By Shu-Heng Chen
Chen, Jain, and Tai assemble numerous attention-grabbing purposes of computational intelligence techniques of their edited Computational Economics: A standpoint from Computational Intelligence publication. Contributions during this quantity express how combos of neural networks, genetic algorithms, wavelets, fuzzy units, and agent-based modeling are used in fixing a number of managerial decision-making difficulties. the quantity is wealthy with purposes in monetary modeling, alternative pricing, market-making, optimization of industry suggestions, optimization for site visitors coverage, expense estimation, coverage appraisal in a legal justice procedure, capital keep watch over, and fixing association concept difficulties.
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Extra resources for Computational Economics: A Perspective from Computational Intelligence
Simulating the evolution of portfolio behavior in a multiple-asset agent-based artificial stock market. Paper presented at the 9th International Conference on Computing in Economics and Finance, University of Washington, Seattle, July. -H. (2001a). Evolving traders and the faculty of the business school: A new architecture of the artificial stock market. Journal of Economic Dynamics and Control, 25, 363-393. -H. (2001b). Toward an integration of social learning and individual learning in agent-based computational stock markets: The approach based on population genetic programming.
Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. Financial Modeling and Forecasting 29 memory (GM) and Laguarre memory (LM). With Focused topology only the past of the input is remembered. It is noted that using a TLRN with Focused TDNN memory has a similar effect to using multiple samples for the inputs to a basic MLP. The primary difference between the two methods is that, focused TDNN memory only allows for one memory depth to be used for all of the inputs, whereas the lag input settings allow me to specify various memory depths.
A ratio of final and initial wealth overcomes the dependence on the final prices. , on initial wealth). Absolute or relative wealth on its own as an evaluation criterion largely ignore risk aversion, and favor riskier strategy with higher return. For modeling risk-averse traders in computational settings, a composite index, combining wealth and risk, was proposed. For example, Beltratti, Margarita and Terna (1996) mix the change in wealth and the sum of the absolute values of the exposure to risky assets.
Computational Economics: A Perspective from Computational Intelligence by Shu-Heng Chen