Stock prediction based on financial correlation
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Daily stock prediction using neuro-genetic hybrids
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Recursive Bayesian recurrent neural networks for time-series modeling
IEEE Transactions on Neural Networks
A system for efficient portfolio management
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Neuro-genetic system for stock index prediction
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Evolutionary neural networks for practical applications
Pattern Recognition
Hybridizing data stream mining and technical indicators in automated trading systems
MDAI'11 Proceedings of the 8th international conference on Modeling decisions for artificial intelligence
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Boosting GARCH and neural networks for the prediction of heteroskedastic time series
Mathematical and Computer Modelling: An International Journal
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We simulate daily trading of straddles on financial indexes. The straddles are traded based on predictions of daily volatility differences in the indexes. The main predictive models studied are recurrent neural nets (RNN). Such applications have often been studied in isolation. However, due to the special character of daily financial time-series, it is difficult to make full use of RNN representational power. Recurrent networks either tend to overestimate noisy data, or behave like finite-memory sources with shallow memory; they hardly beat classical fixed-order Markov models. To overcome data nonstationarity, we use a special technique that combines sophisticated models fitted on a larger data set, with a fixed set of simple-minded symbolic predictors using only recent inputs. Finally, we compare our predictors with the GARCH family of econometric models designed to capture time-dependent volatility structure in financial returns. GARCH models have been used to trade volatility. Experimental results show that while GARCH models cannot generate any significantly positive profit, by careful use of recurrent networks or Markov models, the market makers can generate a statistically significant excess profit, but then there is no reason to prefer RNN over much more simple and straightforward Markov models. We argue that any report containing RNN results on financial tasks should be accompanied by results achieved by simple finite-memory sources combined with simple techniques to fight nonstationarity in the data