Using Quantitative Credit Risk Metrics for Sustained Alpha Generation. Matteo Namari, CQF

Transcription

1 Using Quantitative Credit Risk Metrics for Sustained Alpha Generation Matteo Namari, CQF 21 st November 2016

2 Contents 1. Moody s Analytics intro 2. CreditEdge metrics: a brief overview 3. How mispriced PD risk leads to alpha generation 4. Using CreditEdge data to minimize credit risk while maintaining yields and returns 5. EDFs as providers of a granular view of risk and opportunity 6. Using CreditEdge metrics to construct smart beta strategies for corporate bond portfolios 2

3 1 Moody s Analytics intro

4 4 Moody s Analytics helps our global clients measure, understand, and mitigate risk beyond ratings Independent provider of credit rating opinions and related information for nearly 100 years. Leading provider of data, software, research related professional services for financial risk management. 4

5 2 CreditEdge metrics: a brief overview

6 Over 300 clients worldwide rely upon CreditEdge Canada Norway United Kingdom Sweden Finland Ireland Denmark Germany United States Mexico Netherlands Switzerland Austria Switzerland Portugal France Israel India Bermuda Italy Qatar China Hong Kong Malaysia Japan Korea Thailand Brazil Philippines South Africa Singapore Australia o o 80+ Banking Institutions 40+ Government Institutions, including over 10 Banking/Financial Regulators and Supranational Entities

7 The EDF model uses equity market data and company financial statements to derive estimates of the market value of assets and asset volatility. As with traditional credit analysis, the model assumes that a firm defaults when its assets are worth less than its liabilities. Unobserved Value Changes Assets The greater the cash flow generative power of the assets, the more they're worth, and the higher the stock price A company s stock price changes because the market s assessment of the assets future CF generation changes. We can see the former, but not the latter. Liabilities Going from equity value and equity volatility to asset value and asset volatility Equity Observed Price Changes 7

8 Distance to Default. Vasicek-Kealhofer model. Summarized A key concept is distance to default (DD) which encapsulates the three main pieces of information: the market value of assets, the default point, and the asset volatility. DD ln( A 0 ) ln( X ) A Where: The numerator of the DD equation captures the firm s financial leverage: Measured by the gap between the market value of a firm s assets and the book value of its liabilities The denominator captures its business risk: Measured by the volatility of the market value of a firm s assets Note: An EDF of 1% means that out of a portfolio of 100 firms, we would expect one to default over a given horizon, usually 12 months

9 The movements of the drivers of a firm s EDF as it approached default are instructive. Note the contrast with book assets. SunEdison s EDF, key drivers, and book value of assets Default

10 Peaks in EDFs have led peaks in the default cycle. The levels are usually right as well. Average US high yield EDF and average US high yield default rate 10

11 The Fair Value Spread (FVS) model encompasses the common drivers of credit spreads. EDF TM (Expected Default Frequency, i.e., probability of default) metrics are a key input to FVS. A simplified/stylized view of the Fair Value Spread Model at the bond level** FVS T 1 z f ( zi ) T N N 1 CEDF it im m T LGD Company Size Market Risk Premium (broad market) Correlation of Company asset value to market Company EDF Market Price of Risk Term of the bond Expected Recovery Value (sector and bond-based) Expected Loss 11

12 Most of our research has involved bond Alpha Factors, which compare market spreads to modeled spreads A Bond s Alpha Factor = OAS/Fair Value Spread 12

13 3 How mispriced PD risk leads to alpha generation

14 We use rank order studies to identify how CE data generates alpha. These are proofs of concept, not actual portfolio exercises. Bond eligibility» Must be a member of a leading market value-weighted index (US IG, US HY, EUR IG)» Must have an Alpha Factor metric from Moody s Analytics Methodology» Divide the eligible securities into five duration buckets and two sectors buckets (FIN and IND/UTL, for IG only) per duration cell, so 10 in total (5 in HY).» Within each duration/sector bucket, rank the bonds by their Alpha Factors.» Select the bonds in the top 20% and bottom 20% of each duration bucket s rank order by bond count.» On a monthly basis, calculate the market value-weighted total returns of the top and bottom 20% bucket, as well as for all the bonds in each duration/sector bucket.» Combine the duration bucket results on a market value-weighted basis. 14

15 The firms in the top 20% bucket outperform, on average, with lower levels of credit risk and higher spreads (for IG) Summary results ( for US IG and HY, for EUR IG) US Investment Grade US High Yield Euro IG Averages Best 20% Index Worst 20% Best 20% Index Worst 20% Best 20% Index Worst 20% Annualized Tot. Ret. 8.8% 6.4% 4.6% 12.3% 7.2% 3.0% 6.7% 4.7% 3.5% - Excl. 2008/ % 6.4% 5.1% 9.9% 7.1% 3.5% 5.5% 4.4% 3.5% Yield 3.0% 3.0% 2.7% 5.5% 6.6% 6.9% 0.9% 0.8% 0.5% Spread Price Duration Years to Maturity EDF 0.39% 0.55% 0.91% 0.51% 2.63% 6.12% 0.45% 0.67% 0.98% Rating Allocation (% of total) * Aaa/Aa for IG, Ba for HY 15% 13% 9% 66% 49% 42% 15% 13% 5% * A for IG, B for HY 32% 41% 61% 31% 36% 49% 32% 42% 58% * Baa for IG, Caa-C for HY 53% 46% 29% 3% 15% 9% 53% 45% 37% 15

16 The value gain/loss after a bond is added to the top/bottom 20% groups can persist for up to 24 months EUR IG: Average cumulative bond spread change vs. the benchmarks, bp ( ) Best 20% Worst 20% 16

17 When sliced on an annual basis, the top 20% of issues outperform the bottom 20% and the index in almost every year EUR IG: Ann. tot. ret. (duration-matched) for the top/bottom 20% of issues and the index 17

18 The average EDF for best 20% bucket is significantly lower, so its outperformance comes with a lower level of credit risk. EUR IG: Average EDF for the top/bottom 20% of issues and the index 18

19 The average spreads of the index and top 20% buckets are close together (most of the time). The worst 20% is often wider. EUR IG: Average Spread for the top/bottom 20% of issues and the index 19

20 The value picture for euro investment grade assets also looks good when measured on the basis of spread vs. EDF Average EUR IG spread vs. the average EUR IG 1yr EDF 20

21 This is not the case for US high yield Average US high yield spread vs. the average US high yield 1yr EDF 21

22 4 Using of CreditEdge data to minimize credit risk while maintaining yields and returns

23 4 Using of CreditEdge data to minimize risk while maintaining yields and returns 1. Euro investment grade

24 EDFs provide a way to limit downgrade risk for IG bonds while maintaining yield levels EUR IG:12mo D/G rates (left) and bond YTMs (right), div. by EDF and YTM quartiles. 1yr downgrade/upgrade ratios Average yield to maturity High High DG periods: July 2007-Feb 2009, Mar Nov 2011 YTM Quartile YTM Quartile Low Low High Low High EDF Quartile EDF Quartile 24

25 During the crisis periods EDF data boosted total return performance across all yield quartiles EUR IG:12mo annualized total returns, div. by EDF and YTM quartiles High Annualized Tot. Ret. (%) Sharpe Ratio High DG periods: July 2007-Feb 2009, Mar Nov 2011 YTM Quartile YTM Quartile Low Low High Low High EDF Quartile EDF Quartile 25

26 In the current market, away from the top row investors can find bonds with good yields, but with low EDFs EUR IG; average EDFs and yields to maturity (October 2016) High Average EDF (%) Average Yield to Maturity (%) YTM Quartile YTM Quartile Low Low High Low High EDF Quartile EDF Quartile 26

27 4 Using of CreditEdge data to minimize risk while maintaining yields and returns 2. US high yield

28 You can use EDFs to find the right balance between default rate reduction and yield give-up 12mo US HY def rates (left) and bond YTMs (right), divided by EDF and YTM quartiles High 1yr Default Rate 4 4.9% 7.5% 6.9% 13.6% Average yield to maturity Crisis periods ( , ) YTM Quartile 3 0.3% 0.2% 1.5% 1.2% % 0.1% 0.1% 0.6% Low 1 0.0% 0.0% 0.0% 0.3% Low High Low High EDF Quartile EDF Quartile 28

29 Except for the top quartile by yield, EDFs also led to better annualized total returns on high yield bonds during the crisis periods Annualized total returns based on YTM and EDF quartiles, US HY bonds High Annualized Tot. Ret. (%) Sharpe Ratio Crisis periods ( , ) YTM Quartile Low Low High Low High EDF Quartile EDF Quartile 29

30 In the current market, away from the top row investors can find bonds with good yields, but with low EDFs US HY; average EDFs and yields to maturity (October 2016) Average EDF Average Yield to Maturity (%) High % 1.85% 4.70% 21.82% YTM Quartile % 0.55% 1.18% 3.97% % 0.33% 0.69% 1.88% Low % 0.20% 0.51% 1.62% Low High Low High EDF Quartile EDF Quartile 30

31 5 EDFs as providers of a granular view of risk and opportunity

32 When credit stress rises, risk is increasingly concentrated in the tail of the distribution. This is to be expected for HY Distribution of EDFs for euro high yield issuers (linear scale) 32

33 but it s also true in investment grade Distribution of EDFs for euro investment grade issuers (linear scale) 33

34 Another benefit is that EDFs provide a much more refined view of risk than broad, relative rank (i.e., ordinal) scales like ratings Distribution of 1yr EDFs for EUR firms rated A, Baa, Ba, and B (October 2016) 34

35 6 Using CreditEdge metrics to construct smart beta strategies for corporate bonds

36 The ALFI Index approach: reweighting market value indices according to their Alpha Factors Bond eligibility» Must be a member of a leading market value-weighted index (US IG, US HY, EUR IG)» Must have an Alpha Factor metric from Moody s Analytics Index construction» Divide the eligible securities by duration bucket» In each duration bucket, divide the bonds between those sold by industrial/utility issuers and those sold by financial institutions (this step only for investment grade indices)» Within each duration/sector group, rank all the bonds by their Alpha Factors For each rank order of issues, exclude the worst 10% of the bonds on an issue count basis, i.e., those with the lowest Alpha Factors» Weigh each issue by its AF Score (see p.6) Index rebalancing rules» AF Scores for index-eligible bonds are recalculated semi-annually on the first business days of April and October using the methodology described in the Index Construction section» The AF Scores used for rebalancing are determined using the Alpha Factors as of the last business day of March and September. 36

37 ALFI Index construction, maintenance, and returns calculation (cont.) New issue inclusion and exclusion of existing issues» Newly issued index-eligible bonds enter the index in the month after they become eligible» New issues entering an index are assigned AF Scores that correspond to their place in the AF rank order for their duration bucket and sector» Bonds that no longer meet the index s eligibility criteria leave the index at the prices prevailing at the end of the month that they lose their eligibility Bond weight and index returns calculations» Within each duration bucket and sector group, determine each issue s Alpha Factor Score. This is based on its rank order in the bucket» Determine each issue s ALFI index weight by multiplying its market value by its Alpha Factor Score» Determine the total return of the ALFI index based on the AF Score-based weight of each bond. 37

38 Each bond s AF Score is determined by its AF rank order in its duration and sector* bucket. The scores range from 0.2 to 1.8. A bond s ALFI index weight is (AF Score* Market Value). Alpha Factor Scores (per duration bucket and sector*) Alpha Factor Scores * No sector breakdown for the high yield index Bond Alpha Factor rank order (from low to high) 38

39 The ALFI indices outperform comparable market-weighted benchmarks in the majority of the years. In all cases performance is boosted by Annual and cumulative excess returns for the three ALFI indices 39

40 The ALFI indices match up reasonably well vs. their market-weighted counterparts. Exceptions are the IG sector split, and HY yield, spread, and coupon levels, which reflect different ratings breakdowns. ALFI and market-weight indices; a selective comparison Market Index ALFI Mkt Val- Wt. ALFI/Mkt Wt ALFI Mkt Val- Wt. ALFI/Mkt Wt ALFI Mkt Val- Wt. Number of issues 5,701 7,164 80% 1,381 1,977 70% 1,338 2,331 57% Total Market Value (billions) 5,145 6,160 84% 988 1,327 74% 1,205 1,947 62% Market Value (IND/UTL, billions) 3,715 3, % ,004 81% Market Value (FIN, billions) 1,429 1,589 90% % Alfi/Mkt Wt Average Issue Face Value (mill.) % % % Average Yield (%) % % % Average Spread (OAS to Tsy, bp) % % % Average Coupon (%) % % % Average Price % % % Average Duration (years) % % % Average life, i.e., years to maturity % % % Average EDF (%) Average Moody's Rating A3 A3 - B1 B1 - A3 A3 - Rating Allocation (% of total) US Investment Grade US High Yield Euro Investment Grade * Aaa/Aa for IG, Ba for HY 13% 13% - 58% 49% - 12% 13% - * A for IG, B for HY 34% 41% - 37% 36% - 39% 42% - * Baa for IG, Caa-C for HY 53% 46% - 4% 15% - 49% 45% - 40

41 Client Portfolio Strategy I: Using Alpha Factors for bond selection, with the goal of boosting the return of a tracker portfolio Year Client Portfolio Total returns Alpha Factor Portfolio Barcap Index Averages Index: A customized global IG bond index. The portfolio is duration-matched to the index, with returns hedged back to the client s home currency. Aims to track the index, holding around 20% of the index s bonds. We replicated the client portfolio s characteristics regarding size, turnover, liquidity, etc., except that we used Alpha Factor metrics to select the bonds.. Since 2011, the AF strategy outperformed the existing portfolio with lower risk and similar characteristics (yield, rating and duration etc.) We assume transaction costs as follows. 50 bp ( ), 75 bp (Jan to Oct 15), 1% (Nov15 to now) 41

42 Client Portfolio Strategy II: Using CE to select a corporate bond/equity portfolio with superior performance characteristics Excess Returns TR (%) Best20 Equity/Bond 50/50 40/60 30/70 20/80 10/90 Equity Bond ,444 1,386 1,328 1,271 1, Average Avg. excl We use the S&P 500 index and US IG bond index. Use EDF (for equity) and Alpha Factor (for bond) and select top 20%. Then, create two portfolios and combine them. Calculate the excess returns for each portfolio and combined them in various ways. Since 2007, the EDF and AF strategy outperformed the 50/50 portfolio. When we combine 10/90 (equity/bond), the excess returns are positive every single year. 42

43 Momentum Strategy: finding the optimal mix between US IG and HY Investment Grade Current Yr. Previous Year OAS-EDF Avg. TR% Q1-Q3 Q4 % chg % % % % % % % % 2016 (ann.) % + High Yield Current Yr. Previous Year OAS-EDF Avg. TR% Q1-Q3 Q4 % chg % % % % % % % % 2016 (ann.) % Weighting metrics: OAS EDF Ways to calculate the weighting factor (i.e., the over/underweight of the IG and HY markets: Average for Q4 vs that for Q1-Q3 Calculate the relative strength of the signals over time to determine how to set the rebalancing. The stronger the signal, the bigger the rebalancing (1.25X market value, otherwise, 0.75X market value) Then combine the IG and HY indices based on the weighting. IG and HY Combined TR (%) OAS-EDF Before Rebal. After Rebal. Difference (ann.)

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