FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313294444> ?p ?o ?g. }

• W4313294444 endingPage "294" @default.
• W4313294444 startingPage "273" @default.
• W4313294444 abstract "As the number of exchange-traded commodity contracts and their volatility increase, risk management through financial hedging gains importance for commodity-purchasing firms. Existing data-driven optimization approaches for hedging decisions include linear regression-based techniques. As such, they assume linear price–feature relationships and, thus, do not automatically detect nonlinear feature effects. We propose an alternative, nonlinear data-driven approach to commodity procurement based on deep learning. The prescriptive algorithm uses artificial neural networks to allow for universal approximation and requires no a priori knowledge regarding underlying price processes. We reformulate the periodic review procurement problem as a multilabel time series classification problem as the optimal bang-bang type procurement policy allows us to treat the hedging decision for each demand period as an individual subproblem that is independent of the other periods. Thereby, we are differentiating between optimal and suboptimal hedging decisions in each period and introduce a novel opportunity cost–sensitive loss function. We train maximum likelihood classifiers based on different deep learning architectures and test their performance in numerical experiments and case studies for natural gas, crude oil, nickel, and copper procurement. We show comparable performance to the state of the art for linear price–feature relationships and considerable advantages in the nonlinear case. Funding: Financial support received through the DFG as part of the AdONE GRK2201 [Grant 277991500] is gratefully acknowledged." @default.
• W4313294444 created "2023-01-06" @default.
• W4313294444 creator A5001601724 @default.
• W4313294444 creator A5004504286 @default.
• W4313294444 creator A5028447744 @default.
• W4313294444 creator A5030675775 @default.
• W4313294444 creator A5042175719 @default.
• W4313294444 date "2023-07-01" @default.
• W4313294444 modified "2023-10-16" @default.
• W4313294444 title "Deep Learning for Commodity Procurement: Nonlinear Data-Driven Optimization of Hedging Decisions" @default.
• W4313294444 cites W1569990960 @default.
• W4313294444 cites W2060909347 @default.
• W4313294444 cites W2064675550 @default.
• W4313294444 cites W2101997935 @default.
• W4313294444 cites W2110485445 @default.
• W4313294444 cites W2112760722 @default.
• W4313294444 cites W2141089220 @default.
• W4313294444 cites W2141186877 @default.
• W4313294444 cites W2145224101 @default.
• W4313294444 cites W2169247634 @default.
• W4313294444 cites W2174312558 @default.
• W4313294444 cites W2770188460 @default.
• W4313294444 cites W2783805549 @default.
• W4313294444 cites W2809441681 @default.
• W4313294444 cites W2892035503 @default.
• W4313294444 cites W2910187721 @default.
• W4313294444 cites W2937302337 @default.
• W4313294444 cites W2962752580 @default.
• W4313294444 cites W2969714402 @default.
• W4313294444 cites W2971724044 @default.
• W4313294444 cites W2975867066 @default.
• W4313294444 cites W2997604048 @default.
• W4313294444 cites W3080591126 @default.
• W4313294444 cites W3121346496 @default.
• W4313294444 cites W3122167207 @default.
• W4313294444 cites W3136027957 @default.
• W4313294444 cites W3158988942 @default.
• W4313294444 cites W3217014542 @default.
• W4313294444 cites W4206932883 @default.
• W4313294444 cites W4224232484 @default.
• W4313294444 cites W4230410911 @default.
• W4313294444 cites W4233479037 @default.
• W4313294444 cites W4247451115 @default.
• W4313294444 cites W4249694896 @default.
• W4313294444 cites W4251616545 @default.
• W4313294444 cites W435377112 @default.
• W4313294444 doi "https://doi.org/10.1287/ijoo.2022.0086" @default.
• W4313294444 hasPublicationYear "2023" @default.
• W4313294444 type Work @default.
• W4313294444 citedByCount "0" @default.
• W4313294444 crossrefType "journal-article" @default.
• W4313294444 hasAuthorship W4313294444A5001601724 @default.
• W4313294444 hasAuthorship W4313294444A5004504286 @default.
• W4313294444 hasAuthorship W4313294444A5028447744 @default.
• W4313294444 hasAuthorship W4313294444A5030675775 @default.
• W4313294444 hasAuthorship W4313294444A5042175719 @default.
• W4313294444 hasConcept C119857082 @default.
• W4313294444 hasConcept C121332964 @default.
• W4313294444 hasConcept C126255220 @default.
• W4313294444 hasConcept C149782125 @default.
• W4313294444 hasConcept C154945302 @default.
• W4313294444 hasConcept C158622935 @default.
• W4313294444 hasConcept C162324750 @default.
• W4313294444 hasConcept C187736073 @default.
• W4313294444 hasConcept C18903297 @default.
• W4313294444 hasConcept C201650216 @default.
• W4313294444 hasConcept C21547014 @default.
• W4313294444 hasConcept C2778813691 @default.
• W4313294444 hasConcept C33923547 @default.
• W4313294444 hasConcept C41008148 @default.
• W4313294444 hasConcept C50644808 @default.
• W4313294444 hasConcept C62520636 @default.
• W4313294444 hasConcept C70771513 @default.
• W4313294444 hasConcept C86803240 @default.
• W4313294444 hasConcept C91602232 @default.
• W4313294444 hasConceptScore W4313294444C119857082 @default.
• W4313294444 hasConceptScore W4313294444C121332964 @default.
• W4313294444 hasConceptScore W4313294444C126255220 @default.
• W4313294444 hasConceptScore W4313294444C149782125 @default.
• W4313294444 hasConceptScore W4313294444C154945302 @default.
• W4313294444 hasConceptScore W4313294444C158622935 @default.
• W4313294444 hasConceptScore W4313294444C162324750 @default.
• W4313294444 hasConceptScore W4313294444C187736073 @default.
• W4313294444 hasConceptScore W4313294444C18903297 @default.
• W4313294444 hasConceptScore W4313294444C201650216 @default.
• W4313294444 hasConceptScore W4313294444C21547014 @default.
• W4313294444 hasConceptScore W4313294444C2778813691 @default.
• W4313294444 hasConceptScore W4313294444C33923547 @default.
• W4313294444 hasConceptScore W4313294444C41008148 @default.
• W4313294444 hasConceptScore W4313294444C50644808 @default.
• W4313294444 hasConceptScore W4313294444C62520636 @default.
• W4313294444 hasConceptScore W4313294444C70771513 @default.
• W4313294444 hasConceptScore W4313294444C86803240 @default.
• W4313294444 hasConceptScore W4313294444C91602232 @default.
• W4313294444 hasIssue "3" @default.
• W4313294444 hasLocation W43132944441 @default.
• W4313294444 hasOpenAccess W4313294444 @default.
• W4313294444 hasPrimaryLocation W43132944441 @default.

• next