A novel text mining approach to financial time series forecasting

Neurocomputing

Volumn 83, Issue , 2012, Pages 136-145

  Wang, Baohua ^a,b   Huang, Hejiao ^a   Wang, Xiaolong ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b SHENZHEN UNIVERSITY   (China)

Author keywords

ARIMA; Financial time series forecasting; Market sentiment; Support vector regression

Indexed keywords

ARIMA; FEATURE VECTORS; FINANCIAL TIME SERIES FORECASTING; FINANCIAL TIME SERIES PREDICTIONS; FORECASTING MODELS; MARKET SENTIMENT; MODEL-BASED OPC; NONSTATIONARY; SECURITY COMPANY; SUPPORT VECTOR REGRESSION; SUPPORT VECTOR REGRESSIONS; TEXT MINING; TEXTUAL DATA; TEXTUAL INFORMATION;

COMMERCE; FORECASTING;

FINANCIAL DATA PROCESSING;

ACCURACY; ARTICLE; CHAOTIC DYNAMICS; CONTROLLED STUDY; FINANCIAL MANAGEMENT; FORECASTING; INTERMETHOD COMPARISON; LEARNING ALGORITHM; LINEAR SYSTEM; MATHEMATICAL MODEL; PREDICTION; PRIORITY JOURNAL; PROCESS DEVELOPMENT; PROCESS MODEL; SUPPORT VECTOR MACHINE; TIME SERIES ANALYSIS;

EID: 84862784752     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.12.013     Document Type: Article

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