Matches in SemOpenAlex for { <https://semopenalex.org/work/W2785770483> ?p ?o ?g. }
Showing items 1 to 87 of
87
with 100 items per page.
- W2785770483 abstract "The success of convolutional neural networks in the field of computer vision has attracted the attention of many researchers from other fields. One of the research areas in which neural networks is actively used is financial forecasting. In this paper, we propose a novel method for predicting stock price movements using CNN. To avoid the high volatility of the market and to maximize the profit, ETFs are used as primary financial assets. We extract commonly used trend indicators and momentum indicators from financial time series data and use these as our features. Adopting a sliding window approach, we generate our images by taking snapshots that are bounded by the window over a daily period. We then perform daily predictions, namely, regression for predicting the ETF prices and classification for predicting the movement of the prices on the next day, which can be modified to estimate weekly or monthly trends. To increase the number of images, we use numerous ETFs. Finally, we evaluate our method by performing paper trading and calculating the final capital. We also compare our method's performance to commonly used classical trading strategies. Our results indicate that we can predict the next day's prices with 72% accuracy and end up with 5:1 of our initial capital, taking realistic values of transaction costs into account." @default.
- W2785770483 created "2018-02-23" @default.
- W2785770483 creator A5048947308 @default.
- W2785770483 creator A5050114655 @default.
- W2785770483 creator A5050981174 @default.
- W2785770483 date "2017-11-01" @default.
- W2785770483 modified "2023-10-11" @default.
- W2785770483 title "A deep learning based stock trading model with 2-D CNN trend detection" @default.
- W2785770483 cites W1966676388 @default.
- W2785770483 cites W1967408516 @default.
- W2785770483 cites W2004463884 @default.
- W2785770483 cites W2066289130 @default.
- W2785770483 cites W2066995518 @default.
- W2785770483 cites W2284153934 @default.
- W2785770483 cites W2963749793 @default.
- W2785770483 cites W3105448877 @default.
- W2785770483 cites W3125950889 @default.
- W2785770483 doi "https://doi.org/10.1109/ssci.2017.8285188" @default.
- W2785770483 hasPublicationYear "2017" @default.
- W2785770483 type Work @default.
- W2785770483 sameAs 2785770483 @default.
- W2785770483 citedByCount "63" @default.
- W2785770483 countsByYear W27857704832018 @default.
- W2785770483 countsByYear W27857704832019 @default.
- W2785770483 countsByYear W27857704832020 @default.
- W2785770483 countsByYear W27857704832021 @default.
- W2785770483 countsByYear W27857704832022 @default.
- W2785770483 countsByYear W27857704832023 @default.
- W2785770483 crossrefType "proceedings-article" @default.
- W2785770483 hasAuthorship W2785770483A5048947308 @default.
- W2785770483 hasAuthorship W2785770483A5050114655 @default.
- W2785770483 hasAuthorship W2785770483A5050981174 @default.
- W2785770483 hasConcept C10138342 @default.
- W2785770483 hasConcept C102392041 @default.
- W2785770483 hasConcept C108583219 @default.
- W2785770483 hasConcept C111919701 @default.
- W2785770483 hasConcept C119857082 @default.
- W2785770483 hasConcept C127413603 @default.
- W2785770483 hasConcept C131562839 @default.
- W2785770483 hasConcept C149782125 @default.
- W2785770483 hasConcept C154945302 @default.
- W2785770483 hasConcept C162324750 @default.
- W2785770483 hasConcept C204036174 @default.
- W2785770483 hasConcept C2778751112 @default.
- W2785770483 hasConcept C41008148 @default.
- W2785770483 hasConcept C50644808 @default.
- W2785770483 hasConcept C75949130 @default.
- W2785770483 hasConcept C77088390 @default.
- W2785770483 hasConcept C78519656 @default.
- W2785770483 hasConcept C81363708 @default.
- W2785770483 hasConcept C91602232 @default.
- W2785770483 hasConceptScore W2785770483C10138342 @default.
- W2785770483 hasConceptScore W2785770483C102392041 @default.
- W2785770483 hasConceptScore W2785770483C108583219 @default.
- W2785770483 hasConceptScore W2785770483C111919701 @default.
- W2785770483 hasConceptScore W2785770483C119857082 @default.
- W2785770483 hasConceptScore W2785770483C127413603 @default.
- W2785770483 hasConceptScore W2785770483C131562839 @default.
- W2785770483 hasConceptScore W2785770483C149782125 @default.
- W2785770483 hasConceptScore W2785770483C154945302 @default.
- W2785770483 hasConceptScore W2785770483C162324750 @default.
- W2785770483 hasConceptScore W2785770483C204036174 @default.
- W2785770483 hasConceptScore W2785770483C2778751112 @default.
- W2785770483 hasConceptScore W2785770483C41008148 @default.
- W2785770483 hasConceptScore W2785770483C50644808 @default.
- W2785770483 hasConceptScore W2785770483C75949130 @default.
- W2785770483 hasConceptScore W2785770483C77088390 @default.
- W2785770483 hasConceptScore W2785770483C78519656 @default.
- W2785770483 hasConceptScore W2785770483C81363708 @default.
- W2785770483 hasConceptScore W2785770483C91602232 @default.
- W2785770483 hasLocation W27857704831 @default.
- W2785770483 hasOpenAccess W2785770483 @default.
- W2785770483 hasPrimaryLocation W27857704831 @default.
- W2785770483 hasRelatedWork W2337926734 @default.
- W2785770483 hasRelatedWork W2731899572 @default.
- W2785770483 hasRelatedWork W2963958939 @default.
- W2785770483 hasRelatedWork W3133861977 @default.
- W2785770483 hasRelatedWork W3173182854 @default.
- W2785770483 hasRelatedWork W4311257506 @default.
- W2785770483 hasRelatedWork W4312417841 @default.
- W2785770483 hasRelatedWork W4320802194 @default.
- W2785770483 hasRelatedWork W4321369474 @default.
- W2785770483 hasRelatedWork W4366224123 @default.
- W2785770483 isParatext "false" @default.
- W2785770483 isRetracted "false" @default.
- W2785770483 magId "2785770483" @default.
- W2785770483 workType "article" @default.