Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/32259


Title: Risk Preference, Forecasting Accuracy and Survival Dynamics:Simulations Based on a Multi-Asset Agent-Based Artificial Stock Market
風險偏好與預測能力對於市場生存力的重要性
Authors: 黃雅琪
Huang, Ya-Chi
Contributors: 陳樹衡
Chen, Shu-Heng
黃雅琪
Huang, Ya-Chi
Keywords: 基因演算法
代理人基人工股市
Genetic algorithms
Autonomous agents
Date: 2005
Issue Date: 2009-09-14 13:31:30 (UTC+8)
Abstract: 風險偏好與預測精確性對生存力的重要性吸引進來許多理論學者的注意。一個極端是認為風險偏好完全不重要,唯一重要是預測精確性。然而此乃基於柏拉圖最適配置之下。透過代理人基模型,我們發現相異的結果,即風險偏好在生存力上扮演重要角色。
The relevance of risk preference and forecasting accuracy to the survival of investors is an issue that has recently attracted a number of recent theoretical studies. At one extreme, it has been shown that risk preference can be entirely irrelevant, and that in the long run what distinguishes the agents who survive from those who vanish is just their forecasting accuracy.
Being in line with the market selection hypothesis, this theoretical result is, however,
established mainly on the basis of Pareto optimal allocation. By using agent-based computational
modeling, this dissertation extends the existing studies to an economy where adaptive
behaviors are autonomous and complex heterogeneous, and where the economy is notorious
for its likely persistent deviation from Pareto optimality. Specifically, a computational multiasset
artificial stock market corresponding to Blume and Easley (1992) and Sandroni (2000)
is constructed and studied. Through simulation, we present results that contradict the market
selection hypothesis. Risk preference plays a key role in survivability. And agents who
have superior forecasting accuracy may be driven out just because of their risk preference.
Nevertheless, when all the agents are with the same preference, the wealth share is positively
correlated to forecasting accuracy, and the market selection hypothesis is sustained, at least
in a weak sense.
Reference: [1] Arifovic, J. and M. Maschek (2003), “Expectations and Currency Crisis–An Experimental
Approach,” paper presented at the 9th International Conference on Computing
in Economics and Finance, University of Washington, Seattle, July 11-13.
[2] Arthur, W.B., J. Holland, B. LeBaron, R. Palmer, and P. Tayler (1997), “Asset Pricing
under Endogenous Expectations in an Artificial Stock Market,” in W.B. Arthur, S.
Durlauf and D. Lane (eds.), The Economy as an Evolving Complex System II, Addison-
Wesley, Reading, MA, pp. 15-44.
[3] Barberis, N. and R. Thaler (2002), “A Survey of Behavioral Finance,” in G. Constantinides,
M. Harris and R. Stulz (eds.), Handbook of the Economics of Finance, pp.
1051-1121.
[4] Blume, L. and D. Easley (1992), “Evolution and Market Behavior,” Journal of Economic
Theory, Vol. 58, pp. 9-40.
[5] Blume, L. and D. Easley (2001), “If You’re So Smart, Why Aren’t You Rich? Belief
Selection in Complete and Incomplete Markets,” working paper.
[6] Bullard, J. and J. Duffy (1999), “Using Genetic Algorithms to Model the Evolution of
Heterogenous Beliefs,” Computational Economics, Vol. 13, No. 1, pp. 41-60.
[7] Chen, S.-H. and C.-H. Yeh (2001), “Evolving Traders and the Business School with Genetic Programming: A New Architecture of the Agent-based Artificial Stock Market,”
Journal of Economic Dynamics and Control, Vol. 25, pp. 363-393.
[8] Chen, S.-H. and Y.-C. Huang (2004), “Risk Preference, Forecasting Accuracy and Survival
Dynamics: Simulations Based on a Multi-Asset Agent-Based Artificial Stock
Market,”Working Paper Series 2004-1, AI-ECON Research Center, National Chengchi
University.
[9] Chen, S.-H. and Y.-C. Huang (2005a), “Risk Preference and Survival Dynamics,” in T.
Terano, H. Kita, T. Kaneda and K. Arai (eds.), Agent-Based Simulation: From Modeling
Methodologies to Real-World Applications, Springer Series on Agent-Based Social
Systems, Springer, pp. 141-149.
[10] Chen, S.-H. and Y.-C. Huang (2005b), “On the Role of Risk Preference in Survivability,”
in Lipo Wang, Ke Chen, Yew S. Ong (eds.), Advances in Natural Computation,
Lecture Notes in Computer Science 3612, Springer, pp. 612-621.
[11] Constantinides, G. M., J, B. Donaldson, and R. Mehra (2002), “Junior Can’t Borrow:
A New Perspective on the Equity Premium Puzzle,” Quarterly Journal of Economics,
Vol.117, pp. 269-297.
[12] Epstein, L. G. and S. E. Zin (1991), “Substitution, Risk Aversion, and the Temporal
Behavior of Consumption and Asset Returns: An Empirical Analysis,” Journal of Political
Economy, Vol. 99, pp. 263-286.
[13] Feldman, J. (1962), “Computer Simulation of Cognitive Processes,” in H. Borko (ed.),
Computer Applications in the Behavioral Sciences, Prentice Hall.
[14] Friend, I. and M. E. Blume (1975), “The Demand for Risky Assets,” American Economic
Review, Vol. 65, pp. 900-922.
[15] Goldman, M. B. (1974), “A Negative Report on the ’Near Optimality’ of the Max-
Expected-Log Policy as Applied to Bounded Utilities for Long Lived Programs,” Journal
of Financial Economics, Vol. 1, pp. 97-103.
[16] Gordon, M. J. , G. E. Paradis and C. H. Rorke (1972), “Experimental Evidence on
Alternative Portfolio Decision Rules,” American Economic Review, Vol. 62, pp. 107-
118.
[17] Hansen, L. P. and K. Singleton (1982), “Generalized Instrumental Variables Estimation
of Nonlinear Rational Expectation Models,” Econometrica,Vol. 50, pp. 1269-1286.
[18] Holland, J. and J. Miller (1991), “Artificial Adaptive Agents in Economic Theory,”
American Economic Review, Vol. 81, No. 2, pp. 365-370.
[19] Huang, C. F. and R. H. Litzenberger (1988), Foundations for Financial Economics,
Prentice Hall.
[20] Jorion, P. and A. Giovannini (1993), “Time Series Tests of a Non-expected-Utility
Model of Asset Pricing,” European Economic Review, Vol. 37, pp. 1083-1100.
[21] Kandel, S. and R. F. Stambaugh (1991), “Asset Returns and Intertemporal Preferences,”
Journal of Monetary Economics, Vol. 27, pp. 39-71.
[22] Kelly, J. L. (1956), “A New Interpretation of Information Rate,” Bell System Technical
Journal 35, pp. 917-926.
[23] Laibson, D. (1998), “Life-Cycle Consumption and Hyperbolic Discount Functions,”
European Economic Review, Vol. 42, pp. 861-871.
[24] Lettau, M. (1997), “Explaining the Facts with Adaptive Agents: the Case of Mutual
Fund Flows,” Journal of Economic Dynamics and Control, Vol. 21, No. 7, pp. 1117-
1147.
[25] Lucas, D. (1994), “Asset Pricing with Undiversifiable Risk and Short Sales Constraints:
Deepening the Equity Premium Puzzle,” Journal of Monetary Economics, Vol. 34, pp.
325-341.
[26] Luenberger, D. G. (1993), “A Preference Foundation for Log Mean-Variance Criteria
in Portfolio Choice Problems,” Journal of Economic Dynamics and Control, Vol. 17,
pp. 887-906.
[27] Mankiw, G. N., J. J. Rotemberg, and L. H. Summers (1985), “Intertemporal Substitution
in Macroeconomics,” Quarterly Journal of Economics, Vol. 100, pp. 225-251.
[28] Merton, R. C. and P. A. Samuelson (1974), “Fallacy of the Log-Normal Approximation
to Optimal Portfolio Decision-Making over Many Periods,” Journal of Financial
Economics, Vol. 1, pp. 67-94.
[29] Obstfeld, M. (1994), “Risk Taking, Global Diversification, and Growth,” American
Economic Review, Vol. 84, pp. 1310-1329.
[30] Rabin, M. (1998), “Psychology and Economics,” Journal of Economic Literature, Vol.
36, pp. 11-46.
[31] Samuelson, P. A. (1970), “The Fundamental Approximation Theorem of Portfolio
Analysis in terms of Means, Variances, and Higher Moments,” Review of Economic
Studies, Vol. 37, pp. 537-542.
[32] Samuelson, P. A. (1971), “The ’Fallacy’ of Maximizing the Geometric Mean in Long
Sequences of Investing or Gambling,” Proceedings of the National Academy of Sciences,
Vol. 68, pp. 2493-2496.
[33] Sandroni, A. (2000), “Do Markets Favor Agents Able to Make Accurate Predictions?”
Econometrica, Vol. 68, No. 6, pp. 1303-1341.
[34] Sciubba, E. (1999), “The Evolution of Portfolio Rules and the Capital Asset Pricing
Model,” DAE Working Paper n. 9909, University of Cambridge.
[35] Tay, N. and S. Linn (2001), “Fuzzy Inductive Reasoning, Expectation Formation and
the Behavior of Security Prices,” Journal of Economic Dynamics and Control, Vol. 25,
pp. 321-361.
[36] Tesfatsion, L. (2001), “Introduction to the Special Issue on Agent-Based Computational
Economics,” Journal of Economic Dynamics and Control, Vol. 25, pp. 281-293.
[37] Tesfatsion, L. and K. L. Judd (2006), Handbook of Computational Economics Vol. 2:
Agent-Based Computational Economics, North-Holland, forthcoming.
[38] Vriend, N. J. (2000), “An Illustration of the Essential Difference between Individual
and Social Learning, and its Consequences for Computational Analyses,” Journal of
Economic Dynamics and Control, Vol. 24, pp. 1-19.
[39] Zeldes, S. P. (1989), “Consumption and Liquidity Constraints: An Empirical Investigation,”
Journal of Political Economy, Vol. 97, No. 2, pp. 305-346.
Description: 博士
國立政治大學
經濟研究所
88258502
94
Source URI: http://thesis.lib.nccu.edu.tw/record/#G0882585022
Data Type: thesis
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