Comparing Neural Networks and ARMA Models in Artificial Stock Market
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F11%3A10102906" target="_blank" >RIV/00216208:11320/11:10102906 - isvavai.cz</a>
Alternative codes found
RIV/67985556:_____/11:00361537
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Comparing Neural Networks and ARMA Models in Artificial Stock Market
Original language description
We create a new way of comparing models for forecasting stock prices. Our idea was to create a simple game in which the individual models would compete against each other. We were inspired by the heterogeneous agent models and we created an artificial market. Models act in our artificial market as a forecasting strategies of each agent who trades on the market. Each agent uses his own model for predicting future prices of risky asset and its dividends. Delayed prices of risky asset and dividends provided the basis for predictions. The way how agents trade affects the price of risky asset, which in turn influences their expectations and therefore their decisions whether to buy or sell. Moreover, each agent can recalculate his strategy, if he is not satisfied with its performance. So the forecasting strategies and the artificial market evolve side by side. The models we confront are neural networks VARMA models. The winning model is the one which earns the most money.
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GD402%2F09%2FH045" target="_blank" >GD402/09/H045: Nonlinear Dynamics in Monetary and Financial Economics. Theory and Empirical Models.</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2011
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Bulletin of the Czech Econometric Society
ISSN
1212-074X
e-ISSN
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Volume of the periodical
18
Issue of the periodical within the volume
28
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
13
Pages from-to
53-65
UT code for WoS article
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EID of the result in the Scopus database
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