Liquidity Financial Crisis Resolution 20110912

7/29/2019 Liquidity Financial Crisis Resolution 20110912

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Liquidity Based Indicators of the Financial Crisis and Its Resolution

Bill HuAssistant Professor of Finance

College of BusinessArkansas State University

State University, AR 72467

Phone: (870) 972-2470Fax: (870) 972-3417E-Mail: [email protected]

Chinmay JainDoctoral Candidate of Finance

Fogelman College of Business and EconomicsThe University of Memphis

Memphis, TN 38152Phone: (901) 652-9319

E-Mail: [email protected]

Pankaj JainAssociate Professor of Finance

Fogelman College of Business and Economics

The University of MemphisMemphis, TN 38152Phone: (901) 678-3810

E-Mail:[email protected]

September, 2011


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Liquidity Based Indicators of the Financial Crisis and Its Resolution

Abstract

Equity trading costs increase almost threefold during the recent financial crisis. Using

Granger causality tests, we document that liquidity providers funding constraints lead to

significant declines in stock market liquidity. Furthermore, this effect is heightened during the

crisis period for all firms, and is especially strong for the troubled firms, particularly after the

announcement of TARP. The relationship between funding and market liquidity is always

stronger for financial firms compared to non-financial firms, and is the strongest for too-big-to

fail firms.

JEL Classification: G1, G2

Key Words: Financial crisis, market liquidity, funding liquidity


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1. Introduction

Market liquidity is a key determinant of the stability and efficiency of capital markets. In

a liquid capital market, there are buyers and sellers willing to trade large quantities quickly

without moving the market price too much. What happens to market liquidity during market

crashes? Is liquidity readily available when it is needed the most? Bernanke (1983) predicts that

financial crisis increases the cost of intermediation and thus leads to increases in trading costs.

Brunnermeier and Pedersen (2009) show that market liquidity deteriorates when the supply of

capital is tight. They predict that market liquidity and funding liquidity decrease together and this

phenomenon leads to liquidity spirals. In his 2010 AFA presidential address, Duffie (2010)

points out that slow movement in capital significantly influences asset pricing dynamics. Supply

or demand shocks cause a significant price change in one direction initially, followed by a

gradual reversal in the opposite direction. Taking the immediate price impact into consideration,

trading costs may increase sharply at first and then slowly reverse to normal levels. According to

these models, when facing funding constraints, traditional liquidity providers may become

reluctant to take on new positions, especially capital intensive positions.

In this study, we test three predictions about stock market liquidity and its relationship

with funding liquidity in the context of the recent financial crisis. First, using a battery of market

liquidity measures, we investigate the deterioration of stock market liquidity during the recent

financial crisis as well as its subsequent recovery after the bailout measures. Second, we test

whether funding liquidity is a key determinant of market liquidity. Brunnermeier and Pedersen


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dynamics between stock market liquidity and funding liquidity during the financial crisis and the

subsequent recovery periods. Specifically, we examine the importance of funding liquidity

separately for financial versus non-financial firms, large financial versus small financial firms,

too-big-to-fail firms, TARP recipient firms, and during crisis versus normal periods.

We find that market liquidity indeed deteriorates during the recent financial crisis as

predicted by Bernanke (1983). The relative effective spread increases almost threefold from 5.97

basis points in the pre-crisis benchmark period to 16.34 basis points during the financial crisis

which began with the collapse of Lehman Brothers. When the U.S. Treasury and the Federal

Reserve System began to bail out large banks and insurance companies, the relative effective

spread reduced marginally to 13.33 basis points. Only in the long-term recovery period ending

one year after the crisis, relative effective spread fell to 8.57 basis points. Other measures of

liquidity such as relative quoted spread and relative realized spread responded similarly to the

crisis and recovery efforts. During the crisis period, the relative effective spreads are higher than

the relative quoted spread. It seems that liquidity demanders are more aggressive and trading

quantities beyond the quoted depth. Time series comparisons of quoted and realized spreads

suggest that liquidity providers become very sensitive to the uncertainty created by the crisis and

conservatively quote wider spreads even though there is no evidence of any increase in adverse

selection costs from trading against more informed traders. The increased quoted spreads

appeared to have passed on to liquidity providers in the form of higher relative realized spreads.

The deterioration in market liquidity occurs despite an increase in trading activities. For


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in their liquidity relative to matching non-financial stocks. For example, although the difference

in the relative quoted spread between financial and non-financial stocks is insignificant at 0.06

basis points during the pre-crisis benchmark period, this number increases to a significant 3.04

basis points during the crisis period. The difference in relative quoted spread declines during the

bailout and long-term recovery period, but remains economically and statistically significant. In

summary, several measures of trading costs increase significantly during the crisis, especially for

financial stocks, but are gradually restored to the pre-crisis level within a period of one year.

Next, we examine the relationship between funding liquidity and market liquidity.

Brunnermeier and Pedersen (2009) describe several real-world margin constraints and funding

requirements for hedge funds, commercial and investment banks, and market makers. We use

two alternative measures of funding constraints, the TED spread and NETACQ. The TED spread

is the difference between the 3-month LIBOR (London Interbank Offered Rate) and the 3-month

U.S. Treasury bill rate.NETACQ is the net acquisition of financial assets by security brokers and

dealers and is available from the Federal Reserve Statistical Release about flow of funds

accounts. Adrian and Shin (2008) suggest that aggregate liquidity co-move with the aggregate

balance sheet of the financial intermediaries. When asset prices fall, the size of intermediaries

balance sheet shrinks and such developments interfere with their liquidity supply function. If

liquidity providers approach their funding limits, they may actually start demanding liquidity

through aggressive selling of securities. If other traders send large blocks of shares to be sold in

the market too, transaction costs would increase significantly and at the same time the prices


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the dynamic nature of the relationship between funding liquidity and market liquidity, which is

more acute during the crisis period than during normal or bailout periods. Another unique feature

of our analysis is a Granger causality test which indicates that a decline in funding liquidity

deteriorates market liquidity, but not vice-versa.

We continue our investigation to understand the determinants of the relationship between

funding liquidity and stock market liquidity. The Federal Reserve and other central banks have

implemented various measures such as TARP to enhance funding liquidity in response to the

financial crisis. For troubled firms, the relationship between funding and market liquidity is more

intense, particularly after the announcement of TARP. The relationship is always stronger for

financial firms compared to non-financial firms, and is the strongest for too-big-to fail firms.

Finally, our study provides a detailed analysis of the liquidity changes in response to the U.S.

governments intervention in resolving the crisis. Crisis resolution measures stalled a further

decline in market liquidity but it took more than a years work and wait before liquidity could be

restored to its pre-crisis levels.

2. Background, literature review and hypotheses development

2.1. The financial crisis of 2007-2009 and regulatory responses

The financial crisis of 2007-2009 started with seemingly small subprime mortgage losses,

which were only about 5 percent of overall stock market capitalization. As more and more

people defaulted on underwater home loans and house prices declined precipitously, the problem


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only 11 bank failures during 2003-2007.1 Credit markets came to a grounding halt and stock

markets plunged as well. During the first two weeks of October 2008, both the Dow Jones

Industrial Average and the Standard and Poor 500 Index declined by over 20 percent. Stock

markets worldwide had mostly declined alongside.

To rescue the financial system and avoid economic recession, various short term and

long-term strategies were developed and implemented by the United States and other countries.

The United States executed two stimulus packages, totaling nearly $1 trillion during 2008 and

2009. The U.S. government also implemented a 700 billion troubled asset relief program (TARP)

aimed at restoring liquidity in the financial markets. The U.S. Treasury took over Fannie Mae

and Freddie Mac in September 2008 in an attempt to shore up the nation's falling housing market.

Gorton and Huang (2004), who model liquidity, efficiency, and bank bailouts, show that

governments can efficiently provide liquidity by issuing government securities to bailout the

banking system.

In the long run, many regulatory changes have been proposed by economists, politicians,

journalists, and business leaders to minimize the impact of the financial crisis and prevent its

recurrence. The United States introduced a series of regulatory proposals to address consumer

protection, executive pay, bank financial cushion or capital requirements, expanded regulation of

the shadow banking system and derivatives, and enhanced authority for the Federal Reserve to

safely wind-down systemically important institutions, among others. Regulations targeted at

corporate disclosure may further improve confidence in financial markets. For example, Bushee


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liquidity. Eleswarapu, Thompson, and Venkataraman (2004) find that market liquidity improved

after the Regulation Fair Disclosure (Reg. FD).

The U.S. bailout of troubled assets and coordinated international rescue seem to inject

some confidence in the financial markets. By the second quarter of 2009, major stock market

indices had stabilized and started to bounce back. The results of stress tests conducted on major

banks in the United States in May 2009 were much better than feared and helped drive the stock

prices of these banks significantly higher from their lows. The TED spread, which peaked at 458

basis points in October 2008 had gradually fallen back to the normal levels around 30 basis

points in August 2009. The U.S. GDP growth rate also returned to a positive territory albeit the

unemployment rate remained high.

2.2. Impact of the financial crisis on liquidity and transaction costs

There are three ways a financial crisis can affect the real economy (Bernanke (1983)).

First, the crisis reduces the wealth of banks shareholders. Second, the crisis may lead to a rapid

fall in the money supply. Third, the crisis increases the cost of intermediation and reduces the

effectiveness of financial sectors in performing market making between borrowers and lenders,

or buyers and sellers. Grossman and Miller (1988) demonstrate that it is costly for market

makers to maintain market presence during a crisis. Duffie (2010) predicts that market liquidity

worsens when there is a large negative shock in asset prices. In this study, we empirically study

the effects of increasing cost of intermediation on market liquidity. Specifically, we hypothesize:

Hypotheses 1: Market liquidity deteriorates during the financial crisis.


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liquidity spirals may occur under certain conditions because market liquidity and funding

liquidity are mutually reinforcing. As stock prices decline significantly, market makers hit their

maintenance margin and are forced to liquidate their existing positions. The liquidation may

cause further decline in stock prices and thus the liquidity spirals. They suggest that liquidity

providers funding would be a driving force for variations in market liquidity. The model

predicts that when the supply of capital tightens, market liquidity declines and the sensitivity of

funding liquidity and market liquidity to market makers capital is larger for risky and illiquid

securities. Limits-to-arbitrage models argue that arbitrageurs face mark-to-market losses as asset

prices decrease (Kyle and Xiong (2001); Xiong (2001)). To maintain their margin requirements,

arbitrageurs liquidate their positions and become liquidity demanders. As the demand for

liquidity increases, trading costs may increase accordingly.

Slow movement of capital plays a significant role in market liquidity and asset pricing

dynamics ( Duffie (2010)). For example, Coval and Stafford (2007) examine asset fire sales in

equity markets and find that a reduction of existing positions by funds experiencing large

outflows creates price pressure in the securities held in common by those distressed funds. Allen

and Gale (2004) model financial fragility and show that small shocks to the demand for liquidity

cause either high asset-price volatility or bank defaults or both. Mitchell, Pedersen, and Pulvino

(2007) provide several examples illustrating how slow moving capital affects asset prices, such

as merger arbitrage, the stock market crash of 1987, and the convertible bond market in 2005

when convertible hedge funds faced large redemptions of capital from investors. They find that


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this study, we investigate the impact of slow moving capital and funding liquidity on market

liquidity. Specifically, we hypothesize:

Hypothesis 2: A decline in funding liquidity Granger causes a decline in stock market liquidity

by.

Hypotheses 3: There exists a dynamic relationship between market liquidity and funding

liquidity. (a) The relationship varies across firms and is stronger for the financially distressed

firms. (b) The relationship is stronger during a financial crisis relative to the non-crisis periods.

There is a stream of related literature showing that lower asset prices reduce market

liquidity, and vice versa. Acharya and Pedersen (2005) find that there is a difference between the

highest and lowest liquidity portfolio returns of 4.6 percent per year, of which 3.5 percent is

compensation for expected illiquidity and the remaining 1.1 percent is compensation for liquidity

risk. Korajczyk and Sadka (2008) also find that systematic liquidity is priced in the cross-section

of stock returns. Conversely, Hameed, Kang, and Viswanathan (2010) provide empirical

evidence that negative market returns reduce stock liquidity. However, the samples in the studies

mentioned above are restricted to NYSE ordinary stocks in periods prior to December 2003. In

this study, we examine the deterioration in market liquidity for all stocks during the recent

financial crisis, a time period which really stress tested the hypothesized relationships.

There is indeed a significant time series variation in the level of stock market liquidity.

Chordia, Roll, and Subrahmanyam (2008) document a dramatic increase in liquidity measured by

share turnover in the decade preceding the financial crisis. They find that stocks with larger


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reduction and a significant increase in institutional trading costs during the crisis. They argue

that institutions tend to sell less liquidity-sensitive stocks during the crisis because they would

get a very poor price by selling other stocks that are more sensitive to money supply; the market

liquidity of latter stocks tends to decline when funding liquidity deteriorates.

Although our paper is closely related to Anand, et al. (2010), it is different in the

following ways. First, we extend the analysis to the entire market instead of focusing on a subset

of investors. Second, in our Granger-causality test, we find a unidirectional impact of funding

liquidity on market liquidity, but not the other way around. Third, we examine the dynamic

relationship between funding liquidity and market liquidity in context of the financial crisis,

bailout measures such as TARP, financial versus non-financial firms, and too-big-to fail firms.

3.Data

We use Trades and Automated Quotations (TAQ) dataset to obtain bid price, ask price,

bid depth, ask depth, trade price, and trading volume for each stock time stamped to the seconds.

We use Center for Research in Security Prices (CRSP) data to obtain closing prices and Standard

Industry Classification (SIC) codes. We retain only common stocks (CRSP share codes 10 and

11), which means we exclude securities such as warrants, preferred shares, American Depositary

Receipts, closed-end funds, and REITs. Our sample includes 5,179 stock listed on NYSE,

Nasdaq, and AMEX, which are present in the intersection set of CRSP and TAQ from January

2006 to December 2009.


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data archives.2 To compute TED spreads, we obtain historical 3-month Libor rates at quarterly

and daily frequency from the Wall Street Journal and the Federal Home Loan Bank of Des

Moines, respectively.3

We obtain historical 3-month Treasury bill rate from the Federal

Reserve.4 We use Highline Financial database to identify firms that received TARP funding. We

follow Jagtiani and Brewer (2011) to define too-big-to-fail firms as the financial firms that are

one of the 11 largest organizations in each year based on the market capitalization at the end of

the previous year. There are 17 too-big-to-fail firms throughout our sample period.

We compute several measures of liquidity, trading activity, and volatility for every stock.

For each quote update, we determine the best bid (bid) and the best ask (ask) available at the

moment. We use Lee and Ready (1991) without time lag to infer trade direction. Next, we

compute volume-weighted averages of effective spreads and realized spreads, where each trades

weight is the number of shares transacted. We compute time-weighted averages of relative

quoted spread and relative bid depth, where each quotes weight is the time between two

adjacent quotes in seconds. These weighting methods are well established in the market

microstructure literature, for example, see McInish and Wood (1992).

For parsimonious presentation, each measure is first averaged at the daily level separately

for each stock. Each stock-day becomes an observation in our regression dataset. For univariate

reporting, we average the measures across stocks for each day and then average them over all

days within the respective benchmark or treatment periods. We report three different liquidity

measures in percent:


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Relative effective spreads = (100*I*2*(pricet-(askt+bidt)/2)/((askt+bidt)/2), where I=1 forbuyer-initiated trades and I= -1 for seller-initiated trades), and

Relative realized spreads = (100*I*2*(pricet-( askt+5+ bidt+5)/2)/((askt+5+ bidt+5)/2), wheretis measured in minutes).

In addition, since the liquidity on the bid side is more important during periods of crisis

due to potential imbalance between buy and sell orders, we also examine relative bid depth

following Diether, Lee, and Werner (2009):

Relative bid depth = (100*(biddepth askdepth)/(biddepth + askdepth))We also calculate total number of trades, number of shares traded, dollar volume traded,

average trade size, relative price range (days high minus low price, divided by closing price)

and intraday price volatility (in cents) for every stock-day in our sample.

The comparison of liquidity variations during the recent crisis for financial and non-

financial firms entails the creation of a matched sample. Following Bessembinder (2003) and

Huang and Stoll (1996), we match stocks based on five stock characteristics, namely, market

capitalization, price at the beginning of the benchmark period, average dollar trading volume,

average daily number of trades, and average intraday return volatility during the benchmark

period. These stock attributes are closely related to liquidity measures (for examples, see

Demsetz (1968), McInish and Wood (1992), Lin, Sanger, and Booth (1995)).

For each financial stock, we use the following equation to identify the comparable non-

financial stock that has the lowest composite score computed as follows:


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whereXi represents one of the five stock characteristics identified above;FandNFrefer to

financial and non-financial stocks, respectively. To minimize the initial difference between

financial and non-financial firms, we impose the constraint that the matching score must be less

than 1.5. For our sample of 648 financial stocks (SIC codes from 6000 to 6799), we create a

matched sample of non-financial stocks with replacement.5

4. Results

4.1. Market liquidity surrounding the financial crisis

In this section, we first investigate the changes in overall market liquidity during the

recent financial crisis. We then explore the changes in market liquidity for financial firms and

their matching non-financial firms. We further differentiate the banks that received TARP

funding. Finally, we partition the financial firms into size quintiles and also separately analyze

the too- big-to-fail firms.

4.1.1. Liquidity measures

We begin by plotting cross-sectional average of effective spread in cents and relative

effective spread in basis points around the crisis period in Figure 1 and Figure 2. We see a

dramatic increase in trading costs during the crisis period accompanied by a decline in S&P 500

index. S&P 500 index fell from peak level of 1565 during the benchmark period to 752 during

the crisis period. We define crisis period as the 4-month period between September 2008

December 2008, a period during which Lehman Brothers went bankrupt and S&P500 index


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out distressed firms after the crisis and thus, we defined short-term recovery period as the 4-

month period of January 2009 April 2009, a period during which U.S. government made 351

capital purchases in troubled firms. Finally, we define a long-term recovery period as September

2009 December 2009, a 4-month period, exactly a year after the crisis period, when the

S&P500 recovered to a level of 1,128, or roughly 50 percent higher than the lows of the crisis

period. During this period, Bank of America fully repaid $45 billion of TARP payment that it

had received from the Government, and Citibank repaid $20 Billion of TARP Trust Preferred

Securities.

[Insert Figure 1 and Figure 2 here]

In Table 1, we present summary statistics using time-series average of daily average

liquidity, trading, and volatility measures for each of these periods. In Panel A, we find that all

transaction cost measures are significantly higher during the crisis period compared to the

benchmark period. Relative quoted spread increases from 7.08 basis points in the benchmark

period to 11.67 basis points in the crisis period. Similarly, relative effective spread increases

from 5.97 basis points to 16.34 basis points in the crisis period. These results supportHypothesis

1. Comparing quoted and effective spreads leads to an interesting conclusion about the effects of

crisis on trade executions. During the benchmark period, relative effective spread was lower than

relative quoted spread by 1.11 basis points, implying that more trades executed inside the best

bid and offer quotes (BBO) and fewer trades executed outside the BBO. However, a higher

relative effective spread compared to relative quoted spread during the crisis period implies that


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Comparing quoted and realized spreads help us assess the ex-ante quoting behavior and

the ex-post realized profits of liquidity suppliers. The increased quoted spreads appear to have

passed on to liquidity providers in form of higher relative realized spreads, which increase from

1.61 basis points during the benchmark period to 4.07 basis points during the crisis period. It

appears that liquidity providers become very sensitive to the uncertainty created by the crisis and

conservatively quote wider spreads even though there is no evidence of any increase in adverse

selection costs from trading against more informed traders. Quoted depths also signify the extent

of perceived information asymmetry risk in the market. Liquidity providers were not willing to

undertake the risk of trading in the uncertain environment, especially when the liquidity

providers were on the buy side. Therefore we find a decrease in relative bid depth from 0.40

percent to -0.66 percent during the crisis period.

We observe an improvement in liquidity and a decline in all spread measures both during

the short-term recovery period and the long-term recovery period. During the long-term recovery

period, the relative quoted spread reverts back to its benchmark level while the relative effective

spread is still higher than relative quoted spread. It seems that liquidity demanders are still quite

aggressive and trading quantities beyond the quoted depth during the post crisis periods.

However, when those bargains are available, liquidity providers are willing to exploit them as is

evident from relative bid depths, which revert to 0.50 during long-term recovery, even higher

than levels observed during the benchmark period.

[Insert Table 1 here]


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trades increases from 34,040 in the benchmark period to 88,640 in the crisis period, and

subsequently decline to 44,740 during the long-term recovery period. Dollar trading volume

shows a similar pattern of increasing during the crisis and reverting back in the recovery periods.

The increase in the number of trades is consistent with the aggressive selling behavior reflected

by the higher relative effective spread compared to the relative quoted spread during the crisis

period. Trade size is significantly lower during the crisis period at 190.89 shares compared to

247.91 shares during the benchmark period or 239.81 shares during the long-term recovery

period. The reason that traders are trading only small quantities might be due to the difficulty in

asset valuation during the crisis. The reduction in the trade size also suggests that liquidity

providers are reluctant to quote larger quantities even though they have increased the quoted

spread. In Panel C, both price volatility and relative price range are higher during the crisis

period and decline during the short-term and the long-term recovery periods. Relative price

range increases from 2.76 percent during the benchmark period to 7.04 percent in the crisis

period. It reverts back to 2.66 percent during the long-term recovery period. Price volatility in

cents shows a similar trend; it increases during the crisis periods and drops back during the long-

term recovery period.

4.1.3. Financial versus non-financial firms

In this section, we compare 648 financial stocks with their matched sample. Figure 3

shows that trading costs are at similar levels for financial firms and matched non-financial firms

during the benchmark period. Although trading costs increase for all firms during the financial


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We calculate the average difference in liquidity, trading activity, and volatility measures

between each financial firm and its corresponding matched non-financial firm and present the

respective time-series average of these differences for each period in Table 2. In Panel A, the

difference in the relative quoted spread between financial and non-financial firms is insignificant

during the benchmark period. The magnitude of this difference increases to a significant 3.04

basis points during the crisis period. The increase is somewhat permanent with no signs of

reversal even in the long-term recovery period, where the difference is 1.91 basis points. The

relative effective spread and the relative realized spread show similarly increasing trends during

and after the crisis.

Liquidity providers respond to uncertain valuation environment both by widening spreads

and by reducing depths. Lee, Mucklow, and Ready (1993) find that liquidity providers change

both spreads and depth in response to information asymmetry. Spreads increase and depths

decline in response to high volume during times of information asymmetry. In our sample, the

financial stocks generally have much higher relative bid depths than the non-financial stocks in

the benchmark period. Relative bid depth is significantly lower for the financial stocks during the

crisis period as the difference between financial and non-financial firms is -1.48 percent. In the

long-term recovery period, the difference in relative bid depth is -0.44 percent. A negative

number means that ask depth was greater than the bid depth. It seems that liquidity providers are

not nearly as comfortable buying the financial stocks during the crisis period as they are during

normal periods.


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increasing during the short-term recovery period, but reverts back during the long-term recovery

period to 34,470. We see similar patterns for the number of shares traded and the dollar volume

traded. The difference in trade size continues to increase throughout the crisis and recovery

periods. Higher trade size for the financial firms indicates that market participants are more

aggressive in selling the financial stocks as compared to the non-financial stocks.

In Table 2 Panel C, we note that the difference in price volatility of financial versus non-

financial stocks increases from 0.019 during the pre-crisis benchmark period to 0.159 during the

crisis period. The difference in price volatility reverts during the long-term recovery period to

0.034. The difference in relative price range also increases from 0.40 percent to 3.53 percent

during the crisis period and declines to 2.56 percent during the long-term recovery period.

In summary, all of our liquidity measures deteriorate more dramatically for the financial

stocks than for the non-financial stocks.

4.1.4. Troubled versus non-troubled firms and financial firms of different sizes

In this section, we compare banks that received TARP funding (troubled firms hereafter)

with firms that did not receive TARP funding (non-troubled firms hereafter). In Table 3 Panel A,

we find that the relative effective spread increases by 14 basis points for the troubled firms

during the crisis period. For the non-troubled firms, the increase in the relative effective spread

during the same period is lower at 10 basis points. While the relative effective spread keeps

increasing for the troubled firms during the short-term recovery period, the relative effective

spread of the non-troubled firms declines during this period. Thus, the TARP funding by the U.S.


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period, compared to an increase of 46,350 and 4.08 for the non-troubled firms. The number of

trades and relative price range keep increasing by 60,190 and 1.48 percent, respectively, for the

troubled firms during short-term recovery period. For the non-troubled firms, we find a decrease

in both numbers. The number of trades decreases by 12,120 and the relative price range

decreases by 2.28 percent. Thus, the market continued to interpret TARP funding as a sign of

much deeper trouble at the TARP recipient firms, even as it regained confidence about the

viability of the non-TARP firms. In the long-term recovery period, both sets of firms recovered;

the relative effective spread declines by 9 basis points for the troubled firms and 8 basis points

for the non-troubled firms, from the peak levels during the crisis period. Also in the long-term,

there is a decline in volatility and trading activity for both troubled and non-troubled firms.

Although the immediate effect of the bailout measures was in the opposite directions for the

troubled firms compared to the non-troubled firms, in the long run, the liquidity of both troubled

and non-troubled firms improved.


Popular press had also advocated that firms which represented systemic risk to the

financial system were considered too big to fail and were the primary targets of government

intervention and support. Therefore, we separately analyze financial stocks of varying sizes in

Table 3 Panel B. We make quintiles of all financial stocks in our sample based on market

capitalization at the end of previous year. Jagtiani and Brewer (2011) define too-big-to-fail firms

as the financial firms that are one of the 11 largest organizations in each year based on the


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Lynch and Wachovia Bank drop out of our sample in 2008 and 2009.6 We focus our analysis on

quintile 1 (small firms), quintile 5 (large firms), and too-big-to-fail firms. Since we are studying

time-series variation in liquidity of these too-big-to-fail firms, we keep all 17 firms in our sample

for each period, when available.

The relative quoted spread increases during the crisis period for financial firms in all

quintiles and it reverts back in the long-term recovery period. We observe the highest increase of

72 basis points for small firms and the lowest increase of 3 basis points for too-big-to-fail firms.

In the long-term recovery period, this difference reverts back by 33 basis points and 1 basis point

for small firms and too-big-to-fail firms, respectively. We see a similar trend in the relative

effective spread. The number of trades increases for firms in all three categories. For too-big-to-

fail firms, the number of trades increases by 186,090 and for small firms, by 100. We see a

pronounced decrease of 134,260 in the number of trades for too-big-to-fail firms during the long-

term recovery period. The increase in the relative price range during the crisis period for small

and large firms is 4.32 percent and 7.20 percent, respectively, while for too-big-to-fail firms the

increase is 7.18 percent. Thus, these too-big-to-fail firms are as volatile as other large financial

firms during the crisis period. The reversion in price volatility is 4.28 percent for too-big-to-fail

firms compared to 5.76 percent for other large firms.

In summary, transaction costs increase more for small firms but volatility and trading

activity increase more for big and too-big-to-fail financial firms.

4.2. Impact of firm-specific and market-wide returns on transaction costs


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liquidity as the dependent variable, the key explanatory variables include market-wide return and

firm-specific return closely following the methodology used by Hameed, Wang and Viswanathan

(2010):

, ,,

,,

, , ,

, , , ,,

, where SPRi,t is the weekly average of firm is daily relative quoted spread in weekt. SPRi,t isthe change in spreads relative to previous week. Our main variables of interest are lagged market

return (Rm,t-k) and lagged firm-specific stock return (Ri,t-k). We proxyRm,t-kby the CRSP value-

weighted return. We use 4 lags of market return and 4 lags of firm-specific returns. Other control

variables are defined as follows. STDm,tand STDi,trepresent changes in volatilities of market

returns and stock returns estimated using daily returns over the previous 4 weeks according to

the method used in French, Schwert, and Stambaugh (1987). TURNi,trepresents weekly

changes in turnover, measured as the total weekly share volume traded for firm i divided by its

shares outstanding. ROIBitrepresents weekly changes in relative order imbalance standardized

by the total dollar volume traded during that period. Relative order imbalance is weekly

difference in the dollar amount of buyer initiated trades and seller initiated trades.

We run a time-series regression for each stock separately and then report mean and

median of the estimated regression coefficients across all firms in our sample in Table 4 Panel A.

The coefficients on the return variables are consistent with Hameed, Wang and Viswanathan


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decreasing and significant relationship with current changes in spreads. Therefore, we conclude

that decline in market returns and stock returns increase spreads and reduce stock liquidity.


Since the recent financial crisis may represent a structural break in these relationships, we

allow spreads to react differentially to lagged market returns during the crisis period.

Furthermore, we allow spreads to react differentially to positive and negative lagged stock

returns and run the following regression:

, ,,

,, ,

,,

,,,,,

, , ,

, , , ,,

,

whereDDown,i,tis a dummy variable indicating negative returns and we set it equal to one if and

only ifRi,tis less than zero. Crisis is a dummy variable that is equal to 1 for the 4-month period

of September December 2008, and 0 otherwise. The product terms represent interactive

variables. Other variables retain their definitions from the previous equation. We report the

results of this regression in Table 4 Panel B. When we sum the coefficients forRm,t-1 andRm,t-1 *

crisis, the impact of lagged market return changes from -0.36 during the non-crisis period to -

0.81 during the crisis period, implying a higher impact on spreads. In summary, lagged negative

market returns causes the spreads to widen more during periods of the financial crisis.


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We defineDCAP,tas a dummy variable that equals 1 if weektis a period of lower funding

liquidity compared to weekt-1 and 0 otherwise. We use the TED spread as a measure of funding

liquidity. For example, if the TED spread increases from 1.10 percent to 1.15 percent during a

week, we assignDCAP,t a value of 1 for that week. We add this variable to our regression

equation to obtain the following model:

, ,,

,, ,

,.,, , ,,

,,,,

, , ,

, , , ,,

,

We report the results of this regression in Table 4 Panel C.

We find that market-wide price declines lead to a particularly greater increase in the

relative quoted spread during times of declining funding liquidity. The coefficient of -0.36 on

Rm,t-1 suggests that the relative quoted spreads increase by 36 basis points when market declines

by 1 percent during normal periods. This effect is more acute during the crisis period. In

particular, when we sum the coefficients forRm,t-1,Rm,t-1 * crisis, andRm,t-1 * crisis *DCAP,t-1, we

find that the impact of lagged market return changes from -0.36 during the normal period to -

0.88 during the crisis period when there is a simultaneous decline in funding liquidity. Thus, we

find evidence that liquidity dry-ups during the recent financial crisis were stronger during


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liquidity by considering a direct measureNETACQ, which is the net acquisition of financial

assets by security brokers and dealers. We also consider the TED spread as a measure of funding

liquidity used by previous studies (e.g., Anand, et al. (2010)). The TED spread is the difference

between the LIBOR (London Interbank Offered Rate) and the U.S. Treasury bill rate. The TED

spread increases from 1.25 percent in October 2007 to 3.39 percent in October 2008.

Brunnermeier and Pederson (2009) note that the TED spread widens during the financial crisis

because of two reasons. In times of uncertainty, banks charge higher interest for unsecured loans,

which increases the LIBOR rate. Further, Treasury bonds become more attractive for banks as

first rate collaterals, pushing down the Treasury bond rate. Thus, higher TED spread causes a

decrease in funding liquidity. Following McInish and Wood (1992), we control for other widely

used determinants of trading costs, such as market capitalization, dollar trading volume, and

volatility.

We further employ an additional proxy for funding liquidity and estimate the following

two-stage equations:

Stage 1:Net acquisition of financial assets =f(TED spread, T-bill rate) + u

Stage 2:Relative effective spread = f(TED spread, market capitalization, dollar trading

volume, volatility, u) +

In stage 1, we orthogonalize net acquisition of financial assets by security brokers and

dealers (in billion dollars) at quarterly frequency. We run a regression of net acquisition of

financial assets on the TED spread and the T-bill rate and store the regression residuals. Then in


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the results of these regressions in Table 5. Stage 1 estimates are in Panel A and stage 2 estimates

are in Panel B.


We use three models for the second stage regression. In model 1, we regress the relative

effective spread on the cost of funding capital alone. The proxy for the cost of funding capital is

the contemporaneous TED spread. We find a positive relationship between relative effective

spread and the TED spread. An increase of 1 percent in TED spread on average increases relative

effective spread of a stock by about 17 basis points. Therefore stock market liquidity declines

when liquidity providers face higher funding costs.

In model 2, we add control variables including volatility, firm size, and trading volume

averaged over each quarter. We find that trading costs are lower for high market capitalization

stocks and stocks with higher dollar trading volume. An increase in volatility is related to an

increase in trading cost. In model 3, we also include the residuals from the regression in Table 5

Panel A as an independent variable to capture funding liquidity unexplained by the TED spread.

We find that the coefficient of the residuals is negative and significant. The higher the levels of

funding liquidity, the lower are the spreads. Thus, the coefficient suggests a positive relation

between funding liquidity and market liquidity. A 100 billion dollar acquisition (sale) of

financial assets which is unexplained by increase in the TED spread causes the relative effective

spread to decrease (increase) by 0.36 basis points.

4.4. Granger causality test


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changes in the causal variables should precede changes in the affected variables. The model

specifications are as follows:

Market liquidityt=0 + 1 Funding liquidityt-1 + 2 Market liquidityt-1+ Control variables + t

Funding liquidityt=0 + 1 Funding liquidityt-1 + 2 Market liquidityt-1+ Control variables + t

We use the TED spread as a measure of funding liquidity and the relative effective spread

as a measure of market liquidity. If both 1 0 and 2 0, we would infer an endogenous bi-

directional relationship between funding liquidity and market liquidity. If1 0 but 2= 0, it is

more likely that funding liquidity affects market liquidity. If1 = 0 but 2 0, it is more likely

that the market liquidity affects funding liquidity. If both 1 = 0 and 2= 0, funding liquidity has

no causal relation with market liquidity. We control for T-bill rate, log (market capitalization),

and log (dollar trading volume). We report the Granger causality test results in Table 6.[Insert Table 6 here]

We find that 1 is positive and significant, and 2 is insignificant. These findings reveal a

uni-directional causality from funding liquidity to market liquidity and support ourHypothesis 2.

4.5. Dynamic relationship between funding liquidity and market liquidity

In this section, we use higher frequency daily data to study the dynamic relationship

between a stocks relative effective spread and the TED spread. At quarterly frequency, the TED

spread reaches a peak value of 2.43 percent during the last quarter of 2008, while the daily TED

spread captures full variation of TED spread from a low of 1.53 percent to a high of 4.58 percent

during the same period. We run a pooled regression of stock-level liquidity on funding liquidity


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also include control variables such as volatility, firm size, and trading volume. We find a similar

positive relationship between funding liquidity and market liquidity. In model 3, we only include

firms that received TARP funding. We further interact the TED spread with the TARP event:

TED * before Tarp event, where before TARP eventtakes a value of 1 before the TARP issue

date, and 0 otherwise, and TED * after Tarp event, where after Tarp eventtakes a value of 1 after

the TARP issue date, and 0 otherwise. The coefficient ofTED * after Tarp event0.4752 as

compared to coefficient ofTED * before Tarp event, which is 0.1416. The signs and relative

magnitudes of these coefficients suggest that the relationship is stronger for these troubled firms

than non-troubled firms and it becomes even stronger after the announcement of TARP. In

model 4, we include two interactive variables for financial and non-financial firms; TED *

financial, wherefinancialtakes a value of 1 for all financial firms, and 0 otherwise, and TED *

non-financial, where non-financialtakes a value of 1 for all non-financial firms, and 0 otherwise.

The coefficient ofTED * financial is 0.0943 as compared to coefficient ofTED * non-financial,

which is 0.0918. Thus, we find that the relationship is stronger for the financial firms. In model 5,

we include two interactive variables, one for too-big-to-fail firms and one for firms which are not

too-big-to fail firms as follows; TED * too-big-to-fail, where too-big-to-failtakes a value of 1 for

11 largest financial firms during each year based on the market capitalization, and 0 otherwise,

and TED * not too-big-to-fail, where not too-big-to-failtakes a value of 1 for all firms that are

not categorized as too-big-to-fail, and 0 otherwise. The coefficient ofTED * too-big-to-failis

0.6207 as compared to coefficient ofTED * not too-big-to-fail, which is 0.0904. Therefore the


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crisis takes a value of 1 for the period September-December of 2008, and 0 otherwise, and TED

* non-crisis, where non-crisis takes a value of 1 for the period excluding September-December

of 2008. The coefficient ofTED * crisisis 0.1215 as compared to coefficient ofTED * non-

crisis, which is 0.0175. These results show a stronger relationship between funding liquidity and

market liquidity during the crisis period. In model 7, we re-confirm this finding by excluding the

crisis period from our sample. After excluding the crisis period, we find that the relationship is

still positive, but the coefficient has declined from 0.0922 in model 2 including the crisis period

to 0.0155 in this model excluding the crisis period. Therefore the financial crisis elevates the

importance of funding liquidity for equity market makers liquidity provision functions. These

results supportHypothesis 3b.


5.ConclusionIn this paper, we analyze the dynamic nature of the relationship between funding liquidity,

stock market liquidity, trading activity, and volatility. Our analysis periods include a benchmark

period, the recent financial crisis period, a short-term recovery period, and a long-term recovery

period. We also study the variations among financial versus non-financial firms, troubled versus

non-troubled firms, small versus large firms, and too-big-to fail firms.

Although trading activity intensifies during the financial crisis period, stock market

liquidity deteriorates and prices become more volatile. The relative effective spread increases


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market liquidity in response to capital injections, credit easing, and other policy changes targeted

at resolving the financial crisis. The relative effective spread declines to 13.33 basis points

immediately following the bailouts such as TARP. While bailouts appear to have prevented a

further deterioration in liquidity, trading costs during the bailout period continue to be twice of

their benchmark levels. The relative effective spread during the long-term recovery period is

8.57 basis points, indicating that trading costs are restored back almost to their pre-crisis levels

within one year of the peak of the financial crisis. Similarly, volatility more than doubles during

the crisis period but is restored back to its pre-crisis level a year after the crisis.

We separately analyze troubled firms that received TARP funding and non-troubled firms

that did not receive TARP funding. We find that the troubled firms have a much higher

deterioration in stock liquidity during the crisis period compared to the non-troubled firms.

Similarly, there is a much higher surge in trading activity for these troubled firms compared to

the non-troubled firms. Volatility spike is also more remarkable for the troubled firms. Next, we

look at the size quintiles of financial firms and find that liquidity declines significantly in all

quintiles. The liquidity in the small stocks was hit the hardest. The relative effective spread for

the small stocks increases by 75 basis points while the increase for too-big-to-fail firms is only

10 basis points. Too-big-to-fail firms do not suffer as much in terms of liquidity perhaps

because of their legacy brand image and a higher level of liquidity before the onset of the

financial crisis. In response to the financial crisis, the Senate Banking Committee passed Senator

Christopher Dodds Financial Overhaul Bill that proposes to create a council to detect systemic


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When comparing financial firms with matched non-financial firms, we find that liquidity

declines in the financial stocks are more severe. Furthermore even one year after the financial

crisis, liquidity didnt normalize for the financial stocks even though it was restored to the pre-

crisis levels for the non-financial stocks. Financial stocks continue to suffer from trading costs

much higher than matched sample of non-financial firms.

We extend the literature studying the impact of market returns and stock returns on stock

market liquidity during the recent financial crisis. We find that both lagged market returns and

lagged stock returns play a role in determining stock liquidity. While, negative market return and

negative stock return worsen future stock liquidity, positive returns improve future liquidity. We

also find that this relationship during the crisis becomes stronger when negative market return is

accompanied by a decline in funding liquidity. To understand the genesis of these relationships,

we examine the impact of funding constraints of liquidity providers on stock market liquidity. If

liquidity providers approach their funding limits, they may discontinue their liquidity supply

function and instead demand liquidity through aggressive selling. In Granger causality tests, we

find a uni-directional relationship where funding liquidity affects stock market liquidity. When

liquidity providers face critical levels of funding constraints, we observe a severe decline in

stock market liquidity, but not vice-versa.

We also study the dynamics of the relationship between funding liquidity and stock

market liquidity. We find that the relationship is much sharper during the crisis period. For the

troubled firms, this relationship is tighter, particularly after the announcement of TARP. The


32/45

financial crisis played a major role in restoring stock market liquidity to the pre-crisis levels.


33/45

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Figure 1: Effective spread from September 2007-December 2009

We take cross-sectional value-weighted average of effective spread in cents for all NYSE,

Nasdaq, and AMEX-listed stocks for each day during the period September 2007 December

2009. Effective spread is defined as the signed difference between the trade price and the quote

mid-point at the time of the trade. We plot the numbers in the figure above using the vertical

scale on the left. We also plot daily closing S&P 500 Index using the vertical scale on the right.

We define crisis period as the 4-month period from September 2008 December 2008.

Effectivespreadincents

S&P

500Index


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Figure 2: Relative Effective spread from September 2007-December 2009

We take cross-sectional value-weighted average of relative effective spread in basis points forall NYSE, Nasdaq, and AMEX-listed stocks for each day during the period September 2007 December 2009. Relative effective spread is defined as the signed difference between the tradeprice and the quote mid-point at the time of the trade divided by the quote mid-point. We plot the

numbers in the figure above using the vertical scale on the left. We also plot daily closing S&P500 Index using the vertical scale on the right. We define crisis period as the 4-month periodfrom September 2008 December 2008.

S&P

500Index

Relativee

ffectivespread


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Figure 3: Trading cost of financial stocks vs. matched non-financial stocks

We define benchmark period as September 2007 - December 2007; Crisis period as September

2008 December 2008; Short-term recovery period as January 2009 April 2009; and long-termrecovery period as September 2009 December 2009. For 648 financial stocks (SIC codes from

6000 to 6799) listed on NYSE, Nasdaq, and AMEX, we create a matched sample of non-

financial stocks during benchmark period. Our matching criteria minimizes the differencebetween market capitalization, price, average dollar trading volume, average daily number of

trades, and average intraday return volatility of financial firms and matched non-financial firms.

For each period, we take time-series average of daily relative effective spread averaged acrossstocks, separately for financial stocks and matched non-financial stocks.


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Table 1

Descriptive statistics

The summary statistics represent a panel of trading data for 5,179 common stocks listed on NYSE,

Nasdaq, and AMEX which are in the intersection set of Center for Research in Security Prices (CRSP)tapes and the Trade and Automated Quotations (TAQ). We define benchmark period as September 2007 -December 2007; Crisis period as September 2008 December 2008; Short-term recovery period asJanuary 2009 April 2009; and long-term recovery period as September 2009 December 2009. Wereport liquidity measures for each period as follows. Mid-quote is the average of the best bid and best askprice. Relative quoted spread is defined in percent as 100*(ask price bid price)/ mid-quote. Relativeeffective spread is defined in percent as 100*2*|Traded price mid-quote|/mid-quote. Relative realizedspread is defined in percent as 100*2*(Pricet mid-quotet+5)/ mid-quotet+5 for buys and

100*2*(Midquotet+5 -pricet)/ mid-quotet+5 for sells where trades are matched to quotes in force 5 minutesfollowing the trade. Relative bid-depth is defined in percent as 100*(bid depth - ask depth)/(ask depth +bid depth). In Panel B, we report number of trades, dollar volume traded, number of block trades (tradesize > 10,000 shares), and average trade size in number of shares. In Panel C, we report volatilitymeasures. Relative price range is the intraday highest price minus the lowest price divided by the closingprice and price volatility is calculated as the standard deviation of intraday trade prices in cents. Numbersin parentheses represent differences, the formulas for which are shown in the top row header of the table.

Benchmark Crisis

(Crisis -Benchmark)

Short-term recovery

(Short-term recovery -Crisis)

Long-term recovery

(Long-term recovery -Crisis)

Panel A: Liquidity measures

Relative quoted Spread 0.0708 0.1167 0.1015 0.0695

(0.0459 ***) (-0.0151 ***) (-0.0472 ***)

Relative effective spread 0.0597 0.1634 0.1333 0.0857

(0.1037 ***) (-0.0302 ***) (-0.0777 ***)

Relative realized spread 0.0161 0.0407 0.0314 0.0224

(0.0246 ***) (-0.0093 ***) (-0.0182 ***)

Relative bid depth 0.3988 -0.6584 0.2122 0.4956

(-1.0572 ***) (0.8706 ***) (1.1540 ***)

Panel B: Trade activity measures

N Trades (In '000) 34.04 88.64 76.57 44.74 (54.60 ***) (-12.06 ***) (-43.90 ***)

$ Volume traded (In '000) 535,315 699,105 555,901 478,333(163,790 ***) (-143,204 ***) (-220,772 ***)

Trade size 247.91 190.89 197.89 239.81

(-57.03 ***) (7.00 ***) (48.92 ***)

Panel C: Volatility measures


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Table 2

Descriptive statistics for financial stocks vs. matched non-financial stocks

The summary statistics represent the time-series average of the cross-sectional paired differences

between each financial firm and the matched non-financial firm for each period. We use a sample of 648financial stocks and 648 matched non-financial stocks listed on NYSE, Nasdaq and AMEX. The includedstock-days are required to have data available on both Center for Research in Security Prices (CRSP)tapes and the Trade and Automated Quotations (TAQ). We define benchmark period as September 2007 -December 2007; Crisis period as September 2008 December 2008; Short-term recovery period asJanuary 2009 April 2009; and long-term recovery period as September 2009 December 2009. Mid-quote is the average of the best bid and best ask price. Relative quoted spread is defined in percent as100*(ask price bid price)/ mid-quote. Relative effective spread is defined in percent as 100*2*|Traded

price mid-quote|/mid-quote. Relative realized spread is defined in percent as 100*2*(Pricet mid-quotet+5)/ mid-quotet+5 for buys and 100*2*(Midquotet+5 -pricet)/ mid-quotet+5 for sells where trades arematched to quotes in force 5 minutes following the trade. Relative bid-depth is defined in percent as100*(bid-depth - ask-depth)/(ask-depth + bid-depth). In Panel B, we report number of trades, dollarvolume traded, number of block trades (size > 10,000), and average trade size in number of shares. InPanel C, we report volatility measures. Relative price range is the intraday highest price minus the lowestprice divided by the closing price and price volatility is calculated as the standard deviation of intradaytrade prices in cents.

Financial Minus non-Financial firms

PeriodBenchmark Crisis

Short-termrecovery

Long-termrecovery

Panel A: Liquidity measures

Relative quoted Spread 0.0006 0.0304 *** 0.0195 ** 0.0191 ***

Relative effective spread 0.0032 *** 0.0582 *** 0.0384 *** 0.0206 ***

Relative realized spread 0.0026 *** 0.0120 *** 0.0064 ** 0.0033 *

Relative bid depth 0.4478 ** -1.4845 *** -0.1896 -0.4398 ***

Panel B: Trading activity measures

N Trades (In '000) 6.29*** 67.20 ** 97.07*** 34.47***

$ Volume traded (In '000) -562.42 207,052 ** 365,365*** 269,108***

Trade size -10.96*** 17.93 ** 41.86*** 153.44***

Panel C: Volatility measures

Relative price range (%) 0.40*** 3.53 ** 4.36*** 2.56***Price volatility 0.019*** 0.159 ** 0.151*** 0.034***

***, ** and * represent significance at 1%, 5% and 10% respectively.


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Table 3

Descriptive statistics for troubled firms vs. non-troubled firms and for financial firms by size

In Panel A, the summary statistics represent the time-series averages of the cross-sectional statistics,

separately for firms which received tarp funding and firms which did not receive tarp funding. The

included stock-days are required to have data available in both Center for Research in Security Prices

(CRSP) tapes and the Trade and Automated Quotations (TAQ). We define benchmark period as

September 2007 - December 2007; Crisis period as September 2008 December 2008; Short-term

recovery period as January April 2009; and long-term recovery period as September 2009 December

2009. Mid-quote is the average of the best bid and best ask price. Relative quoted spread is defined in

percent as 100*(ask price bid price)/ mid-quote. Relative effective spread is defined in percent as

100*2*|Traded price mid-quote|/mid-quote. We also report number of trades, and relative price range.Relative price range is the intraday highest price minus the lowest price divided by the closing price.

Panel B reports summary statistics by firm size. We sort all financial firms into quintiles based on the

market capitalization of each firm. Following Jagtiani and Brewer (2011), we define too-big-to-fail

firms as the 11 largest firms at the end of each year based on their market capitalization.

Crisis Benchmark

Short-termrecovery

Crisis

Long-termrecovery Crisis

Panel A: Troubled vs. non-troubled firms

Relative quoted spread Troubled firms 0.06*** 0.02*** -0.03***

Non-troubled firms 0.04*** -0.02*** -0.05***

Relative effective spread Troubled firms 0.14*** 0.01 -0.09***


N Trades (In '000) Troubled firms 167.28*** 60.19*** -106.48***


Relative price range (%) Troubled firms 7.09*** 1.48 -3.54


Panel B: Firms by size quintiles and too-big-to-fail firms

Relative quoted spread Quintile 1 (small) 0.72*** 0.17*** -0.33***

Quintile 5 (large) 0.06*** 0.00 -0.05***

Too big to fail 0.03*** 0.02*** -0.01***

Relative effective spread Quintile 1 (small) 0.75*** 0.45*** -0.20***

Quintile 5 (large) 0.14*** -0.02** -0.10***

Too big to fail 0.11*** 0.00 -0.07***

N T d (I '000) Q i il 1 ( ll) 0 10*** 0 02 0 06**


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40

Table 4Spreads, returns, and, impact of the funding constraints

We estimate the following regression equation following Hameed, Wang and Viswanathan (2010):

, ,,

,,

, We run the time-series regression for each stock and report the mean and median of the estimated regression coefficients across all firms in our

sample in Panel A. SPRi,t is the average of firm is daily relative quoted spread in week t.SPRi,t is the change in weekly spreads. Our main

variables of interest are lagged market return (Rm,t-k) and lagged stock return (Ri,t-k). We proxyRm,t-kby the CRSP value-weighted return. The firm-specific weekly control variables are: turnover (TURNi,t); relative order imbalance (ROIBi,t); and idiosyncratic volatility (STDi,t) and volatility ofthe market return in weekt(STDm,t). Similarly, in Panel B we report the mean and median of the estimated regression coefficients across all stocksfrom the following regression:

, ,,

,,,

,,

,, ,,,

,

DDown,i,tis a dummy variable indicating negative returns and we set it equal to one if and only ifRi,t is less than zero. Crisis is a dummy variablethat is equal to one for the period from September 2008 December 2008.

Next, in Panel C we report the mean and median of the estimated regression coefficients across all stocks from the following regression:

, ,,

,,,

,.,, , ,

DCAP,tis a dummy variable that takes a value of one only if weektis associated with periods of lower funding liquidity compared to weekt-1.

Panel A: Spreads and lagged returns

Estimated Coefficients Rm,t-1 Rm,t-2 Rm,t-3 Rm,t-4 Ri,t-1 Ri,t-2 Ri,t-3 Ri,t-4

Mean -0.5207*** -0.3363*** -0.0378*** 0.1232*** -0.0737*** -0.0622*** -0.0441*** -0.0447***

Median -0.3107 -0.2145 -0.0041 0.0948 -0.0572 -0.0438 -0.0335 -0.0354

Estimated Coefficients STDm,t-1 STDi,t-1 Turni,t-1 OIBi,t-1 STDm,t STDi,t

Mean 0.0166 -0.0070 0.0000 0.0068*** 0.1969*** 0.0366***

Median 0.0705 -0.0002 0.0000 0.0070 0.1293 0.0147


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41

Panel B: Spreads and signed lagged returns

Estimated Coefficients Rm,t-1 Rm,t-2 Rm,t-3 Rm,t-4 Ri,t-1 Ri,t-2 Ri,t-3 Ri,t-4

Mean -0.3608*** -0.3499*** -0.0135 0.0212 -0.0639*** -0.0617*** -0.0567*** -0.0699***Median -0.1914 -0.2316 0.0145 0.0441 -0.0691 -0.0407 -0.0452 -0.0526

Estimated CoefficientsRm,t-1 *crisis

Rm,t-2 *crisis

Rm,t-3 *crisis

Rm,t-4 *crisis

Ri,t-1 *Ddown,i,t-1

Ri,t-2 *Ddown,i,t-2

Ri,t-3 *Ddown,i,t-3

Ri,t-4 *Ddown,i,t-4

Mean -0.4496*** -0.0722*** -0.0526* 0.1542*** -0.0201 -0.0020 0.0314*** 0.0658***

Median -0.2169 -0.0067 -0.0459 0.0096 0.0094 -0.0024 0.0250 0.0482

Panel C: Spreads and funding constraints

Estimated Coefficients Rm,t-1 Rm,t-2 Rm,t-3 Rm,t-4Rm,t-1 *crisis Rm,t-2 * crisis

Rm,t-3 *crisis

Rm,t-1 *crisis *DCAP,t-1

Mean -0.3604*** -0.3534*** -0.0145 0.0164 -0.4117*** -0.0843** -0.0338 -0.1108

Median -0.1870 -0.2338 0.0170 0.0383 -0.1208 0.0051 -0.0461 -0.0869



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Table 5

Effect of funding liquidity on market liquidity

Panel A: Stage I

We orthogonalize net acquisition of financial assets by security brokers and dealers (in 100 billiondollars) at quarterly frequency. We run the following regression and use the residuals of this model as anindependent variable in Panel B:Net acquisition of financial assets =f(TED spread, T-bill rate) + u

TED is defined as the difference between the LIBOR (London Interbank Offered Rate) and the U.S.

Treasury bill rate.

Variable Net acquisition of financial assets

Intercept 5.62TED -12.34***

T-bill rate 1.95**

Adjusted R Square 0.6808

Number of Observations 16

Panel B: Stage IIWe report coefficient estimates from regressions of trading cost (relative effective spread) on TEDspread which is a proxy for funding liquidity and residuals from Panel A regression. The model isestimated over 16 quarters in our sample. Other control variables include volatility, log (marketcapitalization) and log (dollar trading volume) averaged over each quarter. Volatility for each stock-day iscomputed as standard deviation of quote mid-point returns during the day.

Dependent variable Relative Effective Spread

Model 1 Model 2 Model 3

Intercept 0.5858*** 0.5849*** 3.9181***TED 0.1746*** 0.1759*** 0.1346***

Residuals from Stage I -0.0073*** -0.0036***

Log(Market capitalization) -0.0821***

Log(Dollar trading volume) -0.167***

Volatility 5.6734***

Adjusted R Square 0.0124 0.0144 0.6762Number of Observations 71,862 71,862 71,862



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Table 6Granger causality test of funding liquidity and market liquidity

We take cross sectional average of all the stock level variables during our sample and run the following

time series regressions:Funding liquidityt=0 + 1Funding liquidityt-1 + 2Market liquidityt-1+ t

Market liquidityt=0 + 1Funding liquidityt-1 + 2Market liquidityt-1+ Control variables + tTED spread is a proxy for funding liquidity and relative effect spread is a measure of market liquidity.We control for contemporaneous T-bill rate in model 1 and contemporaneous market volatility in model 2.

Variable TEDt Relative effective spreadt

Intercept -0.0040*** 0.0001

TEDt-1

0.1025*** 0.0432***

Relative effective spreadt-1 -0.0250 -0.4800***

T-bill ratet -0.9962***

Market volatilityt -0.0047

Adjusted R Square 0.8327 0.2387

Number of Observations 980 980



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Documents

Liquidity Financial Crisis Resolution 20110912