Altman Z-Score Models After 50 Years, Where We Are in the Credit Cycle & Outlook and the Italian Mini- Dr. Edward Altman NYU Stern School of Business

Credit Risk & Strategy Seminar Classis Capital Piemonte, Italia December 02, 2017

1 Scoring Systems • Qualitative (Subjective) – 1800s • Univariate (Accounting/Market Measures) – Rating Agency (e.g. Moody’s (1909), S&P (1916) and Corporate (e.g., DuPont) Systems (early 1900s) • Multivariate (Accounting/Market Measures) – Late 1960s (Z-Score) - Present – Discriminant, Logit, Probit Models (Linear, Quadratic) – Non-Linear and “Black-Box” Models (e.g., Recursive Partitioning Neural Networks, 1990s) • Discriminant and Logit Models in Use for – Consumer Models - Fair Isaacs (FICO Scores) – Manufacturing Firms (1968) – Z-Scores – Extensions and Innovations for Specific Industries and Countries (1970s – Present) – ZETA Score – Industrials (1977) – Private Firm Models (e.g., Z’-Score (1983), Z”-Score (1995)) – EM Score – Emerging Markets (1995) – Specialized Systems (1990s) – SMEs (e.g. Edmister (1972), Altman & Sabato (2007) & Wiserfunding (2016)) • /Contingent Claims Models (1970s – Present) – Risk of Ruin (Wilcox, 1973) – KMVs Credit Monitor Model (1993) – Extensions of Merton (1974) Structural Framework2 Scoring Systems (continued) • Artificial Intelligence Systems (1990s – Present) – Expert Systems – Neural Networks – Machine Learning • Blended Ratio/Market Value Models/Macro Data – Altman Z-Score (Fundamental Ratios and Market Values) – 1968 – Bond Score (Credit Sights, 2000; RiskCalc Moody’s, 2000) – Hazard (Shumway), 2001) – Kamakura’s Reduced Form, Term Structure Model (2002) – Z-Metrics (Altman, et al, Risk Metrics©, 2010) • Re-introduction of Qualitative Factors/FinTech – Stand-alone Metrics, e.g., Invoices, Payment History – Multiple Factors – Data Mining (Big Data Payments, Governance, time spent on individual firm reports [e.g., CreditRiskMonitor’s revised FRISK Scores, 2017], etc.) – Enhanced Blended Models (2000s)

3 Major Agencies Bond Rating Categories

Moody's S&P/Fitch

Aaa AAA Aa1 AA+ Aa2 AA Aa3 AA- A1 A+ A2 A A3 A- Baa1 BBB+ Baa2 Investment BBB Baa3 Grade BBB- Ba1 High BB+ Ba2 ("Junk") BB Ba3 BB- B1 B+ B2 B B3 B- Caa1 CCC+ Caa CCC Caa3 CCC- Ca CC C C D 4 Size of the US High-Yield

1978 – 2017 (Mid-year US$ billions)

$1.800 $1,622 $1.600

$1.400

$1.200

$1.000

$800 $ (Billions) $ $600

$400

$200

$-

1980 1994 2013 1978 1979 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2014 2015 2016 2017

Source: NYU Salomon Center estimates using Credit Suisse, S&P and Citi data. 5 Size of Western European HY Market

500 450 476 483 476

400 418

350 370

300

250 283 (Billions) 200 € 194 150 154

100 108 89 84 81 79 80 77 81 50 61 70 2 5 9 14 27 45 0

Includes non-investment grade straight corporate debt of issuers with assets located in or revenues derived from Western Europe, or the bond is denominated in a Western European . Floating-rate and convertible bonds and preferred are not included.

Source: Credit Suisse

6 The Italian Mini-bond Market

 Europe High-yield bond market is still lagging behind the US one, but the growth has accelerated in the last 3 years.

 In Italy, the market for SME bonds is known as Extra-MOT PRO “Mini-bond” market.

 The new segment of the Extra-MOT market dedicated to listing of bonds, , and project finance bonds started in February 2013.

 The total amount of listed issuances since February 2013 is 177, for a total issued amount of about Euro 7,146bn. As of March 2016, there is Euro 4.491bn outstanding, from 130 issues.

 In Q2 2016, 13 new issues have been launched.

We believe “Mini-bonds” can be a success in Italy as long as the market supplies an attractive risk/return tradeoff to investors as well as affordable and flexible financing for borrowers.

7 Problems With Traditional Financial Ratio Analysis in Predicting Corporate Financial Distress

1 Univariate Technique 1-at-a-time

2 No “Bottom Line”

3 Subjective Weightings

4 Ambiguous

5 Misleading

8 Forecasting Distress With Discriminant Analysis Linear Form

Z = a1x1 + a2x2 + a3x3 + …… + anxn

Z = Discriminant Score (Z Score)

a1 an = Discriminant Coefficients (Weights)

x1 xn = Discriminant Variables (e.g. Ratios)

Example x x x x x x x x x x x x x x x EBIT x x x x x x x x x x x x x TA x x x x x x x x x x x

9 EQUITY/DEBT Z-Score Component Definitions and Weightings

Variable Definition Weighting Factor

X1 Working Capital 1.2 Total Assets

X2 Retained Earnings 1.4 Total Assets

X3 EBIT 3.3 Total Assets

X4 Market Value of Equity 0.6 Book Value of Total Liabilities

X5 Sales 1.0

Total Assets 10 Zones of Discrimination: Original Z - Score Model (1968)

Z > 2.99 - “Safe” Zone 1.8 < Z < 2.99 - “Grey” Zone Z < 1.80 - “Distress” Zone

11 Time Series Impact On Corporate Z-Scores

• Migration - Greater Use of Leverage - Impact of HY Bond & LL Markets - Global Competition - More and Larger Bankruptcies

• Increased Type II Error

12 Estimating Probability of Default (PD) and Probability of Loss Given Defaults (LGD) Method #1 • Credit scores on new or existing debt • Bond rating equivalents on new issues (Mortality) or existing issues (Rating Agency Cumulative Defaults) • Utilizing mortality or cumulative default rates to estimate marginal and cumulative defaults • Estimating Default Recoveries and Probability of Loss or Method #2 • Credit scores on new or existing debt • Direct estimation of the probability of default

• Based on PDs, assign a rating 13 Median Z-Score by S&P Bond Rating for U.S. Manufacturing Firms: 1992 - 2013

Rating 2013 (No.) 2004-2010 1996-2001 1992-1995

AAA/AA 4.13 (15) 4.18 6.20* 4.80*

A 4.00 (64) 3.71 4.22 3.87

BBB 3.01 (131) 3.26 3.74 2.75

BB 2.69 (119) 2.48 2.81 2.25

B 1.66 (80) 1.74 1.80 1.87

CCC/CC 0.23 (3) 0.46 0.33 0.40

D 0.01 (33) -0.04 -0.20 0.05

*AAA Only. Sources: Compustat Database, mainly S&P 500 firms, compilation by NYU Salomon Center, Stern School of Business. 14 Mortality Rates by Original Rating All Rated Corporate Bonds* 1971-2016 Years After Issuance

1 2 3 4 5 6 7 8 9 10

AAA Marginal 0.00% 0.00% 0.00% 0.00% 0.01% 0.02% 0.01% 0.00% 0.00% 0.00% Cumulative 0.00% 0.00% 0.00% 0.00% 0.01% 0.03% 0.04% 0.04% 0.04% 0.04%

AA Marginal 0.00% 0.00% 0.20% 0.06% 0.02% 0.01% 0.01% 0.01% 0.02% 0.01% Cumulative 0.00% 0.00% 0.20% 0.26% 0.28% 0.29% 0.30% 0.31% 0.33% 0.34%

A Marginal 0.01% 0.03% 0.11% 0.12% 0.09% 0.05% 0.02% 0.24% 0.07% 0.04% Cumulative 0.01% 0.04% 0.15% 0.27% 0.36% 0.41% 0.43% 0.67% 0.74% 0.78%

BBB Marginal 0.32% 2.34% 1.24% 0.98% 0.49% 0.22% 0.25% 0.16% 0.17% 0.33% Cumulative 0.32% 2.65% 3.86% 4.80% 5.27% 5.48% 5.71% 5.86% 6.02% 6.33%

BB Marginal 0.92% 2.04% 3.85% 1.95% 2.42% 1.56% 1.44% 1.10% 1.41% 3.11% Cumulative 0.92% 2.94% 6.68% 8.50% 10.71% 12.11% 13.37% 14.32% 15.53% 18.16%

B Marginal 2.86% 7.67% 7.78% 7.75% 5.74% 4.46% 3.60% 2.05% 1.73% 0.75% Cumulative 2.86% 10.31% 17.29% 23.70% 28.08% 31.29% 33.76% 35.12% 36.24% 36.72%

CCC Marginal 8.11% 12.40% 17.75% 16.25% 4.90% 11.62% 5.40% 4.75% 0.64% 4.26% Cumulative 8.11% 19.50% 33.79% 44.55% 47.27% 53.40% 55.91% 58.01% 58.28% 60.05%

*Rated by S&P at Issuance Based on 3,280 issues 15 Source: Standard & Poor's (New York) and Author's Compilation

Mortality Losses by Original Rating

All Rated Corporate Bonds* 1971-2016 Years After Issuance

1 2 3 4 5 6 7 8 9 10

AAA Marginal 0.00% 0.00% 0.00% 0.00% 0.01% 0.01% 0.01% 0.00% 0.00% 0.00% Cumulative 0.00% 0.00% 0.00% 0.00% 0.01% 0.02% 0.03% 0.03% 0.03% 0.03%

AA Marginal 0.00% 0.00% 0.03% 0.02% 0.01% 0.01% 0.00% 0.01% 0.01% 0.01% Cumulative 0.00% 0.00% 0.03% 0.05% 0.06% 0.07% 0.07% 0.08% 0.09% 0.10%

A Marginal 0.00% 0.01% 0.04% 0.05% 0.05% 0.04% 0.02% 0.02% 0.05% 0.03% Cumulative 0.00% 0.01% 0.05% 0.10% 0.15% 0.19% 0.21% 0.23% 0.28% 0.31%

BBB Marginal 0.23% 1.53% 0.70% 0.58% 0.26% 0.16% 0.10% 0.09% 0.10% 0.18% Cumulative 0.23% 1.76% 2.44% 3.01% 3.26% 3.42% 3.51% 3.60% 3.70% 3.87%

BB Marginal 0.55% 1.18% 2.30% 1.11% 1.38% 0.74% 0.78% 0.48% 0.73% 1.09% Cumulative 0.55% 1.72% 3.98% 5.05% 6.36% 7.05% 7.78% 8.22% 8.89% 9.88%

B Marginal 1.92% 5.38% 5.32% 5.20% 3.79% 2.45% 2.34% 1.13% 0.91% 0.53% Cumulative 1.92% 7.20% 12.13% 16.70% 19.86% 21.82% 23.65% 24.52% 25.20% 25.60%

CCC Marginal 5.37% 8.68% 12.49% 11.45% 3.42% 8.61% 2.32% 3.34% 0.40% 2.72% Cumulative 5.37% 13.58% 24.38% 33.04% 35.33% 40.89% 42.27% 44.19% 44.42% 45.93%

*Rated by S&P at Issuance Based on 2,714 issues 16 Source: Standard & Poor's (New York) and Author's Compilation

Z Score Trend - LTV Corp.

3.5 Safe Zone 2.99 3 BBB- 2.5 BB+ Grey Zone 1.8 2 1.5 Distress Zone 1 B- B- CCC+ 0.5 CCC+ Z Score Z 0 -0.5 -1 -1.5 D 1980 1981 1982 1983 1984 1985 1986 Year Bankrupt July ‘86

17 IBM Corporation Z Score (1980 – 2001)

6 5.5 5 4.5 Operating Co. 4 Safe Zone 3.5 Consolidated Co. July 1993: 3 Downgrade AA- to A 2.5 Z Score Z Grey Zone BBB 2 BB 1.5 1 0.5 B 1/93: Downgrade 0 AAA to AA- 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 Year

18 Z-Score Model Applied to GM (Consolidated Data): Bond Rating Equivalents and Scores from 2005 – 2016

Z- Score: General Motors Co.

2.00

B B B B B 1.50 B B- B- CCC+ Full Emergence 1.00 from Bankruptcy Upgrade to BBB- CCC+ 3/31/11 by S&P 9/25/14 0.50

CCC

Score -

Z 0.00

09 10 11 12 05 06 07 08 13 14 15 16

------

Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec -0.50 Dec Emergence, New Co. Only, from D Ch. 11 Filing Bankruptcy, 7/13/09 -1.00 6/01/09 D -1.50

Z-Score

19 Additional Altman Z-Score Models: Private Firm Model (1968) Non-U.S., Emerging Markets Models for Non Financial Industrial Firms (1995) e.g. Latin America (1977, 1995), China (2010), etc. Sovereign Risk Bottom-Up Model (2010) SME Models for the U.S. (2007) & Europe e.g. Italian (2016), U.K. (2017), Spain (?)

20 Z” Score Model for Manufacturers, Non-Manufacturer Industrials; Developed and Emerging Market Credits (1995)

Z” = 3.25 + 6.56X1 + 3.26X2 + 6.72X3 + 1.05X4

X1 = Current Assets - Current Liabilities Total Assets

X2 = Retained Earnings Total Assets

X3 = Earnings Before and Taxes Total Assets

X4 = Book Value of Equity Total Liabilities

21

US Bond Rating Equivalents Based on Z”-Score Model

Z”=3.25+6.56X1+3.26X2+6.72X3+1.05X4 Rating Median 1996 Z”-Scorea Median 2006 Z”-Scorea Median 2013 Z”-Scorea AAA/AA+ 8.15 (8) 7.51 (14) 8.80 (15) AA/AA- 7.16 (33) 7.78 (20) 8.40 (17) A+ 6.85 (24) 7.76 (26) 8.22 (23) A 6.65 (42) 7.53 (61) 6.94 (48) A- 6.40 (38) 7.10 (65) 6.12 (52) BBB+ 6.25 (38) 6.47 (74) 5.80 (70) BBB 5.85 (59) 6.41 (99) 5.75 (127) BBB- 5.65 (52) 6.36 (76) 5.70 (96) BB+ 5.25 (34) 6.25 (68) 5.65 (71) BB 4.95 (25) 6.17 (114) 5.52 (100) BB- 4.75 (65) 5.65 (173) 5.07 (121) B+ 4.50 (78) 5.05 (164) 4.81 (93) B 4.15 (115) 4.29 (139) 4.03 (100) B- 3.75 (95) 3.68 (62) 3.74 (37) CCC+ 3.20 (23) 2.98 (16) 2.84 (13) CCC 2.50 (10) 2.20 (8) 2.57(3) CCC- 1.75 (6) 1.62 (-)b 1.72 (-)b CC/D 0 (14) 0.84 (120) 0.05 (94)c 22 aSample Size in Parantheses. bInterpolated between CCC and CC/D. cBased on 94 Chapter 11 bankruptcy filings, 2010-2013. Sources: Compustat, Company Filings and S&P. Z and Z”-Score Models Applied to Sears, Roebuck & Co.: Bond Rating Equivalents and Scores from 2014 – 2016 Z and Z”- Score: Sears, Roebuck & Co.

3,00

2,50 CCC 2,00 B+ CCC

1,50 B B-

1,00

0,50 D

0,00 2014 2015 2016 Z-Score Z"-Score

23 Source: E. Altman, NYU Salomon Center Z and Z”-Score Models Applied to Toys “R” Us, Inc.: Bond Rating Equivalents and Scores from 2014 – 2Q17 Z and Z”- Score: Toys “R” Us, Inc.

4,00 B- B- 3,50 B- CCC+ 3,00

2,50

2,00 B B B B 1,50

1,00 CCC/CC Ch. 11 Filed 0,50 9/18/17 CC/D 0,00 2014 2015 2016 1Q17 2Q17 (July)

Z-Score Z"-Score

24 Source: E. Altman, NYU Salomon Center Financial Distress (Z-Score) Prediction Applications

External (To The Firm) Analytics Internal (To The Firm) & Research Analytics

 Lenders (e.g., Pricing, Basel Capital Allocation)  To File or Not (e.g., General Motors)

 Bond Investors (e.g., Quality Junk Portfolio  Comparative Risk Profiles Over Time

 Long/Short Investment Strategy on (e.g.  Industrial Sector Assessment (e.g., Energy) Baskets of Strong Balance Sheet Companies &  Sovereign Default Risk Assessment Indexes, e.g. STOXX, Goldman, Nomura)  Purchasers, Suppliers Assessment  Analysts & Rating Agencies  Accounts Receivables Management  Regulators & Government Agencies  Researchers – Scholarly Studies  Auditors (Audit Risk Model) – Going Concern  Chapter 22 Assessment  Advisors (e.g., Assessing Client’s Health)  Managers – Managing a Financial Turnaround  M&A (e.g., Bottom Fishing) Current Conditions and Outlook in Global Credit Markets

26 26 Benign Credit Cycle? Is It Over?

• Length of Benign Credit Cycles: Is the Current Cycle Over? No.

• Default Rates (no)

• Default Forecast (no)

• Recovery Rates (no)

• Yields (no)

• Liquidity (no)

27 Default Rates on High-Yield Bonds

Quarterly Default Rate and Four -Quarter Moving Average 1989 – 2017 (3Q)

6.0% 16.0%

14.0% 5.0%

12.0%

4.0% 10.0%

3.0% 8.0%

6.0%

2.0%

Quarter Average Moving Quarter -

Quarterly Default Rate Quarterly 4.0% 4 4 1.0% 2.0%

0.0% 0.0%

Quarterly Moving Source: Author’s Compilations 28 Default and Recovery Forecasts: Summary of Forecast Models

2016 (12/31) 2017 (12/31) 2018 (9/30) Default Rate Default Rate Default Rate Forecast as of Forecast as Forecast as Model 12/31/2015 of 12/31/2016 of 9/30/2017 Mortality Rate 4.50% 4.20% 4.20%

Yield-Spread 6.00%a 2.18%c 1.81%e

Distress Ratio 4.38%b 1.94%d 1.77%f

Average of Models 4.96% 2.77% 2.59% Recovery Rates* 37.5% 43.8% 44.6%

* Recovery rate based on the log Linear equation between default and recovery rates, see Altman, et al (2005) Journal of Business, November and Slide 45. a Based on Dec. 31, 2015 yield-spread of 699.8bp. b Based on Dec. 31, 2015 Distress Ratio of 24.7%. c Based on Dec. 31, 2016 yield- spread of 412.1. d Based on Dec. 31, 2016 Distress Ratio of 7.4%. e Based on Sep. 30, 2017 yield-spread of 383.4bp. f Based on Sep. 30, 2017

Distress Ratio of 6.16%. 29 Source: All Issuance and Authors’ Estimates of Market Size in 2016 & 2017. Recovery Rate/Default Rate Association: Dollar-Weighted Average Recovery Rates to Dollar Weighted Average Default Rates, 1982 – 3Q17

70% y = -2.6781x + 0.5456 y = -0.116ln(x) + 0.0217 2007 R² = 0.4752 R² = 0.5855 2006

2014 2 -6.664x 1987 y = 39.123x - 7.3671x + 0.6211 y = 0.5471e 2005 R² = 0.5693 R² = 0.5244 60% 2011 1983 2012 2017 1993 2004 1997 2013 1996 50% 1992 1984 2010 1985 2003 1988 2008 1995 40% 1994 1982 1989 1991 2009 1998 1986

2015

Recovery Rate Rate Rate Rate Rate Rate Rate Rate Rate Rate Rate Rate Rate Rate Rate Rate Recovery Recovery Recovery Recovery Recovery Recovery Recovery Recovery Recovery Recovery Recovery Recovery Recovery Recovery Recovery Recovery

30% 2016 1999 2000 2001 2002 1990

20%

10% 0% 2% 4% 6% 8% 10% 12% 14%

Default Rate 30 Source: E. Altman, et. al., “The Link Between Default and Recovery Rates”, NYU Salomon Center, S-03-4. YTM & Option-Adjusted Spreads Between High Yield Markets & U.S. Treasury Notes June 01, 2007 – November 10, 2017

Yield Spread (YTMS) OAS Average YTMS (1981-2016) Average OAS (1981-2016)

12/16/08 (YTMS = 2,046bp, OAS = 2,144bp) 2,200

2,000

1,800

1,600

1,400

1,200

1,000

800 YTMS = 539bp, 600 OAS = 544bp

400 6/12/07 (YTMS = 260bp, OAS = 249bp) 11/10/17 (YTMS = 398p, OAS = 376bp)

200

6/1/2007 8/9/2010 9/5/2016 3/7/2011 5/2/2012 7/9/2015 2/8/2016 4/5/2017

9/14/2007 7/29/2008 9/24/2009 10/3/2011 8/15/2012 8/26/2014 12/9/2014 11/1/2017 4/15/2008 2/26/2009 6/11/2009 1/11/2010 4/26/2010 6/20/2011 1/18/2012 3/15/2013 6/28/2013 1/28/2014 5/13/2014 3/26/2015 5/23/2016 7/19/2017

11/11/2008 11/22/2010 11/28/2012 10/11/2013 10/22/2015 12/19/2016 12/31/2007

31 Sources: Citigroup Yieldbook Index Data and Bank of America Merrill Lynch. Comparative Health of High-Yield Firms (2007 vs. 2012/2014/3Q 2016)

32 Comparing Financial Strength of High-Yield Bond Issuers in 2007& 2012/2014/3Q 2016

Number of Firms Z-Score Z”-Score 2007 294 378 2012 396 486 2014 577 741 2016 (3Q) 581 742

Average Z-Score/ Median Z-Score/ Average Z”-Score/ Median Z”-Score/ Year (BRE)* (BRE)* (BRE)* (BRE)* 2007 1.95 (B+) 1.84 (B+) 4.68 (B+) 4.82 (B+)

2012 1.76 (B) 1.73 (B) 4.54 (B) 4.63 (B)

2014 2.03 (B+) 1.85 (B+) 4.66 (B+) 4.74 (B+)

2016 (3Q) 1.97 (B+) 1.70 (B) 4.44 (B) 4.63 (B)

*Bond Rating Equivalent 33 Source: Authors’ calculations, data from Altman and Hotchkiss (2006) and S&P Capital IQ/Compustat. U.S. Non-financial Corporate Debt to GDP: Comparison to 4- Quarter Moving Average Default Rate

January 1, 1987 – March 31, 2017

% NFCD to GDP (Quarterly) 4-Quarter Moving Average Default Rate 47% 16%

46% 14% 45% 12% 44% 10% 43%

42% 8%

41% 6% 40% 4% 39% 2% 38%

37% 0%

Jan-93 Jan-15 Jan-87 Jan-88 Jan-89 Jan-90 Jan-91 Jan-92 Jan-94 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Jan-01 Jan-02 Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11 Jan-12 Jan-13 Jan-14 Jan-16 Jan-17

Sources: FRED, Federal Reserve Bank of St. Louis and Altman/Kuehne High-Yield Default Rate data. QUALITY JUNK STRATEGY

35 Return/Risk Tradeoffs – Distressed & High-Yield Bonds

As of December 31, 2012

5.000

4.500

4.000 C A 3.500

3.000

2.500

OAS (bp) OAS 2.000

1.500

1.000

500 D B

0 0,00 1,00 2,00 3,00 4,00 5,00 6,00 7,00 8,00 CCC- B- BB BBB- Z"-Score (BRE) A = Very High Return / Low Risk Z” = 3.25 + 6.56X1 + 3.26X2 + 6.72X3 + 1.05X4 B = High Return / Low Risk C = Very High Return / High Risk X1 = CA – CL / TA; X2 = RE / TA; X3 = EBIT / TA; X4 = BVE / TL D = High Return / High Risk 36 Italian High-Yield Bond Market My Work with Classis Capital & Wiserfunding

Providing a Credit Market Discipline to the Italian Mini-bond Market

Models to Assess the Risk & Return Trade-Off for Investors & Issuers of Mini-bonds

37 The importance of SMEs

 SMEs comprise a major share of economic activity in advanced economies. They account for over 95% of enterprises, 60% of employment and over 50% of value added in the Private sector. In the EU, SMEs have created 85% of net new jobs from 2002/2010.  After the last financial crisis, being heavily reliant on traditional bank lending, the majority of SMEs were faced with significant financing constraints in a deleveraging environment and with restricted credit availability from . Despite recent central banks’ supportive stimulus, capital market bond financing is increasingly attractive.  Non-bank market-based financing increasingly appeared as an option to improve the flow of credit to SMEs, while enhancing diversity and widening participation in the financial system.  Since 2012, new channels have become increasingly important for SMEs to satisfy their funding needs. Examples of these new sources of funding are crowdfunding, P2P lending, equity participation, , and Mini-bonds. However, in Europe, SME financing is still heavily reliant on bank lending.

38 What are the constraints to the success of the Italian ExtraMOT PRO Mini-bond market?  All bond face three main risks (Market, Liquidity and Credit), but it is credit risk that is perhaps most critical for relatively unknown, smaller enterprises.  Since the ExtraMOT PRO market is still quite young, there are not as yet aggregate default and recovery statistics. We prefer, therefore, to concentrate on issuer default & return analytics based on Italian SME experience.

The objective of our model is to help:  Italian SMEs to grow and succeed by assessing their risk profile and suggesting what would be the best funding option for them  Lenders and investors to assess the risk-return trade offs in investing in either individual or portfolios of Italian SME mini-bonds

39 SME ZI-Score: Summary of Results

 We segmented the Italian SMEs by industrial sectors and developed four default prediction models for Manufacturing, Services, Retail and Real Estate firms.

 Models have been developed on a representative sample of more the 14.500 SMEs located in the north of Italy and then certified for their relevance at national level.

 Prediction power of the models is significantly high due to the use of informative variables and appropriate techniques applied.

 In addition to the Score, Firms/Analysts/Investors also receive an estimated Bond Rating Equivalent and Probability of Default.

 The SME ZI-Score improves the matching of demand and supply in the capital markets between SMEs looking for funding options and investors.

40 The Dataset

 Initially, financial data of 15,362 active and 1,000 non-active companies were extracted from AIDA (BvD) covering the years 2004 to 2014 (1).

 Few companies (1,852) had to be dropped due to missing financial information.

 The shape and size of the final development sample is reported below

Number Percentage

Non- defaulted firms 13,990 96.4. %

Defaulted firms 520 3.6. %

Total 1 4 ,510 100%

(1): We thank CLASSIS Capital and ASSOLOMBARDA for supporting this research by providing Italian SMEs data

41 Sector Analysis

Sector

6000 Performance Defaulted Non-defaulted 5000

4000

t n

u 3000

o C

2000

1 000

0 Sector s l s re E g g A i u R ce in in P ta ce lt i r n e i u & rv u i R rv c s t i n se c M e r o l fa S g ti a u A c ci n u n a tr a M s n n i o F C

42 Variables Selection

 Consistent with a large number of studies, we choose five accounting ratio categories describing the main aspects of a company’s financial profile: liquidity, profitability, leverage, coverage and activity.

 For each one of these categories, we create a number of financial ratios identified in the literature as being most successful in predicting firms’ bankruptcy and transform them in highly predictive variables

 Next, we apply a statistical forward stepwise selection procedure to the selected variables and estimate the full model for each of the four sectors eliminating the least helpful covariates, one by one, until all the remaining input variables are efficient, i.e. their significance level is above the chosen critical level.

43 The Results

44 The Bond Rating Equivalent

In order to provide additional measures of credit worthiness, we introduce the concept of Bond Rating Equivalents (BRE) and Probabilities of Default (PD). Our benchmarks for determining these two critical variables are comparisons to the financial profiles of thousands of companies rated by one of the major international rating agencies (Standard & Poor’s) and the incidence of default given a certain bond rating when the bond was first issued. The latter is based on updated data from E. Altman’s Mortality Rate Source: Altman & Kuehne, NYU Salomon Centre, 2016 Approach (Altman, Journal of Finance, 1989).

45 Risk Profile of Mini-bond issuers (2015)

Bond Rating Equivalent # SMEs % SMEs Avg. Yield AA 2 2% 0,057 Applying our SME Z -Score on the A 4 4% 0,062 I BBB 24 25% 0,065 mini-bond issuers as of 2015, we BB 18 19% 0,055 find that: B 31 32% 0,059 CCC 14 14% 0,065  Risk profile of SMEs CC 2 2% 0,030 doesn’t seem to influence C 2 2% 0,060 the bond pricing; Source: Firms listed on Borsa Italiana Extra MOT, calculations by the authors  Majority of existing mini- bond issuers classified as non-investment grade;

 The risk profile of the mini-bond issuers is better (i.e. less risky) than total SME sample.

Source: Firms listed on Borsa Italiana Extra MOT, calculations by the authors

46 Wiserfunding Ltd.: Helping Italian SMEs to Succeed

 Mission is to support small business growth by reducing information asymmetry by providing a common set of information to all market participants.

 The SME ZI-Score should not to be used in isolation. Other factor (e.g. debt capacity, cash flow, recovery profile, market outlook, directors’ experience) are assessed when evaluating SMEs’ financial strength.  We believe that by providing lenders/investors and small businesses with the same set of information, we can help them speak the same language.  We are working with Classis Capital, Borsa Italiana, Confindustria, several PMI organizations and SMEs to apply our model effectively.

47 MANAGING A FINANCIAL TURNAROUND: APPLICATIONS OF THE Z-SCORE MODEL

THE GTI CASE

48 Objectives

• To demonstrate that specific management tools which work are available in crisis situations

• To illustrate that predictive models can be turned “inside out” and used as internal management tools to, in effect, reverse their predictions

• To illustrate an interactive, as opposed to a passive, approach to financial decision making

49 Z-Score Component Definitions

Variable Definition Weighting Factor

Working Capital X 1.2 1 Total Assets Retained Earnings X 1.4 2 Total Assets EBIT X 3.3 3 Total Assets Market Value of Equity X 0.6 4 Book Value of Total Liabilities Sales X .999 5 Total Assets 50 Z-Score Distressed Firm Predictor: Application to GTI Corporation (1972 – 1975)

Z-Score 6.00 EPS = $0.09 5.00 EPS = $0.52 Safe 4.00 Zone EPS = $0.19 3.00 Grey Zone 2.00

1.00 Distress Zone 0.00 EPS = ($1.27) 1972 1973 1974 1975

51 Management Tools Used

• Altman’s Distressed Firm Predictor (Z-Score)

• Function / Location Matrix

• Financial Statements

• Planning Systems

• Trend Charts

52 Managerial & Financial Restructuring Actions and Impact on Z-Score

Strategy Reason Impact

Consolidated Locations Eliminate Underutilized Z-Score Assets

Drop Losing Eliminate Unprofitable Z-Score Product Lines Underutilized Assets

Reduce Debt Using Reduce Liabilities Z-Score Funds Received from and Total Assets Sale of Assets

53 Z-Score Distressed Firm Predictor Application to GTI Corporation (1972 – 1984)

Z-Score

9.0 $0.40 8.0 $0.34 7.0 $0.70 Safe 6.0 Zone EPS = $0.09 5.0 $0.52 4.0 $0.19 $0.15 3.0 $0.28 ($0.29) Grey 2.0 Zone

1.0 Distress Zone 0.0 ($1.27) 1972 1974 1976 1978 1980 1982 1984

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