Data Analysis Machine Learning and Applications Episode 3 Part 1 pdf

506 Wolfgang Bessler and Peter Lückoff References AVRAMOV, D. (2002): Stock Return Predictability and Model Uncertainty. Journal of Finan- cial Economics, 64, 423–458. AVRAMOV, D. and CHORDIA, T. (2006): Asset Pricing Models and Financial Market Anomalies. Review of Financial Studies, 19, 3, 1001–1040. BESSLER, W. and OPFER, H. (2004): Eine Empirische Untersuchung zur Bedeutung makroökonomischer Einflussfaktoren auf Aktienrenditen am deutschen Kapitalmarkt. Fi- nanzmarkt und Portfoliomanagement, 4, 412–436. CAMPBELL, J. D. and SHILLER, R. J. (1989): The Dividend-price Ratio and Expectations of Future Dividends and Discount Factors. Review of Financial Studies, 1, 3, 195–228. CREMERS, K. J. M. (2002): Stock Return Predictability: A Bayesian Model Selection Per- spective. Review of Financial Studies, 15, 4, 1223–1249. FAMA, E. F. and FRENCH, K. R. (1988): Dividend Yields and Expected Stock Returns. Journal of Financial Economics, 22, 3–25. FERSON, W. E. and SARKISSIAN, S. (2003): Spurious regressions in financial economics? Journal of Finance, 58, 4, 1393- ˝ U1412. HODRICK, R. J. (1992): Dividend Yields and Expected Stock Returns: Alternative Proce- dures for Inference and Measurement. Review of Financial Studies, 5, 3, 357–386. KAUL, G. (1996): Predictable Components in Stock Returns. In: G. S. Maddala, C. R. Rao (Eds.): Statistical Methods in Finance. Elsevier Science, Amsterdam, 269-296. LITTERMANN, R. B. (1986): Forecasting with Bayesian Vector Autoregressions ˝ UFive Years of Experience. Journal of Business and Economic Statistics, 4, 1, 25- ˝ U38. SARANTIS, N. (2006): On the Short-term Predictability of Exchange Rates - A BVAR Time- varying Parameters Approach. Journal of Banking and Finance, 30, 2257- ˝ U2279. The Evaluation of Venture-Backed IPOs – Certification Model versus Adverse Selection Model, Which Does Fit Better? Francesco Gangi and Rosaria Lombardo Faculty of Economics, Department of Strategy and Quantitative Methods Second University of Naples, Italy {francesco.gangi, rosaria.lombardo}@unina2.it Abstract. In this paper we aim to investigate the consistency of the certification model against the adverse selection model with respect to the operational performances of venture-backed (VB) IPOs. We analyse a set of economic-financial variables an italian IPOs sample between 1995 and 2004. After non-parametric tests, to take into account the non-normal, multivariate nature of the problem, we propose a non-parametric regression model, i.e. Partial Least Squares, as appropriate investigative tool. 1 Introduction In financial literature the performance evaluation of venture backed IPOs has stim- ulated an important debate. Two are the main theoretical approaches. The first one has pointed out the certification role and the value added services of venture capitalists. The second one has emphasized the negative effects of adverse selection and opportunistic behaviours on IPOs under-performance, especially with respect to the timing of the IPOs. In different studies (Wang et al., 2003; Brau et al., 2004; Del Colle et al., 2006) parametric tests and Ordinary Least Squares regression have been proposed as investigative tools. In this work we investigate complicated effects of adverse selection and conflict of interests by non-parametric statistical approaches. Underlining the non-normal data distribution, we propose as appropriate instruments non-parametric tests and Partial Least Squares regression model (PLS; Tenenhaus, 1998; Durand, 2001). At first we test if the differences of operational performances are significant between the pre-IPOs sample and post-IPOs sample. Next, given the complicated multivariate nature of the problem, we study the dependence relationships of firm performance (measured by ROE) from quantitative and qualitative variables of context (like market conditions). 508 Francesco Gangi and Rosaria Lombardo 2 The theoretical financial background: the certification model and the adverse selection model The common denominator of theoretical approaches on venture capitalist role is represented by the asymmetric information management. On one hand, the certification model considers an efficient solution of this question, due to scouting process and ac- tivism of private equity agents. More specifically, the certification model takes into account the selection capacity and the monitoring function of venture capitalists that allow to make better resources allocation and better control systems than other financial solutions (Barry ed al., 1990; Sahlman, 1990; Magginson e Weiss, 1991; Jain e Kini, 1995; Rajan e Zingales, 2004). Consequently, this model predicts good performances of venture backed firms, even better than non backed ones. The causes of this effect ought to be: more stable corporate relations; strict covenants; frequent operational control activities; board participation; stage financing options. These aspects should compensate the incomplete descriptive contractual structure that follows ev- ery transaction, allowing a more efficient management of the asymmetric information problem. So, venture backed IPOs should generate good performances in terms of growth, profitability and financial robustness, even better if they are compared with non backed ones. On the other hand, IPOs under-performance could be related to adverse selection pro- cesses, even if these companies are participated by a venture capitalist. In this case two related aspects should be considered. The first one is that not necessarily the best firms are selected by venture capital agents. The second one is that the timing of IPO cannot coincide with a new cycle of growth or with an increase in profitability. Relatively to the first matter, some factors could determine a disincentive to accept the venture capital way in, such as latent costs, loose of control rights and income sharing. At the same time, the quotation option could not match an efficient signal towards the market. According to the packing order theory, the IPO choice can be neglected or rejected at all by the firms that are capable to create value by them- selves, without the financial support of a fund or the stock exchange. At first, low quality company, could receive more incentives to the quotation if the value assigned by the market exceeds inside expectations, especially during bubble periods (Ben- ninga, 2005; Coakley et al. 2004). In this situation, venture capitalist could assume an insider approach too, for example stimulating an anticipated IPO, as described by the grandstanding model (Gompers, 1996; Lee and Wahal, 2004). At second, venture capitalists could be in conflict of interests towards the market when they have to accelerate the capital turnover. This is a big question if the venture capitalist operate like an intermediary of resources obtained during the fund raising process. In this case, the venture capitalist assumes a double role: he is a principal with respect to the target company; but he is an agent with respect to the fund, configuring a more complex, onerous, therefore less efficient agency nexus model. So the hypothesis is that a not efficient management of asymmetric information can also explain the VB IPOs under-performance, confuting the assumption of superior IPOs results compared to non- VB IPOs (Wang et. Al, 2003; Brau et al., 2004). The opportunistic behaviours of previous shareholders could not be moderated by venture capitalist’s The Evaluation of VB IPOs Performance 509 Table 1. Wilcoxon Signed Rank Test in VB IPOs: Test1=H0: Me T1 = Me T2 ; Test2= H0:Me T1 = Me T3 ; Test3= H0:Me T2 = Me T3 Ratios Me T1 Me T2 Me T3 Test1 Test2 Test3 ROS 9.97 7.34 5.39 -0.87 -1.78* -1.66* ROE 9.75 6.84 -1.51 -1.16 -1.91* -1.66** ROI 7.33 6.69 3.30 -1.35 -1.79* -1.73* Leverage 292.67 79.75 226.96 -3.29*** -0.09 -2.54*** Table 2. Mann-Whitney Test comparison in VB IPOs: Test1=H0: Me VB T2 = Me NonV B T2 ; Test2=H0:Me VB T3 = Me NonV B T3 Ratios VB T2 NonV B T2 VB T3 NonV B T3 Test1 Test2 ROS 9.52 3.93 5.39 2.16 103 116 ROE 6.7 3.83 3.3 2.01 111 110 ROI 6.85 3.83 -1.51 1.5 113 105 Leverage 79.75 72.52 226.96 88.28 120 58** governance solutions. Furthermore, venture capitalists could even incentive a speculative approach to maximize and anticipate the way out from low quality companies, dimming their hypothetical certification function. 3 Data set and non-parametric hypothesis tests The study of the Italian venture backed IPOs is based on a sample of 17 companies listed from 1995 to 2004. The universe consists of 28 manufacturing companies that have gone public after the way in of a formal venture capitalist with a minority participation. In addition to the principal group, we have composed a control sample represented by non-venture backed IPOs comparable by industries and size. The performance analysis is based on balance sheets ratios. In particular, the study assumes the profitability and the financial robustness as the main parameters to evaluate operational competitiveness before and after the quotation. Ratios are referred to three critical moments, or terms of the factor, called events, consisting in deal-year (T1), IPO-year (T2)andfirst year post-IPO (T3). At first we test the performance differences of balance sheet ratios within the venture backed IPOs with respect to the three events (T1, T2, T3). Successively we test significant difference between the two independent samples of VB IPOs and non-VB IPOs. For the particular sample characteristics (non-normal distribution and eteroschedasticity) we consider non-parametric tests like Wilcoxon signed rank test (Wilcoxon and Wilcox, 1964) for paired depen- dent observations and Mann-Whitney test (Mann and Whitney, 1947) for compar- isons of independent samples. Coherently with the adverse selection model, we test 510 Francesco Gangi and Rosaria Lombardo if the venture backed companies show an operational underperformance between the pre-IPO and post-IPO phases. Subsequently, coherently to the certification model, we test if the venture backed companies have the best performance if compared with non venture backed IPOs. The statistics of VB IPOs show an underperformance trend of venture backed companies during the three defined terms. In particular, all the profitability ratios decline constantly. Moreover, we find an high level of leverage (Debt/Equity) at the deal mo- ment, and in the first year post-IPO the financial robustness goes down again very rapidly. So the prediction of a re-balancing effect on financial structure has been considered only with respect to the IPOs events (see table 1). The results of Wilcoxon Signed Rank Test have been reported in table 1. The null hypothesis is confirmed for profitability parameters comparing ratio medians of T1 and T2 moments, whereas the differences between ratio medians of T1 and T3 and T2 and T3 are significant (the significant differences are marked by the symbols: *=10%, **=5%, ***=1%). So the profitability breakdown is mainly a post-IPO problem, with a negative effect of leverage. These results suggests that venture capitalists do not add value in the post-IPO period, otherwise, the adverse selection moderates the certification function and the best practice effects expected from venture capital solutions. Furthermore we test the hypothesis that VB IPOs generate superior operating performance compared with non-venture IPOs. Using the Mann-Whitney test, we compare IPO-ratios of the two independent samples. The findings show no significant difference between the samples at the IPO-time and at the first year post-IPO; only the leverage level shows an higher growth in the venture group than in non-venture one, confirming the contraction of financial robustness and the loss of the re-balancing effect on financial structure produced by the IPOs (see table 2). In conclusion the test results are more consistent with the adverse selection theory. Underlining the multivariate, non-normal nature of the problem, after hypothesis tests, we propose to investigate VB performance by a suitable non-parametric regression model. 4 Multivariate investigation tools: Partial Least squares regression model In presence of a low-ratio of observations to variables and in case of multicollinearity in the predictors, a natural extension of the multiple linear regression is PLS regression model. It has been promoted in the chemiometrics literature as an alternative to ordinary least squares (OLS) in the poorly or ill-conditioned problems (Tenenhaus, 1998). Let Y be the categorical n,q response matrix and X the n, p matrix of the predictors observed on the same n statistical units. The resulting transformed predictors are called latent structures or latent variables. In particular, PLS chooses the latent variables as a series of orthogonal linear combinations (under a suitable con- straint) that have maximal covariance with linear combinations of Y. PLS constructs a sequence of centered and uncorrelated exploratory variables, i.e. the PLS (latent) The Evaluation of VB IPOs Performance 511 components (t 1 , , t A ). Let E 0 = X and F 0 = Y be the design and response data ma- trices, respectively. Define t k = E k−1 w k and u k = F k−1 c k , where the weighting unit vectors w k and c k are computed by maximizing the covariance between linear com- promises of the updated predictor and response variables, max[cov(t k ,u k )]. Update the new variables E k and F k as the residuals of the least-squares regression on the component previously computed. The number A of the retained latent variables, also called the model dimension, is usually estimated by cross-validation (CV). Two particular properties make PLS attractive and establish a link between the geo- metrical data analysis and the usual regression. First, when A = rank X, PLS(X,Y) ≡{OLS(X,Y j )} j=1, ,q , if the OLS regression exists. Second, the principal component analysis, PCA, of X can be viewed as the “self- PLS" regression of X onto itself, PLS(X,Y = X) ≡ PCA(X). PLS regression model has the following properties: efficient in spite of low ratio of observations on column dimension of X;efficient in the multi-collinear context for predictors (concurvity); robust against extreme values of predictors (local poly- nomials). The PLS regression model examines the predictors of ROE at IPO-year (T2) as variables of performance of VB IPOs companies. The predictor variables are: one quantitative (the leverage measured at the year of the venture capital way in, LEVERAGET1) and four qualitative: 1) the short time interval between the deal and the IPO time (1 year by-deal, 1Yby deal; 2 year by-deal, 2Yby deal); 2) the size of companies listed, (SME; Large); 3) the trend of Milan Stock Exchange, (Hot Market Hotmkt, Normal Market, NORMmkt); 4) the origin of fund, (Bank Fund; non-Bank Fund, N-Bank Fund). The building-model stage consists of finding a balance between goodness of fit and prediction and thriftness. The goodness of fitis valued by R 2 (A), in our study is equal to 60%, and the thriftness by PRESS criterion, the dimension space suggested by PRESS is A = 1. By PLS regression we want to verify the effects of some variables which could subtend opportunistic approaches. Moreover, the analysis is concentrated on the effect of independent variables that could allow the recognition of a conflict of interests between venture agents and the new stockholders. The importance of each predictors on the response is evaluated by looking at regression coefficients (E) whose graphical representation is given in figure 1. For example the regression coefficient value of leverage at the deal-time is a predictor of under-performance in the IPO year (E LEVERAGET1 = −0.36). This finding is consistent with the assumption that adverse selection at the deal reflect its effects when the target firm is listed, especially when the gap between these two moments is very short. We could also say that pre-IPO poorly performing firms continue to produce bad performance afterward too. Concerning the qualitative predictors, the interval time (E 1Ybydeal = −0.17) and the 512 Francesco Gangi and Rosaria Lombardo firm size (E SME = −0.17) are useful variables to capture the influence of a too early quotation, similarly to the grandstanding approach. The market trend (E HOTmkt = −0.13) is useful to verify the impact of a speculative bubble on IPOs performance. Furthermore, the origin of fund (E FundBank = −0.17) it’s necessary to evaluate the potential conflict of interest of an agent that covers a double role: banking and venture financing. All these variables summarize the risk of an adverse selection pro- Fig. 1. Decreasing Influence of Qualitative and Quantitative Predictors on ROE-T2. cess and speculative approach that can contrast the certification function of venture capitalist investments. So, in the first place the leverage, reached after the venture capitalist way in, is the most negative predictor of ROE at IPO time. In the second place, the shorter are the time intervals between the deal and IPO time, the worst is the influence on ROE. In the third place, the firm size SME is a relevant predictor too. In fact, smaller and less structured enterprises have a negative incidence on IPOs operating performance. In the fourth place, even the market trend seems to assume a significant role to explain the VB IPO under-performance. More specifically, hot issues HOTmkt determine a negative effect on ROE. Finally, in a less relevant posi- tion there is the fund origin Fund Bank, for this variable the theoretical assumption is confirmed too, because of the negative influence of bank based agents. In synthe- sis we can say that ROE under-performance depends from the following predictors: LEVERAGE1, 1Yby deal, HOTmkt, SME. So, coherently with inferential tests, the PLS findings related to the IPO segment of the Italian Private Equity Market move away the venture finance solution from the theoretical certification function. The Evaluation of VB IPOs Performance 513 5 Conclusion The results of the non-parametric tests as well as the more complete multivariate dependence model show that operational performances of VB IPOs are significantly consistent with the adverse selection and opportunistic model. Specifically, a large part of IPOs under-performance is due to the leverage ”abuse” at the Deal-Time, and the PLS regression shows that too early quotation by-deal, hot issues and small firm size are all predictors of profitability falls. Probably we should rethink a ”romantic” vision about the venture capitalist role: sometimes he is simply an agent in conflict of interest, or he has not always the skill to select the best firms for the financial market. Obviously there are a lot of implications for further research and developments of this work. An international comparison with other financial systems and a further supply and demand analysis ought to be carried out. Acknowledgments This work was supported by SUN-University funds 2006, responsible Rosaria Lom- bardo and Francesco Gangi. The paper was written by both authors in particular sections 1,2,3,5 are mainly attributed at Francesco Gangi and section 4 at Rosaria Lombardo. References BARRY, C., MUSCARELLA, C., PEAVY, J. and VETSUYPENS, M. (1990): The role of venture capital in the creation of public companies. Evidence from the going public process. Journal of Financial Economics, 27, pp. 447-471. BENNINGA, S., HELMANTEL, M. and SARIG, O. (2005): The timing of initial public of- fering. Journal of Financial Economics, 75, pp. 115-132. BRAU, J., BROWN, R. and OSTERYOUNG, J. (2004): Do venture capitalists add value to small manufacturing firms? An empirical analysis of venture and non-venture capital- backed initial public offerings. Journal of Small Business Management, 42, pp. 78-92. COAKLEY, J., HADASS, L. and WOOD, A. (2004): Post-IPO operating performance, venture capitalists and market timing. Department of Accounting, Finance and Management, University of Essex, pp. 1-32. DEL COLLE, D.M., FINALI RUSSO, P. and GENERALE, A. (2006): The causes and conse- quences of venture capital financing. An analysis based on a sample of italian firms. Temi di discussione Banca d’Italia, 6-45. DURAND, J.F. (2001): Local Polynomial additive regression through PLS and Splines: PLSS. Chemometrics & Intelligent Laboratory Systems, 58, pp. 235-246. GOMPERS, P. (1996): Grandstanding in the venture capital industry. Journal of Financial Economics, 42, pp. 1461-1489. JAIN, B. and KINI, O. (1995): Venture capitalist participation and the post-issue operating performance of IPO firms.Managerial and Decision Economics, 16, pp. 593-606. LEE, P. and WAHAL, S. (2004): Grandstanding, certification and the underpricing of venture capital backed IPOs. Journal of Financial Economics, 73, pp. 375-407. 514 Francesco Gangi and Rosaria Lombardo MANN, H.B. and WHITNEY, D.R. (1947): On a test of whether one of 2 random variables is stochastically larger than the other. Annals of mathematical statistics, 18, pp. 50-60. MEGGINSON, W., WEISS, K. (1991): Venture capital certification in initial public offerings. Journal of Finance, 46, pp. 879-903. RAJAN, R. G. and ZINGALES, L. (2003): Saving capitalism from the capitalists. Einaudi, Torino. SAHLMAN, W.A. (1990): The structure and governance of venture-capital organiza- tions.Journal of Financial Economics, 27. TENENHAUS, M. (1998): La Regression PLS, Theorie et Pratique. Editions Technip, Paris. WANG, C., WANG, K. and LU, Q. (2003): Effects of venture capitalists’ participation in listed companies.Journal of Banking & Finance, 27, pp. 2015-2034. WILCOXON, F. and WILCOX, A.R. (1964): Some rapid approximate statistical procedures. Lederle Lab., Pearl River N.Y. Using Multiple SVM Models for Unbalanced Credit Scoring Data Sets Klaus B. Schebesch 1 and Ralf Stecking 2 1 Faculty of Economics, University "Vasile Goldi¸s", Arad, Romania kbsbase@gmx.de 2 Faculty of Economics, University of Oldenburg, D-26111 Oldenburg, Germany ralf.w.stecking@uni-oldenburg.de Abstract. Owing to the huge size of the credit markets, even small improvements in classification accuracy might considerably reduce effective misclassification costs experienced by banks. Support vector machines (SVM) are useful classification methods for credit client scoring. However, the urgent need to further boost classification performance as well as the stability of results in applications leads the machine learning community into developing SVM with multiple kernels and many other combined approaches. Using a data set from a German bank, we first examine the effects of combining a large number of base SVM on classification performance and robustness. The base models are trained on different sets of reduced client characteristics and may also use different kernels. Furthermore, using censored outputs of multiple SVM models leads to more reliable predictions in most cases. But there also re- mains a credit client subset that seems to be unpredictable. We show that in unbalanced data sets, most common in credit scoring, some minor adjustments may overcome this weakness. We then compare our results to the results obtained earlier with more traditional, single SVM credit scoring models. 1 Introduction Classifier combinations are used in the hope of improving the out-of-sample classification performance of single base classifiers. It is well known (Duin and Tax (2000), Kuncheva (2004), Koltchinskii et al. (2004)), that the results of such combiners can be both better or worse than expensively trained single models and also that combiners can be superior when used on relatively sparse empirical data. In general, as the base models are less powerful (and inexpensive to produce), their combiners tend to yield much better results. However, this advantage is decreasing with the quality of the base models (e.g. Duin and Tax (2000)). Our past credit scoring single- SVM classifiers concentrate on misclassification performance obtainable by different SVM kernels, different input variable subsets and financial operating characteristics (Schebesch and Stecking (2005a,b), Stecking and Schebesch (2006), Schebesch and Stecking (2007)). In credit scoring, classifier combination using such base models [...]... Coulomb 11 95 0 619 11 42 530 7 14 814 17 158 16 009 17 158 0.6 53 0.077 96 1 035 11 4 15 9 13 17 158 0 . 31 4 12 42 860 0 633 6 51 112 4 516 498 25 14 767 17 158 15 149 17 158 16 009 17 158 0.6 43 0.640 0 .14 8 M1 M2 * M3 ** 287 19 1 35 48 850 33 1 33 1 299 256 818 15 722 17 158 12 425 13 2 03 12 425 17 158 0.505 0.655 0.7 43 * 39 55 credit clients could not be classified ** Default class bad for 39 55 credit clients and by w = 11 49 for bad... Schebesch and Ralf Stecking best (elite) base models from each sorted list of the l -1- o errors This again leads to 31 combiners The experiments shown in fig.2 (lhs plot) indicate that, when used on 53 53 MEAN TRAIN ERROR MEAN L 1 O ERROR 49 49 L 1 O ERROR VARIATION BASE SVM 45 45 41 41 37 37 33 33 29 29 25 25 21 21 I60 E3 LINCUM 17 13 4 8 12 17 E CORRIDOR 13 16 20 24 28 32 36 4 8 12 16 20 24 28 32 36 Fig... probability p part (n) that the partition corresponding to the complete histogram H(D) of all co-inspections 546 Andreas W Neumann and Andreas Geyer-Schulz 0.6 0.4 1+ 1 +1+ 1 +1+ 1 2 +1+ 1 +1+ 1 2+2 +1+ 1 2+2+2 3+ 1+ 1 +1 3+ 2 +1 3+ 3 4 +1+ 1 4+2 5 +1 6 0.0 0.2 probability 0.8 1. 0 Partition probabilities for k = 6 (n = 6 to 50) 10 20 30 40 50 n Fig 2 Inspection probabilities p part (n) for k = 6 and growing n in POMICI with... for simple rule I60 on SA and for SVM(I60) on SA Note that combination procedures with such characteristics can be 34 34 SVM(E3) L 1 O ERR SVM(E3) TRAIN ERR SVM(I60) L 1 O ERR 30 LINCUM ON S B 30 LINCUM ON S A 26 26 22 22 18 18 14 14 10 10 6 6 SVM(I60) TRAIN ERR 2 2 4 8 12 16 20 24 28 32 36 4 8 12 16 20 24 28 32 36 Fig 3 Axes description same as in fig 2 Lhs plot: training and validation errors of supervized... P (1) P(2) P (3) P(4) P(5) P(6) P(7) P(8) 10 20 30 40 50 n Fig 1 Inspection probabilities p j (n) for k = 8 and growing n in POSICI n p j (n) = P n Ai i =1 = ( 1) =1 1 1≤i1 < 10 ,000,000 Number of total co-inspected documents 527 ,36 3 255,248 Average market basket size 4.9 2.9 Av aggregated co-inspections per document 11 7.4 5.4 544 Andreas W Neumann and Andreas Geyer-Schulz Table 1 shows some... consideration set ⊇ choice set, Kotler (19 80) p 1 53) have been developed to describe this situation (Narayana and Markin (19 75), Spiggle and Sewall (19 87)) Narayana and Markin have investigated the size of the awareness set for several branded products empirically E g., they report a range from 3 11 products with an average of 6.5 in the awareness set for toothpaste and similar results for other product . 1 035 11 4 15 9 13 17 158 0 . 31 4 Polynomial (3rd degree) 12 42 633 516 14 767 17 158 0.6 43 RBF 860 6 51 498 15 149 17 158 0.640 Coulomb 0 11 24 25 16 009 17 158 0 .14 8 M1 287 850 299 15 722 17 158 0.505 M2 * 19 1. Me NonV B T3 Ratios VB T2 NonV B T2 VB T3 NonV B T3 Test1 Test2 ROS 9.52 3. 93 5 .39 2 .16 1 03 11 6 ROE 6.7 3. 83 3 .3 2. 01 111 11 0 ROI 6.85 3. 83 -1. 51 1.5 1 13 10 5 Leverage 79.75 72.52 226.96 88.28 12 0 58** governance. 0.505 M2 * 19 1 33 1 256 12 425 13 2 03 0.655 M3 ** 35 48 33 1 818 12 425 17 158 0.7 43 * 39 55 credit clients could not be classified. ** Default class bad for 39 55 credit clients. and by w = 11 49 32 3 for bad credit

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