Innovation and Financial Stability

Transcription

1 Innovation and Financial Stability Russ Moro 1,3 Saeideh Aliakbari 1 Giuditta de Prato 2 Daniel Nepelski 2 1 Brunel University, London 2 Institute for Prospective Technological Studies, Seville 3 DIW econ, Berlin INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 1/ 31

2 The Ways of Innovation are Unpredictable Villemard, 1910 INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 2/ 31

3 The Ways of Innovation are Unpredictable Villemard, 1910 INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 3/ 31

4 The Ways of Innovation are More Than Just Unpredictable Crazy patents 250,000 links on the Google INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 4/ 31

5 Innovation is a Risk Innovation is a risky business and increases the probability of failure innovation and entrepreneurship: similarities dot-com crisis 2000 high failure rate of innovative start-ups: only 1/3 survive beyond 10 years (EU/US) INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 5/ 31

6 Innovation is the Solution. Technological Competition 84% executive say innovation is extremely or very important to their companies growth strategy (McKinsey Global Survey, 2010) Samsung, the worlds largest consumer electronics company, spent 9% of its revenue on R&D, registered 1,600 patents and employed 27,000 researchers (2004) The most innovative survive in the crisis: Innovation loves a crisis, J.-Y. Yun, the CEO of Samsung INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 6/ 31

7 How Helpful is Innovation for Company Success and Survival? Inventors: the surest way of ruining a business or Innovation is essential for survival and success Despite the acknowledged connection between innovation and financial risk and stability, little or no attention is paid to it in modelling or policy formulation INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 7/ 31

8 Outline 1. Motivation 2. Patent and Financial Data 3. Common Characteristics of Innovative and Financially Stable Companies 4. Predictors of Innovation and Distress 5. Identification of Financially Stable Companies: Patent Applications INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 8/ 31

9 Patent Data Patent data based on the information of the European Patent Office (EPO) Global coverage, information collected from 90 patent offices Period covered in this study: Patents are assigned to a country based on the residence of applicants INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 9/ 31

10 Patent Issuing Offices. Singapore Patent Office No. Patents Share, % Cumulative, % USA 3, Singapore 1, UK Japan European Union Germany Other INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 10/ 31

11 Company Data Company data provided by the Risk Management Institute of the National University of Singapore (RMI NUS) Available data: Europe, North America, Asia, Latin America Listed private manufacturing companies, quarterly data S - sales; TA - total assets; NI - net income; OI - operating income; OK - own capital; CL - current liabilities; TD - total debt; STD - short term debt; QA - quick assets; CA - current assets; WC - working capital; INV - inventories; AR - accounts receivable; AP - accounts payable; CS - cost of sales; INT - interest payments; D - debt; CASH - cash and equivalents; P - stock price; etc. INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 11/ 31

12 Data Distribution. Asia and Pacific ( ) Country Distressed firms Solvent firms Australia 6 (4.03 %) 143 China 4,182 (7.22 %) 53,739 Hong Kong 19 (0.34 %) 5,505 India 148 (0.51 %) 28,775 Indonesia 70 (1.12 %) 6,186 Japan 258 (0.36 %) 71,380 Malaysia 1,100 (3.12 %) 34,173 Philippines 267 (4.16 %) 6,154 Singapore 77 (1.08 %) 7,050 South Korea 232 (0.46 %) 50,153 Taiwan 604 (2.47 %) 23,809 Thailand 486 (2.77 %) 17,028 INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 12/ 31

13 Definition of Distress Most common credit events identifying distressed firms: restructuring liquidation legislation initiated by creditors failing in coupon and principle payments Credit event codes and , RMI database Time to default 2 years (similar results for other horizons) 2 year cumulative Probability of Default (PD) INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 13/ 31

14 Company Classification Problem INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 14/ 31

15 Company Classification Problem Learning is carried out on the training set: {(x i, y i )} n i=1 with the distribution P (x i, y i ). x i is a vector of d characteristics describing company i e.g. financial ratios; y i = {1; 0} is the class of the i-th company. y i = 1 for distressed, y i = 0 for solvent firms or y i = 1 for companies with patent(s), y i = 0 without patents Objective: find a decision rule f(x) : X d {1; 0} determining the class y of any comany described with x which can generalise well INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 15/ 31

16 Statistical Classification Techniques Logistic regression Support Vector Machine (SVM) as a robust alternative non-parametric: flexibility (in contrast to logit) Tychonoff regularised: complexity control to lower modelling risk convex: a unique stable solution (in contrast to neural networks) solid theoretical foundations: statistical learning theory The SVM used here has a Gaussian kernel and the parameter R = 2.5 and C = 1 INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 16/ 31

17 Combined Company and Patent Data Merging criterion: company name Note: many companies appear with different names, e.g. IBM and International Business Machines, which was taken into account Country: China; quarterly listed company data 2,200 companies (230 with patents) 58,000 observations (4,200 distressed) INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 17/ 31

18 Summary Statistics (1) Patent Distressed Yes No Solvent Distressed Variable Med IQR Med IQR Med IQR Med IQR Leverage OK/TA CL/TA TD/TA Activity TA/S INV/S AR/S AP/CS Med median IQR interquartile range INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 18/ 31

19 Summary Statistics (2) Patent Distressed Yes No Solvent Distressed Variable Med IQR Med IQR Med IQR Med IQR Liquidity STD/D CASH/TA CASH/CL QA/CL CA/CL WC/TA CL/TL Size log(ta) log(s) INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 19/ 31

20 Summary Statistics (3) Patent Distressed Yes No Solvent Distressed Variable Med IQR Med IQR Med IQR Med IQR Profitability NI/TA NI/S OI/TA OI/S EBIT/TA EBIT/S INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 20/ 31

21 Summary Statistics (4) Patent Distressed Yes No Solvent Distressed Variable Med IQR Med IQR Med IQR Med IQR Cost Structure INT/D EBIT/INT Dynamics S Growth NI Growth INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 21/ 31

22 Selection of the Predictors of Innovation and Distress Forward variable selection with a bootstrap procedure: 1. Select non-overlapping subsamples as training and testing sets (500 solvent and 500 distressed companies in each) 2. Calibrate the model (Logit or SVM) on the training set and estimate the Accuracy Ratio (AR) on the testing set 3. Repeat steps 1 and 2 for 100 times and estimate the distribution of AR 4. Select a model based on the highest median AR: robustness INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 22/ 31

23 Selection of the Predictors of Innovation Logit SVM Step Var Med AR p Var Med AR p 1 log(s) log(s) WC/TA WC/TA AR/S TA/S Med AR median accuracy ratio p significance level of including an additional variable INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 23/ 31

24 Selection of the Predictors of Distress Logit SVM Step Var Med AR p Var Med AR p 1 TD/TA TD/TA log(s) log(s) CL/TL CL/TL log(ta) NI/TA Med AR median accuracy ratio p significance level of including an additional variable INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 24/ 31

25 Graphical Interpretation. Patent Score Black (white) points firms with (without) patent(s). Isoquants correspond to the average patent score INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 25/ 31

26 Graphical Interpretation. Probability of Distress Black (white) points solvent (distressed) firms. Isoquants correspond to the average distress rate INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 26/ 31

27 Patent Applications and Distress Number of Observations Patent Solvent Distressed No 52,263 (90.23%) 4,182 (7.22%) Yes 1,476 (2.55%) 0 (0.00%) Number of Firms Patent Solvent Distressed No 1,887 (83.02%) 163 (7.17%) Yes 223 (9.81%) 0 (0.00%) here a company before and after it received a patent is treated as two different companies INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 27/ 31

28 Patent Applications and Distress The probability that the result P (Distress Patent= Yes ) = 0 is random: P = (N n)! (N m)! (N n m)! N! = N=2,273 the total number of companies n=163 the number of distressed companies m=223 the number of companies which obtained patents INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 28/ 31

29 Patent Applications and Distress The hypothesis Patent aplications have no significant influence on PD is confidently rejected Companies that possess patents never reported a credit event (distress) Technological competition: companies that have no patents face a stronger competition and a higher PD INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 29/ 31

30 Thank You! INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 30/ 31

31 References Lacerda, A. I. and R. A. Moro (2008). Analysis of the Predictors of Default for Portuguese Firms, working paper 22, Banco de Portugal De Prato, G., D. Nepelski and J. Stancik (2011). Internationalisation of ICT R&D, JRC Scientific and Technical Reports, IPTS, European Commission, Seville Eisdorfer, A. and P.-H. Hsu (2003). Innovate to Survive: The Effect of Technology Competition on Corporate Bankruptcy, SSRN working paper Härdle, W. K., R. A. Moro and D. Schäfer (2009). Estimating Probabilities of Default with Support Vector Machines, submitted INNOVATION AND FINANCIAL STABILITY R. Moro, S. Aliakbari, G. de Prato, D. Nepelski. 31/ 31