On the risk prediction and analysis of soft information in finance reports

DOI10.1016/J.EJOR.2016.06.069zbMath1395.91516OpenAlexW2492461111MaRDI QIDQ1752794

Ming-Feng Tsai, Chuan-Ju Wang

Publication date: 24 May 2018

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2016.06.069

zbMATH Keywords

text miningfinancerisk predictionsentiment analysis

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Statistical methods; risk measures (91G70) Economic time series analysis (91B84) Statistical aspects of information-theoretic topics (62B10) Corporate finance (dividends, real options, etc.) (91G50)

Related Items (6)

Credit default prediction from user-generated text in peer-to-peer lending using deep learning ⋮ Learning risk culture of banks using news analytics ⋮ The value of text for small business default prediction: a deep learning approach ⋮ The interconnectedness of the economic content in the speeches of the US presidents ⋮ News-based forecasts of macroeconomic indicators: a semantic path model for interpretable predictions ⋮ Social collateral, soft information and online peer-to-peer lending: a theoretical model

Uses Software

• FinTS

Cites Work

• Bad news and Dow Jones make the Spanish stocks go round
• Adaptive neural network model for time-series forecasting
• On the predictive ability of narrative disclosures in annual reports
• Measuring rank correlation coefficients between financial time series: a GARCH-copula based sequence alignment algorithm
• Neural network approach to forecasting of quasiperiodic financial time series
• 10.1162/1532443041827916
• Analysis of Financial Time Series
• A NEW MEASURE OF RANK CORRELATION

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