Financial Distress Early Warning Model for Listed Real Estate Companies of China Based on Multiple Discriminant Analysis

Yang Li (), Hong Zhang () and Shuo Huang ()
Additional contact information
Yang Li: Tsinghua University
Hong Zhang: Tsinghua University
Shuo Huang: Tsinghua University

Chapter Chapter 117 in Proceedings of the 17th International Symposium on Advancement of Construction Management and Real Estate, 2014, pp 1153-1161 from Springer

Abstract: Abstract This paper uses annual financial statement data of 99 listed real estate companies from A-share market, adopts multiple discriminant analysis to modify a Z-Score baseline model, and establishes a financial distress early warning model applicable to listed real estate companies in China. The findings indicate that the average accuracy of the financial distress early warning model reaches higher than 90 %, which is greatly improved from the previous Z-score baseline model. In the context of deepening adjustments in Chinese real estate industry, this model not only provides a reference indicator for business managers and market investors, but also helps policy makers timely evaluate the potential financial risks in real estate industry.

Keywords: Financial distress; Early warning model; Multiple discriminant analysis; Listed real estate company (search for similar items in EconPapers)
Date: 2014
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-35548-6_117

Ordering information: This item can be ordered from
http://www.springer.com/9783642355486

DOI: 10.1007/978-3-642-35548-6_117

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().