散货航运交易策略:人工神经网络的应用

Journal of Korean navigation and port research Pub Date : 2016-10-31 DOI:10.5394/KINPR.2016.40.5.337

Heesung Yun, Sangseop Lim, Kihwan Lee

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引用次数: 0

摘要

: Thecoredecisionsofbulkshippingbusinessescanbesummarizedasthetimingandthechoiceofperiodforwhichcarrying capacityistraded。文章frequentdecisionstotradefreighteitherwithrepeatedspottransactionsorwithaone-offlong-term dealcriticallyimpactbusinessperformance。Eventhoughavarietyoffreighttradingstrategiescanbeemployedtofacilitatethedecisions、charteringpractitionershavenotbeenactiveinutilizingthesestrategies andacademicresearchhasrarelyproposedapplicablesolutions。现有的研究没有很好地反映出可交易商品的特定属性，并且据报道，它们在现实世界中的应用存在局限性。Thisresearchfocusedontheestablishmentofapplicablefreighttradingstrategiesbytaking intoaccounttwopropertiesoffreight: timeperishabilityandterm-dependantpricing。Inadditiontotraditionaltradingstrategies、人工neuralnetworkswereappliedforthefirsttimetothetestoffreighttradingstrategies。Theperformancesofthetradingstrategieswere measuredandcomparedtoproducearemarkableoutperformanceoftheANN。本研究可望对租船实务作出重大贡献，提高租船决策的品质，并最终实现对货运风险的有效管理。除了方法上的拓展外，本研究结果也将为解决货运市场效率这一有争议的问题提供一条途径。

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Trading Strategies in Bulk Shipping: the Application of Artificial Neural Networks

:Thecoredecisionsofbulkshippingbusinessescanbesummarizedasthetimingandthechoiceofperiodforwhichcarrying capacityistraded.Inparticular,frequentdecisionstotradefreighteitherwithrepeatedspottransactionsorwithaone-offlong-term dealcriticallyimpactbusinessperformance.Eventhoughavarietyoffreighttradingstrategiescanbeemployedtofacilitatethedecisions, charteringpractitionershavenotbeenactiveinutilizingthesestrategies,andacademicresearchhasrarelyproposedapplicablesolutions. The specificpropertiesoffreightasatradable commodityare notproperlyreflected inexisting studies, and limitationshave been reportedintheirapplicationtotherealworld.Thisresearchfocusedontheestablishmentofapplicablefreighttradingstrategiesbytaking intoaccounttwopropertiesoffreight:timeperishabilityandterm-dependantpricing.Inadditiontotraditionaltradingstrategies,artificial neuralnetworkswereappliedforthefirsttimetothetestoffreighttradingstrategies.Theperformancesofthetradingstrategieswere measuredandcomparedtoproducearemarkableoutperformanceoftheANN.Thisresearchisexpectedtomakeasignificantcontribution tocharteringpracticesbyenhancingthequalityofcharteringdecisionsandeventuallyenablingtheeffectivemanagementoffreightrate risk.Inadditiontomethodologicalexpansion,theresultwillproposeawaytoapproachthecontroversialissueoffreightmarketefficiency.

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Journal of Korean navigation and port research

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