insider trading cds

8/6/2019 Insider Trading CDS

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Insider Trading in Credit Derivatives

Viral V. Acharya and Timothy C. Johnson

London Business [email protected], [email protected].

MOODYS/LBS CREDIT RIS K CONFERENCE26 May 2005


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Introduction

Credit Derivatives.

Perhaps the most important and successful financial innovation of the last

decade.

Deemed by many (including Alan Greenspan) as efficient mechanisms to

transfer risks from banking sector to capital markets.

However, like any insurance market, credit derivatives may also entail unde-

sirable effects:

1. Ex-ante asymmetric information.

2. Ex-postmoral hazard (Duffee and Zhou, 2001).

We are concerned with the first of these effects.


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Recent events

Early 2002. Rumors surround Xerox, Enron, Fujitsu, others.

...firms with large lending departments would always come in and buy

protection at exactly the right moment.

October 2002. PIMCO white paper alleges widespread abuses:

Credit default markets are a mechanism with which friendly commer-

cial bankers ... can profit by betraying and destroying their clientsthrough the use of inside information.

May 2003. Joint Market Practices Forum (BMA/ISDA/LSTA) issues voluntary

guidelines on information sharing inside banks.

July 2003. Rumors surround Mirant, Marconi, others.

May 2004. Financial Stability Forum (IOSCO/BSBS/IAIS) report.


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Recent events (Contd)

Use of material non-public information by securities and credit derivative

market participants will be embraced by these new laws on insider dealing. Knowl-

edge gleaned from bank lending relationships is the main area of scrutiny, so far

as the use of non-public information in these markets is concerned."

(Creditflux, January 2005, www.creditflux.com)

[B]anks must not use private knowledge about corporate clients to trade in-

struments such as credit default swaps (CDS), says a report drawn up by five bod-

ies including the International Swaps and Derivatives Association and the Loan

Market Association... The warning highlights the challenges credit derivatives

pose to banks and regulators trying to build a functioning market infrastructure...

[M]any banks and institutions are trading CDS instruments in the same compa-

nies they finance - sometimes because they want to reduce the risks to their own

balance sheets."

(Financial Times, April 25, 2005 - Banks warned on insider trading threat

posed by market for credit derivatives)


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Why study insider trading in credit derivatives?

Adverse selection may have undesirable consequences both for the liquidity

of the market and for its scope.

Hence both welfare and policy implications may be significant.

Will credit derivatives be sufficiently liquid when required the most?

* For protection against risky credits.

* For protection in periods of systematic stress.

Additional reasons:

Credit derivatives are a good laboratory for studying insider trading.

* Potentially informed players are well-identified :

- Relationship banks

* Nature of private information is unambiguous:

- Purpose of bank monitoring is to identify credit risk.


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Summary of results

Our study is concerned with two facets of the problem

1. Is there evidence of actual insider trading in CDS markets?

Methodology: Time-series, cross-sectional tests of inter-market informa-

tion flow.

2. Is there evidence that insider trading is harmful to the markets?

Methodology: Cross-sectional tests of liquidity and price determinants.

To date, our findings are as follows.

1. There is a significant, permanent information flow from CDS markets to

equity markets:

- Increasing in the number of relationship banks of the firm.

- Consisting exclusively of bad news.

2. No evidence however that this has affected the liquidity and prices in the

CDS or the equity markets.


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Data

We obtained a data set of daily closing quotes for the most widely traded CDS

names (benchmarks") from January 2001 through October 2004.

We focus on North American corporate credits, and obtain matching infor-

mation on the underlying companies from CRSP and Compustat.

We employ a specially constructed proxy for the number of potentially in-

formed traders for any one target entity:

- This is the number of commercial/investment banks which have partici-

pated in an outstanding loan origination or credit facility.

Our results are robust to

- Subset of banks studied

- Inclusion of subsidiary loans

- Using loan amounts or number of facilities

- Relationship duration


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TABLE 1: SUMMARY STATISTICS

Low Median High

CDS level (mid price, BP) 13 81 2400

CDS bid-ask spread (BP) 1 20 2000

Credit rating Ba3/BB- Baa1/BBB+ AAA/Aaa2

Firm size (equity mkt val, $mm) 720 15820 412900

Firm debt (book val, $mm) 9 8874 312684

Firm leverage (debt at book val) 0.00 0.21 0.65

Average stock volume (mm shrs/day) 0.14 2.65 76.1

Average stock turnover (pct/day) 2.0 5.6 26.8

Average stock volatility (ann std dev) 0.24 0.39 0.83

Number of Bond Issues 0 9 71

Number of Bank Relationships (leads) 0 16 50

Number of Bank Relationships (all) 0 29 69

Average Relationship Length (yrs.) 0.3 4.1 7.2Number of Active Facilities 0 4 36

Amount of Active Facilities ($mm) 0 4019 66099

Observations/day 9 46 62


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Preliminary evidence

We examine the cross-correlation between changes in CDS prices and stock

returns for evidence that information is revealed first in the CDS market.

- Maintained assumption: stock market reaction is coincident with public

release of information.

Previous studies of bond responses to stock moves find slow adjustment of

bond prices:

- Bonds have negative cross-correlation with up to several days lag of

stock returns.

- Effects most likely due to poor data; wide spreads.

We would only expect to observe insiders exploiting information in the CDS

market when there is significant negative information.

Hypothesis: If insiders were active in our sample, firms that experienced severe

credit deterioration may exhibit significant cross-covariance of CDS changes

with future stock returns.


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Figure 1: Cross Correlation of Stock Returns and CDS Changes.

5 4 3 2 1 0 1 2 3 4 50.2

0.15

0.1

0.05

0

0.05

Whole Sample

5 4 3 2 1 0 1 2 3 4 5

0.4

0.3

0.2

0.1

0

0.1WorldCom, Enron, Sprint, Tyco

lead/lag (days)

The figure shows the cross-correlation between percent changes in CDS prices at time t and stockreturns at time t+ k as a function of k. In each panel the cross-correlations for individual firmsare averaged across firms.


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Econometric analysis

We first construct innovations of the CDS market.- Orthogonal to coincident and past stock-market innovations.

- Orthogonal to past CDS innovations.

The relationship of CDS and stock-market returns is inherently non-linear.

- We control for this based on Merton (1973) model.

* Schaefer and Strebulaev (2003)

Next, we study the effect on stock-market returns of lagged CDS innovations.

stock returnt = a0 +[b0 +b1(number of insiders)](CDS innovation)t1 +5k=1

ck stock returntk+


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Figure 2: Merton (1973) model credit-spread elasticity.

10 15 20 25 30 35 40 45 501.5

1

0.5

0

CDS

levelelasticityw.r.t.Stocklevel

1 / CDS Level

sigma = 0.125sigma = 0.25sigma = 0.375


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TABLE 2:

STOCK RETURNS ON CDS INNOVATIONS

(A) (B) (C) (D)

b0 -0.0080 0.0094

(2.75) (1.64)[.003] [.206]

b1 -0.00093

(3.55)

[.001]

b+0 -0.0088 0.0216

(1.98) (2.63)[.138] [.037]

b+1 -0.0016

(4.38)

[.000]

b0 -0.0073 -0.0033

(1.61) (0.39)

[.107] [.650]b1 -0.0002

(0.58)

[.600]


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Alternative hypotheses

We control for several possible reasons for uninformed trade.

(stock return)t = a0 +[b0 +b1(number of banks)+b(other controls)](CDS innovation)t1 +t

Is more banks just more borrowing?

- Size of equity and debt.

- Number of public bond issues.

- Presence of convertible debt.

Is this just about lead banks?

Relative liquidity of debt and equity markets.

- CDS bid-ask spread.

- Volume, Turnover, and Price-impact in equity markets.


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For which firms is this effect stronger?

Sub-sample approach.

Credit conditions

- A one-day decline in CDS level of 50 basis points in future.

- Permanent widening of the CDS level to 100+ basis points in future.

- Low credit rating (A3/A- or worse).

(stock return)t = a0 +5

k=1

[b0,k+ bD0,k (Credit-condition Dummy)t](CDS innovation)tk

+5

k=1

[c0,k+ cD0,k (Credit-condition Dummy)t](stock return)tk+ t.


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TABLE 4: ROBUSTNESS TO CREDIT-CONDITION SUB-SAMPLES.

(A) (B) (C)

a0 0.0003 0.0003 0.0003

(2.19) (2.21) (2.04)

5k=1 b0,k 0.0033 0.0050 0.0111

(0.44) (0.66) (0.91)

5k=1 b

D0,k 0.0492 0.0465 0.0224

(2.36) (2.52) (1.51)

5k=1 c0,k 0.0413 0.0399 0.0183

(3.22) (3.00) (0.97)

5k=1 c

D0,k 0.1917 0.1338 0.0488

(5.90) (4.68) (2.02)


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Cross-sectional analysis

Motivation

- Controls for firm-level heterogeneity in the CDS-stock market linkage.

- Gives equal weight to all firms.

Two-stage analysis: Similar to Hou and Moskowitz (2005).

- Calculate information flow measure for each entity.

- Relate the measure to entity-level characteristics.

(stock return)i,t = afi +

5

k=1

bf

i,k(CDS innovation)i,tk+ t,

i =5

k=1

bfi,k.

Bank relationships emerge as the only robust determinant.


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TABLE 6

PANEL B: PROPERTIES (MEDIANS) OF FIRMS IN DIFFERENT -QUINTILES

Q1 Q2 Q3 Q4 Q5

Average 11% 2% 1% 4% 8%

CDS level (mid price, BP) 185 108 101 79 68

CDS bid-ask spread (BP) 26 21 20 18 17Credit rating 27 27 26 27 26

Firm size (equity mkt val, $mm) 28021 12477 9663 12677 13862

Firm debt (book val, $mm) 12785 7178 4136 6864 6380

Firm leverage (debt at book val) 0.28 0.32 0.33 0.33 0.27

Average stock volume (mm shrs/day) 8.07 2.10 1.37 1.71 2.49

Average stock turnover (pct/day) 5.4 6.4 4.8 5.0 5.6

Average stock volatility (ann std dev) 0.40 0.37 0.33 0.33 0.33

Number of Bond Issues 12.6 7.1 5.2 12.0 5.8

Number of Bank Relationships (leads) 26.1 18.1 14.5 10.6 12.1

Amount of Active Facilities ($mm) 3085 1429 978 1358 1050


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TABLE 6

PANEL C: SECOND-STAGE CROSS-SECTIONAL DETERMINANTS OF

(A) (B) (C) (D) (E)

constant 0.0511 0.0110 0.0608 0.0513 -0.0017(2.95) (0.72) (0.49) (2.65) (0.01)

banks 0.0026 0.0026 0.0027

(3.17) (3.12) (3.17)

CDS 4.8555 0.1008

(105) (0.56) (0.01)

rating 0.0021 0.0020

(0.46) (0.46)


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Is insider trading harmful to these markets?

The uncovered information flow between CDS and stock markets is one-

sided: only on adverse credit news.

The resulting lemons problem may

- Widen the gap in prices between buyers and sellers.

- Increase the cost of default insurance.

- Lower the equilibrium quantity of insurance provision.

We focus on relative liquidity provision (bid/ask spreads) as a measure of

market perception of information asymmetries.

Hypothesis 1: If adverse selection is problematic for market liquidity, then moreinformed insiders will imply wider bid/ask spreads.

Hypothesis 2: If adverse selection is problematic for market liquidity, then more

informed insiders will imply higher CDS levels.


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TABLE 7: ILLIQUIDITY REGRESSIONS

STOCK ILLIQ CDS % B/A

FM Panel FM Panel

size -0.0016 -0.0021 0.0480 0.0499

(3.38) (2.93) (8.59) (3.55)

volume -0.0086 -0.0083 -0.0414 -0.0429

(5.88) (2.88) (10.59) (6.02)

r1mo 0.0028 0.0002 -0.0144 -0.0364

(1.95) (0.17) (0.48) (1.42)

1mo 0.0130 0.0071 0.0561 0.0780

(5.01) (2.28) (1.71) (2.42)

banks -0.00004 -0.00002 -0.0029 -0.0026(1.50) (0.37) (8.18) (2.75)

obs 667 39109 947 44932

R2 0.5266 0.3822 0.2646 0.1365


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TABLE 8: CREDIT SPREAD REGRESSIONS

(A) (B)

FM Panel FM Panel

r6mo -88.76 -101.4 -84.75 -102.8

(3.60) (5.57) (3.49) (5.50)

6mo 390.7 542.2 370.6 524.4

(9.32) (7.15) (7.70) (6.98)

debt 16.75 11.31 14.13 14.04(2.50) (1.07) (1.64) (1.73)

leverage 93.78 138.4 103.3 132.4

(4.77) (2.18) (3.88) (2.21)

tangible 64.89 47.29 68.63 45.04

(2.08) (1.24) (2.41) (1.27)

rating -21.42 -17.14 -20.65 -17.75

(6.17) (3.72) (5.38) (4.20)

banks -0.040 0.407

(0.11) (0.40)

bid/ask -27.50 2.67

(0.71) (0.05)

obs 891 39988 891 39988

R2

0.5867 0.5167 0.6079 0.5161


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Possible interpretations

Informed players take" liquidity when the value of information is high, and

make" liquidity at other times.

- Bloomfield and OHara (1999, 2000), Hong and Rady (2002), Bloom-

field, OHara and Saar (2005).

Informed trade when liquidity is high.

- Endogeneity of informed trading.

- Table 3 results inconsistent with this notion.

Multiple informed players with same information cause immediate release of

information into prices.

- Holden and Subrahmanyam (1992).

- Why dont the informed exploit the stock markets?

Disentangling between these requires further investigation.


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Conclusions

We believe there is prima facie evidence that insider trading exists in the

credit derivatives markets.

The information flow from CDS to stock markets implies an informed revi-sion of quotes.

However, there is no evidence that this has affected adversely the liquidity

and pricing in the CDS or equity markets.

The merits for the regulatory response thus needs to be cautiously evaluated.


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Future tests

1. Obtain more information on credit derivatives trades and quotes.

We currently use only benchmark data of CreditTrade, but may be able

to purchase:

- Intra-day quotes (anonymous).- Trade prices based on actual transactions.

2. Examine insider trading more directly.

Develop more precise measures of information asymmetry.

- Exploit microstructure-based proxies for quantity and likelihood of

insider trading.

3. Data on non-US names as well as sovereign entities.

Will enable us to study more events.


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TABLE 3: CONTROLS FOR UNINFORMED TRADE

(A) (B) (C) (D) (E) (F) (G)

b0

-0.0022 -0.0049 -0.0022 -0.0062 -0.0224 0.0448 0.0126

(0.05) (0.11) (0.05) (0.14) (0.51) (0.92) (0.27)

banks -0.0010 -0.0011 -0.0011 -0.0008 -0.0009 -.0009 -.0009

(2.93) (3.05) (2.11) (1.73) (2.55) (3.26) (2.39)

size 0.0003 -0.0004 -0.0003 0.0006 0.0013 0.0063 0.0057

(0.08) (0.11) (0.08) (0.18) (0.37) (1.38) (1.48)

debt 0.0009 0.0031 0.0009 0.0014 0.0038 0.0021

(0.24) (0.75) (0.23) (0.32) (0.92) (0.56)

bonds 0.0128 0.0145 0.0128 0.0134 0.0161 0.0132

(1.43) (1.60) (1.42) (1.49) (1.76) (1.46)

-0.0005 0.0001 -0.0030 -0.0018 -0.0017

(1.39) (0.08) (0.58) (3.55) (3.39)

CB ind loans loan amt nonleads nonleads

volume -0.0098 -0.0066(2.15) (2.16)

turnover 0.0024

(0.40)

ILLIQ -0.0063

(0.03)

CDS b/a 0.0071

(0.35)


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TABLE 5: ROBUSTNESS TO RELATIONSHIP SUB-SAMPLES.

(A) (B) (C)

a0 0.0003 0.0005 0.0003

(2.10) (3.35) (1.81)

5k=1 b0,k 0.0192 0.0146 0.0035

(2.01) (1.79) (0.43)

5k=1 b

D0,k 0.0475 0.0714 0.0006

(3.41) (4.17) (0.03)

5k=1 c0,k 0.0583 0.0211 0.0008

(3.

46) (1.

56) (0.

06)

5k=1 c

D0,k 0.0909 0.0343 0.0584

(3.86) (1.23) (1.91)


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TABLE 6

PANEL A: PROPERTIES OF

Mean = 0.0043

t-stat = 0.4600

Min = 0.1961

Max = 0.3262

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