Improving accuracy of financial distress prediction by considering volatility: an interval-data-based discriminant model

DOI10.1007/S00180-019-00916-9zbMath1485.91244OpenAlexW2969622383WikidataQ127347484 ScholiaQ127347484MaRDI QIDQ782624

Haitao Zheng, Rong Guan, Hui-Wen Wang

Publication date: 28 July 2020

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00180-019-00916-9

zbMATH Keywords

predictioninterval datafinancial distressChina's stock marketquarterly financial ratio

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05) Financial networks (including contagion, systemic risk, regulation) (91G45)

Related Items (2)

Forecasting interval-valued crude oil prices using asymmetric interval models ⋮ Service data analytics and business intelligence 2017

Uses Software

• SODAS

Cites Work

• Unnamed Item
• Unnamed Item
• Theory of an interval algebra and its application to numerical analysis
• Linear disriminant analysis for interval data
• The comparison of enterprise bankruptcy forecasting method
• Symbolic Data Analysis and the SODAS Software
• Analysis of symbolic data. Exploratory methods for extracting statistical information from complex data
• Discriminant analysis of interval data: an assessment of parametric and distance-based approaches

This page was built for publication: Improving accuracy of financial distress prediction by considering volatility: an interval-data-based discriminant model