Identifying Emerging Markets Bond Mispricing with Social Development

Metrics

ASHTA – Drullinsky, Ellison, Hamilton, Pandya

Development metrics may provide additional information that could lead to better pricing

• Our hypotheses:• Relative risk of default, non-performance, and sub-performance are

directly tied to a country’s underlying human capital indicators (HCIs) in addition to the business environment indicators (BEIs)

• Development of human capital tends to precede changes in business environment and hence HCIs could be valuable leading indicators without needing to elaborately timeshift data

• A combination of HCIs and BEIs could lead to a trading strategy with improvements over traditional fixed-income trading strategies

• Mispricing is likely to be reflected both in government bonds (sovereign spreads, CDS) as well as corporate bonds (investment grade, high yield)

We divided the project into discrete milestones for planning and execution

• Understand the emerging market (EM) landscape

• Source data on EM sovereign and corporate high-yield bonds – spreads, maturities, life/duration

• Select and process indicators that would have the highest likelihood of representative information

• Measure the statistical correlation between indicators and bond metrics using multivariate regressions

• Develop a custom trading strategy based on past performance

• Evaluate mispricing assuming perfect “20-20” hindsight

• Suggest refinements to the perfect trading strategy and potential future project expansions

Agenda

Emerging Market Debt – An Introduction

Data Selection, Processing, and Challenges

Analysis of Indicators using Multivariate Regression Models

Perfect Trading Strategies

Recommendations and Refinements

Emerging Markets Debt – An IntroductionN. Drullinsky

We researched emerging market bond logistics

What are EM Corporate Bonds?

Why Invest? How to invest?

• Debt issued by EM corporations to fund their operation

• Denominated in U.S currency and local currency

• Traded at a credit spread relative to U.S. Treasury spreads. Exposed to:‐ EM political risk,‐ Foreign exchange rate risk‐ Illiquidity risk ‐ Bankruptcy risks‐ Other factors

• Allows a deeper portfolio diversification, as they tend to be less closely correlated with domestic assets

• Offers investors a way to capitalize on growing companies in developing countries

• Two main alternatives to be exposed to EM Corporate Bonds:‐ EM mutual funds‐ ETFs

EM Corporate debt issuance is higher in volume than sovereign debt

Source: JP Morgan as of December 31, 2010

Currently the total value of the asset class is more than $1 trillion, similar to the US high yield market.

EM corporate debt is higher in volume than sovereign debt in some countries

Source: JP Morgan as of December 31, 2010

EM Corporate debt issuance is growing

• Evolution of EM Corporate Debt Issuance by region

Source: Bond Radar, Dealogic, C-Bonds, Banamex, Debentures.com and JP Morgan estimates.

Emerging Market Corporate Debt Issuance by Region (in $USD)

2000-2010

Data Selection, Processing, and ChallengesK. Ellison

We compiled data from multiple sources

We looked at the following countries from 2005 to 2012

A significant amount of time was spent on cleaning and assembling data

• Data cleaning• Removal of bad data and missing data• Translation of data from European format (commas to periods) and

into common units (percentages, basis points, etc)• Roughly 40% of the data needed to be reformatted or cleansed

• Data standardization• Some data was annual; others were quarterly and monthly• Where data was geometric and cumulative (such as returns), we

used geometric means• Where data was discrete (such as rankings), we used arithmetic

averaging

• All data was assembled into a master file in order to facilitate cuts for various statistical analyses

We normalized variables to a standard range (0 to 1) in order to facilitate comparison

• Normalization of the data sources was performed across time for a particular variable

• Where available, absolute normalization (normalization relative to all countries) was used

• This was primarily done for WB variables with finite limits

• When not possible, relative normalization between the sample set was performed

• Primarily for EOB quantitative metrics like days to enforce to a contract

• Other variables like GINI were normalized out of the box

We encountered many challenges in selecting, cleaning, and normalizing the data

Credit spreads are fluid and but many economic and/or social indicators little to no change, even in the long term

Dynamic Credit Spreadsvs. Static Indicators

Brazil’s political risk ranking may not change despite changes in its credit spread making it difficult to forecast spreads based on a regression output

CEMBI data was reported monthly where as economic and social indicators were reported annually

Monthly Data vs. Annual Data

Monthly data was annualized using a geometric mean in order to run regressions yet limits ability to extrapolate periods of high volatility

CEMBI and World Bank credit spreads, as well as World Bank, IFC and CIA indicators are limited in historic data points, often to less than a decade

Limited Historic Data Points

Limited time series led to less variable inputs into the regressions and certain countries could not be included because of a lack of time series consistency across data points

Limited data exists on baskets for corporate credit in emerging markets, buying a particular basket of credit is not readily available

Illiquid Markets for Trading Strategies

Difficult to test specific trade ideas, such as shorting Chilean corporate HY’s because that trade would likely be negotiated between an intermediary

Situation Example

Analysis and DiscussionS. Pandya

We used multivariate regression models to tease out correlations between indicators and spreads

• Dependent variable datasets and listings• CEMBI – Spreads, Future Options (Duration, Volatility)• EMBI – Spreads, Future Options (Duration, Volatility)

• Independent variable datasets and listings• ICRG – Political and Economic/Financial Risk Ratings• World Bank – Life Expectancy (Male/Female), Child Mortality, Labor Force

Participation, Mandatory Years Education,…• World Bank/IFC – Cost to Enforce Contracts, Procedures to Start Business,

Investment Protection Rating, Credit Info Index,…• CIA (Global Factbook) – GINI, Unemployment, Industrial Production, Inflation,

and GDP Real Growth Rate

• In order to be thorough, we explored relationships on all permutations of data sets

• Most effective model in each permutation is presented• Additionally, we used static point values instead of deltas (future option)

For EMBI-ICRG, the optimal model uses both variables as well as on-going time index

Regression Statistics: Model 4 for EMBI_ICRG_Spreads (3 variables, n=198)

R-Squared Adj.RSqr Std.Err.Reg. # Cases # Missing t(2.50%,195) Conf. level

0.774 0.770 194.211 198 0 1.972 95.0%There is no universally accepted definition of R-squared and adjusted R-squared for a model w ith no intercept.The mean value of the residuals is not necessarily zero w hen there is no intercept. In this model it is 0.026.

Summary Table: Model 4 for EMBI_ICRG_Spreads (3 variables, n=198)

Variable Coefficient Std.Err. t-Stat. P -value Lower95% Upper95%

ICRG_Eco___Finance_Risk -15.276 2.033 -7.513 0.000 -19.287 -11.266ICRG_Political_Risk -17.409 1.844 -9.443 0.000 -21.045 -13.773time 1.293 0.089 14.581 0.000 1.118 1.468

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Residual RangeAnderson-Darling statistic is 2.714, which is significant at the 1% level (>1.092), indicating a non-normal

error distribution and/or one or more large outliers.

Histogram of ResidualsModel 4 for EMBI_ICRG_Spreads (3 variables, n=198)

Actual

Theoretical

The fit is distorted for CEMBI due to limited sample size (see data challenges)

Regression Statistics: Model 2 for CEMBI_Spreads (3 variables, n=66)

R-Squared Adj.RSqr Std.Err.Reg. # Cases # Missing t(2.50%,63) Conf. level

0.866 0.860 136.364 66 0 1.998 95.0%There is no universally accepted definition of R-squared and adjusted R-squared for a model w ith no intercept.The mean value of the residuals is not necessarily zero w hen there is no intercept. In this model it is -0.052.

Summary Table: Model 2 for CEMBI_Spreads (3 variables, n=66)

Variable Coefficient Std.Err. t-Stat. P -value Lower95% Upper95%

ICRG_Eco___Finance_Risk -5.099 2.651 -1.924 0.059 -10.395 0.198ICRG_Political_Risk -10.758 3.089 -3.483 0.001 -16.931 -4.586time 0.725 0.128 5.662 0.000 0.469 0.981

The optimal sovereign debt model using WB metrics combines 3 BEIs with one HCI

Regression Statistics: Model 7 for EMBI_ICRG_Spreads (4 variables, n=84)

R-Squared Adj.RSqr Std.Err.Reg. # Cases # Missing t(2.50%,80) Conf. level

0.701 0.686 172.847 84 0 1.990 95.0%There is no universally accepted definition of R-squared and adjusted R-squared for a model w ith no intercept.The mean value of the residuals is not necessarily zero w hen there is no intercept. In this model it is -0.306.

Summary Table: Model 7 for EMBI_ICRG_Spreads (4 variables, n=84)

Variable Coefficient Std.Err. t-Stat. P -value Lower95% Upper95%

WB_Credit_Info_Index 265.437 86.168 3.080 0.003 93.958 436.917WB_Invest_Protect_Index -549.481 145.124 -3.786 0.000 -838.287 -260.675WB_Proced_Start_Biz 238.325 95.103 2.506 0.014 49.063 427.587WB_Years_Mandatory_Edu 321.857 119.217 2.700 0.008 84.608 559.106

An optimal model for corporate bonds using consists of 2 BEIs, one HCI, no time/intercept

Regression Statistics: Model 6 for CEMBI_Spreads (3 variables, n=56)

R-Squared Adj.RSqr Std.Err.Reg. # Cases # Missing t(2.50%,53) Conf. level

0.821 0.811 161.660 56 0 2.006 95.0%There is no universally accepted definition of R-squared and adjusted R-squared for a model w ith no intercept.The mean value of the residuals is not necessarily zero w hen there is no intercept. In this model it is 1.725.

Summary Table: Model 6 for CEMBI_Spreads (3 variables, n=56)

Variable Coefficient Std.Err. t-Stat. P -value Lower95% Upper95%

WB_Credit_Info_Index 106.214 117.991 0.900 0.372 -130.447 342.875WB_Invest_Protect_Index -148.311 98.397 -1.507 0.138 -345.671 49.048WB_Years_Mandatory_Edu 570.967 158.932 3.593 0.001 252.190 889.745

The CIA-based model offered limited explanatory power due to non-HCI indicators

Regression Statistics: Model 4 for EMBI_ICRG_Spreads (2 variables, n=197)

R-Squared Adj.RSqr Std.Err.Reg. # Cases # Missing t(2.50%,195) Conf. level

0.719 0.716 217.365 197 0 1.972 95.0%There is no universally accepted definition of R-squared and adjusted R-squared for a model w ith no intercept.The mean value of the residuals is not necessarily zero w hen there is no intercept. In this model it is 3.898.

Summary Table: Model 4 for EMBI_ICRG_Spreads (2 variables, n=197)

Variable Coefficient Std.Err. t-Stat. P -value Lower95% Upper95%

Gini_Normalized 134.190 31.566 4.251 0.000 71.936 196.444Inflation_Normalized 899.359 76.088 11.820 0.000 749.297 1,049

The corporate model has limited applicability due to lower sample size

Regression Statistics: Model 3 for CEMBI_Spreads (2 variables, n=95)

R-Squared Adj.RSqr Std.Err.Reg. # Cases # Missing t(2.50%,93) Conf. level

0.827 0.823 144.242 95 0 1.986 95.0%There is no universally accepted definition of R-squared and adjusted R-squared for a model w ith no intercept.The mean value of the residuals is not necessarily zero w hen there is no intercept. In this model it is 2.382.

Summary Table: Model 3 for CEMBI_Spreads (2 variables, n=95)

Variable Coefficient Std.Err. t-Stat. P -value Lower95% Upper95%

Gini_Normalized 294.681 34.717 8.488 0.000 225.740 363.623Inflation_Normalized 627.264 141.556 4.431 0.000 346.161 908.367

Trading StrategiesM. Hamilton

We developed a sovereign trading strategy using CDS instruments and assuming 20-20 hindsight

• EM Sovereign Bonds are often illiquid with little generic pricing data available

• USD 10Y CDS utilized to trial trade strategy

• Execution:• When spreads shrink (tighten) bond prices increase and vice versa• CDS and corresponding bonds inversely correlated - not perfectly• Investors sell CDS when think spreads will tighten and vice versa• If EMBI Spreads > Ashta Adjusted Spreads = Buying Opportunity (Sell CDS)• If EMBI Spreads < Ashta Adjusted Spreads = Selling Opportunity (Buy CDS)

We were able to test the performance of the strategy using WB indicators

• World Bank Data• 2005-2011• Buying or selling 1000 CDS

contracts annually based on modeled trading strategy

• Tends to award exposure to high risk sovereigns

• Does not factor in defaults (key)

Country Profit/Loss Argentina 5,831,210.00 Bulgaria 1,265,398.00 South Africa 1,017,775.00 Colombia 714,680.00 Russia 451,055.00 Egypt (373,932.00)China (443,164.00)Brazil (789,970.00)Mexico (1,035,631.00)Peru (1,409,767.00)

Total 5,227,654.00

Using the CIA indicators, the trading strategy seems even more profitable

• CIA Data• 2005-20011• Buying or selling 1000 CDS

contracts annually based on modeled trading strategy

• Tends to award exposure to high risk sovereigns

• Does not factor in defaults

Country Profit/Loss Venezuela 6,498,531.00 Ukraine 6,419,213.00 Argentina 6,245,371.00 Bulgaria (65,205.00)Peru (85,162.00)Panama (192,597.00)Brazil (249,182.00)China (282,932.00)Colombia (561,521.00)South Africa (807,084.00)Mexico (1,314,231.00)Turkey (1,437,515.00)Russia (2,033,231.00)

Total 12,134,455.00

Recommendations and RefinementsTeam

If we had more time and/or resources, here are the refinements we would have wanted to make

• Expanding regressions to monthly or daily returns

• Accessing premium indexing and pricing services (i.e. Barclays Point)

• Exploring value in local currency debt while managing the FX and default risks

• Identifying further risk metrics commonly used/misused by market participants

• Expanding the testing to using HCI metrics from the CIA and potentially leveraging the broad metric set of the Factbook and the World Bank

• Getting and incorporating in metrics and indicators from UNDP, IFC, Transparency International, Human Rights Watch, and other groups

Thank you!Questions?