Corporate Bond Trading by Retail Investors in a Limit Order. Book Exchange

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1 Corporate Bond Trading by Retail Investors in a Limit Order Book Exchange by Menachem (Meni) Abudy 1 and Avi Wohl 2 This version: May Graduate School of Business Administration, Bar-Ilan University, Ramat Gan, Israel, phone: , 2 Recanati Business School, Tel Aviv University, Tel Aviv, Israel, phone: , e- mail: We thank Azi Ben-Rephael, Robby Goldenberg, Marco Pagano, Dimitri Vayanos, Kumar Venkataraman and participants in the Summer Finance Conference IDC Herzliya for helpful comments and suggestions. We thank the Tel Aviv Stock Exchange for providing the trading data. We thank valuation.co.il and Eran Ben-Horin for providing the corporate bonds data.

2 Corporate Bond Trading by Retail Investors in a Limit Order Book Exchange Abstract Corporate bonds are traded at the Tel Aviv Stock Exchange like stocks, by a limit order book, with high investor participation and narrower spreads relative to the stock market. Using data with trader identification we focus on small retail investors. Their average execution costs (0.067%), which are lower than for stocks, are lower than half of the quoted spreads because they tend to act as "makers" or delay "taking" when spreads are high. These findings are in contrast to U.S. corporate bond OTC markets where corporate bond spreads are higher, retail participation is very low and trading costs are high. 2

3 1. Introduction Corporate bonds (hereafter c-bonds) are mostly traded worldwide in over-thecounter (OTC) markets while stocks are mostly traded by an open limit order book on exchanges. The c-bond OTC markets are characterized by large market share of institutional investors and very low participation of retail investors, who pay high trading costs. 1 Several natural questions arise: (a) Are c-bonds fundamentally not suitable for trading in an open limit order book exchange? (b) Are c-bonds fundamentally less liquid than stocks? (c) Would retail investors be as reluctant to trade c-bonds in an exchange as they are to trade them in OTC markets? (d) If retail investors participate in c-bond trading via an exchange, are their trading costs as high as they are in OTC markets? Biais and Green (2007) find that bond trading was quite active on the NYSE until the 1940s and the trading costs of retail investors were lower than today. They conclude that there are multiple equilibria regarding securities trading, and that the bond market in the U.S. reached an inefficient equilibrium of OTC dominance. 2 This paper investigates the case of the Tel Aviv Stock Exchange (hereafter TASE), where government bonds and c-bonds have been traded for many years by the same open limit order book system as stocks. We find an active and liquid c-bond market, with spreads that are much narrower than for TASE stocks. Focusing on retail 1 See Biais and Green (2007) and Section 1 in Schultz (2001). 2 Hendershott and Madhavan (2015) show the viability of a system called MarketAxess that allows an investor to query multiple dealers electronically. An ending time (5-20 minutes) is specified for the auction and only at the end of the auction does the investor review the dealer responses and select the best quote. Hendershott and Madhavan (2015) find that the trading costs through this system are considerable lower than for a regular dealer search. 3

4 investor trading in c-bonds, we find high retail investor participation (much higher than in the U.S.) with low trading costs (much lower than in the U.S.). We rely on a proprietary database that includes all TASE transactions and trader identification (the broker's identity and the client's account number in the broker's records) for both sides of each transaction. 3 It includes all the transactions executed on the TASE (including stocks, government bonds, c-bonds, ETNs and warrants, but excluding options). Most of the instruments (except for some stocks and a few government bonds) are traded only on the TASE. To the best of our knowledge, there are no Israeli corporate bonds that are publicly traded abroad. That is, there are no competing exchanges, dark pools, etc. Our sample period is We find comparable trading volume characteristics for the TASE c-bonds and stocks: the daily average exchange trading volume in c-bonds (stocks) is 781 (958) million NIS (NIS New Israeli Shekels), with only 9.5% (10.0%) of the NIS trading volume of c-bonds (stocks) being done outside the exchange by negotiated deals. 4,5 The turnover of c-bonds is higher than that of the stocks 55.5% relative to 30.4%. 6 The number of accounts participating in the c-bond market is 206,820, which is comparable to the number in the stock market: 246,672. Next, we compare the bid-ask spreads in the c-bond and stock markets. We focus on a sample of 216 c-bonds that were traded on at least 95% of the 245 trading days of our sample period (2012) and a comparison sample of 116 stocks filtered by 3 It is possible that a trader trades through different exchange members or through different accounts of a given exchange member. 4 See The TASE get the information about off-exchange transactions from its members. In this report stock volume (in the exchange and off exchange) also includes warrants but their trading volume is very small. 5 In 2012, one U.S. dollar was equal to 3.84 NIS on average. 6 Part of the difference can be explained by the fact that about 41% of the aggregate stock value is held by large block-holders (more than 5% of firm stocks) that trade them infrequently. For block-holder holdings see 4

5 the same criteria in Abudy and Wohl (2015). 7 We find that the average quoted spread (and also the effective spread) in the c-bond market is relatively low 0.18% (0.15%) and much smaller than for the rest of the stock sample 0.62% (0.51%). Weighted by the security's NIS volume, the quoted spread (effective spread) in the c-bond market is also narrower than in the stock market 0.108% (0.091%) relative to 0.159% (0.127%). Looking at a subsample of stocks and c-bonds that were issued by the same firm, we also find that the c-bond spreads are significantly narrower than the stock spreads. Therefore, we conclude that c-bonds are more liquid than stocks as expected by their low variability (and probably the difference in information asymmetry). Moreover, we find than even after controlling for the security's standard deviation of return (hereafter STD) c-bonds have narrower quoted and effective spreads than stocks. Altogether, this volume and spread evidence seems to indicate that c-bonds are suitable for trading through a limit order book and that in this case they are more liquid than stocks. Since the main difference between OTC and exchange trading is retail participation and trading costs we are interested in retail investor trading. Because our database does not include trader types, we rely on investors' trading volumes for their classification. We focus on "low-volume" investors with an annual trading volume of less than 1 million NIS (roughly $260,000 in 2012) in all the securities that are traded on the TASE (including stocks, government bonds, c-bonds, ETNs and warrants, but not options, hereafter "excluding options"). 8 These low-volume investors traded a small number of securities (5.1 on average) infrequently during 2012 (5.5 trading days on average) and they are probably small retail investors (denoted hereafter SRI). Their aggregate trading volume is about 12.2% of the total aggregate volume of c-bonds. It 7 Abudy and Wohl (2015) investigate stock trading costs of small retail stock investors. 8 Options are excluded for technical reasons. 5

6 should be noted that this fraction is larger than the trading volume share of this investor group in stocks 8.7%. We choose the very low cutoff of an annual volume of 1 million NIS to ensure that these are indeed retail investors. However, investors with an annual trading volume in all TASE securities of 1-2 million NIS are probably retail investors as well. Adding this group to the SRI group, we get 17.6% of the total c-bond volume, which is probably still an underestimation of the retail share in trading. 9 For comparison, Ronen and Zhou (2013) state that in the U.S. retail traders account for only 1.8% of the trading dollar volume of c-bonds. Therefore it seems that the open limit order book is indeed a trading system that attracts c-bond retail investors. While the bid-ask spread measures can serve as an initial proxy for the execution costs (hereafter EC) of the SRI that are the subject of our investigation, our database allows us more direct observation. 10 We estimate the EC of each transaction by comparing the transaction price to the trading day's closing price: a price increase means a positive (negative) cost for the seller (buyer) and the opposite for a price decrease. The price changes are adjusted to contemporaneous changes in an index of all the c-bonds included in the sample (the sum of EC across all traders is zero by definition). This EC definition is consistent with the fundamental microstructure theoretical modeling [for example Kyle (1985), Glosten and Milgrom (1985), Hellwig (1980) and many subsequent papers] where the trading gains or losses are measured against a future realization of asset value The NIS fraction of the retail investors' transactions may be smaller because in some cases both sides of the transactions are retail (and in such a case the transaction is counted twice). 10 Other costs, which are also not the focus of this paper, are explicit trading costs such as commissions, broker fees, and transaction taxes. 11 We prefer to compare the transaction price to a future price rather than to a past price because, as noted by Harris (2003), past prices provide biased estimates when traders use momentum or contrarian trading strategies. 6

7 When we group the investors according to their annual trading volume in all TASE securities we find as expected that EC are higher for lower volume investors. For the lowest volume group of less than 1 million NIS (the SRIs) the average EC are 0.067% (see Figure 1). 12 For comparison, in the c-bond OTC market of the U.S. the comparable costs are more than 10 times higher. 13 Hence, trading through open limit book is characterized by low EC for SRIs even in a small market such as the TASE. In only 9.5% of the cases do we find both buying and selling of a certain c-bond by the same retail investor during This means that the typical holding period of our investor sample is quite long, mitigating the welfare effect of these (already small) EC. An intuitive measure for EC is half of the average quoted spread, which reflects the average cost for an investor who trades immediately after arriving in the market. The average EC (0.067%) is significantly smaller than half of the average for the c-bond's quoted spread, which is 0.110%, and less than half of the effective spread (for each transaction we look at the relevant c-bond effective spread): 0.094%. There are two reasons for these differences. We find that in roughly a third of the cases the SRI are "makers" rather than "takers". In these cases they incur positive EC because of the adverse selection their limit orders face, though these costs are small on average (0.046%), lowering the average of the EC. Moreover, we find that the likelihood of the SRI being "makers" rather than "takers" increases with the c-bond's 12 Our basic unit of analysis is corporate bond-investor-day. That is, the day's EC is the weighted average cost of all the transactions on that day by this investor. 13 Edwards, Harris and Piwowar (2007) estimate a cost of 0.75% for $5,000 transactions in the corporate bond market. Goldstein, Hotchkiss and Sirri (2006) find the half dealer markup for corporate bonds to be 1.18% (1.20%) [see their Table 6]. Hendershott and Madahvan (2015) estimate trading costs to be 0.88% (1.22%) for $1-100K transactions for investment grade (high yield) bonds. All these papers find a negative relation between transaction size and execution costs. The costs they estimate reflect a reduction in transaction costs following an implementation of post-trade transparency by the TRACE system in 2002 [see Bessembinder, Maxwell and Venkataraman (2006), Edwards, Harris and Piwowar (2007) and Golstein, Hotchkiss and Sirri (2006)]. The execution costs in the municipal bond market are even higher than in the c-bond market (see Harris and Piwowar, 2006 and Green, Hollifield and Schurhoff, 2007). 7

8 average quoted spread. That is, the larger the spread the greater the extent to which the SRI lower their EC by using more limits orders. Acting as "makers" is of course possible in an open limit order book such as that at the TASE but it is not possible in OTC markets. When SRI act as "takers" their transaction half spread is narrower than the corresponding c-bond quoted half-spread (0.080% vs %) meaning that SRI tend to act as "takers" when spreads are narrower than average and tend to delay their trade when spreads are larger than average. Altogether, it seems that even some of the SRI, who trade very infrequently, delay their trade or compete with existing quotes if spreads are large. Therefore, half the quoted spread is an upwardly biased estimate of the EC of SRI. Of course, using limit orders has disadvantages that are not captured by the EC (the risk of non-execution, the risk of price change until execution and the time needed for monitoring the order), but because investors use limit orders by choice they evidently find suffering the disadvantages preferable to paying the spread by "taking". The average EC of SRI "takers", 0.079%, are lower than the average EC for the same investor group in TASE stocks (as reported by Abudy and Wohl 2015) 0.172%. Controlling for the security's STD and investor characteristics we find that EC of SRI are lower for c-bonds than for stocks but the difference shrinks as the security's STD increases. 14 By and large, we do not find economically significant differences in SRI's EC for c-bonds and stocks after controlling for the security's STD. We find that the EC of SRI sellers are lower than the EC of buyers. This result is opposite to the finding of Abudy and Wohl (2015) regarding SRI stock trading and is quite surprising. There is a notion that because of short selling limitations/costs buys are more information motivated than sells and therefore informationless traders 14 The EC of SRI "makers" are larger in c-bonds than in stocks (after controlling for the security's return and investor characteristics), consistent with the lower spreads in c-bonds. 8

9 incur more trading costs as sellers. 15 A possible explanation for our opposite result is that institutional c-bond investors accumulate information while they hold their bonds and therefore their sells are more information motivated than their buys. Studies on the U.S. c-bond market find a negative relation between transaction size and EC (see footnote 31), reflecting the higher EC of retail investors. In this study, where we focus on SRI transactions, we find a positive relation between SRI transaction size and EC. C-bond characteristics that are positively related to the EC of takers and makers are STD and duration. As for the log of daily trading volume, we find a positive relation with the EC of "takers" (as in the stock case) but a negative relation with the EC of "makers". A c-bond's quoted half bid-ask spread (or effective spread) is positively related to the EC of "takers" but not related to the EC of "makers". Since in the cross-section of bonds the quoted half bid-ask spread (or the effective spread) is well explained by STD, the log of daily trading volume and the c-bond's duration (the average R 2 across the daily regressions is =17..3), it does not contribute much to explaining takers' EC. That is, daily data are practically enough to explain crosssectional differences in c-bond illiquidity. The rest of the paper is organized as follows. Section 2 describes the market and the data. Section 3 describes the trading volume and investor participation in the c-bond market and compares them to the stock market. Section 4 compares the bidask spread measures of corporate bonds and stocks. Section 5 describes the small retail investors' participation in the markets for c-bonds and stocks. Section 6 analyzes the execution costs of small retail investors. Section 7 concludes. 15 See Saar (2001) for a theoretical model that explains the permanent price impact asymmetry between buyer-initiated and seller-initiated block trades. See Ranaldo (2004) and Kalay, Sade and Wohl (2004) for empirical evidence suggesting that buy orders tend to be more information motivated than sell orders. Table 1 of Barber and Odean (2000) shows different execution costs (denoted there as "spread") for retail buyers and retail sellers. 9

10 2. Market Description and Data 2.1 Market description The Tel Aviv Stock Exchange (TASE) is the only exchange in Israel. All publicly traded securities are traded through this exchange: stocks, government bonds, c-bonds, warrants, convertibles, ETNs (substitutes for ETFs) and various types of options. All the instruments are traded by continuous limit order book trading. In addition all instruments except options (including stocks and bonds) have opening and closing auction trading sessions. 16 The opening stage of the trade in c-bonds takes place between 9:30 and 9:35, the exact time for each bond being arbitrary. The pre-opening stage, where orders are posted, starts at 9:00 am. The closing call auction stage takes place at 16:25, with the pre-closing stage starting at 16:15. In all stages the limit orders are executed by price and time priority, and there are no hidden limit orders. 17 The continuous bilateral stage is conducted throughout the trading day between the opening and the closing sessions. A minimum amount of 10,000 NIS (New Israeli Shekels), for all c-bonds applies for orders placed during the continuous stage. 18 There are 27 exchange members at the TASE. These members are banks and brokerage firms through which traders can submit orders to all the securities that are traded on the TASE. The exchange members provide their clients with online access to the exchange without any human intervention: the clients can see the status of the order book online and submit orders, which are transmitted immediately (after computerized checks) to the exchange. All the traders can observe the three best bids 16 Very illiquid securities are traded by daily auctions only. 17 TASE also allows "fill or kill" and "immediate or kill" orders, but they are rarely used. Hidden orders were introduced in October 2014 but this is not relevant for our sample period For stocks the minimum is 2,000 NIS (for stocks in TA-25 index the minimum is 5,000 NIS). 10

11 and offers of each side of the market in all securities. The identity of the member firms and traders submitting orders is unknown to the market participants. The tick size on the TASE is a function of each security's market price. 19 The major c-bonds indices are the Tel-Bond 20 and the Tel-Bond 60. These indices include the 20 and 60 fixed-interest and CPI-linked c-bonds with the highest market capitalization matching certain criteria, where the Tel-Bond 60 index includes the Tel-Bond 20 (the Tel-Bond 40 is the index that includes the c-bonds that are traded on the Tel-Bond 60 and not on the Tel-Bond 20). The indices are updated twice a year (in the middle of April and the middle of October). 2.2 The TASE database We use a unique and proprietary database of the TASE that includes order records with unique trader identification, and transaction records in which both sides of the transaction are identified. The identification includes the identity of the exchange member and a code that identifies the trader within the member's list of traders. The database does not include the trader's classification (for example retail, institutional etc.). In addition, the database documents the transaction time, whether the transaction was "buyer initiated" or "seller initiated", and the trading stage in which the transaction was executed. We also use data from valuation.co.il, which collects daily data on corporate bonds traded on the TASE. This data includes the c-bond's yield to maturity, yield spread, duration, credit rating and whether the c-bond is CPI-linked. 19 In corporate bonds, for prices below or equal to NIS the tick size is NIS; for prices above NIS the tick size is 0.01 NIS. The mean average tick size of our corporate bond sample (which is detailed in Section 4) is 0.01% and the maximum is 0.03%. 11

12 3. Corporate Bond Trading and Investor Participation Table 1 provides summary statistics of the trading of different groups of securities during The daily average NIS trading volume on the exchange in c- bonds (stocks) is 781 (958) million NIS, with only 9.5% (10.0%) of the NIS trading volume of c-bonds (stocks) being done outside the exchange by negotiated deals (see footnote 4). So we find a minor trading volume outside the exchange in c-bonds, roughly the same as in stocks. The aggregate market cap of the c-bonds (stocks) traded on the TASE was on average 344 (771) billion NIS. 20 Based on the volume and market value we estimate the annual turnover on the exchange: number of trading days during 2012 * market cap average daily NIS volume The turnover of c-bonds is higher than that of stocks 55.5% relative to 30.4%. The difference can be partly explained by the fact that about 41% of the aggregate stock value is held by large block-holders (a block-holder is defined by the TASE as an entity that holds more than 5% of a firm's stocks) that trade them infrequently. Taking the extreme assumption that block-holders never trade on the exchange we reach an estimation for the turnover of other investors of 30.4% (1 41%) 52%. This estimation is still lower than the estimation for the turnover of c-bonds. Another market characteristic is the number of participants. The number of accounts participated in the c-bond market is 206,820, which is comparable to the corresponding number in the market for stocks: 246,672. [INSERT TABLE 1 ABOUT HERE] 20 These are average of market values on and

13 All these pieces of evidence are consistent with the notion that c-bonds are suitable for trading in an exchange in the same way as stocks but to complete the comparison we compare, in the following section, bid-ask spread measures of c-bonds and stocks. 4. Comparing Bid-Ask Spread Measures of Corporate Bonds and Stocks To compare bid-ask measures we focus on a sample of c-bonds and stocks that were traded on the TASE on at least 95% of the 245 trading days of The stock sample is the sample that Abudy and Wohl (2015) use to analyze retail stock trading at the TASE. As in this paper, Abudy and Wohl (2015) require that the security was traded on at least 95% of the trading days of Their sample consists of 116 stocks. 21 We found 216 c-bonds that met this requirement, of which only 33 had less than 245 trading days: 18 bonds with 244 trading days, 13 bonds with 243 trading days, one bond with 241 trading days and one bond with 234 trading days. The trading volume of these 216 c-bonds was 95.3% of the total trading volume of the 570 different c-bonds traded on the TASE throughout Most of the c-bonds in our sample are investment-grade: at the end of 2012, only 13 bonds (out of the 216 c-bonds) were rated speculative-grade (below BBB) and only 10 bonds were not rated. 23 Most of the bonds in the sample (149) are CIP-linked and at the end of 2012, 56 bonds were included in the main c-bond index of the TASE the Tel-Bond Abudy and Wohl (2015) focus on stocks that are traded only on the TASE (that is, stocks that are not dually listed) and they filtered out two stocks with an average tick size larger than 0.5%. 22 In 2012, 661 c-bonds were traded on the TASE. However, only 570 were traded the entire year: 36 bonds were issued during the year and 55 bonds matured during the year (our sample consists of 85.1% the total NIS volume of all 661 bonds). 23 Israel has two rating agencies - Maalot and Midroog. These rating agencies are subsidiaries of global rating agencies: Maalot is a subsidiary of S&P and Midroog a subsidiary of Moody's. The rating in Israel is local, meaning that the firms are rated relative to other Israeli firms and do not take into account the country risk. 13

14 Table 2 reports the summary statistics of the c-bond sample during According to Panel A of Table 2, the c-bonds have a mean (median) daily return of 0.02% (0.03%) and a mean (median) daily STD of 0.70% (0.44%). Their average (median) daily volume is 2.64 (1.76) million NIS. [INSERT TABLE 2 ABOUT HERE] An intuitive measure of liquidity is half the average quoted spread, the intuition being that this is the average cost of an investor who trades a small quantity immediately after arriving in the market. Thus, it may be seen as reflecting the EC of unsophisticated small traders presumably our SRI. An additional intuitive measure is half the effective spread which compares the prices of the market and marketablelimit orders to the mid-quote prevailing before the transactions. At the TASE a transaction cannot occur inside the spread but the effective spread may be systematically different than the quoted bid-ask spread. There are two possible reasons for this: a. Transactions tend to occur where bid-ask spreads are relatively narrow. b. Large quantity orders that "walk on the book", that is, are executed against different layers of the limit order book. The advantage of investigating these measures is that they do not rely on our SRI definition and they can give an assessment of the EC of various types of traders. The quoted half bid-ask spread (QHBAS) and the half effective spread (HES), are calculated as follows: Quoted half bid-ask spread (QHBAS): The QHBAS is the ratio of the quoted bid-ask spread and the bid-ask midpoint: 14

15 HBAS i, j, t Ask Bid i, j, t i, j, t, Mid i, j, t where Mid i,j,t =(Ask i,j,t + Bid i,j,t )/2, Ask i,j,t and Bid i,j,t are ask and bid quotes prevailing on day i for c-bond j at hour t. For each c-bond on each trading day, we calculate the bid-ask spread at each hour during the continuous trading. We obtain seven daily HBASi, j, t measures, from 10:00 until 16:00. The i, j, t HBAS is winsorized in the rare cases (0.15%) where the bid or ask are missing or it is greater than 10%. We average the observations over each corporate bond day and then average all daily averages of each c-bond to get the measure of c-bond j: HBAS j Half effective spread (HES): the half effective spread for each transaction is measured as the absolute value of the difference between the transaction price and the quote midpoint prior to the transaction, divided by the quote midpoint. Formally, the HES on day i of c-bond j on transaction t is calculated as: HES i, j, t price Mid i, j, t i, j, t Mid i, j, t The HES i,j,t of the transaction is winsorized in the rare cases where it is greater than 10% or in cases where there is no valid bid-ask spread (0.0083% of the sample). The daily average for each c-bond HES i,j is calculated as the average of the half effective spreads during the continuous trading stage. If there are no transactions during the continuous stage of the trading day, the observation is omitted (0.102% of the sample). HES j, the half effective spread of c-bond j, is the average of HES i,j. As can be seen in Panel A of Table 2, QHBAS (HES) ranges from 0.03% to 1.26% (0.02% to 1.02%), the average being 0.18% (0.15%) and the median 0.13% (0.11%). The QHBAS and HES of c-bonds are considerably lower than the comparable measures in the stock sample of Abudy and Wohl (2015) reported in their Table 1: 15

16 QHBAS (HES) ranges from 0.07% to 2.71% (0.04% to 2.06%), the average being 0.62% (0.51%) and the median 0.52% (0.40%). The t-statistic for the difference between the average of QHBAS (HES) in the c-bond and stock series is highly significant: (11.96). Weighted by the security's NIS volume, the quoted spread (effective spread) in the c-bond market is also lower than in the stock market 0.108% (0.091%) relative to 0.159% (0.127%). To further demonstrate that on the TASE c-bonds are more liquid than stocks, we focus on a sub-sample of c-bonds and stocks that were issued by the same firm. As specified above, the total sample includes 216 c-bonds and 116 stocks issued by 135 firms. Of this sample, 38 firms have both stocks and bonds: 38 stocks and 81 bonds. 24 We calculate the average QHBAS and HES for the c-bonds of each firm and compare them to the firm's stock QHBAS and HES, respectively. The average QHBAS of the c- bonds is 0.17% and of stocks it is 0.38%. Therefore the average of the series of the differences is -0.21%. This average is statistically significant different than zero (tstatistic of -4.82). The effective spread HES follows a similar pattern: the c-bond's (stock's) HES is 0.15% (0.33%) with a t-statistic of 4.45 for the series of differences. Panel A (Panel B) of Figure 1 presents in a scatter plot the 38 pairs of QHBAS (HES). In 35 (34) out of the 38 cases the points are below the 45 0 line, indicating that the average QHBAS (HES) of a firm's c-bonds are lower than the corresponding figures for the firm's stocks. [INSERT FIGURE 1 ABOUT HERE] 24 In some cases a firm that has c-bonds is not included in the above-mentioned sample of 135 firms because the stock is dual-listed or because the bonds were not traded throughout 2012 (they were issued after the beginning of the year or expired before the end of the year) or because the stock or the c-bond were not traded on more than the 95% of the trading days of

17 The U.S. c-bond market is an OTC market and its quotes are only indicative [see Foucault, Pagano and Roell (2013)]. Therefore, there are no perfectly comparable U.S. bid-ask spread measures. The closest comparisons are estimates of trading costs for small size transaction. These estimates around 1% (see footnote 13) are very high relative to the TASE c-bond market and also relative to U.S. equity market. According to Biais and Green (2007): "That bonds command larger transactions costs than stocks, at least for small and medium sized trades, is surprising. Risk is one of the main components of the cost of supplying liquidity. Bonds are less risky than stocks. They should have lower spreads." In the TASE case, where the trading mechanism of c-bonds and stocks is identical, we find indeed that the spreads of c- bonds are lower than the spreads of stocks. This finding gives rise to the conjecture that the opposite situation in the U.S. is due to the difference in trading mechanisms for c-bonds and stocks. As Biais and Green (2007) mention, the spreads are related to the security's risk. Indeed in the cross-section of c-bonds (stocks) the correlation between QHBAS and STD is 0.64 (0.14) and the correlation between HES and STD is 0.66 (0.19). The question is whether conditional on their STD the c-bonds have larger spread measures. We find that even after controlling for STD c-bonds have narrower quoted and effective spreads than stocks. As an illustration let us look at Panel A of Figure 2, which depicts the QHBAS of the securities versus their STD. Because there are only two stocks with STD less than 1% and only one c-bond with STD greater than 4% we zoom in to the range of STD between 1% and 4% (see Panel B of Figure 2). It can be seen that the square points of the c-bonds tend to be below triangular points of the stocks. The picture for HES is qualitatively the same. 17

18 [INSERT FIGURE 2 ABOUT HERE] Formally, we run regressions in the cross-section of 332 securities (c-bonds and stocks) where a security's QHBAS or HES are explained by STD and a dummy variable DUMMY_CB that takes the value 1 if the security is a c-bond and 0 otherwise. As reported in regressions (2) and (6) of Table 3, the coefficient of this dummy variable is negative and highly significant (the t-statistics are and for explaining QHBAS and HES, respectively). When we add the interaction term of DUMMY_CB and STD, the dummy variable remains negative and significant, and the interaction term is insignificant. [INSET TABLE 3 ABOUT HERE] As a robustness check, we perform the regressions of Table 3 on the sample of securities issued by the 38 firms that have stocks and c-bonds in our sample (see Figure 1). 25 In the regressions of QHBAS and HES on DUMMY_CB, STD and an interaction variable, DUMMY_CB is negative and statistically significant and the other two variables are insignificant. In sum, in answer to the first two questions we raised in the introduction: we do not find any indication that c-bonds are fundamentally not suitable for trading in an open limit order book; further, we find that c-bonds are more liquid than stocks. 5. Participation of Small Retail Investors in the Corporate Bond Market The third question we raise in the introduction is: would retail investors be as reluctant to trade c-bonds in an exchange as they are to trade them in OTC markets? 25 If a firm has more than one c-bond then their STD, QHBAS and HES are averaged to create one observation. 18

19 According to Table 1, the number of unique accounts participating in the c-bond market during 2012 is 206,820 not much less the corresponding number in the stock market: 246,672. This implies that many small traders participate in the both markets. 26 Because we do not have a classification of our traders (algo, institutional, retail, etc.) we rely on trading volume as an indication for investor classification as "retail". We find 468,457 "low-volume" investors with an annual trading volume of less than 1 million NIS (roughly $260,000 during the sample period 2012) in all the securities that are traded on the TASE (excluding options). These low volume investors, who traded small number of securities during 2012 (5.1 on average) and very infrequently (5.5 trading days on average), are probably small retail investors (SRI). In c-bonds, their aggregate trading volume is about 12.22% of the aggregate total volume. For comparison, this fraction is larger than the trading volume share of this investor group in stocks 8.7%. Since the cutoff of 1 million NIS is arbitrary, we also examine the group of investors with a trading volume up to 2 million NIS in all the securities that are traded on TASE. The trading volume of this group is 17.59% (13.05%) of the total c-bond (stock) volume, and this is probably still an underestimation for the retail share in trading. For comparison, Ronen and Zhou (2013) state that in the U.S. retail traders account for only 1.8% of the c-bond trading. Next, we focus on SRI investors (investors with an annual trading volume of less than 1 million NIS in all securities). Panel A of Table 4 reports the summary statistics of the activity of such investors that had at least one transaction in our sample of c-bonds. Their number is 163,503, which is 83.5% of the number of traders in our c-bond sample. Panel A shows that their trading activity is relatively low and infrequent: an investor in this group has an average (median) trading activity of 26 The number of unique accounts in the government bond market is also around 200,632 and in all the bond market (government and corporate) it is 11.,

20 269,521 (190,532) NIS in all TASE securities excluding options, with a trading volume of 112,135 (68,235) NIS in the sample of c-bonds. Therefore, although this group of investors is 83.5% of the traders in our c-bond sample, their aggregate annual trading volume (18.3 billion NIS) is only 13.16% (6.58%) of the volume (the double counted volume) of these c-bonds. The SRI trade on the TASE on an average (median) of 5.39 (4.00) trading days (out of the 245 possible trading days), of which 2.56 (2.00) are trading days in c-bonds. The investor at the ninetieth percentile of trading days traded on 11 trading days. For comparison, Panel B of Table 4 presents the same statistics for the rest of the traders (32,407 investors with an annual trading volume of more than 1 million NIS in all securities) that have at least one transaction during 2012 in our c-bond sample. This group is much more active, with an average (median) total trading volume of (1.95) million NIS. [INSERT TABLE 4 ABOUT HERE] Table 5 presents the activity of the SRI in the different trading phases. According to the table, 94.68% of the SRI trading volume occurs in the continuous trading stage, on which we focus our analysis hereinafter. In addition, roughly one third of the SRI volume is not executed immediately. That is, the orders stay in the book some time before execution ("making" transactions, denoted MKR). [INSERT TABLE 5 ABOUT HERE] 6. Execution Costs of Small Retail Investors (SRI) In this section we take advantage of our detailed database of c-bond quotes and transactions with trader identification and calculate the actual execution costs 20

21 (EC) of SRI. 27 We show that they are lower than QHBAS (the quoted half bid-ask spread), our measure for the EC of SRI based on the intuition that this is the average cost of an investor who trades immediately after arriving in the market. We focus on the continuous trading stage, which attracts about 95% of the c- bond trading volume of SRI Execution cost estimation We measure EC for sellers as the c-bond's closing price divided by the transaction price adjusted for the contemporaneous c-bond equally weighted index return minus one; for buyers it is this value with a minus sign. 29 The intuition is that the closing price is the benchmark price for the transaction, or a proxy for the unperturbed "true" value. A price increase means a loss or a cost for the seller and the opposite for the buyer. Our basic unit of analysis is bond-investor-day and a day's EC is the weighted average of all investor transactions on that day. It should be noted that since we are dealing with SRI, transactions of a given c-bond on two consecutive trading days are rare, occurring in about 0.37% of the corporate bond-investor-day observations. For the SRIs we do not find a significant difference between the current day's closing price and the next trading day's closing price: the average EC calculated using the current day's closing price is 0.067% and using the next day's closing price it is 0.075%, and the difference between these ECs is statistically insignificant Other costs, which are not the focus of this paper, are the costs that arise from execution delay or from non-execution. These costs, which are sometimes called "the implementation shortfall" (see Perold, 1988), are even harder to estimate than the trading execution costs. 28 The call auction trading mechanism in the opening and closing phases is very different than the mechanism of the continuous trading and requires a different type of analysis than the continuous stage. 29 This measure is winzorized at 10%. The index value is calculated every five minutes. 30 The average is by day and then across days. 21

22 Therefore we prefer the estimate using the current day's price which is less noisy. Besides, it does not seem natural to call returns of several days "execution costs". Figure 3 presents the average EC per transaction of investors' groups according to the total trading volume in all securities. Similar to the findings of Abudy and Wohl (2015) for stocks, the group of investors with an annual trading volume of less than 1 million NIS also incurs the highest EC in c-bonds. The average EC of this group is around 0.067%, while for investors with an annual volume between 1 and 2 million NIS the average cost is around 0.062%, and the cost further decreases with trading volume. 31 [INSERT FIGURE 3 ABOUT HERE] A natural question is why use a future price (the trading day's closing price) as a benchmark rather than a price prior to the transaction (say the opening price) or the VWAP the value-weighted average of transaction prices of the day (which is used extensively by practitioners as the benchmark price for EC of institutional investors in equities)? 32 Using a future price is consistent with fundamental microstructure theoretical modeling [for example Kyle (1985), Glosten and Milgrom (1985), Hellwig (1980) and many subsequent papers] where the trading gains or losses are measured against a future realization of the asset's value. Furthermore, as noted by Harris (2003), past prices such as opening prices provide biased estimates when traders use momentum or contrarian trading strategies. In our case, the SRIs tend to be contrarians. 33 That is, they tend to buy (sell) on days of price decrease (increase). This fact biases down the EC. To illustrate, we calculate the EC with the opening price of the trading day as the benchmark price (averaging by day and then across days) and 31 To be consistent with the analysis in the next sections of the paper, the average is value weighted within bond-investor-day transactions and then averaged within each day and then across days. 32 See Harris (2003) for the usage of the VWAP. 33 Consistent with Barber and Odean (2000) who report contrarian trading of retail stock investors. 22

23 get an estimate of % with a t-statistic of It seems that since SRI are contrarians, using past prices as benchmark prices is very problematic. The VWAP mixes future prices with past prices, and using the past prices biases this measure. The estimated EC using VWAP is %. 6.2 Execution costs of SRI compared to the quoted spread As can be in Table 6 (column "All Transactions") the average of EC (0.067%) is smaller than the average of c-bond's quoted half bid-ask spread (QHBAS), which is 0.110%, (for each transaction we look at the relevant c-bond's QHBAS). 34 The difference between these averages is highly significant. There are two reasons for the difference between EC and QHBAS: The first is that in roughly a third of the cases the SRI are "makers" rather than "takers" and the EC of "makers" are lower than the EC of "takers". Moreover, the larger the spread the greater the extent to which the SRI lower their EC by using more limit orders. The second reason is that when SRI act as "takers" their transaction half spread is narrower than the corresponding QHBAS meaning that SRI tend to act as "takers" when spreads are narrower than average and tend to delay their trade when spreads are larger than average. Let us start by showing the first reason for the QHBAS-EC difference. To measure EC we average each day's observations and then analyze the series of 245 daily averages. 35 As can be seen from row (A) of Table 6 the average EC of 0.067% is an average of two very different cases "takers" and "makers". The average EC for "takers" is 0.079% and for "makers" it is 0.046% (the difference between these 34 Our basic unit of analysis is corporate bond-investor-day. That is, the day's EC is the weighted average cost of all the transactions on that day by this investor. 35 In all the cases where we report t-statistics we verify that the observations are not serially correlated. Since we do not detect first-order autocorrelation in any of the relevant cases we do not check for higher order auto-correlation. 23

24 numbers is highly statistically significant). The number of taking observations is about twice the number of making observations and therefore the average EC is closer to the EC of "takers" than to the EC of "makers". [INSERT TABLE 6 ABOUT HERE] The mean EC of "takers" (0.079%) is roughly the same as their corresponding half transaction spread (0.08%), meaning that for these investors all the spread is transmitted to EC. 36 Another interpretation is that these investors are informationless and therefore their transactions do not convey an adverse selection component for the "makers", who eventually earn all the spread. The average EC of "makers" is small (0.046%) but significantly positive. The meaning of this finding is that SRI that are "makers" have an average adverse selection component in their transactions that is larger on average than half the transaction spread and therefore they incur small EC. To check formally the difference in EC between "taking" and "making" transactions, we run 245 daily regressions of EC on a battery of control variables and RATIO_TKR, which represents the fraction of the NIS volume arising from "taking" transactions. 37 Usually this variable takes the value of 0 or 1, but for about 10.4% of the observations it has a value between 0 and 1, meaning that a part of the daily volume of the SRI in the corporate bond arises from "taking" transactions and part from "making" transactions. The control variables are: LOG_BOND_VOL the average over the trading days of the log of daily NIS trading volume in the c-bond. 36 For the investors that are not SRI (see their activity in Table 4, Panel B), the EC of "taking" transactions is much lower 0.02%. 37 As we check that the coefficients of consecutive days are not significantly correlated, the Newey- West (1987) method for adjusting the t-statistics is not needed. This is the case in all other instances where we examine significance for series of daily coefficients. 24

25 STD the standard deviation of the c-bond's daily returns. 38 DURATION the c-bond's duration. LOG_INVESTOR_VOL the log of the trading volume of the investor during 2012 (i.e., the sample period) in all the securities traded on the TASE (excluding options). LOG_TRADED_VOL the log of the NIS volume of the trader in the c-bond during the continuous trading phase on the observation day. The average of the coefficients of RATIO_TKR in explaining EC is 0.059% and the t- statistic of this series is This means that the EC of "takers" are as expected larger than those of "makers". It seems that SRI reduce their EC significantly by using "making". Moreover, the larger the quoted spread or the effective spread of the c-bond, the more the SRI use "making" rather than "taking". To illustrate this, we divide our c-bond sample into four groups according to their QHBAS. For each day we average all RATIO_TKR in each group's observations and then average across days. The averages are 75% (for the narrowest QHBAS bonds), 66%, 58% and 49% (for the largest QHBAS bonds). The significance of the relation between RATIO_TKR and QHBAS is verified by running 245 daily regressions where the explained variable is RATIO_TKR and the explanatory variables are QHBAS plus control variables (LOG_INVESTOR_VOL and LOG_TRADED_VOL). The average of the coefficients of QHBAS is negative (-0.612) and highly significant (the t-statistic is ). As can be seen, the larger the quoted 38 See Section 3.2 of Vayanos and Wang (2013) for a theoretical justification of the relation between return variability and illiquidity and a survey of the evidence regarding this relation. 25

26 spread, the smaller the likelihood of using "taking". 39 Abudy and Wohl (2015) find this relation in stocks as well. As for the second reason, as can be seen in Table 6 (the "Taking" column) the mean EC in "taking" transactions is 0.079% while the mean of the corresponding QHBAS of the "taking" transaction is 0.098%. The difference between these means is highly statistically significant. 40 Altogether, this evidence seems to indicate that even small retail investors, who trade very infrequently in the market, delay their trade or compete with existing quotes if spreads are large. Therefore, half the quoted spread is an upwardly biased estimate of the EC of SRI. Of course, using limit orders has disadvantages that are not captured by the EC (the risk of non-execution, the risk of price change until execution and the time needed for monitoring the order), but since investors use limit orders by choice they evidently find suffering the disadvantages preferable to paying the spread by "taking". To sum up, we find that in the c-bond market at the TASE the quoted bid-ask spreads are small and the actual EC of SRI are even smaller, because sometimes they delay their trades to get better execution. 6.3 Comparing execution cost for bonds and stocks In Section 4 we show that the bid-ask spread measures (QHBAS and HES) of c-bonds are lower than those of stocks. Moreover, we find that the bid-ask measures of c-bonds are lower than those of stocks even after controlling for the security's STD. Because we find in Section 6.2 that actual EC differ from bid-ask measures 39 Because the correlation between QHBAS and HES (the effective spread) in the cross-section of c- bonds is 0.993, all the relations mentioned above are valid for HES as well. 40 For each day we look at the average difference between EC and the corresponding QHBAS. The mean of this series of 245 numbers is 0.015% and the t-statistic is

27 substantially (due to "making" transactions and "taking" when spreads are narrower than average) we now compare the EC of SRI in c-bond transactions with those in the TASE stock transactions analyzed in Abudy and Wohl (2015). The EC of stocks are indeed higher than the EC reported here: The average EC in stocks is 0.099% while in bonds it is 0.067%. For "taking" transactions it is 0.172% in stocks and 0.079% in bonds. The differences between the averages are highly significant. To check this difference formally we run 245 daily regressions of EC on a dummy variable for the security being c-bond and three variables that capture investor characteristics: LOG_INVESTOR_VOL and dummy variables for investor trading activity only in c-bonds (CB_TRADER) or only in stocks (STOCK_TRADER). As can be seen from regression (3) of Table 7, which investigates the EC of "taking" transactions of SRI, the average of DUMMY_CB is and its t-statistic is That is, the EC of SRI in "taking" transactions is lower by 0.068% in c-bonds than in stocks. For "making" transactions the difference is positive (0.032) and marginally significant (the t-statistic is 1.96 see regression (5)), while for all transactions ("taking" and "making") the difference is and its t-statistic is That is, the EC of SRI are lower in c-bonds than in stocks by 0.022%. The main reason for the difference is probably the fact that stocks are riskier than c-bonds. In order to check if there is a difference between SRI's EC of c-bonds and stocks conditional on their riskiness, we add two variables to the regressions of Table 7: STD and an interaction variable DUMMY_CB*STD. For "taking" transactions (regression (4)) the coefficient of DUMMY_CB is (the t-statistic is -3.01) but the coefficient of the interaction term is (the t-statistic is 3.54). The interpretation of these two coefficients is that the SRI's EC of "taking" transactions are lower in c-bonds than in stocks but the difference diminishes as the STD of the 27

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