Short Sales and Trade Classification Algorithms

This paper describes the market for borrowing corporate bonds using a comprehensive dataset from a major lender. The cost of borrowing corporate bonds is comparable to the cost of borrowing stock, between 10 and 20 basis points, and both have fallen over time. Factors that influence borrowing costs are loan size, percentage of inventory lent, rating, and borrower identity. There is no evidence that bond short sellers have private information. Bonds with CDS contracts are more actively lent than those without. Finally, the 2007 Credit Crunch does not affect average borrowing costs or loan volume, but does increase borrowing cost variance.Keywords: Short Sales, Securities Lending, Corporate Bonds, CDS JEL Classifications: G12, G14 * We thank seminar participants at the JACF Conference in Honor of Stew Myers, the Harvard Finance lunch, Talinn Demirjian and Jeri Seidman for comments. In addition, we are grateful to Sharat Alankar, Joseph Keith, Ted Keith, Patrick Sissman, and Caroline Hane-Weijman for research assistance. We also thank a number of practitioners for answering our questions about how this market works. Finally, we thank the Q Group for their financial support. The views and opinions expressed do not necessarily reflect those of State Street Corporation. The Market for Borrowing Corporate BondsThis paper describes the market for borrowing corporate bonds using a comprehensive dataset from a major lender. The cost of borrowing corporate bonds is comparable to the cost of borrowing stock, between 10 and 20 basis points, and both have fallen over time. Factors that influence borrowing costs are loan size, percentage of inventory lent, rating, and borrower identity. There is no evidence that bond short sellers have private information. Bonds with CDS contracts are more actively lent than those without. Finally, the 2007 Credit Crunch does not affect average borrowing costs or loan volume, but does increase borrowing cost variance.3

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“…Asquith, Oman and Safaya (2010) find for a sample of NYSE and NASDAQ stocks, that short trades are 27.9% of trading volume in 2005.…”

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confidence: 94%

The market for borrowing corporate bonds

Asquith¹,

Covert

et al. 2013

Journal of Financial Economics

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106

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“…To estimate the structural models on market microstructure, our sample period is restricted to 2001–2006, due to the availability of relevant intraday transaction data with trader type. Our data contain a time‐ordered record of every stock transaction on the Korea Exchange (KRX) and information regarding the types and order numbers of buyers and sellers, which enables us to clearly identify the initiator of each trade without employing the Lee and Ready () algorithm, which is reported to have misclassification problems (Ellis et al ., ; Odders‐White, ; Asquith et al ., ; Chakrabarty et al ., ). We define the initiator of a trade as the trader who placed his/her order later than the other among the buyer and seller, following the chronological definition of Odders‐White () .…”

Section: Methodsmentioning

confidence: 99%

“…We use transaction data with information on the trade initiator and the trader type (domestic individual, domestic institution, or foreigner) from 2001 to 2006 in the Korean stock market. In the related literature, the failure of the Lee and Ready () algorithm to specify the trade initiator is shown to be problematic (Ellis et al ., ; Asquith et al ., ; Hwang et al ., ). Hwang et al .…”

Section: Introductionmentioning

confidence: 99%

PIN, Adjusted PIN, and PSOS: Difference of Opinion in the Korean Stock Market

Eom

Kang

Kwon

2017

Asia-Pac J of Fin Stud

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Duarte and Young (2009) decompose the probability of information-based trading PIN into adjusted PIN (AdjPIN) and probability of trading caused by symmetric order flow shocks (PSOS). We explore sources of PSOS in the Korean stock market and examine the relation between PSOS and stock returns. Using transaction data with trader types and initiator information, we find that AdjPIN is not priced, while PSOS is negatively priced, a finding that Lai et al. (2014) label "puzzling." We find that the negative price of PSOS comes from differences of opinion among domestic individual investors on the significance of public news. PIN shows little correlation with other measures for information asymmetry while others are closely correlated with each other; it also shows an insignificant relation with the expected stock returns Choe and Yang (AdjPIN generally shows a significant relation with the expected stock returns while PIN does not. The original PIN model does not fit Korean stock market data, but is only useful for detecting the private-information risk that occurs during an extremely short period of time Hwang et al. (

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“…Chordia, Roll and Subrahmanyam (2005) examine the within-stock daily and intraday relations between returns and this proxy with a focus on how the relation weakened from 1996 to 2002. Frictions such as short-sale constraints, that is, short sales only being allowed to occur on upticks in price, induce imperfections in proxies for total order flow measured by trade-signing algorithms (Asquith, Oman and Safaya, 2010).…”

Section: Datamentioning

confidence: 99%

Informed Trading and Portfolio Returns

Boulatov

Hendershott

Livdan

2012

The Review of Economic Studies

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We solve a multi-period model of strategic trading with long-lived information in multiple assets with correlated innovations in fundamental values. Market makers in each asset can only condition their pricing functions on trading in each asset. Using daily non-public data from the New York Stock Exchange, we test the model's predictions on the conditional and unconditional lead-lag relations of institutional order flow and returns within portfolios. We find support for the model prediction of positive autocorrelations in portfolio returns as well as the predictions for how informed order flow positively predicts future returns and future informed order flow. We show that these relations strengthen for portfolios formed from assets within the same industry, which likely have higher correlation of fundamental values. Furthermore, we discuss issues that arise when testing implications of strategic models with imperfect proxies for the underlying strategic behaviour.

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Short Sales and Trade Classification Algorithms

Cited by 15 publications

References 12 publications

The market for borrowing corporate bonds

The market for borrowing corporate bonds

PIN, Adjusted PIN, and PSOS: Difference of Opinion in the Korean Stock Market

Informed Trading and Portfolio Returns

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