A holistic auto-configurable ensemble machine learning strategy for financial trading

Carta S.;Corriga A.;Ferreira A.;Recupero D. R.;Saia R.
2019-01-01

Abstract

Financial markets forecasting represents a challenging task for a series of reasons, such as the irregularity, high fluctuation, noise of the involved data, and the peculiar high unpredictability of the financial domain. Moreover, literature does not offer a proper methodology to systematically identify intrinsic and hyper-parameters, input features, and base algorithms of a forecasting strategy in order to automatically adapt itself to the chosen market. To tackle these issues, this paper introduces a fully automated optimized ensemble approach, where an optimized feature selection process has been combined with an automatic ensemble machine learning strategy, created by a set of classifiers with intrinsic and hyper-parameters learned in each marked under consideration. A series of experiments performed on different real-world futures markets demonstrate the effectiveness of such an approach with regard to both to the Buy and Hold baseline strategy and to several canonical state-of-the-art solutions.
2019
2019
Inglese
7
4
1
25
25
https/www.mdpi.com/2079-3197/7/4/67
Comitato scientifico
internazionale
scientifica
ensemble strategy; financial market forecasting; independent component analysis; machine learning
no
Carta, S.; Corriga, A.; Ferreira, A.; Recupero, D. R.; Saia, R.
1.1 Articolo in rivista
info:eu-repo/semantics/article
1 Contributo su Rivista::1.1 Articolo in rivista
262
5
open
File in questo prodotto:
File Dimensione Formato  
computation-07-00067-v2.pdf

accesso aperto

Tipologia: versione post-print
Dimensione 716.53 kB
Formato Adobe PDF
716.53 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Questionario e social

Condividi su:
Impostazioni cookie