Computational Economics: A Perspective from Computational by Shu-Heng Chen
By Shu-Heng Chen
Chen, Jain, and Tai collect a number of attention-grabbing purposes of computational intelligence ways of their edited Computational Economics: A point of view from Computational Intelligence e-book. Contributions during this quantity convey 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 amount is wealthy with functions in monetary modeling, choice pricing, market-making, optimization of industry innovations, optimization for site visitors coverage, expense estimation, coverage appraisal in a felony justice process, capital keep an eye on, and fixing association thought difficulties.
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Additional resources for Computational Economics: A Perspective from Computational Intelligence
In terms of risk exposure, the primary strategy is less risky than B/H benchmark. Regarding the predictive ability of the primary strategy, accuracy, correlation and IEP are marginally better than the overall average (all three statistics decrease in values in the testing set comparing to the training and cross validation sets). The bootstrap method used to test the significance of the profitability and predictive ability produced p-values, indicating statistically different performance from a random walk with drift.
Modeling the stylized facts in finance through simple nonlinear adaptive systems. In Proceedings of the National Academy of Science,(pp. 7221-7228). , & White, H. (1989). Multilayer feed-forward networks are universal approximators. Neural Networks, 2, 359-366. Jordan, M. I. (1986). Attractor dynamics and parallelism in a connectionist sequential machine. Paper presented at the Proceedings of the 8th annual Conference of the Cognitive Science Society, Hillsdale, NJ. Jordan, M. I. ). (1989). Serial order: A parallel, distributed processing approach.
Help and manual. , & Willams, J. T. (1977). Estimating betas from nonsynchronous data. Journal of Financial Economics, 5(3), 309-327. Sweeney, R. J. (1988). Some filter rule tests: Methods and results. Journal of Financial and Quantitative Analysis, 23, 285-301. ENDNOTES 1 2 3 CI is a development paradigm of intelligent systems with data-driven methodologies to model intelligence observed from natural behavior. It includes such areas, as artificial neural networks, evolutionary computation, fuzzy logic, as well as machine learning, probabilistic belief networks, and so forth.