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Comparing Neural Networks and ARMA Models in Artificial Stock Market

Jiri Krtek & Miloslav Vošvrda

Bulletin of the Czech Econometric Society2011article
ABDC C
Weight
0.26

What the paper says

Neural networks - feed-forward neural networks and Elman's simple recurrent neural networks - are compared with vector ARMA models - VAR and VARMA - in this paper. They are compared in anartifical stock market. One risk free and one risky asset are traded in the market. There are only trend followers in this model, which use the mentioned models for forecasting change of a price of the risky asset and the dividend. traded in the market

Cite this paper

@article{jiri2011,
  title        = {{Comparing Neural Networks and ARMA Models in Artificial Stock Market}},
  author       = {Jiri Krtek & Miloslav Vošvrda},
  journal      = {Bulletin of the Czech Econometric Society},
  year         = {2011},
}

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Comparing Neural Networks and ARMA Models in Artificial Stock Market

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Evidence weight

0.26

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.00 × 0.4 = 0.00
M · momentum0.20 × 0.15 = 0.03
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.