Financial Networks and Trading in Bond Markets

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 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 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 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 2.1 The Bonds and Bills Market The Market 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 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 Turkish lira (TL) for a 100,000 TL face value bond, with 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 not learn the identities of the parties, but they 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 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 Data and Summary Statistics 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 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 market These financial institutions have branch and liaison offices not only in New York and Europe but also in Bahrain, Tokyo, and Moscow.3 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 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 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 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 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% 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 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 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 with a standard deviation of TL 0.11 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 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 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: PikD    ik Tikj   k I k   ik , (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 D D change ( Pik ) of a trade in bond k initiated by investor i For Pik to be defined, there must be a 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 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 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 16 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 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 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 financial institutions Table 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 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 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 17 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 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 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 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 18 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 References Amihud, Y., 2002 Illiquidity and stock returns: cross-section and time-series effects, 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Journal of Financial Markets 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 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 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 Informativeness of Different Financial Institutions Panel A reports the regression results of equation (2) for all financial institutions:  ik    1 SDUM ik  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 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 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 and 2), for short-term bonds only (Columns and 4), and for long-term bonds only (Columns 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 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 and 2), for short-term bonds only (Columns and 4), and for long-term bonds only (Columns 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 GDUM SDUM LPRF (1) 0.0058 [0.000] 0.0059 [0.000] 0.0742 [0.000] LPRF *GDUM LPRF *SDUM VOLINT -1.2653 [0.000] Constant -0.0374 [0.000] Participation Equation LPRF -0.1157 [0.035] VOLINT 26.7774 [0.000] Inv Mill Ratio Rho N Censored Uncensored 0.014** * 0.131 36,483 14,179 22,304 (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] (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 14,948 007** 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 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 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 ... foreign and domestic financial institutions reported in Table For instance, again, large financial institutions earn higher day -trading profits than local small financial institutions, and global financial. .. 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... 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