Short selling and margin trading: Evidence from Chinese intra-day data

Transcription

1 Shor selling and margin rading: Evidence from Chinese inra-day daa Absrac Uilizing daily daa on Chinese socks shor selling and margin rading aciviies and inraday ick sock rading daa, we examine he relaionship beween he informaional efficiency of sock prices and shor selling or margin rading. Our main findings are as follows. Firs, here have been subsanial shor selling and margin rading aciviies going on since he incepion of such rading in China. Second, prices become significanly more efficien when socks are allowed o be sold shor or raded on margin. Third, shor selling (margin rading) aciviies become significanly more inense around major informaion evens including large changes in sock price, earnings announcemens, and insider rading. Fourh, regression resuls confirm a significan associaion beween he informaion efficiency of sock price and he inensiy of shor selling and margin rading. In paricular, higher rading volume in shor selling (margin rading) conribues o more efficien sock prices around bad (good) news days. Taken ogeher, our empirical evidence suppors he conjecure ha shor selling and margin rading allow sock prices o absorb new informaion more efficienly. JEL clarificaion: G14; G18 Keywords: Efficiency of sock price; High-frequency daa; Shor selling 1

2 1. Inroducion Ever since he beginning of shor selling and margin rading, hey have been generaing debae over heir impac on he capial marke. In March 2010, China, one of he larges capial markes, began o allow seleced socks o be sold shor or bough on margin. In his sudy, we invesigae wheher shor selling and margin rading affec he informaional efficiency of sock prices, and, if so, how. There exiss an abundan sream of lieraure on shor selling and margin rading. 1 Mos of he sudies use daily or monhly daa on sock prices and shor selling or margin rading aciviies, mainly because higher-frequency daa are no available. 2 Our sudy is one of he very few sudies o use inraday ick ransacion daa and daily shor selling and margin rading daa o examine he link beween shor selling and margin rading and sock price formaion process, and is he firs of such sudies for Chinese marke. Several sudies on China are of relevance. Sharif, Anderson, and Marshall (2013) examine how shor selling and margin rading affec he difference in sock price beween socks eligible for shor selling and margin rading and similar socks ha are no eligible. They find ha he difference in prices declines afer socks become eligible, and hus conclude ha he impac of shor selling dominaes ha of margin rading. Sharif, Anderson, and Marshall (2012) invesigae how he sock prices of he eligible socks reac o he announcemens ha hey are eligible for shor selling and margin rading and heir prices on he day when such rading begins. They find significanly negaive abnormal reurns around boh of he above evens, and conclude ha heir evidence is consisen wih he overvaluaion hypohesis proposed in Miller (1977). Our sudy is differen from Sharif e al. (2012, 2013) in ha we sudy how shor selling and margin rading affecs 1 Miller (1977) is one of he mos influenial early sudies. For recen developmens, see Desa Ramesh, Thiagarajan, and Balachandran (2002), Bris, Goezmann, and Zhu (2007), and Engelberg, Reed, and Ringgenberg (2012), among ohers. For sudies on inernaional markes, see Aiken, Frino, McCorry, and Swan (1998) for Ausralia, and Chang, Cheng, and Yu (2007) for Hong Kong. 2 One noiceable sudy is Aiken e al. (1998), who use inraday daa on Ausralian socks. 2

3 he informaion efficiency of sock prices. Anoher relevan sudy is Chang, Luo, and Ren (2013), who, similar o Sharif e al. (2012), also find a negaive abnormal reurn on he day when a sock becomes eligible for shor selling and margin rading. Chang e al. (2013) also find ha here is inensified shor selling for socks wih low hisorical reurn and high conemporaneous reurn, and inensified shor selling is marginally associaed wih lower fuure reurns. Similar o our sudy, Chang e al. (2013) examine he price efficiency of eligible socks. They use 3 measures of price efficiency: sock reurn synchroniciy (Rsquared), cross-auocorrelaion beween sock reurn and lagged marke reurn, and he raio of he variance of monhly reurns o ha of weekly reurns. Our sudy is differen from Chang e al. (2013) in ha all heir 3 measures of price efficiency are annual measures calculaed using weekly reurns. Our measure of price efficiency is esimaed from inraday ick ransacion daa. Change e al. (2013) examine he relaionship beween annual measures of price efficiency and he yearly average of shor selling and margin rading aciviies, while our sudy invesigaes he relaionship beween daily price efficiency measure and daily shor selling and margin rading aciviies. We believe our sudy is beer able o illusrae how shor selling and margin rading affecs price efficiency for he following reasons. Firs, exising lieraure indicaes ha sock prices reac o new informaion wihin a shor ime period. For example, Chordia, Roll, and Subrahmanyam (2005) find ha i akes beween 5 and 60 minues for sock prices o converge o marke efficiency. Therefore, our inraday ransacion daa and daily shor selling and margin rading daa allow us o beer invesigae he dynamics beween sock price efficiency and such rading aciviies. Second, aking advanage of he high frequency of our daa, we are able o apply a shock o he normal sock rading process and observe how shor selling and margin rading are employed by invesors o reac o he sock. We do so by invesigaing he abnormal shor selling and margin rading aciviies around days when significan informaion arrives (he shocks), and 3

4 by examining how such abnormal rading aciviies affec how efficienly sock prices incorporae he new informaion. In sum, building on he exising relevan sudies, our paper sheds addiional new insigh on he relaionship beween shor selling, margin rading, and sock price efficiency. Our sudy also has imporan pracical implicaion. Since he incepion of he shor selling and margin rading experimen in March 2010, China has coninued o expand he lis of eligible socks wice: once in he end of 2011 and he second ime in early Wheher China should coninue o increase he scale of he experimen depends on a clear undersanding of he impac of shor selling and margin rading. Our paper is aimed o conribue o such undersanding. We organize he paper as follows. We describe he sample and daa in secion 2, presen he empirical ess and resuls in secion 3, and draw our conclusions in secion Sample and Daa Our sample includes all socks lised on Shanghai or Shenzhen Sock Exchanges ha are allowed o be sold shor or raded on margin (hereafer eligible socks). The firs day for shor selling or margin rading o ake place was March Because some of our empirical ess require comparing he pre-eligibiliy and he pos-eligibiliy periods, our sample period sars on January and ends on March For he sample socks, we collec from he websies of he wo sock exchange daily daa on he number of shares sold shor during he day, he shor ineres on ha day (ha is, he ne shor posiion ousanding in he sock in number of shares as of ha day), he margin rading value in yuan during he day, and he ne value of he margin rading ousanding as of ha day. Daily daa on sock price and number of shares ousanding, and analys earnings forecas daa are colleced from Daasream. 4

5 Earnings announcemen daes and insider rading daa are hand-colleced from he sock exchanges websies. We also obain ick rading daa from SIRCA. Panel E of Table 1 presens he ime-series disribuion of Chinese firms eligible for shor selling and margin rading over our sample period. The end of March and April 2010 mark he beginning of China s pilo shor selling and margin rading program, when 88 socks (abou 16% of he sample firms) became eligible. Then December 2011 and January 2013 winess anoher wo spikes in he number of eligible socks during our sample period, wih 188 (approximaely 33%) and 268 (47%) socks becoming eligible, respecively. This paern reflecs he changes in regulaions on shor selling and margin rading: Shanghai Sock Exchange adjused (expanded) he lis of eligible socks on December and January , and Shenzhen Sock Exchange did so on November and January There are alogeher 565 eligible firms. Among hem, 446 firms have he daa available for our empirical analyses, and hey consiue our sample. Panel A of Table 1 shows saisics on sock characerisics. For an average sample firm, during he sample period is marke capializaion is abou 23 million yuan, price is around 13 yuan, i earns a daily reurn of 0.04%, and is monhly sandard deviaion of daily reurns is approximaely 2.78%. Panel D of Table 1 indicaes ha he shor selling and margin rading aciviy is subsanial. For a sample firm, during he sample period, he average (median) daily margin rading value is more han 12 (6) million yuan. The average (median) daily ne margin posiion ousanding eaches around 210 (140) million yuan. In erms of shor sales, he average (median) daily shor sale volume is around 170,000 (47,000) shares, and he average (median) daily shor ineres exceeds 362,000 (146,000) shares. Given he significan shor sale and margin rading 5

6 aciviies, i is vial o improve our undersanding of heir roles in he informaional efficiency of sock prices. 3. Empirical Resuls 3.1. The impac of shor selling and margin rading Wha impac, if any, does he acive shor selling and margin rading have on Chinese socks? In his subsecion, we ry o find an answer. Firs, based on Miller (1977), lifing resricions on shor sale and margin rading will allow negaive and posiive informaion o be more effecively incorporaed ino prices. Consequenly, sock prices should become more efficien. Hence, we define eligible as being allowed for shor selling and margin rading, and we pu forh hypohesis 1: socks prices during he pos-eligibiliy period are significanly more efficien han he pre-eligibiliy period. Second, boh he pre- and he pos-eligibiliy periods include days when significan new informaion arrives and days when here is no significan news. Given ha shor selling and margin rading are he mechanisms ha faciliae processing informaion, we should expec ha i is more difficul for a sock o promply incorporae significan informaion when he sock is no eligible for shor selling and margin rading han when i is eligible. Based on his reasoning, we propose hypohesis 2: Sock prices during he periods of significan informaion evens are significanly more efficien when he socks are eligible for shor selling and margin rading han heir sock prices during he periods of he same ype of significan informaion evens when hey are no eligible for shor selling and margin rading. We examine 3 ypes of significan informaion evens. The firs ype is he large price changes, which is defined as a price change above 7.5%. I is reasonable o believe ha here mus be some maerial informaion arriving o make he large price changes jusified. The second ype is earnings surprises. Earnings are one of he mos imporan figures o invesors. The announcemens of realized earnings, and hence he 6

7 availabiliy of earnings surprises, are significan informaion evens. The hird ype is insider rading. Invesors usually closely wach insiders purchases and sales of heir own firms socks, because hese rades may reveal inside informaion. To es our hypoheses, we need a measure of he informaional efficiency of sock prices. We use PE, he price efficiency, as he measure. PE is esimaed from high-frequency inra-day ick daa obained from SIRCA. Using such daa, we esimae a PE measure for each of our sample (and conrol) socks on each day of our sample period. 3 We use esimaion mehodology deailed in he appendix of Boehmer and Wu (2013), which is based on Hasbrouck (1993) VAR model. The inuiion behind his measure is as follows. Hasbrouck decomposes he observed ransacion price ino an efficien price and a pricing error. Then he sandard deviaion of he price error divided by he sandard deviaion of he ransacion price (which we call PE) becomes a nice measure of he informaional efficiency of sock prices. The higher he PE is, he less efficien is he sock price. In addiion, because he sandard deviaion of pricing error is scaled by ha of he ransacion prices, PEs are comparable across differen firms. To conduc he ess, we also need o have a conrol group of socks. The reason is because he sock price efficiency is known o be affeced by some firm and sock characerisics, for example, he indusry in which a firm operaes, firm size, price level, and volailiy. In order o isolae ou he impac of shor selling and margin rading on price efficiency, we need o conrol for hose characerisics. Therefore, for each sample firm, we selec from all he socks lised on Shanghai or Shenzhen Sock Exchanges a conrol firm according o he following procedure, which is similar o ha in Bacidore and Sofianos (2002). We firs require ha a sample firm and is conrol be in he same indusry. We follow he indusry 3 The esimaion is a very daa-inensive process. Following Boehmer and Wu (2013), we exclude all PEs ha are greaer han or equal o 1. 7

8 clarificaion made by China Securiies Regulaory Commission (CSRC), he counerpary of Securiies and Exchange Commission (SEC) in he US. We hen selec he firm whose characerisics minimize he following value: 3 i1 X X Sample i Sample i X X 2 Conrol i Conrol i 2 where X i refers o one of he 3 firm and sock characerisics: sock price, marke capializaion, and volailiy. We presen he firm and rading summary saisics for our conrol firms in Panel B of Table 1, and also include such saisics for he combined group of sample and conrol firms in Panel C of Table 1. To es hypohesis 1, we calculae he average daily PEs for sample firms, conrol firms, and sample minus conrol firms (we defined adjused PE as he difference beween he PE for a sample firm and he PE for is conrol) over he days when he sample firms are no eligible for shor selling and margin rading (ha is, he pre period) and over he days when he sample firms are eligible (he pos period). We hen compare hese wo averages. A significan decrease in adjused PE from he pre o he pos period will suppor hypohesis 1. Panel A of Table 2 shows he resuls. There is a significan increase in he PE for boh he sample and he conrol firms from he pre o he pos period, and he increase is significanly larger for he conrol firms. The adjused PE for sample firms shows a subsanial decrease of abou 261%, and he decline is saisically significan a he 1% level. This finding suppors hypohesis 1 and indicaes ha being able o sold shor or raded on margin significanly improves he informaional efficiency for sock prices of Chinese firms. 8

9 Tess in Panel A include all he days over he sample periods, irrespecive of wheher here is any significan news occurring on a day. To furher illusrae he role of shor selling and margin rading, we zoom in on he days wih significan informaion evens, because i is on such days ha he availabiliy of shor selling and margin rading is mos likely o be uilized by invesors. We design our ess as follows. For each sample firm, we firs idenify he daes for all of he following 3 ypes of informaion evens during he sample period: large price changes, earnings surprises, and insider rading. For each even, we calculae he average PE over he 11 days around he even day (ha is, -5 o +5). We hen group all evens ino he evens during he pre-eligibiliy and he pos-eligibiliy periods, and calculae he average (across all he evens during each period) of he average PEs (over he 11 days even window). We finally es he difference in he average PE beween he pos and he pre periods. For each conrol firm, we follow he same process excep ha is eligibiliy dae is aken o be he eligibiliy day of he corresponding sample firm. We presen he resuls for he 3 caegories of informaional evens in Panels B, C, and D of Table 2. Excep for he finding ha he PE surrounding he announcemens of earnings surprises for our sample firms declines significanly when he sample firms become eligible for shor selling and margin rading, he oher resuls are similar o hose in Panel A. The PEs during he informaion periods increase significanly from he pre o he pos periods, and conrol firms exhibi much larger increase. The adjused PEs over he informaion even window declines significanly from he pre o he pos periods for sample firms. This indicaes ha, when a firm is eligible for shor selling and margin rading, is sock price incorporaes significan news more efficienly, and hence becomes more informaionally efficien. These resuls suppor hypohesis The shor selling and margin rading aciviies around informaion evens 9

10 For our sample socks, being eligible for shor selling and margin rading does no necessarily mean ha hey have acually been sold shor or raded on margin. In he previous subsecion, we find resuls consisen wih our hypoheses 1 and 2: when socks are eligible, heir prices are more efficien. In order o srengh furher he link beween shor sale, margin rading and price efficiency, we need o prove ha our sample socks are indeed acively sold shor or raded on margin. Oherwise, our findings in Table 2 may be spurious. Hence, we formulae hypohesis 3: sample socks show significanly higher rading volume (value) in shor selling and margin rading around significan informaion evens. To es hypohesis 3, we need o measure he inensiy of shor selling and margin rading. One advanage of using Chinese daa is ha he daily shor ineres and daily ne margin balance are available. We herefore use he following daily daa iems hand-colleced from he sock exchanges: margin rading volume on he day (mr), ne margin balance ousanding as of he day (mrb), shor selling volume on he day (ss), and shor ineres as of he day (ssb). To measure wheher here is any significan movemen in he level of shor selling and margin rading, we adop an approach similar o ha used o calculae abnormal reurns. For a window period around an even, for example, (-5, -1), we calculae he average daily abnormal value for each of he aforemenioned 4 measures as follows. Average daily abnormal measure (-5, -1)= average daily measure over (-5,-1)/average daily measure over he pre-even esimaion window of (-40, -15)-1. A posiive abnormal measure indicaes an increasing rend in he rading aciviy. To es hypohesis 3, we calculae he average abnormal measures for margin rading value, margin rading balance, shor rading volume, and shor ineres over he 11 days even window for he 3 imporan informaion evens. We hen es wheher he average abnormal measures are significanly differen from zero. A significan and posiive average suppors hypohesis 3. 10

11 Table 3 presens he resuls. For each informaion even, we classify i ino good new or bad news according o he following crieria. If he large price change is posiive (negaive), i is good (bad) news. If he earnings surprise is posiive (negaive), i is good (bad) news. If an insider rade is a purchase (sale), i is good (bad) news. We presen he resuls for good and bad news separaely for each of he 3 ypes of informaion caegories, and we calculae he average abnormal rading over 3 windows: he enire window of he 11 days around an even; he pre-even window of (-5,-1), and he pos-even window of (+1, +5). Firs, we observe ha he average abnormal rading during he enire even window (he columns wih he heading (-5,+5)) is saisically significanly posiive and generally large for all he 3 ypes of informaion evens. In addiion, he abnormal rading occurs during boh he pre- and he pos-even windows. These findings suppor hypohesis 3 and alleviae he concern ha he link beween shor selling, margin rading and price efficiency is spurious. Second, he significan and posiive abnormal margin rading during he 5 days pre-even windows for good news and he significan and posiive abnormal shor selling during he 5 pre-even days for bad news show ha a leas some shor selling and margin rading aciviies are done eiher by informed invesors who know he informaion in advance or by invesors who have superior analyical skills ha allow hem o correcly predic he news. This finding furher srenghens he argumen ha shor selling and margin rading faciliae price processing new informaion more efficienly Evidence from mulivariae regressions In his subsecion, we examine he relaionship beween margin rading, shor selling, and he informaional efficiency of sock prices in a mulivariae regression seing. To his end, we run he following regressions: 11

12 Adjused PE abmrb 8 mvdiff abmr 9 1 prdiff ( or abssb 8 2 rediff abss 9 3 ) voldiff 4 yr yr yr (1) where adjused PE is he difference beween in PE beween a sample firm and is conrol. Mvdiff is he difference in he naural logarihm of he marke value beween a sample firm and is conrol. Prdiff, rediff, and voldiff are defined analogously for sock price, reurn, and volailiy, respecively. Alhough we have already mached each sample firm wih a conrol firm on marke value, price, and volailiy, we sill include hem as conrol variables. The reason is because he crieria used by he sock exchanges o selec socks for shor selling and margin rading include requiremens on he marke capializaion and liquidiy. Usually he sample socks end o be large socks and componen socks of an index. Consequenly, i may be difficul o find a perfec mach for all he sample firms. Panels A and B of Table 1, for example, indicae ha he conrol firms are indeed smaller han he sample firms. Yr2011, yr2012, and yr2013 are he year dummies. The base case is he year of Abmrb, abssb, abmr, and abss are he abnormal ne margin rading balance ousanding as of he day, he abnormal ne shor posiion ousanding as of he day, he abnormal margin rading value during he day, he abnormal shor selling volume during he day, respecively. These 4 abnormal measures on day are calculaed as he measure on day scaled by he average of he measure over a rolling pre-even esimaion window of (-40, -15) hen minus 1. The firs 2 abnormal balance measures are included o conrol for he accumulaed ne posiion ousanding for margin rading and shor selling. The las 2 abnormal rading measures over he day are our es variables. A significan and negaive relaionship beween adjused PE and he abnormal margin rading (abmr) or shor selling (abss) aciviies will suppor he conjecure ha such rading improves price efficiency. The regressions are run a he firmeven level by pulling ogeher daily observaions over he 11 days informaion even 12

13 windows. In all he regressions, we include firm and year fixed effecs and winsorize all variables excep for he dummies a he 1 s and he 99 h perceniles. We presen he resuls from 6 regressions for he 3 ypes of informaion evens in Panels A, B, and C of Table 4, wih each panel consising of resuls for good news and bad news. Firs, among he conrol variables, sock price and reurn volailiy seem o be significanly relaed o price efficiency. In 5 of he 6 regressions, he adjused PE is significanly negaively relaed o price and volailiy. Higher-priced socks may have beer informaion environmen, which conribues o more efficien price. Informaional efficiency is posiively associaed wih reurn volailiy, which is consisen wih he finding in French and Roll (1986) ha volailiy is mainly caused by privae informaion ha moves prices when informed invesors rade. None of he oher conrol variables, including he cumulaive margin rading or shor selling balance ousanding, shows a clear relaionship wih sock price efficiency. Second, shor selling and margin rading significanly improve sock price efficiency. In all he 6 regressions, he coefficien esimaes for he abnormal shor selling or margin rading volume are negaive, and hey are significanly so in 5 ou of he 6 regressions. In paricular, around bad news evens, sock prices are more efficien when here is more abnormal shor selling aciviy during he day. Around he days when good news arrives, prices become more efficien when more margin rading occurs. These findings provide clear evidence ha shor selling (margin rading) faciliaes he incorporaion of bad (good) news ino sock prices. 4. Concluding Remarks Uilizing daily daa on Chinese socks shor selling and margin rading aciviies and inraday ick sock rading daa, we examine he relaionship beween he informaional efficiency of sock prices and shor selling or margin rading. Our main findings are as follows. Firs, here have been subsanial shor selling and margin rading aciviies going on 13

14 since he incepion of such rading in China. Second, prices become significanly more efficien when socks are allowed o be sold shor or raded on margin. Third, shor selling (margin rading) aciviies become significanly more inense around major informaion evens including large changes in sock price, earnings announcemens, and insider rading. Fourh, regression resuls confirm a significan associaion beween he informaion efficiency of sock price and he inensiy of shor selling and margin rading. In paricular, higher rading volume in shor selling (margin rading) conribues o more efficien sock prices around bad (good) news days. Taken ogeher, our empirical evidence suppors he conjecure ha shor selling and margin rading allow sock prices o absorb new informaion more efficienly. Our sudy conribues new empirical evidence from an increasingly imporan marke on how shor selling and margin rading affec he sock price formaion process. I also has imporan implicaion ha capial marke regulaors may find relevan. A he minimum, he evidence in his paper validaes he decision o lif he ban in March 2010 on shor selling and margin rading of socks lised in China. 14

15 References Michael J. Aiken, Alex Frino, Michael S. McCorry, and Peer L. Swan, 1998, Shor Sales Are Almos Insananeously Bad News: Evidence from he Ausralian Sock Exchange, Journal of Finance 53, Ronald C. Anderson, David M. Reeb, and Wanli Zhao, 2012, Family-Conrolled Firms and Informed Trading: Evidence from Shor Sales, Journal of Finance 67, Jeffrey M. Bacidore, and George Sofianos, 2002, Liquidiy Provision and Specialis Trading in NYSE-Lised Non-US Socks, Journal of Financial Economics 63, Henk Berkman, Valenin Dimirov, Prem C. Jain, Paul D. Koch, and Sheri Tice, 2009, Sell on he News: Differences of Opinion, Shor-Sales Consrains, and Reurns around Earnings Announcemens, Journal of Financial Economics 92, Ekkehar Boehmer, and Juan (Julie) Wu, 2013, Shor Selling and he Price Discovery Process, Review of Financial Sudies 26, Aruro Bris, William N. Goezmann, and Ning Zhu, 2007, Efficiency and he Bear: Shor Sales and Markes around he World, Journal of Finance 62, Bidisha Chakrabary, and Andriy Shkilko, 2013, Informaion Transfers and Learning in Financial Markes: Evidence from Shor Selling around Insider Sales, Journal of Banking and Finance 37, Eric C. Chang, Joseph W. Cheng, and Yinghui Yu, 2007, Shor-Sales Consrains and Price Discovery: Evidence from he Hong Kong Marke, Journal of Finance 62, Eric C. Chang, Yan Luo, and Jinjuan Ren, 2013, Shor-selling, Margin-rading, and Price Discovery: Evidence from he Chinese Marke, working paper, he Universiy of Hong Kong. Tarun Chordia, Richard Roll, and Avanidhar Subrahmanyam, 2005, Evidence on he Speed of Convergence o Marke Efficiency, Journal of Financial Economics 76, Sephen E. Chrisophe, Michael G. Ferr and James J. Angel, 2004, Shor-Selling Prior o Earnings Announcemens, Journal of Finance 59, Sephen E. Chrisophe, Michael G. Ferr and Jim Hsieh, 2010, Informed Trading Before Analys Downgrades: Evidence from Shor Sellers, Journal of Financial Economics 95, Hemang Desa K. Ramesh, S. Ramu Thiagarajan, and Bala V. Balachandran, 2002, An Invesigaion of he Informaional Role of Shor Ineres in he Nasdaq Marke, Journal of Finance 57, Joseph E. Engelberg, Adam V. Reed, and Mahew C. Ringgenberg, 2012, How Are Shors Informed? Shor Sellers, News, and Informaion Processing, Journal of Financial Economics 105,

16 Kenneh R. French, and Richard Roll, 1986, Sock Reurn Variances: The Arrival of Informaion and he Reacion of Traders, Journal of Financial Economics 17, Joel Hasbrouck, 1993, Assessing he Qualiy of a Securiy Marke: A New Approach o Transacion-Cos Measuremen, Review of Financial Sudies 6, Jonahan M. Karpoff, and Xiaoxia Lou, 2010, Shor Sellers and Financial Misconduc, Journal of Finance 65, Lianfa L and Belon M. Fleisher, 2004, Heerogeneous Expecaions and Sock Prices in Segmened Markes: Applicaion o Chinese Firms, Quarerly Review of Economics and Finance 44, Edward M. Miller, 1977, Risk, Uncerainy, and Divergence of Opinion, Journal of Finance 32, Saqib Shrif, Hamish D. Anderson, and Ben Marshall, 2012, The Announcemen and Implemenaion Reacion o China s Margin Trading and Shor Selling Pilo Programme, working paper, Massey Universiy Saqib Shrif, Hamish D. Anderson, and Ben Marshall, 2013, Agains he Tide: The Commencemen of Shor Selling and Margin Trading in Mainland China, Accouning and Finance, forhcoming. 16

17 Table 1. Overview of Sample Firms and Their Shor Selling and Margin Trading Panel-A Sample Firms (# of Obs.: 363,589) Variable Mean Median Sd Dev Minimum Lower Quarile Upper Quarile Maximum mv 23, , , , , ,972, pr re volailiy Panel-B Conrol Firms (# of Obs.: 363,589) Variable Mean Median Sd Dev Minimum Lower Quarile Upper Quarile Maximum mv 8, , , , , , pr re volailiy Panel-C Sample and Conrol Firms (# of Obs.: 727,178) Variable Mean Median Sd Dev Minimum Lower Quarile Upper Quarile Maximum mv 15, , , , , ,972, pr re volailiy Saisics are based on daily daa for he 446 sample firms and heir conrol firms over he sample period (January ill March ). Volailiy is monhly sandard deviaion of daily reurns. Mv is he marke capializaion in housands of Chinese yuan. 17

18 Panel-D Sample Firms (# of Obs.: 72,818) Variable Mean Median Sd Dev Minimum Lower Quarile Upper Quarile Maximum margin (in yuan) 12,364, ,882, ,110, ,998, ,683, ,639, margin balance (in yuan) 209,897, ,331, ,078, ,139, ,756, ,436,982, shor (in shares) 170, , , , , ,154, shor balance 362, , , , , ,132, (in shares) abmr , abmrb abss , abssb , Saisics are based on daily daa for sample firms over he sample period (saring from he firs day when a sock is eligible for shor sale or margin rading). Panel E. Disribuion of sample firms by he firs ime when hey were sold shor or raded on margin Monh 03/ / / / / / / / / / / / / /2013 No. of Sample Firms Toal: (7%) 46 (8%) (33%) (47%)

19 Table 2 The impac of shor selling and margin rading on informaional efficiency Panel-A: Full sample Pre Pos # of Obs Mean Sd Dev Mean Sd Dev Difference -sa CPE *** PE *** AdjPE *** Panel-B: Large price change Pre Pos # of Obs Mean Sd Dev Mean Sd Dev Difference -sa CPE *** PE *** AdjPE *** Panel-C: Earning surprise Pre Pos # of Obs Mean Sd Dev Mean Sd Dev Difference -sa CPE *** PE * AdjPE *** Panel-D: Insider rading Pre Pos # of Obs Mean Sd Dev Mean Sd Dev Difference -sa CPE *** PE *** AdjPE *** Pre and Pos refer o he pre-eligibiliy and he pos eligibiliy windows, respecively. The means are he average daily price efficiency measures across he sample firms or heir conrol firms over he enire period in Panel A and over he 11 days even window periods in Panels B, C, and D. 19

20 Table 3 Abnormal shor selling and margin rading aciviies around informaional evens Panel A: Large price change Good news Bad news (-5,+5) (-5,-1) (+1,+5) (-5,+5) (-5,-1) (+1,+5) Abmr 1.752*** (8.658) 1.210*** (3.460) 2.050*** (7.561) Abss 7.469*** (6.913) 9.051*** (4.176) 5.169*** (6.521) # of obs # of obs Abmrb 0.963*** (21.521) 0.739*** (17.449) 1.163*** (15.702) Abssb ** (1.980) 4.418*** (7.033) * (1.857) # of obs # of obs Panel B: Earning surprise Good news Bad news (-5,+5) (-5,-1) (+1,+5) (-5,+5) (-5,-1) (+1,+5) Abmr 3.334*** (3.163) 2.879* (1.837) 3.698** (2.408) Abss 7.246*** (5.657) 2.281*** (2.862) # of obs # of obs Abmrb 5.612*** (3.169) 3.833** (1.983) 8.367** (2.241) Abssb 2.797*** (8.327) 1.807*** (5.680) # of obs # of obs * (1.773) 1.334*** (3.049) Panel C: Insider rading Good news Bad news (-5,+5) (-5,-1) (+1,+5) (-5,+5) (-5,-1) (+1,+5) Abmr 0.156*** (7.443) 0.183*** (5.917) 0.132*** (3.988) Abss 0.744*** (7.845) 0.723*** (4.780) # of obs # of obs Abmrb 0.354*** (22.384) 0.319*** (16.142) 0.379*** (13.746) Abssb 0.346*** (7.533) 0.270*** (6.702) 0.756*** (5.066) 0.411*** (4.402) # of obs # of obs This able presens he average daily abnormal rading measure over 3 window periods around 3 ypes of informaion evens. The average daily abnormal margin rading value during he window of (-5, +5) is calculaed as he average margin rading value over (-5, +5) scaled by he average margin rading value over he pre-even esimaion period of (-40, -10) hen minus 1. The average daily abnormal values for he oher rading measures over he oher even windows are defined analogously. The numbers in he parenheses are he - saisics. 20

21 Table 4 Mulivariae regression resuls Panel-A: Big price change # of Obs. 2,644 3,007 Bad news Good news Esimae -sa p-value Esimae -sa p-value Inercep Inercep mvdiff mvdiff prdiff <.0001 prdiff rediff rediff voldiff <.0001 voldiff yr yr yr yr yr yr abssb abmrb <.0001 abss abmr Panel-B: Earning surprise # of Obs. 1, Bad news Good news Esimae -sa p-value Esimae -sa p-value Inercep Inercep mvdiff mvdiff prdiff prdiff rediff rediff voldiff voldiff yr yr yr yr yr yr abssb abmrb abss abmr Panel-C: Insider rading # of Obs. 1,573 2,038 Bad news Good news Esimae -sa p-value Esimae -sa p-value Inercep Inercep <.0001 mvdiff <.0001 mvdiff prdiff <.0001 prdiff rediff rediff voldiff <.0001 voldiff yr yr yr yr <.0001 yr yr abssb abmrb abss abmr

22 This able shows he resuls from pooled regressions for he eligible period. The regressions are run over he 11 days firm even periods. The dependen variable is he adjused PE. All variables are winsorized a he 1 s and he 99 h perceniles. All regressions include he firm and year fixed effecs. 22