Systematic M&A Arbitrage by Yin Luo at QuantCon 2016

Deutsche Bank

Systematic M&A Arbitrage

April 9, 2016

Deutsche Bank does and seeks to do business with companies covered in its research reports. Thus, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of this report. Investors should consider this report as only a single factor in making their investment decision. DISCLOSURES AND ANALYST CERTIFICATIONS ARE LOCATED IN APPENDIX 1. MICA(P) 072/04/2012

Global Quantitative Strategy

Yin Luo, CFA ▪ 212 250 8983 ▪ [email protected]

Managing Director & Global Head of Quantitative Strategy

Deutsche Bank

Yin Luo, CFA ▪ 1.212.250.8983 ▪ [email protected]

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— Yin Luo, CFA

Global Head of Quantitative Strategy

mailto:[email protected]

http://eqindex.db.com/gqs

Deutsche Bank


04/04/2016 15:40:54 2010 DB Blue template

— Our data set covers 40,000 M&A deals over 25 years of

history. We use merger types and moods to measure

post-announcement drift.

— We provide a model predicting the probability of a merger

going through: the MAS model.

— A multifactor model provides the expected alpha of each

merger: the MAA model.

— We find certainly characteristics of the deal have

significant impact on the duration of the transaction: the

MAD model.

— Combining the MAA, MAS and the expected duration

(MAD model) of a deal, we deliver a systematic M&A

portfolio with an annualized return of 15% and a Sharpe

ratio of 1.6x.

Abstract

2

Performance of the systematic M&A (SMA) strategy

Source: Thompson Reuters, Compustat, IBES, Russell, S&P, Deutsche Bank Quantitative Strategy

0

2

4

6

8

10

12Russell 3000

Naïve M&A strategy

Final M&A portfolio


Deutsche Bank


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Other Cash only Stock only Cash and stock


Thomson Reuters’ Deals M&A database Number of deals of each type Percentage of each type

Percentage of each style Percentage of each mood

3

0

500

1000

1500

2000

2500

Other Cash only Stock only Cash and stock

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Stake purchases Undisclosed dollar value

Disclosed dollar value Self tenders, recaps

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Friendly Neutral Hostile Not appl. Unsolic.


Deutsche Bank


0.97

0.98

0.99

1.00

1.01

1.02

1.03

1.04

1.05

Deal goes through Deal fails

Post-announcementdrift

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

US Mergers International Mergers


Other important deal features Percentage US vs. international Average final control sought

Distribution of deal success/failure by deal age Average profit of successful deals

4

0%

10%

20%

30%

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60%

70%

80%

90%

100%

Percentage of deals going through

0.0

0.1

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0.7

0.8

0.9

1.0

Pro

po

rtio

n o

f m

erg

ers

go

ing

thro

ugh

Announcement year

Average: 61%

drops 20% during crisis

0.00

0.01

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0.05

0.06

0.07

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0.09

0.10

Fre

qu

en

cy

Days until deal is cancelled/closed

Deal closed successfully Deal withdrawn

Pre- and post-announcement drift

0.80

0.85

0.90

0.95

1.00

1.05

1.10

1.15

1.20

1.25

1.30

Deal goes through Deal fails

Pre-announcementdrift

Announcement jump


Deutsche Bank


0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Month

Merton's distance to default

Price-to-52 week high

Normalized abnormal volume

Target price implied return

Return on invested capital (ROIC)

Operating earnings yield, trailing 12M, Basic

Recommendation, mean

Earnings yield, FY0

Expected dividend yield

Asset turnover

Skewness, 1Y daily

12M-1M price return

Total return, 12M

Price-to-operating EPS, trailing 12M, Basic

# of days to cover short

Return on net operating assets (RNOA)

Altman's z-score

Price-to-sales, trailing 12M

Earnings yield x IBES 5Y growth

IBES FY1 EPS up/down ratio, 1M

Barry ratio

Factor strength

Fact

or

nam

e

5

Factor strength in predicting merger success (red = dec. blue = inc.)

— A logit model predicts eventual success of deal each month. This is done on the most up-to-date information set

of that month over the point-in-time population of merger targets to avoid any look ahead bias.

— Fully dynamic. Probability reflects both the age of the deal, and its deteriorating or improving fundamental

characteristics.

Dynamic prediction of M&A success: the MAS model

Factor strength evolution over time


-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

Fact

or

Stre

ngt

h

Calibration year (expanding window)

Month

Merton's distance to default


Normalized abnormal volume

Target price implied return


Operating earnings yield, trailing 12M, Basic

Recommendation, mean

Earnings yield, FY0

Expected dividend yield


Deutsche Bank


MAS model with M&A type, style and mood

6

The impact of deal type, style and mood on the transaction success probability

Other useful factors

— Factors such as deal type, style, and mood are not included in the MAS model, because these factors may

change over time and are sometimes backfilled, which leads to potential look-ahead bias.

— It is interesting to see that deals with undisclosed value, hostile takeovers, and unsolicited transactions have

much higher failure rates.


-5

-4

-3

-2

-1

0

1


Deutsche Bank


MAS performance

— Bold range of predictions as the model

commonly boasts out-of-sample convictions

ranging from 10% to 90%

— Accurate predictions where the predicted

probability matches the out-of-sample frequency

of success

— Positive alpha as screening out deals likely to fail

improves merger arbitrage performance

7

Estimated vs realized success frequencies Performance for above and below median probability of success

Distribution of MAS predictions


0

200

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600

800

1000

1200

1400

1600

Nu

mb

er

of

dat

a p

oin

ts

Estimated probability of success

y = 0.8809x + 0.1554R² = 0.9493

0.00

0.10

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0.90

1.00

0.00 0.20 0.40 0.60 0.80 1.00

Re

aliz

ed

fre

qu

en

cy o

f su

cce

ss

Estimated probability of success

0.96

0.97

0.98

0.99

1

1.01

1.02

1.03

low probability of success high probability of success


Deutsche Bank


0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

Mood: Hostile

Mood: Not Appl.

month

Type: Cash only



Return on Asset

Valuation spread

80

90

100

110

120

130

140

150

160

170

1 2 3 4 5 6 7 8 9 10 11 12

Exp

ect

ed

re

mai

nin

g d

ura

tio

n in

day

s

Age of deal in months

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

1 2 3 4 5 6 7 8 9 10 11 12

Z-sc

ore

fac

tor

valu

e


Price-to-52 week low YoY change in # of shares outstanding

IBES 5Y EPS growth Price to book ratio

Long-term debt/equity

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0.1

0.2

0.3

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0.5

0.6

0.7

1 2 3 4 5 6 7 8 9 10 11 12

Ave

rage

pro

bab

ility

of

goin

g th

rou

gh



The impact of age: the deal duration MAD model Probability of success as a function of age Fundamentals deteriorate as the deal ages

Remaining duration as a function of deal age – four-month is the

critical value Factor coefficients for a Cox survival model (red = long; blue = short)

8


Deutsche Bank


The takeover target universe

9

M&A universe outperforms the market over the long run

Plain vanilla risk arbitrage strategy as our benchmark

— All M&A targets that are in the Russell 3000 universe at the

announcement date

— Close our trade when the deal closes successfully or when is withdrawn,

with a maximum holding period of one year

— Monthly rebalance – a stock enters the M&A universe at the closest month

end after its announcement date

— Long only, no leverage, fully invested

Coverage of the M&A universe

0

50

100

150

200

250

1

10

100

Russell 3000

M&A universe


Sharpe ratio

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0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Russell 3000 M&A universe


Deutsche Bank

Yin Luo, CFA ▪ 1.212.250.8983 ▪ [email protected] 10

Annualized return

— For M&A transactions involving acquirer’s stocks, a long-only strategy takes significant unnecessary risk. For

the stock deals, risk arbitrageurs would long the target company shares and short the acquire company

stocks. The hedge ratio on the short side is calculated based on the deal structure.

— Similar to the naïve long-only risk arbitrage strategy, we weight each deal by the market cap of the target

companies at every month end. For the stock deals in which the acquire companies are also in the Russell

3000 iIndex, we short the acquirer company using the appropriate hedge ratio. If a stock deal closes

successfully or is withdrawn within a month, both the long and the short positions are closed intra month, on

the same day. Otherwise, the positions are rebalanced at the end of each month.

A naïve long/short risk arbitrage strategy

Sharpe ratio


0%

2%

4%

6%

8%

10%

12%

14%

16%

Long only Long/short

0.0

0.1

0.2

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0.5

0.6

0.7

0.8

0.9

1.0

Long only Long/short


Deutsche Bank


-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%Rank IC

12 month average

Avg = 7.60%Min = -47%Max = 64%Avg/Std.Dev.= 0.41

0%

2%

4%

6%

8%

10%

12%

14%

16%

1 2 3 4 5 6 7 8 9 10 11 12 M&A

Source: Thompson Reuters, Compustat, IBES, Russell, S&P, TAQ, Deutsche Bank Quantitative Strategy

Multifactor M&A alpha: the MAA model Common quant factors also predict the returns of takeover targets (IC) The MAA model strongly predicts the future return of targets (IC)

Forecasting power as a function of forecasting horizon Investing in deals with strong alpha based on the MAA model

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7%

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7

8

9M&A universe

MAS model

MAA model


Deutsche Bank

Yin Luo, CFA ▪ 1.212.250.8983 ▪ [email protected] 12

Average bias statistic: realized future risk / historical volatility

— Equity risk model (i.e., the covariance matrix) is generally estimated based on historical data (returns and factors).

— For takeover target companies, however, the future risk is more of a binary issues – if the deals close on their

original terms, the risk tends to be fairly minimal; on the other hand, if the deals break up, the downside risk is

tremendous.

— Our MAS model predicts deal success probability and can significantly improve deal risk estimation.

— We use the bias statistic to measure the accuracy of our risk prediction. The bias statistic is the ratio of realized risk

and predicted risk. A perfect risk model should have a bias statistic close to 1.0.

Using the MAS model to improve deal risk estimation

The higher the deal success probability, the lower realized risk,

@COR(model scores, bias statistic)


0.00

0.50

1.00

1.50

2.00

2.50M&A universe

Russell 3000

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

MAS model MAA model

)exp( ,,, tititi pVolNewVol


Deutsche Bank



More sophisticated portfolio construction technique – risk parity

Sharpe ratio Max drawdown

13

— More sophisticated portfolio construction techniques can further improve performance

— Equally weighted MAA: in this portfolio, we simply equally weight all takeover targets ranked in the top

half by the MAA model;

— Inverse volatility: for the same target stocks, we apply the inverse volatility weighting scheme;

— Risk parity – sample covariance matrix: we weight the same target firms, using the risk parity algorithm.

The covariance matrix is based on sample return data.

— Risk parity – MAS matrix: we weight the same target companies, using the risk parity scheme.

However, we adjust the covariance matrix using our MAS model.

— Weighting stocks by inverse volatility helps, while taking into account of correlation (risk parity) further

improves performance. Lastly, our MAS-adjusted covariance matrix boosts return and cuts down risk.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Equally weighted MAA

Inverse volatility Risk parity - sample covariance matrix

Risk parity - MAS matrix

-50%

-45%

-40%

-35%

-30%

-25%

-20%

-15%

-10%

-5%

0%

Equally weighted MAA

Inverse volatility Risk parity - sample covariance matrix

Risk parity - MAS matrix


Deutsche Bank


The final systematic M&A (SMA) portfolio

— Start with the universe of targets of all US target

companies in the Russell 3000 index

— Use the MAA model and adjust the expected alpha by

the expected duration from the MAD model for a

duration-adjusted alpha signal

— Use the MAS model to predict the deal success

probability, which is then used to improve our

covariance matrix estimation

— Finally, we apply a mean-variance optimization to

construct our systematic M&A arbitrage (SMA) portfolio

14

Max drawdown

Sharpe ratio

Annualized returns


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Deutsche Bank


04/04/2016 15:40:58 2010 DB Blue template

Appendix 1 Important Disclosures Additional Information Available upon Request

DOUBLE CLICK IN

For disclosures pertaining to recommendations or estimates made on securities other than the primary subject of this research, please see the

most recently published company report or visit our global disclosure look-up page on our website at

http://gm.db.com/ger/disclosure/DisclosureDirectory.eqsr

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http://gm.db.com/ger/disclosure/DisclosureDirectory.eqsr

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Special Disclosures N/A

Analyst Certification

The views expressed in this report accurately reflect the personal views of the undersigned lead analyst(s). In addition, the

undersigned lead analyst(s) has not and will not receive compensation for providing a specific recommendation or view in this report.

[Yin Luo]

Hypothetical Disclaimer

Backtested, hypothetical or simulated performance results discussed herein have inherent limitations. Unlike an actual performance

record based on trading actual client portfolios, simulated results are achieved by means of the retroactive application of a backtested

model itself designed with the benefit of hindsight. Taking into account historical events the backtesting of performance also differs

from actual account performance because an actual investment strategy may be adjusted any time, for any reason, including a

response to material, economic or market factors. The backtested performance includes hypothetical results that do not reflect the

reinvestment of dividends and other earnings or the deduction of advisory fees, brokerage or other commissions, and any other

expenses that a client would have paid or actually paid. No representation is made that any trading strategy or account will or is likely

to achieve profits or losses similar to those shown. Alternative modeling techniques or assumptions might produce significantly different

results and prove to be more appropriate. Past hypothetical backtest results are neither an indicator nor guarantee of future returns.

Actual results will vary, perhaps materially, from the analysis.

16


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Regulatory Disclosures 1. Important Additional Conflict Disclosures Aside from within this report, important conflict disclosures can also be found at https://gm.db.com/equities under the “Disc losures Lookup” and “Legal” tabs. Investors are strongly encouraged to review this information before investing.

2. Short-Term Trade Ideas Deutsche Bank equity research analysts sometimes have shorter-term trade ideas (known as SOLAR ideas) that are consistent or inconsistent with Deutsche Bank’s existing longer term ratings. These trade ideas can be found at the SOLAR link at http://gm.db.com.

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18


Economy & Finance

Systematic M&A Arbitrage by Yin Luo at QuantCon 2016