Abstract
This paper introduces agent-based model for simple prediction of financial markets, where each agent predicts development of selected subset of assets pairs in time by separately examining the similarities between ask and bid assets histories. Agent’s fitness is proportional to the wealth accumulated by exercising long and short trading positions, with regards to predicted development of assets. Although the model is iterative and operates on equidistant price data, agents are encouraged to optimize their trading frequency to maximize simulated wealth (fitness). The model evolves by enforcing competitive behavior through optimization processes.
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Kočišová, J., Horváth, D., Kasanický, T., Buša, J. (2012). Prediction of Financial Markets Using Agent-Based Modeling with Optimization Driven by Statistical Evaluation of Historical Data. In: Adam, G., Buša, J., Hnatič, M. (eds) Mathematical Modeling and Computational Science. MMCP 2011. Lecture Notes in Computer Science, vol 7125. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28212-6_38
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DOI: https://doi.org/10.1007/978-3-642-28212-6_38
Publisher Name: Springer, Berlin, Heidelberg
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