UK Fund Manager Cascading and Herding Behaviour: New Evidence from he Sock Marke Yang-Cheng Lu Deparmen of Finance, Ming Chuan Universiy 250 Sec.5., Zhong-Shan Norh Rd., Taipe Taiwan E-Mail ralphyclu1@gmail.com, yclu@mail.mcu.edu.w Hao Fang Deparmen of Asses and Propery Managemen, Hwa Hsia Insiue of Technology No. 111, Gong Jhuan Rd., Chung Ho, Taipe Taiwan 23568, R.O.C. E-Mail marian0307@yahoo.com.w ABSTRACT This paper firs exends Sias (2004) and Wylie (2005) o examine he exisence of and he reasons for UK fund managers herding behaviours in he sock marke. Wheher heir herding behaviours are differen during bullish and bearish periods and wheher or no heir herding behaviours are informaional are explored. Our resuls demonsrae ha UK fund managers cascades primarily resul from heir herding, and habi invesing does no primarily drive heir herding behaviour. Differen from he resuls of Sias (2004) and Wylie (2005), we find ha momenum rading can be regarded as one of he main reasons for UK fund managers herding. Because hey are more likely o herd in large capialisaion securiies, heir herding may resul from invesigaive herding, which is conrary o he finding of Sias (2004). Moreover, our resuls find ha UK fund managers cascades primarily resul from heir herding, which does no change in bullish and bearish sock markes. We also find ha growh-ype and inernaional-ype funds are more likely o herd wih funds similar o heir ypes while value-ype funds are more likely o herd wih funds differen from heir ypes. In addiion, we confirm ha posiive correlaion exis beween he fracion of fund managers buying and subsequen sock reurns, which is consisen wih he resuls of Wermers (1999), Choe e al. (1999) and Sias (2004). According o he reasoning of Hung e al. (2010), UK fund managers herding behaviours migh aribue o informaional herding wihin he subsequen year. To improve porfolio performance, oher invesors could follow UK fund managers o purchases socks overbough by hem wih a leas 15 raders quarerly in he following one year period, especially for growh, specific secor and inernaional funds. Keywords: Characerisic herding, Invesigaive herding, Momenum rading, Informaional herding, Repuaional herding, Informaional cascade, Habi invesing, Muual fund, UK. JEL Classificaion: G11, G14, G21, C21. Hao Fang is a corresponding auhor. 1
1. Inroducion Relaive o he sock holding raio and rading amoun for muual funds in he sock marke of emerging counries, hose of developed counries are significanly large. 1 Based on heir dominance in he sock marke, he influence of fund managers rading of socks on sock prices and price flucuaions is significanly larger han ha of oher invesors rading aciviies. The relevan lieraure on muual funds a presen focuses more on he exploraion of he American marke, bu he componen sock ransacions of he European muual fund are indeed he main srengh ha influences price flucuaions in his sock marke. 2 The UK muual fund ha accouns for he highes proporion of European muual funds is abundan 3 and is dedicaed o daa collecion, analysis and professional invesmen. Their sock selecion sraegy and rading behaviour are more raional and have more advanageous informaion relaive o oher invesors. 4 As Wermers (1999) noed, based on he informaion asymmery and agency problem, muual funds followed he behavioursof each oher (i.e., herding) for some securiies. The herding behaviour of muual funds in he sock marke will decrease he informaion qualiy of sock prices, expand sock price volailiy and drive prices away from fundamenals (Waler and Weber, 2006; Scharfsein and Sein, 1990). On he whole, do UK muual funds paricipae in herding behaviours in he sock marke and wha is heir reason for herding? In deail, do he herding behaviours of UK muual funds resul from characerisic herding, invesigaive 1 Muual funds prevail in Wesern counries. A repor by he American Invesmen Company Insiue in 2010 indicaes ha he oal ne asses of European muual funds occupy 32% of hose of global muual funds. The daa source comes from hp: //www.ici.org. 2 In paricular, he European marke inegraed ino a single marke in 1992, and boh currency and ariffs were simplified from a complex siuaion, undoubedly like a iger ha has grown wings for he European sock marke in erms of srong, original economic srengh. 3 A repor by he American Invesmen Company Insiue in 2010 indicaes ha he oal ne asses of UK muual funds occupy 11% of hose of European muual funds. The daa source comes from hp: //www.ici.org. 4 Briain began o develop muual fund as early as he 19 h cenury, and funds mainly held socks and bonds, and he majoriy of heir holdings were socks. 2
herding, informaional cascades, momenum rading or repuaional herding? Moreover, wha ypes of UK muual funds are more likely o follow similar ypes and differen ypes of funds separaely? Furhermore, does he prohibiion agains shor selling socks for muual funds reduce he expansion of herding behaviours of UK fund managers in he bearish marke period so as o make heir herding behaviours similar during bullish and bearish marke periods? Finally, are pos-herding reurns posiive for he enire and each ype of UK muual funds, clarifying wheher heir herding behaviours are value-relevan informaion? These issues have no ye been discussed in deph. Dennis and Weson (2000), Chakravary (2001) and Sias, Sarks and Timan (2002) concluded ha he relaionship beween changes in insiuional ownership and reurns measured over he same period resuls primarily from price effecs associaed wih insiuional rading. These sudies are consisen wih he hypohesis ha insiuions rading in he same direcion have an impac on securiy prices, bu hey do no allow one o conclude ha insiuional invesors are herding because some securiies experience ne changes in insiuional ownership simply by chance. An LSV measure proposed by Lakonishok, Shleifer and Vishny (1992) examined he degrees of cross-secional variaion in he proporion of fund managers who buy a sock across all socks in a specific period, which has become a sandard in he lieraure. 5 Wermer (1999), revising he LSV measure as he condiional buying and selling herding measures, applied he LSV measure o demonsrae herding by US muual funds even wih lile variaion in he herding level wih he number of managers rading he sock. 6 Since managers of UK muual funds are prohibied from underaking shor sales, Wylie (2005) employed he LSV measure adjused 5 The finding of LSV (1992) shows evidence of herding by equiy pension fund managers in he US, especially wih more herding in small capialisaion socks. Grinbla, Timan and Wermers (1995) use he LSV measure o find sronger evidence of herding by managers in US muual funds for socks raded by large numbers of managers in a period. 6 Choe, Kho and Sulz (1999), also using he LSV measure, indicaed large levels of herding in he Korean sock marke in 1997. 3
for biases o examine herding among UK fund managers. Their empirical resuls show he exisence of fund manger herding in he larges size and smalles size UK socks bu lile herding in oher socks. However, he saic LSV herding measure indirecly ess for cross-secional dependence by recognising ha laer insiuional raders following earlier insiuional invesors rades wihin a period will resul in he highes number of insiuional raders on one side of he rade wihin ha period. On he conrary, Sias (2004) esed he cross-secional correlaion beween insiuional invesors rades in one period and oher insiuional invesors rades in he nex period o direcly examine wheher insiuional invesors follow each oher s rades. Tha is, he dynamic measure of Sias (2004) redefined herding as a group of insiuional invesors buying and selling he same socks by following each oher s ransacions. Differen from he adjused LSV measure of Wylie (2005) on a semi-annual basis, his sudy exends he Sias (2004) model o use cross-secional correlaion of he fracion of UK fund managers increasing heir posiions over adjacen quarers o explore wheher hey are engaged in herding behaviours in he sock marke. The cross-secional correlaion beween he fracions of fund managers buying over adjacen quarers can be direcly decomposed ino he porion resuling from an individual fund manager following his own rades and he porion resuling from fund managers following oher fund managers rades. Hence, he firs objecive of his paper is o apply he dynamic herding measuremen from Sias (2004) o analyse wheher significan herding of UK muual fund managers exiss in he sock marke o compare our resuls wih he findings for US and emerging markes. Previous sudies (Graham (1999), Nofsinger and Sias (1999), Sias (2004) and Wermers (1999)) have indicaed ha he heoreical base for insiuional herding could be divided ino five ranges: informaional cascades, invesigaive herding, characerisic herding, repuaional herding and fads. Among hem, Banerjee (1992) and Bikhchandan 4
Hirshleifer and Welch (1992) deemed ha informaional cascades occur when insiuional invesors ignore heir own noisy informaion and rade wih he herd because hey infer informaion from oher rading behaviour. In such a siuaion, heir acion choice is uninformaive o laer observers. Froo, Scharfsein and Sein (1992) and Hirshleifer, Subrahmanyam and Timan (1994) proposed ha invesigaive herding exiss when he informaion of insiuional invesors is posiively cross-secional correlaed, possibly resuling from following he same signals. Wermers (1999) assered ha informaional cascades are more likely o occur in small-capialisaion securiies, while invesigaive herding is more likely o occur in large-capialisaion securiies. Thus, he second objecive of his paper is o examine wheher herding by UK muual fund managers in he sock marke resuls from informaional cascades or invesigaive herding. Moreover, ha insiuional invesors may herd is he resul of many of hem being araced o securiies wih specific characerisics (Falkensein (1996), Del Guercio (1996) and Gompers and Merick (2001)). A special case of characerisic herding is habi invesing in which insiuional invesors follow each oher ino and ou of he same socks because he grea appeal of securiies wih similar characerisics cause hem o hold similar porfolios. Thus, he hird objecive of his paper is o deermine wheher heir herding is he resul of habi invesing. Many sudies, such as Grinbla e al. (1995), Wermers (1999, 2000), Jones and Winers (1999) and Sias e al. (2002) demonsraed he momenum rade of insiuional invesors. Jones and Winers (1999) and Sias (2004) also showed ha insiuional cascading clearly exiss even afer accouning for momenum rading. The findings of Sias (2004) furher repored ha insiuional demand is more srongly relaed o lag insiuional demand han lag reurns. However, conrary o he finding ha US muual fund managers are momenum invesors, Wylie (2005) found ha UK muual fund 5
managers end o herd ou of large socks afer large posiive excess reurns. 7 Thus, his sudy would like o analyse wheher UK muual fund managers apply a conrarian or a momenum sraegy. Hence, he fourh objecive of his paper is o explore wheher he cascading behaviour of UK fund managers is sill eviden afer considering conrarian or momenum rading, or wheher heir conrarian or momenum rading is more conspicuous han heir herding in he sock marke. Benne, Sias and Sarks (2003) noed ha he environmen faced by insiuional invesors was dynamic; herefore, herding by muual funds migh change over ime 8. The bullish and bearish sock marke period was he mos dynamic environmen of all ypes of financial markes faced by muual fund managers. In addiion o he lieraure of Hwang and Salmon (2004), who hough ha herding behaviour exised in boh bearish and bullish sock marke periods, and of Waler and Weber (2006), who found ha he level of buy-side herding is higher in a bull marke, mos lieraure such as McQueen, Pinegar and Thorley (1996), Chang, Cheng and Khorana (2000) and Gleason e al. (2004) verified he conclusion ha he herding behaviours of invesors in he bearish marke were more significan han hose in he bullish marke. However, due o he naure of shor selling resricion for muual funds, he fifh objecive of his paper is o examine wheher his resricion significanly alleviaes he herding behaviours of fund managers in a bearish marke period relaive o a bullish marke period, or he prohibiion agains shor selling for muual funds reduces he expansion of heir herding behaviours in he bearish marke period resuling from heir quick response o negaive news so as o creae heir herding behaviours regardless of wheher in a bullish or bearish sock marke period. 7 They herd ou of he larges socks wih large posiive excess reurns during he pre-herding 12 monhs and ino socks wih low excess reurns in he same periods. 8 Sias (2004) hough ha growh in relaive sock holdings migh significanly differ among differen insiuional ypes as ime passed, and Campbell, Leau, Malkiel and Xu (2001) and Chordia, Roll and Subrahmanyam (2001) empirically found ha marke characerisics changed significanly as ime passed, implying ha herding behaviour of muual funds migh change over ime. 6
Del Guercio (1996) and Benne e al. (2003) proposed ha he differences in he environmens (such as regulaory requiremens, holding periods and compeiion) faced by differen ypes of insiuional invesors may influence he likelihood ha hese invesors herd and ha herding is only wihin classificaions or wheher some ypes of insiuions lead oher ypes of insiuions. They indicaed ha, if insiuions favour socks wih he same characerisics and hose preferences differ across insiuional classes, hey end o be more likely o follow similar ypes of insiuions han differen ypes because of characerisic herding. Scharfsein and Sein (1990) and Trueman (1994) deemed ha insiuions wih less informaion follow hose wih more precise informaion because of he agency problem and repuaion coss. Sias (2004) repored ha, because of repuaional herding, insiuional invesors should exhibi he sronges endency o herd wih similar ypes of insiuional invesors. The herding behaviours of differen ypes of muual funds can be regarded as hose of differen ypes of insiuional invesors. Based on repuaional concerns, muual funds srongly end o herd wih similar ypes wih whom hey direcly compee han differen ypes of muual funds. Moreover, Wermers (1999) found ha he herding endency of growh-ype funds is larger han ha of income-ype funds because growh-ype funds have less informaion on he fuure income of invesing socks, which promoes growh-ype funds o produce more herding behaviours. In pracice, he regulaory requiremens, holding periods and compeiion faced by growh-ype and inernaional-ype funds are sronger han hose faced by oher ypes of funds. Hence, he sixh objecive of his paper is, on one hand, o es wheher UK muual funds are more likely o follow similar ypes of funds because of repuaion herding and characerisic herding. On he oher hand, we examine wheher he herding behaviour of growh-ype funds is more significan han ha of oher ypes of funds and wheher he growh ype and inernaional ype of funds are more likely o herd wih similar ypes of funds han differen ypes. 7
Previous numerous sudies demonsraed he price effecs of insiuional herding, bu heir herding has differen price impacs (such as Nofsinger and Sias, 1999; Wermers, 1999; Dennis and Weson, 2000; Chakravary, 2001; Sias e al., 2002; Sias, 2004). The resuls of Sias (2004) and Grinbla e al. (1995) showed ha insiuional herding is weakly posiively correlaed wih fuure reurns. Hung, Lu and Lee (2010) proposed ha if informaion for insiuional herding is impounded ino securiy prices, hen here is he absence of price reversals hrough insiuional herding (such as DeLong e al., 1990; Choe e al., 1999; Grinbla e al., 1995; Wermers, 1999; Sias, 2004). However, he empirical resuls of Dennis and Weson (2000), Chakravary (2001) and Sias e al. (2002) indicaed ha he subsequen reurns of insiuional herding from fads, repuaion herding or characerisic herding are significanly reversed. Tha is, if insiuional herding behaviour occurs because of non-informaional reasons, also deemed by Hung e al. (2010), such herding may drive reurn reversals in pos-herding periods. Hence, he final objecive of his paper is o examine wheher or no pos-herding reurns of UK muual fund and separae fund-ype managers are posiive o deermine wheher heir herding behaviour is informaional or non-informaional. The remainder of his paper proceeds as follows. Secion 2 repors he daase and characerisics of UK muual funds used by his paper. Secion 3 describes he mehodology and empirical resuls on he herding behaviour of UK fund managers. Secion 4 analyses he causes of herding by UK fund managers. Secion 5 explores wheher he herding behaviour of UK fund managers changes in bullish and bearish periods. Secion 6 clarifies wheher he herding of UK muual funds is aribuable o herding among funds of he same ype or differen ypes. Secion 7 deermines wheher he herding behaviours of UK fund managers are informaional. Secion 8 concludes our paper. 2. Daa Scope and Analysis 8
The original daa adoped by he sudy was derived from quarerly holding individual sock reurns, he number of ousanding sock companies, capialisaion of Briish muual funds from January 2002 o December 2009 from Thomson ONE Banker s ownership daabase and closing prices of lised socks on he London Sock Exchange (LSE). The sudy divides he funds ino wo ypes pursuan o he P/E raios of he socks held by he fund: aggressive and core growh fund and growh fund, and divides he funds ino wo ypes pursuan o marke price-o-ne value raio: a GARP fund above a mean and a core value and deep value fund below a mean. Meanwhile, his sudy divides he funds pursuan o invesmen region: inernaional fund and emerging marke fund. In addiion, he sudy also akes he specific secor fund as a research objec. The fund ype seleced by he sudy is based on he urnover rae over an average sandard among Briish muual funds, and focuses on analysing funds wih socks having beer liquidiy. The Briish muual fund ypes seleced by he sudy are divided as follows: 18 aggressive and core growh funds, 32 growh funds, 23 core value and deep value funds, 12 GARP funds, 87 inernaional funds, 28 emerging marke funds and 18 specific secor funds. Panel A of Table 1 repors he oal marke value of he asses held by he funds in he daa se a he end of he years of he sample period January 2002 o December 2009 and he oal number of unique socks held in hose funds. The oal holding of UK-lised equiies by higher urnover UK muual funds was US$24,521 billion a year-end 2002 and US$91,464 billion a year-end 2009. The oal number of unique socks held in hose funds was 3,916 a year-end 2002 and 9,936 a year-end 2009. Excep for a sligh decrease in he oal asses of hose funds in 2006 and 2007, possibly because of he impac from he onse of he subprime morgage crisis, here was seady growh in he oal asses of hose funds and oal number of unique socks held in hose funds. Panel B of Table 1 repors he number of muual funds for each year and each quarer of he sample period. Coninuous growh in he oal number of hose funds for each year and for each quarer occurred. 9
Panel C records he average marke value of he asses of each ype of fund. The average size of all ypes of funds is US$398.70 million. Among hose fund ypes, he average marke value of asses held by he aggressive growh and core growh funds is he larges, a an average of US$811.90 million, ha of asses held by growh funds is second larges, a an average of US$520.87 million and ha of asses held by he GARP funds is he smalles, a an average of US$148.44 million. Panel D shows he average number of socks ha each ype of fund holds in is porfolios. Aggressive growh and core growh ype of funds hold he larges number of socks, wih an average of 217, he core value and deep value funds hold he second highes number of socks, wih an average of 214, and he specific secor ype of fund hold he lowes number of socks, wih an average of 81. In sum, UK muual funds increased he number of socks held in heir porfolios over he period, paricularly he aggressive growh and core growh and he GARP funds. Panel E repors he number of each ype of fund. The average number of inernaional funds is highes a 52, and he average number of GARP funds is he lowes a 7. Panel F repors he average number of socks raded by a leas 1, 5, 10 and 15 fund managers in each quarer of a specific year. The panel shows ha here were 5,670 socks wih a leas one fund manager and 409 socks wih a leas fifeen fund managers in each quarer. Noably, here was an increasing rend in he average number of socks regardless of wheher hey were raded by 1, 5, 10 or 15 fund managers in each quarer, which ends o promoe herding among UK muual funds. 3. Tess for Herding by UK Fund Managers Wermers (1999) indirecly esed for cross-secional emporal dependence wihin a cerain period, and found ha when laer insiuional raders followed earlier insiuional invesors rading behaviour, his resuled in he highes number of insiuional raders on 10
he same side of a rade wihin ha period. This sudy adops he insiuional herding measures of Sias (2004) o direcly invesigae wheher UK muual funds follow oher muual funds rades in he sock marke. In oher words, we examine he cross-secional correlaion beween some fund managers rades in one period and oher fund managers rades in he nex period. We follow Sias (2004) and calculae he raw fracion of he number of fund managers buying securiy i during quarer : Raw Δ n = No. of fund buying i, /(No. of fund buying i, + No. of fund selling i, ) (1) A fund manager is defined as a buyer if his ownership in he sock increases and a seller if his ownership in he sock decreases during he quarer. Since he denominaor is greaer han zero, a securiy mus have a leas one fund manager rading i during he quarer. To allow for aggregaion over ime and o direcly compare coefficiens of momenum rading and measures of fund managers demand, we sandardise he fracion of fund managers buying securiy i in quarer (denoed Δ ) as follows: Δ Raw Δ = σ Raw Δ ( Raw Δ ), (2) where Raw Δ is he cross-secional average (across i securiies) raw fracion of fund managers buying in quarer and σ ( Raw Δ ) is he cross-secional sandard deviaion (across i securiies) of he raw fracion of fund managers buying in quarer. This sudy esimaes a cross-secional regression of he sandardised fracion of fund managers buying securiy i ( Δ ) in he curren quarer on he sandardised fracion of fund managers buying securiy i in he previous quarer ( Δ i, 1 ): Δ = β Δ + ε (3) i, 1 i, 1 i, Sias (2004) proposed ha he correlaion beween he curren fracion and he lag fracion 11
of fund managers buying can be decomposed as a fund manager following iself ino and ou of he same securiies and oher fund managers over adjacen periods; hus, we wrie he slope coefficien in Equaion (3) as follows: β = ρ I ( Δi,, Δi, 1) = [1/( i 1) σ( RawΔ ) σ( RawΔ 1) 1 + [ 1/( i 1) σ( RawΔ ) σ( RawΔ 1) I N N i= 1 n= 1 m= 1, m n ( D n, N i= 1 n= 1 ( D n, RawΔ )( D RawΔ )( D m, 1 RawΔ n, 1 1 RawΔ )/ N N 1 1 )/ N N 1, (4) If fund managers end o follow heir own rades over adjacen quarers, he firs erm on he righ-hand side of Equaion (4) will be posiive. 9 If manager m buys (sells) securiy i in quarer -1 and manager n buys (sells) securiy i in quarer, he second erm will be posiive. 10 The average coefficiens of 31 regressions and associaed -saisics compued from he ime-series sandard errors in Equaion (3) are repored in he firs column of Table 2. The resuls in Table 2 consisenly show ha significan evidence exiss ha UK muual funds follow oher funds or hemselves ino and ou of he same securiies for all securiies wih 1, 5, 10 and 15 fund managers rading. Their cascading behaviours are significan on securiies raded a all frequencies, which is consisen wih he resuls of Sias (2004). However, no securiy had 20 UK fund managers rading in he sock marke over he sample period, possibly he resul of lile aciviy in relaion o insiuional rades, conrary o he scenario in he US. The coefficiens in he regressions of insiuional demand on lag insiuional demand are correlaions from sandardised daa and a single independen variable. The cross-secional correlaions beween insiuional demand his quarer and las quarer 9 Alernaively, if fund managers end o reverse heir previous quarer s rades, he firs erm will be negaive. If an individual fund manager s ransacions in he quarer are independen of his own ransacions, he firs erm will be zero. 10 If fund managers end o sell (buy) securiies ha oher fund manager purchased (sold) in he previous quarer, his erm will be negaive. If fund managers ransacions in he quarer are independen of oher fund managers ransacions in he previous quarer, his erm will be zero. 12
average 0.0630, 0.1405, 0.1621 and 0.2690 for securiies wih 1, 5, 10 and 15 fund managers rading, respecively, all significanly differen from zero a he 1% level. The resuls of Table 2 show ha, on average, he majoriy of he correlaions (i.e., 0.0764/0.0630 for securiies wih 1 hey rading, 0.1424/0.1405 for securiies wih 5 hey rading, 0.1708/0.1621 for securiies wih 10 hey rading, 0.2708/0.2690 for securiies wih 15 hey rading) beween he fracion of fund managers buying his quarer and he fracion buying las quarer in he sock marke resuls from oher fund managers cascades (i.e. herding), which is saisically significan a he 1% level. Own cascades accoun for an obvious minoriy of he correlaion (i.e., 0.0133/0.0630 for securiies wih 1 hey rading, 0.0018/0.1405 for securiies wih 5 hey rading, 0.0086/0.1621 for securiies wih 10 hey rading, 0.0017/0.2690 for securiies wih 15 hey rading) beween he fracion of fund managers buying his quarer and he fracion buying las quarer, and individual fund managers coninue o buy (sell) he securiies hey sold (bough) he previous quarer, which negaively reaches a saisically significan level only for securiies wih 1 fund managers rading. Thus, he empirical resuls repor ha UK fund managers cascades mainly resul from heir herding, which is consisen wih he findings of Sias (2004). 4. Causes of UK Fund Managers Herding 4.1 UK fund managers herding resuling from habi invesing or cascades To es wheher habi invesing explains fund managers herding and following heir own lag rades, we examine he correlaion beween he fracion of fund managers increasing heir porfolio weighs in a given quarer and hose increasing hem in he previous quarer. If fund managers follow hemselves and each oher s ino and ou of he same securiies as a resul of habi invesing, hen porfolio weighs should be independen over adjacen quarers. Alernaively, if fund managers follow hemselves and each oher 13
ino he same securiies for reasons oher han ime-series and cross-secional correlaion in ne flows (habi invesing), hen he fracion of fund managers increasing heir porfolio weighs will be posiively correlaed over adjacen quarers. To purge reurn-induced noise from he measure of he fracion of fund managers increasing heir porfolio weighs, his sudy follows Sias (2004) by using changes in reurn-adjused porfolio weighs raher han changes in raw porfolio weighs o accuraely idenify wheher fund manager n is a buyer or a seller. 11 The reurn-adjused porfolio weigh is defined as wha he end-of-quarer porfolio weigh would be if a fund manager s increase in securiy value did no rebalance his porfolio oward he iniial weigh. V n i,, is defined as he value of he price a he end of quarer imes he number of shares held by fund manager n a he end of quarer, which is regarded as fund manager n s posiion in securiy i a he end of quarer. If fund manager n s end-of-quarer porfolio weigh is greaer han his reurn-adjused beginning-of-quarer porfolio weigh, hen fund manager n is classified as increasing his reurn-adjused porfolio weigh (say, a buyer): V n, > I V i = 1 n, V n, 1 I i= 1Vn, 1 ( 1+ R ) ( 1+ R ) (5), where R i, is he reurn for securiy i over quarer. If he sign is reversed in Equaion (5), fund manager n is classified as a seller. 12 Thus, he raw fracion of fund managers increasing heir securiy i reurn-adjused porfolio weighs in quarer is defined as: Raw RA Δ = (No. of fund managers wih increased reurn-adjused weigh i, ) 11 We focus on reurn-adjused porfolio weighs since he fracion of insiuions increasing raw porfolio weighs is highly correlaed wih same-period reurns. 12 If he lef-hand side and he righ-hand side of Equaion (5) are equal, hen dealer n is no classified as a buyer or a seller. 14
/(No. of fund managers wih increased reurn-adjused weigh i, + No. of fund managers wih decreased reurn-adjused weigh i, ). (6) Table 3 shows he ime-series average correlaion, is componens and he associaed -saisics wih reurn-adjused porfolio weighs. Similar o he resuls in Table 2, UK fund managers herding significanly accouns for heir cascades for securiies wih 5, 10 and 15 rades. In deail, he correlaion beween he fracion of fund managers increasing heir reurn-adjused porfolio weighs and he lag fracion is primarily aribued o heir herding (i.e., 0.0584/0.0209 for securiies wih 5 rades, 0.0971/0.0653 for securiies wih 10 rades and 0.1032/0.0744 for securiies wih 15 rades). Similar o he resuls of changes in posiion, individual UK fund managers following heir own reurn-adjused porfolio weigh changes accoun for he minoriy of he correlaion (i.e., 0.0375/0.0209 for securiies wih 5 rades, 0.0318/0.0653 for securiies wih 10 rades and 0.1032/0.0744 for securiies wih 15 rades). However, heir own cascades negaively and significanly accoun for heir cascades for securiies wih 5, 10 and 15 rades. In erms of changes in reurn-adjused porfolio weigh, fund managers negaively and significanly follow heir own lag rades for securiies wih a leas 1 rade. More imporanly, he analyical resuls are consisen wih he conclusions of Sias (2004) ha UK fund managers herding is no primarily driven by habi invesing for securiies wih a leas 5, 10 and 15 rades. 4.2. UK fund managers herding resuling from momenum rading Furhermore, recen sudies such as Wermers (1999, 2000) and Sias e al. (2002) proposed ha fund managers herd owards (away from) socks wih high (low) pas reurns. Tha is, fund managers may follow each oher ino and ou of he same socks because of heir momenum rading. This sudy follows Sias (2004) and adds a lag reurn o Equaion (3) o evaluae fund managers momenum rading o explain he relaionships in heir 15
buying cascades. We regress he quarerly sandardised fracion of fund managers buying on he lag quarerly sandardised fracion of fund managers buying and he lag quarerly sandardised reurn, which is expressed as follows: 13 Δ = β Δ + β R + ε (7) i, 1 i, 1 2 i, 1 i, The resuls of he sandardised regression of UK fund managers demand on lag managers demand and lag reurns in Table 4 indicae ha, excep for securiies wih 1 rade, fund managers posiive feedback rading is significan. Even afer aking ino accoun momenum rading, fund managers cascading behaviour is significan only for securiies wih 10 rades on regression 2, bu all of heir cascading behaviour is significan on regression 1. Adding a sandardised lag reurn o he regression has lile impac on he average coefficien associaed wih he previous fracional increase in fund managers posiion bu has obvious changes on ha of heir reurn-adjused porfolio weighs. Moreover, our resuls find ha he UK fund managers demand is more evidenly relaed o heir lag reurns han heir lag demand, excep for securiies wih 5 rades in regression 1. Tha is, he momenum rading of UK fund managers is larger han heir cascading behaviours, which is differen from he findings of Sias (2004) in he US and Wylie (2005) in he UK. Hence, momenum rading is possibly regarded as one of he primary sources of UK fund managers herding in he sock marke. 4.3. UK fund managers herding from invesigaive herding or informaional cascades Wermers (1999) deemed ha informaional cascades are more likely o occur in small-capialisaion securiies because insiuional invesors end o aach a clearly larger weigh o wha he herd is doing and less weigh o heir own noisy privae informaion. Sias (2004) proposed ha he cross-secional correlaion beween signals is likely o be 13 The coefficien of β 1 represens he exen of fund managers cascading, and ha of β 2 represens he exen of heir feedback rading. 16
sronger for larger socks wih less noisy signals. We follow he hypohesis of Wermers (1999) and Sias (2004) ha fund managers following ohers rades end o inensively rade in smaller socks or ha hose following he correlaed signals end o inensively rade in larger socks. If fund managers herding primarily arises from informaional cascades, herding should be sronges in small-capialisaion securiies. Alernaively, if fund managers herding primarily arises from invesigaive herding, herding should be sronges in large-capialisaion securiies. Since he number of samples is no large in his sudy, compuing he average following heir own rades conribuion and herding conribuion for each securiy quarer is limied. Thus, we do no follow Sias (2004) o adjus he average conribuion from following heir own rades and ohers rades, bu we direcly examine he cross-secional correlaion beween he fracion of fund managers buying his quarer and he fracion buying las quarer for socks wihin small, middle and large capialisaion quiniles. Through his procedure, we es wheher evidence exiss of following heir own rades and herding wihin each capialisaion quinile. Because he capialisaion disribuion is exreme excep for securiies wih 5 rades, Table 5 repors he ime-series averages of he 31 cross-secional averages and associaed -saisics for securiies wihin each capialisaion quinile only for securiies wih rading frequency. The second columns in Table 5 show ha he average following-heir-own rades are negaive and saisically significan for boh small and large capialisaion quiniles; however, he resuls in he hird column in Table 5 provide significanly posiive evidence of UK fund managers following oher managers rades only for he large capialisaion quinile. Thus, fund managers herding is more likely focused on large capialisaion securiies han on small capialisaion securiies, especially for securiies wih 5 rades. However, similar o he resul of Sias (2004), his is no a monoonic posiive relaion beween he average herding iem and capialisaion. 17
UK fund managers are more likely o negaively follow heir own prior-quarer rades in small securiies, which is consisen wih he hypohesis ha insiuions negaively following heir own lag rades may be based on he adjusmen sock posiions and rading coss. Moreover, fund managers are more likely o herd in large capialisaion securiies, which is consisen wih he hypohesis ha insiuions herding resuls primarily from he cross-secional correlaion indicaors, possibly as a resul of heir following he same signals. Tha is, he main cause of UK fund managers herding may come from invesigaive herding raher han informaional cascades in he sock marke. Meanwhile, oher invesors could follow fund managers cascades o rade in large capialisaion socks because abnormal reurns of he pos-herding of hese socks are high as hey possibly follow he correlaed indicaors. 5. UK fund managers herding changes in bullish and bearish markes Alhough some lieraure, such as Hwang and Salmon (2004), found ha herding behaviours exised in boh bearish and bullish sock marke periods, mos lieraure, such as McQueen e al. (1996), Chang e al. (2000) and Gleason e al. (2004), verified ha he herding behaviours of invesors in a bearish marke were more significan han hose in a bullish marke. However, due o he naure of shor sell resricion for muual funds, he sudy waned o undersand wheher such a resricion will significanly alleviae he herding behaviours of fund managers in a bearish marke relaive o a bullish marke, or wheher heir quick response o negaive news in a bearish marke will increase he significance of he herding behaviours during such a period, or wheher he prohibiion agains shor selling for muual funds reduces he expansion of herding behaviors of fund managers in he bearish marke period so as o creae heir significan herding behaviours in eiher bullish or bearish markes. We use he deermining crierion proposed by Fabozzi and Francis (1979), which 18
saes ha in a bullish marke he sock price index rose for hree consecuive monhs, and in a bearish marke he sock price index dropped for hree consecuive monhs. This sudy used he MSCI world index as he sock index since he majoriy of UK muual funds under his sudy were inernaional funds, which occupy 52% of all ypes of funds in he UK, as shown in Table 1. Thus, we can divide he oal sample period ino many subperiods based on bullish or bearish periods. 14 We separaely compue hese ess wih he cross-secional regressions for he enire sample of firms as well as for he subsamples of firms wihin each capialisaion quinile o clarify wheher fund managers herding changes in bullish and bearish periods. If he deposiion componens of fund managers cascades exhibi sabiliy in bullish and bearish periods, hen he inerpreaions of he herding and own cascades by fund managers will no change. We also limi he sample o securiies wih 5 rades because he sample disribuion is uniform only for rading frequency. Panel A of Table 6 shows he average correlaions and decomposiions of own and oher cascades for he enire sample in he bullish and bearish periods and an F-saisic wih he null hypohesis ha he ime-series mean in he bullish period equals ha in he bearish period. The resuls consisenly indicae ha we accep he hypohesis ha he correlaion for he enire sample is he same in he bullish and bearish periods. Moreover, we demonsrae ha he fund managers herding is significanly larger han heir own cascades, and his phenomenon will no change in a bullish or a bearish period. Then, we compue he average correlaions and decomposiions for each bullish, bearish and capialisaion quinile o combine hese wih he resuls across capialisaion quiniles. Excep for he F-saisics for esing he equaliy of he ime series mean for own and oher cascades, each capialisaion quinile also repors he F-saisics wih he null hypohesis ha he ime-series mean in he bullish subperiod 14 The crierion is he similariy of marke changes because i is consruced by he weighed sock index. 19
equals ha in he bearish subperiod. The resuls in Panel B of Table 6 show ha no significan differences exis beween he bullish and bearish periods for he average own cascades and herding in all quiniles. Our resuls find ha UK fund managers herding significanly exiss in boh bullish and bearish sock markes possibly as a resul ha he naure of shor selling resricion for muual funds reduce he expansion of heir herding behaviours in he bearish marke period resuling from heir quick response o negaive news. On he oher hand, we again demonsrae ha fund managers cascades mainly resul from heir herding, and ha fund managers herding is larger han heir own cascades is he same in boh bullish and bearish sock markes. In addiion, by examining he F-saisics, we demonsrae ha UK fund managers herding is significanly larger han heir own cascades in he larges-capialisaion securiies boh in bullish and bearish periods, perhaps because of invesigaive herding of UK fund managers in he sock marke. 6. Herding by he similar and differen insiuional ype Del Guercio (1996) and Benne e al. (2003) indicaed ha, if characerisic herding or repuaional concerns drove insiuional herding, insiuions are more likely o follow similar ypes of insiuions han differen ypes of insiuions. Sias (2004) furher proposed ha, because insiuional invesors are more likely o follow similar ypes of insiuional invesors wih whom hey were direcly compeing han differen ypes of insiuions, hey exhibi he sronges endency o herd based on repuaional herding. We follow he procedure by Sias (2004) and regress he sandardised fracion of growh-ype funds buying his quarer on he sandardised fracion of all ypes of funds buying las quarer. We limi he sample o securiies wih specific rade frequency for which herding by growh-ype funds resuled in heir cascades his quarer and specific rade frequency for which herding by all ypes of funds resuled in heir cascades las quarer. Then, we replace he dependen variable wih each ype of fund and repea he compuaional 20
process. Δ = β Δ + ε (8) q q i, 1 Since individual fund managers follow heir own lag rades and he lag rades of oher fund managers, he fracion of buyers for a specific fund class is relaed o he lag fracion of all fund buyers. The second erm can be furher decomposed ino following oher raders of he same fund classificaion and following raders belonging o a differen fund class. For example, he correlaion among growh-ype funds can be decomposed as follows: 15 ( ) I Qi, q q q q = Δ, Δ,, 1 [1/( 1) ( ) (,, 1 ) (,, )(,, 1 1 )/ i i = i Raw Δ RawΔ i i Dqi RawΔ Dqi Raw Δ Qi, Ni, 1 i= 1 q= 1 β ρ σ σ + [1/( i 1) σ( RawΔ ) ( RawΔ ) ( D RawΔ )( D RawΔ ) / Q N I Qi, Qi, 1 q q σ i, i, 1 qi,, mi,, 1 1 i, i, 1 i= 1 q= 1m= 1, m q, m B I Qi, Ni, 1 Qi, 1 + q q [1/( i 1) σ( RawΔ ) σ( RawΔ,, 1 ) (,, )(,, 1 1 ) /,, 1, i i Dqi RawΔ D mi RawΔ QiNi i= 1 q= 1 m= 1, m B (9) where D qi,, is a dummy variable ha equals one (zero) if growh-ype fund manager q is a buyer (seller) of sock i in quarer and Q i, is he number of growh-ype fund managers rading sock i in quarer. Similarly, Q is defined for quarer -1. i, 1 N, 1 Q i i, 1 is he number of non-growh-ype fund managers rading sock i in quarer -1. The decomposiion includes he quarerly cross-secional average of following own rades of growh-ype fund managers, following he rades of oher growh-ype fund managers (he same-ype herding) and following he rades of non-growh-ype fund managers (differen-ype herding). By limiing he sample o securiies wih 5 rades resuling from he uniformiy of he sample disribuion only for his rading frequency, we compue he ime-series 15 The firs erm in Equaion (9) represens he porion of he correlaion aribued o growh-ype funds following heir own lag rades. The second erm represens he porion of he correlaion aribued o growh-ype funds following oher growh-ype funds. The las erm represens he porion of he correlaion aribued o growh-ype funds following non-growh-ype funds. 21
averages and associaed -saisics of he cross-secional averages for aggressive and core growh, GARP and growh funds (we denoe aferward as growh-ype funds) based on he P/E or M/B raio in he firs row in panel A of Table 7. The same averages and saisics of core and deep values (we denoe aferward as value-ype funds) are repored in he second row. The hird row repors F-saisics abou he null hypohesis ha he esimaes are equal across fund ypes. Then, he analysis is repeaed for muual funds based on invesing regions and secor specific or non-secor-specific in panels B and C of Table 7, respecively. The las column of Table 7 repors F-saisics abou he null hypohesis ha he esimaes beween same-ype and differen-ype herding are equal for each ype of UK muual fund in his sudy. The resuls in he las row in panel C of Table 7 show ha we can rejec he null hypohesis of equaliy across fund ypes based on secor specific or non-secor-specific for he average own-rades. This means ha specific secor funds negaively follow heir own lag rades more ofen han non-specific secor funds, perhaps as a resul of he adjusmen of specific-secor funds for pas sock posiions. In addiion, he resuls in he las row of panel A of Table 7 show ha we can rejec he null hypohesis of equaliy for he average herding. We find ha UK growh-ype funds will herd significanly more ofen han non-growh-ype funds, which is consisen wih he finding of Wermers (1999). Moreover, he resuls in he las row of panels A and B of Table 8 show ha we can rejec he null hypohesis of equaliy for he average same-ype and differen-ype herding, respecively. In deail, he growh-ype and inernaional-ype funds more ofen follow same-ype herding bu he value-ype funds more ofen follow differen-ype herding. Our resuls coincide wih he assumpion of Del Guercio (1996), Benne e al. (2003) and Sias (2004) ha he endency o herd is influenced by he differen environmens faced by hese invesors. Our resuls in panel A and he resuls of inernaional-ype funds in panel B rejec he 22
hypohesis of equaliy and reveal ha he growh-ype and inernaional-ype funds are more likely o follow similar ypes of funds, bu he value-ype funds are more likely o follow differen ypes of funds. The reasons may be ha he invesing arges of growh-ype funds are companies wih long-erm increases in sock prices and invesing arges of inernaional-ype funds are regions wih possible increases and dispersed risk in sock prices. Moreover, based on he sronger compeiion faces by hese funds, growh-ype and inernaional-ype funds are more likely o herd wih similar ypes, possibly because of heir repuaional and characerisic herding in he sock marke. This coincides wih he finding of Sias (2004) and Benne e al. (2003). Alernaively, he value-ype funds are more likely o herd wih differen ypes of funds (i.e., growh-ype funds), possible because he leadership of growh-ype funds. 7. Herding by he value-relevan informaion or he non-informaion Many empirical sudies concluded ha he price effecs of insiuional herding are differen (e.g. Grinbla, e al., 1995; DeLong e al., 1990; Choe e al., 1999; Nofsinger and Sias, 1999; Wermers, 1999; Dennis and Weson, 2000; Chakravary, 2001; Sias, e al., 2002; Sias, 2004). As Hung e al. (2010) proposed, insiuional herding behaviour is value-relevan informaion, or sabilises sock prices if under-priced (over-priced) securiies are bough (sold). Based on his assumpion, a few sudies show ha insiuional herding is weakly posiively correlaed wih fuure reurns (Grinbla, e al., 1995; DeLong e al., 1990; Choe e al., 1999; Wermers, 1999; Sias, 2004). On he conrary, Hung e al. (2010) deemed ha insiuional herding behaviour is non-informaional or desabilises sock prices if over-priced (under-priced) securiies are bough (sold). According o his hypohesis, a few sudies found ha he price impac of insiuional herding driven by fads, repuaional herding or characerisic herding is reversed in he following period. To examine he relaionships beween UK fund managers herding and fuure sock reurns o 23
explore wheher fund managers herding is informaional or non-informaional in he sock marke, his sudy uses a cross-secional regression of he reurn of fund managers buying R i + j securiy i in he same and following quarer j (, managers buying securiy i in he curren quarer ( Δ ) on he sandardised fracion of fund ) as follows: Ri, + j = βδ i, + ε i,, j = 0,1,2,3,4,5 (10) Table 8 shows he resuls of pos-herding reurns of UK muual funds and separae fund-ype managers. On he whole, our resuls demonsrae ha weak posiive correlaions exis beween he fracion of fund managers buying and sock reurns wihin he following year irrespecive of wheher enire or separae funds are considered. Thus, he resuls in Tables 8 are consisen wih he findings of Wermers (1999), Choe e al. (1999) and Sias (2004). Tha is, he herding behaviours of UK fund managers are based on value-relevan informaion as proposed by Hung e al. (2010), bu informaional herding is wihin he following he year. Our resul is similar o he resul of Sias (2004) for he US, bu he persisen period of UK fund managers herding is longer han ha of US fund managers herding, possibly for he following wo reasons. One reason may be ha he volailiy in he UK sock marke is significanly lower han ha in he US sock marke since he raio of FDI/GDP (i.e., foreign direc invesmen occupying gross domesic produc) in he UK 16 is obviously lower han ha in he US. The oher reason may be ha he ineres rae in he UK has been higher han ha in he US in recen years, making he reinvesmen yield of cash dividends in he UK higher han ha in he US when he holding period increases. In sum, we find no evidence ha UK fund managers herding drives prices from fundamenal values, which is no consisen wih Dennis and Weson (2000), Chakravary 16 The main invesing region of UK muual funds is he UK sock marke, and he main invesing region of US muual funds is he US sock marke. 24
(2001) and Sias e al. (2002). Moreover, regardless of enire or separae funds, we consisenly find insignifican relaions beween he fracion of fund managers buying and reurns in he following five quarers for all samples wih a leas 1, 5, 10 and 15 raders. Tha is, compared wih he herding quarer and he following one o four quarers, he correlaion in he following five quarers consisenly decreases. Hence, he impac of such informaional herding is consisenly reversed in he following five quarers. On average, he larges relaion occurs in he following year for samples wih a leas 15 raders, possibly because of he highes liquidiy in he sock marke. More specifically, pos-herding reurns for growh funds are he larges, hose for specific-secor funds are he second larges and hose for inernaional funds are he hird larges. Thus, oher invesors in he invesed sock marke could follow UK fund managers and purchase overbough socks wih a leas 15 raders quarerly in he following year, especially for growh funds, specific-secor funds and inernaional funds, o improve porfolio performance. 8. Conclusion Differen from he adjused LSV measure of Wylie (2005), his paper firs exends he dynamic model of Sias (2004) o explore wheher he herding behaviours of UK fund managers significanly exis in he sock marke. Our resuls demonsrae ha heir cascading behaviours are significan for securiies wih 1, 5, 10 and 15 raders bu no for securiies wih 20 raders. UK fund managers cascades mainly resul from heir herding. In erms of changes in he fracion of heir reurn-adjused porfolio weighs, fund managers herding also mosly resuls in heir cascades. Similar wih he findings of Sias (2004), UK fund managers herding is no primarily driven by habi invesing for securiies wih 5, 10 and 15 raders. Excep for securiies wih 5 raders in he regression of he previous fracional 25
increase in heir posiion, our resuls show ha UK fund managers demand is more evidenly relaed o heir lag reurns han heir lag demand. Differen from he resuls of Sias (2004) in he US and Wylie (2005) in he UK, our resuls show ha UK fund managers momenum rading is larger han heir cascades. Thus, momenum rading can be regarded as one of he main reasons for UK fund managers herding in he sock marke. Moreover, fund managers are more likely o herd in large capialisaion securiies, possibly because hey follow he cross-secional correlaion signals. Hence, one of he main reasons for UK fund managers herding may resul from invesigaive herding oher han informaional cascades in he sock marke, which is no consisen wih he finding of Sias (2004). Combining he resuls for he enire sample and each capialisaion quinile of bullish and bearish quiniles, his paper clarifies wheher, under he resricion ha UK fund managers canno shor sell all socks, UK fund managers are similar wih heir herding behaviours in bullish and bearish sock marke periods. Our resuls find ha fund managers are engaged in herding behaviours in boh bullish and bearish sock markes, possibly because he prohibiion agains shor selling for muual funds reduces he expansion of heir herding behaviours in he bearish marke period resuling from heir quick response o negaive news. Moreover, we again confirm ha managers cascades mainly resul from heir herding, which does no change in bullish and bearish sock markes. Our resuls show ha he herding behaviours of UK growh-ype funds are more significan han hose of non-growh-ype funds, which is consisen wih he resul of Wermers (1999). Furher, we find ha growh-ype and inernaional-ype funds in he UK are more likely o herd wih similar ypes, possibly because of he sronger compeiions hey face, which coincides wih he assumpion of repuaional and characerisic herding (Sias, 2004; Benne e al., 2003). On he oher hand, we find ha value-ype funds are more likely o herd wih heir differen ypes, possibly because of he leadership of 26
growh-ype funds. Our resuls demonsrae ha he endency o herd is influenced by he differen environmens faced by hese invesors, which is consisen wih he hypohesis of Del Guercio (1996), Benne e al. (2003) and Sias (2004). Regardless of enire or separae funds, we demonsrae ha weak posiive correlaions exis beween he fracion of fund managers buying and subsequen sock reurns, consisen wih he resuls of Wermers (1999), Choe e al. (1999) and Sias (2004). According o he argumen of Hung e al. (2010), he herding behaviours of UK fund managers are value-relevan informaion bu he informaional herding occurs during he nex year. Our resul finds ha he persisen period of UK fund managers herding is longer han ha of US fund managers herding. Mos imporanly, oher invesors in he invesed sock marke could follow fund managers and purchase socks overbough by hem wih a leas 15 raders quarerly in he following year, especially for growh funds, specific secor funds and inernaional funds, o improve heir porfolio performance. References American Invesmen Company Insiue., (2010), A Supplemenary Repor abou Global Muual Fund, Naional muual fund associaions and European fund and asse manage mea associaion (EFAMA), hp://www.efama.org/. Banerjee, A., (1992), A Simple Model of Herd Behavior, Quarerly Journal of Economics, 107, 797-817. Benne, J., R. Sias and L. Sarks (2003), Greener Pasures and he Impac of Dynamic Insiuional Preferences, Review of Financial Sudies, 16, 1203-1238. Bikhchandan S., D. Hirshleifer and I. Welch (1992), A Theory of Fads, Fashion, Cusom, and Culural Change as Informaional Cascades, Journal of Porfolio Economy, 100, 992-1026. Campbell, J., M. Leau, B. Malkiel and Y. Xu (2001), Have Individual Socks Become More Volaile? An Empirical Exploraion of Idiosyncraic Risk, Journal of Finance, 56, 1-43. Chakravary, S., (2001), Sealh Trading: Which Traders' Trades Move Prices?, Journal of Financial Economics, 61, 289-307. Chang, E.C., J.W. Cheng and A. Khorana (2000), An Examinaion of Herd Behavior in 27
Equiy Markes: An Inernaional Perspecive, Journal of Banking and Finance, 24, 1651-1679. Choe, H., B.C. Kho and R.M. Sulz (1999), Do Foreign Invesors Desabilize Sock Marke? The Korean Experience in 1997, Journal of Financial Economics, 54, 227-264. Chordia, T., R. Roll and A. Subrahmanyam (2001), Marke Liquidiy and Trading Aciviy, Journal of Finance, 56, 501-530. Del Guercio, D., (1996), The Disoring Effec of he Pruden-man Laws on Insiuional Equiy Invesmen, Journal of Financial Economics, 40, 31-62. De Long, B.J., A. Shleifer, L.H. Summers and R.J. Waldmann (1990), Pasiive Feedback Invesmen Sraegies and Desablizing Raional Speculaion, Journal of Finance, 45, 379-396. Dennis, P., and J. Weson (2000), Who's Informed? An Analysis of Sock Ownership and Informed Trading, Working paper, Universiy of Virginia and Rice Universiy. Fabbzz F.J., and J.C. Francis (1979), Muual Fund Sysemaic Risk for Bull and Bear Markes: An Empirical Examinaion, Journal of Finance, 34, 1250-1254. Falkensein, E.G., (1996), Preferences for Sock Characerisics A Revealed by Muual Fund Porfolio Holdings, Journal of Finance, 51, 111-135. Froo, K.A., D.S. Scharfsein and J.C. Sein (1992), Herd on he Sree: Informaional Inefficiencies in A Marke wih Shor-erm Speculaion, Journal of Finance, 47, 1461-1484. Gleason, K.C., I. Mahur and M.A. Peerson (2004), Analysis of Inraday Herding Behavior Among he Secor ETFs, Journal of Empirical Finance, 11, 681-694. Gompers, P., and A. Merick (2001), Insiuional Invesors and Equiy Prices, Quarerly Journal of Economics, 116, 229-260. Graham, J.R., (1999), Herding Among Invesmen Newsleers: Theory and Evidence, Journal of Finance, 54, 237-268. Grinbla, M., S. Timan and R. Wermers (1995), Momenum Invesmen Sraegies, Porfolio Performance, and Herding: A Sudy of Muual Fund Behavior, American Economic Review, 85, 1088-1105. Hirshleifer, D., A. Subrahmanyam and S. Timan (1994), Securiy Analysis and Trading Paerns when Some Invesors Receive Informaion before Ohers, Journal of Finance, 49, 1665-1698. Hung W.F., C.C. Lu and C.F. Lee (2010), Muual Fund Herding Is Impac on Sock Marke: Evidence from he Taiwan Sock Marke, Pacific-Basin Finance Journal, 18, 477-493. Hwang, S., and M. Salmon (2004), Marke Sress and Herding, Journal of Empirical Finance, 11, 585-616. 28
Jones, S.L., and D.B. Winers (1999), Delayed Reacion in Socks wih he Characerisics of Pas Winners: Implicaions for Momenum, Value, and Insiuional Following, Quarerly Journal of Business and Economics, 38, 21-39. Lakonishok, J., A. Shleifer and R.W. Vishny (1992), The Impac of Insiuional Trading on Sock Prices, Journal of Financial Economics, 32, 23-43. McQueen, G., M. Pinegar and S. Thorley (1996), Delayed Reacion o Good News and he Cross-auocorrelaion of Porfolio Reurns, Journal of Finance, 51, 889-919. Nofsinger, J., and R.W. Sias (1999), Herding and Feedback Trading by Insiuional and Individual Invesors, Journal of Finance, 54, 2263-2295. Scharfsein, D.S., and J.C. Sein (1990), Herd Behavior and Invesmen, American Economic Review, 80, 465-479. Sias, R., L. Sarks and S. Timan (2002), The Price Impac of Insiuional Trading, Working Paper, Washingon Sae Universiy and Universiy of Texas. Sias, R.W., (2004), Insiuional Herding, The Review of Financial Sudies, 17, 165-206. Trueman, B., (1994), Analys Forecass and Herding Behavior, The Review of Financial Sudies, 7, 97-124. Waler, A., and F.M. Weber (2006), Herding in he German Muual Fund Indusry, European Financial Managemen, 12, 375-406. Welch, I., (1992), Sequenial Sales, Learning and Cascades, Journal of Finance, 47, 695-732. Wermers, R., (1999), Muual Fund Herding and he Impac on Sock Prices, Journal of Finance, 54, 581-622. Wermers, R., (2000), Muual Fund Performance: An Empirical Decomposiion ino Sock-picking Talen, Syle, Transacions Coss, and Expenses, Journal of Finance, 55, 1655-1695. Wylie, S., (2005), Fund Manager Herding: A Tes of he Accuracy of Empirical Resuls Using U.K. Daa, Journal of Business, 78, 381-403. 29
Table 1 Descripive saisics of UK muual funds Year 2002 2003 2004 2005 2006 2007 2008 2009 Average A. Toal value and numbers of sock Toals asses of funds (billion US$) 24,520.94 37,689.01 34,230.35 69,509.03 60,097.45 61,065.46 71,141.85 91,463.99 56,214.76 Toal unique socks held 3,916 4,647 4,211 6,221 6,869 7,654 9,263 9,936 6,590 B. Number of Funds Reporing Toal 323 371 416 734 850 1,012 1,303 1,439 806 Q1 76 78 105 173 197 229 304 366 191 Q2 83 91 106 178 208 250 320 344 198 Q3 81 99 103 187 208 257 331 376 205 Q4 83 103 102 196 237 276 348 353 212 C. Average Fund Asse Value (million US$) Aggres. Gr. & Core Growh 794.06 1,014.87 895.96 879.37 590.47 519.64 804.00 996.82 811.90 GARP 155.51 171.39 138.74 133.33 149.17 149.24 131.43 158.70 148.44 Growh 477.39 646.97 538.86 779.37 300.30 281.58 290.91 851.60 520.87 Core Value & Deep Value 407.43 394.62 388.70 524.21 475.84 513.64 475.84 500.03 460.04 Inernaional 172.84 176.77 180.30 195.99 235.84 214.50 215.57 195.58 198.42 Emerg. Mks. 264.38 406.16 425.51 507.77 394.55 429.93 379.96 561.28 421.19 Specific Secor 206.75 198.94 229.65 269.59 232.94 250.95 224.17 227.53 230.06 30
Table 1 Descripive saisics of UK muual funds (coninued) Year 2002 2003 2004 2005 2006 2007 2008 2009 Average D. Average Number of Socks held Aggres. Gr. & Core Growh 196 163 169 181 204 196 248 380 217 GARP 156 154 86 86 114 159 381 385 190 Growh 130 128 186 153 130 116 138 260 155 Core Value & Deep Value 145 145 168 229 218 224 249 332 214 Inernaional 212 192 144 114 107 97 132 183 148 Emerg. Mks. 124 125 116 115 113 108 149 212 133 Specific Secor 62 69 59 68 70 74 105 140 81 E. Fund Number Aggres. Gr. & Core Growh 7 10 11 12 10 13 14 16 12 GARP 7 7 6 7 8 8 7 8 7 Growh 7 10 11 23 24 20 22 21 17 Core Value & Deep Value 8 9 8 18 20 18 20 19 15 Inernaional 28 34 32 62 62 67 68 65 52 Specific Secor 7 7 7 10 12 10 16 16 11 F. Average number of securiies wih 1 rader 2,028 3,606 3,369 5,260 6,131 6,855 8,404 9,704 5,670 5 raders 178 688 641 1,634 2,015 1,975 2,972 3,737 1,730 10 raders 21 216 179 536 695 747 1,514 2,081 749 15 raders 3 86 65 225 311 348 892 1,338 409 31
Table 2 Tess for herding for raw fracion of numbers---if buyer increased posiion Δ = β Δ + ε. i, i, 1 i, Pariioned slope coefficien Average coefficien ( β ) managers following managers following heir own rades ohers rades Panel A: Securiies wih 1 rader 0.0630-0.0133 0.0764 (8.8247***) (-2.5559**) (11.9785***) Panel B: Securiies wih 5 raders 0.1405-0.0018 0.1424 (9.6523***) (-0.5130) (10.5643***) Panel C: Securiies wih 10 raders 0.1621-0.0086 0.1708 (4.5970***) (-1.0927) (5.0901***) Panel D: Securiies wih 15 raders 0.2690-0.0017 0.2708 (4.1512***) (-0.1288) (3.8361***) Average 2 R 0.5512% 2.6109% 6.3646% 18.5854% Noe: 1. Numbers in ( ) indicae -saisics and numbers in [ ] indicae p-values. 2. ***, ** and * indicae saisical significance a he 1, 5 and 10 per cen levels, respecively. Table 3 Tess for herding---if buyer increased reurn-adjused porfolio weigh Δ = β Δ + ε. i, i, 1 i, Pariioned slope coefficien Average coefficien ( β ) managers following managers following heir own rades ohers rades Panel A: Securiies wih 1 rader -0.0253-0.0449 0.0195 (-1.4343) (-3.3606***) (2.4645**) Panel B: Securiies wih 5 raders 0.0209-0.0375 0.0584 (1.6505*) (-5.4226***) (4.1743***) Panel C: Securiies wih 10 raders 0.0653-0.0318 0.0971 (2.6808***) (-3.7930***) (4.1235***) Panel D: Securiies wih 15 raders 0.0744-0.0288 0.1032 (1.9202**) (-3.0690***) (2.7160***) Average 2 R 0.9732% 0.7424% 2.1530% 4.9149% Noe: 1. Numbers in ( ) indicae -saisics and numbers in [ ] indicae p-values. 2. ***, ** and * indicae saisical significance a he 1, 5 and 10 per cen levels, respecively. 32
Table 4 Sandardised regression of UK fund managers demand on lag demand and lag reurn Δ = Δ β Average coefficien associaed wih lag heir demand ( ) 1 β 1, 1 + 2, R 1+ ε Average coefficien associaed β wih lag reurn ( β ) Panel A: Securiies wih 1 rader Regression 1---if buyer increased posiion 0.0440 0.0518 (2.1918**) (1.1887) Regression 2---if buyer increased reurn-adjused porfolio weigh -0.0730 0.0639 (-1.4070) (2.6769***) Panel B: Securiies wih 5 raders Regression 1---if buyer increased posiion 0.1192 0.0976 (5.3431***) (2.1203**) Regression 2---if buyer increased reurn-adjused porfolio weigh 0.0016 0.0911 (0.0903) (3.5264***) Panel C: Securiies wih 10 raders Regression 1---if buyer increased posiion 0.1397 0.3542 (3.0066***) (3.7084***) Regression 2---if buyer increased reurn-adjused porfolio weigh 0.0617 0.1008 (1.9436*) (2.1003**) Panel D: Securiies wih 15 raders Regression 1---if buyer increased posiion 0.3016 0.4464 (4.3745***) (4.0435***) Regression 2---if buyer increased reurn-adjused porfolio weigh 0.0610 0.2045 (1.2575) (1.9871**) 2 Average 2 R 1.0199% 1.2470% 3.4480% 1.6111% 11.8728% 3.9321% 26.2498% 10.4670% Noe: 1. Numbers in ( ) indicae -saisics and numbers in [ ] indicae p-values. 2. ***, ** and * indicae saisical significance a he 1, 5 and 10 per cen levels, respecively. Table 5 Average coefficien s from following UK fund managers own rades and ohers rades for securiies wih 5 raders. Pariioned slope coefficien Capializaion managers following managers following β heir own rades ohers rades quinile Average coefficien ( ) Average 2 R Small firms 0.0191-0.0488 0.0679 (0.3594) (-2.0304**) (1.2158) Quinile 2-0.0149-0.0418 0.0269 (-0.3544) (-3.7630***) (0.6188) Large firms 0.0230-0.0361 0.0592 (1.7278*) (-4.9934***) (4.7709***) 0.8271% 0.5155% 5.7030% Noe: 1. Numbers in ( ) indicae -saisics. 2. ***, ** and * indicae saisical significance a he 1, 5 and 10 per cen levels, respecively. 33
Table 6 Average coefficiens for following UK fund managers own rades and ohers rades for he bullish and bearish periods for securiies wih 5 raders Panel A: The enire sample for he bullish and bearish periods Bullish period Pariioned slope coefficien managers following managers following Average coefficien heir own rades ohers rades 0.0160-0.0398 0.0558 (0.8683) (-5.6253***) (3.3449***) Bearish period 0.0325-0.0322 0.0647 (1.0813) (-1.9200*) (2.3776***) F-saisic 0.2314 0.2452 0.0827 [p-value] [0.6342] [0.6243] [0.7759] Panel B: Each capializaion quinile Pariioned slope coefficien managers following managers following Small firms Bullish period Average coefficien heir own rades ohers rades 0.0045-0.0472 0.0516 (0.0729) (-1.4555) (0.7659) Bearish period 0.0534-0.0527 0.1061 (0.4822) (-1.5992) (1.0117) F-saisic [p-value] 0.1723 0.0110 0.1939 [0.6812] [0.9185] [0.6630] Quinile 2 Bullish period -0.0205-0.0399 0.0194 (-0.5123) (-3.2608***) (0.5017) Bearish period -0.0019-0.0464 0.0445 (-0.0172) (-1.8680*) (0.3749) F-saisic [p-value] 0.0397 0.0706 0.0681 [0.8434] [0.7923] [0.7960] Large firms Bullish period 0.0209-0.0389 0.0598 (1.3940) (-5.1543***) (4.1157***) Bearish period 0.0280-0.0299 0.0579 (0.9752) (-1.7383*) (2.3058**) F-saisic [p-value] 0.0574 0.3175 0.0048 [0.8124] [0.5776] [0.9452] Noe: 1. Numbers in ( ) indicae -saisics and numbers in [ ] indicae p-values. 2. ***, ** and * indicae saisical significance a he 1, 5 and 10 per cen levels, respecively. F-value (Prob) 27.8334 [0.0000] 9.1899 [0.0079] 2.1261 [0.1642] 1.7446 [0.1941] 2.1402 [0.1513] 0.5616 [0.4645] 36.3259 [0.0000] 8.2321 [0.0107] 34
Table 7 Average coefficiens from following UK fund managers own rades, herding, same-ype herding and differen-ype herding for securiies wih 5 raders Trader Average Following Following ype coefficien( β ) own rades ohers rades Same ype Differen ype Panel A: based on P/E or M/B raio Aggres. and Core Growh 0.0508-0.0317 0.0675 0.0803 0.0195 & GARP (2.6360**) (-2.3978**) (3.9029***) (2.1168**) (1.7672**) & Growh Core and 0.0177-0.0167 0.0193 0.0068 0.0607 Deep Value (0.4279) (-2.3528**) (1.7401*) (2.4235**) (3.4572***) F-value 3.8941 0.1086 5.5165 3.8622 3.9358 [Prob] [0.053] [0.743] [0.022] [0.054] [0.052] Panel B: based on invesing regions Inernaional 0.0415-0.0325 0.0740 0.975 0.0565 (2.9885***) (-4.3133***) (6.6651***) (9.9429***) (6.8202***) Emerg. Mks. 0.0808-0.0179 0.0986 0.0581 0.0405 (4.4860***) (-2.9956***) (5.8573***) (5.0990***) (3.0877***) F-value 2.9444 2.3370 1.4927 9.9705 1.0602 [Prob] [0.089] [0.132] [0.227] [0.003] [0.095] Average F-value 2 R (Prob) 0.47% 3.0797 [0.084] 1.37% 9.1799 [0.004] 0.75% 14.5868 [0.000] 1.63% 1.0297 [0.314] Panel C: based on Specific Secor or no Specific Secor 0.0233-0.0146 0.0380 0.0058 0.0321 (1.1938) (-1.7914*) (2.1983***) (0.8944) (1.8775*) Non- Specific 0.0612 0.0027 0.0584 0.0218 0.0366 Secor (5.3669***) (1.0458) (5.2676***) (3.2208***) (3.9677***) F-value 2.6969 3.8785 0.9673 2543.77 0.0510 [Prob] [0.106] [0.054] [0.329] [0.093] [0.822] Noe: 1. Numbers in ( ) indicae -saisics and numbers in [ ] indicae p-values. 2. ***, ** and * indicae saisical significance a he 1, 5 and 10 percen levels respecively. 1.20% 2.0697 [0.155] 0.74% 1.6746 [0.201] Table 8 Tess for correlaion beween UK fund managers demand and he same and following reurns Ri, + j = βδ i, + ε i,, j = 0,1,2,3,4,5 Same Following Following Following Following quarer 2 quarer 3 quarer 4 quarer 5 quarer reurn reurn reurn reurn reurn Panel A: The enire sample Securiies wih 1 rader 0.0103 0.0091 0.0120 0.0114-0.0363 Securiies wih 5 raders (5.3289***) (1.9974***) (2.6441***) (2.3098***) (-1.0541) 0.0130 0.0048 0.0000-0.0018 0.0000 Securiies wih 10 raders (2.0227***) (0.3602) (0.0067) (-0.1085) (0.0375) 0.0209 0.0215 0.0272 0.0260 0.0054 Securiies wih 15 raders (4.3439***) (3.5294***) (3.7140***) (2.4265***) (1.1017) 0.0187 0.0353 0.0375 0.0377 0.0019 (2.6547***) (3.6973***) (3.7067***) (2.7851***) (0.2295) Panel B: Aggres. and Core Growh Securiies wih 1 rader 0.0131 0.0051-0.0004-0.0057-0.0029 Securiies wih 5 raders (3.2260***) (0.8060) (-0.0589) (-0.7343) (-0.6155) 0.0193 0.0159 0.0129 0.0137-0.0000 Securiies wih 10 raders (2.2065***) (1.1854) (0.8557) (0.8240) (-0.1476) 0.0198 0.0212 0.0295 0.0306 0.0061 Securiies wih 15 raders (4.0790***) (2.9821***) (3.0829***) (2.6375***) (1.1673) 0.0204 0.0396 0.0460 0.0516 0.0000 (2.6347***) (3.4802***) (3.2202***) (2.6881***) (0.0443) Panel C: GARP 35
Securiies wih 1 rader 0.0067-0.0039 0.0014 0.0099 0.0043 Securiies wih 5 raders (1.0243) (-0.3156) (0.1025) (0.8277) (1.0365) 0.0114-0.0136-0.0267-0.0285-0.0000 Securiies wih 10 raders (1.1508) (-0.6079) (-1.1018) (-1.0600) (-0.1384) 0.0235 0.0251 0.0347 0.0356 0.0025 Securiies wih 15 raders (4.0046***) (2.6417***) (2.5627***) (2.3882***) (0.4278) 0.0172 0.0286 0.0315 0.0481 0.0046 (1.3106) (1.9315**) (2.0606***) (3.0067***) (0.5349) Panel D: Growh Securiies wih 1 rader 0.0081 0.0034 0.0062 0.0000-0.0024 Securiies wih 5 raders (1.4333) (0.2910) (0.5040) (0.0255) (-0.6136) 0.0132-0.0007-0.0033-0.0059-0.0018 Securiies wih 10 raders (1.1992) (-0.0297) (-0.1223) (-0.1979) (-0.3223) 0.0218 0.0280 0.0357 0.0327 0.0072 Securiies wih 15 raders (4.0293***) (4.0336***) (3.8377***) (2.6241***) (1.4653) 0.0195 0.0425 0.0499 0.0589-0.0017 (2.2629***) (4.4063***) (3.2528***) (2.8274***) (-0.1946) Panel E: Core and Deep Value Securiies wih 1 rader 0.0184 0.0159 0.0169 0.0150 0.0000 Securiies wih 5 raders (4.0062***) (2.1977***) (2.1308***) (1.5002) (0.2016) 0.0154 0.0133 0.0125 0.0157 0.0011 Securiies wih 10 raders (2.1933***) (0.9981) (0.8206) (0.8753) (0.3332) 0.0186 0.0148 0.0221 0.0231 0.0051 Securiies wih 15 raders (3.1478***) (1.1122) (1.3181) (1.2502) (0.9843) 0.0224 0.0385 0.0375 0.0414 0.0030 (3.0966***) (3.4888***) (3.3188***) (2.8343***) (0.2942) Panel F: Inernaional Securiies wih 1 rader 0.0154 0.0137 0.0131 0.0096-0.0038 Securiies wih 5 raders (3.0449***) (1.7282***) (1.4526) (0.9013) (-0.8241) 0.0182 0.0142 0.0163 0.0167 0.0034 Securiies wih 10 raders (2.4295***) (1.0706) (1.1214) (1.0044) (0.6405) 0.0215 0.0235 0.0357 0.0361 0.0060 Securiies wih 15 raders (1.6820) (2.9521***) (3.5424***) (3.1385***) (0.9461) 0.0161 0.0326 0.0428 0.0518 0.0089 (1.6820) (2.9521***) (3.5424***) (3.1385***) (0.9461) Panel G: Emerg. Mks Securiies wih 1 rader 0.0245 0.0216 0.0093-0.0121 0.0090 Securiies wih 5 raders (3.6029***) (1.7750**) (0.6890) (-0.7248) (1.1264) 0.0208 0.0141 0.0098-0.0056 0.0034 Securiies wih 10 raders (3.9617***) (1.2101) (0.6802) (-0.2843) (0.3638) 0.0263 0.0096 0.0014-0.0028 0.0183 Securiies wih 15 raders (2.1193***) (0.5429) (0.0698) (-0.0976) (1.5945) 0.0549 0.0619 0.0202 0.0014 0.0092 (2.2805***) (1.5931) (0.3992) (0.0219) (0.3971) Panel H: Specific Secor Securiies wih 1 rader 0.0130 0.0186 0.0222 0.0228-0.0018 Securiies wih 5 raders (3.2162***) (3.7277***) (3.6903***) (3.2279***) (-0.3706) 0.0090 0.0079 0.0043 0.0088-0.0000 Securiies wih 10 raders (1.0149) (0.8946) (0.4779) (0.7296) (-0.0859) 0.0072-0.0506-0.0437-0.0325 0.0011 Securiies wih 15 raders (0.2712) (-0.5775) (-0.4867) (-0.4022) (0.0843) 0.0460 0.0518 0.0343 0.0576 0.0099 (2.1039***) (1.5510) (1.0456) (1.6030*) (0.9104) Noe: 1. Numbers in ( ) indicae he -saisics, and numbers in [ ] indicae he p-values. 2. ***, ** and * indicae saisical significance a he 1, 5 and 10 percen levels respecively. 36