The Liquidiy and Volailiy Impacs of Day Trading by Individuals in he Taiwan Index Fuures Marke Robin K. Chou Professor, Deparmen of Finance, Naional Chengchi Universiy George H. K. Wang Research Professor of Finance, School of Managemen, George Mason Universiy Yun-Yi Wang Assisan Professor, Deparmen of Finance, Feng Chia Universiy ABSTRACT We invesigae he invesmen sraegies of individual day raders in he Taiwan Index Fuures marke, along wih heir impac on marke liquidiy and volailiy. Our resuls indicae a endency among mos individual day raders o behave as irraional conrarian raders. We also presen consisen evidence o show ha mos individual day raders provide marke liquidiy by reducing he bid-ask spread, emporary price volailiy and he emporal price impacs. Our resuls, which are consisen wih he experimenal resuls of Bloomfield e al. (2009), provide no suppor for he general criicism ha day rading desabilizes he marke while also exacerbaing marke volailiy. Keywords: Individuals; Day rading; Foreign insiuional rading; Trading sraegies; Liquidiy; Volailiy. JEL Classificaion: G12; G14 * Yun-Yi Wang (he corresponding auhor), Deparmen of Finance, College of Finance, Feng Chia Universiy, 100 Wenhwa Road, Taichung, Taiwan, ROC. Tel: +886-4-24517250 ex: 4175; Fax: +886-4-24513796; E-mail: yyiwang@fcu.edu.w. 1
1. INTRODUCTION The rapid growh in he number of individual day raders in he equiy and fuures markes has araced significan aenion from boh regulaors and he financial press. 1 Individual raders in he sock or fuures markes who ener ino rades and hen hasily exi from he rades in order o make quick profis are known as day raders; such raders close heir posiions a he end of each day, wih heir acions generally being aribuable o shor-run price dynamics and liquidiy. Day raders are ofen seen as a good proxy for noise raders, since hey provide liquidiy o he marke in much he same way as noise raders (Black, 1986; Malkiel, 1999). On he oher hand, however, here are numerous heoreical papers which claim ha noise raders end o exacerbae marke volailiy, essenially as a resul of engaging in a momenum rading sraegy or hrough he creaion of speculaive price pressure on asse prices. 2 There has been considerable debae regarding he ways in which he rading behavior of day raders affecs sock markes, wih Barber and Odean (2001), for example, noing ha day raders may add o marke deph by providing insan liquidiy, whereas hose raders who ry o profi from shor-erm momenum cycles 1 See for example, Pulliam (2000) and US SEC (2000), Special Sudy: Repor of Examinaion of Day-rading Broker-dealers, Permanen Subcommiee on Invesigaions of he Commiee on Governmenal Affairs; Unied Saes Senae: Saff Memorandum, 24 February 2000, Day Trading: Everyone Gambles Bu he House ; and Unied Saes General Accouning Office (2001) Repor o Congressional Requesers: Updaes on Acions Taken o Address Day Trading Concerns. 2 Examples include Black (1986), DeLong, Shleifer, Summers, and Waldmann (1990a), Campbell and Kyle (1993), Campbell, Leau, Malkiel, and Xu (2001) and Scheinkman and Xiong (2003). 2
probably increase he level of marke volailiy. However, he quesion of which effec dominaes remains empirically unresolved. The prior lieraure on day rading has been relaively scan, essenially as a resul of he lack of availabiliy o academic researchers of accurae rading records on day raders. We aim o fill he curren gap in he lieraure in he presen sudy by carrying ou a comprehensive empirical examinaion using unique and exremely comprehensive rading daa on all ypes of raders in Taiwan obained from he Taiwan Fuures Exchange (TAIFEX). In one paricular line of research, he focus was placed on he profiabiliy of day rading sraegies, wih he relaed sudies demonsraing ha afer aking ino consideraion heir rading coss, day raders generally end o lose money. 3 Linnainmaa (2005) provided evidence showing he exisence of a negaive relaionship beween shor-erm day rading profis and he long-erm performance of day raders in Finland, while Harris and Schulz (1998) had earlier demonsraed ha he small order execuion sysem (SOES) bandis of he NASDAQ, whose sole inenion was o follow he shor-erm price rend, ended o make some profi, essenially because some marke makers are slow o adjus heir quoes. In anoher line of research, analysis is underaken ino day rading and marke 3 See for example, Jordan and Dilz (2003), Linnainmaa (2005) and Barber, Lee, Liu, and Odean (2011). 3
volailiy; for example, from heir analysis of SOES rading and marke volailiy using inraday daa from 1 June 1995 o 26 July 1996, Baalio, Hach, and Jennings (1997) found ha over one-minue inervals, high numbers of maximum-sized SOES rades caused excess volailiy. However, over a longer period, greaer numbers of maximum-sized SOES rades were found o cause lower volailiy. They herefore concluded ha more maximum-sized SOES rades ended o lead o more efficien price discovery. From an examinaion of wheher day rading was relaed o volailiy in he Finnish sock marke, Kyrolainen (2008) found a posiive correlaion beween sock price volailiy and he number of rades underaken by individual day raders, while from a subsequen examinaion of he impac of day rading on volailiy and liquidiy on he Korea Sock Exchange, Chung, Choe, and Kho (2009) found ha individual day raders ended o use shor-erm conrarian sraegies giving rise o negaive impacs on bid-ask spreads. 4 They also found ha excessive day rading aciviy led o greaer reurn volailiy, alhough he impac ended o dissipae wihin an hour. The presen sudy differs from he prior research in his field in a number of ways. Firsly, our unique daase enables us o race he rading record of each accoun by 4 The definiion of day raders adoped by Chung e al. (2009) is where an invesor buys and sells he same sock on he same day, alhough hey may no buy and sell an equal number of shares; however, heir definiion is no consisen wih he general definiion of day raders (hose who hold zero invenories overnigh) and his may in fac lead o biased resuls. Improving on heir definiion, we adop a more sric definiion of day raders by requiring raders o close heir daily ousanding posiions. 4
differen ypes of raders; hus, we can specifically idenify individual day raders and furher explore heir dominan rading sraegies, as well as heir impac on boh marke liquidiy and volailiy, as compared o oher ypes of raders. The prior sudies invariably used proxies for day rading aciviy (such as message board aciviy), analyzing he impac of individual day rading wih no conrols in place for he impacs of he rading aciviies of oher ypes of raders. 5 Secondly, we invesigae wheher he ne rading behavior (buys minus sells) of individual day raders in he fuures marke ends o follow a posiive feedback (momenum) sraegy or a negaive feedback (conrarian) sraegy; we hen carry ou an examinaion of he impacs of he ne rading of individual day raders on marke liquidiy and volailiy. 6 To he bes of our knowledge, few empirical sudies have been able o simulaneously examine he ne rading behavior of individual day raders along wih he impacs of heir rading sraegies on price liquidiy and volailiy. Thirdly, very few sudies have invesigaed he inraday liquidiy-rading sraegy relaionship for differen ypes of rader; here, we examine he impac on marke liquidiy and volailiy of he ne rading behavior (he dominan rading sraegy) of individual day raders, foreign insiuional raders and oher ypes of raders. 5 For example, Koski, Rice, and Tarhouni (2008) used message board aciviy o proxy for day rading aciviy, adoping daa on large NASDAQ socks over wo ime periods, 3Q 1999 and 3Q 2002, o es he hypohesis ha increased day rading in 1999 caused higher volailiy. 6 Trading in he Taiwan index fuures markes requires less capial, has lower ransacion coss and less shor selling consrains, which may make i more aracive for day raders o ake shor-erm gains. 5
We also conribue o he exan lieraure of he fuures marke by providing a more reliable volailiy-volume relaionship for differen ypes of raders. Alhough an examinaion of he volailiy-volume relaionship by rader ypes was carried ou by Daigler and Wiley (1999), heir daase suffered from he common drawback of he unclear ideniy of he differen ypes of raders. For example, hey found ha he posiive volailiy-volume relaionship was driven by he general public (CT4), a group of raders ouside of he exchanges. Alhough he group of CT4 raders is undersood o comprise of individual raders, managed funds, insiuional raders and hedgers, Daigler and Wiley (1999) were unable o clearly disinguish beween individual raders and insiuional raders. In he presen sudy, however, we are able o disinguish beween four differen ypes of raders: foreign insiuional raders, domesic insiuional raders, fuures proprieary firm raders and individual raders, from which we obain several ineresing resuls. We find ha mos individual day raders end o behave as irraional conrarian raders, and also provide consisen evidence o show ha hey provide marke liquidiy by reducing he bid-ask spread, emporal price impacs and emporary price volailiy. Our resuls are consisen wih he experimenal resuls of Bloomfield, O'Hara, and Saar (2009), bu conradic he general criicism ha day rading desabilizes he marke and exacerbaes marke volailiy. 6
The remainder of his paper is organized as follows. Our empirical hypoheses based upon he prior empirical lieraure and heoreical models on he rading sraegies of individual raders are presened in Secion 2, along wih an examinaion of he relaionship beween individual raders, marke liquidiy and volailiy. A discussion on he daa, empirical model and variable measures used in his sudy is provided in Secion 3. Secion 4 begins wih he presenaion and discussion of he empirical resuls on rading sraegy and he profiabiliy of individual day raders and oher ypes of raders, and hen goes on o repor he empirical resuls on he impacs on marke liquidiy and volailiy aribuable o individual day raders and oher ypes of raders. Tess for robusness are presened in Secion 5, wih Secion 6 finally reporing he conclusions drawn from his sudy. 2. LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT We begin wih a review of he relaed empirical lieraure and an examinaion of a heoreical model on he relaionship beween marke volailiy and he rading behavior of invesors. The prior empirical findings and suggesions inferred by he model will form he hypoheses for he empirical ess in his sudy. The popular heoreical model of DeLong, Shleifer, Summers, and Waldmann (1990b) suggess ha when shor-erm noise raders follow a posiive feedback rading sraegy, his will end o creae excess marke volailiy, wih he raionale 7
behind heir argumen being as follows. In any financial marke, here are wo ypes of raders, value invesors and noise raders. Value invesors base heir rading on fundamenals, wih such rading involving buying (selling) socks when he marke price is below (above) he fundamenal value. This negaive feedback rading sraegy also referred o as a conrarian sraegy ends o reduce he overall level of sock price volailiy by pushing he sock price back o is fundamenal value. Conversely, shor-erm noise raders end o follow a posiive feedback rading sraegy, which involves buying and selling socks based on heir previous price movemens or he resuls of some echnical analysis; hus, heir primary focus is on making money, regardless of wheher he price movemens are raional. Alhough value invesors may well make money in he long run, if he noise raders in he marke have significan influence, and are able o keep prices from revering o heir fundamenal value, hen his will force he value invesors o liquidae heir posiions premaurely. Thus, DeLong e al. (1990b) claimed ha noise raders ended no only o creae excess volailiy, bu also o desabilize he marke. There is a rapidly growing sring of empirical lieraure involving invesigaions ino he rading behavior of individual raders. For example, Choe, Kho, and Sulz (1999) provided evidence o show ha individual invesors in Korea ended o follow a shor-erm negaive feedback (conrarian) sraegy, while Grinbla and 8
Keloharju (2000, 2001) also found ha individual invesors in Finland were conrarian invesors. From heir examinaion of he rading behavior of individual and insiuional invesors in Nasdaq 100 securiies, Griffin, Harris, and Topaloglu (2003) provided furher evidence o show ha ne individual rading ended o follow a conrarian sraegy. Bloomfield e al. (2009) adoped an experimenal marke approach o invesigae he behavior of noise raders, as well as he impac of heir rading on he markes. They also noed ha noise raders ended o follow a conrarian sraegy, and ha hey provided liquidiy while reducing he emporary price impac of rades. Chung e al. (2009) found ha Korean day raders used shor-erm conrarian sraegies, wih heir excessive day rading aciviies negaively affecing bid-ask spreads, which in urn, affec fuures volailiy. Based on our review of he prior lieraure, we propose he following hypoheses on he ways in which individual day raders affec he index fuures marke in Taiwan: Hypohesis H 0 : If dominan individual day raders follow a momenum (posiive feedback) rading sraegy, he ne rading aciviy of individual day raders will demand marke liquidiy and raise marke volailiy. Hypohesis H A : If dominan individual day raders follow a conrarian (negaive feedback) rading sraegy, he ne rading aciviy of individual day raders will provide marke liquidiy and reduce emporary 9
marke volailiy. 3. DATA AND METHODOLOGY 3.1 Daa The sample adoped for his sudy comprises of all Taiwan Capializaion Weighed Sock Index (TAIEX) fuures conracs raded on he Taiwan Fuures Exchange (TAIFEX) beween January 2006 and December 2008. The TAIFEX is an order-driven elecronic fuures marke wihin which here are no designaed marke makers; hus, liquidiy is generaed endogenously by marke paricipans. Trading on he TAIFEX is carried ou from 8:45 am o 1:45 pm, from Mondays o Fridays (excluding public holidays). The TAIEX is a value-weighed index of all common socks lised on he Taiwan Sock Exchange (TSE), where he rading unis of he TAIEX fuures are he index value of he TAIEX 200 New Taiwan Dollars (NT$). The nearby conracs are used in our analysis essenially because hey are he mos liquid conracs. 7 The daase adoped for his sudy, which is obained from he TAIFEX, conains comprehensive deails on all ransacions underaken in he marke. The TAIFEX ransacion daa include he dae and ime of he ransacion, he direcion (buy or sell) of he ransacion, he quaniy, ransacion price and he 7 During he mauriy monh, when he rading volume of he firs deferred conrac is greaer han he rading volume of he nearby conrac, he nearby fuures prices are rolled over o he firs deferred conrac, wih hese rollovers ofen occurring in he middle or laer pars of he mauriy monh. 10
ideniy of he raders. The rader idenificaion enables us o caegorize four ypes of raders, foreign insiuional raders, domesic insiuional raders, fuures proprieary firm raders and individual raders. Day rading is prevalen wihin he markes of Taiwan (Barber, Lee, Liu, and Odean, 2009; Chou, Wang, Wang, and Bjursell, 2011), and wha makes he daase used in his sudy quie unique, is is provision of accoun idenificaion, which enables us o race he rading aciviy for each accoun; i herefore also enables us o disinguish beween day raders and oher ypes of raders. Day raders are defined in he presen sudy as hose who buy and sell exacly he same amouns of fuures on he same day; ha is, hose raders who carry no invenory overnigh. The descripive saisics of he daily dollar rading volume of all rades and day rades, by rader ypes, are repored in Table 1, from which we can see ha he larges proporion of ransacions is aribuable o individual raders, wih heir overall rade value accouning for 70.43% of all rades. Fuures proprieary firms rank second (19.44%) and foreign insiuional raders rank hird (8.17%), wih domesic insiuional raders accouning for only 1.97%. <Table 1 is insered abou here> I is worh noing ha individual day raders are responsible for a paricularly high proporion of all rades; indeed, hey accoun for 33.67% of all daily rade value. 11
The proporions of day rades vis-à-vis all rades accouned for by oher raders are jus 0.57% for foreign insiuional raders, 0.33% for domesic insiuional raders and 0.15% for fuures proprieary raders. 3.2 Empirical Models We use a wo-equaion srucural model framework o esimae he poenial impac on marke liquidiy and price volailiy aribuable o he ne rading aciviy of individual day raders. The wo empirical models are specified as follows, beginning wih he bid-ask equaion, Equaion (1): 8 4 BAS = α + α TV + α IV + α BAS + oher conrols + e (1) 0 1i i 1 2 1 3 1 i= 1 where bid-ask spread (BAS ) is a funcion of inraday rading volume (lagged by one period) for he four ypes of raders (TV i 1). Addiional explanaory variables include one-period lagged bid-ask spreads (BAS 1), price risk (lagged by one period, and measured by price volailiy, IV 1) and oher conrols, which are inraday dummy variables aking ino accoun inraday periodiciy. The price volailiy equaion, Equaion (2), is expressed as: 4 IV = δ + δ TV + δ BAS + δ IV + oher conrols + ε (2) 0 1i i 1 2 1 3 1 i= 1 where price volailiy (IV ) is a funcion of rading aciviy measured by he rading 8 In order o avoid simulaneous equaion bias, for all ypes of raders, price volailiy and rading volume are lagged by one period in Equaion (1), while bid-ask spreads and rading volume are lagged by one period in Equaion (2). Refer o Wang and Yao (2000) for furher discussion on his issue. 12
volume (lagged by one period) for he four ypes of raders (TV i-1 ); bid-ask spread (BAS -1 ), lagged by one period, is used o ake ino accoun he volailiy aribuable o he bid-ask bounce. One-period lagged price volailiy (IV 1) capures he persisen effecs of marke volailiy. We expec o find a posiive relaionship beween price volailiy and bid-ask spreads, since marke makers demand wider bid-ask spreads when hey rade wih informed raders or when hey adop a posiion in opposiion o a large rade (ha is o say, hey demand a larger liquidiy premium when faced wih such rades). Thus, greaer ransacion price movemens may also be aribuable o significan variaions in bid-ask spreads. The ordinary leas squares (OLS) mehod is used o esimae he parameers of he model, wih he Newey and Wes heeroskedasiciy and auocorrelaion consisen covariance marix also being used o esimae he consisen sandard errors of he parameers. 3.3 Variable Measures 3.3.1Spread measures Boh he effecive and realized spreads from he inraday bid-ask quoes are used o measure marke liquidiy in he presen sudy; hese are calculaed as follows: Effecive Spread = 2D (P M ) (3) 13
Realized Spread (n) = 2D (P M +n ) (4) where D is an indicaor variable, which is equal o 1 for cusomer buy orders and 1 for cusomer sell orders; P is he ransacion price a which he rade is execued; M is he midpoin of he reference bid and ask quoes, which is assumed o be he rue value of he asse; and M +n is he midpoin of he quoaions which are in effec a n periods afer he rade. 9 Effecive Spread is a measure of rading cos and Realized Spread is a measure of emporary or non-informaional price impac. 3.3.2 Alernaive volailiy measures We use wo volailiy measures o examine he impacs of day raders on inraday volailiy: (i) Realized Volailiy (Andersen, Bollerslev, Diebold, and Ebens, 2001); and (ii) Transiory Volailiy. The firs of hese, Realized Volailiy, is calculaed as: n 2 ˆ σ = ( ) (6) R i i= 1 where R i is he 5-minue inraday reurn; and n is he number of 5-minue inraday reurns. Following he mehodology of Bae, Jang, and Park (2003), 10 we decompose oal volailiy ino ransiory volailiy and informaional volailiy. To assis in he esimaion of he ransiory variance and informaional variance, ransacion price is assumed o follow a random walk wih ransiory noise. The local level 9 10 Following he seing of Bessembinder (2003), n=30minues afer he reference quoe. The local level model was applied in Bae e al. (2003) o esimae and decompose he ransacions ino efficien and ransiory price componens. 14
model is specified as follows: P = m + ξ m = m + ν ζ ~ NID (0, ν ~ NID(0, 2 σ ζ ) 2 σ ν ), (7) where P is he ransacion price; m is he unobserved equilibrium (efficien) price, which follows a random walk; and ξ is he ransiory componen. 11 The Kalman filer echnique is used in his sudy o esimae he parameers of Equaion (7) for each 15-minue inerval, and σ ξ is also used as our measure of ransiory volailiy in each 15-minue inerval, since his allows us o perform a direc es on he effecs of rading aciviy on he inraday volailiy componen aribuable o changes in he demand and supply of marke liquidiy. 4. EMPIRICAL RESULTS 4.1 Trading Sraegies and Profis Bloomfield e al. (2009) argued ha he behavior of uninformed raders may manifes iself in a number of differen ways, as skillful echnical raders, profiable liquidiy providers or irraional noise raders. As a firs sep owards gaining an undersanding of he impac ha individual day raders have on he markes, we invesigae wheher he ne rading behavior of individual day raders ends o follow 11 For furher discussion on his unobserved componen (local level) model, refer o Harvey (1989) and Hasbrouck (1996). 15
a posiive feedback (momenum) rading sraegy or a negaive feedback (conrarian) rading sraegy. We also idenify he ne rading sraegies adoped by individual non-day raders, fuures proprieary firm non-day raders and foreign insiuional non-day raders in order o faciliae our comparaive analysis. 12 The exen o which rades are dependen upon pas price movemens is quie imporan, since a conrarian rading sraegy may well miigae price volailiy, whereas a momenum rading sraegy will end o have he opposie effec. The price movemens ha have occurred prior o rades provide us wih he basis for he classificaion of he rading sraegies of marke paricipans, as eiher momenum or conrarian raders. If raders follow conrarian rading sraegies by buying during down markes and selling during up markes, we expec o find buy (sell) rades following negaive (posiive) prior reurns, whereas, if raders follow momenum sraegies, a converse relaionship should be discernible. Following Keim and Madhavan (1995), we calculae he average marke reurns prior o buy and sell rades for several pre-decision ime inervals, comprising of 10, 20, 30, 60, 90, 120, 180 and 300 seconds prior o he rades aking place. The marke pre-rade average reurns, by rader ypes, are repored in Table 2, from which we 12 Domesic insiuional raders are omied from he sample, essenially because, as shown in Table 1, domesic insiuional raders accoun for only 1.97% of all rades. 16
can see ha for individual day raders, he prior average marke reurns of buy (sell) ransacions are all significanly negaive (posiive). For example, he 10-second marke reurn prior o a buy ransacion is found o be 0.00644%, while ha for a sell ransacion is found o be 0.01058%, hereby indicaing ha mos individual day raders end o follow a conrarian sraegy. This resul echoes he findings of he experimenal sudy underaken by Bloomfield e al. (2009), who concluded ha noise raders ended o follow conrarian rading sraegies and were likely o enhance marke liquidiy. <Table 2 is insered abou here> Ineresingly, we observe quie opposie paerns for individual non-day raders, foreign insiuional non-day raders and fuures proprieary firm non-day raders, mos of whom are found o buy when here is an increase in price and sell when here is a price decline, which is fairly consisen across he differen pre-decision ime inervals (10, 20, 30, 60, 90, 120, 180 and 300 seconds prior o rades). Our findings sugges ha he ne rading behavior of all non-day raders, including individual raders, foreign insiuional raders and fuures proprieary firm raders reveals a endency among all of hese non-day raders o follow a momenum rading sraegy. In order o provide furher confirmaion of he resuls on he ne rading behavior of individual day raders and oher ypes of marke raders, we 17
apply he price-seing order imbalance es procedure, as suggesed by Choe e al. (1999), and compue he price-seing order imbalance of each of he differen ypes of raders, condiional on he sign of he marke reurn of he prior 5, 15 and 60 minues. 13 We compue he price-seing order imbalance for each ype of rader in he prior 5-minue rading inerval (as an example) as he buy volume minus he sell volume wihin a 5-minue rading inerval; his is hen scaled by he inraday rading volume for each ype of rader. The means of he inraday price seing order imbalance for he differen ypes of raders, which are repored in Table 3, are condiional on he sign of he marke reurn of he prior 5 minues (Panel A), 15 minues (Panel B) and 60 minues (Panel C). <Table 3 is insered abou here> Based upon he prior 5-minue negaive marke reurns, we observe from Panel A of Table 3 ha he average order imbalance of individual day raders is posiively significan, whereas based upon he prior 5-minue posiive marke reurns, i is found o be negaively significan. The ess on he difference beween he means of he average order imbalances, condiional on prior posiive and negaive marke reurns, for each ype of rader, are repored in he las column of Panel A of Table 3, 13 We calculae he marke reurn of he prior 5, 10, 15, 30 and 60 minues and find ha he resuls are qualiaively similar; hus, for space saving purposes, only he resuls for 5, 15 and 60 minues are presened here. 18
from which we can see ha hey are all saisically significan a he 1% level. Table 3 also repors he resuls of our ess on he differences in he average order imbalance beween individual day raders and oher ypes of raders, condiional on previous posiive and negaive marke reurns, clearly showing ha he differences are all saisically significan a he 5% level, or beer. These resuls sugges ha he rading sraegies praciced by individual day raders differ significanly from hose of oher ypes of raders. The saisical es resuls obained from he calculaion of he order imbalances based upon he prior 15-minue (Panel B) and prior 60-minue (Panel C) marke reurns are also found o be similar. Thus, he resuls once again indicae ha individual day raders buy (sell) following negaive (posiive) marke reurns. Conversely, precisely he opposie rading paerns are exhibied by individual non-day raders, foreign insiuional non-day raders and fuures proprieary firm non-day raders. We now urn our aenion o he rading sraegies adoped over he course of he rading day in order o explore wheher a conrarian rading adoped by individual day raders has any direc effec on marke efficiency. Bloomfield e al. (2009) demonsraed ha he effec of conrarian rading on marke efficiency was dependen upon he amoun of marke informaion ha was available. When new informaion arrives, conrarian rading has a derimenal effec on marke efficiency, 19
essenially because a conrarian sraegy keeps prices from adjusing o such new informaion when hey are far from heir rue value. Conversely, in he absence of he arrival of any new informaion, conrarian rading is found o render ransacion prices more efficien, essenially because he addiional liquidiy supplied by conrarian raders leads o a reducion in he price impac of heir rades. Based upon heir experimenal analysis, Bloomfield e al. (2005, 2009) found ha individual day raders exhibied a much sronger conrarian rading paern in he mornings, as compared o he afernoons, when here is more informaion. Thus, individual day raders are likely o be noisy conrarian raders. The price seing order imbalances of individual day raders for 5-10-, 15-, 30- and 60-minue marke reurns are repored in Table 4, wih a comparison also being underaken beween he morning and afernoon reurns. As he resuls show, he conrarian rading paern is found o be sronger during morning rading han afernoon rading; indeed, following negaive (posiive) marke reurns, he price-seing order imbalance is found o be saisically and significanly larger (smaller) for morning rading han afernoon rading. <Table 4 is insered abou here> For example, as shown in Panel A of Table 4, following negaive (posiive) marke reurns, he order imbalance for morning rading is found o be 1.490% 20
( 0.481%), which is significanly larger (smaller) han ha for afernoon rading, a 0.423% ( 0.374%). These resuls sugges ha when new informaion arrives in he marke, he conrarian rading sraegies in which individual day raders end o engage may well be derimenal o he efficiency of he marke, essenially because a conrarian rading sraegy reduces he speed of price adjusmen o he rue sock price level. In heir experimenal marke analysis, Bloomfield e al. (2009) found ha liquidiy raders ended o provide more liquidiy during he morning, as compared o less liquidiy owards he end of he rading period. 14 Our resuls furher indicae ha, in line wih he findings of Bloomfield e al. (2009), individual day raders are more likely o behave as liquidiy raders essenially because hey reveal a sronger conrarian rading paern during morning rading han afernoon rading. Neverheless, he provision of liquidiy by individual day raders is no necessarily raional, and indeed, i can resul in such raders accruing posiive profis; herefore, in an aemp o deermine wheher or no his anicipaed characerisic of individual day raders providing liquidiy o he marke is indeed raional, we go on o furher examine he overall profiabiliy of individual day raders, wih he resuls 14 Bloomfield e al. (2009) classified raders ino hree groups: informed, uninformed and liquidiy raders, wih uninformed raders choosing o rades despie having no informaional advanage or exogenous moivaion o rade, and liquidiy raders rading for exogenous reasons or hedging purposes. Wihin he uninformed raders group, hey furher classified uninformed raders ino hree ypes: (i) skillful echnical raders; (ii) raional liquidiy providers (marke makers); and (iii) irraional conrarian noise raders. 21
being repored in Table 5. The resuls on gross rading reurns are repored in Panel A of Table 5, while he resuls on ne reurns (wih all commissions and ransacion axes aken ino consideraion) are repored in Panel B of Table 5. 15 The reurns are calculaed as he sum of paper reurns and realized reurns, wih he paper reurns being calculaed by esimaing he oal reurns for he open posiions held by each rader a 12:00 pm on each rading day, and he realized reurns being hose reurns ha are accrued as a resul of raders liquidaing heir posiions. <Table 5 is insered abou here> Several ineresing resuls are obained from Table 5. Firsly, we find ha, wih regard o non-day rading, only foreign insiuional raders end o make profis, while fuures proprieary firm raders and individual raders all end o lose money. These resuls are consisen wih he findings of several of he prior sudies, in which i was concluded ha foreign insiuional raders ended o make profis essenially because hey had an informaional advanage over domesic raders. 16 Secondly, as shown in Panel A of Table 5, we find ha all hree ypes of day raders accrue significanly posiive gross reurns; however, afer aking ino accoun 15 This commission varies among he differen brokerage houses, wih he average being abou NT$ 150. During our sample period, from 1 January 2006 o 5 Ocober 2008, he ransacion ax was 1 basis poin; however, on 6 Ocober 2008, he Taiwanese governmen reduced he ax levied on fuures ransacions on he TAIFEX from 1 o 0.4 basis poins. 16 Examples include Grinbla and Keloharju (2000), Chou and Wang (2009) and Huang and Shiu (2009). 22
all commissions and ransacion axes, only foreign insiuional day raders are found o make any significanly posiive ne reurns, while individual raders are found o experience significanly negaive ne reurns. Since he expeced characerisic of individual day raders providing liquidiy o he marke usually leads o hem losing money, hey essenially mach he definiion of irraional noise raders. Thirdly, we find ha foreign insiuional raders and fuures proprieary firm raders end o make more profis during he morning han during he afernoon, wih he difference being saisically significan. Conversely, for individual raders, he differences beween morning and afernoon reurns are all found o be significanly negaive. This suggess ha informed raders make larger profis in he morning sessions when here is relaively greaer availabiliy of informaion relevan o he marke (Bloomfield e al., 2009). In summary, our resuls indicae ha as opposed o being characerized as skillful echnical raders or raional liquidiy providers, individual day raders rading on he TAIFEX are more likely o be regarded as irraional conrarian noise raders. 4.2 Marke Liquidiy Based upon he above analysis, we have clearly demonsraed ha individual day raders behave as irraional conrarian raders; we herefore go on in his secion o use Equaion (1), he bid-ask equaion, o invesigae he ways in which heir ne 23
rading behavior affecs marke liquidiy. We use wo spread measures, Percenage Effecive Spread and Percenage Realized Spread, wih boh measures being normalized by conrac value in order o conrol for he spread flucuaions caused by changes in he value of he underlying fuures conrac. Volailiy is measured by realized volailiy based upon 5-minue reurns. Table 6 repors he empirical resuls on he inraday Effecive Spread and Realized Spread of he 5-minue inervals (Panel A), 30-minue inervals (Panel B) and 60-minue inervals (Panel C). 17 As we can see from he able, mos of he coefficiens on oal volume are found o be significanly negaive, a resul which is consisen wih he findings in he prior sudies of a negaive relaionship beween bid-ask spread and rading volume (Wang, Yau, and Bapise, 1997). <Table 6 is insered abou here> Wih an increase in oal rading volume, here will be greaer opporuniies for raders o offse heir undesirable posiions, hereby reducing he price risk. This, in urn, will lead o a reducion in bid-ask spreads. For all models in Table 6, he coefficiens on volailiy are found o be posiive and saisically significan, resuls which are no unexpeced, essenially because an increase in price volailiy implies ha marke makers are faced wih increased invenory risk as well as he risk of 17 We also include inraday inerval dummy variables o conrol for he U-shaped paern in inraday bid-ask spreads; however, for space saving purposes, hese resuls are no presened in Table 6. 24
rading wih informed raders. More imporanly, in all hree panels, all of he coefficiens on individual day raders are found o be significanly negaive, whereas he coefficiens on non-day raders (individual non-day raders, foreign insiuional non-day raders and fuures proprieary firm non-day raders) are found o be insignifican. These resuls remain consisen boh across he differen spread measures and across differen ime inervals (Panels A, B and C of Table 6). The significanly negaive coefficiens on volume for individual day raders confirm ha hese raders provide marke liquidiy by reducing boh he effecive spread and he realized spread (a measure of emporary price impac). Our resuls are consisen wih he experimenal resuls obained by Bloomfield e al. (2009) where i was demonsraed ha rading by uninformed raders reduced he bid-ask spread while also providing marke liquidiy. 4.3 Marke Volailiy We use he price volailiy equaion (Equaion (2) presened in Secion 3 of his paper) o esimae he impac on marke volailiy aribuable o he inraday rading aciviies of individual day raders, as well as oher ypes of raders. 18 Table 7 repors he inraday price volailiy regressions on 5-minue (Panel A), 30-minue (Panel B) 18 Since Andersen and Bollerslev (1997) revealed an inraday paern of volailiy, we also include inerval dummies as conrol variables; for space saving purposes, he resuls are no presened in Table 7. 25
and 60-minue (Panel C) inervals. As we can see from Model (1) in Table 7, wih realized volailiy, all of he coefficiens on oal volume are found o be significanly posiive, hereby indicaing ha higher volailiy is associaed wih large rading volume, a finding which is consisen wih he posiive volailiy-volume relaionship found in he prior sudies. 19 As expeced, all of he coefficiens on he spreads are found o significanly posiive, essenially because he inraday price volailiy componen is due o variaions in he bid-ask spreads. This resul is also consisen wih he findings of he prior sudies (Wang e al., 1997; Wang and Yau, 2000). <Table 7 is insered abou here> As shown in Models (2) and (3) in Table 7, in all hree ime inervals, all of he coefficiens on volume for individual day raders (non-day raders) are found o be significanly negaive (posiive), wih he negaive coefficiens on he volume of individual day raders confirming ha an increase in individual day rading reduces marke volailiy. In an aemp o enhance he robusness and reliabiliy of our resuls, we also adop a Bivariae vecor auoregressive model of realized volailiy, as well as he volume for individual day raders during he 5-, 30- and 60-minue inervals, wih 19 See Karpoff (1987) for a review. 26
effecive spread being conrolled by a one-period lag. Once again, we find ha he firs hree coefficiens on rading volume for individual day raders are negaive and significan. 20 Our resuls run conrary o he findings in he prior sudy underaken by Daigler and Wiley (1999) whore pored ha he coefficiens on general public rading volume (CT4) in heir daily volailiy equaion on he S&P 500 index were found o be posiive and significan. 21 Our resuls do no provide suppor for he hypohesis ha day rading desabilizes he marke while also creaing excess volailiy; 22 and indeed, our empirical evidence suggess ha individual day rading provides an overall benefi o he marke by reducing he level of volailiy. In order o furher invesigae he ways in which day rading direcly affecs ransiory marke volailiy (emporary marke volailiy), we use he sae space model mehodology (Secion 3.3.1) o decompose volailiy ino ransiory and informaional volailiy. The resuls of he ransiory price volailiy equaion, which are presened in Table 8, are based upon daa on 15-minue (Panel A), 30-minue (Panel B) and 60-minue (Panel C) inervals, from which several 20 The Bivariae VAR analyses of volailiy and individual day rading volume are repored in Appendix Table A-1. We also observe, in he individual day rader volume equaion, ha he coefficiens on he firs wo lagged volailiy regressions are posiive and highly significan. This resul indicaes higher volailiy which will also induce higher individual day rading aciviy. 21 Daigler and Wiley (1999) inerpreed ouside raders in he marke as noise raders. 22 In our unrepored resuls, we also used he high-low esimaor proposed by Parkinson (1980) for each ime inervals, and found ha he resuls were very similar o hose repored in Table 7. 27
ineresing resuls are obained. For ransiory volailiy (repored in Models (2) and (3) in each of he panels), all of he coefficiens on individual day raders are found o be significanly negaive, whereas hose for non-day raders (individual non-day raders and fuures proprieary firm non-day raders) are found o be significanly posiive. These resuls, which are fairly consisen across he various inraday inervals shown in Models (2) and (3), indicae ha individual day rading (non-day rading) reduces (increases) ransiory volailiy, because individual day raders (non-day raders) are likely o supply (demand) marke liquidiy. <Table 8 is insered abou here> In summary, we show ha individual day raders behave as irraional conrarian raders, reducing emporary price volailiy and providing marke liquidiy o oher ypes of raders a heir own expense. The ne rading aciviies of individual day raders, following conrarian rading sraegies, reduces (increases) price efficiency during periods of relaively high (low) informaion, because day rading slows down he speed of informaion compounded ino ransacion prices, when informaion is relaively high and day rading reduces bid-ask spreads, making ransacion prices closer o heir rue prices, when informaion is relaively low. We herefore rejec he null hypohesis and accep he alernaive hypohesis 28
ha he ne rading aciviies of individual day raders generally end o follow a conrarian (negaive feedback) rading sraegy; consequenly, he ne rading aciviy of individual day raders will generally provide marke liquidiy while also reducing emporary marke volailiy. 5. ROBUSTNESS CHECKS 5.1 Alernaive Mehods of Idenifying Trading Sraegies We follow Kaniel, Saar, and Timan (2008) o invesigaive marke reurns prior o inense rading so as o es he robusness of our empirical resuls on alernaive mehods of idenifying momenum or conrarian rading sraegies. The inraday ne rading measure is compued as inraday buy dollar volume less he sell dollar volume aribued o each ype of invesor, scaled by inraday rading dollar volume for each ype of invesor. Each ne rading measure is pu ino 1 of 10 deciles according o he ne rading measure. The resuls show ha inense buying (selling) by individual day raders follows a reducion (increase) in marke reurns a five minues prior o rading. Conversely, individual non-day raders, foreign insiuional non-day raders and fuures proprieary firm non-day raders exhibi an opposing paern. Similar resuls are obained when inense rading is condiional on marke reurns 10, 15, 30, 60 minues prior o rading. 29
30 Furhermore, following Griffin e al. (2003), we also use vecor auoregression (VAR) models o examine he ime-series behavior of he buy-sell imbalance and marke reurns on an inraday basis, and find ha hese have a significanly negaive relaionship wih prior reurns, whereas he buy-sell imbalances of non-day raders are found o have a significanly posiive relaionship wih prior reurns. 23 The resuls once again sugges ha individual day raders engage in conrarian rading sraegies, whereas individual non-day raders, foreign insiuional non-day raders and fuures proprieary firm non-day raders end o adop momenum rading sraegies. The robusness checks underaken in his secion confirm ha our resuls are no sensiive o differen mehods of idenifying he rading sraegy. 5.2 Alernaive Measures of Transiory Volailiy We go on o use he componen GARCH model (Engle and Lee, 1999) o decompose volailiy ino permanen and emporary volailiy. The componen GARCH model is specified as: NDT VOL IDT VOL Q Q Q Q Q r r ) ( ) ( ) ( ) (, 9 8 1 2 1 7 1 2 1 6 2 2 1 2 1 5 3 1 4 3 1 2 1 + + + + = + + = + = α α σ α ε α σ σ ε α α α α α α (8) where r is he reurn a ime ; VOL_IDT refers o individual day rader volume; 23 For space saving consideraions, we do no presen he resuls of he addiional analyses here; however, ineresed readers can obain he resuls from he auhors upon reques.
2 VOL_NIDT denoes non-day rader volume; andσ Q is ransiory volailiy. The resuls on he impacs on ransiory volailiy aribuable o individual day rading and non-day rading are presened in Table 9, from which we can once again see ha, wih he excepion of Model (4), he coefficiens on he volume for individual day raders (non-day raders) are all found o be significanly negaive (posiive). <Table 9 is insered abou here> 6. CONCLUSIONS We se ou in his sudy, using a unique daase, o examine he ways in which rading by individual day raders affecs marke liquidiy and volailiy in he Taiwan index fuures marke. The unique daase, obained from he TAIFEX, provides deailed informaion on rading records and accoun idenificaion, which enables us o race he rading aciviy for each accoun. We can herefore explore he rading sraegies adoped by day raders in order o examine he impac of hese sraegies on he markes. Several imporan conclusions are obained, as described below. Firsly, we find a endency among individual day raders o follow a conrarian rading sraegy, whereas non-day raders end o adop momenum rading sraegies. These resuls echo he findings repored in he experimenal sudy of Bloomfield e al. (2009), who found ha noise raders ended o follow a conrarian rading 31
sraegy, resuling in enhanced marke liquidiy. Secondly, afer aking ino consideraion all commissions and ransacion axes, we find ha he vas majoriy of he individual day raders examined in his sudy experienced significanly negaive ne reurns; herefore, mos individual day raders on he TAIFEX end o mach he definiion of irraional noise raders. Thirdly, by exploring he rading sraegies and reurns of individual day raders over he course of a rading day, we find ha conrarian rading paerns end o be sronger in he morning han in he afernoon, indicaing ha wih he arrival of new marke informaion, conrarian rading by individual day raders is derimenal o marke efficiency. Furhermore, informed raders (foreign insiuional raders and fuures proprieary firm raders) end o make greaer profis in he morning han in he afernoon, while liquidiy raders (individual raders) end o lose more money in he morning han in he afernoon. These resuls sugges ha informed raders will make larger profi during periods when more informaion is available. Finally, we presen consisen empirical evidence o show ha individual day rading provides liquidiy o he Taiwan index fuures marke by reducing boh he bid-ask spreads and he emporary price impac. Thus, he null hypohesis ha he ne rading behavior of individual day raders generally ends o follow a momenum sraegy, wih resulan desabilizaion of he marke and he creaion of excess 32
volailiy in he TAIEX is rejeced by he empirical resuls of he presen sudy. These resuls, which are based upon real world daa, provide suppor for he resuls of he experimenal sudy of Bloomfield e al. (2009). Finally, we should poin ou ha if he ne rading behavior of individual day raders in oher fuures markes is found o follow a momenum rading sraegy, hen our empirical resuls on he ways in which individual day raders affec he TAIFEX canno be readily exended o oher fuures exchanges. 33
REFERENCES Andersen, T. G. and T. Bollerslev, 1997, Inraday Periodiciy and Volailiy Persisence in Financial Markes, Journal of Empirical Finance 4, 115-158. Andersen, T.G., T. Bollerslev, F.X. Diebold, and H. Ebens, 2001, The Disribuion of Realized Sock Reurn Volailiy, Journal of Financial Economics 61, 43-76. Bae, K.H., H. Jang, and K.S. Park, 2003, Traders Choice beween Limi and Marke Orders: Evidence from NYSE Socks, Journal of Financial Markes 6, 517-538. Barber, B.M. and T. Odean, 2001, The Inerne and he Invesor, Journal of Economic Perspecives 15, 41-54. Barber, B.M., Y.T. Lee, Y.J. Liu, and T. Odean, 2009, Jus How Much do Individual Invesor Loss by Trading? Review of Financial Sudies 22, 609-632. Barber, B.M., Y.T. Lee, Y.J. Liu, and T. Odean, 2011, The Cross-Secion of Speculaor Skill Evidence from Day Trading, working paper. Baalio, R.H., B. Hach, and R. Jennings, 1997, SOES Trading and Marke Volailiy, Journal of Financial and Quaniaive Analysis 32, 225-238. Bessembinder, H., 2003, Issues in Assessing Trade Execuion Coss, Journal of Financial Markes 6, 233-257. Black, F., 1986, Noise, Journal of Finance 41, 529-543. Bloomfield, R., M. O Hara, and G. Saar, 2005, The Make or Take Decision in an Elecronic Marke: Evidence on he Evoluion of Liquidiy, Journal of Financial Economics 75, 165-199. Bloomfield, R.J., M. O Hara, and G. Saar, 2009, How Noise Trading Affecs Markes: An Experimenal Analysis, Review of Financial Sudies 22, 2275-2302. Campbell, J.Y. and A.S. Kyle, 1993, Smar Money, Noise Trading and Sock Price Behavior, Review of Economic Sudies 60, 1-34. Campbell, J. Y., M. Leau, B.G.M. Malkiel, and Y. Xu, 2001, Have Individual Socks Become More Volaile? An Empirical Exploraion of Idiosyncraic Risk, Journal of Finance 56, 1-43. 34
Choe, H., B.C. Kho, and R.M. Sulz, 1999, Do Foreign Invesors Desabilize Sock Markes? The Korean Experience in 1997, Journal of Financial Economics 54, 227-264. Chou, R.K. and Y.Y. Wang, 2009, Sraegic Order Spliing, Order Choice and Aggressiveness: Evidence from he Taiwan Fuures Exchange, Journal of Fuures Markes 29, 1102-1129. Chou, R.K., G.H.K. Wang, Y.Y. Wang, and J. Bjursell, 2011, Individual Trades, Insiuional Trades and Inraday Fuures Price Behavior: Evidence from he Taiwan Fuures Exchange, Pacific-Basin Finance Journal 19, 41-70. Chung, J.M., H. Choe, and B.C. Kho, 2009, The Impac of Day-Trading on Volailiy and Liquidiy, Asian-Pacific Journal of Financial Sudies 38, 237-275. Daigler, R.T. and M.K. Wiley, 1999, The Impac of Trader Type on he Fuures Volailiy-Volume Relaion, Journal of Finance 54, 2297-2316. DeLong, J.B., A. Shleifer, L.H. Summers, and R.J. Waldmann, 1990a, Noise Trader Risk in Financial Markes, Journal of Poliical Economy 98, 703-738. DeLong, J.B., A. Shleifer, L.H. Summers, and R.J. Waldmann, 1990b, Posiive Feedback Invesmen Sraegies and Desabilizing Raional Speculaion, Journal of Finance 45, 379-395. Engle R.F and G.G.J. Lee, 1999, A Long-Run and Shor-Run Componen Model of Sock Reurn Volailiy, in Engle, R. F and Whie, H. edied Coinegraion, Causaliy, and Forecasing. Oxford Universiy Press, Griffin, J.M., J.H. Harris, and S. Topaloglu, 2003, The Dynamics of Insiuional and Individual Trading, Journal of Finance 58, 2285-2320. Grinbla, M. and M. Keloharju, 2000, The Invesmen Behavior and Performance of Various Invesor Types: A Sudy of Finland s Unique Daa Se, Journal of Financial Economics 55, 43-67. Grinbla, M. and M. Keloharju, 2001, Wha Makes Invesors Trade, Journal of Finance 56, 589-616. Harris, J.H. and P.H. Schulz, 1998, The Trading Profis of SOES Bandis, Journal of Financial Economics 50, 39-62. 35
Harvey, A.C., 1989, Forecasing, Srucural Time Series Models and Kalman Filer. Cambridge Universiy Press. Hasbrouck, J., 1996, Modeling Marke Microsrucure Time Series in: Maddala, G., S., Rao, (Eds.) Handbook of Saisics, 14,647-692. Huang, R.D. and C.Y. Shiu, 2009, Local Effecs of Foreign Ownership in an Emerging Financial Marke: Evidence from Qualified Foreign Insiuional Invesors in Taiwan, Financial Managemen 38, 567-602. Jordan, D. J. and J.D. Dilz, 2003, The Profiabiliy of Day Traders, Financial Analyss Journal 59, 85-94. Kaniel, R., G. Saar, and S. Timan, 2008, Individual Invesor Trading and Sock Reurns, Journal of Finance 63, 273-310. Karpoff, J.M., 1987, The Relaion beween Price Changes and Trading Volume: A Survey, Journal of Financial and Quaniaive Analysis 22, 109-126. Keim, D.B. and A. Madhavan, 1995, Anaomy of he Trading Process Empirical Evidence on he Behavior of Insiuional Traders, Journal of Financial Economics 37, 371-398. Koski, J. L., E.M. Rice, and A. Tarhouni, 2008, Day Trading and Volailiy: Evidence from Message Board Posings in 2002 vs. 1999, Working Paper, Universiy of Washingon. Kyrolainen, P., 2008, Day Trading and Sock Price Volailiy, Journal of Economics and Finance 32, 75-89. Linnainmaa, J., 2005 The Individual Day Trader, Working paper, The Anderson School a UCLA. Malkiel, B.G., 1999, Day Trading, and Is Dangers, Wall Sree Journal, Augus 3, Page A22. Scheinkman, J.A. and W. Xiong, 2003, Overconfidence and Speculaive Bubbles, Journal of Poliical Economy 6, 1183-1220. Parkinson, M., 1980, The Exreme Value Mehod for Esimaing he Variance of he Rae of Reurn, Journal of Business 53, 61-65. Permanen Subcommiee on Invesigaions of he Commiee on Governmenal Affairs, Unied Saes Senae. Day Trading: Everyone Gambles Bu he 36