What We Know and Don’t Know About Risk - … What We Know and Don’t Know About Risk ... Black Swans cannot be predicted consistently, ... not simply driven by beta.

© John Longo and The MDE Group 2014

All Rights Reserved.

WEALTH LEADERSHIP SINCE 1987

John M. Longo, Ph.D., CFA Rutgers Business School

The MDE Group

What We Know and Don’t Know About Risk

The Danish Forum for Performance Measurement Copenhagen, Denmark, June 12, 2014




Outline

1) Motivation for Presentation

2) What We Know About Risk

3) What We Don’t Know About Risk

4) Methods for Navigating Risk in an

Uncertain World

2




Motivation

Well known financial textbook, Brealey & Myers

What we know about Finance:

Net Present Value

The Capital Asset Pricing Model

Efficient Capital Markets

Value Additivity and Law of Conservation of Value

Capital Structure Theory

Option Theory

Agency Theory

3




Motivation (2)

What we don’t know about Finance:

What determines project risk and present value?

Risk and return – what have we missed?

How important are the exceptions to Efficient Market

Theory?

Is management an off balance sheet liability?

How can we explain the success of new securities and

new markets?

What risks should a firm take?

What is the value of liquidity?

How can we explain merger waves?

Why are financial systems so prone to crisis?

4




Mark Twain Quote

5




We Know: Long-Term Risk-Return Relationship: Unfortunately it May Take 20 Years to Work !

6




Detail on Long-Term: Decades with Zero Return

7




We Know: Performance Rankings Are Erratic

Source: JP Morgan Guide to Markets

8




We Know: Diversification Reduces Risk

9

My ventures are not in one bottom

trusted, Nor to one place; nor is my

whole estate upon the fortune of this

present year: Therefore my

merchandise makes me not sad.

Antonio, in William Shakespeare's

"The Merchant of Venice", Act 1,

Scene 1.

Shakespeare on Risk Management (1597)




We Know: Diversification Is A (Modest) Free Lunch

Source: JP Morgan Guide to the Markets, 2Q 2014.

10




68% 66%

88%

Cash Outperforms

32%

Cash Outperforms

34%

Cash Outperforms

12%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Cash vs. Stocks Cash vs. Bonds Cash vs. Both Stocks and Bonds

Bonds Outperform

Stocks Outperform

Either Stocks or Bonds

Outperform

FREQUENCY (RATE) OF OUTPERFORMANCE FOR CASH Vs. STOCKS & INCOME

OVER 1-YEAR PERIODS (1926-2011)

We Know: Cash is Rarely King

Source: Fidelity Investments

11




We Know: Interest Rates are Tied to Inflation

Source: J.P. Morgan Guide to Markets, 2Q 2014

12




We Know: Interest Rates Impact Risk Through the Time Value of Money

13

Buffett:

“Portfolio Theory was

invented by Aesop in 600

B.C. when he said, ‘A

bird in the hand is worth

two in the bush’.”




We Know: The Taylor Rule and the Fed Funds Rate

Interest rates can’t fall below

zero, so the Fed or ECB must

use atypical monetary policy

in combinations with large

government stimulus.

Source: Paul Krussgman, “Zero lower bound

blogging,” New York Times, January 17, 2009.

14




Although we cannot agree on the precise definition of risk, there is a more general consensus on the factors or levers that drive risk:

Market Exposure (e.g. Beta, Duration, etc.)

Leverage

Concentration

Liquidity

Quality

Transparency

Lesson: Watch the risk levers ex-ante

We Know: Most of the Drivers of Risk: Unfortunately, We Can’t Forecast Risk Well

15




We Know: Reversion to the Mean Over the Long Term

VIX Example: Eventually reverts back to around 18-20 after shocks dissipate.

Many other examples (e.g. P/E, credit spreads, etc.)

16




Bubbles don’t repeat exactly, but the boom / bust cycle continues. Fear and greed are inextricable parts of human nature.

Tulip Bubble

South Sea Bubble

“Nifty Fifty” of the 1970s

Merger Mania of the 1980s

Internet Bubble

Credit and Real Estate Bubble

Social Media Bubble (e.g. WhatsApp, Snapchat)

Cloud Computing Bubble

We Know: Bubbles Repeat Due to Human Nature

17




We Know: Prices May Be Nonlinear (Bubbles Possible)

18




We Know: Prices May Be Nonlinear (WhatsApp)

19

Price tag:

$19

Billion!




We Know: Prices May Be Nonlinear (Snapchat)

20

Snapchat

rejects $3

billion cash

offer from

Facebook




We Know: Security Prices Not Normally Distributed Dow Jones Example (Actual Distributions)

Source: PIMCO

21




We Know: Black Swans Exist

22




Black Swans cannot be predicted consistently, but their impact may be limited if we control the risk levers carefully and buy (cheap) insurance.

Examples of cheap insurance:

Deep out of the money options or other derivatives, before volatility spikes.

• Your portfolio will still take a hit after the black swan arrives, but losses may be more manageable.

Dealing With Black Swans

23




We have known for many years that volatility tends to follow volatility (e.g. ARCH / GARCH models).

Q: Why? A: Investor psychology, delayed reaction to news, momentum trading, margin calls, and other reasons.

Hence, some volatility can be predicted (e.g. the second leg).

Risk Outside of Black Swans

24




Predicting the 2nd Leg of Risk

25




Cut your losses short and let your winners run.

Gain more on your winners and lose less on your losers (i.e. impact ratio).

Never fight the tape.

Bet bigger if more trades are going in your favor; otherwise, bet lesser amounts of capital.

Try to be greedy when others are fearful, be fearful when others are greedy. (Attributed to Warren Buffett)

We Know: Conventional Wall Street Wisdom On Risk

26




There is no uniformly agreed upon definition of risk.

Most people view risk as the chance of losing money or

not achieving an important goal.

Others may view risk as volatility, or performance relative

to a benchmark.

Modern finance theory suggests that risk is to be voided,

unless you are compensated with extra return.

We also can’t effectively forecast the first leg of risk (e.g. Black Swan events).

We Don’t Know: How to Precisely Define Risk (Even Though We Know Most Of Its Levers)

27




We Don’t Know: Equity Correlations Over Time

Source: J.P. Morgan

28




We Don’t Know: Country vs. Industry Effects Ratio Of Country Effects To Sector Effects

Source: BARRA

29




We Don’t Know: How Market Risk Changes

Using equities as an example, we know market risk is not simply driven by beta. Other factors are important, but not in any consistent manner:

Examples: Industry effects, interest rates, oil prices, political risk (e.g. Mideast, Europe), etc.

Suggests APT Model probably more applicable to the real world (e.g. BARRA type models).

30




We Don’t Know: What Is Optimal Diversification?

The answer, according to various sources, ranges from 20 to 300 or more.

Famous “stock pickers” and early academic studies suggest something closer to the lower number.

They claim it is important to know what is in the basket rather than having so many eggs (i.e. stocks) that you cannot adequately follow.

Arguably, the stock market has become more volatile in recent years.

• Increases in volatility may be due to increases in hedge funds, program trading, higher debt loads, higher correlations due to globalization, and short-term investor mindset.

31




Snapshot of Buffett / Berkshire Stock Portfolio

Source: WhaleWisdom.com

32

Top 13F Holdings on 03/31/2014

Stock % of Portfolio

Wells Fargo & Company 21.7943%

The Coca-Cola Company 14.62%

American Express Company 12.9046%

INTERNATIONAL BUSINESS MACHINES

12.4396%

Wal-Mart Stores Inc. 4.1948%

Berkshire’s Top

10 holdings

comprise 82% of

its portfolio.

http://whalewisdom.com/stock/wfc

http://whalewisdom.com/stock/ko



http://whalewisdom.com/stock/axp

http://whalewisdom.com/stock/ibm

http://whalewisdom.com/stock/ibm

http://whalewisdom.com/stock/wmt






We Don’t Know: The Distribution of Security Prices Fat Tailed Distribution 1: Levy Distribution

33




We Don’t Know: The Distribution of Security Prices Fat Tailed Distribution 2: Cauchy Distribution

34




We Don’t Know: Why Decoupling Doesn’t Hold

Emerging

markets are not

decoupled from

developed

markets despite

growing GDP.

They are part of

the “Risk On”

trade.

35




Because of These Unknown Risks We Often Use Band-Aids (i.e. Heuristics)

36




How to Deal with Risk

Watch risk levers, buy cheap insurance (e.g. deep out of the

money put options or other derivatives).

Use investment policy constraints to prevent big bets

Follow a macroeconomic approach

Utilize dashboards

Utilize bubble risk charts

Conduct stress tests

Examine linkages among risks (Davos Economic Forum)

Combine fundamentals with technicals and behavioral

finance

37




Global Macro Approach: Effective In Dealing With Risk?

38




Sample Macro Indicator: ISM PMI Index

39




Macro Indicator: China Bank Lending / Electricity Consumption

40




Investment Mosaic or Dashboard

41




42

Risk Factor Dashboard - Global Model Mix

Subprime Spillover

Recession

Terrorist Attack

Supply Chain Disruption

Natural Disaster

Surge in Int Rates

Depreciating DollarPolitical Turmoil

Real Estate Collapse

Market Crash

Health Epidemic

Tax Increase

Hedge Fund Failure

(Inv)Bank Failure

Private Equity Problems

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0% 50.0% 55.0%

Probability of Event in %

Mag

nitu

de o

f Los

s in

%




Stress testing empirically tests how the fund would have performed during historical or hypothetical high risk scenarios

Examples: Crash of 1987, Bear Markets of 2000-2002, 2007-2009,etc.

Stress testing is not very effective when dealing with “Black Swan” type events.

Stress Testing Funds

43




VaR has a big practical problem

It doesn’t tell you how bad things will get in the left part of the distribution (e.g. bottom 5%) .

Need to examine Conditional VaR, also known as Expected Shortfall to attempt to more fully quantify left tail risk

Conditional VaR or Expected Shortfall Calculation

Take the average of the worst expected outcomes (e.g. bottom 5% of left tail).

Conditional VaR: Model Left Tail Risk

44




Showing Linkages Among Risks

45




Evolution of Risks: Likelihood

Source: World Economic Forum

46




Evolution of Risks: Impact

Source: World Economic Forum

47




Ben Graham Analogy: Market As A Pendulum

48

Fear, March 2009 Greed, Internet Bubble

Long Run = Fair Value




Technical analysis was once had the same level of respect as astrology. However, technical analysis does add some value due to the following:

(Illegal) Insider Trading

Legal Insider Trading (Director Trades, Bill Ackman 10% purchase of Allergan before takeover announcement)

News does not come out of a vacuum (Sometimes analysts do research, take a position, and give the information to reporters).

Investor psychology (chasing performance)

Self fulfilling prophecy(“placebo effect”)

Why Technical Analysis ≠ Astrology

49




Coca Cola: Bear Market Overrides Fundamentals

50




Bank of America Example: News Revealed Later

51

Bank of America stock down 30%+ from Nov 11th – Dec 31st. Stock up

45% from Jan 1st to Feb 14th. What drove this strange performance? On

Feb 14th it was revealed John Paulson sold all of his shares in Bank of

America during the 4th quarter. (Initial stake was $2.2 billion)




Behavioral Finance: Back to the Future

52

John Maynard

Keynes:

"The markets are

moved by animal

spirits, and not by

reason."




Behavioral Finance: Back to the Future

53

Benjamin Graham:

"In the short-run, the market is a voting

machine."

(It is a popularity contest, influenced by

psychology)

"In the long-run, the market is a

weighing machine."

(It weighs or values earnings)

"The investor's chief problem – and

even his worst enemy - is likely to be

himself."




Two people can look at the same exact picture (company or market) and come to different conclusions.

One says “Buy,” the other says “Sell.”

There is a human element in pricing (even adjusting for different time horizons and information sets).

One Lesson From The Picture

54




Cycle of Market Emotions Impacts Performance

55




Investor Performance Is Impacted by Behavioral Biases

56




Investor Performance Consistently Subpar

57




Positive Neutral Negative

Credit Conditions

Corporate Earnings

Monetary Policy

Energy Prices

Inflation

Valuations

Economic Activity

Geopolitical

Fiscal Policy

Dealing With Conflicting Signals Balanced Scorecard

Source: Fidelity Investments

58




Questions?

59




Biographical Sketch: John M. Longo, PhD, CFA

Dr. Longo is a Clinical Professor of Finance & Economics at Rutgers Business School, and Chief

Investment Officer (CIO) and Chairman of the Investment Committee for The MDE Group

(www.mdegroup.com), a registered investment advisor with $1.5 billion under management. The MDE

Group has been ranked as the 4th best independent investment advisor in the USA by Barron’s in

2007 and 2008. He is also CIO of MDE’s sister company, Acertus Capital Management.

Dr. Longo has appeared on CNBC, Bloomberg TV, Bloomberg Radio, Fox Business, BBC World,

wsj.com (video),The (Ron) Insana Quotient, Greatinvestors.tv and several other programs. He has

been quoted in the The Wall Street Journal, Thomson Reuters,Dow Jones MarketWatch, The Chicago

Tribune, The Star Ledger, Hedge Fund Alert, FundFire, and dozens of other periodicals.

He is author / editor of Hedge Fund Alpha: A Framework for Generating and Understanding

Investment Performance. He is a member of the Editorial Boards of The Journal of Performance

Measurement and The Journal of Financial Planning & Forecasting. He has served as consultant to

many financial service firms on a global basis and twice led Rutgers students to a personal visit with

Warren Buffett. Previously, he was a Vice President at Merrill Lynch & Co., Inc.

60

http://www.mdegroup.com/








Legal Disclaimers: Information contained in this document does not constitute personalized investment advice.

Information contained in this document does not constitute an offer to sell or solicitation to buy securities.

Any offer to sell The Alternative Fund, LLC or any other non-registered security will be made only to qualified investors pursuant to a

confidential offering memorandum.

All information contained in this presentation has been gathered from sources which The MDE Group, Inc. deems to be accurate.

However, inadvertent errors may occur.

Investing involves risks which may lead to losses. Different types of investments involve varying degrees of risk and there can be no

assurance that any specific investment will be profitable. The MDE Group, Inc. does not make any representation that any of its

investments on behalf of clients, including but not limited to any investments in The Alternative Fund, LLC or the Planned Return

Strategy, Accelerated Return Strategy, or the Third Rail Strategy or any other investment strategy, will or is likely to achieve returns

similar to those shown in the performance results in this presentation.

Past performance is not a predictor of future results. It should not be assumed that any information discussed herein will prove to be

profitable or that decisions in the future will be profitable or provide similar results.

Calculation methodologies are available from The MDE Group, Inc. upon request.

Any discussion of tax matters contained within this communication should not be used for the purpose of avoiding U.S. tax related

penalties or promoting, marketing, or recommending to another party any transaction or matter addressed herein.

The MDE Group, Inc. disclaims liability for any expense incurred or any damage or loss sustained which may be attributable, directly or

indirectly to the use of or reliance upon any information provided.

61

What We Know and Don’t Know About Risk - … What We Know and Don’t Know About Risk ... Black Swans cannot be predicted consistently, ... not simply driven by beta.

Documents