Analyzing financial distress of listed companies using neural network

Author

Wang, Qiang ; Yang, Qian ; Zhang, Miaomiao

Author_Institution

Sch. of Econ. & Manage., Beijing Univ. of Aeronaut. & Astronaut., Beijing, China

Volume

2

fYear

2012

fDate

20-21 Oct. 2012

Firstpage

64

Lastpage

67

Abstract

Corporation financial distress has been an important issue for study in the financial fields. This paper uses traditional BP neural network model and proposes PNN model to predicate financial distress. The sample consists of 276 companies listed on the Shanghai Stock Exchange and Shenzhen Stock Exchange over the period 2001-2010. Factor analysis is used to lower correlation and reduce dimensionality. The results demonstrate that the PNN model has higher explanatory power in predicating financial distress than BPN model.

Keywords

backpropagation; financial data processing; neural nets; probability; BP neural network model; Shanghai Stock Exchange; Shenzhen Stock Exchange; corporation financial distress; factor analysis; financial distress predication; financial field; listed companies; probabilistic neural network; Accuracy; Companies; Mathematical model; Neural networks; Predictive models; Probabilistic logic; Training; BP neural network; Financial predication; Probabilistic Neural Network;

fLanguage

English

Publisher

ieee

Conference_Titel

System Science, Engineering Design and Manufacturing Informatization (ICSEM), 2012 3rd International Conference on

Conference_Location

Chengdu

Print_ISBN

978-1-4673-0914-1

Type

conf

DOI

10.1109/ICSSEM.2012.6340809

Filename

6340809