Alpha Strategies: Summary and Introductionportal.idc.ac.il/en/main/research/caesareacenter/annualsummit/... · Alpha Strategies Boudoukh Please do not duplicate without permission

Alpha Strategies Boudoukh

Please do not duplicate without permission

Alpha Strategies:

Summary and Introduction

Jacob Boudoukh

Rothschild Caesarea Center, Arison School of Business, IDC

http://www.faculty.idc.ac.il/kobi/ Google: “Jacob Boudoukh”

1

Outline

• Briefly review HF strategies and their typical characteristics

• Briefly review some key anomalies

• Briefly discuss some crisis related lessons

2



Broad Trend in Asset Management

• EMH notion: Cannot “beat the market”, ie, predict “risk-adjusted” returns using publicly available information

� active management: α α α α = -fee

Contributed to a jump in index investing

• Until the mid 90’s there was scant evidence to the contrary

• In the past ~15yrs evidence suggestive of some EMH-doubt is

has been mounting

Contributed to jump in alpha investing

• Net result:

• Crowding out of traditional active long-β in favor of αααα−−−−ββββseparation: INDEX investing and ALT-INV investing thrive

� Alpha is not dead, but HFs are the right org form

3

Increase in Idexation

Source: ICI Factbook 2012

X2.5 from 1997 to 2011 in equity index funds

4



Explosion in ETF Assets

Source: ICI Factbook 2012

X12 from 2001 to 2011 in ETF assets

5

HFs + FoHFs AUM

Source: The Economist Jan126



HF Strategies and Typical Characteristics

strategy Hold

pd

Capacity ββββexposure

Liqu/

Sntimexposure

#of

holdings

Expected

Sharpe

Equity long/short Mnths M Med Med Med 1.5

Dedicated short bias Mnths M <0 ~ 0 L ?

Value investing Mnths M-H H Lo L 1

Convertible arb Mnths M L-M M-H L-M 1.5

FixInc rel val Mn-Wk M-H 0 H 1.5

Quant strats Wks H 0 M H 1.5

Global Macro Wks vH Lo L L 1.5

Stat arb Dy-Sec L 0 M-H H 2

(ultra)hi frequency (mi)Sec vL 0 M-H H 2.5+

Emerging Markets ? ? vH vH ? ?

7

Outline

In this lecture my goal is to

• Briefly review HF strategies and their typical

characteristics



8



Anomalies (not and exhaustive list…)

1. Event based

2. Technical

analysis

3. Fundamental

strategies

4. …hybrids

5. …misc

Events:

I. Financing Decisions

Dividends

Repurchases

Stock Splits

Stock Offerings

II. Corporate Restructurings

M&A

Spinoffs/Carveouts

III. Information Events

Earnings Announcements

Analysts Recommendations

Insiders’ Transactions

Short Interest

9

The General Pattern

Stock returns tend to persist after certain events that can be described as

♦ A signal by management or other informed investors

♦ Permanent changes in the corporation

Story:

Impact of the event is not fully captured by the market at the time of announcement :

Markets underreact to the initial news release

⇒ Slow drift in prices over the weeks/months following the event

10



Event Time Portfolios

Given an event-type:

� All events are equal regardless of their frequency

� Abnormal return is measured relative to a benchmark portfolio based on the risk of the firm

� Calculate average abnormal return for each week/month following the event

� Two alternatives for measuring abnormal returns:

– Cumulative Abnormal Return (CAR) (assumes equally weighted portfolio each month)

– Buy and Hold (better from a statistical and economic perspective, except biases can get accentuated)

11

Calendar Time Portfolios

� Create an event portfolio each calendar month

� Include firms with event in the previous N months

� Use equal / value / time-decay / buy&hold weights

� Measure abnormal performance of the portfolio per month

� The number of “observations” is the number of months you create the strategy for and not the number of events

� Use an asset pricing model w/ benchmark portfolio(s) to adjust the risk of strategy portfolio

� Obtain αααα, , , , information ratio, Sharpe etc.

12



Choosing the Benchmark

• Market Index, but which one?

� The one you are trying to beat (spx, msci)

� Academic studies usually use VW index of all NYSE, AMEX,and NASDAQ firms

• Add closet betas

• HML Size

• HML BM

• HML Momentum

• Liquidity

• Industry index

• Matching firm

13

The Moving Sands of αααα : Dividends Minicase

Why expect predictable returns after dividend changes?

• Dividend policy is used as a signal by managers

• Signal involves insiders’ view about the future of earning growth (perhaps linked to the post-earnings-announcement drift)

• Also changes clienteles: institutional investors tend to buy stocks with higher dividend yield… Higher dividend ⇒ higher demand for shares ⇒ upward drift in price

• If information is incorporated slowly by markets, then dividend policy is a good place to start looking

14



Changes in Regular Dividend

• Sample period 1979-1991

• 255 dividend cuts, 4249 dividend increases, split into quintilesbased on % increase in dividend.

15

The Changing Nature of Payouts

Source: Boudoukh, Michaely, Richardson Roberts, 2007, On the Importance of Measuring Payout Yield: Implications for Empirical Asset Pricing

16



Strategy Adjustment

DivYld Payout(CF) Payout(TS) NetPayout

Dec 1 10 1 10 1 10 1 10

AvgRet 1.04 1.25 1.22 1.74 1.16 1.60 0.95 1.68

∆∆∆∆ 0.21 0.52 0.44 0.73

Source: Boudoukh, Michaely, Richardson Roberts, 2007, On the Importance of Measuring Payout Yield: Implications for Empirical Asset Pricing

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ConclusionsSignificant evidence on anomalies in stock returns:

markets underreact to a medley of events upon news release:

∗ Dividends (in direction of change)

∗ Stock repurchase (+)

∗ Stock split (+)

∗ Equity offerings (-)

∗ Acquisitions: cash (+), stock (-)

∗ Spin-offs (+)

∗ Earnings (+)

∗ Analyst recommendations (+)

∗ Short interest (-)

∗ Insider buy (+)

� Dozens of papers on each…

� Moving sands effect seems to repeat itself (slow learning)

18



Anomalies

1. Event based

2. Technicalanalysis

3. Fundamental

strategies

4. …hybrids

5. …misc

Technical Analysis

I. Short-term strategies

II. Momentum

19

I. Short-Term Strategies

Traditional technical trading

� Moving average and trading range break rules

– Non-parametric techniques, e.g., genetic algorithms

� Contrarian strategies

– Buy losers, sell winners

– Conditioning information augmented with volume data in order to discern over-reaction from lagged adjustment

� Alive and kicking at all frequencies…

� Key issues include transaction costs and slippage in execution

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• Positive momentum stocks tend to outperform negative

momentum stock in the intermediate term.

♦Define strength portfolios based on returns in month -1 to

month -4,…,-13

♦Positive momentum firms are those in the highest return

portfolio; negative momentum firms are those in the lowest

return portfolio.

� Hold the portfolio for 3-12 months.

II. Momentum

21

Corroborating Evidence Minicase:Momentum Everywhere

22



Anomalies

1. Event based

2. Technical

analysis

3. Fundamentalstrategies

4. …hybrids

5. …misc

23

Fundamental Strategies: Intuition

The explanation behind the ability of fundamental strategies

to generate αααα is essentially the same, regardless of the

specific implementation:

stock prices may differ from their fundamental values due to mistakes, biases, or misperceptions by investors

Economic stories may involve under-reaction, over-reaction,

Naïve extrapolation etc.

24



Identifying Value Stocks

“Value stocks” are characterized by low prices relative to accounting/financial variables (that is, high values of the ratios).

Typically:

� book to market equity (B/M)

� [forecasted] earnings [growth] to price (E/P)

� [forecasted] cash flow [growth] to price (C/P)

� dividend yield (D/P)

25

A Simple Value Strategy

Source: Lakonishok, Shleifer, Vishny (1994)26



“Double Conditional” Minicase:CP Conditional on Sales Growth

average returns 1968-89

Source: Lakonishok, Shleifer, Vishny (1994)27

Anomalies

1. Event based

2. Technical

analysis

3. Fundamental

strategies

4. …hybrids

5. …misc

If a number of effects exist and

they are not perfectly correlated,

then a strategy exploiting the

additional conditioning

information will perform better

28



Hybrid Strategies Minicase:Cross Value Momentum

The most visible example:

� Value premiums are strongly positively correlated

(0.5-0.8)

� They tend to exhibit weak performance

� Momentum on its own is somewhat stronger, but still borderline on its own

However!

�Momentum is negatively correlated with value

29

Cross Value/Momentum Strategy

Source: Asness (1997)

30



Corroborating Evidence Minicase2:Value-Momentum

Source: Value and Momentum Everywhere, Asness, Moskowitz, Pedersen, 2009 31





Anomalies

1. Event based

2. Technical

analysis

3. Fundamental

strategies

4. Aggregate

predictability

5. Misc

Miscellaneous

I. The closed end fund puzzle

II. Index inclusion/exclusion

effect

III. Carry and the forward

premium puzzle

IV. Fixed income arb strategies

V. (ultra)fast trading strategies

VI. Option surface trading

strategies

VII. …

34



Outline

In this lecture my goal is to

• Briefly review HF strategies and their typical

characteristics



35

Broad-brush Dichotomies

� Asset gatherers vs Performers

– What do we make of the mega-funds?

» Economically sensible?

» Exit possible?

» Hold more and more PE-style assets

– “2&20” – one size fits all?

» Tough to justify for mega funds

» Messes up incentives

�More varied fee structures, and linked to lockups

� Discretionary / Opportunistic vs Systematic / Disciplined

– Alpha of the former is tough to interpret

36



Slow Moving Capital:Convertible Arb Minicase

� Imminent failure of prime brokers cause sudden decrease

in arb-leverage

� Asset (converts) liability (repo-loans) mismatch

� RelVal trades shut down

� Mispricings spiked, and took time to correct itself

Demonstrates

- the problem of fire sales and

- the role of arbs in maintaining relative prices aligned

Source: Mitchel Pulvino Arbitrage Crashed and Speed of Capital (2011) 37

1. prime broker spreads

Source: Mitchel Pulvino Arbitrage Crashed and Speed of Capital (2011) 38



2. allowable leverage

39

3. Y(straight)-Y(convert)

40



So, are HFs Good Doers Under Constraints?

� Do HFs really provide the archetypical Friedmanite service they are ascribed to?

� According to Brunnermeier and Nagel’s “Hedge Funds and the Technology Bubble” (JF04), perhaps not…

� BN document that HFs, during the tech bubble, were heavily invested in technology stocks

� Position was not the result of unawareness of the bubble:

– HFs captured the upturn, but avoided much of the downturn!

� Do speculators always stabilize prices?

� Instead: pros ride bubbles banking on predictable investor sentiment and limits to arbitrage

41

BN Copycat Fund

42



Some Final Thoughts

� Opaque, illiquid, ill-regulated HFs weren't those who brought down the global financial system by over leveraging and holding toxic assets

� Opaque, illiquid, ill-regulated HFs’ illiquid holdings did not bring down the global financial system: liquidity mechanisms, in general, functioned well under stress

� Pre and Post crisis GlobMac, Distressed, Hi-Freq funds provided important service (s.t. limits of arb.)

But,

� jury is out on size of industry relative to NET alpha opportunities

� jury is out on the economic rational behind mega-HFs

� jury is out on whether arbs in general (ultra hi fequency in particular) play a positive role

43

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Alpha Strategies: Summary and Introductionportal.idc.ac.il/en/main/research/caesareacenter/annualsummit/... · Alpha Strategies Boudoukh Please do not duplicate without permission