Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-27T08:17:14.643Z Has data issue: false hasContentIssue false
  • English
  • Français

DYNAMIC PREDICTOR SELECTION AND ORDER SPLITTING IN A LIMIT ORDER MARKET

Published online by Cambridge University Press:  07 August 2017

Ryuichi Yamamoto
Ryuichi Yamamoto*
Affiliation:
Waseda University
*
Address correspondence to: Ryuichi Yamamoto, School of Political Science and Economics, Waseda University, Tokyo 169-8050, Japan; e-mail: ryuichi@waseda.jp.
Get access Rights & Permissions [Opens in a new window]

Abstract

Recent empirical research has documented the clustered volatility and fat tails of return distribution in stock markets, yet returns are uncorrelated over time. Certain agent-based theoretical models attempt to explain the empirical features in terms of investors' order-splitting or dynamic switching strategies, both of which are frequently used by actual stock investors. However, little theoretical research has discriminated among the behavioral assumptions within a model and compared the impacts of the assumptions on the empirical features. Nor has the research simultaneously replicated the return features and empirical features on market microstructure, such as patterns of order choice. This study constructs an artificial limit order market in which investors split orders into small pieces or use fundamental and trend-following predictors interchangeably over time. We demonstrate that, on one hand, the market that features strategies with order splitting and dynamic predictor selection can independently replicate clustered volatility and fat tails with near-zero return autocorrelations. However, we also show that patterns of order choice do not match those found in certain previous empirical studies in both types of economies. Thus, we conclude that, in reality, the two strategies can work to generate the empirical return features but that investors may also use other strategies in actual stock markets. We also demonstrate that the impact of both strategies on the volatility persistence tends to be greater as the number of traders increases in the market; this finding implies that the order-splitting strategy and dynamic predictor selection are more crucial for the empirical phenomena pertaining to larger capital stocks.

Keywords

Dynamic Predictor SelectionOrder SplittingVolatility ClusteringFat TailUncorrelated ReturnLimit Order MarketAgent-Based Modeling
Type
Articles
Information
Macroeconomic Dynamics , Volume 23 , Issue 5 , July 2019 , pp. 1757 - 1792
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

I thank William A. Barnett (the Editor) and two anonymous referees who provided useful suggestions and feedback on an earlier draft. I gratefully acknowledge the financial support from Waseda University.

References

REFERENCES

Alfarano, S., Lux, T., and Wagner, F. (2008) Time variation of higher moments in financial markets with heterogeneous agents: An analytical approach. Journal of Economic Dynamics and Control 32, 101–136.Google Scholar
Alfi, V., Cristelli, M., Pietronero, L., and Zaccaria, A. (2009) Minimal agent based model for financial markets. European Physical Journal B 67, 385–397.Google Scholar
Alfonsi, A., Fruth, A., and Schied, A. (2010) Optimal execution strategies in limit order books with general shape functions. Quantitative Finance 10, 143–157.Google Scholar
Almgren, R. and Chriss, N. (2000) Optimal execution of portfolio transactions. Journal of Risk 3, 5–39.Google Scholar
Almgren, R. and Lorenz, J. (2007) Adaptive arrival price. In Bruce, B. R. (ed.), Algorithmic Trading III: Precision, Control, Execution. New York: Institutional Investor Journals.Google Scholar
Anufriev, M., Arifovic, J., Ledyard, J., and Panchenko, V. (2013) Efficiency of continuous double auctions under individual evolutionary learning with full or limited information. Journal of Evolutionary Economics, 23, 539–573.Google Scholar
Anufriev, M. and Panchenko, V. (2009) Asset prices, traders' behavior and market design. Journal of Economic Dynamics and Control 33, 1073–1090.Google Scholar
Bershova, N. and Rakhlin, D. (2013) The non-linear market impact of large trades: Evidence from buy-side order flow. Quantitative Finance 13, 1759–1778.Google Scholar
Bertsimas, D. and Lo, A. (1998) Optimal control of execution costs. Journal of Financial Markets 1, 1–50.Google Scholar
Bessembinder, H., Panayides, M. A., and Venkataraman, K. (2009) Hidden liquidity: An analysis of order exposure strategies in electronic stock markets. Journal of Financial Economics 94, 361–383.Google Scholar
Biais, B., Hillion, P., and Spatt, C. (1995) An empirical analysis of the limit order book and the order flow in the Paris Bourse. Journal of Finance 50, 1655–1689.Google Scholar
Boswijk, H. P., Hommes, C. H., and Manzan, S. (2007) Behavioral heterogeneity in stock prices. Journal of Economic Dynamics and Control 31, 1938–1970.Google Scholar
Bouchaud, J.-P., Gefen, Y., Potters, M., and Wyart, M. (2004) Fluctuations and response in financial markets: The subtle nature of ‘random’ price changes. Quantitative Finance 4, 176–190.Google Scholar
Brock, W. A. and Hommes, C. H. (1997) A rational route to randomness. Econometrica 65, 1059–1095.Google Scholar
Brock, W. and Hommes, C. (1998) Heterogeneous beliefs and routes to chaos in a simple asset pricing model. Journal of Economic Dynamics and Control 22, 1235–1274.Google Scholar
Chen, S.-H., Chang, C.-L., and Du, Y.-R. (2012) Agent-based economic models and econometrics. Knowledge Engineering Review 27 (2), 187–219.Google Scholar
Chiarella, C., He, X.-Z., and Wei, L. (2015) Learning, information processing and order submission in limit order markets. Journal of Economic Dynamics and Control 61, 245–268.Google Scholar
Chiarella, C., Iori, G., and Perelló, J. (2009) The impact of heterogeneous trading rules on the limit order book and order flows. Journal of Economic Dynamics and Control 33, 525–537.Google Scholar
Ding, Z., Granger, C., and Engle, R. (1993) A long memory property of stock market returns and a new model. Journal of Empirical Finance 1, 83–106.Google Scholar
Duong, H. N., Kalev, P., and Krishnamurti, C. (2009) Order aggressiveness of institutional and individual investors. Pacific-Basin Finance Journal 17, 533–546.Google Scholar
Farmer, J. D. and Lillo, F. (2004) On the origin of power laws in financial markets. Quantitative Finance 4, C7–C10.Google Scholar
Foucault, T., Kadan, O., and Kandel, E. (2005) Limit order book as a market for liquidity. Review of Financial Studies 18, 1171–1217Google Scholar
Frankel, J. A. and Froot, K. A. (1990) Chartists, fundamentalists and the demand for dollars. In Courakis, A. S. and Taylor, M. P. (eds.), Private Behaviour and Government Policy in Interdependent Economies, pp. 73–126. New York: Oxford University Press.Google Scholar
Goettler, R., Parlour, C., and Rajan, U. (2005) Equilibrium in a dynamic limit order market. Journal of Finance 60, 2149–2192.Google Scholar
Gomes, C. and Waelbroeck, H. (2015) Is market impact a measure of the information value of trades? Market response to liquidity vs. informed metaorders. Quantitative Finance 15, 773–793.Google Scholar
Gould, M., Porter, M., Williams, S., Fenn, D., and Howison, S. D. (2013) Limit order books. Quantitative Finance 13 (11), 1709–1742.Google Scholar
Griffiths, M., Smith, B., Turnbull, A., and White, R. (2000) The costs and the determinants of order aggressiveness. Journal of Financial Economics 56, 65–88.Google Scholar
Hall, A. and Hautsch, N. (2006). Order aggressiveness and order book dynamics. Empirical Economics 30, 973–1005.Google Scholar
Handa, P., Schwartz, R., and Tiwari, A. (2003). Quote setting and price formation in an order driven market. Journal of Financial Markets 6, 461–489.Google Scholar
Hommes, C. H. (2006) Heterogeneous agent models in economics and finance. In Tesfatsion, L. and Judd, K. L. (eds.), Handbook of Computational Economics: Vol. 2. Agent-Based Computational Economics, pp. 1109–1186. Amsterdam: North-Holland.Google Scholar
Kirman, A. (1993) Ants, rationality, and recruitment. Quarterly Journal of Economics 108, 137–156.Google Scholar
Kovaleva, P. and Iori, G. (2015) The impact of reduced pre-trade transparency regimes on market quality. Journal of Economic Dynamics and Control 57, 145–162.Google Scholar
LeBaron, B. (2001) Evolution and time horizons in an agent based stock market. Macroeconomic Dynamics 5, 225–254.Google Scholar
LeBaron, B. (2006) Agent-based computational finance. In Tesfatsion, L. and Judd, K. L. (eds.), Handbook of Computational Economics: Vol. 2. Agent-Based Computational Economics, vol. 2, pp. 1187–1233. Amsterdam: North-Holland.Google Scholar
LeBaron, B., Arthur, B., and Palmer, R. (1999) Time series properties of an artificial stock market. Journal of Economic Dynamics and Control 23, 1487–1516.Google Scholar
Lillo, F. and Farmer, D. (2004) The long memory of the efficient market. Studies in Nonlinear Dynamics & Econometrics 8, Article 1.Google Scholar
Lillo, F., Mike, S., and Farmer, D. (2005) Theory for long memory in supply and demand. Physical Review E 71, 066122.Google Scholar
Lorenz, J. and Almgren, R. (2011) Mean-variance optimal adaptive execution. Applied Mathematical Finance 18, 395–422.Google Scholar
Lui, Y.-H. and Mole, D. (1998) The use of fundamental and technical analysis by foreign exchange dealers: Hong Kong evidence. Journal of International Money and Finance 17, 535–545.Google Scholar
Lux, T. (1995) Herd behavior, bubbles and crashes. Economic Journal 105, 881–896.Google Scholar
Lux, T. (2001) The limiting extremal behaviour of speculative returns: An analysis of intra-daily data from the Frankfurt stock exchange. Applied Financial Economics 11, 299–315.Google Scholar
Lux, T. and Marchesi, M. (2000) Volatility clustering in financial markets: A microsimulation of interacting agents. International Journal of Theoretical and Applied Finance 3, 675–702.Google Scholar
Mandelbrot, B. (1972) Statistical methodology for non-periodic cycles: From the covariance to R/S analysis. Annals of Economic and Social Measurements 1, 259–290.Google Scholar
Menkhoff, L. (2010) The use of technical analysis by fund managers: International evidence. Journal of Banking and Finance 34, 2573–2586.Google Scholar
Menkhoff, L. and Taylor, M. (2007) The obstinate passion of foreign exchange professionals: Technical analysis. Journal of Economic Literature 45, 936–972.Google Scholar
Mike, S. and Farmer, D. (2008) An empirical behavioral model of liquidity and volatility. Journal of Economic Dynamics and Control 32, 200–234.Google Scholar
Obizhaeva, A. (2008) Information vs. Liquidity: Evidence from Portfolio Transition Trades. Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=965743.Google Scholar
Obizhaeva, A. and Wang, J. (2005). Optimal Trading Strategy and Supply/Demand Dynamics. NBER working paper 11444.Google Scholar
Parlour, C. (1998) Price dynamics in limit order markets. Review of Financial Studies 11, 789–816.Google Scholar
Perold, A. F. (1988) The implementation shortfall: Paper versus reality. Journal of Portfolio Management 14, 4–9.Google Scholar
Ranaldo, A. (2004) Order aggressiveness in limit order book markets. Journal of Financial Markets 7, 53–74.Google Scholar
Roşu, I. (2009) A dynamic model of the limit order book. Review of Financial Studies 22, 4601–4641.Google Scholar
Taranto, D. E., Bormetti, G., and Lillo, F. (2014). The adaptive nature of liquidity taking in limit order books. Journal of Statistical Mechanics: Theory and Experiment. doi: 10.1088/1742-5468/2014/06/P06002.Google Scholar
Tedeschi, G., Iori, G., and Gallegati, M. (2012) Herding effects in order driven markets: The rise and fall of gurus. Journal of Economic Behavior & Organization 81, 82–96.Google Scholar
Tóth, B., Palit, I., Lillo, F., and Farmer, D. (2015) Why is order flow so persistent?. Journal of Economic Dynamics and Control 51, 218–239.Google Scholar
Vaglica, G., Lillo, F., Moro, E., and Mantegna, R. (2008) Scaling laws of strategic behaviour and size heterogeneity in agent dynamics. Physical Review E 77, 036110.Google Scholar
Yamamoto, R. (2011) Order aggressiveness, pre-trade transparency, and long memory in an order-driven market. Journal of Economic Dynamics and Control 35, 1938–1963.Google Scholar
Yamamoto, R. and LeBaron, B. (2010) Order-splitting and long-memory in an order-driven market. European Physical Journal B 73, 51–57.Google Scholar
Zarinelli, E., Treccani, M., Farmer, D., and Lillo, F. (2015). Beyond the square root: Evidence for logarithmic dependence of market impact on size and participation rate. Market Microstructure and Liquidity 1, 1550004.Google Scholar