A hybrid recurrent neural networks model based on synthesis features to forecast the Taiwan stock market

International Journal of Innovative Computing, Information and Control

Volumn 8, Issue 8, 2012, Pages 5559-5571

  Wei, Liang Ying ^a   Cheng, Ching Hsue ^b  

^a YUANPEI UNIVERSITY   (Taiwan)

^b NATIONAL YUNLIN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

Author keywords

Recurrent neural network; Synthesis feature; Technical indicators

Indexed keywords

CORRELATION MATRIX; ELMAN NEURAL NETWORK; FINANCIAL STATEMENTS; FORECASTING MODELS; FORECASTING PROBLEMS; HIGH DIMENSIONAL DATA; HYBRID MODEL; LINEAR TIME SERIES MODEL; NEURAL NETWORKS MODEL; PERFORMANCE INDICATORS; RECURRENT NETWORKS; ROOT MEAN SQUARED ERRORS; STEPWISE REGRESSION; STOCK EXCHANGE; STOCK FORECASTING; STOCK INDICES; STOCK PRICE; SYNTHESIS FEATURES; TAIWAN STOCK MARKETS; TECHNICAL ANALYSIS; TECHNICAL INDICATOR;

BENCHMARKING; DECISION TREES; FINANCE; RECURRENT NEURAL NETWORKS;

FORECASTING;

EID: 84866037441     PISSN: 13494198     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

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