8
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- Figure 1. Initial shape of order book
- Figure 2. Examples of simulated price dynamics
- Figure 3. Examples of simulated price dynamics with metaorder traded every 10 time units
- Figure 4. Arrival cost distribution for various frequencies of metaorder trading
- Figure 5. Market impact as a function of metaorder participation rate
- Figure 6. Arrival cost distribution for various limit prices
- Figure 7. Arrival costs and probability of order to be fully filled
- Figure 8. Market impact for various speed of trading and limit prices
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- Table 1. Description of combined process transitions
- Table 2. Descriptive statistics for various frequencies (participation rates) of metaorder execution
- Table 3. Execution cost and trading stats for various metaorder limits
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- Abergel, F., Anane M., Chakraborti A., Jedidi A., & Toke, I. (2016). Limit Order Books. Cambridge University Press.
- Alfonsi, A., Fruth, A., & Schied, A. (2010). Optimal execution strategies in limit order books with general shape functions. Quantitative Finance, 10(2), 143-157.
- Allen, D. (1991). What are transaction costs? Journal of Behavior and Organization, 14, 1-18.
- Almgren, R., & Chriss, N. (2001). Optimal execution of portfolio transactions. Journal of Risk, 3, 5-39.
- Almgren, R., Thum, C., Hauptmann, E., & Li, H. (2005). Direct estimation of equity market impact. Risk, 18(7), 58-62.
- Berkowitz, S. A., Logue, D. E., & Noser, E. A. (1998). The Total Cost of Transaction on the NYSE. The Journal of Finance, 43(1), 97-112.
- Bershova, N., & Rakhlin, D. (2013). The non-linear market impact of large trades: Evidence from buy-side order flow. Quantitative Finance, 13(11), 1759-1778.
- Biais, B., Hillion, P., & Spatt, C. (1995). An empirical analysis of the limit order book and the order flow in the Paris Bourse. Journal of Finance, 50(5), 1655-1689.
- Bonart, J., & Gould, D. (2017). Latency and liquidity provision in a limit order book. Quantitative Finance, 17(10), 1601-1616.
- Cohen, K. J., Maier, S. F., & Schwartz, R. A. (1981). Transaction Costs, Order Placement Strategy and Existence of Bid-Ask Spread. Journal of Political Economy, 89(2), 287-305.
- Collins, B., & Fabozzi, F. (1991). A Methodology for Measuring Transaction Costs. Financial Analyst Journal, 47(2), 27-36.
- Cont, R., Stoikov, S., & Talreja, R. A. (2010). Stochastic Model for Order Book Dynamics. Operations Research, 58(3), 549-563.
- Donier, J., Bonart, J., Mastromatteo, I., & Bouchaud, J. (2015). A fully consistent, minimal model for non-linear market impact. Quantitative Finance, 15(7), 1109-1121.
- Farmer, J. D., Gerig, A., Lillo, F., & Waelbroeck, H. (2013). How efficiency shapes market impact. Quantitative Finance, 13(11), 1743-1758.
- Grinold, R., & Kahn, R. (1999). Active Portfolio Management (2nd ed.) (616 p.). New York: McGraw-Hill.
- Handa, P., & Schwartz, R. (1996). Limit order trading. Journal of Finance, 51, 1835-1861.
- Harris, L., & Hasbrouck J. (1996). Market vs. Limit orders: The SuperDOT evidence on order submission strategy. Journal of Financial and Quantitative Analysis, 31(2), 213-231.
- Konishi, H. (2002). Optimal Slice of a VWAP Trade. Journal of Financial Markets, 5, 197-221.
- Nevmyvaka, Y., Kearns, M., Papandreou, M., & Sycara, K. (2005). Electronic trading in order-driven markets: efficient execution. Paper presented at Seventh IEEE International Conference on E-Commerce Technology (CEC’05) (pp. 190-197). Munich, Germany.
- Said, E., Ayed, A., Husson, A., & Abergel, F. (2018). Market Impact: A systematic study of limit orders (Working Papers hal-01561128). HAL.
- Taranto, D., Bormetti, G., Bouchaud, J., Lillo, F., & Toth B. (2018). Linear models for the impact of order flow on prices I. History dependent impact models. Quantitative Finance, 18(6), 903-915.
- Torre, N. G. (1997). Market Impact Model Handbook. BARRA Inc., Berkeley.
- Toth, B., Lemperiere, Y., Deremble, C., Lataillade, J., Kockelkoren, J., & Bouchaud, J. (2011). Anomalous price impact and the critical nature of liquidity in financial markets. Physical Review, X1(2), 1-11.
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Determinants of price reversal in high-frequency trading: empirical evidence from Indonesia
Perdana Wahyu Santosa
doi: http://dx.doi.org/10.21511/imfi.17(1).2020.16
Investment Management and Financial Innovations Volume 17, 2020 Issue #1 pp. 175-187 Views: 1082 Downloads: 494 TO CITE АНОТАЦІЯThis article analyzes whether the factors of the mechanism of high-frequency trading (HFT) or intraday trading affect the process of price reversal and continuation. The price reversal phenomenon is gaining importance rapidly due to the increasingly intensive use of IT/Fintech-based trading automation facilities on the Indonesia Stock Exchange. However, one knows little about how their trading affects volatility and liquidity pressures that cause price reversals. A new research approach uses the factors of market microstructure mechanism based on high-frequency data (HFD-intraday). The research method uses purposive random sampling, which classified price fractions into three groups, specifically low price, medium price, and high price, which are analyzed by logistic panel regression. The research variables used include price reversal (dependent), stock return, trading volume, transaction frequency, volume/frequency (V/F) proxy, volatility, and liquidity. According to low price model research findings, all variables show a significant effect on price reversal; for medium price model, all variables except liquidity show a significant effect on price reversal; and for high price model, all variables have a significant effect on price reversal, except trading volume and volatility. In conclusion, low price shares tend to have higher price reversal probability compared to continuity because they tend to be liquid, low institutional ownership, and minimal reporting/analysis and are controlled by HFTs (uninformed traders). Some variables are not significant because of the bounce effect around the bid-ask spread.
Acknowledgment
Many thanks to Armida S. Alisjahbana, Roy H. Sembel, Budiono, Rahardi S. Rahmanto, and the anonymous referee/reviewer for valuable inputs and feedback. -
Are Asian exchanges outliers? A market quality criterion
Ranjan R. Chakravarty , Sudhanshu Pani
doi: http://dx.doi.org/10.21511/imfi.18(2).2021.06
Investment Management and Financial Innovations Volume 18, 2021 Issue #2 pp. 64-78 Views: 635 Downloads: 243 TO CITE АНОТАЦІЯThis paper provides a practical, empirical and theoretical framework that allows investment managers to evaluate stock exchanges’ market quality when choosing among different plausible international trading venues. To compare trading exchanges, it extends the hypothesis of market microstructure invariance to trading across exchanges. A measure ω, the ratio of the market-wide volatility to microstructure invariance, is introduced. The paper computes ω for the exchanges around the world. Its value for the NSE (India) is 24.5%, the Korea Exchange (Korea) is 7.9%, the Shanghai Exchange (China) is 3.5%, and the Shenzhen Exchange (China) is 4.4%, which is significantly different from that of major exchanges in the USA (NYSE – 0.8%, NASDAQ – 1.3%) and Europe (LSE (UK) – 0.4). This country risk dimension clearly identifies which equity exchanges cannot hold their own direct correlational hedges and therefore mandatorily require derivative positions, and has significant implications for the decision making of global long-short equity asset allocators in the Asian listed equity markets.
