using neural networks and genetic algorithms to predict stock market returns

using neural networks and genetic algorithms to predict stock market returns

ID:14226734

大小:1.08 MB

页数:166页

时间:2018-07-27

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1、USINGNEURALNETWORKSANDGENETICALGORITHMSTOPREDICTSTOCKMARKETRETURNSATHESISSUBMITTEDTOTHEUNIVERSITYOFMANCHESTERFORTHEDEGREEOFMASTEROFSCIENCEINADVANCEDCOMPUTERSCIENCEINTHEFACULTYOFSCIENCEANDENGINEERINGByEfstathiosKalyvasDepartmentOfComputerScienceOctober2001ContentsAbstract6Declaration7Copyright

2、andOwnership8Acknowledgments91Introduction111.1AimsandObjectives........................................................................................111.2Rationale.........................................................................................................121.3StockMarketPredic

3、tion..................................................................................121.4OrganizationoftheStudy................................................................................132StockMarketsandPrediction152.1TheStockMarket.....................................................

4、.......................................152.1.1InvestmentTheories.....................................................................................152.1.2DataRelatedtotheMarket..........................................................................162.2PredictionoftheMarket...............

5、...................................................................172.2.1Definingthepredictiontask.........................................................................172.2.2IstheMarketpredictable?...........................................................................182.2.3Predictio

6、nMethods.....................................................................................192.2.3.1TechnicalAnalysis...............................................................................202.2.3.2FundamentalAnalysis...................................................................

7、......2022.2.3.3TraditionalTimeSeriesPrediction......................................................212.2.3.4MachineLearningMethods.................................................................232.2.3.4.1NearestNeighborTechniques.........

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