Evolino Recurrent Neural Network Ensemble for Speculation in Exchange Market in Time of Anomalies

Autor: Nijolė Maknickienė, Algirdas Maknickas
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Applied Artificial Intelligence, Vol 34, Iss 13, Pp 957-980 (2020)
Druh dokumentu: article
ISSN: 0883-9514
1087-6545
08839514
DOI: 10.1080/08839514.2020.1790249
Popis: Sharp falls or explosive growths in exchange markets, whether expected or not, generates new challenges for investors who want to protect their investments or achieve an optimum benefit during and after the turmoil. An anomaly of the exchange market, instigated by the Swiss National Bank, occurred when the Swiss Franc decoupled from the euro unexpectedly. The United Kingdom (UK) vote to withdraw from the European Union (Brexit), in contrast, was feared but expected. A comparison of the consequences of the anomalies gives us an unprecedented opportunity to investigate prediction capabilities of the EVOLINO Recurrent Neural Network Ensemble (ERNN) model following an anomaly. By introducing this new information to the ERNN model and analyzing its response, we increase investor resources during large exchange rate fluctuations; this will provide them with additional information that will help them construct different portfolios. Reaction to the anomaly was visible only after the anomaly occurred, this is when the model began to acquire data influenced by the extreme change. Comparing different strategies which are related or unrelated to the anomaly and orthogonal or not orthogonal for conservative, moderate, or aggressive trading shows that in order to profit from the anomaly, speculation depends on prediction-accuracy and on the sets of exchange-rate associated with the anomaly.
Databáze: Directory of Open Access Journals
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