Volatility, Returns and Liquidity: The Relation Between Online Trading and Stock Market Behavior

Transcription

1 Volailiy, Reurns and Liquidiy: The Relaion Beween Online Trading and Sock Marke Behavior Xuemin (Serling) Yan and Sephen P. Ferris* This Draf: Augus 2004 * We would like o hank John Howe, Ming Liu, Tim Loughran, Cyndi McDonald, Shawn Ni, John Sowe, Paul Weller, David Wes, and seminar paricipans a he Universiy of Missouri - Columbia and Iowa Sae Universiy for helpful commens. We acknowledge he financial suppor from he Universiy of Missouri Sysem Research Board and he Universiy of Missouri Columbia Research Council. We are graeful o Donna Tom of Media Merix for assisance on obaining he web raffic daa and Mike Ancell of Bank of America for sharing wih us online brokerage indusry repors. Xuemin (Serling) Yan is assisan professor of finance and can be reached a 427 Cornell Hall, College of Business, Universiy of Missouri Columbia, Columbia, MO , phone: (573) , yanx@missouri.edu. Sephen Ferris is professor of finance and can be reached a 404 Cornell Hall, College of Business, Universiy of Missouri Columbia, Columbia, MO , phone: (573) , ferriss@missouri.edu.

2 Volailiy, Reurns and Liquidiy: The Relaion Beween Online Trading and Sock Marke Behavior Absrac Using he level of Web raffic experienced by online brokers as a proxy for online equiy rading, his paper examines he dynamic relaions beween online rading and aggregae measures of sock marke behavior. We iniially observe ha online rading is posiively relaed o marke volailiy. However, once we conrol for he oal rading by small raders, online rading no longer conribues o marke volailiy. This resul is inconsisen wih he claim ha he expansion of online rading increases sock marke volailiy. We find a significanly posiive relaion beween online rading and conemporaneous marke reurns. This resul is consisen wih he presence of sysemaic noise as well as posiive-feedback rading by online invesors. Finally, we find ha online rading is posiively relaed o wo measures of marke liquidiy, he bid-ask spread and he quoed deph. This join resul concerning liquidiy likely benefis insiuional invesors who end o place large orders, while increasing he cos of rading o individual invesors who are likely o submi small orders. Keywords: online rading; marke volailiy; liquidiy; Web raffic JEL Classificaions: G12/G14

3 Volailiy, Reurns and Liquidiy: The Relaion Beween Online Trading and Sock Marke Behavior I. Inroducion Online rading has exploded in recen years, due o he bull marke of he lae 1990s and developmens in informaion echnology, especially he Inerne. 1 As recenly as 1994, here was no online rading of socks over he Inerne. From 1995 o 2000, invesors opened over 19 million online brokerage accouns (U.S. General Accouning Office (2000, 2001)). Salomon Smih and Barney (2002) esimaes ha a year-end 2001, oal online brokerage asses exceeded one rillion dollars. Thus, his las decade has winessed he birh and remendous growh of a new way o rade equiies. In spie of is populariy, scholarly analysis of online rading is limied. Barber and Odean (2002) argue ha online invesors end o become overconfiden due o illusions of knowledge and conrol. They find ha invesors rade more frequenly, more speculaively, bu less profiably afer hey go online. Choi, Laibson and Merick (2002) analyze he impac of a webbased rading channel on rader behavior in wo large corporae 401(k) plans. They find ha he rading frequency for heir sample firms wih Web access is double ha of firms lacking a Web channel. Our sudy builds upon his iniial lieraure by explicily examining he relaion beween online rading and more aggregae componens of marke behavior such as volailiy, reurns, and liquidiy. These issues are of ineres no only o academics, bu also o marke paricipans, policy makers, and marke regulaors. The media and some academics have suggesed ha online 1 The muliple benefis of online rading such as lower commissions, quicker execuion, and easier access o research informaion has furher enhanced is populariy among invesors. 1

4 rading desabilizes financial markes by inducing excessive rading, greaer risk aking, and higher volailiy in he sock marke. 2 Shiller (2000), for insance, conends ha he expansion of online rading will increase he level of sock marke volailiy. Moreover, some criics conend ha online rading played an imporan role in boh he growh and ulimae burs of he recen bubble in echnology socks. For example, Thaler (1999) appears o believe ha online and day raders are a leas parially responsible for he Inerne bubble, as evidenced from he following quoe: I hope someday soon a scholar will acquire a daa se of online and day raders. Unil such daa become available, we will never fully undersand wha I hink will become known as he Grea Inerne Sock Bubble. Furher, policy makers and marke regulaors are concerned wih he impac of online rading on good order and discipline in he financial markeplace. For insance, hen SEC Chairman Arhur Levi (1999a, 1999b, 1999c) issued a series of cauionary policy saemens in 1999 concerning online and day rading. This sudy makes wo conribuions o he lieraure on invesor and marke behavior, especially as hey are influenced by he emergence of online rading. Using a new daabase, we consruc an innovaive proxy for online rading. Specifically, we use Web raffic for six leading online brokers as a proxy for aggregae online rading. Such a proxy is especially appropriae for invesigaing he aggregae relaions beween online invesors and criical dimensions of sock marke behavior. The sample firms over which we consruc our proxy represen an 80% share of he online rading marke while heir Web raffic is based on he sampling of 50,000 inerne users. This is he firs sudy ha empirically examines he dynamic relaions ha exis beween online rading and aggregae marke volailiy, reurns, and liquidiy. Earlier sudies focus on he rading behavior of individual invesors and use disaggregaed daa. By focusing on aggregae 2 See, for example, Choi, Laibson, and Merick (2002) and he references in heir foonoe 1. 2

5 marke relaionships in he U.S., his sudy complemens previous research and provides a more complee view of he effecs of online rading on he equiy marke. We begin by examining he relaion beween online rading and marke volailiy. Many sudies documen a posiive relaion beween price volailiy and rading volume in he financial markes. This relaion is robus o various daa frequencies and financial markes (see Karpoff (1987) for a review). Consequenly, one migh expec a posiive relaion beween online rading and marke volailiy. Online rading and price volailiy migh also be posiively relaed due o overconfidence. Odean (1998) develops a model in which invesors are overconfiden. Odean shows ha boh rading volume and price volailiy increases as invesor overconfidence increases. Gervais and Odean (2001) develop a dynamic model in which overconfidence is deermined endogenously and changes dynamically. They show ha rading volume and price volailiy are boh posiively relaed o he degree of self-aribuion bias, which is he underlying cause for overconfidence in heir model. In pracice, he degree of overconfidence is no observed. If one is willing o consider he level of online rading as a proxy for he degree of online raders overconfidence, one migh argue based on Odean (1998) and Gervais and Odean (2001) ha online rading should be posiively relaed o price volailiy. Nex we examine he relaion beween online rading and sock marke reurns. Individual invesors are rouinely viewed as unsophisicaed, uninformed, and as noise raders in he lieraure (see, for example, Nofsinger and Sias (1999), Kumar and Lee (2002), Barber, Odean, and Zhu (2003), and Griffin, Harris and Topaloglu (2003)). Shleifer and Summers (1990) and Shleifer and Vishny (1997) argue ha demand by noise raders could cause asse prices o deviae from heir fundamenal values because of limis o arbirage. Kumar and Lee (2002) and 3

6 Barber, Odean and Zhu (2003) sudy he behavior of individual invesors and find ha rading by individual invesors is sysemaic. As a resul, hey argue ha noise rading has he poenial o affec asse prices. If online raders are noise raders, i is useful o examine he exen o which online rading can influence marke prices. Lasly, we examine he relaion beween online rading and marke liquidiy. If online raders end o be uninformed, hen an increase in online rading should reduce bid-ask spreads by lowering he probabiliy of informed rading (see, for example, Copeland and Galai (1983), Glosen and Milgrom (1985), and Easley and O Hara (1987)). However, if online rading ends o be sysemaic, hen an increase in online rading migh widen bid-ask spreads by creaing or exacerbaing he marke-maker s invenory problems (see, for example, Demsez (1968), Ho and Soll (1981), and Soll (1979)). Therefore, i remains an imporan empirical quesion as o how an increase in online rading would affec marke liquidiy. The remainder of he paper proceeds as follows. Secion II discusses he relaed lieraure. Secion III presens our new proxy for online rading and describes he sample we use in our analysis. In Secion IV we discuss he resuls from our preliminary daa analysis. Secion V invesigaes he relaion beween online rading and sock marke volailiy. In Secion VI, we es for he possibiliy of online rading influencing price formaion by examining he relaion beween online rading and conemporaneous marke reurns. Secion VII conains he resuls from our examinaion of he relaion beween online rading and marke liquidiy. We conclude wih a brief summary and inerpreaion in Secion VIII. II. Relaed Lieraure Several sudies examine he rading behavior of online invesors. Barber and Odean 4

7 (2002) argue ha online invesors end o become overconfiden due o illusions of knowledge and conrol. They analyze he rading aciviies of 1,607 invesors of a large discoun broker ha swiched from phone-based rading o online rading. They find ha invesors rade more frequenly, more speculaively, bu less profiably afer hey go online. Glaser and Weber (2003) repor resuls from a survey of inerne raders and find ha hose who idenify hemselves as more overconfiden rade more ofen. Choi, Laibson and Merick (2002) analyze he impac of a web-based rading channel on rader behavior and performance in wo large corporae 401(k) plans. They find ha he rading frequency for heir sample firms wih Web access is double ha of firms lacking a Web channel. Jackson (2002) examines cross secional differences beween inerne and radiional invesors and concludes ha inerne invesors are more sensiive o boh recen reurns and volailiy in hose reurns. Our analysis of he relaion beween online rading and marke volailiy is relaed o a large lieraure on he volume-volailiy relaion and wo heoreical papers on overconfidence. Numerous sudies documen a posiive relaion beween price volailiy and rading volume in financial markes. For example, Karpoff (1987) summarizes he resuls of nineeen empirical sudies and repors ha eigheen ou of nineeen sudies find a posiive correlaion beween absolue price change and rading volume. Many recen sudies of volume-volailiy relaions eiher use a (wo-sage) regression model or a generalized auoregressive condiional heeroskedasiciy (GARCH) model. For example, Schwer (1990), Bessembinder and Seguin (1993), Jones, Kaul, and Lipson (1994), and Chan and Fong (2000) adop he regression approach while Lamoureux and Lasrapes (1990) use he GARCH model. Odean (1998) develops a model in which uninformed raders, informed raders, and marke makers are all overconfiden. Specifically, hese raders overesimae he precision of 5

8 heir signals. Odean shows ha boh rading volume and price volailiy increase as he invesor s overconfidence increases. This resul holds wheher he invesor is informed or uninformed. Gervais and Odean (2001) develop a dynamic model in which overconfidence is deermined endogenously hrough a self-aribuion bias. In heir model, a rader s level of overconfidence changes dynamically wih his success and failures. They show ha expeced rading volume and price volailiy are boh posiively relaed o he exen of he self-aribuion bias. In pracice, he degree of overconfidence is no observed. Bu since online raders end o become overconfiden, one migh argue based on Odean (1998), Gervais and Odean (2001), and Barber and Odean (2002) ha heir rading, reflecive of heir overconfidence, should be posiively relaed o price volailiy. Sysemaic rading by individual invesors is one of he necessary condiions for he biases and senimen of individual invesors o have a cumulaive effec on asse prices (Barber, Odean, and Zhu (2003)). 3 Kumar and Lee (2002) and Barber, Odean, and Zhu (2003) boh documen ha he aggregae rading of individual invesors is sysemaic. Barber, Odean, and Zhu show ha he rading of individual invesors is highly correlaed and argue ha his coordinaed rading is likely driven by he disposiion effec, he represenaiveness heurisic, and limied aenion. Kumar and Lee find ha he buy-sell imbalance in individual invesor rades conains a sysemaic componen. Moreover, his sysemaic componen has incremenal explanaory power for small-cap reurns. Our analysis of he relaion beween online rading and marke liquidiy builds on several recen sudies ha examine commonaliy in liquidiy and marke liquidiy. Chordia, Roll, and Subrahmanyam (2000), Hasbrouck and Seppi (2000), and Huberman and Halka (2001) 3 The oher necessary condiion is he exisence of limis of arbirage. 6

9 documen commonaliy in he liquidiy of individual socks. This resul emphasizes he imporance of sudying he behavior of marke liquidiy. In paricular, here is now evidence ha sysemaic liquidiy variaion is a priced facor (Pasor and Sambaugh (2003)). Chordia, Roll, and Subrahmanyam (2001, 2002) sudy how marke liquidiy varies over ime. They find ha recen marke reurns, marke volailiy, macroeconomic variables, and rading imbalances are imporan deerminans of marke liquidiy. This paper conribues o his burgeoning lieraure by focusing on he relaion beween aggregae online rading and marke liquidiy. III. Daa and Variables A. New Proxy for Online Trading Aggregae online rading daa more frequen han monhly for he U.S. are no readily available o academic researchers. Consequenly, in his sudy we make use of a new daa source ha conains Web raffic daa for a number of online brokers. More specifically, we use he oal Web raffic from six leading online brokers websies as a proxy for aggregae online rading. The six online brokers we use in our proxy consrucion are Amerirade, Daek, E*rade, Fideliy, Schwab, and TD Waerhouse. Because he online brokerage indusry is highly concenraed, our sample of six online brokers is sufficien o allow meaningful analysis. Indeed, McMillan (1999, 2000) noes ha he six online brokers of our sample are he indusry s six larges online brokers and represen a combined marke share of 80%. We believe ha he level of Web raffic observed for an online broker is an effecive proxy for he online rading acually experienced by a broker for several reasons. Firs, a porion of he Web raffic is direcly relaed o online rading because invesors mus log-on o heir brokers websies o execue online rades. The remaining Web raffic is likely o be posiively 7

10 correlaed wih online rading because of wo behavioral facors. Researchers in psychology such as Fesinger (1957) find ha people prefer cogniive consonance beween heir acions. Hence, invesors who visi a broker s websie are more likely o rade eiher immediaely or in he fuure. Web raffic and acual rading are concepually consonan aciviies which will resul in a posiive correlaion beween hem, hus allowing one o proxy for he oher. Researchers in he area of markeing noe he exisence of a mere exposure effec in individual aiudes owards a produc (e.g., Krugman (1977), Bara and Ray (1986), Suar, Shimp, and Engle (1987), and Janiszewski and Warlop (1993)). According o his effec, consumer aiudes owards a produc can change wihou cogniion since mere exposure o he produc has he abiliy o make an individual s aiude more favorable. Thus, as invesors visi a broker s websie, hey become increasingly aware of available financial producs and are ulimaely more likely o place a rade. Finally, we examine he correlaions beween online brokers Web raffic and acual aggregae rading levels by small raders. We find ha correlaions beween Web raffic and small rades, defined as 500 shares or fewer, are significanly posiive. The correlaion exceeds 0.40 when calculaed wih variable levels and is over 0.60 when using changes in he variables. 4 These consisenly srong correlaions beween Web raffic and acual rading levels provide empirical suppor for our argumen ha he Web raffic of online brokers can proxy for online rading. B. Daa and Sample Our sample period exends from December 8, 1999 o July 28, This consiues a oal sample period of 138 weeks and corresponds o he period for which we have Web raffic 4 Using changes in variables miigaes a concern ha his high correlaion simply reflecs a common underlying rend or oher commonaliies. 8

11 daa. We obain weekly Web raffic daa for six leading online brokers for his period from Media Merix. Media Merix is a leading inerne raing firm ha provides hird-pary audience daa used by cliens o make business decisions. Media Merix uses a random recruimen mehod o form a represenaive sample, or panel, of inerne users. The panel consiss of 50,000 individuals in he Unied Saes. Media Merix provides a number of differen merics for inerne usage. In his sudy, we focus on one paricular measure, he average daily number of unique visiors. This measure represens he average number of differen individuals ha visi a specific websie per day during he course of he reporing week. 5 To complee our empirical analysis, we collec equiy reurn and price daa from several differen sources. We obain S&P 500 index daa from CRSP. We collec NASDAQ composie index daa from he NASDAQ websie. 6 To consruc marke rading and liquidiy variables, we obain inra-daily rade and quoe daa for all common socks from he NYSE Trade and Quoe (TAQ) daabase. C. Variable Consrucion In his secion we describe he consrucion of he variables ha we use in our analysis. Since he Web raffic daa are weekly, our subsequen analysis is weekly. Again, we use he aggregae weekly Web raffic from he six leading online brokers websies as a proxy for aggregae online rading. Because wo of hese brokers, Fideliy and Schwab, offer a wide range of financial producs, i migh be ha much of heir Web raffic is unrelaed o equiy rading, 5 Media Merix daa are used by a number of sudies in finance and accouning including Lazer, Lev, and Livna (2001), and Trueman, Wong, and Zhang (2000). 6 We also examine he Russell 2000 index and he AMEX Inerne index and find qualiaively similar resuls o ha of S&P 500 index and Nasdaq index. Hence, we do no separaely repor hese findings. 9

12 Hence, we calculae an alernaive measure ha aggregaes Web raffic across he remaining four more focused online brokers. We consruc weekly S&P 500 index reurns (RETSPX) and weekly NASDAQ composie index reurns (RETNASD) by aggregaing heir respecive daily reurns. We use he high-low reurn, which is defined as he difference beween he highes and he lowes logarihm of price during a day, as a proxy for marke volailiy. This range-based volailiy proxy is widely used among academics and praciioners. 7 We follow Chordia, Roll and Subramanyam (2001, 2002) o consruc marke-wide liquidiy and rading aciviy measures. We apply a number of daa screens ha exclude specific rades or quoes o ensure ha erroneous daa are no included in he analysis. Online invesors are individual invesors and end o engage in small rades. To deermine if online invesors have an incremenal effec on he sock marke, we mus conrol for he aggregae rading aciviy of individual invesors. Following many sudies in he lieraure including Lee (1992), Lee and Radhakrishna (2000), Hvidkjaer (2003), and Malmendier and Shanhikumar (2003), we use rade size o disinguish beween individual and insiuional rades. We classify rades of 500 shares or fewer as individual rades. 8 To mainain his sudy s focus, we place he deails concerning he consrucion of marke liquidiy and rading aciviy variables in he Appendix. IV. Preliminary Daa Analysis A. Web raffic and Reurn Variables In Table 1 we presen summary saisics for a variey of Web raffic and reurn variables. We observe in Panel A ha he daily average number of unique visiors o he six online brokers 7 See Alizadeh, Brand, and Diebold (2002) and references herein. 8 Using an alernaive cuoff such as 1000 shares for small rades does no affec any of our resuls. 10

13 websies is 924,950. This measure is our proxy for aggregae online rading, and for ease of exposiion is referred o as online rading or aggregae online rading hroughou his sudy. Boh online rading (OL) and is logarihm (LOGOL) are persisen ime-series wih firs order auocorrelaions of 0.85 and 0.84 respecively. Over our sample period of December 1999 o July 2002, we observe he effec of he collapse of he Inerne bubble and he subsequen weak economy on equiy values. We calculae a mean weekly reurn of -0.38% o he S&P 500 index. The weekly reurns o he Nasdaq composie index are even lower, wih a mean of -0.74%. As noed above, boh OL and LOGOL are quie persisen. To es wheher hese wo variables are saionary, we conduc a uni roo es. Panel B conains our resuls. Overall, we canno rejec he hypohesis ha OL and LOGOL conain uni roos, wheher we allow for a linear rend or no. This resul suggess ha OL and LOGOL migh be non-saionary. I is wellknown ha using non-saionary variables in regressions can generae spurious resuls (see, for example, Ferson, Sarkissian, and Simin (2003)). Consequenly, in our subsequen regression analysis, we use he firs difference of eiher online rading ( OL) or he logarihm of online rading ( LOGOL). Panel C conains a summary descripion of he Web raffic for each of he six online brokers, Amerirade, Daek, E*Trade, Fideliy, Schwab, and TD Waerhouse. B. Marke Liquidiy and Trading Aciviy Variables In Table 2 we examine he characerisics of hose variables which capure marke liquidiy and rading aciviy. In Panel A, we presen a series of univariae summary saisics. We examine wo measures of bid-ask spreads, he quoed spread and he effecive spread, in boh absolue and percenage erms. Anoher imporan dimension of liquidiy is capured by he 11

14 quoed deph. We also examine several marke rading aciviy variables, including share volume, number of rades, and order imbalance. We consruc he marke-wide liquidiy and rading aciviy variables from an average of 4,870 socks. This is less han he number of socks covered by he CRSP and TAQ daabase because of our screening crieria described in he Appendix. The average quoed spread over our sample period is 9.20 cens while he average percenage quoed spread is 0.91%. The average effecive spread is 7.36 cens and he average percenage effecive spread is 0.73%. The finding ha he effecive spreads are smaller han he quoed spreads is expeced because effecive spreads incorporae he possibiliy ha rades migh occur inside he bid-ask prices. The average quoed deph is 1,562 shares. The average weekly rading volume is billion shares for all sized rades and 2.28 billion shares for small-sized rades. On average, here are million rades per week, and million of hem are small rades. Small rades represen 72.5% of he oal number of rades, bu only 18.4% of share volume. The average order imbalance for small rades is posiive wheher we measure i by share volume or by he number of rades. This resul suggess ha small invesors are ne buyers over our sample period, which is characerized by a generally declining sock marke. The NYSE and NASDAQ have evolved differen marke microsrucures o rade socks (see, Harris (2003) for a comprehensive review). The NYSE is an order-driven marke, based on a cenralized public limi order book, which is handled by a single specialis. The NASDAQ is primarily a quoe-driven marke, based on muliple dealers who compee for order flow. These differences have imporan implicaions for inerpreing he quoed deph and rading volume of hese exchanges. In paricular, he NASDAQ rading volume is likely o be inflaed relaive o ha repored for he NYSE, and he NASDAQ quoed deph likely undersaes he rue deph of 12

15 he marke. These differences raise issues regarding he appropriaeness of aggregaing across NYSE and NASDAQ socks. To miigae such concerns, we consruc several marke liquidiy and rading aciviy variables separaely using NYSE and NASDAQ socks. The resuls from hese calculaions are conained in Panel B. The average percenage quoed spread is 1.11% for NASDAQ socks and only 0.40% for NYSE socks. Similarly, he average percenage effecive spread is 0.92% for NASDAQ and only 0.26% for NYSE. One should be cauious, however, abou concluding ha rading coss on he NYSE are lower han hose of NASDAQ, because we do no conrol for sock characerisics ha are relaed o rading coss. One of he reasons why he average spread is higher on he NASDAQ is ha mos of he NASDAQ socks are hose of small firms. As expeced, we find ha NYSE socks have greaer quoed deph. The average NYSE deph is 3,086 shares. This compares o 1,562 shares for socks of all exchanges (Panel A). Small rades appear o be concenraed on NASDAQ issues. For example, each week here are on average 8.11 million small rades on he NASDAQ, bu only 2.23 million small rades occur on he NYSE. Figure 1 plos our marke-wide liquidiy variables over he sample period. Panel A plos he absolue quoed spread, percenage quoed spread, absolue effecive spread, and percenage effecive spread. No surprisingly, all four measures of bid-ask spreads move closely wih each oher. Furhermore, here is a downward rend in all four measures, indicaing ha bid-ask spreads generally decline over our sample period. Panel B in Figure 1 plos he quoed deph across all exchanges as well as separaely for he NYSE. Consisen wih exising evidence on decimalizaion (Bessembinder (2003)), we find ha he quoed deph decreases subsanially afer decimalizaion in January In our 13

16 subsequen analysis of he quoed deph, we remove he week ha NYSE decimalizaion becomes effecive as well as he following week from our sample period for wo reasons. We eliminae he week of decimalizaion because decimalizaion represens a srucural change raher han a response o marke condiions or rading informaion. Our use of firs differences in consrucing measures of he dependen variable requires we also eliminae he week following decimalizaion. C. Correlaions beween Online Trading and Marke Trading In Table 3 we presen he resuls from a correlaion analysis beween our proxy for online rading and various measures of marke rading aciviy. Because Fideliy and Schwab offer an exensive se of services oher han sock rading, we include an addiional variable, OL4, which measures aggregae Web raffic for he oher four online brokers in our sample. We use boh he share volume and he number of rades as our measures of marke rading aciviy. In Panel A we observe ha he correlaion beween Web raffic for he six brokers, OL, and ha for he focused brokers, OL4, is This high correlaion suggess ha Fideliy and Schwab s diversified menu of financial producs does no conaminae he use of heir Web raffic when consrucing our proxy for online rading. We find ha for boh OL and OL4, he correlaions wih our wo measures of acual rading by small raders are significanly posiive and in excess of These resuls are consisen wih our use of Web raffic as a proxy for online rading. Because we can no rejec he exisence of a uni roo for OL, we examine in Panel B he correlaions beween weekly changes in Web raffic and corresponding changes in rading volume. We again noe he high correlaions (>0.60). This resul suggess ha he posiive 14

17 correlaion beween Web raffic and acual rading volume is no driven by common rends or oher commonaliies. D. Deerminans of Online Trading To deermine wha facors affec online rading, we develop and hen es a simple linear model. Lamoureux and Lasrapes (1990) and Gallan, Rossi, and Tauchen (1992) are among many researchers who find ha rading volume is persisen. Hence, we conjecure ha online rading will likewise demonsrae persisence and anicipae ha i will be posiively relaed o is lagged values. We include lagged sock reurns, RET -1, in our regression because Saman and Thorley (2003) find ha high rading volume is associaed wih high sock reurns in previous weeks. They inerpre his resul as evidence in favor of he overconfidence and disposiion effecs. Saman and Thorley conend ha in rising markes invesors end o aribue success o heir own abiliies more han hey should. As a resul, hey become overconfiden and rade more acively. Alernaively, when he marke declines, invesors end o hold heir losers due o loss aversion and consequenly rade less acively. We expec ha online rading will be lower during weeks which include holidays. We include a dummy variable, Holiday, ha assumes a value of 1 if a naional holiday falls wihin he week of ineres and is 0 oherwise. 9 Noe ha our daa frequency is weekly. Therefore, markes are sill open on some days even during a holiday week. In summary, we specify our model of online rading as follows: OL = α + β OL 1 + γ OL 2 + θ RET 1 + ψ Holiday + ε (1) In Table 4 we provide our regression esimaes, using boh he level of online rading (Panel A) and is logarihmic ransformaion (Panel B). The resuls in Table 4 illusrae he 9 Our se of naional holidays is: New Year s Day, Memorial Day, Independence Day, Labor Day, Thanksgiving, and Chrismas. 15

18 imporance of emporal variables in deermining he level of online rading. We observe ha online rading is highly auo-correlaed and ha he pas wo weeks level of rading is an imporan deerminan of nex week s rading. Furher, we noe ha he holiday variable is negaively relaed o he level of rading, consisen wih reduced rading around holidays. We examine he impac of lagged aggregae marke reurns by including as regressors he reurns o he S&P 500 index as well as he NASDAQ composie index. The impac of lagged aggregae marke reurns is consisenly negaive, bu saisically insignifican for boh measures of marke performance. These resuls do no suppor he presence of overconfidence and disposiion effecs in online rading. The naure of previous reurns appears no o influence he rading levels of online invesors. Because he resuls are qualiaively idenical for OL and is logarihmic ransformaion, we elec o repor findings for only OL in our subsequen analyses. V. Online Trading and Marke Volailiy The media and academics sugges ha online rading desabilizes financial markes by inducing excessive rading and higher volailiy in he sock marke. For example, Shiller (2000) suggess ha he expansion of online rading will increase sock marke volailiy. The findings of Odean (1998) and Gervais and Odean (2001) regarding invesor overconfidence also sugges ha online rading migh posiively impac marke volailiy. Consequenly, we examine in his secion how online rading influences he sock marke s volailiy. We employ wo differen models in our analysis. The firs is a regression model while he second is he sandard GARCH model. A. Regression Model 16

19 In he firs approach, we proxy volailiy wih high-low reurns and hen employ a regression model o examine he impac of online rading on marke volailiy. We use he highlow reurn as our dependen variable since i beer capures inraday volailiy han absolue reurn when he difference beween he open and closing prices is small. Specifically, we esimae he following regression model: SPXHL = α + β OL + γ SPXHL 1 + δ NTS + ψ VOLS + ε (2a) NASDHL = α + β OL + γ NASDHL 1 + δ NTS + ψ VOLS + ε (2b) The dependen variables are he high-low reurns of he S&P 500 index in equaion (2a) and he Nasdaq composie index in equaion (2b). The independen variables include he change in online rading, lagged high-low reurns, he change in oal share volume of small rades, and he change in he number of small rades. We showed earlier ha we canno rejec he exisence of a uni roo for online rading. Therefore, we use firs differences in our analysis. We include as regressors changes in he oal share volume of small rades (VOLS), and he oal number of small rades (NTS) o examine wheher online rading impacs marke volailiy differenly from ha of oher individual invesors rading. In Table 5 we presen he resuls for boh S&P 500 volailiy (Panel A) and NASDAQ volailiy (Panel B). Consisen wih he exising lieraure (e.g., Bollerslev, Engle and Nelson (1994)), we find sock volailiies o be persisen. More imporanly, changes in online rading are posiively and significanly relaed o sock index volailiy, wheher we examine he S&P 500 or NASDAQ volailiy. This is as expeced. Many sudies repor a posiive relaion beween rading volume and volailiy. To he exen ha online rading represens a porion of he oal rading volume, i should be posiively relaed o volailiy. 17

20 Once we inroduce he oal volume of small rades ino our model, however, online rading is no longer significanly relaed o volailiy. The posiive relaion beween online rading and volailiy is compleely subsumed by he aggregae rading volume of small rades, wheher we use share volume or he number of rades. 10 This resul suggess ha online rading does no have a posiive impac on marke volailiy above and beyond he well-documened rading volume effec. If online rading provides an incremenal posiive impac on marke volailiy, we would expec he coefficien of online rading o be significanly posiive. I appears ha online rading does no conribue o excessive volailiy in he sock marke. B. GARCH Model In he second approach, we employ he sandard GARCH model o examine he impac of online rading on marke volailiy. Specifically, we consider a GARCH (1,1) framework. r = a + b r 1 + u where σ ε ε ~ Ν (0,1) u = σ ω + α u + βσ + γ OL + δ VOLS + η NTS (3) = 1 1 Table 6 conains he esimaion resuls. Panel A presens he resuls for he S&P 500 index while Panel B presens he resuls for he NASDAQ composie index. In each panel, we esimae hree models. In Model 1, we include online rading in he variance equaion. Model 2 adds he oal volume of small rades o he variance equaion while model 3 includes he number of small rades in he variance equaion. 10 We repor earlier ha online rading is posiively correlaed wih oal rading volume of small rades, wheher i is measured in shares or number of rades. Therefore, i is possible ha our resuls are affeced by mulicollineariy. We do no believe, however, ha his is a serious issue in our analysis because he coefficiens on oal rading by small raders are saisically significan. Noneheless, we conduced a robusness check by orhogonalizing online rading agains oal rading volume of small rades. Our resuls are no affeced when we use his alernaive mehod. These resuls are no repored, bu are available on reques. We also use he orhogonalizaion mehod for our examinaion of marke liquidiy and marke reurns. Again, we find qualiaively similar resuls. 18

21 Our resuls are very similar o hose obained by using he regression approach (as repored in Table 5). When used alone, online rading has a significan posiive relaion wih marke volailiy. However, when he volume of small-sized rades or he number of small-sized rades is included in he variance specificaion, he significance of online rading vanishes. Consisen wih previous GARCH analyses of high frequency daa, we find ha he ARCH and GARCH parameers are posiive and heir sum is close o one. This indicaes ha volailiy is persisen. Overall, we conclude from his analysis ha online rading is posiively relaed o marke volailiy. However, afer conrolling for oal rading volume of small raders, online rading is no longer significanly relaed o marke volailiy. The impac of online rading on marke volailiy is dominaed by he oal rading volume of small raders. These resuls show ha online rading does no have an incremenal impac on marke volailiy. VI. Online Trading and Conemporaneous Marke Reurns Individual invesors are frequenly viewed as noise raders in he lieraure (Kumar and Lee (2002), Barber, Odean, and Zhu (2003), and Griffin, Harris and Topaloglu (2003)). The radiional view is ha noise raders do no affec equilibrium asse prices because arbirageurs can arbirage away any deviaions from he fundamenal values. More recenly, sudies such as Shleifer and Summers (1990) and Shleifer and Vishny (1997) allow for he possibiliy ha demand by noise raders migh cause asse prices o deviae from heir fundamenal values for exensive periods of ime because of limis o arbirage. Barber, Odean and Zhu (2003) conend ha rading by individual invesors is surprisingly sysemaic due o he presence of limied 19

22 aenion, he represenaiveness heurisic, and a disposiion effec. 11 Because he rading aciviy of individual invesors is highly correlaed, hey argue ha noise rading has he poenial o affec asse prices. Therefore, i is useful o deermine he exen o which online rading can influence marke prices. We begin by examining he relaion beween online rading and he conemporaneous order flow of small rades. Panels A and B of Table 7 conain he resuls from a regression analysis of he aggregae order flow of small rades on aggregae online rading. We find ha online rading has a significan and posiive relaion wih order imbalance, wheher i is measured in he number of shares or in he number of rades. Our resul is robus o he inclusion of conrol variables such as lagged order imbalance, he oal share volume of small rades, he oal number of small rades, and lagged marke reurns. This resul is consisen wih he view ha higher levels of online rading are associaed wih greaer ne buying and consequenly a more bullish senimen among online invesors. Having esablished he relaion beween online rading and invesor senimen, we now examine he relaion beween online rading and conemporaneous marke reurns. We esimae he following regression model: RETSPX = α + β OL + γ RETSPX 1 + δ NTS + ψ VOLS + ε (4) Panel C presens he regression resuls. We find a significanly posiive relaion beween online rading and conemporaneous marke reurns. Conrolling for oal rading volume does no aler his resul. This resul also holds when we use he NASDAQ Composie index (Panel 11 Barber and Odean (2002) conend ha invesors manage he coss associaed wih evaluaing he housands of socks available for purchase by focusing on hose ha have gained heir aenion. This phenomenon is more characerisic of sock buying han selling. The represenaiveness heurisic (Tversky and Kahnemann (1974)) assers ha individuals expec small samples and shor inervals of ime-series daa o be represenaive of he underlying populaion or daa. The disposiion effec (Shefrin and Saman (1985)) is an applicaion of prospec heory o invesmens, and is he endency of an individual o hold losers and o sell winners. 20

23 D). There are wo possible explanaions for his resul. One possibiliy is ha online rading moves prices. This would be consisen wih he sysemaic noise argumen of Kumar and Lee (2002) and Barber, Odean, and Zhu (2003). The oher possibiliy is ha online raders engage in inra-week posiive feedback rading. Tha is, online invesors rade more acively afer he marke rises. Unforunaely, we are unable o disinguish beween hese wo possibiliies wih he weekly daa ha is available. Overall, we find srong evidence ha online rading is posiively relaed o conemporaneous marke reurns. While his resul migh be consisen wih he view ha online rading moves prices, i is also consisen wih he hypohesis ha online invesors simply engage in inra-week posiive feedback rading. VII. Online Trading and Marke Liquidiy In his secion we examine he relaion beween online rading and wo criical dimensions of marke liquidiy: he bid-ask spread and quoed deph. 12 A. Bid-ask Spreads Online rading is likely o impac bid-ask spreads for wo reasons. Firs, assuming ha online raders are noise raders, an increase in online rading decreases he probabiliy ha he marke maker rades wih informed raders, hereby reducing he spread s adverse selecion componen. Second, if online rading is sysemaic, hen an increase in online rading also increases he order imbalance. An increase in order imbalance, even absen any informaion conen, is likely o cause an increase in he spread because i creaes or exacerbaes he markemaker s invenory problem. Thus, we have wo conflicing predicions regarding he effec of 12 A hird criical dimension of liquidiy is he deph on he limi order book. Unforunaely, he limi order daa are no publicly available. 21

24 online rading on he bid-ask spread. Our subsequen empirical analysis will deermine which effec dominaes by esimaing he following regression model: Spreads = α + β OL + γ VOLS ( or NTS ) + θ RETSPX + ε (5) where Spreads are quoed or effecive spreads expressed in eiher absolue or percenage erms. Since we canno rejec he exisence of a uni roo in OL, we perform our regression analysis using changes in he variables of ineres. Specifically, we regress he changes in spreads agains he change in online rading and oher conrol variables. To examine wheher online rading effecs marke liquidiy differenly from ha of oher individual invesors rading, we include as regressors he changes in oal share volume of small rades (VOLS) and he oal number of small rades (NTS). Chordia, Roll, and Subrahmanyam (2001) find ha he marke liquidiy improves in up markes and worsens in down markes. Therefore, we also conrol for he reurn o he S&P 500 index (RETSPX). In esimaing he above model we eliminae hose observaions falling wihin he week of and he week following decimalizaion because of is impac on marke spreads and deph. Noe ha since our dependen variables are changes (no levels) of spreads, i is sufficien o drop jus wo weeks of daa. Table 8 presens he esimaion resuls for regression equaion (5). Panel A conains he resuls for absolue spreads while Panel B uses percenage spreads. In boh panels, we find ha changes in online rading are significanly and posiively relaed o changes in spreads, wheher we consider quoed or effecive spreads. This resul appears o be consisen wih he conenion ha greaer online rading leads o increased order imbalance, which causes wider spreads because of invenory concerns. I migh also sugges ha any order imbalance effec resuling from online rading dominaes he reducion in he adverse selecion componen of he bid-ask spread aribuable o he noise rading of online invesors. This posiive relaion beween online 22

25 rading and bid-ask spreads holds while conrolling for he reurn o he S&P 500, he oal share volume of small-sized rades, and he oal number of small-sized rades. As we discuss in Secion IV.B, he NYSE and NASDAQ have differen marke microsrucures. Therefore, i is useful o examine he relaion beween online rading and he bid-ask spreads of NYSE and NASDAQ socks separaely. To accomplish such an analysis we separaely esimae he mean quoed (effecive) spread for NYSE and NASDAQ socks. Table 9 presens he resuls of our analysis of he relaion beween online rading and spreads by individual exchange. Overall, Table 9 conains findings very similar o hose in Table 8. The coefficiens for online rading are significanly posiive for all measures of spread and across boh exchanges. These resuls indicae ha high volumes of online rading are associaed wih poor liquidiy as refleced in large bid-ask spreads. Again, we find a significanly negaive relaion beween changes in spreads and conemporaneous sock reurns. Overall, conrolling for NASDAQ or NYSE rading volume separaely does no aler our basic conclusion ha online rading is posiively relaed o he bid-ask spread. B. Quoed Deph Online rading migh affec quoed deph hrough is effecs on rading volume. As rading volume increases, he marke maker faces less invenory risk and hus will be willing o quoe greaer deph. However, if online rading is sysemaic, an increase in online rading would exacerbae he invenory problem faced by he marke maker, who can be expeced o respond by changing he quoed deph. Specifically, facing large invenory imbalance on one side of he marke, marke makers likely respond wih lower deph on he same side of he quoe, bu wih greaer deph on he opposie side of he quoe. Thus, an order imbalance will likely yield 23

26 canceling effecs on he quoed deph. Overall, we predic a weak posiive relaion beween online rading and quoed deph. In Table 10 we examine he relaion beween online rading and anoher dimension of liquidiy, quoed deph. We analyze quoed deph wih he following regression model: Deph = α + β OL + γ VOLS ( or NTS ) + θ RET + ε (6) The above model is similar o he quoed deph regression of Chordia, Roll, and Subrahmanyam (2001). We measure quoed deph for he combined sample of NYSE/AMEX/NASDAQ socks as well as for he NYSE socks separaely. Again, because of he impac of decimalizaion on marke deph, we exclude hose observaions falling wihin he week of and he week following decimalizaion. Regardless of he deph measure, we observe ha he coefficiens for online rading are significanly posiive across all model specificaions. Thus, i appears ha an increase in online rading is associaed wih an increase in marke deph. The combined resuls of Tables 8, 9 and 10 provide mixed evidence concerning he naure of online rading s influence on marke liquidiy. We observe ha online rading is associaed wih greaer quoed deph. Simulaneously, however, we find ha online rading ends o be posiively relaed o bid-ask spreads, hus reducing marke liquidiy. The combined effec of wider spreads and greaer deph hurs individual invesors who end o submi small orders, bu likely benefis insiuional invesors who are more likely o submi large orders. VIII. Conclusions Many in he media as well as academia argue ha online rading conribues o excessive rading, increased sock marke volailiy, and he perverse machinaions of he Inerne bubble of lae 1990s and early Using a new daabase of he Web raffic of he six larges online 24

27 brokers allows us o gain new insighs regarding he effecs of online rading on aggregae marke. Specifically, we examine he dynamic relaions beween online rading and he volailiy, reurn, and liquidiy of he U.S. equiy marke. During our examinaion of he relaion beween online rading and volailiy, we iniially find ha online rading is posiively relaed o sock marke volailiy. However, once we conrol for he aggregae volume of small rades, online rading ceases o be a significan explanaory facor for marke volailiy. Raher, i is he rading aciviy of all small raders ha influences marke volailiy. This suggess ha he claim ha online rading generaes excess marke volailiy migh be over-saed. We also analyze wheher online rading migh be able o impac marke prices. We find ha online rading is significanly and posiively relaed o conemporaneous marke reurns. Such a resul is consisen wih Barber, Odean and Zhu (2003) and Kumar and Lee (2002) in ha he sysemaic noise of individual invesors impacs equiy prices. I migh also be he resul of inra-week posiive feedback rading. The coarseness of our weekly Web raffic daa, however, does no permi a facile disincion beween hese wo possible explanaions. Finally, we find ha online rading has a mixed relaion wih marke liquidiy. Higher online rading appears o be relaed o wider bid-ask spreads, wheher hey are measured as quoed or effecive spreads. Simulaneously however, we find ha online rading is posiively relaed o he quoed deph. This combined resul of wider spreads and greaer deph likely benefis insiuional invesors who end o place large orders, while increasing he cos of rading o individual invesors who are likely o submi small orders. We conclude from our analysis ha online rading is relaed o he volailiy, reurns, and liquidiy of he sock marke, bu hese relaions are more suble han ha generally porrayed in he popular media. 25

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