Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/1047
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dc.contributor.authorHristu-Varsakelis, Dimitrios-
dc.contributor.authorChalvatzis, Chariton-
dc.date.accessioned2021-11-15T19:10:57Z-
dc.date.available2021-11-15T19:10:57Z-
dc.date.issued2020-11-
dc.identifier10.1016/j.asoc.2020.106567en_US
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2020.106567en_US
dc.identifier.urihttps://ruomo.lib.uom.gr/handle/7000/1047-
dc.description.abstractAutomated asset trading typically involves a price prediction model – of as high an accuracy as possible – together with a trading strategy, sometimes as simple as buying or selling when the price is predicted to rise or fall, respectively. Despite the fact that the model’s effectiveness in generating profits may depend on the particular trading strategy it is used with, these two components are often designed separately, in part because of the difficulty involved in jointly optimizing them. Motivated by this interplay between model performance and trading strategy, this work presents a novel automated trading architecture in which the prediction model is tuned to enhance profitability instead of accuracy, while the trading strategy attempts to be more sophisticated in its use of the model’s price predictions. In particular, instead of acting simply on whether the price is predicted to rise or fall we show that there is value in taking advantage of the model-specific distribution of predicted returns, and the fact that a prediction’s position within that distribution carries useful information about the expected profitability of a trade. Our proposed approach was tested with tree-based models as well as one deep long short-term memory (LSTM) neural networks, all of which were kept structurally simple and generated predictions based on price observations over a modest number of trading days. Tested over the period 2010–2019 on the S&P 500, Dow Jones Industrial Average (DJIA), NASDAQ and Russell 2000 stock indices, and our best overall model achieved cumulative returns of 350%, 403%, 497% and 333%, respectively, outperforming the benchmark buy-and-hold strategy as well as other recent efforts.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.sourceApplied Soft Computingen_US
dc.subjectFRASCATI::Natural sciences::Computer and information sciencesen_US
dc.subjectFRASCATI::Social sciences::Economics and Business::Financeen_US
dc.subject.otherFinanceen_US
dc.subject.otherLSTMen_US
dc.subject.otherAutomatic tradingen_US
dc.subject.otherDeep Learningen_US
dc.titleHigh-performance stock index trading via neural networks and treesen_US
dc.typeArticleen_US
dc.contributor.departmentΤμήμα Εφαρμοσμένης Πληροφορικήςen_US
local.identifier.volume96en_US
local.identifier.firstpage106567en_US
Appears in Collections:Department of Applied Informatics

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