New Algorithm for Evolutionary Selection of the Dynamic Signature Global Features

Author(s):  
Marcin Zalasiński ◽  
Krystian Łapa ◽  
Krzysztof Cpałka
2006 ◽  
Author(s):  
Giovanni Nardinocchi ◽  
Stanislaw Jankowski ◽  
Marco Balsi

2011 ◽  
Vol 6 (5) ◽  
pp. 431-440 ◽  
Author(s):  
Eloy Gonzales ◽  
Shingo Mabu ◽  
Karla Taboada ◽  
Kaoru Shimada ◽  
Kotaro Hirasawa

2012 ◽  
Vol 4 (4) ◽  
pp. 35-64 ◽  
Author(s):  
Mikhail Anufriev ◽  
Cars Hommes

In recent “learning to forecast” experiments (Hommes et al. 2005), three different patterns in aggregate price behavior have been observed: slow monotonic convergence, permanent oscillations, and dampened fluctuations. We show that a simple model of individual learning can explain these different aggregate outcomes within the same experimental setting. The key idea is evolutionary selection among heterogeneous expectation rules, driven by their relative performance. The out-of-sample predictive power of our switching model is higher compared to the rational or other homogeneous expectations benchmarks. Our results show that heterogeneity in expectations is crucial to describe individual forecasting and aggregate price behavior. (JEL C53, C91, D83, D84, G12)


2011 ◽  
Vol 23 (3) ◽  
pp. 663-688 ◽  
Author(s):  
Mikhail Anufriev ◽  
Cars H. Hommes ◽  
Raoul H. S. Philipse

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