Advanced Applied Finance Javier Estrada Winter, 2014 Assessment - - PDF document

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Advanced Applied Finance

Javier Estrada Winter, 2014

Assessment

Javier Estrada IESE Business School Barcelona Spain ADFIN Winter/2014

Background: Mean Returns

• AM v. GM v. DWM
• Three different questions, three different answers
• What has been the return in a ‘typical’ period?

 The AM (Not as widely used as typically believed)

• What has been the periodic rate at which a passively‐

invested capital evolved over time, compounded?

 The GM (This is what we typically call ‘mean return’)  Remember: AM ≥ GM and AM‒GM = f+ (Volatility)

• What has been the periodic return of an active

investor?

 The DWM (The investor’s IRR)  Remember: DWM ≷ GM  The difference between GM and DWM goes to the heart

• f the active/passive management debate

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Javier Estrada IESE Business School Barcelona Spain ADFIN Winter/2014

Long‐Term Trends – Returns

• Motivation
• Long‐term trends provide useful perspective
• Finance models are long‐term (equilibrium) models
• Evidence: In the long‐term …
• S outperformed B in every country
• The compounding power of S is much higher

 The ERP varies substantially across countries (Remember this when using the CAPM)  World‐market ERP ≈ 5%

• B reduced purchasing power in many countries
• Q: Can the difference in returns between S and B

be explained by differences in risk?

Javier Estrada IESE Business School Barcelona Spain ADFIN Winter/2014

Long‐Term Trends – Risk

• First and foremost
• Risk just cannot be assessed independently from

the holding period

• What is risk?
• In the short term
• Volatility, spreads, worst‐case scenarios, …

 The evidence suggests that S are riskier than B

• In the long term
• Shortfall probability (Key benchmark: Inflation)

 The evidence suggests that S are less risky than B

• Time diversification
• The longer the holding period, the more likely is a

higher exposure to risk to turn into higher return

• This idea is at the heart of lifecycle strategies and

standard advice about asset allocation

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Javier Estrada IESE Business School Barcelona Spain ADFIN Winter/2014

Long‐Term Trends – Forecasting

• Predictability tends to increase with …
• the aggregation of the portfolio
• the length of the holding period
• The RDM (R1 ≈ DY0 + g1

E + ∆P/E1)

• Observed and expected returns must be the sum of

these three components

• Returns have investment/speculative components
• Long‐term returns basically are investment returns
• Valuation plays a critical role in long‐term forecasts
• But valuation does not give timing signals
• Usefulness of the forecasting matrix
• What are the conditions that support a prediction?

 How plausible are those conditions?

Javier Estrada IESE Business School Barcelona Spain ADFIN Winter/2014

Risk Revisited – Downside Risk

• An alternative to the standard framework
• Focuses on the way most investors think about risk
• Aims to isolate ‘bad’ outcomes (downside potential)
• Measures of downside risk
• Semideviation
• Measures volatility below a chosen benchmark
• Introduces a distinction between good/bad volatility
• VaR (Value at Risk)
• Measures ‘really bad’ observed/expected outcomes
• Easy to interpret and communicate
• Trivial to calculate only under normality

 Most of the time normality may be badly misleading

• Downside beta and Morningstar risk

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Javier Estrada IESE Business School Barcelona Spain ADFIN Winter/2014

Risk Revisited – The 3‐Factor Model

• An alternative way to estimate required returns
• Accounts for the size and value effects
• Has CF and PM applications
• Keep in mind: The 3FM …
• is based on pervasive and global evidence
• assumes that value and small‐cap stocks are riskier
• But it is controversial whether this is really the case
• argues that higher exposure to the size/value

effects (higher risk) calls for higher required return

• Argues that size/value tilts increase expected return
• is very widely used in PM
• Estimation of a manager’s alpha

Javier Estrada IESE Business School Barcelona Spain ADFIN Winter/2014

Risk & Return – Risk‐Adjusted Return

• Performance can be the result of luck, risk taking,
• r skill (or, typically, a mix of them)
• No reason to reward being lucky or taking risk
• The only reason to pay a manager is for his ‘skill’
• Most of the popular rankings are flawed
• Based on short‐term returns
• Skill can be assessed by ranking managers by their

risk‐adjusted, long‐term performance

• Measures of risk‐adjusted performance
• Jensen and Treynor: Based on beta
• Sharpe and RAP: Based on volatility
• Sortino: Based on downside volatility
• Information ratio: Based on alpha and its volatility

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Javier Estrada IESE Business School Barcelona Spain ADFIN Winter/2014

Risk & Return – Stars and Costs

• Stars (Morningstar)
• Returns should be measured net of all costs
• Hence cost‐and‐risk‐adjusted return
• This is what the Morningstar stars measure

 Proprietary, objective, relative, and easy to explain

• Costs
• Explicit (Public)
• Loads, management fee, incentive fee
• ‘Hidden’ (Not known ex‐ante)
• Trading, bid‐ask spreads, taxes
• Critical factor determining/predicting performance
• This is the big edge of passive management
• The less you pay, the more you get
• Keep a very close eye on the fees you pay

Javier Estrada IESE Business School Barcelona Spain ADFIN Winter/2014

Portfolio Optimization – Excel

• Optimization tool
• Can easily handle …
• any goal
• any number of assets
• any number and type of restrictions
• Inputs
• ERs and Var‐Cov matrix (and sometimes Rf)

 Remember GIGO!  Remember observed short/long‐term patterns  Remember the portfolio’s intended holding period

• Outputs
• Weights ⇒ Ep / SDp / Sp / GMp

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Javier Estrada IESE Business School Barcelona Spain ADFIN Winter/2014

Portfolio Optimization – GMM

• GMM is an attractive alternative to SRM
• Designed to deal with a multiperiod horizon and

the reinvestment of capital

• Maximizes the probability than GMp and WT will be

higher than with any other strategy

• Typically yields concentrated and volatile portfolios
• Particularly plausible for …
• aggressive investors
• long‐term investors
• investors unlikely to have to bail out along the way
• Simple to implement
• Requires the same information than SRM

Javier Estrada IESE Business School Barcelona Spain ADFIN Winter/2014

Emerging Markets – Performance

• During 1988‐2013 the evidence shows …
• EMs equity …
• Produced annualized returns of 12.1% with volatility
• f 23.6%
• Outperformed the US/Europe/World markets

(Not so over the past five years / Still below ‘07 peak)

• Still provide substantial diversification benefits
• Call for a 10‐15% allocation of equity portfolio
• EMs debt …
• Produced annualized returns of 9.8% with volatility of

13.9% (‘DM‐equity‐like’ performance)

• Were clearly re‐rated in the last few years
• Had wildly‐fluctuating spreads

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Javier Estrada IESE Business School Barcelona Spain ADFIN Winter/2014

Emerging Markets – Risk

• The popular perception is that EMs are ‘risky’
• This is largely due to a focus on …
• the volatility of individual (equity) markets
• political risk

 Big question 1: Should it be priced? (Is it diversifiable?)  Big question 2: How to measure it? (Spreads, CCRs, …)

• But EMs are much less risky when considered

appropriately from a portfolio perspective

• Correlations across EMs are relatively low

 A large part of the risk gets diversified away

• Critical issue: Where to account for risk?
• In the CFs (scenarios)? In the DR (risk premium)?
• These two alternatives are interconnected
• Remember the implications of an arbitrarily‐high DR

Javier Estrada IESE Business School Barcelona Spain ADFIN Winter/2014

Emerging Markets – Cost of Capital

• Models
• L: Systematic country and industry risk
• GE: Yield spread / Total country risk
• GS: Improves GE’s double‐counting adjustment
• SSB: Detailed political risk adjustment / Subjective
• Relevant issues to keep in mind
• There is a wide variety of proposed methodologies,

none of which is currently widely accepted

• Risk adjustments can be made through the DR or

through scenarios in CFs

• Avoid an arbitrary estimation of the DR
• Can you defend your approach and DR estimate?

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Javier Estrada IESE Business School Barcelona Spain ADFIN Winter/2014

Emerging Markets – Project Evaluation

• Expected cash flows
• Typically estimated first in local currency
• Then ‘somehow’ converted into strong‐currency CFs

 Here (not in the DR) is where currency risk is considered

• Consider scenarios specific for EM projects
• Large devaluations, expropriations, …
• Discount rate
• Carefully consider the ‘best’ model for your situation
• Which model (hence DR estimate) could you plausibly

defend in front of a client?

• Sensitivity analysis
• Critical part of the evaluation
• On CFs: Prices, exchange rates, …
• On the DR: Models, assumptions, …

Javier Estrada IESE Business School Barcelona Spain ADFIN Winter/2014

And Two More for the Road

• What have we done?
• We have discussed a broad cross‐section of issues
• Mostly on portfolio management

 Some with corporate finance applications

• Some of them will be useful in your future jobs

 Some others that have simply expanded your knowledge

• What do you take with you?
• An expanded toolbox
• All tools have pros (strengths) and cons (weaknesses)
• All tools are more/less useful depending on your goal
• Knowledge of more tools, how to apply them, and

their pros and cons make you a little (or a lot) wiser

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Javier Estrada IESE Business School Barcelona Spain ADFIN Winter/2014

And Two More for the Road

“Understanding the why, where, and how is better than trying to follow a black-box

• solution. The worst thing a practitioner

can ask is: ‘Give me a rule of thumb I can follow without thinking’.”

David Nawrocki “ A Brief History of Downside Risk Measures”

Thank you!