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Can Local Long-term Institutional Ownership Alleviate Information Asymmetry in Bank Loan Pricing?* Kiyoung Chang Ying Li Ha-Chin Yi Abstract We use local institutional ownership as a proxy for ownership that is informed and exogenous and show that local long-term institutional ownership (LLTIO) is negatively associated with the spread charged by lenders. LLTIO features geographic proximity and long investment horizons that alleviate information asymmetry between the borrower and syndicated lenders. The negative relation between LLTIO and loan spread is salient only when a gap for geography-related soft information exists and when conflicts of interest between equity and debt holders are unlikely. We show that better monitoring through LLTIO’s long-term equity commitment is more likely the reason for the LLTIO effect. JEL classifications: G14, G21, G32 * Chang, [email protected], College of Business, University of South Florida Sarasota-Manatee, Sarasota, FL 34343; Li (corresponding author), [email protected], School of Business, University of Washington, Bothell, WA 98011; Yi, [email protected], McCoy College of Business Administration, Texas State University, San Marcos, TX 78666. We thank Kee-Hong Bae, Soku Byoun, Hinh Khieu, Jin-Mo Kim, Yong-Cheol Kim, Jieun Lee, Deming Wu, seminar participants at University of Washington, Bothell, and conference participants at the FMA 2014 Annual Meeting for comments and suggestions. We thank the review committee of the Joint Conference and Symposium of All Five Finance-Related Korean Academic Associations Annual Meeting for recognizing our work with a best paper award. All remaining errors are our own. I. Introduction Banking theories posit the presence of information asymmetry between borrowers and lenders, which may cause adverse selection and moral hazard problems (Diamond, 1984). They also suggest that informed ownership can serve as a signal (Leland and Pyle, 1977) and that an informative signal is a valuable indicator of due diligence and monitoring (Holmstrom, 1979; Holmstrom and Tirole, 1997). As informed ownership of the borrower is associated with improved due diligence and monitoring, it can serve as a positive signal to the lender in the process of evaluating the borrower’s credit worthiness and therefore influence the loan pricing terms. In this paper, we show that the borrower’s long-term informed equity ownership reduces the loan spread in the syndication process by alleviating information asymmetry between the lead lender and the borrower. We also show that the local long-term institutional ownership (LLTIO, hereafter) at the borrowing firm is likely to influence the loan spread through its monitoring function. It is difficult to evaluate the price effect of information asymmetry in the syndicated loan market due to a host of simultaneity and endogeneity problems (Sufi, 2007). Ownership effect on mitigating information asymmetry is also difficult to show because ownership is usually endogenous (Demsetz and Lehn, 1985). We use ownership by institutional shareholders whose headquarters are geographically proximate to the headquarters of the borrowing firm as a proxy for ownership that carries information on the borrower.1 The major advantage of our proxy of informed ownership is that the main determinant of proximity, namely, the location of the 1 Coval and Moskowitz (2001) and Baik, Kang, and Kim (2010) find that local institutional investors earn substantial abnormal returns in their nearby equity holdings and that the amount of local equity investment is positively correlated with that stock’s expected return. Malloy (2005) shows that analysts possess an informational advantage on local stocks. Finally, Gaspar and Massa (2007) use local ownership as a proxy for private information to demonstrate the trade-off between monitoring and liquidity effects. investor, is reasonably exogenous (Gaspar and Massa, 2007). 2 Our formal endogeneity test using instrumental variable regressions also suggests that LLTIO is exogenous.3 The presence of local institutional ownership could serve as a signal to lenders, which is informative and influences loan pricing. Since local institutional equity ownership exists outside of the loan syndicate, its effect on loan pricing does not involve simultaneity problems. In this study, we examine the effect of the ownership by institutional investors that are both geographically close and belong to the ten largest shareholders of a borrowing firm (Top10LIO) on loan spreads. We focus on the top10 local shareholders as informed owners with large stakes are subject to great underdiversification risks (Leland and Pyle, 1977). Top10LIO thus provides a convincing signal to lenders. Whereas the local institutional ownership possesses information that entails either improved due diligence or monitoring (Coval and Moskowitz, 2001; Gaspar and Massa, 2007), or both, investment horizons play an important role in how institutions exert their influences (Harford, Kecskes, and Mansi, 2014). Institutional ownerships with a long-term investment horizon are associated with stronger monitoring effects (Gaspar, Massa, and Matos, 2005) than short-term ownerships. We therefore differentiate local institutional ownerships with a long-term and short-term investment horizon and examine their differential effects on loan spreads to gain insights on the mechanism through which informed equity ownership affects the loan spread. Using a sample of borrowing firms from the U.S. syndicated loan market over the 1995 – 2009 period, we find the borrower’s local long-term institutional owners (LLTIOs) who belong to the ten largest shareholders (Top10LLTIO) to be negatively associated with the loan spread charged by lenders. The local short-term institutional owners (LSTIOs) with large stakes 2 Kang and Kim (2008) make a similar argument. Our empirical results also support the exogeneity of local ownership. 3 Our results using only index funds also remain unchanged. (Top10LSTIO) are not associated with lower loan spreads, suggesting LLTIOs’ monitoring functions are more likely the driving force behind reduced information asymmetry. We refer to the negative relation between the LLTIOs and loan spreads as the “LLTIO effect”. The LLTIO effect is salient only under certain conditions: 1) when conflicts of interest are unlikely between creditors and shareholders, such as in the case of investment-grade borrowers and during out-of-crisis periods and 2) when the necessary hard information is in place, for example, when the borrowers have credit rating, which belongs to hard information, yet there is a gap for geography-related soft information, for another example, when the borrower actively invest in research and development (R&D). If the need for geography-related soft information is not present as lenders obtain it through alternative channels, the LLTIO effect does not exist. For example, we do not observe the LLTIO effect when the lead lender is geographically proximate to the borrower, or when the borrowers are close to urban cities. These findings suggest that the LLTIO effect indeed becomes salient only when the presence of the LLTIOs addresses geography-related information asymmetry between the borrower and lenders. They also suggest that the possible dual role of LLTIOs, that is, LLTIOs being both shareholder and creditor of the local firm, is not the likely reason for our findings. When exploring the syndicate structure, however, we find no evidence to support the existence of the LLTIO effect within the loan syndicate. We also use the implementation of Regulation Fair Disclosure (Regulation FD) in 2000 and of the Sarbanes-Oxley Act (SOX) in 2002 as two natural experimental settings to investigate the mechanism(s) through which the LLTIO effect functions. Our findings suggest that the LLTIO effect is associated with improved monitoring, as it becomes statistically and economically more significant after the SOX implementation. Further exploration suggests that when the LLTIOs are present, the likelihood of observing managerial misbehavior such as “lucky” CEO (Bebchuk, Grinstein, and Peyer, 2010) drops significantly. We also find a negative relation between the level of the entrenchment index (E-index) (Bebchuk, Cohen, and Ferrell, 2009) and the proportion of the LLTIOs. Both findings are consistent with the LLTIOs’ monitoring function. Our findings contribute to the existing literature in several ways. First, although past studies have investigated the effect of a geographically proximate lender has on loan pricing (Peterson and Rajan, 2002), the follow-up studies find mixed results. The net effect of a geographically proximate bank on loan pricing could be driven by either a lower premium due to less information asymmetry (Knyazeva and Knyazeva, 2012), or a higher premium due to spatial discrimination (Degryse and Ongena, 2005; Agarwal and Hauswald, 2010). In our study, by focusing on the informational effect of local institutional equity ownership which resides outside of the loan syndicate, we avoid the confounding effects of the hold-up problem and conduct a clean test on how the LLTIO conducive to less information asymmetry between the borrower and lender can be an important factor in bank loan contracting.4 Second, to the best of our knowledge, our paper is the first to demonstrate that the presence of the borrower’s geographically proximate long-term institutional shareholders reduces information asymmetry between the lead bank and the borrowing firm in the context of loan pricing. Even though monitoring is unobservable, we show that lenders give more favorable loan terms when borrowers have observable characteristics (Top10LLTIOs in our study) that provide a signal suggesting improved monitoring. 4 A hold-up problem arises as banks exploit their informational advantage at the borrower’s expense (Rajan, 1992; Sharpe, 1990). Finally, whereas past studies show the importance of geography on collection cost of soft information (Knyazeva and Knyazeva, 2012) and the benefits of soft information (Peterson and Rajan, 1994; Berger and Udell, 1995), the mechanism through which geography-related soft information enters into the loan syndication process is less clear. Our paper suggests that monitoring function is the main mechanism for the LLTIO effect that we document. In summary, our findings contribute to the literature on loan pricing and shed light on the monitoring role that Top10LLTIOs play and the channels through which such monitoring works in the context of bank loans. The rest of the paper is organized as follows. Section 2 describes the syndicated loan market and the features of LLTIOs that could address information asymmetry in a syndicated loan setting. Section 3 presents the data and summary statistics. Section 4 contains our empirical results and Section 5 concludes. 2. LLTIO and Bank Loan Pricing Internationally, syndicated loans represent an important and fast-growing source of financing for corporations, with $1.8 trillion in such loans issued in 2009, more than the total value of corporate borrowing in the global bond markets (Chui et al., 2010). According to Thomson Reuters (the provider of the DealScan data used in this paper), the U.S. leveraged loan issue, a subset of all syndicated loans issued in the U.S., reached $664 billion in 2013. Secondary loan trading in the country also exceeded $600 billion in 2014. Of the 500 largest Compustat firms, 90% have obtained syndicated loans (Sufi, 2007), and 51% of all U.S. corporate financing is in the form of syndicated loans (Weidner, 2000). Loan quality and pricing are functions of both hard and soft information about the borrower (Stein, 2002). Hard information, such as a borrower’s credit rating, whether it pays dividends, and whether it is a component of a major stock index, is easy to collect and verify. On the contrary, soft information, such as the harmonic ongoing relationship among the top management team, middle management, and labor, the prospect for potential of research and development (R&D), and the cultural compatibility between a new CEO and other top executives, is difficult to collect and verify (Agarwal and Hauswald, 2010; Liberti and Mian, 2009), as many involve a dynamic process. Geographic proximity enables easier access to certain soft information (Rajan, Seru, and Vig, 2015) and facilitates monitoring of the borrower (Sufi, 2007). Prior to loan syndication, the lead bank conducts due diligence on the borrower before reaching agreement with it on a target spread range over the London Interbank Offered Rate (LIBOR) (Ivashina, 2009). Although the lead bank possesses a large amount of information on the borrower, much of it could take the form of hard information, leaving significant gaps in soft information, which is harder to evaluate or transfer from a distance (Stein, 2002; Peterson, 2004). A lead bank may also possess soft information from a previous banking relationship with the borrowing firm, but geography-related and previous relationship-related soft information are not necessarily identical. For example, although a distant lead bank may have a good understanding of the business and operations of a borrowing firm based on its previous relationship with that firm, it is likely to be less costly for a local lead bank to evaluate the firm’s current status and future prospects. The composition of ownership may send a signal to outsiders (Fombrun and Shanley, 1990). A high degree of institutional ownership is believed to be associated with a high level of information gathering effort and low level of information assessment errors (Sias, 1996), and firms with a high degree of institutional ownership are therefore likely to be viewed favorably. The largest institutional shareholders are also highly visible to lenders. As LLTIOs possess superior access to geography-related soft information, their presence can serve as a favorable signal in loan pricing. Such a favorable signal from the local institutions with the largest stakes in the borrower is particularly convincing because of the greater risk of under-diversification (Leland and Pyle, 1977). Furthermore, LLTIOs with large stakes can serve as an external monitor for the borrower, alleviating moral hazard problems over the long term. Geographic proximity provides a costbenefit justification for monitoring, and facilitates intense monitoring through frequent interactions with local firms and other stakeholders. For example, Gaspar and Massa (2007) and Kang and Kim (2008) show such proximity to reduce both transportation and communication costs and encourage local investors to get involved. Concentrated ownership and a long-term investment horizon are additional characteristics that contribute to lower monitoring costs (Hartzell and Starks, 2003; Gaspar, Massa, and Matos, 2005). Therefore, LLTIOs with large stakes serve as an external monitoring mechanism for the borrower and alleviate moral hazard problems over the long term. Indeed, Gaspar and Massa (2007) and Chhaochharia, Kumar, and Niessen-Ruenzi (2012) show that local institutional ownership is associated with improved corporate governance, which is documented to lead to a lower cost of capital (Stulz, 1999). Whereas local short-term institutional owners (LSTIOs) may possess private information on the borrowing firm ex ante, their short-term investment horizon suggests that they will have little monitoring incentive (Gaspar, Massa and Matos, 2005; Chen, Harford, and Li, 2007). Frequent trading by LSTIOs leads to more uncertainty, resulting in an offsetting effect on the loan spreads that will confound the study of the local institutions’ information effect. To explore the effect of informed equity ownership of the borrower on information asymmetry, we focus on the LLTIOs. We hypothesize that, ceteris paribus, LLTIOs reduce premiums lenders charge for loans by serving as a signal that fills the gap in geography-related soft information. We also expect the LLTIO effect on loan pricing to be more salient when conflicts of interest between creditors and shareholders are unlikely. Otherwise, a gain to the latter could be synonymous with a loss to the former in case of elevated conflicts of interest between two stakeholders, such as in a high level of financial distress, because shareholders selfishly exert their influence on corporate decisions at the expense of creditors (Jensen and Meckling, 1976; Myers, 1977). Furthermore, in the absence of information asymmetry or a need for geography-related soft information, either because the loans are secured or the lenders are geographically proximate, we expect the LLTIO effect on loan pricing to vanish. 3. Data and Summary Statistics Our bank loan data come from the Thomson Reuters LPC DealScan database, and our information on financial characteristics and stock returns from Compustat and the Center for Research in Security Prices (CRSP), respectively. We also obtain institutional ownership data from the Thomson Reuters 13F database. We match the DealScan dataset with the Compustat dataset using the list of identifiers constructed by Chava and Roberts (2008). Our sample excludes financial and regulated utility industry borrowers and non-U.S. borrowers. The final sample includes 17,308 loan deals with financial and stock information on 3,810 unique borrowing firms, which has non-zero institutional ownership over 1995-2009. We report the variable definitions in Appendix A, and summary statistics in Table 1. 3.1 Loan Data DealScan collects loan-level data, mostly on syndicated loans, from various sources, including annual reports, reports from loan originators, and Securities and Exchange Commission (SEC) filings. Syndicated loans are medium- or large-sized loans extended to firms by a group of lenders. In a typical syndicated loan contract, a small number of lenders, called lead lenders or arrangers, head up a group of participating banks that jointly issue a relatively large loan package to share the risk and meet capital requirements. The role of the lead lenders is to serve as a bridge between the borrowers and participating banks. They serve both sides of the table: for the borrower, the lead bank secures financing, and for the participating banks, it performs credit-screening on borrowers through due diligence and then offers ex-post monitoring. Our research variable is the all-in-drawn spread (spread) for syndicated loans, which, according to the DealScan definition, is the total annual cost in basis points paid over the London Interbank Offered Rate (LIBOR) for each dollar used under the loan commitment. 3.2 Institutional Ownership Data Form 13F mandatory institutional reports are filed with the SEC on a calendar quarter basis, and are compiled by Thomson Reuters (formerly known as the 13F CDS/Spectrum database). The SEC requires all institutions with more than $100 million under management at the end of the year to file Form 13F reporting their long positions in equity5 in the next year. Form 13F filings thus have several limitations: for example, small institutions with less than $100 million under management are not required to report their positions, smaller holdings that do not reach the 10,000-share or $200,000 threshold are not included, and short positions are not reported. Further, Thomson Reuters aggregates holdings reports at the management company level.6 5 The reported positions are those in which the institution owns more than 10,000 shares or with a market value greater than $200,000. 6 A given 13F report may include holdings reported by multiple funds/managers that are not necessarily located in the same area as the headquarters. This problem, which is suffered by most local-related studies using 13F data, constitutes one of the limitations of our study. A firm’s local investors are defined as those located within a short distance. As we cannot differentiate holdings by the local offices of the same institutional investor, we focus on the location of the corporate headquarters of the management company to identify local institutional investors, which is similar to the approach used by Gaspar and Massa (2007) and Baik, Kang, and Kim (2010). Also, similar to Knyazeva, Knyazeva, and Masulis (2013), we obtain corporate headquarters locations and firm-level financial variables from the Compustat database. If information on the corporate headquarters location is missing, we obtain it manually. We identify the institutional location (zip code) by manually searching the SEC EDGAR site for historical 13F filings. Consistent with John, Knyazeva, and Knyazeva (2011), we use the distance between the corporate headquarters of firms and the headquarters of institutional investors to calculate local institutional ownership. Like Baik, Kang, and Kim (2010), we exclude cases in which either the firms or institutional investors are located in Alaska, Hawaii, Puerto Rico, or the Virgin Islands. We first identify the 10 institutional investors with the largest stakes in a firm and calculate the percentage of shares owned by these top-10 owners (Top10IO). We then calculate the percentages of shares owned by long-term and short-term investors7 whose headquarters are located within a 100-mile radius of the firm’s headquarters.8 We use the percentages, including 7 Following Bushee (2001), we categorize ownership by institutional owners who are either dedicated or quasi-index as long-term institutional ownership. According to Bushee (1998), dedicated institutional investors are characterized by large average investments in portfolio firms with extremely low turnover ratios, whereas quasi-indexers are characterized by low turnover and diversified holdings. He argues that both types of investors provide firms with long-term, stable ownership because they are geared toward longer-term benefits, be those benefits dividend income or capital appreciation (Bushee, 2001). We thank Brian Bushee for providing institutional investor classification data (1981–2009) on his website: http://acct3.wharton.upenn.edu/faculty/bushee/. 8 Coval and Moskowitz (1999, 2001) and Gaspar and Massa (2007) use a 100-kilometer radius as a measure of locality, whereas Ivkovic and Weisbenner (2005) set 250 miles as the maximum radius for local investors, and Baik, Kang, and Kim (2010) adopt state identifiers to identify local institutional investors. The distance, 𝑑𝑖,𝑗 , between the headquarters of institutional owner i and firm j is calculated as follows: 𝑑𝑖,𝑗 = arccos(𝑑𝑒𝑔𝑙𝑎𝑡𝑙𝑜𝑛 ) × 2𝜋𝑟 360 , where 𝑑𝑒𝑔𝑙𝑎𝑡𝑙𝑜𝑛 = cos(𝑙𝑎𝑡𝑖 ) × cos(𝑙𝑜𝑛𝑖 ) × cos(𝑙𝑎𝑡𝑗 ) × cos(𝑙𝑜𝑛𝑗 ) + cos(𝑙𝑎𝑡𝑖 ) × sin(𝑙𝑜𝑛𝑖 ) × cos(𝑙𝑎𝑡𝑗 ) × sin(𝑙𝑜𝑛𝑗 ) + those of concentrated overall local, local long-term, and local short-term institutional ownership (Top10LIO, Top10LLTIO, and Top10LSTIO, respectively), as a proxy for informed equity ownership. The overall local institutional ownership (Top10LIO) for firm j is calculated as follows:9 𝑇𝑜𝑝10𝐿𝐼𝑂𝑗 = ∑𝑖∈𝐿 𝑉𝑖,𝑗 𝑗 ∑𝑖∈𝐼 𝑉𝑖,𝑗 , (1) where Lj is the set of the ten largest institutions based on shares of firm j owned that are headquartered within a 100-mile radius of firm j’s headquarters, I is the universe of all ten of the largest institutions based on their stake in firm j, and Vi,j is the dollar value of institutional owner i’s stake in firm j. 𝑇𝑜𝑝10𝐿𝐿𝑇𝐼𝑂𝑗 = 𝑇𝑜𝑝10𝐿𝑆𝑇𝐼𝑂𝑗 = ∑𝑖∈𝐿𝐿𝑇 𝑉𝑖,𝑗 𝑗 ∑𝑖∈𝐼 𝑉𝑖,𝑗 ∑𝑖∈𝐿𝑆𝑇 𝑉𝑖,𝑗 𝑗 ∑𝑖∈𝐼 𝑉𝑖,𝑗 , (2) , (3) Top10LLTIO and Top10LSTIO are calculated similarly as described in Equations (2) and (3), where Top10LLTIOj and Top10LSTIOj are Top10LIOs who have long-term and short-term investment horizons, respectively, according to Bushee’s categorization: long-term institutional investors include dedicated and quasi-indexers and short-term include transient institutions. 3.3 Control Variables We include firm characteristics, loan characteristics, macro-economic variables, and industry dummies as our control variables. Firm characteristics include firm size, asset sin(𝑙𝑎𝑡𝑖 ) × sin(𝑙𝑎𝑡𝑗 ), lat and lon are the latitudes and longitudes of the institutional owner and firm, and r is the radius of the earth (approximately 3,959 miles). 9 Coval and Moskowitz (2001) and Gaspar and Massa (2007) define local ownership as the “excess” local ownership in one firm relative to the benchmark expected for the particular locality in which it is headquartered. We use actual local institutional ownership out of the top-10 largest shareholders, an approach similar in spirit to that adopted by Baik, Kang, and Kim (2010). This measure enables us to calculate changes in ownership and assess the effect on alleviation of information asymmetry. tangibility, membership of the S&P 500 index, profitability, financial distress (modified Z), leverage, credit rating, stock volatility, R&D-to-asset ratio, and institutional ownership. Loan characteristics include whether the loan is secured, loan type, maturity, loan purpose, and relationship status. The other control variables include term spread, credit spread, and the FamaFrench twelve industry effects. The term spread and credit spread are measured on an annual basis, and we omit year fixed effects to avoid multicollinearity due to the presence of term spread and credit spread variables in the model. We use firm size and the average debt issue size as proxies for economies of scale in flotation costs, following Krishnaswami, Spindt, and Subramaniam (1999). Also, large public borrowers are usually covered by many analysts, and accordingly more public information is available on such borrowers. Hence, we expect a negative relation between firm size and spread. Similarly, information asymmetry is less severe for S&P 500 index firms, dividend-paying firms, and borrowing firms with a previous banking relationship with the lead bank (Berger and Udell, 1995; Petersen and Rajan, 1994) and for loans originating with reputable banks (Ross, 2010; Dennis and Mullineaux, 2000). We thus expect a negative relation between S&P500 dummy, Div dummy, Relation dummy, Top3 bank, and spread. Leverage is a proxy variable for the observable default risk, and we expect a positive relation between it and spread (Merton, 1974; Carey, Post, and Sharpe, 1998). As tangible assets are easier to value than intangible assets, we expect a negative relation between asset tangibility, NFA/TA, which is measured as the ratio of net fixed assets to total assets, and spread. Return on assets (ROA) captures borrower profitability, and is expected to have a negative association with spread. Finally, top-10 institutional ownership (Top10IO, the ratio of shares owned by the 10 largest institutions to the shares owned by all institutional investors) captures the concentration level of institutional ownership. Larger, more mature firms are likely to have a large numbers of institutional owners, and the 10 largest shareholders of such firms are thus likely to be less representative of overall institutional ownership compared to other firms. We therefore expect a positive relation between Top10IO and spread. Table 1 presents summary statistics at the loan level. Syndicated loans are issued as a package deal, with each deal possibly comprising multiple revolvers (or credit lines) and term loans (or installment loans). Loan-level presentation provides a good picture of our sample because revolvers and term loans contain different loan specifications. Our sample comprises 17,308 loans over the 1995–2009 period. The average loan spread is about 182 basis points above the LIBOR. There is a wide variation in the spreads for our sample, with a minimum spread of 2.7 basis points and maximum spread of 1,500 basis points.10 Approximately 20% of the sample loans were obtained by S&P 500 firms, and about half were the outcome of repeat loans from the same set of lead lenders and borrowers. Approximately 30% of the loans were issued by the three banks with the largest dollar volume of syndications, namely, JP Morgan Chase, Bank of America, and Citi Bank. According to Ross (2010), these three banks accounted for almost half the total syndicated loan volume, measured in dollars, in the 2000–2008 period. On average, the ten largest institutional investors hold 29% of the equity in a borrowing firm, with long-term investors constituting the majority. Local owners are a relatively minor group (approximately 9% of the sample loans), with the long-term investors among them accounting for roughly 7% of sample loans. About 4% of loans were obtained by borrowing firms located within 100 miles of the lead syndicate lenders. On average, the book value of the sample borrowing firms is approximately $786 million, with a leverage level of 31%, but just over half of all loans (53%) were secured with some form of collateral. Just under half (49%) 10 A closer examination of our sample identifies multiple loans with a spread of more than 1,000 basis points, suggesting that the wide variation in loan spread is unlikely to be a recording mistake. Our results remain largely the same after removing the extreme observations as we use the logarithms of spread to minimize the impact of outliers. were obtained by firms paying dividends. About 24% of loans were obtained by investmentgrade firms (with long-term credit ratings of BBB or above), and the rest were by either speculative-grade firms (with long-term credit ratings below BBB) or firms that do not have a credit rating. Roughly half the loans (47%) are obtained by investment-grade firms. Approximately 56% of loans are revolvers, and 26% are term loans, and the average maturity is about 48 months. [Table 1 about here] Panel A of Table 2 shows the summary statistics for different firm and loan characteristics with high and low Top10LLTIO and Top10LSTIO, respectively. If a loan is associated with greater than 5% Top10LLTIO/ Top10LSTIO, it belongs to the high Top10LLTIO/ Top10LSTIO group, and to the low Top10LLTIO/ Top10LSTIO group otherwise. The univariate statistics show that, relative to those in the low Top10LLTIO group, loans in the high Top10LLTIO group have a lower loan spread (159 basis points versus 190 basis points, on average), are obtained by firms with a previous banking relationship with the current lenders, issued by the top-three reputable banks, from larger, more profitable, more likely dividendpaying and S&P500 firms, with a lower level of leverage, and are less risky as measured by credit ratings. With the exception of the long-term revolvers, all of the mean differences are statistically significant with a confidence level of 1% or better. Relative to those in the low Top10LSTIO group, loans in the high Top10LSTIO group are obtained by firms with smaller size, no dividend, higher volatility in operating cash flow, and higher risk as measured by credit ratings. The loan spread difference for the two groups with high and low Top10LSTIO is not clear, with the t-statistic on logspread difference being insignificant. The results from the univariate test confirm our conjecture that due to their frequent trading and lack of monitoring, LSTIOs’ informational effect is unclear. Panel B of Table 2 shows the breakdown of our sample into two groups: investmentgrade versus speculative-grade loans. About 77% of our sample loans are speculative loans. As expected, safe loans tend to be repeat deals, and are more likely to be issued by one of the topthree banks. Investment-grade firms are more likely to pay dividends, are more profitable, and tend to have more LLTIOs. The results of univariate analysis are consistent with the literature reporting that loans issued to borrowing firms with a high credit rating and previous banking relationship tend to have a lower loan spread (Yi and Mullineaux, 2006; Schenone, 2010). Panel C of Table 2 shows the comparison of mean spread and logspread between borrowing firms by their credit rating and the level of Top10LLTIO using a threshold of 5%, respectively. Whether the borrowing firm has an investment grade or not, the mean differences in spread and logspread associated with high (≥5%) and low (<5%) Top10LLTIO are similar in magnitudes and significant at a better than 1% confidence level. Although the univariate result needs to be verified in a multivariate setting later, it suggests that it is the level of Top10LLTIO is more likely to drive the result than credit ratings. [Table 2 about here] 4. Empirical Results 4.1 The LLTIO Effect Theory tells us that information asymmetry between lenders and borrowers is a key factor driving the terms of loan contracts, which attempt to deal with adverse selection and moral hazard problems (Diamond, 1984). Informed ownership can serve as a signal that mitigates the costs of information asymmetry (Leland and Pyle, 1977), and prior research has demonstrated that such asymmetry can influence the structure and pricing terms of syndicated loans (Dennis and Mullineaux, 2000; Sufi, 2007; Ivashina, 2009; Knyazeva and Knyazeva, 2012). We argue here that because local institutional ownership (LIO) with sizable stakes represents informed ownership, which can play a credible role in ensuring either due diligence, or monitoring, or both (see Holmstrom, 1979), the presence of LIOs in a borrowing firm may induce lenders to reduce the loan spread. Furthermore, as the location of LIOs is exogenous to bank loan contracting terms, our identification strategy is less troubled by endogenous concerns. To investigate the informational effect of LIO on syndicated loan pricing, we estimate the following multivariate regression, which includes both long- and short-term ownership by the top-10 (largest) shareholders with headquarters located within a 100-mile radius of the borrowing firm’s corporate headquarters, as well as the control variables specified in Equation (2), and report the results in Table 3. We use the logarithm of spread (logspread) as the measure of loan spread, similar to other studies in the banking literature (for example, Graham, Li, and Qiu, 2008), in all regression analyses. Loan spread = f (Top10LLTIO, Top10LSTIO, institutional ownership, loan characteristics, firm characteristics, macro-economic variables, industry dummies). (4) Column (1) examines the relation between logspread and overall local ownership with large equity stakes in the borrowing firms (Top10LocalIO). With the exception of the added institutional ownership variables, the results in Column (1) are consistent with the findings from the previous studies of bank loan pricing. That is, a larger, more profitable firm, longer maturity loan, and firm with a prior relationship with the lender obtain loan rate discounts, whereas a highly levered, volatile firm with a low credit rating and a loan backed by collateral pays a higher spread. The coefficient estimates on loan purpose and industry are also generally significant. The coefficient estimate on Top10IO is positive, as we expected, since Top10IO is likely to be a higher percentage at smaller firms and smaller firms tend to face higher loan spreads. It is also significant at the 1% level. Column (2) examines the relation between logspread and top-10 local institutional ownership with different investment horizons (Top10LLTIO for long-term and Top10LSTIO for short-term local institutional ownership, respectively). After controlling for creditworthiness and other firm and loan characteristics, the coefficient estimate on Top10LLTIO is negative and significant with a confidence level of 1%, whereas that on Top10LSTIO is positive and significant with a confidence level of 5%. Short-term investors appear to be better informed and to trade in a way that exploits their informational advantage (Yan and Zhang, 2009), and local short-term institutional investors are able to generate superior returns (Baik, Kang, and Kim, 2010). Banks may view short-term (or transitory) investors as harmful to the stability of a firm due to the higher level of equity trading turnover and volatility they create. Furthermore, transitory ownership is usually opportunistic and lacks ex-post monitoring incentives (Gaspar, Massa, and Matos, 2005). These factors may explain the observed positive relation between Top10LSTIO and logspread. Conversely, the top-10 LLTIOs have incentives to monitor and access to geography-related soft information obtained through a long-term commitment. These features of the LLTIOs help to alleviate the information asymmetry between borrowing firms and lenders and explain the observed negative relation between Top10LLTIO and logspread. Column (3) is identical to Column (2) except for a slightly different definition of the credit rating variable, an indicator variable that captures the default risk of the borrowing firm (Invgrade in Column (2) and Invgrade2 in Column (3)).11 Although the sample size changes, the 11 The dummy variable Invgrade takes a value of one if the S&P rating on a borrowing firm’s long-term debt is BBB- or above. Because many borrowing firms do not have a long-term debt rating, when Invgrade takes a value of results in Column (3) are consistent with those reported in Column (2), with the coefficient estimates on Top10LLTIO negative and significant, both with a confidence level of 1%. When we include bank fixed effects in the regression, the LLTIO effect remains significant, suggesting that it is not driven by specific bank characteristics. We report these results in Column (4). 12 To disentangle the roles played by geographic proximity from long-term investment horizons, as either may be driving the LLTIO effect on logspread, we also report results using the 10 largest long- and short-term institutional owners (Top10LTIO and Top10STIO) as explanatory variables. The results, reported in Column (5) show that Top10LTIO presence is not associated with loan spread, suggesting that geographic proximity is the key element for the existence of the LLTIO effect. Urban areas, especially vicinities of New York City and Connecticut are the headquarters of a large cluster of institutional owners. For example, in results that are not tabulated, we observe that the average level of Top10LLTIO is above 14% in the vicinity of Connecticut and 0.5% in the vicinity of Texas. The level of Top10LLTIO in urban areas is therefore highly skewed and the LLTIO effect we observe could be due to a borrowing firm’s central location. Previous studies (Loughram and Schultz, 2005; John, Knyazeva, and Knyazeva, 2011; Chen, Gompers, Kovner and Lerner, 2010; Cumming and Dai, 2010) show that urban location does matter for a firm’s dividend payout policy and for venture capital success. To address this concern, we exclude firms that are located in one of the ten largest metropolitan statistical areas (MSAs) and re-estimate Equation (4). The results in Column (6) show that the LLTIO effect zero, neither non-investment-grade borrowers nor those without a rating are included. We thus define another dummy variable, Invgrade2, which takes a value of zero only when the borrower has a long-term debt rating and that rating is below BBB-. The sample size changes depending on whether borrowing firms without a long-term debt rating are included, and Columns (2) and (3) thus have different numbers of observations. 12 Since our sample is built on loan facilities, the data is not panel. We therefore rely on the ordinary least squares (OLS) regression instead of panel data techniques for empirical analysis. remains negative and significant in non-urban areas, with a confidence level better than 1%. To compare the effect of LLTIO and LSTIO on loan spreads for the same borrowing firm at the same time, we include firm fixed effects and year fixed effects13 for the non-urban borrowing firm sample and report the results in Column (7). The LLTIO effect remains significant, suggesting that it does not exist in the urban areas only. [Table 3 about here] 4.2 LLTIO as a Proxy for Exogenous Ownership The validity of our argument depends on whether LLTIO can be considered a form of exogenous ownership because the effect of endogenous ownership on asymmetric information is difficult to show (Demsetz and Lehn, 1985). Although following the arguments in Gaspar and Massa (2007) and Kang and Kim (2008) renders Top10LLTIO reasonably exogenous, we also adopt an instrumental variable (IV) approach to formally establish causality between Top10LLTIO and logspread. IV regressions can help alleviate the endogeneity concern, which stems from certain unobservable firm characteristics being omitted from the model but is related to both logspread and Top10LLTIO. We introduce the two following IVs for Top10LLTIO. 13 State Top10LDIO: Annual average of top10 local dedicated institutional owners14 with the largest stakes for all other firms in the same state but in different industries, as defined by their 2-digit SIC code.15 Industry Top10LQIO: Annual average of top10 local quasi-index institutional owners16 with the largest stakes for all other firms within the same industry, as defined by their 2digit SIC code.17 Fixed year effects are subsumed under term spread and credit spread as both variables have annual variation. Following Bushee (1998), we define dedicated institutional ownership as being characterized by large average investments in portfolio firms with extremely low turnover ratios. It is a component of LLTIO. 15 The IV (State Top10LDIOi) for Top10LLTIOi is constructed by including all other firms in the same state, but not the same industry, as firm i, identifying the aggregate Top10LDIO level for each, and calculating the annual average Top10LDIO across firms in a given year. Similarly, we construct our other IV (Industry Top10LQIO) using information on Top10LQIO for all other firms with the same 2-digit SIC codes to calculate the annual average. 14 A valid IV needs to satisfy two conditions: relevance and exclusion. We expect that whether they belong to dedicated (Top10LDIO) or quasi-indexers (Top10LQIO), Top10LLTIO are likely to be indifferent with their targets, if they choose to monitor due to the same reason, that is, lower cost of doing so. Therefore, an institutional investor with monitoring motivation will likely take actions at other firms that are also geographically close. This assumption suggests that our location-based IV, State Top10LDIO, satisfies the relevance condition. The exclusion condition requires that State Top10LDIO affect loan spread at the borrowing firm only through its information asymmetry alleviation effect, not because of other factors that can influence both LLTIO and loan spreads. For example, State Top10LDIO focuses on the Top10LDIO of other borrowing firms in different industries, satisfying the exclusion criterion. Furthermore, in results that are not tabulated here, we find that the LLTIO effect remains negative and significant in a similar regression to Equation (4) after controlling for added state fixed effects of the borrowing firms. With state fixed effects, we focus on the within-state variation of LLTIO and we still find high LLTIO to be associated with lower logspread. This suggests that location in different states does not have a systematic effect on loan spreads and therefore is not a factor that drives our results. We also include Industry Top10LQIO as a second IV to conduct the endogeneity test for Top10LLTIO. Hansen’s J-test confirms that at least one instrument is valid. We report the IV regression results for the overall sample and rated borrower-only sample in Columns (1) – (4) of Table 4. The Chi-square statistics for the endogeneity test are 1.62 and 1.07 for the overall and rated borrower-only samples, with p-values of 0.203 and 0.301, 16 Following Bushee’s (1998) definition, quasi-indexer institutional ownership is characterized by low turnover and diversified holdings. It is the other component of LLTIO. Details of the variables can be found in Appendix A. 17 We similarly construct IVs based on Top10LLTIO for firms in other industries in the same sate, and obtain similar results: Top10LLTIO is not endogenous, and the LLTIO effect remains. respectively, suggesting that Top10LLTIO is, indeed, not endogenous at the conventional significance level. The t-statistics for both instruments are positive and significant at a 1% confidence level. The F-statistic of joint significance from adding the two IVs is 141.24 and 108.29 for the overall and rated-only samples, respectively, suggesting that neither IV is weak. The coefficient estimates from the second stage of the IV regression on the instrumented Top10LLTIO are -0.255 and -0.360 for the overall and rated-only samples, respectively, significant at the 5% level. The results from the IV regressions suggest that Top10LLTIO leads to a lower logspread. [Table 4 about here] 4.3 LLTIO, Relationship Banking, and Soft Information A previous banking relationship constitutes a source of soft information for the lead bank, which mitigates information asymmetry and leads to lower loan spreads (Diamond, 1991; Berger and Udell, 1995; Bharath, Dahiya, Saunders, and Srinivasan, 2011; Boot, 2000). The adverse selection and moral hazard problems in the banking relationship can be mitigated through a strong relationship between the lending bank and borrower based on prior lending experiences or other business ties such as deposit and working capital management (Boot, 2000). Such a relationship gives the lending bank access to intimate soft information on the borrower, possibly including information on its business prospects. To investigate the nature of soft information, we examine whether the LLTIO effect varies depending on the existence of a previous banking relationship. If the soft information that LLTIOs possess is identical to the soft information that a lead bank collects from a previous banking relationship, then the LLTIO effect will vanish in the presence of such a relationship. We create an indicator variable, Relation dummy, which takes a value of one if there is a previous banking relationship between the lead bank and borrowing firm, and zero otherwise. We then interact Relation dummy with Top10LLTIO, and report the results in Column (1) of Table 5. We find that both Top10LLTIO and Relation dummy carry a negative coefficient estimate, significant at the 5% level. The coefficient estimate on the interaction term Relation dummy ×Top10LLTIO is negative and insignificant, whereas the aggregate LLTIO effect when a previous banking relationship exists is negative, with a coefficient magnitude of (-0.087−0.089 = -0.176), and significant at a better than 5% level. Analyses using subsamples of borrowing firms that do and do not have a previous banking relationship with the lead bank also show the LLTIO effect to remain significant, with a magnitude of -0.178 and -0.073 for the subsamples with and without such a relationship, respectively. Both coefficients are significant at a better than 5% confidence level, and the results are reported in Columns (2) and (3) of Table 5. The existence of the LLTIO effect irrespective of a previous banking relationship suggests that the soft information that LLTIOs possess differs from that garnered by lenders from a previous banking relationship. We refer to the soft information that LLTIOs possess as geography-related soft information. [Table 5 about here] 4.4 Conditions that Influence the LLTIO Effect The nature of geography-related soft information and the conditions that influence the LLTIO effect require empirical exploration. We suggest that convenient, frequent interactions between locals help alleviate geography-related soft information gap. Furthermore, because the LLTIOs are outside of the loan syndicate, we argue that the geography-related soft information is secondary to hard information. In other words, LLTIO is a signal that “hardens” geography- related soft information and latches onto existing hard information. In sum, we propose that the LLTIO effect is only salient for a borrowing firm with the necessary hard information in place, but still has a need for geography-related soft information. We use two proxies to capture borrowing firms in which the lender’s dealing of soft information is non-trivial. First, the R&D-to-asset ratio serves as a proxy for the lender’s need for geography-related soft information at the borrowing firm level because it is difficult to discern the future prospects of firms with R&D investments (Lorek, Stone, and Willinger, 1999). Indeed, Cohen, Diether, and Malloy (2013) suggest that the stock market appears unable to distinguish between “good” and “bad” R&D investments. The presence of the LLTIOs provides a better monitoring environment with more frequent updates on R&D investments and helps address information asymmetry. Second, the intangible-to-asset ratio serves as another proxy for the lender’s need of geography-related soft information. The economic value of a firm’s intangible assets such as human capital and customer satisfaction is hard to assess from a distance (Edmans, Heinle, and Huang, 2015). The LLTIOs possess an advantage in conducting such assessment due to their geographic proximity. We therefore expect the LLTIO effect to be more salient in borrowing firms with either high R&D investments or high proportion of intangible assets, as the need for soft information arises from long-term monitoring of the borrowing firm in both cases (Sufi, 2007). Credit rating is the most important piece of information about a potential borrower that a lender can easily obtain (Sufi, 2009), and contains hard information on the borrower’s creditworthiness. We use the borrowing firm’s credit rating as another proxy to capture borrowing firms that have necessary hard information in place so that the addition of soft information is effective. Since borrowers without long-term bond ratings lack the necessary hard information for geography-related soft information to latch onto, the LLTIO effect should disappear. Finally, secured loans can serve as a proxy for the lack of a need for additional information, as little uncertainty is involved in the event of loan default. We expect the LLTIO effect to diminish as the geography-related soft information the LLTIOs offer is not relevant when an information gap does not exist. The results for the subsamples constructed using the above proxies to explore the informational effect of the LLTIOs are reported in Panel A of Table 6. Columns (1) and (2) examine the R&D-to-asset ratio as a measure of the lender’s need for geography-related soft information. The LLTIO effect is salient only for the subsample in which the borrowing firms have a positive R&D ratio, with a coefficient estimate that is negative and statistically different from zero at a 1% level of confidence. This finding suggests that the presence of LLTIOs leads to lower loan spreads only for borrowing firms that involve R&D, where geography-related soft information is helpful. Columns (3) and (4) investigate the intangible-to-asset ratio as another measure of the lender’s need for geography-related soft information. Similarly, the LLTIO effect is salient only for the subsample in which the borrowing firms have a positive intangible asset ratio, with a coefficient estimate that is negative and statistically different from zero at a 1% level of confidence. Columns (5) and (6) consider a borrowing firm’s long-term credit rating as a measure of hard information on its creditworthiness. It can be seen that the LLTIO effect is salient only for the subsample of borrowing firms with a long-term debt rating (rated), with a coefficient estimate that is negative and statistically different from zero at a 1% level of confidence, suggesting that the LLTIO effect based on geography-related soft information is secondary to credit ratings. Columns (7) and (8) examine secured loans as a measure of the need for geography-related soft information revealing that the LLTIO effect is salient only for the subsample with unsecured loans, as indicated by the negative coefficient estimate that is statistically different from zero at a 1% level of confidence. Secured loans remove uncertainty, and the LLTIO effect exists only when there is information asymmetry. If the observed LLTIO effect is the result of geography-related soft information, it may lose its salience when (1) the lead bank is close to the borrowing firm and (2) when the borrowing firm has an urban location because (1) a lead bank that is geographically proximate has easy access to the geography-related soft information that LLTIOs possess and (2) a borrowing firm located in an urban location is subject to greater scrutiny and is better governed, as managerial investment decisions are easily observable (John, Knyazeva, and Knyazeva, 2011). To examine these two conditions, we create two indicator variables, Close Bank and Urban10, which takes a value of one if the headquarters of the lead bank that issued the loan is within a 100-mile radius of the borrowing firm’s corporate headquarters and if the borrowing firm is located in one of the 10 largest MSAs in the U.S., respectively, and zero otherwise. We then interact Top10LLTIO with Close Bank and Urban10, and examine the LLTIO effect for loans (1) with a geographically proximate lead bank and (2) a borrower that is located in one of the 10 largest MSAs by testing whether the respective sum of coefficient estimates, namely, (Top10LLTIO + Close Bank × Top10LLTIO) and (Top10LLTIO + Urban10×Top10LLTIO), is statistically different from zero. We report the results in Columns (1) and (2) of Panel B in Table 6. The coefficient estimates on Top10LLTIO are negative and significant at a better than 5% level of confidence in both columns, suggesting that the LLTIO effect is salient when the lead bank is not geographically close to the borrowing firm and when the borrowing firm does not have an urban location. The coefficient sums, (Top10LLTIO + Close Bank × Top10LLTIO) and (Top10LLTIO + Urban10 × Top10LLTIO), are both insignificantly different from zero (Chisquare statistics of 0.49 and 0.88, respectively), suggesting that the LLTIO effect vanishes when the loan is issued by a lead bank located close to the borrowing firm or when the borrowing firm is located in a large urban area. [Table 6 about here] As LLTIO is a type of equity ownership, we expect the salience of the LLTIO effect to vary with the likelihood of conflicts of interest between creditors and shareholders. Conflicts of interest arise when there is a risk of default. Myers (2001) states: “If debt is totally free of default risk, debtholders have no interest in the income, value or risk of the firm. But if there is a chance of default, then shareholders can gain at the expense of debt investors. Equity is a residual claim, so shareholders gain when the value of existing debt falls, even when the value of the firm is constant” (p. 96). To explore how the LLTIO effect varies with the likelihood of a conflict of interest, which is driven largely by default risk, we employ two proxies for default risk: whether the borrowing firm has an investment-grade rating on its long-term debt and whether the loan was syndicated in the midst of a financial crisis. As the default risk is lower for investment-grade borrowing firms than for their non-investment-grade counterparts, and lower during nonfinancial crisis periods than crisis periods, we expect the LLTIO effect to be more salient in subsamples with a lower default risk in which risk-driven conflicts of interest are also less likely. The results for subsamples constructed using the two foregoing proxies are reported in Table 7. Columns (1)–(2) examine logspread in subsamples of borrowing firms with and without investment-grade long-term debt, with the LLTIO effect salient only in the former. Columns (3)– (4) consider logspread in subsamples with loans issued in financial and non-financial crisis periods, with crisis periods defined as 2000–2002 and 2007–2009. The LLTIO effect is salient only in the non-crisis subsample. In addition, the control variables for a previous banking relationship (Relation dummy) and bank reputation (Top3Bank) exert different influences on logspread in the crisis and non-crisis subsamples.18 Prior research has documented an association between a lower loan spread and a previous banking relationship (Berger and Udell, 1995; Petersen and Rajan, 1994) and lender certification (Ross, 2010), whereas here both Relation dummy and Top3Bank are (negatively) significant only in non-crisis periods. Our conjecture concerning the differential effects of Relation dummy and Top3Bank on logspread is that loans are in greater demand during crisis periods, as concerned borrowers prepare for a potential liquidity squeeze and lenders systematically increase loan spreads to compensate for the greater default risk (Santos, 2011). In this extreme environment, the benefits of lender certification and relationship lending diminish, putting pressure on prospective borrowers. The differential LLTIO effect that we report in Tables 6 and 7 also suggests that it is unlikely due to the dual role of the institutional investor as both a shareholder and a creditor. [Table 7 about here] 4.5 Within-Syndicate LLTIO Effect We also investigate whether the LLTIO effect exists within a loan syndicate, as the lead bank in a syndicate essentially serves as a half-agent for the other participating banks, and there is thus information asymmetry between the lead and participating banks (Sufi, 2007). As monitoring is not observable, lead banks can shirk from their duties, with the other banks possibly bearing the full cost of such shirking. To explore whether the presence of LLTIOs at the borrower level alleviates the severity of within-syndicate information asymmetry, we investigate 18 Because our Investment Grade dummy variable is defined in a way that includes non-rated firms and below BBBrated firms, the differential effects of pos_relation1 and Top3Bank with respect to Investment Grade are unclear. whether the syndicate structure changes with such presence. As Sufi (2007) shows, more severe information asymmetry problems force a lead bank to take a larger stake in a loan. If the LLTIO effect alleviates information asymmetry within the syndicate, we expect a negative relation between Top10LLTIO and LeadShare, that is, the stake held by the lead bank. In unreported results with LeadShare as the dependent variable, and after controlling for firm-, loan-, and macroeconomy-level characteristics and industry effects, we find LLTIOs to have a negative, albeit non-significant, effect on LeadShare. We therefore do not find empirical support for the existence of a within-syndicate LLTIO effect. 4.6 Mechanisms of the LLTIO Effect As information asymmetry leads to both adverse selection and moral hazard problems, the mechanisms driving the LLTIO effect could serve to (1) alleviate the ex ante information asymmetry associated with adverse selection problems, (2) alleviate the ex post information asymmetry associated with moral hazard problems, or (3) alleviate both. Even though we argue that better monitoring is likely the reason for the LLTIO effect, we use the implementation of Regulation FD and SOX as natural experiments to examine whether the LLTIO effect is associated with less severe adverse selection problems or moral hazard problems, respectively. The use of natural experiments helps mitigate concerns over the results being driven by endogenous factors. By promoting the full and fair disclosure at public companies, Regulation FD has greatly reduced the informational advantage of institutional investors (Cornett, Tehranian, and Yalcin, 2007). By imposing higher standards of corporate governance, SOX has encouraged institutional activism through changes in legal and regulatory standards (Gillan, 2006). Although we are aware that the implementation of both Regulation FD and SOX has affected all institutional investors, we argue that their marginal effects are greater for local institutions. First, the information advantage enjoyed by local institutions is widely documented (see, for example, Malloy, 2005) but yet to be established for general institutions.19 Second, we demonstrate in Section 4.1 that it is geographic proximity that drives the LLTIO effect. When reacting to a stronger institutional monitoring environment, however, local institutions are more likely to monitor because the cost of doing so is lower (Kang and Kim, 2008). We adopt a difference-in-difference (DiD) approach that captures the incremental effect of Regulation FD and SOX implementation to examine the mechanisms through which the LLTIO effect operates. If it relies on the mechanism of alleviating adverse selection problems, we expect it to weaken and possibly disappear following Regulation FD implementation. If, in contrast, the LLTIO effect relies on the mechanism of alleviating moral hazard problems, we expect it to strengthen in the post-SOX implementation period. We define an indicator variable, post Regulation FD, which is set to one for the years after 1999 (as Regulation FD was implemented in October 2000), and zero otherwise. Our other indicator variable, post SOX, is set to one for the years after 2001 (as SOX was implemented in July 2002), and zero otherwise. We also include the interaction terms Top10LLTIO×post Regulation FD and Top10LLTIO×post SOX as additional variables in the respective baseline specifications. After controlling for firm-, loan-, and macroeconomic characteristics and industry effects, we compare the LLTIO effect over the two-year subsamples before and after Regulation FD implementation (1998–1999 to 2000–2001), over the two-year subsamples before and after 19 As Coval and Moskowitz (2001) point out, prior studies of mutual fund managers and pension fund managers report that, if anything, their investors consistently underperform the market and other passive benchmark portfolios. See, for example, Carhart (1997), Chevalier and Ellison (1999), and Lakonishok, Shleifer, and Vishny (1992), to name a few. SOX implementation (2000–2001 to 2002–2003), and over 1998–1999 to 2002–2003. Our expectation is that if a due diligence mechanism that alleviates adverse selection problems exists, the LLTIO effect will be weaker over the 2000–2001 period than over the 1998–1999 period, whereas if a monitoring mechanism that alleviates moral hazard problems exists, that effect will be weaker over 2000–2001 than over 2002–2003. Finally, comparison of the LLTIO effect over 1998–1999 and 2002–2003 is informative for comparing the due diligence and monitoring mechanisms. The estimation results are reported in Table 8, with Column (1) showing the effect of Regulation FD implementation by comparing 1998–1999 and 2000–2001. The coefficient estimate on Top10LLTIO is negative and significant with a magnitude of -0.1, whereas that on Top10LLTIO×post Regulation FD is positive and significant, with a magnitude of 0.14, both at a confidence level of 1%, suggesting that the LLTIO effect vanished after the implementation of Regulation FD. This provides evidence for the existence of an adverse selection-alleviating mechanism (due diligence mechanism). Column (2) of Table 8 examines the effect of SOX implementation by comparing 2000– 2001 and 2002–2003. The coefficient estimate on the interaction term Top10LLTIO ×Post SOX is negative and significant, with a confidence level of 1%, suggesting that the LLTIO effect became more salient after the implementation of SOX. This provides evidence for the existence of a moral hazard-alleviating mechanism (monitoring mechanism). Finally, Column (3) examines the relative importance of the two mechanisms by comparing 2002–2003 and 1998–1999. The coefficient estimates for both Top10LLTIO and the interaction term Top10LLTIO ×Post SOX are negative and significant, suggesting that the monitoring mechanism is stronger than the due diligence mechanism. To check for the robustness of both mechanisms, we vary the two-year period before and after the implementation of Regulation FD and SOX used in the DiD regression, respectively. We do not observe the same significant results when the two-year period before and after Regulation FD changes to 2001-2002. The significant decrease in logspread associated with the interaction term Top10LLTIO ×Post SOX remains as we vary the cutoff months of the two-year period in different ways. The highly robust results on the monitoring mechanism suggest that the monitoring function of the LLTIOs is more likely the factor that drives the LLTIO effect.20 [Table 8 about here] 4.7 LLTIO’s Monitoring Role We next explore further evidence for the monitoring role of the LLTIOs and the possible channels for such monitoring function. As Chhaochharia, Kumar and Niessen-Ruenzi (2012) document a number of empirical evidence suggesting that geographic proximity of the institutional investors is associated with improved corporate governance, we investigate direct evidence to establish the causal link between LLTIO and corporate governance using our loan sample. Our proxy for internal governance weakness is “lucky” option grants to CEOs and directors, where lucky option grants are defined as options granted at the lowest stock price of the month. Bebchuk, Grinstein, and Peyer (2010) show that the opportunistic timing of option grants reflects internal governance weakness and that lucky option grants to CEOs and directors suggest weak monitoring. If the LLTIOs monitor, we expect to observe a lower likelihood of lucky CEO option grant. We run a pooled regression with clustered standard errors using the data on lucky CEO option granting21 over the sample period of 1996-2005 and find that this is indeed 20 We also vary the two-year periods to ensure that our results are not sensitive to exclusion of the years in which the two regulations took effect. These results are available upon request. 21 We thank Professor Bebchuk for providing the data on his website at http://www.law.harvard.edu/faculty/bebchuk/data.shtml. the case. The results are displayed in Column (1) of Panel A, Table 9. Here the dependent variable is a dummy variable indicating whether a CEO grant event was lucky and Top10LLTIO is the independent variable of interest. We control for Lucky director, which is a dummy variable indicating whether an independent director grant event was lucky, and other variables like Top10IO, Top10LSTIO, as well as a number of firm characteristics, including firm size, S&P 500 membership, leverage, profitability, R&D, etc. The coefficient estimate on Top10LLTIO is negative and significant with a confidence level of 5%, consistent with our conjecture that the LLTIOs monitor and reduce the likelihood of a lucky CEO grant event. Bebchuk, Cohen, and Ferrell (2009) also show that the entrenchment index (E-index), which is based on six out of the twenty-four provisions that are included in the G-index (Gompers, Isshi and Metrick, 2003), is monotonically associated with economically significant reduction in firm value. If LLTIOs monitor, we expect to observe a negative relation between LLTIO and the level of E-index. To test our conjecture, we run three pooled regressions, with Eindex and G-index22 as the dependent variable, respectively, and with Top10LLTIO as the independent variable of interest. Whether we use OLS or ordered Probit models, the coefficient estimates of Top10LLTIO are negative and significant, with a confidence level of at least 5%, when the dependent variable is E-index, as shown in Columns (2)-(3) of Table 9. The coefficient estimate of Top10LLTIO is insignificant, as shown in Column (4) of Table 9, when the dependent variable is G-index. These results suggest that the LLTIOs monitor material governance provisions that matter for firm valuation. [Table 9 about here] 4.8 Propensity Score Matching Analysis 22 E-index and G-index are constructed following Bebchuk, Cohen, and Ferrell (2009) and Gompers, Isshi, and Metrick (2003), respectively. We thank Professor Bebchuk for providing the E-index on his website at: http://www.law.harvard.edu/faculty/bebchuk/data.shtml. As can be observed from Panel A of Table 2, there are significant differences in most firm and loan characteristics between loans with high and low levels of Top10LLTIO. To ensure that the LLTIO effect that we have documented is not driven by firm or loan characteristics, we match loans using borrowing firm characteristics and loan characteristics based on propensity scores, and then compare the loan spreads based only on one variable for the matched samples: whether Top10LLTIO is higher than 5% (high Top10LLTIO) or not (low Top10LLTIO). The results indicate that the LLTIO effect continues to hold for these matched loans. We conduct propensity matching using a logit model with the following borrowing firm characteristics: S&P 500 index membership dummy, overall institutional ownership, stake held by the 10 largest institutional owners, firm size, leverage usage, Tobin’s Q, R&D-to-asset ratio, ROA, dividend dummy, asset tangibility, and cash flow volatility. The model also incorporates the following loan characteristics: previous banking relationship with lead bank, loan originated by one of the top-three banks in loan syndication, secured loan dummy, short-term revolver dummy, long-term revolver dummy, term loan dummy, other loan dummy, loan maturity, investment grade dummy, term spread, credit spread, various dummies for loan purposes, and Fama-French twelve-industry categorization. Based on the closeness of their propensity scores, we select the nearest syndicated loan-firm observation with similar (matched) characteristics and compare mean spread and logspread based on one variable: whether the level of Top10LLTIO is above 5% or not. We then conduct the same exercise choosing from the three nearest syndicated loan-firm observations, and compare the difference in spread and logspread with respect to Top10LLTIO. The propensity matching results reported in Table 10 show that we are able to match a group of firms than resemble one another within an allowed error margin (caliper) of 0.05. The spread for the high Top10LLTIO (Top10LLTIO ≥5%) group is 31.285 basis points lower than that for the low Top10LLTO (Top10LLTIO <5%) group before matching. The magnitude of the difference in spread after matching using different criteria ranges from -6.650 to -7.437 basis points, remaining negative and significant at a confidence level of 1%. The magnitude of the difference in logspread after matching using different criteria is about 1.06 basis points, remaining negative and significant at a confidence level of 1%. The difference in economic significance with spread and logspread may be due to outlier effect, which is more drastic with spread. [Table 10 about here] 5. Conclusion We propose geographically proximate institutional ownership with a large stake in a borrowing firm as a proxy for exogenous, informed ownership that serves as an informative signal. Using this proxy, we test the theoretical arguments in Leland and Pyle (1977) and Holmstrom (1979), and provide empirical support for the proposition that informed ownership provides an informative and valuable signal in the context of syndicated loan contracting. We find that long-term and not short-term institutional ownership is associated with lower loan spreads at local borrowing firms. 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Zhang, 2009, “Institutional Investors and Equity Returns: Are Short-term Institutions Better Informed?” Review of Financial Studies 22, 832-924. Yi, H. and D. Mullineaux, 2006, “The Informational Role of Bank Loan Ratings,” Journal of Financial Research 29, 481 - 501. Appendix A. Variable definitions Variable Name Definitions and measurements Source Bank loan spread Spread Initial all-in-drawn spread over LIBOR DealScan Log (loan spread) Logspread Log of Initial all-in-drawn spread over LIBOR DealScan S&P500 dummy S&P500 Takes 1 if a firm belongs to S&P500, else 0 Compustat Urban10 Urban10 Close bank Close bank Takes 1 if a belongs to the top 10 urban areas, else 0 Takes 1 if lenders and borrowers are located within 100 miles, else 0 Compustat, DealScan Relation Relation Relation dummy Relation dummy Top10 Institutional Ownership Top10 IO Top10 Local Institutional Ownership Top10 Local IO Top10 short-term institutional ownership Top10 STIO Top10 long-term institutional ownership Top10 LTIO Top10 local short-term institutional ownership Top10 LSTIO Top10 local dedicated institutional ownership Top10LDIO Top10LQIO Number of loan experiences from the same bank Takes 1 if a borrower has borrowed from the same bank before, else 0 # of shares held by largest ten (measured by stakes in the borrowing firm) institutional investors / # of total shares outstanding Ownership by the largest ten (measured by stakes in the borrowing firm) institutional investors that have headquarters within 100 miles from headquarters of the borrowing firm Ownership by the largest ten (measured by stakes in the borrowing firm) institutional investors that are identified as belonging to the transient type by Bushee (1998). Ownership by the largest ten (measured by stakes in the borrowing firm) institutional investors that are identified as belonging to either the dedicated or quasi-indexer type by Bushee (1998). Ownership by the largest ten (measured by stakes in the borrowing firm) institutional investors that are identified as belonging to the transient type by Bushee (1998) and have headquarters within 100 miles from headquarters of the borrowing firm Ownership by the largest ten (measured by stakes in the borrowing firm) institutional investors that are identified as belonging to the dedicated type by Bushee (1998) and have headquarters within 100 miles from headquarters of the borrowing firm Ownership by the largest ten (measured by stakes in the borrowing firm) institutional investors that are identified as belonging to quasi-indexer type by Bushee (1998) and have headquarters within 100 miles from headquarters of the borrowing firm Top10 LLTIO Top10LLTIO=Top10LDIO+Top10LQIO Thompson Reuters 13F Thompson Reuters 13F Total Assets TA at Compustat Log (Total Assets) LogTA Log(at) Compustat Leverage Leverage Total debt / TA Compustat Tobin’s Q Tobin’s Q Market value of assets / Book value of assets Compustat Return on Assets ROA Net Income / TA, ni/at Compustat R&D/Total Assets R&D/TA Xrd/at Compustat Intangible Assets /Total Assets Intangible/TA Intan/at Compustat Dividend dummy Div dummy Takes 1 if a firm pays dividend, else 0 Compustat Net Fixed Assets/Total Assets Standard deviation of cash flows NFA/TA Ppent/at Compustat STD CF Standard deviation of previous 5 year cash flows Compustat Secured loan Secured loan Takes 1 if loan is secured, else 0 DealScan Top10 local quasi-indexer institutional ownership Top10 local long-term institutional ownership DealScan DealScan Thompson Reuters 13F Thompson Reuters 13F Thompson Reuters 13F Thompson Reuters 13F Thompson Reuters 13F Thompson Reuters 13F Short-term revolver loan St revolver Takes 1 if loan is short-term revolver, else 0 DealScan Long-term revolver loan Lt revolver Takes 1 if loan is long-term revolver, else 0 DealScan Term loan Term loan Takes 1 if loan is term loan, else 0 Deal Scan Other loan Other loan Takes 1 if loan is other loan, else 0 Deal Scan Loan Maturity Maturity Maturity of loans, expressed in months Deal Scan Long-term bond credit rating LT CR rating Compustat Investment grade Invgrade Investment grade2 Invgrade2 Rated status Rated 1 (CCC-) to 22 (AAA) Takes 1 if a company’s S&P long-term credit rating is BBB- and above, else 0 (0 includes not rated firms) Takes 1 if a company’s S&P long-term credit rating is BBB- and above and takes 0 if the long-term rating is below BBB- (0 does not include not rated firms) Takes 1 if a company has S&P long-term credit rating, else 0 Term spread Term spread Annual term spread (10 year – 1 year Tbond spread) FED Credit spread Credit spread Annual credit spread (CCC – AAA corporate bond) FED Lucky CEO Lucky CEO Takes 1 when options to the CEO are granted at the lowest stock price of the month, else zero. Lucky director Lucky director Takes 1 when options to directors are granted at the lowest stock price of the month, else zero. E-index E-index Governance index (composed of 6 items) G-index G-index Governance index (composed of 24 items) Compustat Compustat Compustat Bebchuk, Grinstein, and Peyer (2010) Bebchuk, Grinstein, and Peyer (2010) Bebchuk, Cohen, and Ferrell (2009) Gompers, Ishii, and Metrick (2003). Table 1. Summary Statistics Table 1 reports summary statistics for our sample over the period of 1995-2009. An institutional owner is defined as “local” if the headquarters of the institution is within a 100-mile radius of the company’s headquarters. Annual Compustat data are matched to Thomson Reuters DealScan data using the identifiers of Chava and Roberts (2008). We exclude securities with share codes different from 10 or 11, financial and utilities companies, borrowers incorporated or headquartered outside the U.S., loans originated outside of the U.S., loans denominated in foreign currencies, loans with benchmark rates other than the LIBOR, and observations with missing data. The sample includes 17,308 firm-loan observations and the list of variable definitions and measurements is shown in Appendix A. Variable Spread Logspread S&P500 Urban10 Close bank Relation Relation dummy Top3bank IO Top10 IO Top10 STIO Top10 LTIO Top10LSTIO Top10LLTIO Top10LQIO TA (million U$) LogTA Leverage Tobin's Q ROA R&D/TA Intangible/TA Div dummy NFA/TA STD CF Secured loan St revolver Lt revolver Term loan Maturity Modified Z Invgrade N 17308 17308 17308 17308 6983 17308 17308 17308 17308 17308 17308 17308 17308 17308 17308 17308 17308 17308 17308 17308 17308 15471 17308 17308 17308 17308 17308 17308 17308 17308 17308 17308 Mean 181.509 4.894 0.201 0.326 0.036 0.490 0.442 0.394 0.577 0.288 0.072 0.215 0.020 0.069 0.052 3602 6.764 0.309 1.745 0.029 0.015 0.193 0.486 0.325 0.046 0.532 0.123 0.559 0.261 48.226 1.701 0.235 Median 165.000 5.106 0.000 0.000 0.000 0.000 0.000 0.000 0.616 0.300 0.052 0.212 0.000 0.000 0.000 787 6.667 0.286 1.438 0.040 0.000 0.132 0.000 0.269 0.028 1.000 0.000 1.000 0.000 59.000 1.740 0.000 SD 131.781 0.867 0.401 0.469 0.186 0.591 0.497 0.489 0.265 0.159 0.072 0.137 0.061 0.144 0.114 7937 1.710 0.212 0.995 0.094 0.034 0.195 0.500 0.229 0.058 0.499 0.329 0.497 0.439 24.337 1.689 0.424 Min 2.700 0.993 0.000 0.000 0.000 0.000 0.000 0.000 0.003 0.000 0.000 0.000 0.000 0.000 0.000 24 2.827 0.000 0.699 -0.445 0.000 0.000 0.000 0.013 0.003 0.000 0.000 0.000 0.000 1.000 -73.295 0.000 Max 1500.000 7.313 1.000 1.000 1.000 4.000 1.000 1.000 1.000 0.660 0.337 0.580 0.390 0.745 0.615 55272 10.893 1.016 6.565 0.239 0.196 0.776 1.000 0.901 0.386 1.000 1.000 1.000 1.000 264.000 5.308 1.000 Invgrade2 Rated Term spread Credit spread Repay purpose CP backup purpose Working capital purpose Buyback purpose Takeover purpose LBO purpose Project purpose Others purpose 8620 17308 17308 17308 17308 17308 17308 17308 17308 17308 17308 17308 0.472 0.498 0.811 0.882 0.195 0.073 0.182 0.011 0.198 0.042 0.006 0.031 0.000 0.000 0.490 0.810 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.499 0.500 0.856 0.349 0.396 0.260 0.386 0.106 0.399 0.201 0.074 0.174 0.000 0.000 -0.410 0.550 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 1.000 2.830 3.380 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 Table 2. Univariate Tests Panel A. Loan spread by High vs. Low Top10 Local Long-Term and Short-Term Institutional Owners (Top10LLTIO and Top10LSTIO) Panel A reports results from a univariate comparison of firm and loan characteristics between borrowing firms with high and low Top10LLTIO and Top10LSTIO sub-samples. High Top10LLTIO (Top10LSTIO) is a sample with 5% and above of Top10LLTIO (Top10LSTIO), else firm belongs to Low Top10LLTIO (Top10LSTIO) sample. Variables Spread Logspread S&P500 Urban10 Close bank Relation Relation dummy Top3bank IO Top10 IO Top10 STIO Top10LTIO Top10LSTIO Top10LLTIO LogTA Leverage Tobin's Q ROA R&D/TA Intangible/TA Div dummy NFA/TA STD CF Secured loan St revolver Lt revolver Term loan Maturity Modified Z Invgrade Invgrade2 Rated Top10LLTIO High Low 158.875 190.160 4.694 4.971 0.285 0.169 0.440 0.283 0.098 0.023 0.523 0.477 0.463 0.434 0.454 0.371 0.607 0.566 0.313 0.279 0.067 0.073 0.245 0.204 0.045 0.010 0.245 0.002 7.017 6.667 0.289 0.317 1.883 1.693 0.035 0.027 0.019 0.014 0.222 0.182 0.543 0.464 0.267 0.348 0.042 0.048 0.446 0.564 0.159 0.110 0.560 0.558 0.234 0.272 46.276 48.972 1.770 1.674 0.301 0.210 0.586 0.426 0.514 0.492 Difference (low-high) 31.285*** 0.277*** -0.116*** -0.157*** -0.047*** -0.046*** -0.029*** -0.083*** -0.041*** -0.034*** 0.006*** -0.041*** -0.036*** -0.244*** -0.350*** 0.029*** -0.190*** -0.008*** -0.005*** -0.040*** -0.079*** 0.080*** 0.006*** 0.118*** -0.049*** -0.002 0.038*** 2.695*** -0.095*** -0.092*** -0.160*** -0.022*** Top10LSTIO High Low 176.771 182.154 4.909 4.892 0.179 0.204 0.478 0.306 0.095 0.029 0.507 0.487 0.448 0.441 0.409 0.392 0.614 0.572 0.316 0.284 0.111 0.066 0.202 0.217 0.155 0.001 0.164 0.056 6.681 6.775 0.296 0.311 1.937 1.719 0.033 0.029 0.019 0.015 0.219 0.190 0.394 0.498 0.260 0.334 0.053 0.045 0.539 0.531 0.112 0.125 0.568 0.557 0.263 0.261 48.44 48.20 1.607 1.714 0.192 0.241 0.408 0.480 0.470 0.502 Difference (low-high) 5.383* -0.017 0.025*** -0.173*** -0.066*** -0.020 -0.007 -0.017 -0.041*** -0.031*** -0.045*** 0.015*** -0.153*** -0.108*** 0.094** 0.016*** -0.218*** -0.004* -0.004*** -0.030*** 0.104*** 0.074*** -0.007*** -0.008 0.012* -0.011 -0.002 -0.245 0.106** 0.049*** 0.072*** 0.032*** Panel B. Borrowers with Investment Graded- vs. Non-Investment Graded Borrowers Panel B reports results from a univariate comparison of firm and loan characteristics between borrowing firms with investment grade and non-investment grade long-term bonds. Borrowing firms with non-investment grade bonds include non-rated firms. Variables Spread Logspread S&P500 Urban10 Close bank Relation Relation dummy Top3bank IO Top10 IO Top10STIO Top10LTIO Top10LSTIO Top10LLTIO LogTA Leverage Tobin's Q ROA R&D/TA Intangible/TA Div dummy NFA/TA STD CF Secured loan St revolver Lt revolver Term loan Maturity Modified Z Rated Investment grade (1) (N=4066) 78.815 3.976 0.683 0.353 0.044 0.672 0.581 0.598 0.679 0.309 0.049 0.259 0.012 0.078 8.651 0.273 1.885 0.056 0.017 0.184 0.832 0.358 0.026 0.115 0.355 0.483 0.110 39.126 1.993 1.000 Non-investment grade (0) (N=13242) 213.042 5.176 0.053 0.318 0.033 0.434 0.399 0.332 0.546 0.282 0.078 0.202 0.022 0.066 6.184 0.321 1.703 0.021 0.015 0.196 0.380 0.315 0.053 0.660 0.052 0.582 0.308 51.021 1.611 0.344 Difference ( 0 – 1) 134.227*** 1.200*** -0.631*** -0.034*** -0.010* -0.238*** -0.182*** -0.266*** -0.133*** -0.027*** 0.029*** -0.057*** 0.010*** -0.012*** -2.467*** 0.048*** -0.182*** -0.035*** -0.003*** 0.012*** -0.452*** -0.043*** 0.027*** 0.548*** -0.303*** 0.988*** 0.198*** 11.895*** -0.382*** -0.656*** Panel C. Loan Spread by Top10LLTIO and Credit Ratings Panel C reports spread and logspread for borrowing firms with Top10 LLTIO classified by 5% threshold and credit ratings. Investment grade Top10LLTIO Difference Variables High(≥5%) Low(<5%) (low-high) Spread 67.718 84.912 17.815*** Logspread 3.804 4.071 0.267*** Non-investment grade Top10LLTIO Difference High (≥5%) Low(<5%) (low-high) 198.183 218.062 19.878*** 5.077 5.209 0.132*** Table 3. Loan Spread and Institutional Ownership Table 3 reports results from estimating Equation (2), the relation between loan spread and local institutional ownership after controlling for institutional ownership in general, firm characteristics, loan characteristics, macroeconomic variables, as well as industry effects over the period of 1995-2009. An institutional owner is defined as “local” if the headquarters of the institution is within a 100-mile radius of the company’s headquarters. Annual Compustat data are matched to Thomson Reuters DealScan data using the identifiers of Chava and Roberts (2008). We exclude securities with share codes different from 10 or 11, financial and utilities companies, borrowers incorporated or headquartered outside of the U.S., loans originated outside of the U.S., loans denominated in foreign currencies, loans with benchmark rates other than the LIBOR, and observations with missing data. Robust standard errors are two-way clustered at the borrowing firm and year levels. ***, **, * denote statistical significance at the 1%, 5%, and 10% levels, respectively. The list of variable definitions and measurements is shown in Appendix A. Variables (1) (2) (3) Logspread Logspread Logspread (4) (5) (6) (7) Logspread Logspread Logspread Logspread Non urban Non urban Bank fixed Firm fixed S&P500 -0.165*** -0.163*** -0.156*** -0.152*** -0.163*** -0.183*** -0.085* (-4.397) (-4.337) (-4.461) (-4.399) (-4.523) (-4.871) (-1.930) -0.028** -0.029** -0.041*** -0.004 -0.028** -0.028* -0.002 (-2.376) (-2.407) (-2.651) (-0.291) (-2.351) (-1.876) (-0.128) -0.021 -0.021 -0.012 0.813*** -0.020 -0.031 -0.078*** (-1.327) (-1.330) (-0.735) -4.311 (-1.317) (-1.590) (-3.609) IO -0.160*** -0.162*** -0.197*** -0.173*** -0.160*** -0.166*** -0.300*** (-3.669) (-3.676) (-2.648) (-3.852) (-3.660) (-2.907) (-3.765) Top10 IO 0.276*** 0.277*** 0.484*** 0.293*** 0.163 0.291*** 0.203** (4.479) (4.472) (4.899) (4.657) (0.564) (4.166) (2.316) 0.222** 0.300** 0.155** 0.172 0.048 (2.542) (2.020) (1.969) (1.459) (0.349) -0.122*** -0.224*** -0.130*** -0.156*** -0.162** (-3.164) (-3.297) (-3.294) (-2.856) (-2.251) Relation dummy Top3 bank Top10 Local IO -0.054 (-1.612) Top10 LSTIO Top10 LLTIO Top10 STIO 0.235 (0.729) Top10 LTIO 0.072 (0.271) LogTA -0.073*** -0.073*** -0.053*** -0.077*** -0.073*** -0.073*** -0.092*** (-5.700) (-5.661) (-3.315) (-6.178) (-5.555) (-4.517) (-3.962) Leverage 0.463*** 0.463*** 0.435*** 0.431*** 0.460*** 0.447*** 0.296*** (10.850) (10.820) (6.605) (11.150) (10.700) (8.477) (4.248) Tobin’s Q -0.102*** -0.102*** -0.136*** -0.105*** -0.103*** -0.094*** -0.086*** (-9.918) (-9.944) (-7.558) (-9.242) (-9.238) (-7.779) (-7.258) -0.762*** -0.763*** -0.867*** -0.728*** -0.762*** -0.790*** -0.599*** (-7.104) (-7.161) (-4.326) (-7.259) (-7.208) (-6.513) (-4.740) ROA_n R&D/TA Div dummy NFA/TA STD CF Secured loan ST revolver Lt revolver Other loans Maturity Modified Z Invgrade -0.351 -0.33 -0.587 -0.363** -0.351 -0.157 -0.253 (-1.638) (-1.545) (-1.124) (-1.977) (-1.616) (-0.612) (-0.364) -0.075*** -0.073*** -0.057*** -0.061*** -0.074*** -0.077*** 0.004 (-6.054) (-5.951) (-3.040) (-5.081) (-5.963) (-5.028) (0.195) -0.091** -0.092** -0.126** -0.062 -0.086** -0.103** -0.228* (-2.361) (-2.423) (-2.208) (-1.516) (-2.286) (-2.439) (-2.098) 0.802*** 0.789*** 0.488*** 0.725*** 0.796*** 0.699*** 0.340 (6.861) (6.935) (2.843) (7.339) (6.845) (6.143) (1.636) 0.368*** 0.367*** 0.372*** 0.350*** 0.367*** 0.368*** 0.294*** (11.970) (11.990) (9.005) (13.380) (11.690) (12.55) (12.30) -0.480*** -0.480*** -0.520*** -0.443*** -0.481*** -0.507*** -0.339*** (-17.260) (-17.370) (-11.130) (-16.890) (-17.280) (-16.10) (-12.53) -0.319*** -0.319*** -0.340*** -0.290*** -0.319*** -0.316*** -0.217*** (-18.840) (-18.890) (-13.290) (-17.950) (-18.810) (-17.05) (-15.61) -0.090*** -0.090*** -0.059* -0.083*** -0.090*** -0.110*** -0.035 (-3.384) (-3.434) (-1.788) (-3.232) (-3.447) (-3.545) (-1.175) -0.003*** -0.003*** -0.003*** -0.002*** -0.003*** -0.003*** -0.002*** (-6.316) (-6.350) (-3.895) (-7.625) (-6.307) (-7.257) (-3.756) -0.017*** -0.016*** -0.030** -0.016*** -0.016*** -0.016** -0.003 (-2.908) (-2.877) (-2.381) (-2.656) (-2.865) (-2.147) (-0.364) -0.448*** -0.447*** -0.431*** -0.445*** -0.411*** -0.146*** (-12.250) (-12.290) (-12.640) (-11.950) (-11.87) (-3.322) Invgrade2 -0.452*** (-9.911) Rated 0.120*** 0.119*** 0.125*** 0.119*** 0.123*** 0.047 (6.483) (6.500) (6.809) (6.304) (6.127) (1.365) 0.070** 0.071** 0.086** 0.071*** 0.070** 0.071** 0.119*** (2.268) (2.280) (2.570) (2.700) (2.270) (2.304) (4.651) 0.389*** 0.389*** 0.432*** 0.342*** 0.390*** 0.369*** 0.191** (6.471) (6.462) (8.016) (8.134) (6.491) (5.972) (2.883) 5.383*** 5.382*** 5.464*** 5.027*** 5.376*** 5.395*** 5.335*** (67.050) (66.500) (34.950) (45.050) (65.830) (72.56) (27.36) Loan purpose Yes Yes Yes Yes Yes Yes Yes FF12 Industry fixed Yes Yes Yes Yes Yes Yes Yes Bank fixed No No No Yes No No No Term spread Credit_spread Constant Firm fixed No No No No No No Yes Two-way clustered Yes Yes Yes Yes Yes Yes Yes Observations 17308 17308 8620 17206 17308 11600 11600 R-squared 0.657 0.658 0.720 0.695 0.657 0.640 0.822 Table 4. Instrumental Variable (IV) Regressions for Bank Loan Spread Table 4 reports results from instrumental variable regressions for the relation between natural logarithm of loan spread and Top10LLTIO. We use two instruments, State Top10LDIO and Industry Top10LQIO for Top10LLTIO. State Top10LDIO is annual average of top10 local dedicated institutional ownership for all firms in the same state but in different industries defined by 2-digit SIC code. Industry Top10LQIO is annual average of top10 local quasi institutional ownership for all other firms within the same industry defined by 2-digit SIC code. Column (1) and (2) report results from the second stage regressions for overall sample and rated sample only, respectively. An institutional owner is defined as “local” if the headquarters of the institution is within a 100-mile radius of the company’s headquarters. Annual Compustat data are matched to Thomson Reuters DealScan data according to Chava and Roberts (2008). We exclude securities with share codes different from 10 or 11, financial and utilities companies, borrowers incorporated or headquartered outside of the U.S., loans originated outside of the U.S., loans denominated in foreign currencies, loans with benchmark rates other than the LIBOR, and observations with missing data. Robust standard errors are two-way clustered at the borrowing firm and year levels. ***, **, * denote statistical significance at the 1%, 5%, and 10% levels, respectively. The list of variable definitions and measurements is shown in Appendix A. Variables (1) First Stage Top10LLTIO Top10LLTIO S&P500 Relation dummy Top3bank Logat Leverage Tobin’s Q ROA_n R&D/TA Div dummy NFA/TA STD CF Secured loan ST revolver LT revolver Other loans Maturity Modified Z Invgrade Term spread 0.009 (1.08) -0.003 (-1.24) 0.004 (1.05) 0.001 (0.43) 0.001 (0.12) -0.001 (-0.76) 0.024 (1.65) 0.029 (0.46) 0.006 (1.33) -0.042*** (-3.84) 0.004*** (4.14) -0.007* (-1.68) -0.004 (-0.55) 0.001 (0.49) -0.009* (-1.67) -0.000* (-1.74) -0.002 (-1.19) -0.007 (-0.97) -0.002 (2) Logspread Full Sample (Second stage) -0.255** (-2.227) -0.183*** (-5.722) -0.030*** (-2.789) -0.020 (-1.440) -0.070*** (-9.978) 0.501*** (12.67) -0.074*** (-7.407) -0.216* (-1.941) -0.363* (-1.693) -0.073*** (-5.055) -0.122*** (-3.178) 0.015*** (5.402) 0.402*** (25.05) -0.516*** (-19.13) -0.330*** (-29.25) -0.095*** (-3.871) -0.003*** (-9.001) -0.043*** (-5.494) -0.392*** (-13.06) 0.072*** (3) First Stage Top10LLTIO -0.001 (-0.05) -0.002 (-0.54) 0.001 (0.24) 0.006 (1.45) -0.006 (-0.28) 0.003 (1.09) 0.017 (0.86) 0.104 (0.79) -0.001 (-0.21) -0.028* (-1.95) -0.010 (-0.21) -0.004 (-0.64) -0.007 (-0.83) 0.006 (1.59) -0.005 (-0.79) -0.000** (-2.00) -0.002 (-0.42) -0.003 (-0.34) -0.000 (4) Logspread Rated only sample (Second stage) -0.360** (-2.186) -0.165*** (-4.605) -0.041*** (-2.709) -0.009 (-0.470) -0.067*** (-4.821) 0.398*** (6.286) -0.117*** (-5.044) -0.290 (-1.092) -0.518 (-0.958) -0.059*** (-2.861) -0.139** (-2.295) 0.067 (0.413) 0.394*** (15.03) -0.568*** (-15.48) -0.353*** (-21.04) -0.069* (-1.915) -0.004*** (-7.364) -0.050*** (-3.760) -0.464*** (-13.48) 0.090*** Credit spread State Top10LDIO Industry Top10LQIO Constant Loan purpose control FF12 Industry fixed Clustered F-test of excluded Instruments (p-value) Endogenous Test (p-value) Hansen’s J-test Observations R-squared (-1.00) 0.001 (0.35) 1.429*** (14.14) 0.544*** (8.05) 0.044* (1.69) Yes Yes Yes 141.24 (p=0.000) 1.620 (p=0.203) 0.505 (p=0.477) 16719 0.173 (11.03) 0.384*** (19.37) 5.465*** (74.59) Yes Yes Yes 16719 0.649 (-0.02) -0.004 (-0.61) 1.609*** (13.11) 0.482*** (5.56) -0.004 (-0.09) Yes Yes Yes 108.29 (p=0.000) 1.069 (p=0.301) 0.731 (p=0.393) 8275 0.236 (9.814) 0.426*** (14.26) 5.703*** (41.90) Yes Yes Yes 8275 0.717 Table 5. Loan Spread and Previous Lending Relationship Table 5 examines whether soft information from previous lending relationship is identical as geographical-related soft information that the LLTIO possess. An institutional owner is defined as “local” if the headquarters of the institution is within a 100-mile radius of the company’s headquarters. Annual Compustat data are matched to Thomson Reuters DealScan data according to Chava and Roberts (2008). We exclude securities with share codes different from 10 or 11, financial and utilities companies, borrowers incorporated or headquartered outside of the U.S., loans originated outside of the U.S., loans denominated in foreign currencies, loans with benchmark rates other than the LIBOR, and observations with missing data. Robust standard errors are two-way clustered at the borrowing firm and year levels. ***, **, * denote statistical significance at the 1%, 5%, and 10% levels, respectively. The list of variable definitions and measurements is shown in Appendix A. Variables S&P500 Relation dummy Top3 bank IO Top10 IO Top10LSTIO Top10LLTIO Relation * Top10LLTIO Loan purpose Loan Type Loan related variables control Financial variables control FF12 Industry fixed Two-way clustered Observations R-squared (1) Logspread (2) Logspread With Previous Relation subsample (3) Logspread Without Previous Relation subsample -0.163*** (-4.321) -0.023** (-2.006) -0.021 (-1.326) -0.162*** (-3.679) 0.277*** (4.485) 0.225** (2.545) -0.087** (-2.535) -0.089 (-1.116) Yes Yes Yes Yes Yes Yes 17,308 0.658 -0.155*** (-3.449) -0.155*** (-4.425) 0.015 (0.787) -0.206*** (-3.393) 0.436*** (6.289) 0.362*** (2.784) -0.178** (-2.431) -0.050*** (-3.336) -0.138*** (-3.281) 0.151** (2.146) 0.110 (1.085) -0.073** (-2.021) Yes Yes Yes Yes Yes Yes 7,653 0.708 Yes Yes Yes Yes Yes Yes 9,655 0.598 Table 6. Top10LLTIO, Loan Spread, and Soft Information Table 6 examines how the need for geography-related soft information and the value of such information with respect to evaluate the borrower’s credit worthiness influence the relation between logspread and Top10LLTIO. An institutional owner is defined as “local” if the headquarters of the institution is within a 100-mile radius of the company’s headquarters. Annual Compustat data are matched to Thomson Reuters DealScan data according to Chava and Roberts (2008). We exclude securities with share codes different from 10 or 11, financial and utilities companies, borrowers incorporated or headquartered outside of the U.S., loans originated outside of the U.S., loans denominated in foreign currencies, loans with benchmark rates other than the LIBOR, and observations with missing data. Robust standard errors are two-way clustered at the borrowing firm and year levels. ***, **, * denote statistical significance at the 1%, 5%, and 10% levels, respectively. The list of variable definitions and measurements is shown in Appendix A. Panel A. Geography-related Soft Information, Hard Information, and the Effect of LLTIO on Loan Spread Panel A reports the results based on four proxies of the need for geography-related soft information and necessary hard information for soft information to latch onto. These subsamples include (a) with and without positive a R&D to assets ratio, (b) above- (high) and below-and-equal-to median (low) intangible assets, (c) with and without longterm credit rating, and (d) with and without secured status for the loan. Variables S&P500 Relation dummy Top3 bank IO Top10IO Top10 LSTIO Top10 LLTIO Loan purpose Loan Type Loan related variables Control Financial variables control FF12 Industry fixed Two-way clustered Observations R-squared (1) Logspread Pos R&D (2) Logspread No R&D (4) Logspread Low intangible -0.116** (-2.292) -0.034** (-2.299) -0.022 (-1.008) -0.102 (-1.707) 0.244** (2.508) 0.259* (2.029) -0.070 (-1.083) Yes Yes Yes (5) Logspread Rated -0.144*** (-3.684) -0.025** (-2.254) -0.020 (-1.244) -0.159*** (-3.037) 0.196*** (2.846) 0.229** (1.988) -0.055 (-1.068) Yes Yes Yes (3) Logspread High Intangible -0.222*** (-4.126) -0.037** (-2.296) -0.027 (-1.326) -0.187*** (-3.347) 0.289*** (3.373) 0.243* (2.027) -0.164*** (-2.970) Yes Yes Yes -0.167*** (-2.848) -0.032 (-1.449) -0.019 (-0.783) -0.187** (-2.156) 0.437*** (3.244) 0.260** (2.417) -0.199*** (-2.855) Yes Yes Yes Yes Yes Yes 6324 0.689 (7) Logspread Secured -0.156*** (-4.461) -0.041*** (-2.651) -0.012 (-0.735) -0.197*** (-2.648) 0.484*** (4.899) 0.300** (2.020) -0.224*** (-3.297) Yes Yes Yes (6) Logspread Not Rated -0.282*** (-4.934) -0.005 (-0.354) -0.033 (-1.453) -0.088* (-1.909) 0.048 (0.621) 0.176 (1.568) -0.028 (-0.578) Yes Yes Yes 0.058 (1.347) -0.028** (-2.166) -0.051*** (-2.955) -0.246*** (-7.045) 0.101 (1.456) -0.042 (-0.447) -0.002 (-0.0491) Yes Yes Yes (8) Logspread Not Secured -0.163*** (-5.099) -0.018 (-0.992) 0.014 (0.827) -0.083 (-1.242) 0.341*** (4.455) 0.633*** (3.693) -0.209*** (-4.305) Yes Yes Yes Yes Yes Yes 10984 0.624 Yes Yes Yes 7873 0.692 Yes Yes Yes 7598 0.637 Yes Yes Yes 8620 0.720 Yes Yes Yes 8688 0.499 Yes Yes Yes 9203 0.303 Yes Yes Yes 8105 0.638 Panel B. Lender and Borrower Location, and Their Effect on Soft Information Production Panel B examines how urban location and geographically proximate lead bank influences the relation between natural logarithm of loan spread and Top10LLTIO. Two geography variables are created: Close Bank and Urban10, which takes a value of one if the headquarters of the lead bank that issued the loan is within a 100-mile radius of the borrowing firm’s corporate headquarters and if the borrowing firm is located in one of the 10 largest MSAs in the U.S., respectively, and zero otherwise. Variables S&P500 Relation dummy Top3 bank IO Top10IO Top10 LSTIO Top10 LLTIO Urban10 Urban10 * Top10LLTIO (1) Logspread Close Bank -0.162*** (-4.320) -0.028** (-2.411) -0.020 (-1.300) -0.164*** (-3.738) 0.276*** (4.454) 0.237*** (2.717) -0.160*** (-2.955) -0.034** (-2.366) 0.108 (1.430) Close bank Close bank * Top10LLTIO Loan purpose Loan Type Loan related variables control Financial related variables control FF12 Industry fixed Two-way clustered Observations R-squared Yes Yes Yes Yes Yes Yes 17308 0.658 Chi square Test: Top10LLTIO + Urban10 * Top10LLTIO=0 (p-value) Chi square Test: Top10LLTIO + Close * Top10LLTIO=0 (p-value) 0.88 (0.348) (2) Logspread Urban 10 -0.176*** (-3.669) -0.032** (-2.370) -0.077*** (-3.413) -0.155*** (-2.642) 0.307*** (3.888) 0.183 (1.345) -0.169** (-2.257) -0.101* (-1.950) 0.032 (0.144) Yes Yes Yes Yes Yes Yes 6983 0.686 0.49 (0.485) Table 7. Conflict of Interests between Shareholders and Creditors Table 7 examines how the likelihood of conflict of interests between shareholders and creditors influence the relation between natural logarithm of loan spread and Top10LLTIO. An institutional owner is defined as “local” if the headquarters of the institution is within a 100-mile radius of the company’s headquarters. Annual Compustat data are matched to Thomson Reuters DealScan data according to Chava and Roberts (2008). We exclude securities with share codes different from 10 or 11, financial and utilities companies, borrowers incorporated or headquartered outside of the U.S., loans originated outside of the U.S., loans denominated in foreign currencies, loans with benchmark rates other than the LIBOR, and observations with missing data. Robust standard errors are two-way clustered at the borrowing firm and year levels. ***, **, * denote statistical significance at the 1%, 5%, and 10% levels, respectively. The list of variable definitions and measurements is shown in Appendix A. (1) Logspread Inv. grade S&P500 -0.144*** (-3.895) Relation dummy -0.025 (-0.881) Top3 bank 0.021 (0.841) IO 0.135 (0.898) Top10IO 0.396*** (2.932) Top10 LSTIO 0.645** (2.114) Top10 LLTIO -0.210** (-2.127) Loan purpose Yes Loan Type Yes Loan related variables controls Yes Financial related variables control Yes FF12 Industry fixed Yes Two-way clustered Yes Observations 4066 R-squared 0.585 Variables (2) Logspread Non-inv. grade -0.090* (-1.778) -0.017 (-1.412) -0.027 (-1.476) -0.215*** (-5.744) 0.147** (2.147) 0.135 (1.401) -0.061 (-1.411) Yes Yes Yes Yes Yes Yes 13242 0.472 (3) Logspread Crisis -0.172*** (-3.456) 0.009 (0.326) -0.015 (-1.123) -0.121* (-1.886) 0.214** (2.350) 0.076 (1.459) 0.021 (0.537) Yes Yes Yes Yes Yes Yes 4699 0.669 (4) Logspread Non-crisis -0.139*** (-2.691) -0.024* (-1.757) -0.038** (-2.300) -0.204*** (-3.621) 0.288*** (4.167) 0.294*** (2.860) -0.180*** (-5.986) Yes Yes Yes Yes Yes Yes 12609 0.664 Table 8. Mechanisms of the LLTIO Effect: Difference-in-Difference Analyses Table 8 reports results from three difference-in-difference (DID) analyses on the relation between natural logarithm of loan spread and Top10LLTIO, using two exogenous shocks: the implementation of Regulation FD (RegFD) and Sarbanes-Oxley (SOX). By comparing the two-year periods before and after these exogenous shocks, the DID analyses capture the change in LLTIO effect. An institutional owner is defined as “local” if the headquarters of the institution is within a 100-mile radius of the company’s headquarters. Annual Compustat data are matched to Thomson Reuters DealScan data according to Chava and Roberts (2008). We exclude securities with share codes different from 10 or 11, financial and utilities companies, borrowers incorporated or headquartered outside of the U.S., loans originated outside of the U.S., loans denominated in foreign currencies, loans with benchmark rates other than the LIBOR, and observations with missing data. Robust standard errors are two-way clustered at the borrowing firm and year levels. ***, **, * denote statistical significance at the 1%, 5%, and 10% levels, respectively. The list of variable definitions and measurements is shown in Appendix A. Variables S&P500 Relation dummy Top3 bank IO Top10IO Top10 LSTIO Top10 LLTIO Post RegFD(98-99 vs. 00-01) Post RegFD * Top10LLTIO (1) DIF (RegFD) 98-99 vs 00-01 -0.230*** (-6.387) 0.024* (1.733) -0.036*** (-2.763) -0.155*** (-2.873) 0.260*** (3.236) 0.119 (1.212) -0.100*** (-2.880) 0.019 (0.579) 0.144*** (4.271) Post SOX(00-01 vs 02-03) (2) DIF (SOX) 00-01 vs 02-03 -0.151*** (-2.848) 0.002 (0.0834) -0.046** (-2.177) -0.183*** (-3.269) 0.440*** (5.358) 0.113* (1.651) 0.074* (1.911) -0.045 (-1.535) -0.291*** (-4.321) Post SOX * Top10LLTIO Post SOX2(98-99 vs 02-03) Post SOX2 * Top10LLTIO Loan purpose Loan Type Loan related variables control Financial related variables control FF12 Industry fixed Two-way clustered Observations R-squared (3) DIF (SOX) 98-99 vs 02-03 -0.187*** (-4.079) -0.025* (-1.660) -0.056*** (-2.763) -0.125** (-2.499) 0.285** (2.492) 0.184** (2.046) -0.089** (-2.548) Yes Yes Yes Yes Yes Yes 5052 0.705 Yes Yes Yes Yes Yes Yes 4910 0.701 -0.156*** (-5.589) -0.116* (-1.835) Yes Yes Yes Yes Yes Yes 5017 0.707 Table 9. The LLTIOs’ Monitoring Role Table 9 examines Top10 LLTIO’s monitoring role. Dependent variable of Column (1) is Lucky of CEO of Bebchuk, Grinstein, and Peyer (2010). Lucky CEO takes 1 when options are granted at the lowest stock price of the month, else zero. Dependent variables of Column (2) and (3) are E-index of Bebchuk, Cohen, and Ferrell (2009) and dependent variable of column (4) is G-index of Gompers, Ishii, and Metrick (2003). Data is from http://www.law.harvard.edu/faculty/bebchuk/data.shtml. Robust standard errors are clustered at the borrowing firm level. All the regressions are include year and industry (defined as SIC2 digit level) fixed effects. ***, **, * denote statistical significance at the 1%, 5%, and 10% levels, respectively. The list of variable definitions and measurements is shown in Appendix A. Variables Lucky director S&P500 IO Top10IO Top10 LSTIO Top10 LLTIO LogTA Leverage Tobin’s Q ROA_n R&D/TA Div dummy NFA/TA STD CF Post SOX (92-2001 vs. 2002-2009) Constant SIC2 Industry fixed Year fixed Clustered at the firm level Observations (Pseudo) R-squared (1) Lucky CEO (Logit) 3.611*** (9.722) -0.616 (-1.282) 1.500** (2.508) -0.663 (-0.656) 1.263 (0.860) -1.059** (-2.006) -0.035 (-0.252) -0.265 (-0.440) 0.052 (0.348) -1.997 (-1.634) 2.855 (0.843) -0.450** (-2.087) -1.353* (-1.714) -0.773 (-0.507) -0.653 (-1.027) -14.352*** (-10.50) Yes Yes Yes 2263 (0.318) (2) E-index (OLS) (3) E-index (Ordered Probit) (4) G-index (OLS) 0.363*** (4.505) 0.588*** (2.700) -0.468* 0.362*** (4.135) 0.602*** (2.764) -0.480* 1.456*** (7.172) 2.107*** (4.098) -1.371** (-1.851) 0.121 (0.310) -0.557*** (-2.585) -0.130*** (-4.354) 0.105 (0.552) -0.070*** (-2.780) -0.410 (-1.427) -1.262 (-1.497) 0.204*** (3.045) 0.176 (0.809) -0.986 (-1.590) (-1.836) 0.174 (0.402) -0.602** (-2.556) -0.130*** (-3.832) 0.084 (0.446) -0.070** (-2.257) -0.436 (-1.496) -1.173 (-1.268) 0.200*** (2.971) 0.170 (0.715) -0.899 (-1.308) (-2.333) -1.661 (-1.612) -0.042 (-0.0990) -0.251*** (-3.462) 0.058 (0.156) -0.232*** (-3.571) -0.483 (-0.775) -3.203 (-1.479) 0.872*** (5.851) 0.282 (0.575) -2.873** (-2.558) 1.070 (1.155) Yes Yes Yes 8901 0.097 5.126*** (5.002) Yes Yes Yes 8901 (0.035) 5.524*** (5.148) Yes Yes Yes 8901 0.158 Table 10. Propensity Score Matching Analysis Table 10 reports results from a two-stage propensity score match. In the first stage, we use a logit model to estimate propensity scores for each loan observation. We match loan observations which differ in the level of Top10LLTIO, with high Top10LLTIO (5% and above) and low Top10LLTIO (below 5%), respectively, and which are similar in size, S&P 500 index membership or not, Tobin’s Q, R&D intensity, dividend-paying or not, ROA, asset tangibility, cash flow volatility, institutional ownership, secured loan status, type of loan, maturity, investment grade or not, term spread, credit spread, loan purposes, and the borrowing firms are in the same Fama-French 12 industries. We report results from the matches using the nearest one observation and the nearest three observations, which is based on the distance of their propensity scores, as well as requiring the error margin (caliper) to be less than 0.05, respectively below. NN1 refers to the nearest one neighbor and NN3 refers to the nearest three neighbors in conducting the matches. An institutional owner is defined as “local” if the headquarters of the institution is within a 100-mile radius of the company’s headquarters. Annual Compustat data are matched to Thomson Reuters DealScan data according to Chava and Roberts (2008). We exclude securities with share codes different from 10 or 11, financial and utilities companies, borrowers incorporated or headquartered outside of the U.S., loans originated outside of the U.S., loans denominated in foreign currencies, loans with benchmark rates other than the LIBOR, and observations with missing data. Robust standard errors are used. The list of variable definitions and measurements is shown in Appendix A. Difference Caliper 0.05 & NN1 Caliper 0.05 & NN3 Spread After matching (High vs. Low Top10LLTIO) -7.437*** (-3.00) -6.650*** (-3.16) Logspread After matching (High vs. Low Top10LLTIO) -0.058*** (-4.09) -0.056*** (-4.69)