FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2891295326> ?p ?o ?g. }

• W2891295326 endingPage "138" @default.
• W2891295326 startingPage "125" @default.
• W2891295326 abstract "Abstract We study trading systems using reinforcement learning with three newly proposed methods to maximize total profits and reflect real financial market situations while overcoming the limitations of financial data. First, we propose a trading system that can predict the number of shares to trade. Specifically, we design an automated system that predicts the number of shares by adding a deep neural network (DNN) regressor to a deep Q-network, thereby combining reinforcement learning and a DNN. Second, we study various action strategies that use Q-values to analyze which action strategies are beneficial for profits in a confused market. Finally, we propose transfer learning approaches to prevent overfitting from insufficient financial data. We use four different stock indices—the S&P500, KOSPI, HSI, and EuroStoxx50—to experimentally verify our proposed methods and then conduct extensive research. The proposed automated trading system, which enables us to predict the number of shares with the DNN regressor, increases total profits by four times in S&P500, five times in KOSPI, 12 times in HSI, and six times in EuroStoxx50 compared with the fixed-number trading system. When the market situation is confused, delaying the decision to buy or sell increases total profits by 18% in S&P500, 24% in KOSPI, and 49% in EuroStoxx50. Further, transfer learning increases total profits by twofold in S&P500, 3 times in KOSPI, twofold in HSI, and 2.5 times in EuroStoxx50. The trading system with all three proposed methods increases total profits by 13 times in S&P500, 24 times in KOSPI, 30 times in HSI, and 18 times in EuroStoxx50, outperforming the market and the reinforcement learning model." @default.
• W2891295326 created "2018-09-27" @default.
• W2891295326 creator A5017681892 @default.
• W2891295326 creator A5064474000 @default.
• W2891295326 date "2019-03-01" @default.
• W2891295326 modified "2023-10-12" @default.
• W2891295326 title "Improving financial trading decisions using deep Q-learning: Predicting the number of shares, action strategies, and transfer learning" @default.
• W2891295326 cites W1535062413 @default.
• W2891295326 cites W1971470772 @default.
• W2891295326 cites W1976606095 @default.
• W2891295326 cites W1979193912 @default.
• W2891295326 cites W1989275788 @default.
• W2891295326 cites W1996342763 @default.
• W2891295326 cites W2005233533 @default.
• W2891295326 cites W2010161140 @default.
• W2891295326 cites W2016977390 @default.
• W2891295326 cites W2021693371 @default.
• W2891295326 cites W2042105482 @default.
• W2891295326 cites W2052848417 @default.
• W2891295326 cites W2073194514 @default.
• W2891295326 cites W2077791698 @default.
• W2891295326 cites W2084876223 @default.
• W2891295326 cites W2101544483 @default.
• W2891295326 cites W2103467996 @default.
• W2891295326 cites W2107726111 @default.
• W2891295326 cites W2111204393 @default.
• W2891295326 cites W2114653151 @default.
• W2891295326 cites W2117895630 @default.
• W2891295326 cites W2119259871 @default.
• W2891295326 cites W2128561109 @default.
• W2891295326 cites W2131675768 @default.
• W2891295326 cites W2145339207 @default.
• W2891295326 cites W2153146814 @default.
• W2891295326 cites W2165698076 @default.
• W2891295326 cites W2169015875 @default.
• W2891295326 cites W2195085701 @default.
• W2891295326 cites W2344786740 @default.
• W2891295326 cites W2586702902 @default.
• W2891295326 cites W2625101268 @default.
• W2891295326 cites W2793864397 @default.
• W2891295326 cites W2940897671 @default.
• W2891295326 cites W2949585412 @default.
• W2891295326 cites W3123095408 @default.
• W2891295326 cites W3124458746 @default.
• W2891295326 cites W3125266917 @default.
• W2891295326 cites W3125639666 @default.
• W2891295326 cites W32403112 @default.
• W2891295326 cites W4214717370 @default.
• W2891295326 doi "https://doi.org/10.1016/j.eswa.2018.09.036" @default.
• W2891295326 hasPublicationYear "2019" @default.
• W2891295326 type Work @default.
• W2891295326 sameAs 2891295326 @default.
• W2891295326 citedByCount "115" @default.
• W2891295326 countsByYear W28912953262019 @default.
• W2891295326 countsByYear W28912953262020 @default.
• W2891295326 countsByYear W28912953262021 @default.
• W2891295326 countsByYear W28912953262022 @default.
• W2891295326 countsByYear W28912953262023 @default.
• W2891295326 crossrefType "journal-article" @default.
• W2891295326 hasAuthorship W2891295326A5017681892 @default.
• W2891295326 hasAuthorship W2891295326A5064474000 @default.
• W2891295326 hasConcept C119857082 @default.
• W2891295326 hasConcept C121332964 @default.
• W2891295326 hasConcept C150899416 @default.
• W2891295326 hasConcept C154945302 @default.
• W2891295326 hasConcept C173608175 @default.
• W2891295326 hasConcept C188116033 @default.
• W2891295326 hasConcept C2776175482 @default.
• W2891295326 hasConcept C2780791683 @default.
• W2891295326 hasConcept C41008148 @default.
• W2891295326 hasConcept C62520636 @default.
• W2891295326 hasConcept C97541855 @default.
• W2891295326 hasConceptScore W2891295326C119857082 @default.
• W2891295326 hasConceptScore W2891295326C121332964 @default.
• W2891295326 hasConceptScore W2891295326C150899416 @default.
• W2891295326 hasConceptScore W2891295326C154945302 @default.
• W2891295326 hasConceptScore W2891295326C173608175 @default.
• W2891295326 hasConceptScore W2891295326C188116033 @default.
• W2891295326 hasConceptScore W2891295326C2776175482 @default.
• W2891295326 hasConceptScore W2891295326C2780791683 @default.
• W2891295326 hasConceptScore W2891295326C41008148 @default.
• W2891295326 hasConceptScore W2891295326C62520636 @default.
• W2891295326 hasConceptScore W2891295326C97541855 @default.
• W2891295326 hasFunder F4320322010 @default.
• W2891295326 hasLocation W28912953261 @default.
• W2891295326 hasOpenAccess W2891295326 @default.
• W2891295326 hasPrimaryLocation W28912953261 @default.
• W2891295326 hasRelatedWork W2960456850 @default.
• W2891295326 hasRelatedWork W3021430260 @default.
• W2891295326 hasRelatedWork W3133293092 @default.
• W2891295326 hasRelatedWork W3192503984 @default.
• W2891295326 hasRelatedWork W4213299466 @default.
• W2891295326 hasRelatedWork W4225294552 @default.
• W2891295326 hasRelatedWork W4281382123 @default.
• W2891295326 hasRelatedWork W4281645081 @default.
• W2891295326 hasRelatedWork W4294306704 @default.
• W2891295326 hasRelatedWork W4308262314 @default.
• W2891295326 hasVolume "117" @default.

• next