Trading Volume and Price Distortion: An Agent-Based Model with Heterogenous Knowledge of Fundamentals
- 167 Downloads
This paper investigates whether trading volume and price distortion can be explained by the investor’s bounded rationality. Assuming that agents are bounded by their information access and processing, what are the consequences on market dynamics? We expose the result of simulations in an ABM that considers the liquidity as an endogenous characteristic of the market and allows to design investors as bounded rational. In a call auction market, where two risky assets are exchanged, traders are defined as a mix between fundamentalist and trend-follower outlook. Each one differs as to behaviour, order-placement strategy, mood, knowledge, risk-aversion and investment horizon. We place agents in a context of evolving fundamental values and order placement strategy; they perceive the fundamental but they also have some heterogeneous belief perseverance; and they adapt their orders to the market depth so as to maximise their execution probability and their profit. By adding bounded rationality in their information processing, we show that (1) usual features as trend-follower outlook and heterogeneous investment horizon are important features to generate excess volatility of asset prices and market inefficiency; (2) the learning fundamental value stabilises the market price and the trading volume; (3) the order-placement strategy increases trading volume, but reduces market efficiency and stability; (4) the agent’s mood prevents illiquid market and weakly increases the market volatility as classical noise trader agents; (5) the impatience to sell of traders is always present in the market: the market sell orders are always more numerous than the market buy orders.
KeywordsAgent-based modelling Market microstructure Fundamental value Trading volume Efficient market
JEL ClassificationC63 D44 G12 G14
- Arthur, W. B., Holland, J., LeBaron, B., Parlmer, R., & Tayler, P. (1997). Asset pricing under endogenous expectations in an artificial stock market. In W. B. Arthur, D. Lane, & S. N. Durlauf (Eds.), The economy as an evolving complex system. II (pp. 15–44). Reading: Addison-Wesley.Google Scholar
- Barberis, N., & Thaler, R. (2003). A survey of behavioral finance. In: G. M. Constantinides, M. Harris, & R. M. Stulz (Eds.), Handbook of the economics of finance (Chap. 18, 1st ed., Vol. 1, Part 2, pp. 1053–1128). Elsevier.Google Scholar
- Fischoff, B., Slovic, P., & Lichtenstein, S. (1977). Knowing with certainty: The appropriateness of extreme confidence. Journal of Experimental Psychology: Human Perception and Performance, 3, 552–564.Google Scholar
- Hommes, C. (2006). Heterogeneous agent models in economics and finance. In: L. Tesfatsion, & K. L. Judd (Eds.) Handbook of computational economics (Chap. 23, 1st ed., Vol. 2, pp. 1109–1186). Elsevier.Google Scholar
- Hommes, C., Sonnemans, J., Tuinstra, J., & van de Velden, H. (2005b). Coordination of expectations in asset pricing experiments. Review of Financial Studies, 18(3), 955–980 http://EconPapers.repec.org/RePEc:oup:rfinst:v:18:y:2005:i:3:p:955-980.
- LeBaron, B. (2006). Agent-based computational finance. In: Handbook of computational economics (Vol. 2(B), pp. 1187–1227).Google Scholar
- Muranaga, J., & Shimizu, T. (1999). Market microstructure and market liquidity, Vol. 11. Bank for International Settlements. http://EconPapers.repec.org/RePEc:bis:biscgc:11-03.
- Murphy, J. J. (1999). Technical analysis of the financial markets. New York: Institute of Finance.Google Scholar
- O’Hara, M., & Easley, D. (1995). Market microstructure. In: R. Jarrow, V. Maksimovic, & W. Ziemba (Eds.), Handbooks in operations research and management science: Finance. North Holland.Google Scholar
- Orléan, A. (2011). L’Empire de la valeur. Refonder l’économie. La couleur des idées (p. 352). Paris: Seuil.Google Scholar
- Pouget, C. (2000). L’efficience informationnelle revisitée sous l’angle de l’efficience allocative. GDR: In Colloque GDR.Google Scholar
- Salle, I., & Yildizoglu, M. (2012). Efficient sampling and metamodeling for computational economic models. http://cahiersdugretha.u-bordeaux4.fr/2012/2012-18, cahier du GREThA.
- Simon, H. A. (1982). Models of bounded rationality: Behavioral economics and business organization. Cambridge: MIT Press.Google Scholar
- Vriend, N. J. (2006). Ace models of endogenous interactions. In: L. Tesfatsion, & K. L. Judd (Eds.), Handbook of computational economics (Chap. 21, 1st ed., Vol. 2, pp. 1047–1079). Elsevier.Google Scholar
- Westerhoff, F. H. (2004). Multi-asset market dynamics. Macroeconomic Dynamics, 8, 596–616.Google Scholar