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Please use this identifier to cite or link to this item: https://digital.lib.ueh.edu.vn/handle/UEH/64425
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dc.contributor.authorYang Zhaovi
dc.contributor.otherZhonglu Chenvi
dc.date.accessioned2022-09-20T07:58:38Z-
dc.date.available2022-09-20T07:58:38Z-
dc.date.issued2022-06-
dc.identifier.issn2615-9104-
dc.identifier.urihttp://jabes.ueh.edu.vn/Home/SearchArticle?article_Id=100fff74-f496-3c37-a36c-92da3183cddc-
dc.identifier.urihttps://digital.lib.ueh.edu.vn/handle/UEH/64425-
dc.description.abstractPurpose This study explores whether a new machine learning method can more accurately predict the movement of stock prices. Design/methodology/approach This study presents a novel hybrid deep learning model, Residual-CNN-Seq2Seq (RCSNet), to predict the trend of stock price movement. RCSNet integrates the autoregressive integrated moving average (ARIMA) model, convolutional neural network (CNN) and the sequence-to-sequence (Seq2Seq) long–short-term memory (LSTM) model. Findings The hybrid model is able to forecast both linear and non-linear time-series component of stock dataset. CNN and Seq2Seq LSTMs can be effectively combined for dynamic modeling of short- and long-term-dependent patterns in non-linear time series forecast. Experimental results show that the proposed model outperforms baseline models on S&P 500 index stock dataset from January 2000 to August 2016. Originality/value This study develops the RCSNet hybrid model to tackle the challenge by combining both linear and non-linear models. New evidence has been obtained in predicting the movement of stock market prices.vi
dc.formatPortable Document Format (PDF)-
dc.publisherUniversity of Economics Ho Chi Minh Cityvi
dc.relation.ispartofJournal of Asian Business and Economic Studiesvi
dc.relation.ispartofseriesJABES, Vol.29(2)-
dc.subjectStock price movementvi
dc.subjectRCSNetvi
dc.subjectARIMAvi
dc.subjectCNNvi
dc.subjectLSTMvi
dc.subjectS&P 500 indexvi
dc.titleForecasting stock price movement: new evidence from a novel hybrid deep learning modelvi
dc.typeJournal Article-
dc.identifier.doihttp://10.1108/JABES-05-2021-0061-
dc.format.firstpage91-
dc.format.lastpage104-
item.openairetypeJournal Article-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextOnly abstracts-
Appears in Collections:JABES in English
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