FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386600478> ?p ?o ?g. }

• W4386600478 endingPage "121502" @default.
• W4386600478 startingPage "121502" @default.
• W4386600478 abstract "An increasing number of studies have shown the effectiveness of using deep reinforcement learning to learn profitable trading strategies from financial market data. However, a single-agent model is not sufficient to handle complex financial scenarios. To address this problem, a novel approach called Multi-Agent Double Deep Q-Network (Later called MADDQN) is proposed in this study, which reasonably balances the pursuit of maximum revenue and the avoidance of risk under the multi-agent reinforcement learning framework by innovatively employing two different agents represented respectively by two time-series feature extraction networks, TimesNet, and the Multi-Scale Convolutional Neural Network. Furthermore, to achieve a more generalized model suitable for different underlying assets, a mixed dataset containing three major U.S. stock indexes is collected. And the proposed model has been pre-trained in this dataset and subsequently refined for the specified asset. The results from experiments on five different stock indices show that the proposed MADDQN has an average cumulative return of 23.08%, outperforming the other baseline methods. Besides, the multi-agent model demonstrates its advantage in balancing the risk and revenue, in comparison with the single-agent models. Additionally, The generalization experiments confirm that the proposed MADDQN method after pre-training in the proposed mixed dataset could be stably transferred to the other underlying assets with a refinement. These findings indicate that the proposed framework not only achieves good performance in complex financial market environments but also is able to generalize robustly across different scenarios in various markets." @default.
• W4386600478 created "2023-09-12" @default.
• W4386600478 creator A5010838874 @default.
• W4386600478 creator A5044295393 @default.
• W4386600478 creator A5047802926 @default.
• W4386600478 creator A5090615044 @default.
• W4386600478 date "2024-03-01" @default.
• W4386600478 modified "2023-10-14" @default.
• W4386600478 title "A Multi-agent reinforcement learning framework for optimizing financial trading strategies based on TimesNet" @default.
• W4386600478 cites W2074229969 @default.
• W4386600478 cites W2169015875 @default.
• W4386600478 cites W2296599285 @default.
• W4386600478 cites W2786348607 @default.
• W4386600478 cites W2891295326 @default.
• W4386600478 cites W2892260033 @default.
• W4386600478 cites W2917600866 @default.
• W4386600478 cites W2966021938 @default.
• W4386600478 cites W2986670013 @default.
• W4386600478 cites W2997874851 @default.
• W4386600478 cites W2998658946 @default.
• W4386600478 cites W3012333258 @default.
• W4386600478 cites W3019918961 @default.
• W4386600478 cites W3035574064 @default.
• W4386600478 cites W3080232699 @default.
• W4386600478 cites W3083252136 @default.
• W4386600478 cites W3098465353 @default.
• W4386600478 cites W3123095408 @default.
• W4386600478 cites W3126577088 @default.
• W4386600478 cites W3138503612 @default.
• W4386600478 cites W3147314058 @default.
• W4386600478 cites W3151614082 @default.
• W4386600478 cites W3174745599 @default.
• W4386600478 cites W3215072286 @default.
• W4386600478 cites W4200153030 @default.
• W4386600478 cites W4206190611 @default.
• W4386600478 cites W4210271893 @default.
• W4386600478 cites W4212896298 @default.
• W4386600478 cites W4220705239 @default.
• W4386600478 cites W4220794015 @default.
• W4386600478 cites W4225163341 @default.
• W4386600478 cites W4225724460 @default.
• W4386600478 cites W4243641017 @default.
• W4386600478 cites W4281260602 @default.
• W4386600478 cites W4281674262 @default.
• W4386600478 cites W4283262006 @default.
• W4386600478 cites W4285011703 @default.
• W4386600478 cites W4285606719 @default.
• W4386600478 cites W4292513339 @default.
• W4386600478 cites W4296151339 @default.
• W4386600478 cites W4316038443 @default.
• W4386600478 cites W4353080254 @default.
• W4386600478 cites W4361250857 @default.
• W4386600478 doi "https://doi.org/10.1016/j.eswa.2023.121502" @default.
• W4386600478 hasPublicationYear "2024" @default.
• W4386600478 type Work @default.
• W4386600478 citedByCount "0" @default.
• W4386600478 crossrefType "journal-article" @default.
• W4386600478 hasAuthorship W4386600478A5010838874 @default.
• W4386600478 hasAuthorship W4386600478A5044295393 @default.
• W4386600478 hasAuthorship W4386600478A5047802926 @default.
• W4386600478 hasAuthorship W4386600478A5090615044 @default.
• W4386600478 hasConcept C10138342 @default.
• W4386600478 hasConcept C119857082 @default.
• W4386600478 hasConcept C134306372 @default.
• W4386600478 hasConcept C151730666 @default.
• W4386600478 hasConcept C154945302 @default.
• W4386600478 hasConcept C162324750 @default.
• W4386600478 hasConcept C177148314 @default.
• W4386600478 hasConcept C19244329 @default.
• W4386600478 hasConcept C195487862 @default.
• W4386600478 hasConcept C2780299701 @default.
• W4386600478 hasConcept C2780762169 @default.
• W4386600478 hasConcept C33923547 @default.
• W4386600478 hasConcept C41008148 @default.
• W4386600478 hasConcept C50644808 @default.
• W4386600478 hasConcept C86803240 @default.
• W4386600478 hasConcept C97541855 @default.
• W4386600478 hasConceptScore W4386600478C10138342 @default.
• W4386600478 hasConceptScore W4386600478C119857082 @default.
• W4386600478 hasConceptScore W4386600478C134306372 @default.
• W4386600478 hasConceptScore W4386600478C151730666 @default.
• W4386600478 hasConceptScore W4386600478C154945302 @default.
• W4386600478 hasConceptScore W4386600478C162324750 @default.
• W4386600478 hasConceptScore W4386600478C177148314 @default.
• W4386600478 hasConceptScore W4386600478C19244329 @default.
• W4386600478 hasConceptScore W4386600478C195487862 @default.
• W4386600478 hasConceptScore W4386600478C2780299701 @default.
• W4386600478 hasConceptScore W4386600478C2780762169 @default.
• W4386600478 hasConceptScore W4386600478C33923547 @default.
• W4386600478 hasConceptScore W4386600478C41008148 @default.
• W4386600478 hasConceptScore W4386600478C50644808 @default.
• W4386600478 hasConceptScore W4386600478C86803240 @default.
• W4386600478 hasConceptScore W4386600478C97541855 @default.
• W4386600478 hasLocation W43866004781 @default.
• W4386600478 hasOpenAccess W4386600478 @default.
• W4386600478 hasPrimaryLocation W43866004781 @default.
• W4386600478 hasRelatedWork W1912507756 @default.
• W4386600478 hasRelatedWork W260766989 @default.

• next