Senior Management Programme in Banking Module IV: Asset Management Professor Andrew Clare Cass...

Senior Management Programme in Banking

Module IV: Asset Management

Professor Andrew ClareCass Business School

October 2012

Overview

Strategic asset allocation

Tactical asset allocation

The tactical asset allocation game

Alternative investments – how alternative are they?

Liability driven investment

Alpha – what value do active fund managers add?

Choosing a fund manager

Investment strategies – simple strategies for generating alpha


Professor Andrew Clare

Overview

Asset allocation: what’s it all about

Long-term expected returns

Risk premia

Expected risk & risk aversion

Appendix: Yale university's endowment fund

Asset allocation

Emphasis on broad asset categories:

Equities, Bonds, Property, Currencies etc

US v UK equities etc

Main Practitioners:

Life Companies

Pension Funds

Funds of funds

Family offices

Its simple: just buy equities !!!

A historic perspective on asset allocation

10

100

1000

10000

100000

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Re

al i

nd

ice

s, lo

ga

rith

mic

sca

le

UK equity Gilts T-Bills

But most institutional investors do not hold only equities.

Long-term asset holdings of UK’s DB industry

0

20

40

60

80

100

2003 2004 2005 2006 2007 2008 2009 2010 2011

% o

f tot

al h

oldi

ngs

Equities Bonds Other


Strategic refers to longer-term outlook, bedrock of investment goals

Defining a benchmark for tactical asset allocation

Getting it wrong can be very costly

Should the aim be to

maximise expected return, or

maximise expected return, while simultaneously seeking to minimise expected risk ?

Using the MVE framework

Often asset allocators make use of the MV framework

But to do so we need to know: expected returns, variances and covariances to construct the frontier

Exp

ecte

d re

turn

Standard deviation, risk

Individual asset classes

Efficient frontier


Exp

ecte

d re

turn



Efficient frontier


A mean-variance frontier for asset classes

Long-term expected returns

Determining expected returns

Historic returns could be misleading – over the last ten years the FTSE-100 has fallen !!!

So asset allocators try to take a forward-looking view. We will try to do the same and apply this view to:

Cash

Government bonds

Corporate bonds

Equity

Long-term expected return components

There are three components of expected return on all assets

Ex ante real return

Compensation for future inflation

Compensation for risk

Let’s begin by determining the “neutral rate”, which comprises the first two components

The ex ante real return

In a world with no inflation and no risk, investors would still require a return from their investments, but how much ?

It would depend upon the ‘opportunity cost’ of foregone consumption

It’s closely related to the potential growth rate of the real economy

Average real GDP growth since 1970

Long-run economic growth low: ex ante real return should be low too

Average annual, real GDP growth since 1970

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

3.50%

Australia Canada France Japan UK USA

Ave

rage

real

GD

P g

row

th, %

pa

The ex ante real return

Despite many new inventions - railways, telephones, microchip, the internet etc - economic growth has actually been remarkably stable

Perhaps then historic GDP growth will be a good guide to long term future real GDP growth

On the other hand, is the credit crunch a paradigm shifting event … the end of capitalism as we know it ?

Such estimates probably a good proxy for the long term ex ante real return

Yields on long-dated index-linked gilt market can give us a clue to what the market thinks about trend growth

Yields on long-dated index-linked gilt

UK’s real long-term economic growth (was) similar to index-linked bond yield

Real, long term govt bond yield (UK)

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

6.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

6.0

1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011

Yie

ld o

n in

de

x-lin

ked

gilt

, %p

a

Yie

ld o

n in

de

x-lin

ked

gilt

, %p

a

UK recessions

Yields on long-dated index-linked bonds

There’s clearly more to default-free real yields

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

Australia France Italy Japan Sweden UK USA

Sho

rt-t

erm

, rea

l yie

lds,

%pa

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

Australia France Italy Japan Sweden UK USA

Sho

rt-t

erm

, rea

l yie

lds,

%pa

Short term real yields (pre-crisis) Short term real yields (post-crisis)


Inflation expectations affect the nominal expected return on assets

How does one go about forecasting inflation ?

The recent low inflation environment

Will the low inflation environment stick this time?

Inflation in a selection of developed economies since 1960

Source: Thomson Financial

-5.0

0.0

5.0

10.0

15.0

20.0

25.0

30.0

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

An

nu

al i

nfl

ati

on

%

US Japan Germany

UK Canada Italy

Inflation targeting

0

10

20

30

40

50

60

1970s 1980s 1990 1991 1992 1993 1994 1995 1996 1997 1998

Nu

mb

er

of

infla

tion

ta

rge

ting

ce

ntr

al b

an

ksInflation targeting: the 1990’s epedemic

Inflation targeting has had a big impact upon the inflation environment

Source: Bank of England

Inflation targeting

Inflation targets in a selection of developed economies

Country/region Target/inflation goalEuro-area ECB aims to keep CPI inflation below ceiling of 2.0%UK MPC aims to keep CPI inflation within 1.0% of 2.0% targetAustralia Australia’s FRB target inflation between 2.0% to 3.0%Canada Bank of Canada aims to keep CPI inflation within 1.0% of 2.0% target

New Zealand Reserve Bank of New Zealand aims to keep CPI between 1.0% to 3.0%

Sweden Riksbank aims to keep CPI inflation within 1.0% of 2.0% targetUSA Indications from Fed officials that 2.0% for core PCE inflation is “preferred”

Most seem to target between 2 to 3%

Why not target 10% or 0% ?

Market “inflation expectations”

Are market inflation expectations consistent with targets ?

0.0

2.0

4.0

6.0

8.0

10.0

1985 1988 1991 1994 1997 2000 2003 2006 2009

Ten

-yea

r br

eak

even

s, %

pa

UK USA Australia

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

Australia Canada France Italy Japan UK USA

Bre

ak e

ven

infla

tion

rate

s, %

pa

Pre-crisis Dec-11

Break evens over time Ten-year break evens


In the UK it seemed reasonable in the past to assume inflation of around 2.0% (CPI), that is, 2.5% (RPI). But what about now ?

In Europe 2.0%

In USA – the Fed have just launched QE3 – an indefinite commitment to expand the money supply

Today, arguably, the inflation picture hasn’t been this uncertain for some time

Putting it all together: an example

Putting together an estimate of trend growth and expected inflation gives a neutral policy rate for an economy

Neutral rate will be close to expected return on cash

For the UK prior to the credit crunch it might have been:

2.25% for growth

2.5% (RPI) for inflation

Giving a grand total of 4.75%

But what about now ?

The ‘neutral rate’

Policy rates will cycle around their ‘neutral rates’

The return on cash will be closely related

These neutral rates can change themselves if:

trend growth changes (productivity improvements, labour migration, credit crunch)

monetary policy regime changes

The return on cash is the basis for future expected returns on all assets

The risk premium is what distinguishes them

Pre and post crisis policy rates

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

Brazil

Canad

a

China

EUIn

dia

Japa

n

Mex

ico

Poland

Russia

South

Afri

ca

South

Kor

ea

Taiwan UK

USA

Po

licy

rate

, %p

a

Jun-07 Dec-11

Policy rates in Jun ’07 and Dec ‘11

Risk premia

Risk premia

Why do we want to be compensated for bearing risk ?

Risk inherent in investment classes distinguishes expected returns

Measuring risk premia is very problematic

Risk premia on main asset classes

Government bonds – an ‘inflation risk premium’

Corporate bonds – a credit risk premium

Equities – the equity risk premium

The “inflation risk premium”

Biggest risk in holding conventional, govt bonds is inflation

In past governments have arguably “inflated away” their debts – they may be tempted to do this again

Investors demand an additional return, mainly because future inflation is uncertain (other risks too)

It will depend upon the:

the monetary policy framework and

the credibility of monetary authorities

Calculating an “inflation risk premium”

Yield on Conventional government bond (Gilt)

Minus

Yield on index-linked government bond (ILG)

Minus

Estimate of expected inflation (survey based)

Equals

Measure of inflation risk premium

This gives a good proxy for the risk premium on government bonds

Inflation risk premia

Measure of the inflation risk premium for gilts

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

1993 1995 1997 1999 2001 2003 2005 2007 2009

Ban

k of

Eng

land

pol

icy

rate

, %pa

Inflation risk premium

Moving average

Change in BRP (2007-2011)

-3.0%

-2.5%

-2.0%

-1.5%

-1.0%

-0.5%

0.0%

Australia Canada France Italy Japan UK USA

Cha

nge

in b

ond

risk

prem

ia,

%pa

It’s fallen everywhere, but not because of receding fears of inflation


Government bonds – an inflation risk premium = 0.50% to 1.00% ?

Corporate bonds – a credit risk premium


The credit risk premium

Credit premium additional return over equivalent govt bond to compensate for credit risk

Varies according to the type of firm (AAA, AA, A, BBB etc)

Outside US not much history to guide us as to likely future credit risk premium

It’s also very volatile …

Credit premium varies over time

35

The credit risk premia

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

1926 1936 1946 1956 1966 1976 1986 1996 2006

Cre

dit

sp

rea

d, %

pa

Cre

dit

sp

rea

d, %

pa

BAA Spread

AAA Spread

Credit premium varies by rating

36

0.0

5.0

10.0

15.0

20.0

25.0

0.0

5.0

10.0

15.0

20.0

25.0

1973 1978 1983 1988 1993 1998 2003 2008

Cre

dit s

pre

ad

, %

pa

Cre

dit s

pre

ad

, %

pa

Aaa

A

Baa

Spec

Credit premium varies by sector

37

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

1993 1995 1997 1999 2001 2003 2005 2007 2009 2011

Se

cto

ral c

red

it s

pre

ad

, %

pa

Se

cto

ral c

red

it s

pre

ad

, %

pa

Banking Industrial

Telecoms Utilities

Company specific factors

38

0.0

1.0

2.0

3.0

4.0

5.0

2008 2009 2010 2011

European high yield debt/EBITDA

0.0

1.0

2.0

3.0

4.0

5.0

2008 2009 2010 2011

European high yield interest rate coverage

• Company fundamentals play an important part in the premium too

Risk neutrality and the credit premium

39

• However, if investors are risk neutral then they will only asked to be compensated for the potential additional loss compared with a default-free investment

• What we expect to lose from investing in a credit risky entity is simply the product of the probability of experiencing and the scale of that potential loss

Expected loss = probability of loss x (1 – recovery rate)

40

The probability of loss (1920-2008)

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

AAA Aa A Baa Ba B Caa-C

Per

cent

age

Year 5 Year 10 Year 15 Year 20

41

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

1982 1987 1992 1997 2002 2007

Re

cove

ry r

ate

s, %

Sr. Sec Sr. Unsec Sub.

Recovery rates – (rating 5 years before default)

42

The “risk neutral” credit premium

• Credit premium = prob. of loss x (1 – recovery rate)

• For example:

Expected loss rate for Baa over ten years = 5% x 40% = 2%

• Or something like that

• The degree to which the actual spread differs from the risk neutral, or ‘fair’ spread reflects the additional return required by risk averse investors

43

Loss rates (1982-2008)

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

Lo

ss r

ate

s


Government bonds – an inflation risk premium = 0.25% to 0.50%

Corporate bonds – a credit risk premium, the starting point should be the historic loss rate, let’s say = 1.50% to 2.00% for Baa


The equity risk premium

ERP is the additional return required over long-dated government bond for bearing equity risk

But what is equity risk ?

profitability

ongoing viability of company

Equities are a poor hedge against recessions

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010R

ea

l re

turn

Re

al r

etu

rn

UK real equity returns Historic equity risk premia

6.0

3.83.2

5.0 4.9

3.6

2.1

3.94.5

3.9

-2.0

0.0

2.0

4.0

6.0

8.0

Re

al r

etu

rn, %

pa

Equities Bonds ERP

The Dividend Discount Model (DDM)

When we buy equities we purchase a future stream of dividends

All we need to do is calculate the “present value” of each of these dividends and add them all up

But

dividends are paid over a long period

and are uncertain

However, if we assume that they grow at a constant growth rate, maths can help us out …

The Dividend Discount Model (DDM)

ERP + Risk free rate = Dividend Yield + Dividend growth

or

ERP = Dividend Yield + Dividend growth Risk free rate

Dividend yield can be observed

Risk free rate can be observed (government bond yield)

Dividend growth – unobservable

If we apply some macroeconomic theory then we can arrive at a very simple measure of the equity risk premium …

Simplifying the DDM

ERP = Dividend Yield + Growth in dividends Risk free rate

if dividends grow in line with real economy over long periods of time and

if real risk free rate is close to trend growth of the economy then (for the UK):

ERP = Dividend Yield + 2.25% 2.25%

ERP = Dividend Yield

UK’s equity risk premium

In 1970s required additional compensation was high

A measure of the UK’s equity risk premium

0.0

2.0

4.0

6.0

8.0

10.0

12.0

1965 1970 1975 1980 1985 1990 1995 2000 2005

Imp

lied

eq

uity

ris

k p

rem

ium

, %

pa

A DDM matrix

Real risk free rate 2.25%

Real risk free rate 0.70%

FTSE-100 = 5,900 on this day

Risk premium % 2.5% 3.5% 4.55% 3.5% 4.5% 5.5%

1.50% 10,412 6,556 4,720 6,556 4,784 3,766 Real 1.75% 12,207 7,224 5,057 7,224 5,130 3,978 Earnings 2.00% 14,750 8,045 5,446 8,045 5,531 4,214 Growth 2.25% - 9,077 5,900 9,077 6,000 4,481 2.50% - - 6,436 10,412 6,556 4,784 2.75% - - - 12,207 7,224 5,130 3.00% - - - - 8,045 5,531

Risk premium % 2.0% 2.5% 3.00% 3.5% 4.0% 4.5%

1.50 6,436 5,446 4,720 4,165 3,726 3,371 Real 1.75 7,080 5,900 5,057 4,425 3,933 3,540 Earnings 2.00 7,867 6,436 5,446 4,720 4,165 3,726 Growth 2.25 - 7,080 5,900 5,057 4,425 3,933 2.50 - - 6,436 5,446 4,720 4,165 2.75 - - - 5,900 5,057 4,425 3.00 - - - - 5,446 4,720

Issues with this simplification

What if firms increase dividends temporarily ?

What if firms pay no dividends ?

What about share buy backs ?

What if the profits earned by the market are not derived from the underlying economy ?

Adjustments to the model can be made to account for all these issues, but will require considerable user discretion

Assembling the building blocks

Once the asset allocator has come to a view about expected:

economic growth rates

inflation and

risk premia on a range of asset classes

then the expected return jigsaw puzzle can be put together …

Putting it all together: an example

Example of “building block approach” to forecasting long-run asset class returns

This was the orthodox view just under four years ago

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

9.0%

Real economic

growth

Expected inflation

Inflation risk premium

Equity risk premium

Index-linked gilts

Cash Gilts Equity

Expe

cted

retu

rn/e

xpec

ted

retu

rn co

mpo

nent

Expected return component

Long-run expected return

Questions about the building block approach

What might change the asset allocator’s views ?

Should we revisit the pre-crisis assumptions?

What about developing economy asset classes ?

What about the starting point ? (the tactical aspect)

Expected risk: Measuringvolatilities and correlations

Expected returns is the first ingredient

How do we get the other ingredients ?

A mean-variance efficient frontier for asset classes



Efficient frontier

Exp

ecte

d re

turn

: es

tabl

ishe

d vi

a 'b

uild

ing

bloc

k ap

proa

ch'



Efficient frontier

Exp

ecte

d re

turn

: es

tabl

ishe

d vi

a 'b

uild

ing

bloc

k ap

proa

ch'

Historic measures of volatility and correlation

Having determined the expected return, most use historic measures of volatility and correlation

A MVEF can then be constructed

But variances and co-variances change over time …

Time-varying volatility

Asset class volatility can vary substantially over time

UK & US equity return volatility over time

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

1970 1975 1980 1985 1990 1995 2000 2005 2010

Equ

ity m

arke

t vol

atili

ty, s

tand

ard

devi

atio

n, %

pa

USA UK

Time-varying correlations

Asset class correlation can vary substantially over time too

Correlation between UK & US equity returns over time

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

1970 1975 1980 1985 1990 1995 2000 2005 2010

Equ

ity m

arke

t cor

rela

tion,

US

and

UK

equ

ities

Time variation of volatility and correlation is a problem

Many fancy statistical techniques for forecasting future volatility and correlations

But once again, there is no “correct” way to forecast volatilities and correlations

Forecasting volatility and correlations

What about client risk tolerance ?

We now have:

expected returns

expected variances and correlations

an efficient frontier

But what is the client’s appetite for risk ?

May be dictated by “return needs”

Psychologists and economists now put a lot of effort in to trying to determine this

Measuring risk aversion

Risk aversion is very difficult to gauge

Answer depends heavily on the utility function assumed to describe investors’ risk-return trade off

Investment professionals in US use experiments of this kind to determine risk aversion of their clients

These techniques are now in widespread use elsewhere too

Risk aversion is the final ingredient

But remember: ALL optimizers essentially ‘tell’ us what we have ‘told’ them!!!

Choosing a position on the efficient frontier

Standard deviation, risk: established using historic estimates of volatilities and correlations


Efficient frontier

Exp

ecte

d re

turn

: est

ablis

hed

via

'bui

ldin

g bl

ock

appr

oach

'

A

B

C

D

E

F

Standard deviation, risk: established using historic estimates of volatilities and correlations


Efficient frontier

Exp

ecte

d re

turn

: est

ablis

hed

via

'bui

ldin

g bl

ock

appr

oach

'

A

B

C

D

E

F

Source: Fathom

Appendix: Yale University Endowment fund

Overview Yale University's endowment is seen as a "best of breed" multi asset class

investment fund

Has made substantial use of alternative asset classes

The fund aims to support the University's academic activities

And has managed to increase both the absolute size of the fund and the absolute size of the annual support for these University activities

66

Annual spend Fund value

1996 $170m $676m

2010 $1.1bn $16.6bn

(Though the fund has received substantial donations over this period too)

Source: "The Yale Endowment" 2010

Yale University revenue

67

Endowment revenue makes up over 40% of total uni revenue

The "liabilities"

68

The fund is used to support all University activities.

Many of the donations are given with pre-defined activities that the donor wishes to support, but for investment purposes the funds are "co-mingled"

In 2010 the fund supported 41% of the University's $2,681m operating budget

"Spending" policy

69

Conflicting goal: desire to support as much current spending as possible, but preserving the value of assets to support future spending

Goal 1: Aim to produce stable/smooth stream of income for university

Goal 2: protect value of investments relative to inflation

Long-term spending rate, combined with "smoothing rule"

The smoothing rule ensures that income does not fall too far (if at all) in bad years, but does not rise too much (if at all) in good years

(Life companies use similar rules)

"Spending" policy

Spending growth has outstripped the University's specific inflation measure, that is, spending has increased in real terms

Rate of growth is smooth, due to smoothing rule70

Spending rate

The crisis had a big impact on the “smoothed” spending rule

71

Fund value

72

Combination of high annual returns and ongoing contributions has led to a massive increase in the fund's value over time

Investment policy

73

A combination of academic theory and "informed market judgement"

MVA is the starting point, stress tested for different return, vol and correlation assumptions etc

Aim to invest predominantly in asset classes with "equity-like" returns

Avoid the "home bias" of investing in only domestic asset classes

The long-term horizon means that capital can be committed to illiquid asset classes

Yale’s performance

74

Yale’s asset allocation

75

Asset allocation: actual & target

76

The assets used to support the spending aspirations (the liabilities) are relatively diverse

This asset allocation structure is quite different from similar US University funds, and very different from the sort of allocations made by UK life and pension funds

Yale’s illiquidity ‘budget’

77

Yale’s fiscal highlights

78

Absolute returns

79

In 1990 first sizeable institution to invest in absolute return strategies

Identify managers that can enhance long-term real returns by exploiting market inefficiencies. 50% Event driven, other 50% "Value driven strategies"

Expected real return: 5-6%

Expected risk: 10% volatility (event driven)

Expected risk: 15% "Value strat"

Policy:

performance-related fees

hurdle rates

clawback provisions

manager invests own net worth in fund

Performance: 11.5% pa with low correlation to bonds and equities

Domestic equities

80

Lower weighting than similar institutions (7% target)

Expected real return: 6%

Expected standard deviation: 20%

Benchmarked against Wilshire 5000 index

Policy:

commitment to active management

prefer managers with bottom-up research capabilities

acknowledgement that this will focus on small stocks

Performance: over last ten years 6.7%pa outperforming Wilshire 5000 by 7.4%pa

Overseas equities

81

Raison d'etre: exposure to global economy

One half of portfolio invested in high growth, emerging markets


Expected standard deviation: 22.5%

Again commitment to active fund management

Fixed income

82

Attracted by "certainty" of nominal cash flow; a hedge against "financial accidents", but allocation of just 4%


Expected standard deviation: 10%

Benchmark index: Lehman Brothers US Treasury Index

Policy:

(internal) active management

avoiding market timing strategies, call options & credit risk

Private equity

83

Attraction: long-term, risk adjusted returns

Including buy-out funds and venture funds

Expected real return: 10.5%

Expected standard deviation: 27.7%

Policy:

avoid PE funds sponsored by financial institutions because of potential conflicts of interest and staff instability

Actual returns since inception: 30.3%pa

Real assets

84

Investments in real estate, oil and gas and "timberland"

Attractions:

real assets so hedge against expected and unexpected inflation

visible cash flows

low correlation with other asset classes

illiquidity of such assets creates barrier to entry and raises long-term returns

Expected real return: 6%pa

Expected standard deviation: 15.5%pa

Over last ten years the portfolio has returned 10.9%pa

Investment performance

All components of portfolio have outperformed active benchmarks over the last ten years

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