AN ANALYSIS OF THE IMPACT OF SOVEREIGN CREDIT RATINGS ON FINANCIAL STABILITY USING MACHINE LEARNING

2-3 December 2019

OLIVER TAKAWIRA

PhD Candidate

DFIM CBE UJ

• All central banks aim to avoid financial crisis but promote financial stability.• Financial stability (FS) is so broad and difficult to define or identify its

determinants.• FS was split into four categories to find its determinants namely :

-Financial Conditions, Confidence, Banking System and Price Stability .

• Uncertainty of the impact of Sovereign credit rating (SCR) on FS becauseSCRs have an influence the international bond market .

• Credit Rating Agencies (CRA) provide information and monitoring services.• Investors use risk-rating scale to make investment and financial decisions.

Background

Motivation • Forecasting and maintaining FS.

• To advise central banks on the determinants of FS.

• Analyse the impact of sovereign ratings and outlooks on FS.

• Use ML, data mining and econometric models to analyse time series data inorder to solve financial problems.

• Compare Linear Regression Model (LM) and Support Vector Model (SVM)SVM -[or Support Vector Regression (SVR)].

Problem Statement • Unregulated and oligopolistic market dominated by Fitch, Moody’s and Standard

and Poor’s (S&P).

• Opaque methodologies which failed to predict the Asian financial crisis of 1990.

• Over reliance on CRAs which led to the 2008 financial crisis.

• Ceilings & Benchmarks.

• Increase borrowing costs and negatively affect or limit access to funding.

Research QuestionsThe research questions are as follows:

• What constitute financial stability?

- Which economic factors determine financial stability?

- How can Financial Stability (FS) be modelled?

• To what extent do sovereign credit ratings and outlooks influence financial

stability?

Data and Variables -Quarterly time series secondary data on South Africa (SA) starting from 1995 to2018. [Trading Economics, SARB, Quantec Easy data, JSE, Thomson Reuters andStats SA]

-Real effective exchange rates (REER), Prime lending interest rate (PIR), Brentcrude oil prices (BCO), Household saving to disposable income (HSDI), Householddebt to disposable income ratio (HDDI), Unemployment rate (UR), Gross domesticproduct percentage change (GDPpc), Real gross national income (RGNI), Foreigndebt to GDP (FDGDP), Balance of payments (BOP), Current account balance (CAB),Bank index (BANKI), Consumer confidence (CONFI), Financial conditions index(FCI), Fixed income securities yield on government bonds (FBYGB), Financial index– price index (FIPI), Sovereign Credit Ratings (SCR) and Outlook of SovereignRatings (ORI).*Sovereign Credit Ratings and Outlook Index (SCORI).

Characteristics of FS

Characteristics of financial Stability

High Degree of

confidence Stable positions of key financial

institutions

Payment services

Allocation of credit to

investment opportunities

Well-functioning

financial intermediation process

Smooth market functioning

Monetary stability

Capability of financial system to withstand shocks

Source: Anatolyevna and Ramilevna (2013)

Description S&P Moody's Fitch # ScalePrime AAA Aaa AAA 21

High gradeAA+ Aa1 AA+ 20AA Aa2 AA 19AA- Aa3 AA- 18

Upper medium gradeA+ A1 A+ 17A A2 A 16A- A3 A- 15

Lower medium gradeBBB+ Baa1 BBB+ 14BBB Baa2 BBB 13BBB- Baa3 BBB- 12

Non-investment grade speculativeBB+ Ba1 BB+ 11

BB Ba2 BB 10BB- Ba3 BB- 9

Highly speculativeB+ B1 B+ 8B B2 B 7B- B3 B- 6

Substantial risks Extremely speculative In default with little prospect for recovery

CCC+ Caa1 CCC+ 5

CCC Caa2 CCC 4CCC- Caa3 CCC- 3CC Ca CC; C 2

In Default SD; D C DDD; DD; D 1

Literature Review -Financial stability is controversial in its definition, less quantifiable and moredifficult to define (Frenkel et al. 2004).-(Kabundi and Mbelu (2017) noted FCI as the leading indicator of system stress.-Bruha (2018) monetary stability constitute the stability in price level and value of acurrency. Monetary Stability Index (MSI) is a better indicator.-Brown, (2002) amended the classical misconception of visualizing financialstability in terms of managing systemic financial risk.-Unregulated and lowly supervised high level of financial liberalization may renderthe banking sector more fragile (Havrylchyk, 2010)-Liberalization brings innovation.-Ratings downgrades increase the cost of borrowing and high-interest ratesinfluence property values (Alter and Schueler, 2012).-Iyengar (2010) credit rating measures the ability to service debt obligationseffectively and in time.

Research Methodology-1The Principal Component Analysis (PCA) method was used to create an index termed the sovereign credit outlook ratings index (SCORI). 𝑦𝑦𝑖𝑖𝑖𝑖 = 𝑤𝑤1𝑖𝑖𝑥𝑥1𝑖𝑖 + 𝑤𝑤2𝑖𝑖𝑥𝑥2𝑖𝑖 + 𝑤𝑤3𝑖𝑖𝑥𝑥3𝑖𝑖 + ⋯+ 𝑤𝑤𝑝𝑝𝑖𝑖𝑥𝑥𝑝𝑝𝑖𝑖-𝑦𝑦𝑖𝑖𝑖𝑖 represent SCORI a factors resulting from weighted average of numericalscores of the 𝑥𝑥𝑖𝑖 denoting Fitch, Moody’s and S&P ratings; 𝑊𝑊 are weights.

Machine Learning (ML) algorithms were used to classify and analyse FS using economic indicators.- ML Supervised approach in which we know the outcome. - Cross-validation split data into two sets - train set and test set. - Linear Regression Model (LM) and the Support Vector Machine Model (SVM).Diebold-Mariano (DM) test - tests prediction accuracy between unique models.

LM and SVM Diagrams 𝑦𝑦 = 𝑎𝑎 + 𝑥𝑥𝛽𝛽 + 𝜀𝜀

𝑦𝑦 𝑥𝑥 = 𝑤𝑤𝑇𝑇𝜑𝜑 𝑥𝑥 + 𝑏𝑏

Research Methodology-2Linear Regression Model (LM)

𝑦𝑦𝑖𝑖𝑖𝑖 = 𝛽𝛽0 + ∑𝑖𝑖=115 𝑥𝑥𝑖𝑖𝑖𝑖 𝛽𝛽𝑖𝑖 + 𝜀𝜀

𝑦𝑦𝑖𝑖 is the dependent variable at time ‘t’; ‘i’ which can either be FCI, FIPI, BANKIor CONFI (FS categories).

𝑥𝑥𝑖𝑖𝑖𝑖 are independent variables thus the macroeconomic indicator ‘j’ at time ‘t’ this

includes sovereign ratings and outlooks.

- Robust Regression applies the Linear regression by correcting for outliers.

- Structural Vector Auto - Regression Model (SVAR) to test for structural shocksor linkages.

Support Vector Machine(SVM) Model

SVMs seek a separating hyperplane that maximizes the margin between sets of data.

The SVM model used the approach applied by Okasha (2014) :

𝑦𝑦 𝑥𝑥 = 𝑤𝑤𝑇𝑇𝜑𝜑 𝑥𝑥 + 𝑏𝑏

-Introducing Lagrange multipliers to solve the optimization problem. C is referred

to as the constant regularized and the 12𝑤𝑤𝑇𝑇𝑤𝑤 is the weight vector norm. The solution

can be shown to have an expansion:

𝐿𝐿 = 12𝑤𝑤𝑇𝑇𝑤𝑤 + 𝐶𝐶 ∑𝑖𝑖=1𝑁𝑁 𝜉𝜉𝑖𝑖 + 𝜉𝜉𝑖𝑖

∗ − ∑𝑖𝑖=1𝑁𝑁 𝑎𝑎𝑖𝑖(𝜀𝜀 + 𝜉𝜉𝑖𝑖 − 𝑦𝑦𝑖𝑖 + 𝑤𝑤𝑇𝑇𝑥𝑥𝑖𝑖 + 𝑏𝑏) −

∑𝑖𝑖=1𝑁𝑁 𝑎𝑎𝑖𝑖∗(𝜀𝜀 + 𝜉𝜉𝑖𝑖∗ − 𝑦𝑦𝑖𝑖 + 𝑤𝑤𝑇𝑇𝑥𝑥𝑖𝑖 + 𝑏𝑏) − ∑𝑖𝑖=1𝑁𝑁 𝜂𝜂𝑖𝑖𝜉𝜉𝑖𝑖 + 𝜂𝜂𝑖𝑖∗𝜉𝜉𝑖𝑖∗

-SVM was applied through the radial basis kernel function.

Trend Analysis from 1995 to 2018

-4

-3

-2

-1

0

1

2

3

4

10 20 30 40 50 60 70 80 90

ORI SCORI

ORI

SCORI

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

10 20 30 40 50 60 70 80 90

BANKI

80

85

90

95

100

105

110

115

10 20 30 40 50 60 70 80 90

FCI

0

10,000

20,000

30,000

40,000

50,000

10 20 30 40 50 60 70 80 90

FIPI

0

20

40

60

80

100

10 20 30 40 50 60 70 80 90

CONFI

60

70

80

90

100

110

10 20 30 40 50 60 70 80 90

REER

Variable Importance LMVariable Importance - LM

FCI Overall FIPI Overall BANKI Overall CONFI OverallREER 100.000 BANKI 100.000 FIPI 100.0000 BANKI 100.00PIR 47.370 FCI 33.328 BCO 32.1518 HDDI 90.26FDGDP 41.468 REER 32.869 CONFI 27.8001 ORI 62.14BCO 41.022 UR 32.716 RGNI 22.6607 BCO 47.72FIPI 38.146 GDPPC 26.108 UR 19.3936 GDPPC 47.34HSDI 24.347 RGNI 21.796 FBYGB 17.4229 SCORI 42.73BOP 17.972 FBYGB 20.437 BOP 13.1757 BOP 39.38ORI 15.825 SCORI 18.261 PIR 11.8435 FDGDP 37.11FBYGB 15.460 PIR 12.413 FCI 11.3221 PIR 33.89BANKI 13.658 HDDI 10.245 SCORI 5.0546 REER 29.82GDPPC 10.296 FDGDP 9.649 HSDI 3.7153 UR 25.59UR 10.171 ORI 8.709 CAB 2.5104 FBYGB 20.76RGNI 5.658 BOP 7.226 HDDI 2.4111 RGNI 16.39CAB 5.303 BCO 5.092 ORI 1.4522 FIPI 16.14CONFI 2.650 CONFI 3.607 GDPPC 0.9167 CAB 12.59HDDI 1.424 CAB 2.253 REER 0.8018 FCI 11.41SCORI 0.000 HSDI 0.000 FDGDP 0.0000 HSDI 0.00

Variable Importance SVM Variable Importance - SVM

FCI Overall FIPI Overall BANKI Overall CONFI OverallREER 100.000 BANKI 100.000 FIPI 100.00000 BANKI 100.000FDGDP 41.691 REER 56.103 BOP 30.31791 FIPI 96.612CONFI 19.642 FBYGB 44.041 FBYGB 29.34012 HDDI 85.473FBYGB 18.897 PIR 36.784 CONFI 29.17459 GDPPC 79.165PIR 14.671 BOP 36.110 REER 25.51019 REER 59.919FIPI 12.656 BCO 28.831 BCO 23.59755 BCO 52.582GDPPC 12.623 CONFI 27.902 RGNI 19.28243 RGNI 47.280UR 10.801 FDGDP 27.216 PIR 18.41327 UR 46.501BANKI 10.654 FCI 23.556 FCI 15.13472 BOP 44.432HDDI 9.684 GDPPC 18.354 CAB 14.40942 FCI 35.804SCORI 7.871 SCORI 14.659 FDGDP 12.85192 CAB 32.996RGNI 5.566 RGNI 13.793 HDDI 12.11744 FBYGB 30.854BCO 5.056 UR 12.616 GDPPC 11.81264 FDGDP 29.606BOP 4.915 HDDI 11.981 UR 5.53793 PIR 22.088CAB 4.675 CAB 6.930 HSDI 0.19971 HSDI 8.879HSDI 2.852 ORI 1.321 ORI 0.01287 ORI 2.412ORI 0.000 HSDI 0.000 SCORI 0.00000 SCORI 0.000

Correlation AnalysisBCO BOP CAB FDGDP GDPPC HDDI HSDI ORI PIR REER RGNI SCORI UR

BANKI 0,737 -0,502 -0,478 -0,032 0,053 0,644 -0,660 -0,370 -0,563 -0,078 0,375 0,510 0,015BCO 1 -0,370 -0,652 0,106 0,009 0,822 -0,804 -0,409 -0,717 -0,103 0,633 0,590 0,122BOP -0,370 1 0,650 0,141 -0,089 -0,336 0,401 0,152 0,199 0,113 -0,147 -0,174 0,209CAB -0,652 0,650 1 -0,162 0,244 -0,669 0,669 0,440 0,596 0,336 -0,745 -0,180 -0,187CONFI 0,010 -0,329 -0,023 0,274 0,596 -0,048 -0,126 0,378 -0,084 0,070 -0,185 0,391 -0,186

FBYGB -0,728 0,262 0,602 -0,544 0,000 -0,705 0,814 0,266 0,878 0,241 -0,697 -0,703 -0,426FCI -0,487 0,310 0,614 -0,569 0,167 -0,502 0,614 0,322 0,549 0,769 -0,652 -0,230 -0,330

FDGDP 0,106 0,141 -0,162 1 0,000 0,005 -0,272 0,099 -0,439 -0,589 0,383 0,316 0,698FIPI 0,538 -0,242 -0,773 0,311 -0,371 0,623 -0,613 -0,551 -0,692 -0,402 0,962 -0,007 0,395

GDPPC 0,009 -0,089 0,244 0,000 1 -0,191 0,019 0,455 0,054 0,307 -0,417 0,369 -0,294HDDI 0,822 -0,336 -0,669 0,005 -0,191 1 -0,809 -0,449 -0,601 -0,090 0,671 0,529 -0,030HSDI -0,804 0,401 0,669 -0,272 0,019 -0,809 1 0,354 0,740 0,274 -0,657 -0,607 -0,205ORI -0,409 0,152 0,440 0,099 0,455 -0,449 0,354 1 0,461 0,177 -0,590 0,198 -0,149PIR -0,717 0,199 0,596 -0,439 0,054 -0,601 0,740 0,461 1 0,229 -0,765 -0,487 -0,415REER -0,103 0,113 0,336 -0,589 0,307 -0,090 0,274 0,177 0,229 1 -0,406 0,030 -0,507RGNI 0,633 -0,147 -0,745 0,383 -0,417 0,671 -0,657 -0,590 -0,765 -0,406 1 0,093 0,456SCORI 0,590 -0,174 -0,180 0,316 0,369 0,529 -0,607 0,198 -0,487 0,030 0,093 1 0,085UR 0,122 0,209 -0,187 0,698 -0,294 -0,030 -0,205 -0,149 -0,415 -0,507 0,456 0,085 1

FindingsThe variables that were significant on FCI were BCO, FDGDP, PIR and REER. Themodel captured and explained 45.13% of the variation on FCI and the model was agood fit on the dataset.Coefficients: Estimate Std.Error t-value Pr(>|t|)(Intercept) 6.631e-01 4.251e-01 1.560 0.12382BCO -3.148e-02 1.074e-02 -2.932 0.00469 **FDGDP -7.419e-01 2.937e-01 -2.526 0.01407 *PIR 8.525e-01 2.999e-01 2.842 0.00603 **REER 4.609e-01 8.051e-02 5.724 3.11e-07 ***SCORI 4.476e-01 8.717e-01 0.514 0.60939Multiple R-squared: 0.5624, Adjusted R-squared: 0.4513F-statistic: 5.061 on 16 and 63 DF, p-value: 1.463e-06

---5%

FIPI-63.26% only BANKI; BANKI- 24.37%; CONFI-10.65%.

Mean Squared Error ValuesMSE FCI FIPI BANKI CONFI

LM 0.218 538 190 30 829 626 24 296 903

SVM 0.273 630 801 31 032 920 24 390 761

RLR 0.095 630 178 28 982 438 24 293 188

Final Remarks • Sovereign ratings and outlooks are insignificant on the variation of financial

stability, but they have a small influence on CONFI - customer confidence.

• In South Africa the most important variable is REER towards financial stability.

-The other variables of concern are BCO, FDGDP, PIR, BANKI, GDPPC, UR, FCI,

CONFI and HDDI.

Diebold-Mariano (DM) Test Results showed that there is no enough evidence for

either one model to have better accuracy in forecasting than the other.

Final Remarks • The machine learning models can predict with precision but needs caution.

• REER is the most crucial variable as it is significant on most of the FS

categories.

• There is moderate positive correlation between SCORI and BANKI but no

evidence that SCORI granger causes FIPI, FCI, CONFI and BANKI.

• Changes in SCORI are insignificant on the variation in FS.

Recommendations• To sustain financial stability the Central Bank should monitor exchange rates,

foreign debt, interest rates, economic growth, unemployment, financial

conditions, household debt and the confidence of financial sector customer.

• Further researches to include of variables like political stability index,

governance measurement, corruption indices, oil prices and gold prices.

• Compare these results with other BRICS countries or the USA.

Any Questions or Additions?

THANK YOU- THE END!