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References

Agnew, Julie, Pierluiggi Balduzzi, and Annika Sunden, 2000, Portfolio choice, trading,and returns in a large 401(k) Plan, Working paper, Boston College.

Ameriks, John, and Steve Zeldes, 2000, How do household portfolio shares vary withage?, Working paper, Columbia University.

Atchison, Michael, Kirt Butler, and Richard Simonds, 1987, Nonsynchronous securitytrading and market index correlation, Journal of Finance 42, 111-118.

Barber, Brad M., and Terrance Odean, 1999, Online investors: Do the slow die first?,Working paper, UC Davis.

Barber, Brad M., and Terrance Odean, 2000, Trading is hazardous to your wealth: Thecommon stock investment performance of individual investors, Journal of Finance 55,773-806.

Bergstrasser, Daniel, and James Poterba, 2000, Do after-tax returns affect mutual fundflows, National Bureau of Economic Research, Working paper No. 7595.

Chalmers, John M.R., Roger M. Edelen, and Gregory Kadlec, 1999, The wildcard optionin transacting in mutual fund shares, Rodney L. White Center for Financial Research,Working paper No. 00-03.

Chevalier, Judith, and Glenn Ellison, 1997, Risk taking by mutual funds as a response toincentives, Journal of Political Economy 105, 1167-1200. Dugan, Ianthe Jeanne, 1999, Senate hearings to probe day trading; Impact on market,traders at issue, Washington Post (September 14), Financial Section, E01.

Edelen, Roger M., 1999, Investor flows and the assessed performance of open-endmutual funds, Journal of Financial Economics 53 (3). 439-466.

Goetzmann, William N., Zoran Ivkovich, and Geert Rouwenhorst, 2000, Day-tradinginternational mutual funds: evidence and policy solutions, Working paper, YaleUniversity.

Goetzmann, William N., Massimo Massa, Geert Rouwenhorst, 2000, Behavioral factorsin mutual-fund flows, Working paper, Yale University.

Grinblatt, Mark and Matti Keloharju, 1999, What makes investors trade? Working paper,UCLA.

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32

Holden, Sarah, Jack VanDerhei, and Carol Quick, 1999, 401(k) plan asset allocation,account balances, and loan activity in 1998, Investment Company Institute Perspective 6(1).

Kunath, Brian D., 1999, Day trading: The ’Dealerization’ of the Market,Global Investment Magazine (December), 51.

Odean, Terrance, 1998, Are investors reluctant to realize their losses, Journal of Finance53, 1775-1798.

Sirri, Eric and Peter Tufano, 1993, Buying and selling mutual funds: funds, performance,fees, and service, Working paper, Harvard Business School.

Stillwell, Dennis, 2000, High-speed danger alert, Journal of Commerce, April 5,Global Commerce Section, 13.

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Table I401(k) Plan Characteristics

This table presents summary statistics for the 401(k) plans of firms Alpha and Omega.Because of data availability, demographic information is limited to participants who hadpositive plan balances or plan activity in 1998 or 1999.

Alpha Omega

Number of participants1 More than 10,000 More than 50,000

Data range 5/19/97 – 3/3/00 1/27/97 – 1/26/00

Number of investment options 11 36

Company stock available in plan? No Yes

Percent of plan assets in equity2, 3 78.2% 40.4%

Percent of plan assets in companystock2

0.0% 6.6%

Average age2, 4 40.7 52.8

Average years since original hire2, 4 8.6 18.6

Average plan balance2, 4 $68,202 $112,456

Average contribution rate2, 5 6.49% 9.27%

Percent of participants who trade atleast once in sample

41% 45%

Month of Web introduction August 1998 August 1998

Average trades per month perparticipant before Webintroduction6

0.0564 0.0844

Average trades per month perparticipant after Web introduction6

0.1285 0.1407

Average trades per month perparticipant on Web6

0.0666 0.0597

Percent of participants who trade atleast once on Web

24% 15%

1 All participants in sample, including those who drop out of the plan before the end of the sample.2 At year-end 1999.3 Includes all equity mutual fund and company stock balances.4 Participants who had positive plan balances at year-end 1999 or who had plan activity in 1998 or 1999.5 Current employees as of year-end 1999 only.6 All sales and purchases on a given day by a participant are counted as one “trade.”

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Table IIDeterminants of Trading Frequency

The dependent variable, TRADES, is the percent of participants in each company whotrade on each day. WEB is a dummy set to one if Web trading has been introduced.WEB * TIME is the interaction of WEB and TIME, the number of calendar days that havepassed since Web trading was introduced. NON-WEB INDEX is the equally-weightedaverage of the daily percent of plan balances traded between asset classes for 17companies without Web trading. |S&P 500| and |LAG S&P 500| are the absolute valuesof the S&P 500 return today and yesterday, respectively. (S&P 500)2 and (S&P 500)2 arethe squares of |S&P 500| and |LAG S&P 500|, respectively. STD(S&P 500) is the twenty-day lagged standard deviation of the S&P 500 price return. |COMPANY STOCK| and|LAG COMPANY STOCK| are the absolute values of the company stock’s return todayand yesterday, respectively. (COMPANY STOCK)2 and (LAG COMPANY STOCK)2 arethe squares of |COMPANY STOCK| and |LAG COMPANY STOCK|, respectively.STD(COMPANY STOCK) is the twenty-day lagged standard deviation of the companystock price return. START WEEK, END WEEK, START MONTH, and END MONTH aredummies set to one if the day is the first trading day of the week, the last trading day ofthe week, the first trading day of the month, and the last trading day of the month,respectively. RULE CHANGE is a dummy set to one for Omega after the institution of anew rule restricting trading on an international fund. TREND is the number of calendardays that have elapsed since January 1, 1997. Newey-West robust standard errors (fivelags) are reported in parentheses below the OLS point estimates.

SEE NEXT PAGE FOR TABLE

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Independent Variables Alpha Omega

WEB -0.0952(0.0582)

-0.0241(0.0518)

WEB * TIME 0.00072**(0.00020)

0.00064*(0.00028)

NON-WEB INDEX 140.0920**(22.3813)

81.6954**(18.6263)

|S&P 500| 0.2079(1.8021)

3.4935*(1.7030)

(S&P 500)2 60.3637(41.8465)

-1.0643(32.3171)

|LAG S&P 500| -1.5036(1.7831)

-1.1995(1.8917)

(LAG S&P 500)2 202.9359**(37.9670)

149.2298**(52.8017)

STD(S&P 500) -2.7578(3.9289)

-5.2980*(2.4152)

|COMPANY STOCK| 0.9426(0.8968)

2.5601**(0.8864)

(COMPANY STOCK)2 -0.9552(13.0412)

41.8645**(9.5403)

|LAG COMPANY STOCK| 0.2651(0.9386)

0.2604(1.1452)

(LAG COMPANY STOCK)2 -1.4287(12.6027)

32.0787**(12.1468)

STD(COMPANY STOCK) 7.7854**(2.5549)

7.5359**(1.7749)

START WEEK 0.1208**(0.0173)

0.1287**(0.0172)

END WEEK -0.0070(0.0122)

0.0352*(0.0152)

START MONTH -0.0488(0.0410)

-0.0379(0.0298)

END MONTH -0.0149(0.0308)

-0.0113(0.0318)

RULE CHANGE -0.0547(0.0863)

TREND 0.00030(0.00016)

0.00034**(0.00009)

Constant -0.1894*(0.0953)

-0.0696(0.0656)

* Significant at the 5 percent level** Significant at the 1 percent level

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Table IIIDeterminants of Turnover

The dependent variable, TURNOVER, is the daily dollar value of all sales as a percent oftotal balances on that day. WEB is a dummy set to one if Web trading has beenintroduced. WEB * TIME is the interaction of WEB and TIME, the number of calendardays that have passed since Web trading was introduced. NON-WEB INDEX is theequally-weighted average of the daily percent of plan balances traded between assetclasses for 17 companies without Web trading. |S&P 500| and |LAG S&P 500| are theabsolute values of the S&P 500 return today and yesterday, respectively. (S&P 500)2 and(S&P 500)2 are the squares of |S&P 500| and |LAG S&P 500|, respectively. STD(S&P500) is the twenty-day lagged standard deviation of the S&P 500 price return.|COMPANY STOCK| and |LAG COMPANY STOCK| are the absolute values of thecompany stock’s return |LAG COMPANY STOCK|, respectively. STD(COMPANYSTOCK) is the twenty-day lagged standard deviation of the company stock price return.START WEEK, END WEEK, START MONTH, and END MONTH are dummies set to oneif the day is the first trading day of the week, the last trading day of the week, the firsttrading day of the month, and the last trading day of the month, respectively. RULECHANGE is a dummy set to one for Omega after March 19, 1999 to reflect a new ruleinstituted to restrict trading on an international fund. TREND is the number of calendardays that have elapsed since January 1, 1997. Newey-West robust standard errors (fivelags) are reported in parentheses below the OLS point estimates.

SEE NEXT PAGE FOR TABLE

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Independent Variables Alpha Omega

WEB -0.0591(0.0577)

-0.0205(0.0119)

WEB * TIME 0.00038(0.00020)

0.00026**(0.00006)

NON-WEB INDEX 194.0439**(25.9074)

31.6382**(4.3597)

|S&P 500| 0.1820(2.6553)

0.1188(0.3925)

(S&P 500)2 83.1802(73.1748)

11.0200(9.9516)

|LAG S&P 500| -3.3204(2.5653)

-0.4162(0.3861)

(LAG S&P 500)2 302.6174**(64.2860)

44.1060**(8.7565)

STD(S&P 500) -4.1020(4.1555)

-0.3025(0.4919)

|COMPANY STOCK| 0.5350(1.2829)

-0.0198(0.2006)

(COMPANY STOCK)2 9.5484(20.8628)

1.8339(1.8384)

|LAG COMPANY STOCK| -0.4893(1.1520)

-0.0768(0.1800)

(LAG COMPANY STOCK)2 11.0976(13.7542)

-0.2390(1.7718)

STD(COMPANY STOCK) 8.5034**(2.4703)

1.3509**(0.3771)

START WEEK 0.1200**(0.0217)

0.0208**(0.0033)

END WEEK 0.0407*(0.0195)

0.0062*(0.0032)

START MONTH -0.0442(0.0487)

-0.0093(0.0076)

END MONTH -0.0222(0.0408)

-0.0029(0.0064)

RULE CHANGE -0.0586**(0.0147)

TREND 0.00064**(0.00016)

0.00011**(0.00002)

Constant -0.3360**(0.0956)

-0.0643**(0.0180)

* Significant at the 5 percent level** Significant at the 1 percent level

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Table IVDemographics of Web Traders

This table presents the results of a binary logit regression of the likelihood of trading at least onceon the Web in the sample, conditional upon trading at least once since Web trading wasintroduced. Participants must have been enrolled before Web introduction and had a positive planbalance or plan activity in 1998 or 1999 in order to have a full set of right-hand-side variables andbe included in the regression. MALE and MARRIED are dummies set to one if the participant ismale and married, respectively. AGE is the participant’s age at December 31, 1999, and TENUREis the log of the number of years since the participant’s original hire date, as of December 31,1999. SALARY is the log of 1999 salary, and BALANCES is the log of total plan balance at year-end 1999. PARTICIPATION LENGTH is the log of the number of years since the participantoriginally enrolled in the plan. PRE-WEB TRADES PER MONTH is the number of trades permonth the participant executed before the introduction of the Web. CONTRIBUTION RATE is thecontribution rate effective at year-end 1999, in integers (e.g. “5” percent). TERMINATED andRETIRED are dummies set to one if the participant has been terminated or retired, respectively asof year-end 1999. Standard errors are given in parentheses below the point estimates.

Independent Variables Alpha Omega

MALE 0.4093**(0.0675)

MARRIED 0.0564(0.0639)

0.2783**(0.0406)

AGE -0.0369**(0.0039)

-0.0479**(0.0028)

TENURE -0.0059(0.1496)

0.0674(0.0578)

SALARY 0.1879**(0.0231)

0.0619**(0.0063)

BALANCES 0.3264**(0.0444)

0.2080**(0.0205)

PARTICIPATION LENGTH -0.3683**(0.1360)

0.0287(0.0567)

PRE-WEB TRADES PER MONTH -0.3811**(0.0864)

-0.0535(0.0275)

CONTRIBUTION RATE 0.0129(0.0103)

-0.0076*(0.0032)

TERMINATED -0.4862**(0.1615)

-0.1978**(0.0706)

RETIRED -0.2244(0.5898)

-0.3655**(0.0916)

Constant -2.7538**(0.3831)

-0.7128**(0.1927)

* Significant at the 5 percent level** Significant at the 1 percent level

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Table VDistribution of Trade Entry Times By Channel

This table presents the trades that have been entered through each channel at each hour(U.S. Eastern Time) since Web trading was introduced, as a percent of all trades that havegone through each channel since the introduction of Web trading.

Alpha Omega

Time Phone Web Phone Web

12:00 A.M. – 8:59 A.M. 5.8% 12.0% 4.2% 13.6%

9:00 A.M. – 9:59 A.M. 2.6% 4.0% 3.1% 4.1%

10:00 A.M. – 10:59 A.M. 3.5% 4.9% 3.6% 4.7%

11:00 A.M. – 11:59 A.M. 4.2% 4.8% 3.7% 5.1%

12:00 P.M. – 12:59 P.M. 4.4% 5.7% 5.1% 7.0%

1:00 P.M. – 1:59 P.M. 5.2% 6.9% 6.1% 7.9%

2:00 P.M. – 2:59 P.M. 8.6% 8.6% 9.7% 10.5%

3:00 P.M. – 3:59 P.M. 46.4% 26.7% 51.2% 29.1%

4:00 P.M. – 4:59 P.M. 5.3% 4.6% 6.3% 2.9%

5:00 P.M. – 11:59 P.M. 13.9% 21.9% 7.0% 15.0%

Total 100.0% 100.0% 100.0% 100.0%

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Figure 1. Alpha: Daily trading frequency, 20-trading-day moving average. On eachtrading day, the percent of participants enrolled in company Alpha’s 401(k) plan whotraded on that day is calculated.

0.0%

0.2%

0.4%

0.6%

0.8%

1.0%

1.2%

1.4%

1.6%

May

-97

Jun-

97

Jul-

97

Aug

-97

Sep-

97

Oct

-97

Nov

-97

Dec

-97

Jan-

98

Feb-

98

Mar

-98

Apr

-98

May

-98

Jun-

98

Jul-

98

Aug

-98

Sep-

98

Oct

-98

Nov

-98

Dec

-98

Jan-

99

Feb-

99

Mar

-99

Apr

-99

May

-99

Jun-

99

Jul-

99

Aug

-99

Sep-

99

Oct

-99

Nov

-99

Dec

-99

Jan-

00

Web

Phone

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Figure 2. A simulated realization of an x path for an individual trader. This figureplots a simulated path for the current flow cost of an individual’s Web use. Wheneverthe flow cost lies above a critical threshold, x* (see formula in the appendix), theindividual does not pay the flow cost, stays off of the Web, and uses the phone to executetrades. When the flow cost lies below x*, the individual actively uses the Web and isable to trade on the Web when sufficiently appealing trading opportunities arise. Thissimulation assumes the following model calibration: πv =.5, πw = 1, µv = .1, µw = .2, σv =.25, σw =.5, and γ = .05.

Valueof x

1.8

1.6

1.4

1.2

1.0

0.8

0.6

0.40.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Years since introduction of Web

x* = 1.02

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Figure 3. Trading channel tree. This figure traces the channels through whichparticipants trade, starting with their first Web trade. The top number in each box is thenumber of participants who reach that particular node, across both companies. Forexample, 15,421 participants made at least one Web trade (column 1). Column 2 showsthat 9,172+1,241 = 10,413 participants traded again. 9,172 (88% of 10,412) made thatsecond trade on the Web. 1,241 (12% of 10,413) made that second trade on the phone.

Web

15,421100%

Web

9,17288%

Phone

1,24112%

Web

40843%

Phone

54357%

Web

6,26194%

Phone

4106%

Web

4,59796%

Phone

1974%

Web

18458%

Phone

13542%

Web

28581%

Phone

6819%

Web

10323%

Phone

34877%

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Figure 4. Average dollars per trade, 20-trading-day moving average. The dollar valueof each day’s trades on each channel is summed and divided by the number ofparticipants who traded through that channel on that day. A moving average constructedusing the most recent 20 trading days is displayed on the graphs.

Panel A: Alpha

$0

$20,000

$40,000

$60,000

$80,000

$100,000

$120,000

$140,000Ju

n-97

Sep

-97

Dec

-97

Mar

-98

Jun-

98

Sep

-98

Dec

-98

Mar

-99

Jun-

99

Sep

-99

Dec

-99

Phone

Web

Both channels

Panel B: Omega

$0

$20,000

$40,000

$60,000

$80,000

$100,000

$120,000

$140,000

Feb

-97

May

-97

Aug

-97

Nov

-97

Feb

-98

May

-98

Aug

-98

Nov

-98

Feb

-99

May

-99

Aug

-99

Nov

-99

Phone

Web

Both channels

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Figure 5. Average turnover per trader by channel, 20-trading-day moving averages.For each day we form a subsample of all of the participants who traded on that day,calculate their individual turnover rates (percent of day-end balance traded on that day),and then average these individual turnover rates by channel. A moving averageconstructed using the most recent 20 trading days is displayed on the graphs.

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Figure 6. Average balance of traders, 20-trading-day moving average. The averageday-end balances of participants who traded through each channel on each day arecalculated. A moving average constructed using the most recent 20 trading days isdisplayed on the graphs.

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Figure 7. Percent of trades that are “reversed” trades, 20-trading-day movingaverage. “Reversed” trades are defined as trades that at least partially reverse a tradeexecuted no more than five trading days prior, or trades that are at least partially reversedno more than five trading days hence. We divide the number of participants executing areversed trade through each channel on each day by the total number of participantstrading through that channel on that day. A moving average constructed using the mostrecent 20 trading days is displayed on the graphs.

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