Do informed investors manipulate markets using options prior to SEOs?

Donghan Kim†

Korea Advanced Institute of Science and Technology

This version: September, 2016

Abstract

This paper investigates the manipulation possibility using options prior to Seasoned Equity Offerings

(SEOs). The theoretical model identifies a key variable influencing manipulation incentive, the

liquidity order correlation between stock and options market. Informed investors in the markets with

low correlation cannot profit from the market transaction, therefore, they rather manipulate the market

to get larger SEO discounts. Furthermore, high legal costs of stock market manipulation make options

market the attractive venue for manipulation. Using the empirical proxy defined as the correlation of

signed Amihud illiquidity between two markets, the supportive evidence is found that there is a

significant negative relation between the correlation and SEO discounts. Pre-issue market

informativeness and post-issue price transparency worsen as the correlation decreases. Long-run

return reversal is also found in SEOs with low correlation. In the natural experiment regarding legal

costs, the strengthened regulation enhances stock market efficiency, while worsening options market.

This paper provides an evidence that options can be a loophole to anti-manipulation regulation and the

liquidity order correlation is the important factor for multi-market microstructure.

Keywords: Seasoned Equity Offerings, Manipulation, Options, Market Microstructure, Multi-market

trading

JEL Classification: G13, G14, G23, G24, G32

† College of Business, Korea Advanced Institute of Science and Technology (KAIST), 85 Heogiro,

Dongdaemoon-gu, Seoul 02455, Republic of Korea; Tel: +82-2-958-3427; E-mail: kdh8997@

business.kaist.ac.kr

1

I. Introduction

Both academics and practitioners have long-standing interest on the manipulative trading prior to

seasoned equity offerings (SEOs). The issuing firms have an incentive to discount the offering price

to attract more investors, facing with high information asymmetry and weak investor demand

(Altınkılıç and Hansen (2003); Beatty and Ritter (1986); Corwin (2003); Kim and Shin (2004); Mola

and Loughran (2004); Rock (1986 )). Gerard and Nanda ((1993)) develop a model arguing that

investors can benefit from manipulating the stock price to get larger SEO discounts. Henry and Koski

((2010)) find evidence on SEO manipulation that abnormal short-selling is followed by larger SEO

discounts. Regarding this concern, in June 2007, Securities and Exchange Commission (SEC)

strengthened Rule 105 of Regulation M that investors are prohibited to purchase the newly issued

shares if they sold short the equity for the restricted period beginning five days prior to the pricing

and ending at the very pricing1. Even with these efforts, SEC report that they settle more than 40

violations of Rule 105 and collected $42 million as penalties from 2010 to 20132, which is the strong

evidence that there is still illegal manipulation attempts prior to SEOs. Furthermore, SEC seriously

concerns the manipulation possibility through other channels such as derivative, PIPE transactions,

long sales, convertible offerings, or best efforts offerings, which can be a loophole to the rule3. One

of the most feasible scenarios is that manipulators mimic the manipulative strategy in options market,

but they are not subject to Rule 105. Since not all SEOs of optionable stocks are manipulated and the

pricing mechanism becomes much more complicated with options, it is required to more deeply

analyze the mechanism for manipulation through options.

With the presence of information asymmetry, the information diffusion process of optionable stocks

become more complicated. In Black-Scholes-Merton world, options are redundant securities and

1 Before the amendment, investors who sold short for the restricted period can participate the offering, but they

cannot cover the short position with the newly allocated shares. Short-selling other securities are not subject to

the rule before and after the amendment.

2 SEC Office of Compliance Inspections and Examinations Risk alert, Volume III, Issue 4, 17 September 2013,

“Rule 105 of Regulation M: Short selling in connection with a public offering”

3 17 CFR PART 242 page 9 and 32, Release No. 34-56206; File No. S7-20-06

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there should not be superior information in options market. Information asymmetry, however, raises

the possibility of options market leading stock market, since informed investors may prefer options

market as the first venue for their trading. Previous literatures documents that options market is

attractive to informed trading due to options leverage (Easley, O'Hara and Srinivas (1998 )) and

liquidity condition (Back (1993 )), which leads to options’ predictability over future stock return (An,

Ang, Bali and Cakici (2014); Chakravarty, Gulen and Mayhew (2004); Cremers and Weinbaum

(2010); Pan and Poteshman (2006); Xing, Zhang and Zhao (2010 )). Information, therefore, flows

between two markets, which raises the possibility that distorted information can confuse the market

participants, leading to higher SEO discounts. Back (1993) develops a model that information content

on buying stock and options are different with respect to options’ moneyness. Informed stock buying

orders indicate that stock price will appreciate to the level greater than the current value. Informed

options buying orders with different strike price, however, indicate that stock price will appreciate to

the level greater than the strike price. Market makers have, therefore, an incentive to monitor the

information contents in options market. Regarding this mechanism, manipulated options orders can

confuse other market participants. Beside information based rationale, options market markets’

inventory risk can generate equivalent order effect in stock market, due to their hedging activity

(Muravyev (2016 )). Manipulative short-equivalent position in options market, therefore, can

generate the effect of manipulative selling orders in stock markets. In either ways, options market is

the feasible venue for manipulation prior to SEOs.

This paper develops a model of SEO manipulation for optionable stocks and presents the key

variable governing manipulation incentive, with supporting empirical evidence. As aforementioned,

information asymmetry increase SEO discounts due to the fact that investors suffer from winner’s

curse problem (Beatty and Ritter (1986); Corwin (2003); Rock (1986 )). In such events, options

become more important since it affects the information diffusion process. The informativeness of

options price is largely affected by informed trading and informed trading depends on the liquidity of

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the security, which is provided by the liquidity traders’ activity4. In earlier work, Kyle ((1985))

develops a model that higher market liquidity from the liquidity traders stimulates informed trading,

which leads to the more efficient price. In multi-market trading, not only the liquidity in the market

but also the liquidity correlation among markets affects the information diffusion process (Back and

Crotty (2015); Chabakauri, Yuan and Zachariadis (2015); Chowhdry and Nanda (1991 )). Back and

Crotty (2015) argue that there exists cross-market influence on return between bond and stock

markets although the order flows are almost uncorrelated. Chabakauri et al. (2015) develop a model

that the correlation of the liquidity orders between options and stock markets affects informed

investors’ trading behavior, which is related to the price transparency. To the extent, this paper

further argues that the correlation of the liquidity orders is one of the most important variables

affecting the SEO manipulation incentive through options. In nature, it is expected that there is a

negative or at least zero liquidity order correlation, since options are used as hedging device and

some investors only trade in one market (Back (1993)). The markets with uncorrelated liquidity

orders have a various spectrum of order types (variety) and they are capable of clearing the correlated

informed buying or selling orders. As the correlation being negative, the market clearing condition

makes it hard to trade security since informed investors should take the risk of revealing their private

information. In such condition, informed investors rather try to manipulate the markets pursuing the

larger SEO discounts.

In addition to the liquidity order correlation, the heterogeneity in legal costs make options market

the attractive venue for manipulation. The major concern to the manipulators is a legal issue.

Manipulation through stock market is the most efficient way, however, this strategy becomes

infeasible after 2007 since they cannot be allocated the new security. The reported cases for violating

rule 105 indicates that SEC actively monitor the suspicious stock trading, which makes this strategy

less probable 5 . There is a loophole to the rule, however, options can arise as an alternative

4 Liquidity traders in this paper include noise or uninformed traders, although they are not identical concept.

5 Manipulation can still be possible, since some of the violation may not be monitored by SEC. Furthermore,

SEC allows short-seller to get the new shares for the following three exceptions; 1) bona fide purchase, 2)

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manipulation device since it is not subject to the rule. Furthermore, equivalent short position in

options market cannot be easily classified as manipulation. Investors who have a positive information

on the firm also can take position as married-put for hedging purpose. SEOs are highly uncertain

ambiguous events and even informed investors suffer from downside risk (Cumming, Johanning,

Ordu and Schweizer (2015 )). Manipulation through options, therefore, provides manipulators with a

way of escape from accusation by justifying their manipulative options trading as hedging activity. In

short, high legal costs of stock market manipulation make it more preferable to manipulate the

markets using options. Following this conjecture, this paper provides empirical evidence in the

natural experiment that stock market informativenss is improved after the rule amendment, however,

options market informativeness become worsen.

To empirically test the impact of the liquidity order correlation, it is required to measure an order

imbalance. In a daily level, it is well documented that an order imbalance makes the market less

liquid and increase the return movement for a given trading volume (Amihud (2002); Amihud and

Mendelson (1986); Chordia, Roll and Subrahmanyam (2002); Chordia and Subrahmanyam (2004 )).

Signed Amihud6, therefore, can be a proxy for the order imbalance in a daily level. Moreover,

negatively correlated end-user demand on security makes the prices deviate from the other, which

will decrease the inter-market return correlation within arbitrage bounds7 (Garleanu, Pedersen and

Poteshman (2009 )). Aggressive and negatively correlated liquidity orders, therefore, make the return

and signed Amihud correlation less than one. The empirical proxy for the correlation of the liquidity

orders, ρ̂ , is defined as the correlation of signed Amihud illiquidity between options and stock

markets. It is hard, however, to isolate the liquidity orders from the informed order in measuring the

trading in separate accounts, and 3) investment company with registration. Manipulation possibility through

these exceptions also cannot be excluded.

6 The conventional Amihud illiquidity is defined using the absolute of return for a given trading volume. Signed

Amihud means that it excludes the absolute sign and uses raw return for a given trading volume, to get a

directional information.

7 In a perfect market, the security prices in different markets should be identical and return correlation should be

one, however, order imbalance in each markets can make it deviate from each other, especially with downward

sloping demand curves.

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correlation. To minimize this bias, it is estimated 21 trading days prior to the issuance, since informed

trading about short-term movement around the issuance occurs as the issuance nears8 (Kim, Kim and

Seo )).

This paper provides empirical evidence that there is a negative relation between SEO discounts and

ρ̂. This supports the hypothesis that SEOs with low correlation are in the manipulative equilibrium

accompanied by greater SEO discounts. Also, manipulation degenerates the market efficiency, that is,

pre-issue market informativeness and post-issue price transparency decrease as the correlation

diminishes. A large pre-issue market innovation lowers SEO discounts, which supports the

hypothesis that return innovation is induced by the new information arriving. These effects, however,

diminish as the correlation decreases, which indicates that SEOs with low correlation loose pre-issue

market informativeness, possibly due to the manipulation. Interestingly, these effects are prominent

in stock market for the rule 105 restricted period, while options market only shows the effects prior to

the restricted period. It supports the conjecture that manipulators avoid the manipulation through

stocks for the restricted period, however, instead use options as an alternative. Post-issue price

transparency also shows similar implication. A large post-issue return innovation indicates that new

information is not impounded into the price ex ante, that is, the low transparency (Chabakuri et al.

(2015)). A large post-issue innovation is accompanied by SEOs with a low correlation in both stock

and options market. The price discovery for optionable stocks is complicated even after the issuance.

It is possible that information does not reveals in the short-run. Since the negative information

diffuses faster than the positive information does, manipulated good SEOs with low correlation will

take time to recover the price to the fair level. Consistent with this conjecture, it is found that there is

a negative relation between the liquidity order correlation and long-run performance after the

issuance, which can be interpreted as the return reversal following manipulation.

8 Even though it is contaminated with informed trading, it is safe to use the proxy, since higher liquidity order

correlation encourages informed investors to participate and results in higher total order correlation.

6

To the best of my knowledge, this is the first paper to show how options can be used as SEO

manipulation device with theoretical and empirical evidences. The liquidity order correlation plays an

important role in information diffusion process and it influences manipulation incentive prior to

SEOs. This paper sheds light on understanding how the capital market is distorted, and how the tools

expected to enhance market efficiency can ruin the markets. This paper is organized as follows.

Section 2 develops the SEO manipulation model of optionable stocks. Section 3 presents empirical

evidences with the description of variables and the dataset, how an empirical proxy for the correlation

is constructed, and the robustness check results. Section 4 concludes the paper and proposes the

future study.

II. Theory

2.1. The model

Following Gerard and Nanda (1993), there are 5 market participants: market makers, the informed

trader, the liquidity (uninformed /noise) traders, the uninformed bidders, and the security issuer. All

the market participants are risk neutral and trade both stock and options. For simplicity, it is assumed

that there are only European call options in the market9.

< Insert Figure 1>

At two days prior to the issuance (ID-2), the issuing firm announce the equity offerings and

informed traders are given the true value of stock. The ex-post true liquidation value of stock, Ṽ, has

dichotomous values, V+ or V−. The initial price of the stock, Ps0, is V−+θ0ΔV where θ0 is the ex-ante

unconditional probability of V+ and ΔV is V+−V−. The initial price of the option, Po0, is θ0ΔVK where

ΔVK is V+−K and K is the option strike price, for which the range is between V+ and V−. Without the

9 Even though put options are allowed to be traded, the implication of the model doesn’t change.

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loss of generality, θ0 is assumed to be 1/2 for simplicity. At ID-1, informed and liquidity traders trade

and the market price is determined. X(Ṽ) = (xs, xo) are the informed trader’s orders in the stock and

options market, which makes their expected profit maximized. For each states, X(V) can be

X(V+)=(xs+, xo+) or X(V−)=(xs−, xo−). The liquidity orders are independent of the true value. Their

probability distribution of orders in both markets, Ũ=(ũs, ũo), is given as follows.

Ũ =

[

(+us, +uo) with ρ 2⁄

(−us, −uo) with ρ 2⁄

(+us, −uo) with (1 − ρ) 2⁄

(−us, +uo) with (1 − ρ) 2⁄

ρ is the likelihood of uninformed liquidity traders trading both options and the underlying stock in

the same direction10. For high ρ, the liquidity traders tend to simultaneously buy (sell) both stock and

options, which is the correlated liquidity orders from uninformed (sentiment) investors who speculate

on the direction of the stock. Net order flow,Ỹ, is the aggregate of the informed and the liquidity

orders, X(Ṽ) + Ũ. Market makers clear the market after observing the net order flow in both markets.

Market makers cannot distinguish where the orders come from, but deduce the true value of the stock

given the net order flow at P=E[Ṽ|Ỹ]. The security issuers set the offering price after monitoring the

secondary market price. It is exogenously given that the informed bidders demand NI shares and the

uninformed bidders demand NU shares11. Since the total number of shares newly issued, q, is more

than NI but less than NU, the issuing firms have an incentive to discount the offering price in order for

encouraging the uninformed bidders to participate in the issuance. The uninformed bidders will,

therefore, always participate in new issue allocation. The informed traders will, however, avoid the

issuance when the stock is overvalued. The number of new shares allocated to the informed bidders,

αI, and the number of new shares allocated to the uninformed bidders, αU, are given as follows.

10 ρ can be expressed as the correlation between the uninformed orders of options and those of the underlying

stock as referred above, since the correlation is 2ρ – 1.

11 If uninformed bidders’ demand is affected by the last trading price, informed investors have more incentive to

manipulate the market, since smaller uninformed demand results in larger discounts.

8

𝛼𝐼 = {0 𝑤ℎ𝑒𝑛 �̃� = 𝑉−

𝑞 ×𝑁𝐼

𝑁𝐼+𝑁𝑈 𝑤ℎ𝑒𝑛 �̃� = 𝑉+, 𝛼𝑈 = {

𝑞 𝑤ℎ𝑒𝑛 �̃� = 𝑉−

𝑞 ×𝑁𝑈

𝑁𝐼+𝑁𝑈=

𝑞

𝜂 𝑤ℎ𝑒𝑛 �̃� = 𝑉+

At t=ID+1, the true value of the stock is revealed and the option expires.

2.2. Trading Strategy and Equilibrium

With options listed, informed investors have four different choices prior to SEOs; trade both

securities, trade only stock or options, and manipulate the market. It is natural to assume that they will

buy (sell) the securities when the stock is undervalued (overvalued) and X(V+) is different from

X(V−); separating strategy. Due to the market liquidity condition, these two values cannot be

infinitely differentiated but should be constrained12; X(V+) = X(V−) − 2U. When informed investors

decide not to trade stock (options), both xs+ (xo+) and xs− (xo−) are assumed to be identically zero,

while trading on other security is separated; mixed separating and pooling strategy. Finally, as

documented in Gerard and Nanda (1993), manipulators even sell the security in the case of good

offering, that is, X(V+) and X(V−) are assumed to be identical and negative; pooling strategy. These

four different strategies are summarized as follows.

Definition 1 (Informed investors’ strategies) Informed investors can separate (x+=x−+2u) or pool

(x+=x−) their orders. There are four different equilibriums in the market; Trading both (I), trading only

stock or options (MXS or MXO), and manipulation (M).

Depending upon the equilibriums, market clearing condition changes and affects the expected price

informed investors should pay. After observing net order flow, market makers infer the price by

estimating the conditional probability of V+ which is a function of ρ. The Bayes’ rule is applied to the

price estimation process. The conditional probability of V+, θ1, is

12 Details in the Appendix.

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θ1(Ỹ, Si) = Prob(Ṽ = V+|Ỹ, Si) =

Prob(Ṽ = V+) ⋅ f(Ỹ|Ṽ = V+)

Prob(Ṽ = V+) ⋅ f(Ỹ|Ṽ = V+) + Prob(Ṽ = V−) ⋅ f(Ỹ|Ṽ = V−) , (1)

where Si stands for each equilibriums; I, MXS/MXO, and M. Since Ṽ is dichotomic, the stock and

option prices are given as

Ps|Ỹ,Si =E[Ṽ|Ỹ,Si]=V−+ θ1ΔV, Po|Ỹ,Si = E [(Ṽ−K)+|Ỹ,Si] = θ1ΔVK . (2) The price is identical to its conditional expected value since market makers are competitive13. The

offer price and discount are calculated similarly. The expected profit of the uninformed bidders for

participating in the issuance is E[αU(Ṽ − P∗)|Ỹ, Si]. The offer price is set at the break-even, that is,

P∗|Ỹ, Si =E[αUṼ|Ỹ, Si]

E[αU|Ỹ, Si]= E[Ṽ|Ỹ, Si] +

Cov(αU, Ṽ|Ỹ, Si)

E[αU|Ỹ, Si] . (3)

The expected (offer) price conditional on the true value of the stock can be obtained accordingly.

E[P|Ṽ, Si] = ∑P|Yj, Si × f(Ỹ = Yj|Ṽ, Si)

j

. (4)

Following Kyle (1985) and Kyle and Vila (1991) , the equilibrium should satisfy profit

maximization and market efficiency condition. Additionally, since there are more equilibriums other

than the informative equilibrium, beliefs off-the-equilibrium path should be specified (Gerard and

Nanda (1993); Kreps and Wilson (1982 )).

Definition 2. (Definition of the equilibrium) The equilibrium is the sets of a pair X, P satisfying the

three conditions: 1) Profit maximization; E[Π(X,P)|�̃�,Si] ≥ E[Π(X’,P)|�̃�,Si], 2) Market efficiency;

P(𝐘)=E[�̃�|𝐘,Si], and 3) No defection; E[Π(Si)|�̃�,Si] ≥ E[Π(S−i)|�̃�,Si] where Si stands for each strategy.

The expected profit conditional on Ṽ is

E[Π(X, P)|Ṽ, Si] = { Π+ = Π− 𝑤ℎ𝑒𝑛 Π+ = Π−

0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

where Π+=E[(V+−Ps)xs++(V+−K−Po)xo++αI(V+−P*)|V+,Si], and

Π− =E[(V−−Ps)xs−+(−Po)xo−|V−,Si].

13 If there is only one market maker, solving the utility maximization problem for the market maker is required.

However, I assume that the market makers are competitive and their expected profit is set to be zero, as Kyle

(1985).

10

Π+ and Π− are the profit functions the informed traders try to maximize in each states, however, due

to the model’s characteristics, informed investors should set orders where Π+ and Π− are identical, in

order not to reveal their private information. Details are explained in the Appendix.

2.3. Manipulation without Options

First, this study considers the case when there is no option, for understanding how SEOs provide the

incentives for manipulation. Since only stock exists, informed investors choose to trade informatively

(separating) or manipulate the market (pooling). The liquidity orders have dichotomic values, us and –

us, with the probability of bs and 1 − bs, respectively. Since noise traders do not consider the direction

of the price, bs is assumed to be 1/2. The initial stock price at t=ID-2 is V ̶ + ΔV/2. After trading, the

conditional expected price at t=ID-1 is given as follows.

Informative equilibrium: E[P|V+] = V+ − ΔV/4 and E[P|V−] = V ̶ + ΔV/4

(5) Manipulative equilibrium: E[P|V+] = E[P|V−] = V ̶ + ΔV/2.

Compared to the informative equilibrium, the market price in the manipulative equilibrium is less

efficient and has high information asymmetry. Informed investor’s information is impounded into

price only when they try not to manipulate the market. Since information asymmetry is one of the

important factors governing SEO discount, the secondary market informativeness affects the issuing

firm’s decision. SEO discounts are given as follow.

Informative equilibrium: E[DSCT |I] =

1

2(1

2−

1

𝜂+1 )ΔV

(6) Manipulative equilibrium: E[DSCT |M] = (

1

2−

1

𝜂+1 )ΔV.

Unresolved information asymmetry in the manipulative equilibrium results in the greater SEO

discounts, which is the very incentive to do manipulation. As in Definition 2, manipulation is superior

when its profit is greater than the one for informative strategy.

Theorem 1 (Manipulative equilibrium without options) When there is no options allowed,

manipulative equilibrium exists when the following condition is satisfied.

αI 𝜂

𝜂+1 ≥ us. (7)

11

Proof is in the Appendix. Inequality (7) is analogous to equation (Ia) in Gerard and Nanda (1993),

which states that higher information asymmetry and larger offer size make manipulation more

attractive. Left-hand-side (LHS) of the inequality (7) represents the manipulation gain through SEO

discount. Right-hand-side (RHS) is the gain from secondary market trading. The above theorem,

therefore, implies that the manipulative equilibrium exists when the gain from manipulation exceeds

the gain from the market trading, that is, when there is a large SEO, low liquidity, and high

information asymmetry.

2.4. Manipulation with Options

Options make pricing mechanism and information diffusion process complicated. Without options,

informed investors only concerns about the stock market liquidity. If informed orders are cleared by

the liquidity orders, there is no more order imbalance left and price impact is limited. With options,

however, the market makers can get the additional information from the trading pattern in options

market. For example, if informed buying orders from investors with private information of stock

being undervalued is offset by the liquidity selling orders, the market makers’ posterior belief does

not change. However, if market makers observe that there is buying (selling) orders in options market,

they consider calculating the belief again. When the liquidity order correlation is low, buying (selling)

options order imbalance indicates that stock liquidity orders were selling (buying) orders and the

private information was undervaluation (overvaluation). The liquidity correlation, therefore, is one of

the critical parameters in information diffusion process.

In this paper, ρ is exogenously given to the model and its endogenous mechanism is beyond the

scope of this paper. However, some of the considerations give clues to the characteristics of ρ. Back

(1993) comments that it is natural to have a small ρ in the ordinary markets, since some investors only

trade in the stock markets, options are used for hedging, transaction costs are different, and the tax

effect exists. Furthermore, options are unique in the sense that it can be used for volatility trading (Ni,

Pan and Poteshman (2008); Rourke (2014 )). Volatility and the direction of the stock return does not

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need to be correlated, therefore, informed (or uninformed) volatility trading on options can be seen as

uninformed orders in terms of the directional trading. Since trading based on the volatility requires to

purchase both call and put options, it makes the liquidity order correlation with stock lower. Also, the

existence of large liquidity traders can affect the correlation. Chowdhry and Nanda (1991) argues that

large liquidity traders split their orders to minimize price impact, which generate correlated liquidity

orders among different markets with identical security. Finally, hedger also lowers ρ. As Back (1993)

argues, the hedger trades in the opposite direction between two securities, that is, incurring a small ρ.

There is the clientele effect and ρ could be cross-sectionally variant depending on the characteristics

of the investors. In short, due to the nature of the liquidity traders, it is natural to assume that the

liquidity order correlation is negative, which indicates that ρ is lower than 1/2.

Assumption 1 The likelihood of uninformed liquidity traders trading both options and the underlying

stock in the same direction, ρ, is assumed to be less than or equal to 1/2.

As referred in section 2.2, informative trading reveals more information and makes the secondary

market price more efficient. With options, the price informativeness decreases as informed traders

decide not to participate in the transaction.

Trading both (I): E[P|V+] = V+ − (𝜌 4⁄ )ΔV and E[P|V−] = V ̶ + (𝜌 4⁄ )ΔV (8) Trading one security (MX): E[P|V+] = V+ − ρ(1−ρ)ΔV and E[P|V−] = V ̶ + ρ (1−ρ)ΔV

Manipulation (M): E[P|V+] = E[P|V−] = V ̶ + ΔV/2.

For any given ρ, the price transparency, which can be measured as |Ṽ − E[P|Ṽ=V]|, is greater for more

informative equilibrium. As informed investors trade more, the secondary market price becomes

closer to the true value and information asymmetry is resolved. Options price also shows identical

implication as follows.

Trading both (I): E[C|V+] = (1 − ρ 4⁄ )ΔVK and E[C|V−] = (ρ 4⁄ )ΔVK

(9) Trading one security (MX): E[C|V+] = (1 − ρ(1−ρ))ΔVK and E[C|V−] = ρ(1−ρ)ΔVK

Manipulation (M): E[C|V+] = E[C|V−] = ΔVK/2.

As referred above, information asymmetry is the primary factors determining SEO discounts. Since

the liquidity correlation influences informed trading pattern and the market informativeness, SEO

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discounts is also affected by the correlation. The following theorem shows that discounts is a function

of the liquidity order correlation.

Theorem 2 (Manipulation and SEO discount) For any given ρ, more informative equilibrium has

smaller SEO discounts; E[DSCT|M] ≥ E[DSCT|MX] ≥ E[DSCT|I].

Trading both (I): E[DSCT|I] = ρ

4⋅η−1

η+1ΔV

(10) Trading one security (MX): E[DSCT|MX] = ρ(1−ρ)

4⋅

η2−1

(η−1)2ρ(1−ρ)+ηΔV

Manipulation (M): E[DSCT|M] = 1

2⋅η−1

η+1ΔV.

Theorem 2 states that manipulative equilibrium has the largest SEO discount, since the market in this

equilibrium sustains high level of information asymmetry. As informed investors trades stocks and

options, the level of information asymmetry and discounts is lower. Interestingly, within each

equilibrium, discounts are an increasing function of ρ. Similar to the fact that informed investors

easily camouflage their trading with high liquidity, higher ρ provides informed investors with

opportunities to safely trade in the market without much price impact. There is, however, expected to

be a negative correlation between ρ and SEO discounts, since higher ρ moves the market to more

informative equilibrium.

Following Definition 2, the manipulative equilibrium exists when the manipulation profits exceeds

any other strategies.

Theorem 3 (Manipulative equilibrium with options) When there are options listed, manipulative

equilibrium exists when the following condition is satisfied.

ρ < 1

2× [1 − √1 − 4Mk] (11)

where Mk(𝛼𝐼 , �̃�𝑘) =[𝑓(𝛼𝐼 , �̃�𝑘) + √𝑓(𝛼𝐼 , �̃�𝑘)

2 + 32𝜂2(𝜂 − 1)(𝜂2 − 1)𝛼𝐼�̃�𝑘]8(𝜂 − 1)(𝜂2 − 1)�̃�𝑘

⁄ ,

k ∈ {o, s}

𝑓(𝛼𝐼 , �̃�𝑘) = 𝜂(𝜂2 − 6𝜂 + 1)𝛼𝐼 − 4𝜂(𝜂 + 1)�̃�𝑘

�̃�𝑠 = 𝑢𝑠, �̃�𝑜 = 𝛿𝑢𝑜.

14

Proof is in the Appendix. The implication is similar to Theorem 2, the case without options. First, Mk

is an increasing function of αI, that is, high information asymmetry and large issuance makes the

manipulation generate greater gain from manipulated SEOs. Second, Mk is an decreasing function of

�̃�𝑘, which indicates that manipulation is more attractive when informed investors cannot generate

enough profit in secondary market trading. Aggressive trading activity, that is, high �̃�𝑘 helps informed

investors trade more securities without price impact. Finally, lower ρ stimulates manipulation, since

the secondary market trading gain decreases as ρ decreases. Interestingly, ρ does not affect the

decision whether informed investors trade stocks (MXS) or options (MXO). It is solely determined by

each markets’ liquidity condition, �̃�𝑘. In short, ρ influences the condition for trading both, only one or

manipulating the markets. Theorem 2 and Theorem 3 states that lower ρ stimulates manipulation

followed by higher SEO discounts.

2.5. Legal costs of Manipulation

The amended Rule 105 of Regulation M prevents investors from purchasing the new shares if they

sold short the equity for the restricted period. SEC expects that these amendments deter manipulation

through stock market. There is a loophole, however, that other instruments including options can be

an alternative manipulation device as this paper argues. Although SEC monitors manipulation

attempts in other markets, it is not subject to the rule and manipulators can participate in the new

share allocation even though they manipulate options market. It is also more difficult to detect

manipulation through options, since manipulators can argue that they do proper hedging activity. The

heterogeneity in legal costs make options market the attractive venue for manipulation. This paper

further analyzes manipulation possibility regarding heterogeneous legal costs.

Manipulators will have higher legal costs when they manipulate the stock market, that is, short sell

the equity. Their profit function in the manipulative equilibrium should consider the legal costs

denoted by C as follows:

Π+ = ε ΔV xs+ + ε ΔVK xo+ + αIε’ΔV – I(xs+

15

Π− = −ε ΔV xs− − ε ΔVK xo−

where I(xs+

16

options market (Rourke (2014 )). If the market is complete and options are redundant, there should

not be dispersion of the options price from the theoretical value. However, with information

asymmetry, options transactions are informational (Back (1993 )) and end-user demand pressure

affects the option price (Bollen and Whaley (2004); Garleanu, Pedersen and Poteshman (2009 )).

Furthermore, the inventory risk of options moves the price as much as information asymmetry

(Muravyev (2015 )). Therefore, on a day with higher net buying orders, stock and option returns will

appreciate and the amount of the return appreciation per voulme will also be higher. Order imbalance,

therefore, can make stock and options return deviate from its expected values for several reasons;

Information asymmetry; downward sloping demand curve; inventory risk; skewness(Boyer and

Vorkink (2014 )); idiosyncratic risk(Cao and Han (2013 )).

Since order imbalance on a daily level can be measured as the return movement for a given trading

volume, the empirical proxy for ρ, �̂� , is defined as the correlation of signed Amihud (2002)’s

illiquidity measures of options and those of the underlying stock:

�̂� = 𝐶𝑜𝑟𝑟 (𝑟𝑠𝑉𝑠

,𝑟𝑜𝑉𝑜

).

where rs (ro) is the return on stock (options) and Vs (Vo) is the trading volume normalized to shares

outstanding (open interest). Several things should be considered when calculating options return. First,

there are two different kinds of options; call and put options. Option investors can buy (sell) call (put)

options if they think the stock is undervalued (overvalued) and vice versa. To extract the information

in options market, both call and put options should be considered. Furthermore, increases in call (put)

options implied volatility does not necessarily mean that option market investors have optimistic

(pessimistic) view on the stocks, since options price and implied volatility can appreciate by volatility

trading (Ni et al. (2008)). The portfolio of long call and short put options is used as the proxy for

option trading, which is synthetic futures made with options. Signed Amihud illiquidity of the option

is defined as

ro𝑉𝑜

=𝑟𝑐𝑉𝑐

−𝑟𝑝

𝑉𝑝

17

where rc (rp) is the return on call (put) options and Vc (Vp) is call(put) option trading volume

normalized to open interest. Second, options have different time-to-maturity. Longer time-to-maturity

options usually violate the put-call parity due to the limited arbitrage (Ofek, Richardson and Whitelaw

(2004 )). Also, long and short maturity options have different information depending on investment

horizon (Kim et al. (2016)). Since SEO pricing are the events with short-lived information, options

with time to maturity between 10 and 60 days are only considered.

One challenges in measuring liquidity order imbalance is that it is impossible to distinguish the types

of orders. The market makers can only observe total net order flows, which is the aggregate of

liquidity and informed orders. In order to avoid the effect from informed orders, the correlation is

estimated in the period between 84 and 21 trading days prior to issuance. Kim et al. (2016) documents

that informed trading about short-run information mainly occurs near the issuance. Long time-to-

maturity options are lack of short-run information. Furthermore, although total orders are

contaminated with informed orders, the market with higher ρ still have higher total order correlation

between stock and options market, since higher ρ encourages informed investors trade in both markets

which results in correlated informed orders.

For a robustness check, this paper also tests the return correlation (�̂�r), the signed Amihud correlation

using raw trading volume (�̂�v), the signed Amihud correlation with call options (�̂�c), and the minus

signed Amihud correlation with put options (�̂�p). All other variables are defined in Appendix A.

3.2. Observability of order flow

One of the critical assumption in the model is that market makers can observe the order flows of two

markets. In practice, this could not be true, since market makers in one market may have a privilege to

access private information contained in the order flow and the limit order book. However, post-trade

transparency provides a strong enough condition for the observability of order flow (Back and Crotty

(2015 )). Since market makers can infer the order flow from the price movement, post-trade

transparency is an sufficient assumption in model setting. Equity markets are fairly transparent.

Especially, since the 1990s, the NYSE has allowed the floor traders to view the order book. On

18

January 24, 2002, the NYSE gave the public access to real time depth of the limit order book (Baruch

(2005); Boehmer, Saar and Yu (2005); Hasbrouck (2006 )). The options markets also have a fair level

of transparency. In the CBOE, under Rule 6.51, the seller and buyer should report trading detail

within 90 seconds of execution, including price, transaction amount, broker, execution time, type, and

maturity. Also, the Plan for Reporting of Consolidated Options Last Sale Reports and Quotation

Information was approved under the Securities Exchange Act of 1934. This plan is for providing

consolidated information on option quotes and trades. Recently, the Options Price Reporting

Authority (OPRA) was founded as a limited liability company ("LLC") under the plan14. The OPRA

plan helps investors, broker-dealers, and the market get better information and the best price (Battalio,

Hatch and Jennings (2004); Battalio and Schultz (2006); Rourke (2013); Rourke (2014 )). Even

though there is a reporting lag, since this study uses daily returns and volume, it is safe to assume in

the anlaysis that market makers will not miss other markets’ order flows. Furthermore, the 90 second

reporting lag is short enough to assure that there is no reporting lag and enough transparency in daily

level analysis.

3.3. Data and Sample construction

The dataset is based on the SDC New Issue database for Seasoned Equity Offerings (SEOs) of the

U.S. Public Common Stock. The sample period starts in January 1996 and ends in December 2013.

Issues with a missing offering date or CUSIP are excluded. Only firm commitment and accelerated or

block offers in the SDC offer technique are included. These screening criteria yield a sample of 6,992

SEOs. To determine the effective offering date for SEOs which take place after the close of day, the

volume-based offer date correction rule in Corwin (2003) is applied.

The stock price data is from the Center for Research in Security Prices (CRSP). Only the firms

having common stock with share codes 10 or 11, and listed on NYSE/AMEX or Nasdaq are included.

Financial firms (SIC code between 6000 and 6999) and Utilities (SIC code between 4900 and 4999)

are excluded. The CRSP data are merged with Compustat. Firms that do not have book equity value

14 http://www.opradata.com

19

for the fiscal year prior to the issuance in Compustat are excluded. If the issue date is no later than 4

months after the last prior fiscal year, data in the fiscal year before the last are used instead. Firms

with negative book equity are excluded. These data filters leave 3,010 SEOs.

Option data are from OptionMetrics providing detailed information including price, implied

volatilities, open interests, and volumes. Only the at-the-money (ATM) options with positive open

interest and the days to maturity between 10 and 60 are included to ensure enough liquidity. Options

with a |delta| between 0.375 and 0.625 are defined as ATM (Bollen and Whaley (2004 )). Due to the

restrictions on other variables, the final sample contains 1,156 SEOs.

< Insert Table 1>

Table 1 presents summary statistics for the sample with and without options listed. As predicted, all

the correlation measures are close to 1. The average discount is 0.03315, which is similar to previous

studies (Altınkılıç and Hansen (2003); Corwin (2003); Henry and Koski (2010 )). On average, post-

issue performance is negative that the Cumulative Abnormal Return, CAR[1,5], is -0.9%. Consistent

with previous literature, the firms doing SEOs show low long-run performance. The six months Buy-

and-Hold return (BHR), matching firm adjusted abnormal return (MBHAR), and Fama-French value-

weighted portfolio adjusted abnormal return (VWBHAR) are -0.089, -0.031, and -0.103, respectively.

Compared to the sample without options, this group has smaller SEO discounts and worse post-issue

performance. These are due to the fact that firms with options are usually larger and liquid. The

average firm size is $5,021 M (7.403 in the logarithm) in year 2005 dollars and the one without

options is $422 M (5.492 in the logarithm) in year 2005 dollars. The average Market-to-Book (MTB)

is 12.01 (1.712 in the logarithm) whereas the average MTB without options is 8.63 (1.483 in the

logarithm), that is, SEO firms with options are closer to growth firms. Turnover and Amihud is 0.015

and 0.131, respectively, indicating that option listing provides liquidity for the stock or liquid stocks

tend to have options. 40% of the sample firms are listed on New York Stock Exchange (NYSE). Pre-

15 Discount is defined in the logarithm. The discount defined as offer price / stock close price one day prior to

issuance is 3.15% (2.23%) on average (median). Rest of the paper reports log discount as percentage, since these

two values are almost similar in approximation.

20

issue market reaction is much better for the firms with options. CAR[-5,-1] and one-year-prior

abnormal return (ARPR) are -2.9% and 74.66% (0.35 in logarithm), respectively, which are greater

than those without options. That is, the firms issuing new shares outperform the Fama-French value-

weighted portfolio by 74.66%, one year prior to the SEO. SEO firms with options which are large and

growth, therefore, are more likely to issue new shares due to overvaluation of the stock, which is also

supported by the lower post issue performance. Initial day return of IPO, rIPO, is 21.9% where that for

sample without options is 18.7%, which further implies that SEO firms with options are more likely to

time the market. The standard deviation of the stock return is 4%, which is higher than the sample

including SEOs without options. This supports the hypothesis that options make the volatility of

underlying stocks stochastic (Back (1993); Jarrow (1994 )).

Overall, SEO firms with options are large, growth, liquid, and more overvalued. SEO mechanism is

also different with respect to options. 74.2% of the sample SEOs is primary offerings, 24.8% of the

sample SEOs uses accelerated issuance, and 61.9% of the sample SEOs uses shelf-registered offering.

Firms choose the fully marketed offering for enhancing liquidity (Gao and Ritter (2010 )). Since firms

with options are already liquid, they have smaller incentive to do fully marketed offering. The relative

offer size (RelOfrSize) is 13.6%. Since the firms with options are usually large, the offer size is

relatively small. The stock close price one day prior to the SEO is $34.73 (3.362 in logarithm). 73.8%

of the sample does offer price clustering and 71.8% of the sample has a lock-up provision. The lock-

up provision helps the firms to resolve information asymmetry. However, since there is a selection

bias in that the firms with high information asymmetry choose to include the provision, there is a

positive correlation between information asymmetry and the provision (Karpoff, Lee and Masulis

(2013 )).

The options leverage is 1.638, which means that the ATM option return increases by 16.38% as the

stock return increases by 1%. Since only ATM options with a delta of 0.5 are analyzed, it indicates

that the stock is 16.38 times more expensive than options. OTurnover, which is option trading volume

for an open interest, is 0.558, that is, options are trades 0.558 times for one open contract. Pre-issue

options market reaction is negative, measured as call and put options implied volatility difference

21

(CPIV[-5,-1]). Generally, put options are more expensive for hedging demand or pessimistic view on

options market. The average implied volatility is 0.6 (the sum is 1.2), which is much greater than the

historical volatility of 0.04.

< Insert Table 2>

Among SEO firms with options, their characteristics also varies with respect to �̂� as reported in

Table 2. Large and more liquid firms tend to have higher �̂�. As referred above, the correlation proxy

not only measures order imbalance but it also affected by information asymmetry, market inefficiency,

and market sentiment. Higher prior one year return also has a positive relation with �̂�. When the firm

perform well, uninformed speculators more aggressively trade, which generates correlated liquidity

orders between stock and options. Furthermore, in such period, there is a little divergence of opinion.

Accordingly, rIPO and nIPO, which represent market sentiment, also has a positive relation. MTB is,

however, not different among each �̂� groups. In short, high �̂� firms are larger, more liquid, and less

information asymmetric. Overall, different �̂� groups are not largely governed by other firm

characteristics but uniquely defined.

< Insert Table 3>

The correlation of variables are documented in Table 3. In column (1), �̂� decreases as times flows,

therefore, the market suffers from manipulation via option although market itself becomes more

efficient. In column (2), as predicted, the �̂� and SEO discount has a negative relation, which supports

the hypothesis that SEOs with low �̂� are in the manipulative equilibrium. Furthermore, post-issue

innovation measured as |CAR[1,5]| and |CPIV[1,5]| has significant negative relation with �̂�, that is,

information is not revealed prior to SEOs for lower �̂� . Long-run post-SEO performance has a

significant correlation with �̂� of -0.077 at 1% level, which indicates the return reversal in the long run.

In column (3), option leverage has a negative correlation with SEO discount as -0.232. Easley et al.

(1998) posits that higher options leverage encourage informed investors to participate in options

trading, which enhance market transparency leading to smaller SEO discount. Higher stock (options)

22

volume and stock return standard deviation results in higher SEO discount, since these are the proxies

for information asymmetry and divergence of opinion. Aggressive trading volume, however, can also

be an indicator for high liquidity and there is usually a mixed effect. Amihud has a positive

correlation with discount as 0.073, since informed investors avoid trading in illiquid markets. Primary

offering has higher discount and lower post-issue performance due to investors’ concern about free

cash problem (Lee(1997)). Large offers also show higher discount (Corwin (2003); Gerard and Nanda

(1993)). SEOs with lock up provision has higher discounts, since the firms with high information

asymmetry choose to include the provision (Karpoff et al. (2013)). The results in Table 3 are

consistent with the previous literatures.

3.4. Empirical Analysis Results

In this section, the hypothesis about the manipulation and its relation with �̂� is tested.

3.4.1. SEO discount and ρ

As in Table 1, SEO firms with options are more efficient and have smaller SEO discounts. The

results in coulmn (1) of Table 4 supports these findings that SEO firms with options have 1.1% lower

SEO discounts. DOption is the dummy variable of 1 for the stock with listed options and 0 otherwise.

Among SEOs with options, however, these effects are only applicable for SEO with high liquidity

correlation as in column (2). DOption2 has the value of the correlation measure if it has options and 0

otherwise. Following the previous literatures advocating the role of options for market efficiency,

options enhence market informativeness around SEOs, however, it is limited only for high correlation

stock.

To deeply understand the role of the liquidity correlation around SEOs, the sample with options is

more analyzed. One of most important implications in this paper is that the liquidity order correlation,

which is measured using a proxy �̂� , is one of the important variables determining manipulation

possibility. Since informed investors cannot generate enough profits in secondary market trading with

23

a small �̂�, it stimulates manipulation and results in a larger SEO discount. To test this hypothesis, this

paper regress SEO discounts on the liquidity order correlation as in the following model:

Discount = β0 + β1∙�̂� + XControls + DYear + DIndustry + εit (12)

where XControls is the set of control variables, DYear is a dummy variable of years, and DIndustry is a

dummy variable of industries. β1 is expected to be negative, since a small �̂� stimulates manipulation

and a larger SEO discount. The empirical analysis results are reported in Table 4.

< Insert Table 4>

In Table 4, column (3) to (8) present the results for the anlaysis of �̂� and the SEO discount for

various conditions. Column (3) provides the results of univariate regression. Column (4), (5), and (6)

provides the results controlling options market, stock market, and deal specific information,

respectively. All four results show that there is a significant negative relation between �̂� and SEO

discounts; -0.066, -0.057, -0.053, and -0.050. As �̂� decreases, therefore, the market is more likely to

be in the manipulative equilibrium and discount increases. �̂� has an unique information for SEO

discounts including those from options and stock markets. After controlling other information, it

drops a little, however, its coefficient is still significantly negative. Especially, the magnitude of the

coefficients decreases the most after controlling deal specific information, therefore, �̂� contains much

information on the deal and it is one of the important factors affecting SEO process. Column (7)

reports the results controlling all the relavant factors. The coefficient on �̂� is -0.049, which is

statistically and economically significant. As �̂� increases one standard deviation (9.8%), the SEO

discount decreases about 0.48%, which is 14.55 percent and 10.67 percent of the discount mean (3.3%)

and the standard deviation (4.5%), respectively. Column (8) is the multivariate regression results with

year clustering (Petersen (2009 )), which also shows a significant negative relation.

Other control variables are consistent with the conjectures from previous literatures and some shows

interesting results. Unlike the negative relation in Table 3, options leverages (OLev) have a positive

24

coefficient as 0.006 in column (7), after controlling other effects. In the later analysis, the effect of

options leverage is largely affected by �̂�. Options implied skewness measured by CPIV has a negative

relation with the coefficient of -0.061, which partly supports manipulation through options market.

Call and put options implied volatility has a significant positive relation with discount as the

coefficient of 0.026, since it measures information asymmetry and high uncertainty (An et al. (2013)).

Primary and accelerated offering has higher discount by 0.6% and 1.8%, respectively. Consistent with

previous literatures (Altınkılıç and Hansen (2003); Corwin (2003); Mola and Loughran (2004 )), the

level of the price and offer price clustering has a negative and a positive impacts on the SEO discount.

The proxies for information asymmetry, LockUp, have positive but marginally significant coefficients.

3.4.2. Pre-issue Market Informativeness

Detecting manipulation is not an easy task. One of the most well known identification strategy is to

see pre-issue market informativeness. Stock price moves for various reasons including new

information arrival and demand-supply imbalance. If the market is efficient and liquid, large price

movement prior to SEO is induced by new information and it should be followed by lower SEO

discounts. On the other hand, if the market is manipulated, this relation will not be found. With flat

demand curve, which is assumed in the theoretical model that there are enough substitutes and

competative market makers, pre-issue price does not change at all for manipulation possibility. When

loosening this assumuption as demand curve have downward slope, selling orders can create impact

on the price and SEO discounts will be enlarged following the negative market reaction. In short, the

market is in the manipulative equilibrium when the lower discounts are not followed by the large

return innovation.

Previous literatures documents mixed evidence on SEO manipulation following stock return

movement. Henry and Koski (2010) find that a negative CAR prior to the issuance is followed by a

larger discount. That is, a poor market response will increase the SEO discount, which provides

evendence supporting the manipulation hypothesis. On the other hand, Corwin (2003) finds evidence

that a positive pre-issue market reaction increases the discount but a negative reaction has no effect on

25

the SEO discount. It supports the hypothesis that the issuing firm determines the offer price based on

the expected price, not on a recently dropped market price. These mixed evidence, therefore, indicates

that manipulation does not always occur but there must be a sufficient condition.

Manipulation attemps is largely affected by ρ and pre-issue market informativeness is also

determined by ρ. As referred, if the price movement is induced by the new information arriving, there

will be a negative relation between pre-issue return movement and SEO discounts. This effect will

diminish as ρ decrease, since the market are in the manipulative equilibrium. Pre-issue stock market

reaction is measured by the absolte of the cumulative abnormal return (CAR) and the absolute of the

innovation in CPIV is used for options market reaction. To test this hypothesis, this paper regresses

SEO discounts on pre-issue market innovation and its interaction with ρ as follows:

Discount = β0 + β1 |CAR| (or |ΔCPIV|) + β2∙�̂� + XControls + DYear + DIndustry + εit (13)

Discount = β0 + (β1 + β2∙�̂�) |CAR| (or |ΔCPIV|)+ β3∙�̂� + XControls + DYear + DIndustry + εit (14)

where XControls , DYear, and DIndustry are defined as in equation (12). If the price changes are due to the

new information arriving, large abnormal return (innovation) will results in lower discounts, therefore

β1 in equation (13) is negative. Otherwise, it will be non-negative and the markets are in the

manipulative equilibrium. Since the manipulation possibility is governed by ρ, lower ρ mitigates the

price infomativeness, therefore, β1 and β2 in equation (14) is non-negative and negative, respectively.

< Insert Table 5>

In Panel A of Table 5, the analyses on the pre-issue stock market informativeness are reported.

Column (1) to (3) are the results with CAR estimated in the period between five and one trading days

prior to the issuance for both samples with and without options. In column (1), the coefficient on

|CAR| is insignificantly negative, that is, the return innovation prior to the issuance does not

contribute to the lower SEO discounts. In column (2), however, the coefficient on the interaction term

between |CAR| and DOptions is significantly negative as -0.085, which indicates that lower SEO

discounts are followed by large innovation only for SEO with options. The result in column (3)

26

further advocates the role of �̂� that the informative trading only occurs for high �̂� SEO. The

coefficient on the interaction term between |CAR| and DOptions2 is significantly negative as -0.104,

which is more negative than the one in coulmn (1). Overall, pre-issue market informativeness is

greater for SEOs with options, however, it is only found in high �̂� sample.

In column (4) to (6), pre-market informativeness and �̂� are more deeply analyzed for SEOs with

options. As predicted, the coefficient on |CAR| in column (4) is significantly negative that stock return

changes prior to the issuance are induced by the new information arriving. These effecs are, however,

dominant for high �̂� sample that the coefficient on |CAR| and �̂� in coulmn (5) is significantly negative

as -0.721. At the mean of �̂�, the coefficient on |CAR| is -0.067. As �̂� decreases one standard deviation

(0.098), it becomes 0.004. SEO firms with low �̂� have, therefore, worse pre-issue market

informativenss and this paper argues that it is due to manipulation via options market. Additionally,

column (6) decompose the stock market movement into postive and negative CAR (Corwin (2003)).

Consistent with column (4), larger good or bad stock market reaction results in smaller SEO discounts.

The results in column (4) to (6) show that firms with options have overall good price

informativeness and this can be due to the timing on the regulation. Amended Rule 105 of Regulation

M prohibits investors to participate in SEO allocation, who short sell the stock for the restricted period

beginning five trading days prior to the issuance and ending the pricing day. In this trading window,

therefore, there is higher legal cost and firms with options are hard to be manipulated since they are

usually large firms. The manipulators have, therefore, an incentive to manipulate the price prior to the

restricted period. Of course, manipulation in the restricted period is more effective, however, high

legal costs are serious concerns. To test this possibility, column (7) to (9) analyze price

informativeness for the period between ten and six trading days prior to the issuance. Surprisingly, the

coefficient on |CAR| in column (7) are insignificantly positive. Furthermore, the coefficient on CAR−

in column (9) is insignificantly negative, which means that negative innovation increases SEO

discounts, possibly due to manipulation.

Another possible loophole for the regulation is derivative securities. Although SEC seriously

concerns and monitors the manipulation via options market, it is hard to detect options manipulation

27

and importanly it is not subject to the regulation. For the restricted period, therefore, manipulation

through options market can be profitable with lower legal costs. Panel B of Table 5 tests this

possiblity by analyzing the pre-issue options market informativeness. Unlike column (4) in Panel A,

the coefficient on |ΔCPIV| in column (1) is insignificanly positive, which indicates that options market

innovation increases SEO discounts. The coefficient on the interaction term between |ΔCPIV| and �̂� in

column (2) is insignificantly positive, which implies that even firms with high �̂� do not have superior

informativeness in options market. Furthermore, the coefficient on ΔCPIV – in column (3) is negative,

supporting the manipulative hypothesis that negative price presure in options market generate the

enlarged SEO discounts. Column (4) to (6) report the results for the earlier period, when the stock

market is manipulated. In column (5), the coefficient on the interaction term between |ΔCPIV| and �̂� is

significantly negative that firms with high �̂� have superior options market informativeness. At the

mean of �̂� , the coefficient on |CAR| is -0.004. As �̂� increases one standard deviation (0.098), it

becomes -0.048. In this period, therefore, only firms with high �̂� have the informative trading where

those with low �̂� are manipulated.

In short, there is an informed trading in stock market for the restricted period, however, its

informativeness is largely affected by ρ. Eariler than this period, manipulation possibility arises.

Furthermore, to avoid a legal issue, manipulators utilize options as an alternative tools, which is also

governed by ρ.

3.4.3. Post-issue Price Transparency and Reversal

Manipulation deters the pre-issue market informativeness and this should results in greater ex-post

return innovation, since information revealed by manipulator realizing the profit is impounded into

the price after the issuance. Chabakauri et al. (2015) shows that transparent market’s ex-post return

volatility is lower. Similarly, it is shown in this paper that innovation in post-issue market price

diminishes as the markets are in more informative equilibrium. Similar to pre-issue market reaction,

ex-post price transparency is tested with the absolute of CAR and ΔCPIV. Since SEOs with low ρ are

28

in the manipulative equilibrium, there will be a negative relation between ρ and ex-post price

movement. To test this hypothesis, this paper regresses |CAR| and |ΔCPIV| on ρ as follows:

|CAR| or |ΔCPIV| = β0 + β1∙�̂� + XControls + DYear + DIndustry + εit (15)

where XControls , DYear, and DIndustry are defined as in equation (12). Following aforementioned

conjecture, β1 will be negative, since lower ρ stimulates manipulation and degenerate ex-post price

transparency.

< Insert Table 6>

Panel A of Table 6 presents the empirical analyses on the effect of ρ on post-issue price transparency.

Manipulation is based on the fear that negative market reaction can be induced by the actual bad

issuance. Manipulated SEOs are, therefore, sometimes the manipulated good offering but othertimes

actually overvalued issuance. Low �̂� implies that the issuance can be manipulated, however, it does

not provide information on the direction of the stock price. Following this conjecture, the coefficient

on �̂� in column (1) is insignificant, however, �̂� affects the ex-post stock innovation as in column (2).

The coefficient is -0.041 that SEOs with lower �̂� is followed by larger stock return innovation, which

is due to the new information arrival from manipulators realizing the profit. In order for analyzing

asymmetric response in the market, the ex-post return is decomposed as CAR+ and CAR−. The

coefficient on CAR+ in column (3) is negative and that on CAR− in column (2) is positive, which is

consistent with the result in column (2). Interestingly, only the coefficient in (4) is significant that bad

information is revealed faster than good information. As in column (5) to (8), options market shows

similar results.

The results in Panel A further raise possibility that it can take time to reveal the true value of the

stock. Speed of impounding good and bad information is asymmetric, that is, bad information reveals

faster. This may be due to the fact that investors do not want to bear risk on bad information

according to prospects theory (Kahneman and Tversky (1979 )) and market reacts more aggresively to

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the bad information. Following this conjecture, manipulated good SEOs will take time to get their fair

value. For instance, SEOs with high ρ have good price transparency that information asymmetry will

immediately be resolved after the issuance, no matter how they are overvalued or undervalued.

Manipulated good SEOs with low ρ, however, can maintain high level of information asymmetry

even after the issuance for several reasons. First, manipulators have an incentive not to reveal true

value to avoid a legal issue. If price immediately recover to its fair level, the market participants and

SEC will suspect the manipulation. Second, manipulated stock usually has a low liquidity and then it

is highly probable that it is still illiquid after the issuance. In order not to impact the market,

manipulators will prefer to split their orders over time, which lower the speed of new information

impounded. Overall, a negative relation between ρ and long-run post-issue performance, that is, long-

run return reversal is expected. To test this hypothesis, this paper regresses post-SEO long-run

performance on ρ as follows:

Long-run performance = β0 + β1∙�̂� + XControls + DYear + DIndustry + εit (16)

where Long-run performance is measured as raw and risk adjusted buy-and-hold log return (wealth

relative) and XControls , DYear, and DIndustry are defined as in equation (12). β1 is expected to be negative.

Panel B of Table 6 presents the empirical analyses results on the long-run return reversal after SEOs.

MBHAR in column (1) and (2) are 6 months and one year log buy-and-hold return adjusted by size

and book-to-market matched matching firm, respectively. VWBHAR in column (3) and (4) are 6

months and one year log buy-and-hold return adjusted by size and book-to-market matched Fama-

French 25 portfolios, respectively. Finally, BHR in column (5) and (6) are 6 months and one year raw

buy-and-hold return, respectively. Consistent with the prediction, all six coefficients on ρ are

(marginally) significantly negative. Figure 3 further provides supporting evidence that only high ρ

group underperforms for six months. SEO firms with low ρ, therefore, are manipulated and have long-

run return reversal.

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3.4.4. Rule 105 of Regulation M

On 20 June 2007, SEC amended Rule 105 that investors are prohibited to participate in the new share

allocation if they sold short the security for the restricted period. Rule 105 restricted period begins

five days before the offer pricing and ends with the pricing. Although short-seller in the restricted

period cannot cover the position with the newly allocated shared from 1997, this amendment further

enforce the rule to deter the manipulation through public offering. Tandon, Yu and Webb ((2010))

finds evidence that abnormal return at the offer date increases as Rule 105 is amended and as options

are listed. If amended rule 105 is effective, there will be a structral drops in SEO discount after 2007

as Tandon et al. (2010) argue. Furthermore, pre-issue market informativeness will be enhenced. If

manipulation via options market is feasible for smaller legal costs, however, these effects will be

limited for options market, which will be less informative compared to stock market. To test these

arguments, this paper regresses SEO discounts on the interactions among high ρ group dummy, Post-

2007 dummy, and pre-issue innovation as follows:

Discount = β0 + β1∙DOption + (β2 + β3∙DOption)DPost-2007+ XControls + DYear + DIndustry + εit (17)

Discount = β0 + β1∙ DHigh ρ̂ + (β2 + β3DHigh ρ̂)DPost-2007 + XControls + DYear + DIndustry + εit (18)

Discount = β0 + β1∙DPost-2007 + β2∙DHigh ρ̂ + (β3 + β4DPost-2007)|ARPre-market| + … (19)

Discount = β0 + β1∙DPost-2007∙DHigh ρ̂∙|ARPre-market| + … (20)

where DPost-2007 is dummy variable of 1 when the issue date is after 20 June 2007 and 0 otherwise, DHigh

ρ̂ is dummy variable of 1 when the issue is in the group of top fifty percent with respect to ρ̂ and 0

otherwise, |ARPre-market| is |CAR| or |ΔCPIV|, and XControls , DYear, and DIndustry are defined as in equation

(12). Its effect can be tested for the magnitude of the impact on the market efficiency. First, if

amended Rule 105 is effective for all SEOs, β2 and β2 + β3 in equation (17) will be negative. In

equation (18), the effect of the rule for SEOs with options is tested and β2 and β2 + β3 are expected to

be negative if the rule is effective. Since manipulation via options is more attractive strategy, the

effect of the rule will be more pronounced for high ρ̂ group, that is, β3 will be negative. Pre-issue

market informativeness will have different aspect for each markets. Since legal costs are less

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expensive in options market, β4 will be positive (negative) for options (stock) market in equation (19).

Finally, equation (20) analyze whether changes in pre-issue informativeness are induced by which

groups.

< Insert Table 7>

Table 7 presents the analysis results for the effect of amended rule 105. In column (1), the coefficient

on DPost-2007 is insignificantly positive, that is, SEOs without options tend to have greater discounts.

The coefficient on the interaction term between DPost-2007 and DOption is significantly negative, however,

its sum with the coeffiecient on DPost-2007 is positive. Although increment in discounts for SEOs with

options is smaller than those for SEOs without options, SEO discount have gradually increased for all

sample. This raises several possibilities including manipulation or changes in the characteristics of the

issuance (Autore et. al. (2008)). In column (2), only the sample with options is analyzed. The

coefficient on DPost-2007 is significantly positive as 0.016, that is, SEO discounts increase after the rule

amendment for low ρ̂ group. For high ρ̂ group, SEO discounts increased by 0.3% after the rule

amendment, which is much smaller than those for low group (1.6%). Regarding increasing trend in

SEO discounts, high (low) group has smaller (bigger) discounts. Low group, which suffers from

manipulation, tends to be more manipulated, possibly through options market. Although marginally

significant or insignificant, the coefficients on |CAR| and its interaction with DPost-2007 are negative in

column (3). Stock market has good pre-issue market informativeness and it becomes more efficient

after the rule amendment. The coefficient on DHigh ρ̂ x |CAR| x DPost-2007 is significantly negative as -

0.137, indicating that superior stock market informativeness is driven by high group. Options market

has the opposite effect. Although marginally significant or insignificant, the coefficients on |ΔCPIV|

and its interaction with DPost-2007 are positive in column (5). Options market becomes, therefore, less

efficient as the rule is amended, which supports evidence on manipulation via options for smaller

legal costs. Column (6) further analyzes that the coefficient on DHigh ρ̂ x |ΔCPIV| x DPost-2007 is

significantly positive as 0.236, which means that high group losses options market informativeness.

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In short, high legal costs in manipulation via stock market make it more attractive to manipulate

options market prior to SEOs. Regarding SEO having trend of increasing discounts, low ρ̂ group has

much higher SEO discounts after the rule amendment, which is due to the increase in legal costs on

stock market manipulation. Further, pre-issue stock (options) market informativeness becomes better

(worse) after the rule amendment. The overall evidence support hypothesis that legal costs is an

important factor for SEO manipulation and options market is the attractive venue.

3.4.5. The importance of liquidity correlation

There are several factors which are known to affect SEO discounts and market efficiency such as

information asymmetry, liquidity, and offer size. Among many factors, this paper argues that their

impact on SEO discounts largely depends on ρ for the optionable stock. First, Options leverage and

liquidity are two of the most critical factors to market efficiency. Easley et al. (1997) document that

informed investors participate in options market to get a leverage effect of options. Also, they prefer

liquid market since they can easly camouflage their trading intention and do not generate price impact

(An et al. (2014); Kyle(1985); Back(1992 and 1993)). In Table 3, the correlation of discounts with

OLev and Amihud are significantly negative and positive, respectively. Higher options leverage

encourages informed traders to participate in options market and high illiquidity keeps informed

traders away from market transactions, which leads to smaller and larger discounts, respectively.

After controlling the other variables, however, their effects become marginal as in Table 4. Second,

information asymmetry and offer size are known to be related to SEO discounts due to the winner’s

curse problem. Firms adopting lock-up provision have high information asymmetry for sample

selection problem although the provision is expected to alleviate this concerns (Karpoff et. al. (2013)).

Since winner’s curse problem is more severe when the offering size is huge, relative offering size

increases SEO discounts. For SEOs with high ρ, however, the market is already in the informative

equilibrium and the impact of other parameters will be limited. In short, their role will vary depending

33

on the level of ρ. To test this hypothesis, the paper regresses SEO discounts on the interaction of ρ

with options liquidity and Amihud illiquidity as follows:

Discount = β0 + (β1 + β2∙�̂� ) Factors + β3∙�̂� + XControls + DYear + DIndustry + εit (21)

where XControls , DYear, and DIndustry are defined as in equation (12). For low ρ, one unit increase in

Factors which are known to be attractive (unattractive) to investors, it will leads to lower (larger)

SEO discounts. β1 in equation (21) is expected to be negative (positive). As ρ increases, their effects

are mitigated and β2 in equation (21) is expected to be positive (negative).

< Insert Table 8>

Table 8 present the effects of other factors depending on ρ. In column (1), the coefficient on OLev is

significantly negative as -0.053, which indicates that informed investors prefer high options leverage

and market becomes more efficient as the leverage increases for low ρ SEOs. The coefficient on

interaction term with ρ is significanly positive as 0.062, however, that their impact diminish as ρ

increases. For high ρ group, the market is already in the informative equilibrium and the marginal

increse in the options leverage does not contribute to the market informativeness. In column (2), the

coefficients on IV and interaction tern with ρ are 0.099 and -0.082, repectively. Options investors

have information on not only the direction of the stock price but also the volatility. Higher implied

volatility implies greater future volatility, that is, high uncertainty (Ni et. al. (2008)). For low ρ group,

increase in IV contributes to the additional information on the SEO risk, however, its impact

diminishes as ρ increases. In column (3), stock market illiquidity is analyzed. Informed investors

hesitate to trade illiquid security thus it has inferior informativeness. The coefficients on Amihud and

its interaction with ρ are 0.122 and -0.130, respectively. For low ρ group, illiquidity is related to the

large SEO discounts, however, even the illiquid stock have some information for high ρ group.

Besides factors related to market trading, the characteristic of the deal also are affected by ρ . Lock-

up provision is expected to alleviate the winner’s curse problem and the negative relation with SEO

34

discounts is predicted. Empirical results show, however, that there is positive relation and this is due

to the sample selection problem (Karpoff et. al. (2013)). This relation is also found in the results of

column (4). In column (4), the coefficient on LockUp and its interaction with ρ are -0.084 and 0.095,

respectively. For low ρ group, lock-up provision indeed decrease SEO discounts, that is, information

asymmetry is resolved after the firms adopt the provision. For high ρ group, however, these effect is

mitigated and the firms with the provision even have greater SEO discounts. In short, lock-up

provision is more helpful for low ρ group to alleviate the winner’s curse problem, however, high ρ

group has sample selection problem. Finally, in column (5), the coefficients on RelOfrSize and its

interaction with ρ are 0.358 and -0.360, respectively. Greater offering size requires more demand

from uninformed investors, which results in larger discounts. As ρ increases, however, uninformed

investors are more informed about the value of the stock from market price and RelOfrSize does not

affect SEO discounts.

Consequently, although the effects of other variables are obvious, it is largely governed by ρ, which

emphasizes the importance of the intermarket liquidity correlation.

3.5. Robustness Check

The empirical proxy for ρ is designed to capture the correlation of uninformed order imbalances,

however, other factors can affect the �̂�. First, information asymmetry can have a negative effect on �̂�.

As information asymmetry is severe, the market makers and arbitrager will increase the bid ask

spread to avoid possible loss, which can make the price deviate from its fundamental value and

put/call parity is violated. Second, market inefficiency also decreases �̂� for the similar reason. Finally,

high market sentiment can attribute to greater liquidity correlation. Options traders are known to trade

on the momentum when the market sentiment is high (Lakonishok et. al. (2007)). In such an

environment, their trading pattern will increase the �̂�. To elliminate these effects, a residual from the

first stage regression of �̂� on engogineity proxies is tested, which is independent of those concerns.

For proxies for information asymmetry, STD, LockUp, No_Issue, Tangible, and BA_Spread are used.

35

For proxies for market inefficiency, |ARs| and |ARo| are used. Finally, for proxies for market

sentiments, ARPR, rIPO, and nIPO are used.

Panel A of Table 9 reports the first stage regression results and Panel B reports the second stage

results. All the results in Panel B are consistent but the magnitude of the coefficient decreases a little.

This results is natural that the above concerns affect �̂�, however, its impact is limited. The results

confirm the paper’s argument independent of the endogeniety issue.

Another issue that can arise is the measurement of long-run performance. Although the Buy-and-

hold return and wealth relative is a widely used methodology, there is a statistical issue (Fama (1998);

Loughran and Ritter (2000); Lyon, Barber and Tsai (1999 )). For an additional check, a calandar-time

portfolio is formed to test the long-run performance after SEO. For a given month, firms issuing new

shares for the last six months are included in the portfolio. Also, two portfolios are fored based on the

value of ρ. If the firm’s ρ is smaller than the median, it is designated as a low �̂� portfolio. Otherwise,

it is classified as the high ρ portfolio.

Table 10 reports the results. In column (1), the high �̂� group underperforms by -0.845 % per month.

However, the low ρ group in column (2) has an insignificant α. Although the momentum factors are

included in column (3) and (4), the results do not change. Column (5) shows the results for the zero-

investment portfolio analysis. The high �̂� group underperforms the low ρ group by -0.909% per

month, which is consistent with the previous analysis.

The empirical proxy for ρ can be also estimated using the alternative measures. It can be raw return

correlation, Amihud using raw volume, the return of only call or put options. For robustness, most of

the results presented in this paper are tested again using these alternatives.

36

Table 11 presents the results using alternative correlation measures. All the results are consistent

with the previous results, although some are marginally significant.

IV. Conclusion

This paper finds the theoretical and empirical evidence that options can be used as a tool for

manipulating markets prior to SEOs. One of the important parameter affecting manipulation incentive

is the liquidity order correlation between two markets. As the correlation decreases, informed traders

have an incentive to manipulate the markets, since they cannot generate the secondary market profit in

such an environment. Manipulation degenerates market informativeness, which leads to higher SEO

discounts. High legal costs of stock market manipulation attribute to the incentive fo