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Identifying Emerging Markets Bond Mispricing with Social Development Metrics ASHTA – Drullinsky, Ellison, Hamilton, Pandya.

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Presentation on theme: "Identifying Emerging Markets Bond Mispricing with Social Development Metrics ASHTA – Drullinsky, Ellison, Hamilton, Pandya."— Presentation transcript:

1 Identifying Emerging Markets Bond Mispricing with Social Development Metrics ASHTA – Drullinsky, Ellison, Hamilton, Pandya

2 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)

3 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

4 Agenda Emerging Market Debt – An Introduction Data Selection, Processing, and Challenges Analysis of Indicators using Multivariate Regression Models Perfect Trading Strategies Recommendations and Refinements

5 Emerging Markets Debt – An Introduction N. Drullinsky

6 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

7 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.

8 EM corporate debt is higher in volume than sovereign debt in some countries Source: JP Morgan as of December 31, 2010

9 EM Corporate debt issuance is growing Evolution of EM Corporate Debt Issuance by region Source: Bond Radar, Dealogic, C-Bonds, Banamex, and JP Morgan estimates. Emerging Market Corporate Debt Issuance by Region (in $USD)

10 Data Selection, Processing, and Challenges K. Ellison

11 We compiled data from multiple sources

12 We looked at the following countries from 2005 to 2012

13 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

14 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

15 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 Spreads vs. 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 SituationExample

16 Analysis and Discussion S. Pandya

17 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)

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

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

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

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

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

23 The corporate model has limited applicability due to lower sample size

24 Trading Strategies M. Hamilton

25 We developed a sovereign trading strategy using CDS instruments and assuming 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)

26 We were able to test the performance of the strategy using WB indicators World Bank Data 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, Bulgaria 1,265, South Africa 1,017, Colombia 714, Russia 451, 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

27 Using the CIA indicators, the trading strategy seems even more profitable CIA Data 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, Ukraine 6,419, Argentina 6,245, 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

28 Recommendations and Refinements Team

29 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

30 Thank you! Questions?

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