Financial Market Modeling with Quantum Neural Networks Carlos Pedro Gonçalves August 27, 2015 Instituto Superior de Ciências Sociais e Políticas (ISCSP) - University of Lisbon, E-mail:
[email protected] Abstract Econophysics has developed as a research field that applies the for- malism of Statistical Mechanics and Quantum Mechanics to address Economics and Finance problems. The branch of Econophysics that applies of Quantum Theory to Economics and Finance is called Quan- tum Econophysics. In Finance, Quantum Econophysics’ contributions have ranged from option pricing to market dynamics modeling, behav- ioral finance and applications of Game Theory, integrating the empir- ical finding, from human decision analysis, that shows that nonlinear update rules in probabilities, leading to non-additive decision weights, can be computationally approached from quantum computation, with resulting quantum interference terms explaining the non-additive prob- abilities. The current work draws on these results to introduce new tools from Quantum Artificial Intelligence, namely Quantum Artifi- arXiv:1508.06586v1 [q-fin.CP] 26 Aug 2015 cial Neural Networks as a way to build and simulate financial market models with adaptive selection of trading rules, leading to turbulence and excess kurtosis in the returns distributions for a wide range of parameters. Keywords: Finance, Econophysics, Quantum Artificial Neural Networks, Quantum Stochastic Processes, Cognitive Science 1 1 Introduction One of the major problems of financial modeling has been to address com- plex financial returns dynamics, in particular, excess kurtosis and volatility- related turbulence which lead to statistically significant deviations from the Gaussian random walk model worked in traditional Financial Theory (Arthur et al., 1997; Voit, 2001; Ilinsky, 2001; Focardi and Fabozzi, 2004).