Does trading remove or bring frictions?

Lin, William and Sun, David and Tsai, Shih-Chuan (2010): Does trading remove or bring frictions?

Abstract

We explore in this paper how trading noise, when considered as a market friction, reacts to trading activity. Transactions cost is a good explanation for intraday trading behavior in the market according to our data. Particularly, we show that in general trading brings friction to market. However, trading friction at market open is the lowest during the day, as trading causes less friction then relatively. This is due to the behavioral difference among investors. When market opens, individual trading removes, while institutional trading brings, market friction. Situation in the rest of the day is just the opposite, where individual, instead of institutional, trading brings friction. The uneven behavior of trading noise across investors and time of day makes it a specific, rather than general, transactions cost, as opposed to Stoll (2000). Intraday trading activity suppresses both order width and depth, as proxies for trading intensity, therefore creates more noise or friction in the market. Width and depth contribute to trading noise in a polarized way, so that individual trading hurts friction in small cap stocks at open, but benefits it at close. Institutional trading brings extremely strong friction to large cap stocks, but less so at market close. So trading noise as a specific, rather than general, transactions cost is prominent only to certain investors, at certain time and for certain stocks in the market. Our findings lend itself to the justification of the new financial transactions tax proposed by the European Union.

• Item Type: MPRA Paper
• 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

References

1.Admati, A. R., (1991), “The Informational Role of Prices,” Journal of Monetary Economics 28,347-361.

2.Ahn, H. and Cheung, Y.. (1999), “The intraday patterns of the spread and depth in a market without market makers: The Stock Exchange of Hong Kong,” Pacific-Basin Finance Journal 7, 539-556.

3.Amihud, Y. and Mendelson, H.. (1987), “Trading mechanism and stock returns: An empirical investigation,” Journal of Finance 42, 533-553.

4.Avery, C. and Zemsky, P.. (1998), “Multidimensional uncertainty and herd behavior in financial markets,” American Economic Review 88, no. 4, 724-748.

5.Banerjee, A.. (1992), “A Simple Model of Herd Behavior,” Quarterly Journal of Economics 107, 797-817.

6.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.

7.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.

8.Chakraborty, A. and Yilmaz, B., (2004), "Manipulation in market order models," Journal of Financial Markets 7(2), 187-206.

9.Cont, R. and Bouchaud, J. P.. (2000), “Herd Behavior and Aggregate Fluctuations in Financial Markets,” Macroeconomic Dynamics 4, 170-196.

10.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.

11.Diamond, D. W., and Verrecchia, R. E., (1981), “Information Aggregation in a Noisy Rational Expectations Economy,” Journal of Financial Economics 9, 221-235.

12.Dorn, D.; G. Huberman; P. Sengmueller. 2008. “Correlated Trading and Returns.” Journal of Finance 58, no. 2: 885-920.

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.Jarrow, R.. (1992), “Market manipulations, bubbles, corners and short squeezes,” Journal of Financial and Quantitative Analysis 27, 311-336.

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., (2011), “Search Costs and Investor Trading Activity: Evidences from limit order book,” Forthcoming, Emerging Markets Finance and Trade.

27.McKenzie, M.D. (2007). Technical Trading Rules in Emerging Markets and the 1997 Asian Currency Crises, Emerging Markets Finance and Trade, 43, 46-73.

28.Mood, A., (1940), “The distribution theory of runs,” Annals of Mathematical Statistics 11, 367-392.

29.Nofsinger, J. R., and Sias, R.W.. (1999), “Herding and Feedback Trading by Institutional and Individual Investors,” Journal of Finance 54, 2263-2295.

30.Patterson, D., and Sharma, V.. (2006), “Do Traders Follow Each Other at the NYSE?” Working Paper, University of Michigan-Dearborn.

31.Radalj, M., and McAleer, M.. (1993). “Herding, information cascades and volatility spillovers in futures markets,” Working Paper, University of Western Australia, Perth, Australia.

32.Stoll, H.R.. “Friction,” Journal of Finance 55 (2000), 1479-1514.

33.Stoll, H. R., and R. E. Whaley, (1990), “Stock market structure and volatility,” Review of Financial Studies, Vol. 3, pp.37−71.

34.Wermers, R.. “Mutual Fund Herding and the Impact on Stock Prices,” Journal of Finance, 54 (1999), 581-622.