Analysing capital structure of spanish chemical companies using self-organizing maps

Purpose This paper analyses the capital structure of the Spanish chemical industry during the period between 1999 and 2013, with a twofold objective. First, to determine whether the assumptions of Pecking Order Theory are fulfilled throughout the study's timeframe. Second, by using data covering the years before the crisis and the worst years thereof, this study shows how the crisis has affected the capital structure of the companies included in our sample. Design/methodology/approach A particular kind of unsupervised neural network, Self-organizing Maps, are applied. This methodology allows to cluster firms avoiding the need to establish relationships between the different variables involved in the problem beforehand. Findings Companies are clustered into groups with different degrees of accomplishment of the Pecking Order Theory. The hypothesis about risk is the one that experience a greater variation in the period before and after the crisis. Moreover, companies' capital structure has been lightly disr...

[1]  S. Myers Financing of corporations , 2003 .

[2]  David W. Wright,et al.  Using Artificial Neural Networks to Pick Stocks , 1993 .

[3]  Eric Séverin,et al.  Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model , 2011, Decis. Support Syst..

[4]  Bill C. Hardgrave,et al.  An improved method for developing neural networks: The case of evaluating commercial loan creditworthiness , 1996, Comput. Oper. Res..

[5]  Iván Pastor Sanz,et al.  Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks , 2015, Expert Syst. Appl..

[6]  M. C. Jensen,et al.  Harvard Business School; SSRN; National Bureau of Economic Research (NBER); European Corporate Governance Institute (ECGI); Harvard University - Accounting & Control Unit , 1976 .

[7]  Hayne E. Leland,et al.  INFORMATIONAL ASYMMETRIES, FINANCIAL STRUCTURE, AND FINANCIAL INTERMEDIATION , 1977 .

[8]  F. Modigliani,et al.  CORPORATE INCOME TAXES AND THE COST OF CAPITAL: A CORRECTION , 1963 .

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

[10]  Jatinder N. D. Gupta,et al.  Neural networks in business: techniques and applications for the operations researcher , 2000, Comput. Oper. Res..

[11]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[12]  S. Brooks Marshall,et al.  Agency Costs, Risk Management, and Capital Structure , 1999 .

[13]  Desmond Fletcher,et al.  Forecasting with neural networks: An application using bankruptcy data , 1993, Inf. Manag..

[14]  Hannu Vanharanta,et al.  Using the Self-Organizing Map as a Visualization Tool in Financial Benchmarking , 2003, Inf. Vis..

[15]  Hannu Vanharanta,et al.  The language of quarterly reports as an indicator of change in the company's financial status , 2005, Inf. Manag..

[16]  Richard G. Hoptroff,et al.  The principles and practice of time series forecasting and business modelling using neural nets , 1993, Neural Computing & Applications.

[17]  Jeffrey A. Clark,et al.  Off-site monitoring systems for predicting bank underperformance: a comparison of neural networks, discriminant analysis, and professional human judgment , 2001, Intell. Syst. Account. Finance Manag..

[18]  Junsei Tsukuda,et al.  Predicting Japanese corporate bankruptcy in terms of financial data using neural network , 1994 .

[19]  Kin Keung Lai,et al.  Bankruptcy prediction using SVM models with a new approach to combine features selection and parameter optimisation , 2014, Int. J. Syst. Sci..

[20]  R. Rajan,et al.  What Do We Know About Capital Structure? Some Evidence from International Data , 1994 .

[21]  R. Litzenberger,et al.  A State-Preference Model of Optimal Financial Leverage , 1973 .

[22]  Merton H. Miller The Cost of Capital, Corporation Finance and the Theory of Investment , 1958 .

[23]  Amaury Lendasse,et al.  Bankruptcy prediction using Extreme Learning Machine and financial expertise , 2014, Neurocomputing.

[24]  Youngohc Yoon,et al.  Applying Artificial Neural Networks to Investment Analysis , 1992 .

[25]  Ken'ichi Kamijo,et al.  Stock price pattern recognition-a recurrent neural network approach , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[26]  E. H. Kim,et al.  A Mean-Variance Theory of Optimal Capital Structure and Corporate Debt Capacity , 1978 .

[27]  Capital Structure and its Determinants in the UK - a Decompositional Analysis , 2000 .

[28]  Campbell R. Harvey,et al.  The Theory and Practice of Corporate Finance: Evidence from the Field , 1999 .

[29]  Bharat A. Jain,et al.  Artificial Neural Network Models for Pricing Initial Public Offerings , 1995 .

[30]  G. Grudnitski,et al.  Forecasting S&P and gold futures prices: An application of neural networks , 1993 .

[31]  S. Hamid,et al.  Using neural networks for forecasting volatility of S&P 500 Index futures prices , 2004 .

[32]  Anna Vilanova La Dinámica de la estructura de capital. Evidencia para las empresas industriales española , 2007 .

[33]  Vijay S. Desai,et al.  A comparison of neural networks and linear scoring models in the credit union environment , 1996 .

[34]  Kaisa Sere,et al.  Analyzing financial performance with self-organizing maps , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[35]  Ingoo Han,et al.  Hybrid neural network models for bankruptcy predictions , 1996, Decis. Support Syst..

[36]  M. A. F. Izquierdo,et al.  VALIDATING THE PECKING ORDER THEORY IN THE SPANISH CHEMICAL INDUSTRY , 2010 .

[37]  Jun Wang,et al.  Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks , 2015, Neurocomputing.

[38]  Stephen A. Ross,et al.  The determination of financial structure: the incentive-signalling approach , 1977 .

[39]  Zhe George Zhang,et al.  Forecasting stock indices with back propagation neural network , 2011, Expert Syst. Appl..

[40]  Eric Séverin,et al.  Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time , 2012, Eur. J. Oper. Res..

[41]  Carlos Serrano-Cinca,et al.  Self organizing neural networks for financial diagnosis , 1996, Decision Support Systems.

[42]  E. H. Kim,et al.  ON THE EXISTENCE OF AN OPTIMAL CAPITAL STRUCTURE: THEORY AND EVIDENCE , 1984 .

[43]  Murray Z. Frank,et al.  Trade-Off and Pecking Order Theories of Debt , 2007 .

[44]  A. Heshmati,et al.  The Dynamics of Capital Structure , 2000 .

[45]  Ramesh Sharda,et al.  A neural network model for bankruptcy prediction , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[46]  Philippe du Jardin,et al.  Predicting bankruptcy using neural networks and other classification methods: The influence of variable selection techniques on model accuracy , 2010, Neurocomputing.

[47]  B. Yegnanarayana,et al.  Artificial Neural Networks , 2004 .

[48]  Herbert L. Jensen,et al.  Using Neural Networks for Credit Scoring , 1992 .

[49]  E. Fama,et al.  Testing Tradeoff and Pecking Order Predictions About Dividends and Debt , 2000 .

[50]  H. C. Lau On the complexity of manpower shift scheduling , 1996 .

[51]  E. Michael Azoff,et al.  Neural Network Time Series: Forecasting of Financial Markets , 1994 .

[52]  E. Fama The Effects of a Firm's Investment and Financing Decisions on the Welfare of its Security Holders , 1978 .

[53]  S. Dutta,et al.  Bond rating: a nonconservative application of neural networks , 1988, IEEE 1988 International Conference on Neural Networks.

[54]  Kazuo Asakawa,et al.  Stock market prediction system with modular neural networks , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[55]  Philippe du Jardin,et al.  Bankruptcy prediction using terminal failure processes , 2015, Eur. J. Oper. Res..

[56]  Eric Séverin,et al.  Self organizing maps in corporate finance: Quantitative and qualitative analysis of debt and leasing , 2010, Neurocomputing.

[57]  Francisco Javier de Cos Juez,et al.  A hybrid device for the solution of sampling bias problems in the forecasting of firms' bankruptcy , 2012, Expert Syst. Appl..

[58]  Bernardete Ribeiro,et al.  Clustering and visualization of bankruptcy trajectory using self-organizing map , 2013, Expert Syst. Appl..

[59]  R. K. Agrawal,et al.  A combination of artificial neural network and random walk models for financial time series forecasting , 2013, Neural Computing and Applications.

[60]  Ramesh Sharda,et al.  Connectionist approach to time series prediction: an empirical test , 1992, J. Intell. Manuf..

[61]  Soumitra Dutta,et al.  Bond rating: A non-conservative application of neural networks , 1988 .

[62]  Teuvo Kohonen,et al.  An introduction to neural computing , 1988, Neural Networks.

[63]  Soushan Wu,et al.  Credit rating analysis with support vector machines and neural networks: a market comparative study , 2004, Decis. Support Syst..

[64]  Arun Agarwal,et al.  Recurrent neural network and a hybrid model for prediction of stock returns , 2015, Expert Syst. Appl..

[65]  David Enke,et al.  The use of data mining and neural networks for forecasting stock market returns , 2005, Expert Syst. Appl..

[66]  Bo K. Wong,et al.  Neural network applications in finance: A review and analysis of literature (1990-1996) , 1998, Inf. Manag..

[67]  Sangjae Lee,et al.  A multi-industry bankruptcy prediction model using back-propagation neural network and multivariate discriminant analysis , 2013, Expert Syst. Appl..

[68]  Joseph E. Stiglitz,et al.  A Re-Examination of the Modigliani Miller Theorem , 1967 .

[69]  H. White,et al.  Economic prediction using neural networks: the case of IBM daily stock returns , 1988, IEEE 1988 International Conference on Neural Networks.

[70]  Kimmo Kiviluoto,et al.  Predicting bankruptcies with the self-organizing map , 1998, Neurocomputing.

[71]  Sheridan Titman,et al.  The Determinants of Capital Structure Choice , 1988 .

[72]  Kar Yan Tam,et al.  Neural network models and the prediction of bank bankruptcy , 1991 .

[73]  Timothy B. Bell,et al.  Neural nets or the logit model? A comparison of each model’s ability to predict commercial bank failures , 1997 .

[74]  A. Visa,et al.  Industry‐specific cycles and companies' financial performance comparison using self‐organizing maps , 2004 .

[75]  Artur Raviv,et al.  The Theory of Capital Structure , 1991 .

[76]  Stewart C. Myers,et al.  Optimal financing decisions , 1965 .

[77]  Delvin D. Hawley,et al.  Artificial Neural Systems: A New Tool for Financial Decision-Making , 1990 .

[78]  Mu-Yen Chen,et al.  Visualization and dynamic evaluation model of corporate financial structure with self-organizing map and support vector regression , 2012, Appl. Soft Comput..

[79]  R. Sarker,et al.  Artificial Neural Networks in Finance and Manufacturing , 2006 .

[80]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[81]  Richard T. Redmond,et al.  Expert systems for bond rating: a comparative analysis of statistical, rule‐based and neural network systems , 1993 .

[82]  Nevins D. Baxter LEVERAGE, RISK OF RUIN AND THE COST OF CAPITAL* , 1967 .