Transcript
Page 1: Using Morningstar’s Risk Model to Drive Investment

©2018 Morningstar, Inc. All rights reserved.

Madison Sargis, Associate Director of Quantitative ResearchChristopher King, Principal Quantitative Analyst December 5, 2018

Using Morningstar’s Risk Model to Drive Investment Decision Making

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gIntro to the Quant team

gDemystifying risk models

gAn expose on exposures

gSources of risk and return

g3 ways to skin a scenario

Agenda

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Quant Team

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Morningstar Quantitative Research: Who We Are

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51 data scientists, researchers, technologists, engineers and analysts around the globe.

PhDs:BioinformaticsComputer ScienceElectrical EngineeringFinanceFracture MechanicsInformation TechnologyMathematicsMechanical EngineeringPolicy AnalysisStatisticsSystems Science

Other:1 Concrete Nuclear

Building Code Author1 Published Poet1 Undefeated Fantasy

Football Gambler1 Violinist1 Anti Ketogenic1 FIFA champion

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What are risk models?

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gWhat factors is this portfolio exposed to?

gWhere is my portfolio’s alpha coming from?

gWhat type of active risk does my portfolio

have?

gHow does this portfolio hold up under

stress?

gWhat macro events will my portfolio be

insulated from?

gHow do I build a portfolio to meet a specific

objective?

What questions do risk models help to answer?

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gFama-French models are time-series regressions

/One asset over many time periods

/Factor premia are known, exposures are unknown

gRisk models are cross-sectional regressions

/Many assets over one time period

/Factor premia are unknown, exposures are known

gAdvantages of holdings-based approach

/More accurate

/Adapts to change – securities, managed products, portfolios

Difference between risk models and Fama-French models

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Expose on Exposures

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What are the factors?

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gValuation

gEconomic Moat

gMomentum

gFinancial Health

gValue-Growth

gSize

gLiquidity

gVolatility

gValuation Uncertainty

gOwnership Risk

gOwnership Popularity

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Partial economic exposures for sectors, regions, currencies

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Relying on categorical classification to assess sector, regional, or currency risk can be quite limiting as classifications are a binary yes or no. As a consequence, risk may be under- or overstated for many large conglomerates that operate multiple business lines across geographies.

rti − rtf = αi + β1i rt1 − rtf + ⋯+ βki rt11 − rtf + εti

rt𝑖𝑖 = weekly return on the 𝑖𝑖th stock

rtf = weekly return on 3 − mo US TBill

rt𝑘𝑘 = weekly return on the 𝑘𝑘th sector or region or currency benchmark

constraints: 0 < βki < 1; �kβki = 1

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Manager Due Diligence example

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gJPM Euroland Dynamic A perf (acc) EUR

gBenchmark: Morningstar Eurozone NR EUR

gPortfolio Date: 7/31/2018

gMomentum exposure +0.205 suggest that this fund might be pursuing a momentum tilt to generate alpha

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Manager Due Diligence example

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gJPM Euroland Dynamic A perf (acc) EUR

gBenchmark: Morningstar Eurozone NR EUR

gDate Range: 7/1/2015 - 6/30/2018

gMomentum exposure has been consistently greater than the benchmark for the past three years.

gMeanwhile, momentum premia has been positive. Suggesting that this tilt is paying off.

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Sources of risk and return

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Where do returns come from?

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gPortfolio attribution tells you where a portfolio’s returnscome from by decomposing a return stream into component pieces.

gThis is useful to know the importance of specific factor tilts and their impact on returns.

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Manager Due Diligence example

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gJPM Euroland Dynamic A perf (acc) EUR

gBenchmark: Morningstar Eurozone NR EUR

gDate Range: 7/1/2016 – 6/30/2018

gDue to the momentum tilt, an approximate 2.45% of alpha was generated

gFactor bets generated 4.9% of total alpha

gStock specific bets detracted 5.6% of total alpha

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Where does risk come from?

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gPortfolio risk decomposition tells you where a portfolio’s risk comes from by decomposing a portfolio’s realized variance into component pieces.

gThis is useful to know the importance of specific factor tilts and their impact on risk.

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Manager Due Diligence example

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gJPM Euroland Dynamic A perf (acc) EUR

gBenchmark: Morningstar Eurozone NR EUR

gDate Range: 7/1/2017 – 6/30/2018

gFactors account for most risk

gMomentum contributed small amount of risk

gDeveloped Europe accounted for the majority of total risk (38.7%) and active risk (73.8%)

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3 ways to skin a scenario

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gOil prices experienced a sustained rise from around $40/bbl in January 2007 to just less than $150/bbl on July 15, 2008.

gIf oil prices were to rise by 375% in the next 17 months, what is a plausible return outcome for the energy sector and the U.S. market?

Our Scenario

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Three Scenario Analysis Methodologies

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Historical Scenario Analysis

Macro-Financial Scenario Analysis

Market-Driven Scenario Analysis

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Replay a historical scenario exactly as it occurred/aka. capture the full state of the world at a certain

time

g3 General Steps:

1. Take premia for a time period in the past

2. Use current exposures

3. Create return series using past premia and current exposures

Historical Scenario Analysis

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A few scenarios…

/2003 Bond Selloff

/2006 Emerging Market Selloff

/2007-2008 Oil Price Rise

/2007-2009 Financial Crisis

/2010 Greek Crisis

/2014-2015 Oil Price Drop

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gEnergy ETF return at 80% while S&P 500 ETF return at slight loss

gThis takes into account market activities in 2007-2008 which not only had a significant oil price rise, but also market wide losses. This is somewhat unique occurrence.

gThis scenario tells us “What would happen if we took our ETF’s and replayed them back through the 2007-2008 Oil Price rise”

Historical Scenario Analysis Outcome

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Demo TimeHistorical Demo

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Shock multiple macro-financial variables at distinct future times

g3 General Steps:

/Create Macro-Financial model to represent economy

/Link Macro-Financial model with Risk Model premia

/Introduce Macro-Financial “shocks” to create scenario

Macro-Financial Scenario Analysis

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Date Oil-Price Scale Oil Price

9/30/2018 1.09 74.02

10/31/2018 1.11 75.46

11/30/2018 1.17 79.88

12/31/2018 1.16 79.23

1/31/2019 1.24 84.27

2/28/2019 1.36 92.54

3/31/2019 1.33 90.35

4/30/2019 1.47 99.79

5/31/2019 1.57 107.13

6/30/2019 1.74 118.33

7/31/2019 1.68 114.48

8/31/2019 1.71 116.08

9/30/2019 1.75 119.1

10/31/2019 1.93 131.66

11/30/2019 2.07 140.56

12/31/2019 2.3 156.57

1/31/2020 2.46 167.16

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Macro-Financial Scenario Analysis Outcome

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gEnergy ETF return at 52% while S&P 500 ETF return at 32%

gSignificantly different from Historical Scenario Analysis due to much different assumptions

gThis scenario tells us “What would happen if we took our ETF’s and increased oil prices in the same manner as 2007-2008”

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Macro-Financial Scenario Analysis Outcome (alternate)

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gFlexibility of the tool allows us to add additional constraints. Now we also add S&P shock to match 2007-2008 time period.

gThis has dampening effect on returns, as it should

gThis scenario tells us “What would happen if we took our ETF’s and increased oil prices and the S&P 500 acted in the same manner as 2007-2008”

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Demo TimeMacro-Financial Demo

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Run Monte Carlo simulations in order to stress test

g3 General Steps:

/Select index to shock

/Choose 2 of following parameters:

/Percentile of index distribution

/Magnitude of shock

/Duration of shock

/Run simulation of premia while taking into account volatility clustering

Market-Driven Scenario Analysis

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Magnitude = 375% shock of oil pricesPercentile = 95th

Our Model

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Market-Driven Scenario Analysis

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gMean Energy ETF return of 34% from all simulations

g18 months to reach oil shock of 375% at 95th

percentile

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Historical Scenario Analysis

• Replay historical premia that capture “full state of the world”

• “What would happen if we went through another financial crisis?”

Macro-Financial Scenario Analysis

• Shock multiple macro-financial variables at distinct future times

• “What would happen if unemployment rose 2% next month and GDP drops 1% each of the next 6 months?”

Market-Driven Scenario Analysis

• Run Monte-Carlo simulations in order to stress test

• “What would happen if the S&P500 dropped 30% over the next month?”

Quick Comparison

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1. Pick correct tool for the job

2. Powerful, flexible tools

3. 3 current offers. A fourth coming soon…

Scenario Tool Takeaways

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Morningstar Risk Model Roadmap

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gMorningstar Global Equity Risk Model

/Available in USD

/Coming Soon: EUR, GBP

gMorningstar United Kingdom Equity Risk Model

/Coming Soon: EUR, GBP

gMorningstar Multi Asset Risk Model

Morningstar Risk Models Roadmap

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gComing very soon!

gCurrent Coverage

/Corporate, Government, Municipal bonds without optionality

/Interest rate factors

/Shift, twist, curvature

/Currencies

/USD, EUR, GBP, CHF

gExpected coverage at the end 2019

/Credit, duration, and convexity factors

/Additional bond types

Morningstar Multi Asset Risk Model

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Appendix


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