Using Support Vector Machines to Evaluate Financial Fate of Dotcoms

The success of dotcoms has been short lived. Within a short span of a few years the dotcoms experienced a meteoric rise and suffered a dramatic fall. This research is a support vector machine based investigation of dotcoms from a financial perspective. Data from the financial statements of survived and failed dotcoms are collected and 24 financial ratios are computed. The financial ratios are analyzed using support vector machines to find out whether they can predict the financial fate of companies. The results show that support vector machines can predict the financial fate of dotcoms with at most 76% accuracy. Several numerical experiments are conducted to check the impact of size of training sample, size of testing sample, ratio of training and testing sample size, and balance of sample on the classification accuracy of support vector machines.

[1]  Avi Shama,et al.  Dot-coms' coma , 2001, J. Syst. Softw..

[2]  Constantin Zopounidis,et al.  Business failure prediction using rough sets , 1999, Eur. J. Oper. Res..

[3]  H. Frydman,et al.  Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress , 1985 .

[4]  Robert A. Eisenbeis,et al.  PITFALLS IN THE APPLICATION OF DISCRIMINANT ANALYSIS IN BUSINESS, FINANCE, AND ECONOMICS , 1977 .

[5]  James V. Hansen,et al.  Inducing rules for expert system development: an example using default and bankruptcy data , 1988 .

[6]  Francis Eng Hock Tay,et al.  Financial Forecasting Using Support Vector Machines , 2001, Neural Computing & Applications.

[7]  Jaap Spronk,et al.  The Application of the Multi-Factor Model in the Analysis of Corporate Failure , 1998 .

[8]  Edward I. Altman,et al.  FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY , 1968 .

[9]  James A. Ohlson.  FINANCIAL RATIOS AND THE PROBABILISTIC PREDICTION OF BANKRUPTCY , 1980 .

[10]  Melody Y. Kiang,et al.  Managerial Applications of Neural Networks: The Case of Bank Failure Predictions , 1992 .

[11]  Erkki K. Laitinen,et al.  Survival analysis as a tool for company failure prediction , 1991 .

[12]  Constantin Zopounidis,et al.  A survey of business failures with an emphasis on prediction methods and industrial applications , 1996 .

[13]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[14]  F. Tay,et al.  Application of support vector machines in financial time series forecasting , 2001 .

[15]  Marimuthu Palaniswami,et al.  Selecting bankruptcy predictors using a support vector machine approach , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[16]  Bo K. Wong,et al.  A bibliography of neural network business applications research: 1994-1998 , 2000, Comput. Oper. Res..

[17]  Yash P. Gupta,et al.  LINEAR GOAL PROGRAMMING AS AN ALTERNATIVE TO MULTIVARIATE DISCRIMINANT ANALYSIS: A NOTE , 1990 .

[18]  M. Zmijewski.  METHODOLOGICAL ISSUES RELATED TO THE ESTIMATION OF FINANCIAL DISTRESS PREDICTION MODELS , 1984 .