Download 2. The Bonds and Bills Market - The University of Texas at Dallas

Financial Networks and Trading in Bond Markets
G. Geoffrey Booth
Eli Broad Graduate School of Management, Michigan State University
Umit G. Gurun
School of Management, University of Texas at Dallas
Harold H. Zhang
School of Management, University of Texas at Dallas
This paper examines the role of financial networks in influencing asset prices and trading performance.
Consistent with theoretical studies on the role of communication networks in information dissemination, we posit
that financial institutions with more extensive financial networks (global financial institutions) can more efficiently
acquire and process information pertaining to asset trading in global financial markets than financial institutions
with more limited financial networks (local financial institutions). The information advantage affords the global
financial institutions more favorable transaction prices and better trading performance relative to their local
counterparts. Using transaction-level Turkish government bond trading data, we find that financial institutions with
global financial networks exhibit a stronger tendency to trade in the more liquid bonds. Further, they consistently
trade at more favorable prices and enjoy better trading performance than local financial institutions. Together, these
results suggest that global financial institutions have information advantages and benefit from bond trading in an
open emerging market.
Key Words: Financial Networks, Information, Bond Markets
1.
Introduction
Although it is well established that information moves security prices, how information flows
through financial markets and is incorporated in the prices of financial assets is not as well understood.
Traditional asset pricing approaches assume that individual agents behave anonymously with new
information becoming known by all the agents in the market simultaneously, thereby making the
information common knowledge. Information, however, can also gradually spread throughout the market
by word-of-mouth and observational learning. Because of differences in institutional structures and
traders’ information processing abilities, it is unlikely that information diffusion will be amorphous.
Instead, information is likely to spread more rapidly within trading firms than between trading firms, not
only because of the presence of an intra-firm network but also because of financial incentives provided to
traders that are related to firm profitability. As a result, traditional approaches disregard the possibility
that agent behavior (individually and collectively) may be influenced by a communication network.
Models of trading dynamics recognize the presence of asymmetric information. The distinction
between informed and uninformed traders leads to a number of useful insights. For instance, informed
traders tend to respond more quickly to news, tend to trade in more liquid markets, and tend to show
better performance than uninformed traders. Yet it is not entirely clear who the informed traders are or
how they become informed. In this regard several empirical studies show that individuals who reside and
work in the same location tend to make similar financial decisions, which suggests the presence of
internal group communication.1 The idea is that traders who are spatially and electronically close are
exposed to similar information that is diffused via networks within the same group once the information
is received by one or more of the traders.
For example, investors tend to invest locally (e.g., Grinblatt and Keloharju, 2001; Ivković and Weisbenner, 2005; Massa and
Simonov, 2006), as do professional money managers (Coval and Moskowitz, 2001). Investors also tend to follow their
colleagues and neighbors (e.g., Dufflo and Saez, 2003; Hong, Kubik, and Stein, 2005; Ivković and Weisbenner, 2007).
Moreover, Hong, Kubik, and Stein (2004) develop a model in which stock market participation is influenced by social
interaction, and Xia shows (2007) that the influence of information on transaction prices depends on the structure of the network.
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We address this gap in the literature by comparing the information networks of financial
institutions that trade bonds in an emerging market. We select this type of market because, as Biais and
Green (2005) point out, bond markets often provide little pre-trading transparency, which creates
opportunities for informed traders to take advantage of their superior information. We classify the sample
institutions as those that have offices in the local economy only and those that have offices both in the
local economy and in major bond trading markets such as New York City and London. We define a
financial network to be a set of offices that are linked together by an electronic communication system.
Consistent with the implications of theoretical studies on the role of networks in information
dissemination, we posit that a financial institution with a global (more extensive) financial network can
more efficiently acquire and process information related to global financial market movements than an
institution with only a local (less extensive) network. This information advantage is expected to allow
global financial institutions to trade more nimbly and perform better relative to local financial institutions.
We test our hypotheses by empirically investigating day trading in the government bonds of
Turkey, an open emerging market in which financial institutions with different scopes of financial
networks are permitted to participate with limited government interference. We find that financial
institutions with global financial networks trade at more favorable prices and demonstrate better
performance in the Turkish government bond market relative to financial institutions with only local
networks. Our empirical findings thus support the conjecture that financial institutions with a more
extensive financial network such as those with a global network exhibit an information advantage and
benefit from utilizing their superior information relative to financial institutions with a less extensive
financial network such as those with only a local network.
2.
The Bonds and Bills Market
2.1
The Market
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Turkey’s public bond market, the Bonds and Bills Market, is an important investment and trading
venue for financial institutions. Using total market capitalization standardized by GDP as a measure of
importance, according to World Bank Database on Financial Development and Structure, Turkey ranked
9th out of 30 major world bond markets, with its bond market being 2.3 times as large as its equity market
(see Beck et al. (2000) for details of this database).
Almost every month, the Turkish Treasury auctions bonds with maturities ranging from one
month to 10 years. After the primary market allocation, these bonds are traded on an automated secondary
market, the Bonds and Bills Market. This market also facilitates repurchase agreements, but these
transactions are executed separately and excluded from our analysis. The institutions that are authorized
to trade on the Bonds and Bills Market are Istanbul Stock Exchange (ISE) member banks and member
brokerage houses. These financial institutions typically trade on their own accounts. Sometimes they fill
retail buy orders from their inventory, but if their inventory is insufficient they may have to go to market
to meet demand.
Each institution employs multiple traders who form an information network. They are in constant
contact with each other throughout the trading day, permitting them to be better aware of the local buy
and sell order flow. For instance, it is not uncommon for traders to inform the participants in their
network that they have learned that a particular financial institution is a net buyer today or that another
financial institution is trying to liquidate a sizeable position. Some institutions have home offices in
multiple markets while others have branches; such organizational structures create multi-market trader
networks that facilitate the transmission of information relevant to the local market.
Bond market participants are a diverse mix of small and large Turkish financial institutions and
large international financial institutions. These institutions have different arrangements to disseminating
information. International banks, for instance, have their bond trading floors connected by a “hoot”.
Nowadays a “hoot” refers to an electronic communication system, but originally it was a device devoted
to a single trading floor. “Hoot” transmissions tend to flow from New York and London to other markets.
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In contrast, bond traders of Turkish banks (especially large Turkish banks) gather information by making
phone calls to fellow bond traders in overseas financial centers. Of course, information is also available to
all traders whose firms have access to public information networks such as Reuters, Bloomberg, and
similar providers. Different financial institutions, however, may still have different information
processing capabilities, which may lead to differences in interpretation of publicly released information
and in turn trading performance.
2.2
The Trading System
The Bonds and Bills Market is a limit order book market that uses an electronic system to match,
administer, and report transactions. The market operates in two sessions: from 9:30 a.m. to 12:00 noon,
and from 1:00 p.m. to 5:00 p.m. Bonds with same-day and next-day settlement trade until 2:00 p.m.,
which is the settlement time for the day; between 2:00 p.m. and closing, only bonds with next-day
settlement trade. Thus, the number of transactions declines noticeably after 2:00 p.m.
Orders are processed and executed according to price and time priority in an automated trading
system. The ISE uses an order-driven electronic continuous market with no intermediary such as a market
maker and no floor brokers. The majority of the orders are routed electronically via member firms to the
central limit order book through an order processing system that does not require any re-entry by the
member firms. In very rare cases, member firms call representatives at the exchange to have their orders
entered for them. Member firms can execute market orders and limit orders, as well as orders that require
further conditions for execution (e.g., Fill-or-kill and Stop-loss). Member firms are not allowed to enter
orders when the market is not open; however, they are allowed to withdraw their existing orders. It is not
unusual to see traders filling out their order screen prior to opening time and submitting multiple orders at
the open.
Price information on the 20 best bids and offers is continuously available to member firms. The
system does not display quantity demanded or offered at each of these prices, but past transactions can be
viewed by all members. The tick size is 1 Turkish lira (TL) for a 100,000 TL face value bond, with
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minimum (maximum) order size set to 100,000 TL (10 million TL); there exists no formal upstairs market
for block trades. An incoming market order is executed automatically against the best limit orders in the
book. Execution within the inside quotes is allowed.
Once a transaction takes place, a confirmation notice is sent to the parties involved in the
transaction. The other market participants do not learn the identities of the parties, but they do observe
that a transaction took place at a specific price and quantity. All information pertaining to price, yield, and
volume of best orders as well as details of the last transaction and a summary of all transactions are
disseminated to data vendors, including Bloomberg, Reuters, and some local firms, immediately after
each transaction. In addition, all trades are reported to the clearing organization, the ISE Settlement and
Custody Bank Inc. (Takas Bank), at the end of the day to facilitate bookkeeping. We do not have
information on what percentage of the transactions take place in ISE; however, anecdotal evidence
suggests that ISE consolidates more than 97% of the turnover value of the Bonds and Bills Market’s
transactions. The remaining portion is captured by OTC markets.
The Turkish government typically plays a minimal role in the Bonds and Mills Market.
Nevertheless, after the 2001 banking crisis, the Undersecretariat of the Treasury initiated a primary dealer
system that requires some market’s members that participate in the primary market auction to provide
liquidity by quoting a bid and an ask (not necessarily the best bid or ask) in the secondary market. The
quotes are identified as being given by a primary dealer. The rationale for this innovation is that these
members would accommodate liquidity needs that may arise during times of crisis, although anecdotal
evidence indicates that such action by the primary dealers has yet to occur. The number of primary dealer
members (typically between eight and 14) and its composition (foreign or domestic) is determined by the
Undersecretariat. In 2006, the primary dealer system consisted of 12 primary dealers. The most recently
issued bond is designated as the active (or benchmark) bond.
3.
Data and Summary Statistics
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Our sample consists of 1,716,917 tick-by-tick time-stamped transactions beginning May 1, 2001
and ending June 15, 2005 (1,039 trading days). For each transaction, we have detailed information on the
time of order placed and filled, transaction price, and trade size for 177 Turkish lira-denominated
Treasury bills and notes. More important, our data set also contains the identities of the traders on both
sides of a transaction from their unique identification code. The starting date of the sample is two months
after the Turkish financial crisis attributed to liquidity shortages in the banking system that ended in
February 2001. Data availability dictates the sample’s ending date.
One hundred seventy distinct financial institutions participated in the Bonds and Bills Market.
We classify these into local versus global financial institutions. A financial institution is classified as
“global” if it has branches or offices in major financial markets outside Turkey; otherwise, it is classified
as “local”. Based on information collected from the Istanbul Stock Exchange and data from the Turkish
Bank Association (http://www.tbb.org.tr/net/subeler) on overseas branches and offices, we classify 146 as
local financial institutions and 24 as global financial institutions. We use the ISE asset size categories to
divide local financial institutions into 116 small and 30 large financial institutions.2
The roster of global financial institutions includes large foreign banks such as Deutsche Bank,
Citibank, and JP Morgan Chase as well as large domestic financial institutions such as Yapı Kredi
Bankası A.Ş., Vakıflar Bankası A.Ş., and Akbank A.Ş. The foreign global financial institutions have
home offices in New York and throughout Europe, with the latter including offices in London,
Amsterdam, Paris, and Frankfurt to name but a few. Six of the global financial institutions are Turkish
and together account for more than 22% of the global financial institutions’ participation in the Turkish
We also divide financial institutions according to their trading volume using their past month’s transactions and find that the
two proxies for size are highly correlated. This is not surprising since anecdotal evidence suggests that large financial institutions
participate in treasury auctions more frequently and have the ability to obtain more bonds.
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market. These financial institutions have branch and liaison offices not only in New York and Europe but
also in Bahrain, Tokyo, and Moscow.3
Using the trader identification code, our global versus local classification, and ISE asset size
categories, we classify each transaction as being made by a local small, local large, or global financial
institution. Table 1 reports selective summary statistics for our data. Panel A shows the average daily
trading volume in U.S. dollars (USD) of the local small, local large, and global financial institutions
sorted by seller- and buyer-initiated trades and their counterparties.4 Of the USD 640 million of total daily
volume, trading between local large and global financial institutions is the highest, with average daily
trading volume reaching USD 133.8 million for seller-initiated trades and USD 139 million for buyerinitiated trades. Trading among global financial institutions is the second highest, with seller- and buyerinitiated daily trading volume of USD 76.5 million and USD 95.6 million, respectively.
In Panel B, we report the average size and number of tick-by-tick transactions for local small,
local large, and global financial institutions without reference to the initiator. The average volume per
transaction is highest, at USD 0.9 million, between local large and global financial institutions. The
transaction volume for trades among global financial institutions is the second highest at USD 0.65
million, while trades between local small and global financial institutions ranks third. In terms of the
number of transactions, trades between local large and global financial institutions account for 36.9% of
all trades, trades among local large financial institutions account for 21.7%, and trades among global
financial institutions account for 16.1%.
4.
Empirical Analysis and Results
Our analysis consists of three distinct but related analyses. We first examine whether global
financial institutions are more likely to trade more liquid bonds, a practice that allows them to more easily
3
We classify as local financial institutions several small Turkish banks with offices in nearby nations that provide primarily retail
banking services (deposits and remittances). We performed our analyses with these banks classified as global financial
institutions. Our results were not affected.
4 The USD volume is obtained by using the daily closing TL/USD exchange rate for that day. Turkey dropped six zeros from its
currency at the end of 2004. We incorporate this change in our calculations. During the sample period, the average exchange rate
was TL 1.46 = USD 1 with a standard deviation of TL 0.11.
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hide informed strategic trades. We then investigate whether global financial institutions consistently
transact at more favorable prices. Finally, we explore the day-trading profitability by different financial
institutions. The evidence supports the notion that financial institutions with global information networks
are more informed than those institutions with only local networks.
4.1
Strategic Trading
Chowdry and Nanda (1991) show that informed investors tend to trade in more liquid markets,
presumably because of their need to hide strategic transactions that convey information. Combining this
observation with Pasquariello and Vega’s (2007) suggestion that the most liquid bonds are the ones that
are most recently issued, we hypothesize that if global financial institutions have an informational
advantage over local financial institutions, they tend to trade active bonds, i.e., the most recently issued
bonds, relative to the other bonds, which we refer to as passive bonds.
In Table 2 we report different financial institutions’ average daily transactions—measured by
volume of trade (in U.S. dollars) and number of trades—in active bonds and passive bonds and the daily
ratio of trading in active bonds to passive bonds. Consistent with Pasquariello and Vega (2007), active
bonds have the highest transaction volume/number compared to the rest of the bonds. As a robustness
check, we calculate the transaction volume at day t-1 and designate the bond with highest score as the
active bond for the next trading day. Our conclusions are not affected by this alternative definition of
“active” bond.
Next, we split the sample transactions into cases where global financial institutions are on both
sides of the transaction, a global financial institution is on one side and a local financial institution on the
other, and local financial institutions are on both sides. The results for trading volume and number of
transactions are similar, although those for trading volume are more compelling. The ratio of active to
passive bonds is greatest in terms of trading volume (in U.S. dollars) when global financial institutions
are on both sides of the transaction and smallest when local financial institutions are on both sides of the
trade. For example, the ratio of active to passive bonds in terms of trading volume is 2.33 for global
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financial institutions and 1.38 for local financial institutions. The difference in the ratios of active to
passive bonds for global/global and local/local financial institutions is significant (p = 0.000), suggesting
that global financial institutions have a stronger preference for trading active bonds than local financial
institutions.
The results based on the number of transactions are similar. While the ratio of active to passive
bonds for trades involving only global financial institutions is 1.34, the corresponding ratio for only local
financial institutions is 1.19. The difference is again significant (p = 0.000). Our empirical evidence on
preferences of global and local financial institutions with respect to active versus passive bonds thus
supports the conjecture that global financial institutions have an information advantage over local
financial institutions. This evidence may also be consistent, however, with alternative explanations such
as the liquidity concerns of global financial institutions trading large quantities.
4.2
Pricing Advantage
We define a financial institution as better “informed” if it consistently buys (or sells) before other
financial institutions before the market price rises (or declines). Similar to Massa and Simonov (2003), we
determine the degree of informativeness of different financial institutions by examining the delayed price
changes in the same bond in a D-minute interval following each transaction initiated by the financial
institution. For a given bond k, we estimate the following time-series regression to identify the delayed
price changes associated with trades initiated by investor i:
Pik  0  ik Tikj   k I k   ik ,
D
(1)
D
where Pik is the delayed price change of bond k in D minutes following the trade, Ik is a binary
indicator that takes a value of one for bond k (the bond fixed effect), and Tik is the signed transaction
volume in million Turkish liras (positive for purchases and negative for sales). The notation for the
subscript indicates that investor i initiated trade of bond k. θik measures the effect on the delayed price
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D
D
change ( Pik ) of a trade in bond k initiated by investor i. For Pik to be defined, there must be a
transaction in bond k within D minutes after the trade. If there is more than one trade, we use the last
D
transaction within the interval, and if no transaction takes place, we set Pik = 0.
We use a 10-minute time interval (D = 10) as the baseline case in our regression to identify the
price impact of informed trades. Given the total number of trades and trading days in our sample, on
average there are about 30 trades per 10-minute interval. We also perform our analysis using 20- and 30minute time intervals, which increases the average number of trades per interval to approximately 60 and
90, respectively. This analysis allows us to assess how quickly information dissipates in this market.
Excluding the top and bottom deciles of trades from each day to control for the potential impact of
extreme trading does not change our results.
We estimate equation (1) for each financial institution and each bond. The coefficient θik
represents the degree of informativeness of financial institution i, which initiated the trade on bond k. A
positive θik means that the trade initiator, financial institution i, consistently bought (sold) from (to) other
financial institutions before an increase (decrease) in the price of bond k. A significant value for θik
implies that financial institution i is more informed than its trading counterparts with respect to bond k.
The larger the coefficient’s value, the greater the informational content of the trade.
To examine how the relative informativeness of trades varies with type of financial institution, we
estimate the following equation that relates trade informativeness to financial institutions characteristics:
ik   0  1 SDUM ik   2 GDUM ik   it ,
(2)
where SDUM is a dummy variable that takes a value of one for large financial institutions and zero
otherwise, and GDUM is a dummy variable that takes a value of one for global financial institutions and
zero otherwise. The local small financial institution is our benchmark group. Since all global financial
institutions are large in size, the size dummy SDUM takes a value of one if a financial institution is a
global financial institution. Therefore, the coefficient on GDUM measures the additional effect associated
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with being a global financial institution beyond the size effect. We estimate equation (2) using robust
clustered standard error regression. In equation (2), we include all θs estimated in equation (1). Limiting
observations to θs that are statically significant at p = 0.000 does not change our results. We include bond
fixed effect to control for possible unobserved heterogeneity among different bonds.
Table 3 presents the estimation results of equation (2). Panel A presents the results for the full
sample. For the baseline 10-minute time interval, the estimated coefficient on the size effect SDUM
suggests that for every one million Turkish lira that large financial institutions trade, the bond price
changes by TL 0.14 more than if the one million Turkish lira were traded by local small financial
institutions. Given the average bond price of TL 78 for the entire sample, the estimated bond price change
suggests the large financial institutions enjoy an 18 basis point price advantage. Using the average
exchange rate during the sample period, 1.46 TL/USD, this effect corresponds to 10 U.S. cents.
The coefficient estimate on GDUM is also positive and statistically significant. Specifically, for
every one million Turkish lira traded by global financial institutions, the price changes TL 0.09 or 6 U.S.
cents more in a 10-minute interval than if the one million Turkish lira were traded by a typical large
financial institution. Global financial institutions thus enjoy an additional price advantage of 11 basis
points. These results indicate that, on average, global financial institutions enjoy better pricing in bond
trading than local financial institutions, which is consistent with having an information advantage over
local financial institutions. For the 30-minute interval, the coefficient estimate on GDUM is positive but
insignificant. This suggests that the information advantage afforded global financial institutions is shortlived and consistent with the type of market where the information is mostly related to order flows on
global financial markets but not to fundamentals. The coefficient estimate on SDUM, however, remains
positive and highly significant, indicating that large financial institutions benefit from their relative size
when the trading interval is extended to 30 minutes.
To evaluate whether our findings are due to the dichotomy of foreign versus domestic financial
institutions, we perform the regression analysis using only observations on Turkish financial institutions.
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Panel B reports the estimation results excluding foreign financial institutions. The coefficient estimates on
SDUM and GDUM are very similar to those based on the entire sample. The pricing benefit is slightly
higher at TL 0.12 (8 U.S. cents) for global financial institutions than for large financial institutions. The
information advantage afforded global financial institutions disappears after 30 minutes, as when using
the full sample. Further, the estimated size effect is also slightly larger than that in the full sample case
when the pricing impact is measured 30 minutes after trade. These results indicate that our findings on
favorable pricing in Turkish bond trading are more likely attributed to global financial institutions’ more
extensive network (relative to local financial institutions) than to the foreign versus domestic dichotomy.
Panel C reports the estimation results using the subsample of liquid bonds. We identify liquid
bonds based on the Amihud (2002) liquidity measure, calculated for each bond as the absolute value of
daily returns divided by daily dollar volume for the previous day, with low (below-median) Amihud
measure bonds comprising the liquid bonds. Panel D presents the results for the liquid bond subsample
excluding foreign financial institutions. In both panels the coefficient estimates on SDUM and GDUM
remain quantitatively similar to the findings based on the full sample. This suggests that more favorable
pricing by global financial institutions is unlikely to be attributed to differences in bond liquidity.
Finally, in unreported tables, we replicate our analysis by excluding financial institutions that are
designated as primary dealers. As explained above, primary dealers are required to quote a bid and ask
price (though not necessarily the best bid and ask) in the limit order book of some bonds in the secondary
market. If these primary dealers systematically include non-global members, then it is possible that our
results are driven by liquidity provision required from such members. Our results are quantitatively
similar when primary dealers are excluded from the sample. This is in line with the Turkish market not
experiencing a buying/selling frenzy that requires the primary dealers to act as liquidity providers.
4.3
Day-Trading Profitability
We next investigate how different financial institutions perform in terms of day-trading
profitability in the bond market. To do so, we construct daily trading cycles for each bond k by financial
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institution i. We assume that the initial the inventory is zero and that purchases increase inventory while
sales decrease it. As the day unfolds the inventory level will typically hit zero several times. The time
between the adjacent zeros is considered a cycle and the profits associated with the transactions in each
cycle are calculated using the buy and sell prices and corresponding trade volumes.
Using information on the percentage profit and funds invested in trading cycles for all bonds for a
given trader i on day t, we construct the day-trading profitability measure for each investor i on day t as
follows:
PRFit 
K
C
k 1
c 1
K
  Profit
 Invitkc
.
C

k 1
itkc
(3)
Invitkc
c 1
PRF is the weighted average percentage profit per trading cycle for investor i on day t. We ignore direct
transaction costs in our calculation because they are very small at approximately 0.001%. Moreover,
since we are not privy to tick-by-tick TL/USD exchange rates, we calculate profits per cycle in Turkish
lira. Although lira and dollar profits may differ, there is no compelling reason for this difference to create
a short-term systematic bias.
In Table 4, we report the PRF of local small, local large, and global financial institutions. All
investor groups earn on average a negative profit on their day-trading activities. Trades attributed to
global financial institutions, however, have a smaller average loss (-0.02%) than the trades of local large
financial institutions (-0.03%) or local small financial institutions (-0.04%). Overall, 33.3% of day trades
produce a positive profit. The fraction of day trades with positive PRF is 37.2% for global financial
institutions, 34.4% for local large financial institutions, and 29.8% for local small financial institutions. 5
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Analyzing the Taiwanese stock market, Barber et al. (2006) find that individual day traders routinely incur losses, while
institutional day traders, on average, profit. The composition of trades in this market is dramatically different from the Bond and
Bills Market, however. For instance, in the Turkish market all of the traders are institutions, while institutions only account for
10.5% of the Taiwanese trade value.
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Although on average day-trading profitability is negative for all traders, financial institutions may
still make a net profit using other investment strategies, such as buy-and-hold, on certain bonds. In our
sample, the buy-and-hold strategy is used by both financial institutions that do not day trade (48% of the
traders do not participate in day-trading activities) and by day traders as part of their overall trading
strategy. Lending support to this view, day trades make up only 35% of trading volume (in U.S. dollars).
In column 1 of Panel B, we report that global financial institutions earn significantly higher day-trading
profits than local large financial institutions (p = 0.000), which in turn earn higher day-trading profits than
local small financial institutions (p = 0.000).
Barber et al. (2004) suggest that frequent day traders perform better than infrequent day traders.
In addition, if investors are overconfident in their trading skills, prior profitability will likely influence
their participation decisions in day trading. It is also plausible that traders learn from prior trading,
adjusting their trading strategies based on prior day-trading successes and market conditions. Because a
large proportion of financial institutions do not participate in day trading, we conduct joint estimation of
the participation decision and day-trading profitability using Heckman’s (1979) self-selection model,
where we specify the participation decision as a function of the trader’s prior day-trading successes and
market conditions, as proxied by interest rate volatility.
Specifically, we estimate the following model:
PRFit   0  1 SDUM i   2 GDUM i   3 LPRFit   4 LPRFit * SDUM i
  5 LPRFit * GDUM i   6VOLINTt 1   it
PARTit   1 LPRFit   2VOLINTt 1  uit  0,
(4)
where PRFit is the percentage day-trading profit of trader i on day t, LPRFit is the percentage profit of
previous day-trading, PARTit is the participation value, which takes value of one if financial institution i
participates in day-trading on day t, and VOLINTt-1 is the standard deviation of the interest rate on the
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previous day using 30-minute observations. The interaction terms capture the impact of past trading
performance and market conditions on the day-trading profitability of various financial institutions. 6
Table 5 reports the day-trading profitability results. Column (1) shows the estimation results for
the Heckman model for all bonds traded during the sample period. The regression results suggest that,
controlling for interest rate volatility and previous day-trading profitability, large financial institutions
earn 0.005 percentage point higher profits than local small financial institutions on day-trading. Moreover,
global financial institutions earn day-trading profits that are 0.007 percentage points higher than those of
typical large financial institutions. Both estimates are significant (p = 0.000).
The coefficient estimate on lagged day-trading profitability is positive and significant (p = 0.000),
indicating some persistence in day-trading profitability. Consistent with the participation of more
informed financial institutions in day trading, the lagged percentage profit in day-trading negatively
affects the participation decision. A plausible reason for this finding is that an informed financial
institution that has just traded may not immediately trade again unless the new information is received
immediately after the previous trading cycle. Interest rate volatility has a significant negative effect (p =
0.000) on day-trading profitability. This implies that it is more difficult to profit from day-trading when
the interest rate is more volatile. However, higher interest rate volatility has a significantly positive effect
(p = 0.000) on the decision to participate in day-trading on the following day. One explanation is that
informed financial institutions are more likely to day trade and higher volatility reflects more informed
traders in the market. 7
6
Our conclusions remain unaffected if we use alternative selection equation specifications such as including (1)
constant, (2) squared lag profitability, and (2) more lags of prior volatility.
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One difficulty with measuring trading cycles on a daily basis is that a financial institution may have non-zero inventories of
certain bonds at the end of the day. We handle this issue by excluding the ending cycle for these bonds. In addition, we conduct
a robustness exercise by creating weekly trading cycles in the same manner that we constructed daily trading cycles. The signs
and statistical significance of key variables’ weekly estimates are the same as those using the daily trading cycles. The only
exception is interest rate volatility, which is no longer statistically significant at conventional levels. This suggests that our
findings on the trading profitability of different types of financial institutions are robust to extending the trading cycle to a weekly
basis. As anticipated, the magnitudes of coefficient estimates for key variables are larger for the weekly trading cycles than for
the daily trading cycles.
15
To test whether different financial institutions perform differently for different realizations of past
day-trading performance, we introduce the interaction terms LPRF*SDUM and LPRF*GDUM in our
regression analysis. Column (2) presents the regression results. The coefficient on LPRF*SDUM is
significantly negative (p = 0.000). This suggests that the persistence in day-trading profitability is weaker
for large financial institutions than for local small financial institutions. However, the weaker persistence
in day-trading profitability does not apply to global financial institutions.
Balduzzi, Elton, and Green (2001) find that intermediate- and long-term bonds are more
responsive to macroeconomic news than short-term bonds. We therefore also examine whether daytrading profitability behaves differently between short- and long-term bonds. Columns (3) and (4) in
Table 5 report the estimation results for short-term bonds (remaining maturity is one year or less) while
columns (5) and (6) show the results for long-term bonds (remaining maturity is longer than one year).
For the short-term bonds, we find that consistent with the full sample, both SDUM and GDUM have a
significantly positive effect. This indicates that both network size and a global network contribute to daytrading profitability on short-term bonds. Further, the coefficient estimate increases from 0.005 to 0.008
for SDUM and from 0.007 to 0.008 for GDUM, suggesting stronger effects on short-term bonds from size
and a global network. The persistence in day-trading profitability is also stronger for short-term bonds,
whereas the difference in the effect of interest rate volatility is very small. In contrast, for long-term
bonds, neither a network’s size nor the existence of a global network has a significant effect on daytrading profitability. Further, interest rate volatility has a substantially larger negative effect on daytrading profitability for long bonds than for short bonds, consistent with the fact that long-term bonds are
more sensitive to changes in the interest rate, which makes it more difficult to profit from day trading.
Additional results on long-term bonds show that the coefficients on the interaction terms indicate that
large financial institutions again exhibit weaker performance persistence in day-trading profitability than
local small financial institutions, and that a global financial network no longer has any additional effect
on the persistence on day-trading profitability.
16
Next, we test whether media emphasis affects day-trading profitability across different types of
financial institutions, following Morris and Shin (2002) who suggests that bond yields react most to news
emphasized by the media. Specifically, we estimate equation (4) after excluding Turkish scheduled
macroeconomic announcements collected from Bloomberg. The news items include announcements
related to inflation, gross national product, industrial production, current account, trade balance,
unemployment, and capacity utilization. 8 The results, reported in Table 6, show that the coefficient
estimates based on the no-domestic macroeconomic news subsample are qualitatively similar to those
reported in Table 5. In particular, the effects of SDUM and GDUM remain stronger for the short-term
bonds than for the long-term bonds.
A question that remains is whether the differences in day-trading profitability are caused by
differences in the liquidity of bonds traded by different financial institutions. To address this issue, we
examine the day-trading profitability of different financial institutions of bonds with different liquidity.
We partition bonds into liquid and illiquid bonds based on two different measures: (1) the active versus
passive classification used in Section 4.1, with the active bonds classified as liquid bonds; and (2) the
Amihud (2002) liquidity measure used in Section 4.2, with low (below-median) Amihud measure bonds
taken to be the liquid bonds. Table 7 reports the estimation results of equation (4) for different groups of
bonds with different liquidity. Our results suggest that for both liquid and illiquid bonds, the estimated
coefficient on GDUM is positive and statistically significant. The estimates’ magnitudes are also
comparable for liquid and illiquid bonds, and the results suggest similar persistence in day-trading
profitability for bonds with different liquidity.
Finally, just as we test whether our results on pricing are due to differences between foreign
versus domestic financial institutions rather than global versus local networks, we analyze whether our
day-trading profitability results correspond entirely to domestic financial institutions. To accomplish this,
we estimate equation (4) using day trades of 146 local financial institutions and the six Turkish global
We also exclude dates on which Turkey’s sovereign rating is changed by the three principal sovereign rating agencies (Standard
& Poor’s (S&P), Moody’s Investor Services, and Fitch Investor Service). We obtain similar results.
8
17
financial institutions. Table 8 shows that the results based on domestic financial institutions offer similar
conclusions as the results based on both foreign and domestic financial institutions reported in Table 5.
For instance, again, large financial institutions earn higher day-trading profits than local small financial
institutions, and global financial institutions earn higher day-trading profits than other financial
institutions, though the magnitude of outperformance for global financial institutions is slightly smaller
here than with foreign financial institutions included (0.004 percentage points versus 0.007 percentage
points). Also consistent with the findings reported in Table 5, day-trading profitability exhibits weaker
persistence for large financial institutions than for large financial institutions and little persistence for
global financial institutions. Interest rate volatility remains negative and highly significant, indicating that
a more volatile market makes it more difficult to profit from day-trading.
5.
Concluding Remarks
We empirically investigate the role of financial networks in influencing asset prices and trading
performance. Consistent with theoretical studies on the role of communication networks on information
dissemination, we posit that financial institutions with a more extensive financial network (global
financial institutions) can more efficiently acquire and process information than institutions with only a
local network (local financial institutions). We test this hypothesis by examining the information
advantage and associated benefits of global versus local financial institutions in trading government
bonds in an open emerging market.
Using transaction-level data from the Turkish Bonds and Bills Market, we find that global
financial institutions have a greater propensity to trade in the more liquid part of the market than do their
local counterparts, consistent with the need of informed investors to strategically hide trades that might
convey information. In addition, global financial institutions consistently buy (or sell) prior to market
price increases (or decreases), that is, enjoy more favorable pricing in bond trading, relative to local
financial institutions (both large and small), lending support to financial institutions with a global network
having an information advantage. The pricing advantage of global financial institutions disappears
18
quickly, however, suggesting that the information advantage may be related to the order flows in global
financial markets and not to fundamentals. Finally, analysis of day-trading profitability reveals that global
financial institutions consistently and significantly outperform local financial institutions (both large and
small), providing further support to the information advantage hypothesis. These findings are robust to
numerous alternative samples and definitions. Most importantly, they continue to hold when we restrict
our sample to only domestic financial institutions and to bonds with different liquidity, which suggests
that our findings are unlikely to be due to the foreign versus domestic dichotomy or to liquidity.
Overall our empirical analysis provides strong evidence that in a globally integrated financial
market, financial institutions with more extensive global information networks can more efficiently
acquire and process information such as order flows than local financial institutions can, and this
information advantage allows the global financial institutions to enjoy more favorable pricing and
consistently outperform their local counterparts.
19
References
Amihud, Y., 2002. Illiquidity and stock returns: cross-section and time-series effects, Journal of Financial
Markets 5 31–56.
Balduzzi, P., E. Elton, C. Green, 2001. Economic News and Bond Prices: Evidence from the U.S.
Treasury Market, Journal of Financial and Quantitative Analysis 36 523 – 543.
Barber, B.M., Y.-T. Lee, Y.-J. Liu, T. Odean, 2004. Do Individual Day Traders Make Money? Evidence
from Taiwan, Working Paper, The University of California - Berkeley, Berkeley, CA.
Barber, B.M., Y.-T. Lee, Y.-J. Liu, T. Odean, 2006. Just How Much Do Individual Investors Lose by
Trading, Working Paper, The University of California - Berkeley, Berkeley, CA.
Beck, T., A. Demirgüç-Kunt, R. Levine, (2000), A New Database on Financial Development and
Structure, World Bank Economic Review 14 597-605.
Biais, B.M., R. Green, 2005. The Microstructure of the Bond Market in the 20th Century, Working Paper,
Carnegie Mellon University, Pittsburgh, PA.
Chowdhry, B., V. Nanda, 1991. Multimarket trading and market liquidity, Review of Financial Studies 4
483 – 511.
Coval, J.D., T.J. Moskowitz, 2001. The geography of investment: Informed trading and asset prices,
Journal of Political Economy 109 811 – 841.
Duflo, E., E. Saez, 2003. The role of information and social interactions in retirement plan decisions:
Evidence from a randomized experiment, Quarterly Journal of Public Economics 118 815 – 142.
Grinblatt, M., M. Keloharju, 2001. How distance, language and culture influence stockholdings and
trades, Journal of Finance 56 1053 – 1073.
Heckman, J.J., 1979, Sample selection bias as a specification error, Econometrica 47 153 – 162.
20
Hong, H., J.D. Kubik, J.C. Stein, 2004. Social interaction and stock market participation, Journal of
Finance 59 137 – 163.
Hong, H., J.D. Kubik, J.C. Stein, 2005. Thy neighbor’s portfolio: Word-of-mouth effects in the holdings
and trades of money managers, Journal of Finance 60 2801 – 2824.
Ivković, Z., S. Weisbenner, 2005. Local does as local is: Information content of the geography of
individual investors’ common stock investments, Journal of Finance 60 267 – 306.
Ivković, Z., S. Weisbenner, 2007. Information diffusion effects in individual investors’ common stock
purchases: Covet thy neighbors’ investment choices, Review of Financial Studies 20 1327 –
1357.
Massa, M., A. Simonov, 2003. Reputation and interdealer trading: A microstructure analysis of the
Treasury bond market, Journal of Financial Markets 6 99 – 141.
Massa, M., A. Simonov, 2006. Hedging, familiarity and portfolio choice, Review of Financial Studies 19,
633 – 685.
Morris, S., H. Shin, 2002. Social value of public information, American Economic Review 92 1521 –
1534.
Pasquariello, P., C. Vega, 2007. Informed and strategic order flow in the bond markets, Review of
Financial Studies 20 1975 – 2019.
Xia, C., 2007. Communication and Confidence in Financial Networks, Working Paper, University of
Minnesota, Minneapolis, MN.
21
Table 1
Transactions by Financial Institution Type
This table presents summary statistics on transactions by type of financial institution (local small, local large,
and global). Panel A reports the daily mean U.S. dollar trading volume when we focus attention on buyer- and
seller-initiated trades. Panel B presents the mean per-transaction U.S. dollar volume and the total number of
transactions. The sample period is May 1, 2001 to June 15, 2005 (1,039 trading days).
Panel A. Daily Trading Volume (in million U.S. dollars)
Seller
Buyer
Local Large
8.469
60.605
67.383
Global
Local Small
Local Large
Global
Seller Initiated Trades
Local Small
1.939
9.473
9.730
Local Large
8.097
63.297
65.999
Global
Local Small
Local Large
Global
Buyer Initiated Trades
Local Small
1.995
8.578
8.635
8.500
66.414
76.527
9.402
72.974
95.600
Panel B. Transaction Volume and Number
Local Small
Local Large
Global
Mean Per-Transaction Volume (in million U.S. dollars)
Local Small
Local Large
Global
0.078
0.160
0.157
0.346
0.236
0.435
0.248
0.460
0.647
Local Small
Local Large
Global
Number of Transactions
Local Small
Local Large
Global
52,093
107,587
119,444
372,225
80,709
318,635
74,877
315,117
276,230
22
Table 2
Relative Trading Activity in Active and Passive Bonds
This table summarizes active and passive bond transactions by volume in million U.S. dollars (Panel A)
and number of trades (Panel B). We present our measures for transactions involving global financial
institutions on both sides of the trade (buy and sell), global financial institutions on at least one side of the
trade (buy or sell), and local financial institutions on both sides of the trade. The entries corresponding to
“Active” and “Passive” rows represent the volume or number of daily transactions of Active and Passive
bonds, whereas those of “Ratio” refer to the average daily ratio of active to passive bond transactions. An
active bond is the most recently issued bond. p-values are reported for the null hypothesis that the sample
mean of one of the categories involving global financial institutions is greater than the mean of the
category involving only local financial institutions. The percentage of days Ratio>Local/Local Ratio
measures the portion of days that the Global/Global or Global/Local categories are greater than the
Local/Local category.
Panel A: Volume of Transactions
Active
Passive
Local/Local
85.77
76.64
Global/Local
162.30
146.79
Global/Global
96.42
75.78
1.38
0.04
1.38
0.04
2.33
0.30
(0.554)
0.14
53.99
(0.000)
3.10
55.73
Local/Local
315.06
311.84
Global/Local
418.66
341.45
Global/Global
135.98
130.02
1.19
0.03
1.46
0.03
1.34
0.04
(0.000)
14.66
73.53
(0.000)
4.00
52.94
Ratio
Std. error of mean
p-value
t-value
% of days Ratio>Local/Local Ratio
Panel B: Number of Transactions
Active
Passive
Ratio
Std. error of mean
p-value
t-value
% of days Ratio>Local/Local Ratio
23
Table 3
Informativeness of Different Financial Institutions
Panel A reports the regression results of equation (2) for all financial institutions:
ik   0  1 SDUM ik  2 GDUM ik   it The dependent variable, θik, is the coefficient estimated
.
using equation (1) for the transactions of bond k initiated by financial institution i and filled by
trader j (j i) for all trading days. Each column indicates the price change interval used (D = 10, 20,
or 30 minutes). GDUMi (SDUMi) is a dummy variable that takes the value of one if financial
institution i is a global (large) financial institution and zero otherwise. Standard errors are clustered
by bonds. p-values associated with coefficient estimates are provided in brackets. Panel B shows
the results excluding transactions of foreign financial institutions. Panel C presents the results after
excluding transactions involving illiquid bonds. Panel D provides the results using transactions of
local financial institutions involving liquid bonds.
Panel A. All financial institutions
Theta10
0.139
[0.0001]
Theta20
0.104
[0.0204]
Theta30
0.224
[0.0001]
GDUM
0.085
[0.0083]
0.081
[0.0463]
0.065
[0.1277]
Constant
Bond Fixed Effects
Included
Included
Included
Included
Included
Included
5,594
0.41
5,593
0.26
5,594
0.21
SDUM
N
R2
Panel B. Excluding foreign financial institutions
Theta10
0.151
[0.0001]
Theta20
0.114
[0.021]
Theta30
0.240
[0.001]
GDUM
0.121
[0.002]
0.130
[0.013]
0.104
[0.070]
Constant
Bond Fixed Effects
Included
Included
Included
Included
Included
Included
4,855
0.39
4,855
0.25
4,855
0.20
SDUM
N
R2
24
Panel C. All financial institutions using liquid bonds only
Theta10
0.138
[0.001]
Theta20
0.116
[0.010]
Theta30
0.232
[0.001]
GDUM
0.086
[0.007]
0.080
[0.047]
0.058
[0.151]
Constant
Bond Fixed Effects
Included
Included
Included
Included
Included
Included
5,463
0.42
5,463
0.27
5,463
0.22
SDUM
N
R2
Panel D. Excluding foreign financial institutions and illiquid bonds
Theta10
0.149
[0.001]
Theta20
0.126
[0.0113]
Theta30
0.247
[0.0001]
GDUM
0.114
[0.004]
0.124
[0.016]
0.100
[0.076]
Constant
Bond Fixed Effects
Included
Included
Included
Included
Included
Included
4,753
0.40
4,753
0.26
4,753
0.20
SDUM
N
R2
25
Table 4
Day-Trading Profitability
Panel A presents descriptive statistics on percentage day-trading profits (PRF) for global and local financial
institutions. Panel B reports the Pearson chi-square values and their corresponding p-values of the
hypothesis that the median of the first column is equal to the mean of the second column. PRF is calculated
using the methodology described in Section 4.3.
Panel A. Descriptive Statistics
Local Small
Local Large
Global
Obs
16,946
16,880
10,374
All
44,200
Mean
-0.038
-0.027
-0.020
PRF
Std. Dev.
0.118
0.109
0.115
Median
-0.019
-0.013
-0.010
75%
0.007
0.013
0.023
-0.030
0.114
-0.014
0.013
Panel B. Test Statistics of Median Comparisons
Local Small
Local Large
Local Large
Global
Global
Local Small
26
PRF
59.61
[0.000]
18.63
[0.000]
101.96
[0.000]
Table 5
Day-Trading Profits, Prior Profitability, Volatility, and Financial Networks
This table reports the estimates for the Heckman selection model, equation (4), for the entire sample
(Columns 1 and 2), for short-term bonds only (Columns 3 and 4), and for long-term bonds only (Columns 5
and 6). PRFit, the dependent variable, is the day-trading percentage profit of financial institution i on day t,
LPRFit is lagged profitability, and VOLINTt is the standard deviation of the interest rate on the previous day
using 30-minute observations. SDUMi (GDUMi) takes the value of one if the financial institution is a large
(global) financial institution and zero otherwise. p-values associated with coefficient estimates are provided in
brackets and are based on robust standard errors clustered by day. ***, **, and * represent the 0.1%, 1%, and
5% level of significance respectively, of the Inverse Mill’s ratios.
-0.8837
[0.000]
-0.0458
[0.000]
(2)
0.0066
[0.000]
0.0030
[0.029]
0.1186
[0.000]
-0.0162
[0.349]
-0.0665
[0.000]
-0.8732
[0.000]
-0.0444
[0.000]
Participation Equation
LPRF
-0.4293
[0.000]
VOLINT
57.0596
[0.000]
Inv. Mill Ratio
Rho
N
Censored
Uncensored
GDUM
SDUM
LPRF
(1)
0.0068
[0.000]
0.0051
[0.000]
0.0700
[0.000]
LPRF *GDUM
LPRF *SDUM
VOLINT
Constant
0.026***
0.253
44,200
11,065
33,135
(3)
0.0079
[0.000]
0.0081
[0.000]
0.0813
[0.000]
-0.8710
[0.000]
-0.0444
[0.000]
(4)
0.0073
[0.000]
0.0055
[0.002]
0.1384
[0.000]
-0.0380
[0.082]
-0.0713
[0.001]
-0.8693
[0.000]
-0.0425
[0.000]
-2.3909
[0.000]
-0.0381
[0.000]
(6)
0.0035
[0.086]
0.0011
[0.555]
0.1021
[0.000]
0.0339
[0.146]
-0.0733
[0.003]
-2.3937
[0.005]
-0.0372
[0.000]
-0.4295
[0.000]
57.0753
[0.000]
-0.2342
[0.000]
40.5614
[0.000]
-0.2344
[0.000]
40.5730
[0.000]
-0.0355
[0.556]
123.6571
[0.000]
-0.0352
[0.559]
123.6822
[0.000]
0.026***
0.256
44,200
11,065
33,135
0.019***
0.182
32,321
10,915
21,406
0.020***
0.185
32,321
10,915
21,406
0.027***
0.273
22,319
6,363
15,956
0.027***
0.269
22,319
6,363
15,956
27
(5)
0.0027
[0.162]
0.0029
[0.120]
0.0607
[0.000]
Table 6
Day-Trading Profits, Prior Profitability, Volatility, and Financial Networks
Excluding Turkish Macro News Announcement Days
This table reports the estimates for the Heckman selection model, equation (4), after excluding the
Turkish macroeconomic news announcement days, for all bonds (Columns 1 and 2), for short-term bonds
only (Columns 3 and 4), and for long-term bonds only (Columns 5 and 6). PRFit, the dependent variable,
is the day-trading percentage profit of financial institution i on day t, LPRFit is lagged profitability, and
VOLINTt is the standard deviation of the interest rate on the previous day using 30-minute observations.
SDUMi (GDUMi) takes the value of one if the financial institution is a large (global) financial institution
and zero otherwise. p-values associated with coefficient estimates are provided in brackets and are based
on robust standard errors clustered by day. ***, **, and * represent the 0.1%, 1%, and 5% level of
significance respectively, of the Inverse Mill’s ratios.
-1.2653
[0.000]
Constant
-0.0374
[0.000]
Participation Equation
LPRF
-0.1157
[0.035]
VOLINT
26.7774
[0.000]
(2)
0.0054
[0.003]
0.0041
[0.013]
0.1177
[0.000]
-0.0242
[0.252]
-0.0555
[0.008]
-1.2556
[0.000]
-0.0360
[0.000]
Inv. Mill Ratio
Rho
N
Censored
Uncensored
GDUM
SDUM
LPRF
(1)
0.0058
[0.000]
0.0059
[0.000]
0.0742
[0.000]
LPRF *GDUM
LPRF *SDUM
VOLINT
0.014***
0.131
36,483
14,179
22,304
(3)
0.0071
[0.000]
0.0090
[0.000]
0.0780
[0.000]
-1.1068
[0.416]
-0.0377
[0.000]
(4)
0.0060
[0.007]
0.0071
[0.001]
0.1222
[0.000]
-0.0525
[0.041]
-0.0474
[0.073]
-1.1065
[0.000]
-0.0359
[0.000]
-3.2845
[0.000]
-0.0240
[0.000]
(6)
0.0023
[0.366]
0.0028
[0.227]
0.1024
[0.000]
0.0638
[0.021]
-0.0832
[0.005]
-3.3229
[0.000]
-0.0231
[0.000]
-0.1157
[0.035]
26.7800
[0.000]
-0.0475
[0.416]
17.6432
[0.000]
-0.0475
[0.416]
17.6440
[0.000]
0.2020
[0.007]
37.6064
[0.000]
0.2020
[0.007]
37.6092
[0.000]
0.014***
0.132
36,483
14,179
22,304
0.008***
0.0777
27,054
12,106
14,948
0.008***
0.079
27,054
12,106
14,948
0. 007**
0.0727
17,816
7,746
10,070
0.007**
0.074
17,816
7,746
10,070
28
(5)
0.0009
[0.731]
0.0047
[0.036]
0.0640
[0.000]
Table 7
Day-Trading Profitability and Liquidity of Bonds
This table reports the estimates for the Heckman selection model, equation (4), using liquid and illiquid
bonds. The column heading (Active and Amihud or Passive and Amihud) represents the liquidity measure
used to separate bonds. PRFit, the dependent variable, is the day-trading percentage profit of financial
institution i on day t, LPRFit is lagged profitability, and VOLINTt is the standard deviation of the interest rate
on the previous day using 30-minute observations. SDUMi (GDUMi) takes the value of one if the financial
institution is a large (global) financial institution and zero otherwise. p-values associated with coefficient
estimates are provided in brackets and are based on robust standard errors clustered by day. ***, **, and *
represent the 0.1%, 1%, and 5% level of significance respectively, of the Inverse Mill’s ratios.
GDUM
SDUM
LPRF
LPRF *GDUM
LPRF *SDUM
VOLINT
Constant
Participation Equation
LPRF
VOLINT
Inv. Mill Ratio
Rho
N
Censored
Uncensored
Liquid Bonds
Active
Amihud
(1)
(2)
0.0059
0.0062
[0.001]
[0.000]
0.0140
0.0004
[0.000]
[0.775]
0.1751
0.1148
[0.000]
[0.000]
-0.0170
-0.0122
[0.452]
[0.524]
-0.0852
-0.0629
[0.000]
[0.001]
-2.0174
-1.5319
[0.000]
[0.000]
0.0491
-0.0350
[0.000]
[0.000]
Illiquid Bonds
Passive
Amihud
(3)
(4)
0.0049
0.0080
[0.021]
[0.004]
-0.0061
0.0225
[0.001]
[0.000]
0.0917
0.1388
[0.000]
[0.000]
-0.0273
-0.0351
[0.271]
[0.219]
-0.0440
-0.0808
[0.055]
[0.008]
-1.9046
1.7481
[0.000]
[0.087]
-0.0178
0.0538
[0.000]
[0.000]
-0.5611
[0.000]
23.1810
[0.000]
-0.1775
[0.000]
43.2030
[0.000]
0.1909
[0.000]
18.3999
[0.000]
-0.0965
[0.179]
-19.4802
[0.000]
0.113***
-0.832
29,726
10,416
19,310
0.022***
0.212
38,558
11,715
26,843
0.009***
0.089
30,256
11,884
18,372
-0.132*
-0.839
18,456
9,629
8,827
29
Table 8
Day-Trading Profit Analysis Excluding Foreign Financial Institutions
This table reports the estimates for the Heckman selection model, equation (4), excluding foreign
financial institutions. PRFit, the dependent variable, is the day-trading percentage profit of financial
institution i on day t, LPRFit is lagged profitability, and VOLINTt is the standard deviation of the interest
rate on the previous day using 30-minute observations. SDUMi (GDUMi) takes the value of one if the
financial institution is a large (global) financial institution and zero otherwise. p-values associated with
coefficient estimates are provided in brackets and are based on robust standard errors clustered by day.
***, **, and * represent the 0.1%, 1%, and 5% level of significance respectively, of the Inverse Mill’s
ratios.
GDUM
SDUM
LPRF
(1)
0.0038
[0.001]
0.0050
[0.001]
0.0774
[0.000]
-0.7613
[0.001]
-0.0445
[0.000]
(2)
0.0035
[0.004]
0.0029
[0.058]
0.1201
[0.000]
-0.0265
[0.225]
-0.0649
[0.000]
-0.7523
[0.001]
-0.0433
[0.000]
-0.4779
[0.000]
57.6870
[0.000]
-0.4779
[0.000]
57.7021
[0.000]
0.025***
0.241
38,574
9,461
29,113
0.025***
0.244
38,574
9,461
29,113
LPRF *GDUM
LPRF *SDUM
VOLINT
Constant
Participation Equation
LPRF
VOLINT
Inv. Mill Ratio
Rho
N
Censored
Uncensored
30