Lin, William and Sun, David and Tsai, Shih-Chuan (2010): Searching out of Trading Noise: A Study of Intraday Transactions Cost.
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Abstract
We attempt to identify in this paper the role of trading noise as a transactions cost to market participant in the sense of Stoll (2000), especially in the presence of trading concentration. Applying the measures of Hu (2006) and Kang and Yeo (2008), we analyze the noise proportion in intraday stock returns and its interaction with investor herding and search cost. Although this noise is high on individual orders and low on institutional orders, its behavior at market open is entirely different from the rest of the day. Noises for small cap stocks, unlike volatilities, are lower than those for large cap stocks. We also found that noise relates positively to trading volume, but inversely to holdings and turnover ratio of institutional investors. Responses from institutional and individuals are quite the opposite. The noise proportion generated by individual order rises with institutional turnover and search cost encountered, while that of institutional order behaves just oppositely. At market open, behaviors of noise from institutional and individual orders just switch mutually, and then switch back afterwards. Also, noise from high-cap stocks is actually more responsive than that from low-cap ones across investors. So trading noise is a specific transactions cost, prominent to only certain investors, at certain time and for certain stocks in the market, rather than a general market friction as argued in Stoll (2000). This transactions cost is inversely related to search costs encountered in trading, which depends on investor, trading hour of day and market capitalization of stocks.
Item Type: | MPRA Paper |
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Original Title: | Searching out of Trading Noise: A Study of Intraday Transactions Cost |
Language: | English |
Keywords: | Noise, transaction cost, herding, search model, order book |
Subjects: | G - Financial Economics > G1 - General Financial Markets > G12 - Asset Pricing ; Trading Volume ; Bond Interest Rates L - Industrial Organization > L1 - Market Structure, Firm Strategy, and Market Performance > L11 - Production, Pricing, and Market Structure ; Size Distribution of Firms C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D82 - Asymmetric and Private Information ; Mechanism Design D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D83 - Search ; Learning ; Information and Knowledge ; Communication ; Belief ; Unawareness |
Item ID: | 28937 |
Depositing User: | David Sun |
Date Deposited: | 21 Feb 2011 01:31 |
Last Modified: | 27 Sep 2019 23:49 |
References: | 1. Admati, A. R., (1991), “The Informational Role of Prices,” Journal of Monetary Economics 28,347-361. 2. Avery, C. and Zemsky, P.. (1998), “Multidimensional uncertainty and herd behavior in financial markets,” American Economic Review 88, no. 4, 724-748. 3. Banerjee, A.. (1992), “A Simple Model of Herd Behavior,” Quarterly Journal of Economics 107, 797-817. 4. Bikhchandani, S.; Hirshleifer, D.; Welch, I.. (1992), “A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades,” Journal of Political Economy 100, 992-1026. 5. Bouchaud, J.P.. (2002), “An Introduction to Statistical Finance,” Physica A 313, 238-251. 6. Chang, E. C.; Cheng, J. W.; Khoran, A., (2000), “An examination of herd behavior in equity markets: An international perspective,” Journal of Banking and Finance 24, no. 10, 1651-1699. 7. Chakraborty, A. and Yilmaz, B., (2004), "Manipulation in market order models," Journal of Financial Markets 7(2), 187-206. 8. Christie, W. G., and Huang, R. D.. (1995), “Following the pied piper: Do individual returns herd around the market?” Financial Analyst Journal 51, no. 4, 31-37 9. Christoffersen, S. K. and Tang, Y., (2009), “Institutional Herding and Information Cascades: Evidence from Daily Trades,” Working Paper, McGill University. 10. Cont, R. and Bouchaud, J. P.. (2000), “Herd Behavior and Aggregate Fluctuations in Financial Markets,” Macroeconomic Dynamics 4, 170-196. 11. De Long, J. B., A. Shleifer, L. H. Summers, and R. J. Waldmann, (1990), “Positive Feedback Investment Strategies and Destabilizing Rational Speculation,” Journal of Finance 45(2), 374-397. 12. Diamond, D. W., and Verrecchia, R. E., (1981), “Information Aggregation in a Noisy Rational Expectations Economy,” Journal of Financial Economics 9, 221-235. 13. Easley, D., Kiefer, N. M., and O’Hara, M., (1997), “One Day in the Life of a Very Common Stock,” Review of Financial Studies 10, 805-835. 14. Easley, D., and O’Hara, M., (1992), “Time and the Process of Security Price Adjustment,” Journal of Finance 47, 577-605. 15. Finucane, T. J.. (2002), “A Direct Test of Methods for Inferring Trade Direction from Intra-day Data,” Journal of Financial and Quantitative Analysis 35, 553-557. 16. Foucault, T., Kadam, O., and Kandel, E., (2005), “Limit Order Book as a Market for Liquidity, ” Review of Financial Studies 18, 1171-1218. 17. Glosten, L. R. and Milgrom, P. R., (1985), “Bid, ask and transaction prices in a specialist market with heterogeneously informed traders,” Journal of Financial Economics, Vol. 14(1), 71-100. 18. Grossman, S. J., and Stiglitz, J. E., (1980), “On the Impossibility of Informationally Efficient Markets,” American Economic Review 70(3), 393-408. 19. Hu, S.. (2006), “A Simple Estimate of Noise and Its Determinant in a Call Auction Market,” International Review of Financial Analysis 15, 348-362. 20. Huddart, S., Hughes, J. S. and Levine, C. B., (2001), "Public Disclosure and Dissimulation of Insider Trades," Econometrica 69(3), 665-681. 21. Kang, W., and Yeo, W., (2008), “Liquidity beyond the Best Quote: A Study of the NYSE Limit Order Book,” Working Paper, National University of Singapore. 22. Kyle, A. S., (1985), “Continuous Auctions and Insider Trading,” Econometrica 53, 1315-1335. 23. Lakonishok, J.; Shleifer, A.; Vishny, R. W., (1992), “The Impact of Institutional Trading on Stock Prices,” Journal of Financial Economics 32, 23-43. 24. Lee, C. M. C., and Ready, M. J., (1991), “Inferring Trade form Intraday Data,” Journal of Finance 46, 733-746. 25. Lin, J., Sanger, G. C., and Booth, G. G., (1995). “Trade size and components of the bid-ask spread,” Review of Financial Studies 8, 1153−1183. 26. Lin, W. T., Tsai, S. C., and Sun, D. S., (2010), “What causes herding: Information cascade or searching cost?” Forthcoming, Emerging Markets Finance and Trade. 27. Mood, A., (1940), “The distribution theory of runs,” Annals of Mathematical Statistics 11, 367-392. 28. Nofsinger, J. R., and Sias, R.W.. (1999), “Herding and Feedback Trading by Institutional and Individual Investors,” Journal of Finance 54, 2263-2295. 29. Patterson, D., and Sharma, V.. (2006), “Do Traders Follow Each Other at the NYSE?” Working Paper, University of Michigan-Dearborn. 30. Stoll, H.R.. “Friction,” Journal of Finance 55 (2000), 1479-1514. 31. Stoll, H. R., and R. E. Whaley, (1990), “Stock market structure and volatility,” Review of Financial Studies, Vol. 3, pp.37−71. 32. Vayanos, D. and Wang, T.. “Search and Endogenous Concentration of Liquidity in Asset Markets,” Journal of Economic Theory 136 (2007), 66-104. 33. Wermers, R.. “Mutual Fund Herding and the Impact on Stock Prices,” Journal of Finance, 54 (1999), 581-622. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/28937 |