cash management of malaysian banks_ k. g. balachander and santha

8/14/2019 Cash Management of Malaysian Banks_ K. G. Balachander and Santha

1/17

Cash Management of Malaysian Banks

K. G. BalachanderV. Santha

Bala Shanmugam

ABSTRACT. This study aims to identify seasonal variations in the form of

day of the week, month of the year, and time of the month effects on the de-mand and supply of cash based on daily cash data obtained from a sample of

banks in Malaysia. This information would increase the efficiency of vault

cash management policies of banks, which would in turn increase the profit-

ability of banks since the degree to which banks optimize their vault cash hold-

ing has revenue and cost implications. These seasonal effects have been tested

using the one-way ANOVA technique, and the findings revealed that the de-

mand for general cash among Malaysian banks has been on the decline

throughout theyears dueto theintroduction of newchannels of delivery. There

was also sufficient statistical evidence, though to varying extent, that indicates

the presence of month of the year and time of the month effects on banks de-

mand for and supply of cash. [Article copies available for a fee from The Haworth

Document Delivery Service: 1-800-HAWORTH. E-mail address: Website: 2002 by The Haworth Press, Inc. All rights reserved.]

KEYWORDS.Cash management, vault cash, Malaysia, banking, demandand supply of cash

K. G. Balachander and V. Santha are Lecturers, Center for Multimedia Banking, Mul-timedia University, 63100, Cyberjaya, Malaysia.

Bala Shanmugam is Director, Banking and Finance Research Unit, Monash Univer-sity Malaysian Campus, No. 2, Jalan Kolej, Bandar Sunway, 46150 Petaling Jaya,Selangor, Malaysia (Email: [email protected]).

Address correspondence to Bala Shanmugam.

Journal of Asia-Pacific Business, Vol. 4(3) 2002http://www.haworthpressinc.com/store/product.asp?sku=J098 2002 by The Haworth Press, Inc. All rights reserved. 69
http://www.haworthpress/http://www.haworthpressinc.com/store/product.asp?sku=J098http://www.haworthpressinc.com/store/product.asp?sku=J098http://www.haworthpress/


2/17

INTRODUCTION

Malaysia is still far away from the notions of a cashless society. Conse-quently physical cash becomes the most fundamental resource necessaryfor the successful and continued operations of banks. However, as pointedout by Daniel and Baxter (1999), most banks in Malaysia and in Asia havepaid very little consideration to cash management. Thus, while bankswere willing to spend millions of dollars for the latest technology andbusiness process reengineering to augment revenues and minimize costs,they have obviously turned a blind eye toward issues relating to minimiz-ing costs of holding cash (vault).

The general practice among banks in Malaysia is for the head office of

each bank to specify a blanket vault cash management policy, which doesnot take into account factors such as location of the branches andday-to-day variations in the supply and demand for cash into account(Hamid, 1998). Thus, there is great likelihood that the vault cash manage-ment policy of these banks may be far from optimal and hence wasteful.This practice would, no doubt, affect the profitability of banks since thedegree to which banks optimize their vault cash holding has revenue andcost implications.

This study is basically contemporary in nature rather than futuristic andhence focuses in analyzing seasonal variations and trends in the daily de-mand for and supply of cash at commercial banks. To this extent, it mustbe appreciated from the beginning that one of the greatest stumblingblocks to research of this nature is the availability of data.

This study is thus based on data from a small sample of ten bankbranches located in the Klang Valleya region not atypical of urban Ma-laysia. The paper first provides a discussion of the Malaysian banks cashmanagement issues. Second, trends in the demand for cash at Malaysianbanks are analyzed. Third, the seasonal variations in the withdrawals anddeposits of these banks are analyzed. The final section presents the con-clusions and recommendations.

THE VAULT CASH MANAGEMENT ISSUE

The cash held at a banks vault together with any deposits the bank hasplaced with other banks and the banks reserve accounts held with the

Central Bank are often referred to as primary reserves. As argued byWrase (1998), excess reserves may be handy, as such reserves can guardagainst unexpected payment outflows. These cash assets are a banks first

70 JOURNAL OF ASIA-PACIFIC BUSINESS


3/17

line of defense against deposit withdrawals and the first source of funds toturn to when a customer comes up with an unexpected request for cash(Glaze, 1999).

In the banking business, availability of cash is an extremely importantaspect of customer service. Inadequate cash availability can lead to cus-tomer dissatisfaction and hence loss of customer goodwill (De La Rue,1999). There would also be the costs of inter-bank borrowings to meet thebanks liquidity needs. In the case of automated teller machines (ATMs),there would also be the loss of interchange fees. Thus, inadequate cashavailability in a bank would not only have implications for bank costs butalso bank revenues. However, though a 100% availability would implynever running out of cash, the cost of maintaining such high levels of cash

can be prohibitive. In short, there is a trade-off between liquidity and prof-itability for banks.

The demand for cash at any bank or branch or even service centerwould be unique depending on its geographical location and customerbase (GMT, 1999). Furthermore, the demand patterns may be quite erraticdue to trends, seasonal effects, structural shifts, public holidays and spe-cial events. Thus, there is a need for dynamic demand forecasting thattakes these factors into consideration in an effort to determine the optimalcash to be held at each bank on a daily basis. In this context, a general ruleof thumb blanket policy for all banks would certainly be inappropriate(Tan, 1998).

Allen (1998) examined the daily vault cash holdings in the Eighth Dis-

trict banks in the United States to determine whether the observedamounts of vault cash held by these banks are consistent with the funda-mental assumptions of a one-sided (S,s) inventory decision rule. The (S,s)model first developed by Scarf (1960) simply represents an upper limit ofvault cash (S) and a lower limit or replenishment signal (s) which are de-termined based on the intra-day profile of withdrawals and deposits aswell as the costs associated with shipments and the opportunity costs ofstocking out. Allens (1998) findings based on 1997 data appeared to sup-port the idea that banks in the Eighth District (U.S.) have not been manag-ing vault cash holdings very closely. Within the context of the (S,s)inventory model, the variance of net withdrawals and/or the penalty asso-ciated with running out would have to be very high to justify the levels ofvault cash balances held by these banks.

When deciding how much cash to order or to clear, a model which is cus-tomized for each location and takes account of information such as cost fac-tors, carrier parameters, demand patterns and other physical constraints needs

Balachander, Santha, and Shanmugam 71


4/17

to be developed. Whether cash holding costs are minimized depends a greatdeal on the quality of the modeling (Ashford, 2000).

To this extent it is worth noting that the major cost drivers in any cur-rency management environment relate to cash holding, carrier activityand general cash handling. These are competing costs and the aim is tofind the right cash ordering and clearing strategies that minimize the totalcosts.

Baxter and Daniel (1999) indicated that establishing a cost to holding cashwas important because it provides a benchmark for bankers and other cash in-tensive organizations to reengineer their processes. To this extent, they identi-fied the following as the main cost elements of cash holding.

i. interest rate lost (cost of storage)ii. transport cost of replenishments (cost of ordering)iii. security (cost of storage)iv. cost of insurance/fraud (cost of storage)

TRENDS IN MALAYSIAN DEPOSITORSDEMAND FOR CASH

Allen (1998) had used the ratio of commercial bank vault cash to de-mand deposits as a proxy for the depositors or public demand for cash.High values of this ratio may indicate either high depositors demand forcurrency or a conservative cash management policy on the part of the

commercial banks. On the other hand, low values of this ratio may eitherindicate low depositors demand for currency or a more prudent cash man-agement policy on the part of commercial banks.

As can be seen from Table 1 and subsequently illustrated in Figure 1,the vault cash of Malaysian commercial banks as a percentage of demanddeposits appears to indicate a downward trend. This may indicate a de-cline in the demand for cash or currency on the part of the Malaysian pub-lic or an increasingly prudent cash holding policy on the part ofcommercial banks. The former would appear to be a more plausible expla-nation, in view of the developments which have been taking place in theMalaysian economy. Research by Budhiraja and Latiff (1998) stated thegrowing importance of information technology in the banking industryhas helped to reduce the need for carrying much cash and for banks to

maintain a large vault space. Technological advancements and innova-tions in the area of information technology and telecommunications arereshaping consumer behaviour and payment systems, which tend to devi-



5/17

ate from extensive use of cash (Suganthi et al., 2001). This is clearly repre-sented by the increasing use of credit cards, charge or debit cards and, oflate, smart cards by Malaysian consumers.

In addition, businesses need for large volumes of cash for salary pay-ments too have been reduced via the banking services where employee

salaries are credited directly into their accounts and withdrawals are madebased on needs by the employees from automated teller machines(ATMs). Automated teller machines have also reduced the precautionary


TABLE 1. Vault Cash as a Percentage of Demand Deposits

Year Cash at

Commercial

Banks (RM

million)

Demand

Deposits

(RM

million)

Vault Cash

as a

Percentage

of Demand

Deposits

(%)

Year Cash at

Commercial

Banks (RM

million)

Demand

Deposits

(RM

million)

Vault Cash

as a

Percentage

of Demand

Deposits

(%)

1959 51.3 465.7 11 1980 346.7 5326.2 7

1960 56.8 476.5 12 1981 393.3 6234.8 6

1961 48.7 497.3 10 1982 403.0 7223.5 6

1962 59.9 520.0 12 1983 532.5 8062.5 7

1963 54.6 556.5 10 1984 587.0 8088.3 7

1964 68.5 664.7 10 1985 553.2 7950.4 7

1965 82.6 713.8 12 1986 565.6 7598.2 7

1966 84.3 795.0 11 1987 607.0 9017.3 7

1967 87.6 791.8 11 1988 660.8 10032.9 7

1968 92.2 920.3 10 1989 729.7 12762.1 6

1969 89.8 991.4 9 1990 1164.7 15024.8 8

1970 96.3 1068.1 9 1991 1025.3 16650.5 6

1971 84.0 1077.6 8 1992 1031.6 19068.4 5

1972 90.6 1485.9 6 1993 1115.2 29279.9 4

1973 110.4 2030.3 5 1994 1248.4 31777.4 4

1974 151.2 2081.8 7 1995 1434.1 36300.5 4

1975 152.9 2197.4 7 1996 2032.9 44534.8 5

1976 161.8 2728.3 6 1997 3098.5 45800.7 7

1977 185.3 3190.6 6 1998 2295.0 36792.5 6

1978 218.5 3801.9 6 1999 2702.9 48413.3 6

1979 267.6 4548.9 6 2000 2814.7 54627.2 5

Source: Money and Banking in Malaysia (1994), BNM Monthly Statistical Bulletin (2000)


6/17

demand for cash since ATMs are widespread and operate almost 24 hoursa day. This finding certainly has implications for commercial banks vaultcash management policies. If the demand for cash is actually decreasing,then the obvious question that arises is whether the cash held at banks areactually optimal or are they far beyond requirements resulting in idle cashand hence wasteful utilization of a useful resource.

SEASONAL VARIATIONSIN BANK WITHDRAWALS AND DEPOSITS

In relation to cash withdrawals and deposits at commercial banks, theseasonal variations can be classified as day of the week variations, monthof the year variations and time of the month variations. In this context, thedeposits and withdrawals pattern over the week can be expected to varyaccording to the day of the week. Furthermore, since Malaysia is a land ofnumerous festivities occurring in various months of the year, the bankingbusiness activities can also be expected to vary according to the month ofthe year. Finally, the consumer behavioral patterns in relation to depositsand withdrawals may vary at different periods of the month and thus thetime of the month can also influence these cash flows. The seasonal varia-tions in the demand for and supply of cash at commercial banks would

certainly have implications for the efficient vault cash management ofthese institutions. Each one of these seasonal components will thus be an-alyzed in the following sections.


1959

1961

1963

1965

1967

1969

1971

1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

Year

Vau

ltC

as

h

Deman

dDepos

it(%

)

-

14

12

10

8

6

4

2

0

Trends in Vault Cash as a Percentage of Demand Deposits

FIGURE 1


7/17

Day of the Week Effect on Bank Withdrawals and Deposits

The day of the week effect on the withdrawals and deposits are ana-lyzed by using the one-way ANOVA technique. This analysis basicallyinvolves the testing of the following hypotheses for the withdrawals anddeposits, respectively.

Day of the Week Effect on Bank Withdrawals

The result of the one-way ANOVA test on the day of the week effect oncommercial bank withdrawals is presented in Table 2. The results indicatea p-value of 13.6%, which is greater than the 5% level of significance.

Therefore, the null hypothesis is not rejected and it can be concluded thatthere is no evidence of differences in the mean daily withdrawals amongthe six days of the week. Thus, the results show an absence of a significantday of the week effect on the withdrawals at these sample branches.

Day of the Week Effect on Bank Deposits

The results of the one-way ANOVA test on the day of the week effecton commercial bank deposits is presented in Table 3. The results indicatea p-value of greater than 5% level of significance. Therefore, the null hy-


TABLE 2. Summary of ANOVA Test Results for the Day of the Week Effect on

Withdrawals

Source Sum of Squares df Mean Square F-value p-value

Between Days 1.90719E+12 5 3.81439E+11 1.696 0.136

Within Days 6.47909E+13 288 2.24968E+11

Total 6.66981E+13 293

TABLE 3. Summary of ANOVA Test Results for the Day of the Week Effect on

Deposits


Between Days 6.3028E+12 5 1.26056E+12 2.125 0.063

Within Days 1.70832E+14 288 5.93166E+11

Total 1.77135E+14 293


8/17

pothesis is not rejected and it is concluded that there is no evidence of dif-ferences in the mean daily deposits among the six days of the week. Thus,the results show an absence of a significant day of the week effect on thedeposits, which is similar to the result obtained in the case of withdrawals(see Figure 3).

Month of the Year Effect on Bank Withdrawals and Deposits

The month of the year effect on the withdrawals and deposits are alsoanalyzed using the one-way ANOVA technique. This analysis basicallyinvolves the testing of the following hypotheses for the withdrawals anddeposits, respectively.

Month of the Year Effect on Bank Withdrawals

The results of the one-way ANOVA test on the month of the year effecton commercial bank withdrawals is presented in Table 4. The results indi-cate a p-value of smaller than 5% level of significance. Therefore, the nullhypothesis is rejected and it can be concluded that there is evidence ofmonthly seasonal effects on withdrawals.

Since the null hypothesis was rejected, then according to Levine et al.(1999), the Tukey-Kramer procedure is used to determine exactly whichof the monthly means were significantly different from one another. Thefindings are presented in Table 5.

The entries in Table 5 are the mean differences between all possiblepair-wise combinations of the mean monthly withdrawals. The results in-dicate that the mean monthly demand is maximized in May and mini-mized in November. The pattern of variations in the mean monthlywithdrawals is illustrated in Figure 2. The largest amount of withdrawalsin May could be due to the long vacation granted to schools and colleges.Holiday makers would no doubt withdraw their savings to go on spendingsprees. Meanwhile, withdrawals are smaller in November indicating that


TABLE 4. Summary of ANOVA Test Results for the Month of the Year Effect on

Withdrawals


Between Months 2.74458E+13 11 2.49508E+12 17.925 0Within Months 3.92523E+13 282 1.39192E+11

Total 6.66981E+13 293


9/17

TABL

E5.

SummaryoftheTukey-Kramer

Analysis

M

onths

January

February

March

April

May

June

July

Aug

ust

September

October

November

Dec

ember

Ja

nuary

2397544.5

5

2376509.4

3

24

4316.6

8

272838.6

4

152406.2

2

224944.1

32473355.0

7

2382695.0

52458815.6

02512275.3

7233

4579.4

2

Fe

bruary

397544.5

5

21035.1

2

64

1861.2

3

670383.1

9

549950.7

7

622488.6

8

275810.5

2

14849.4

9

261271.0

52114730.8

2

6

2965.1

2

March

376509.4

3

221035.1

2

62

0826.1

1

649348.0

8

528915.6

5

601453.5

6

296845.6

4

26185.6

2

282306.1

72135765.9

3

4

1930.0

1

Ap

ril

2244316.6

82641861.2

3

2620826.1

1

28521.9

6

291910.4

6219372.5

52717671.7

5

2627011.7

42703132.2

82756592.0

5257

8896.1

0

May

2272838.6

42670383.1

9

2649348.0

822

8521.9

6

2120432.4

2247894.5

12746193.7

1

2655533.7

02731654.2

42785114.0

1260

7418.0

7

Ju

ne

2152406.2

22549950.7

7

2528915.6

5

9

1910.4

6

120432.4

2

72537.9

12625761.2

9

2535101.2

82611221.8

22664681.5

9248

6985.6

5

Ju

ly

2224944.1

32622488.6

8

2601453.5

6

1

9372.5

5

47894.5

1

272537.9

1

2698299.2

0

2607639.1

92683759.7

32737219.5

0255

9523.5

5

Au

gust

473355.0

7

75810.5

2

96845.6

4

71

7671.7

5

746193.7

1

625761.2

9

698299.2

0

90660.0

1

14539.4

7

238920.3

0

13

8775.6

5

Se

ptem-

be

r

382695.0

5

214849.4

9

6185.6

2

62

7011.7

4

655533.7

0

535101.2

8

607639.1

9

290660.0

1

276120.5

42129580.3

1

4

8115.6

3

October

458815.6

0

61271.0

5

82306.1

7

70

3132.2

8

731654.2

4

611221.8

2

683759.7

3

214539.4

7

76120.5

4

253459.7

7

12

4236.1

8

No

vember

512275.3

7

114730.8

2

135765.9

3

75

6592.0

5

785114.0

1

664681.5

9

737219.5

0

38920.3

0

129580.3

1

53459.7

7

217

7695.9

4

De

cember

334579.4

2

262965.1

2

241930.0

1

57

8896.1

0

607418.0

7

486985.6

5

559523.5

52138775.6

5

248115.6

32124236.1

82177695.9

4

significantat5%

77


10/17

people are spending less in order to save for the coming festive seasons ofChristmas, New Year, Chinese (Lunar) New Year and the Hari Raya(Muslim) festivals in December and January.

Month of the Year Effect on Bank Deposits

The results of the one-way ANOVA test on the month of the year ef-fects on commercial bank deposits are presented in Table 6. The p-valueof smaller than 5% level of significance indicates that there is evidence ofsignificant monthly seasonal effects on deposits.

This result can also be seen in the time series plot of the mean monthlydeposits in Figure 5.

A comparison of Figure 4 and Figure 5 reveals a great deal of similaritybetween the pattern of mean monthly withdrawals and mean monthly de-posits which is maximized in May and minimized in November.


FIGURE 2

Withdrawals Deposits

H0: mW, Monday = mW, Tuesday = ... = mW, Saturday

H1: Not all mW's are equal

Where

mW = average daily withdrawals

H0: mD, Monday = mD, Tuesday = ... = mD, Saturday

H1: Not all mD's are equal

Where

mD = average daily deposits

FIGURE 3

Withdrawals Deposits

H0: mW, January = mW, February = ... = mW, December

H1: Not all mW's are equal

Where

mW = average monthly withdrawals

H0: mD, January = mD, February = ... = mD, December

H1: Not all mD's are equal

Where

mD = average monthly deposits


11/17


TABLE 6. Summary of ANOVA Test Results for the Month of the Year Effect on

Deposits


Between Months 1.41181E+14 11 1.28347E+13 100.668 0

Within Months 3.59535E+13 282 1.27495E+11

Total 1.77135E+14 293

Dec-97 Jan-98 Feb-98 Mar-98 Apr-98 May-98 Jun-98 Jul-98 Aug-98 Sep-98 Oct-98 Nov-98

Month

RM

Mean MonthlyWithdrawals

1400000

1200000

1000000

800000

600000

400000

200000

0

FIGURE 4

Mean Monthly Deposits

Month

3000000

2500000

2000000

1500000

1000000

500000

0

RM

Dec-97 Jan-98 Feb-98 Mar-98 Apr-98 May-98Jun-98 Jul-98 Aug-98Sep-98Oct-98 Nov-98

FIGURE 5


12/17

Time of the Month Effect on Bank Withdrawals and Deposits

Finally, the third seasonal effect to be investigated in this study is thetime of the month effect on cash withdrawals and deposits. Each month isdivided into three time periods as shown below:

Since salaries and most bills are paid at the beginning of the month(first 10 days) large withdrawals can be expected at this time of the month.Similarly the deposits can also be expected to be high during the start of

the month as a result of receiving incomes during this period of time.By the end of the month, individuals would have gradually exhausted

their funds and hence banking activities can be expected to be low. In viewof this intuitive behavioral pattern the time of month effects on cash with-drawals and deposits are examined.

Time of the Month Effect on Bank Withdrawals

Table 7 presents the results of the ANOVA test for the time of montheffect on bank withdrawals. The results indicate a significant time ofmonth effect on withdrawals. The plot of the mean withdrawals for thethree time periods in Figure 6 indicates a sharp decline in withdrawals dur-

ing the middle of the month. The withdrawals are high during the end ofthe month because it is the period in which salaries are credited into onesaccount. However, the withdrawals would be much higher at the begin-ning of next month because the salaries are only credited towards the veryend of the month.

Time of the Month Effect on Bank Deposits

Table 8 presents the results of the ANOVA test for the time of themonth effects on bank deposits. The results indicate a significant time ofthe month effect on deposits. The plot of the mean deposits for the threetime periods, which is shown in Figure 5, indicates a sharp decline in de-posits during the middle of the month. This might imply that deposits aremade basically at the start and end of the month when salaries and receiv-


Start Middle End

First 10 days Next 10 days Last 10 days


13/17

ables are collected. This is similar to the pattern observed for withdrawals(see Table 8 and Figure 7).

CONCLUSIONS AND RECOMMENDATIONS

In line with developments taking place in Malaysia in relation to infor-mation technology and telecommunications, the behavioral patterns ofconsumers are changing towards being less cash oriented. This is clearlyindicated in the declining ratio of cash to demand deposits, which is usedas a proxy to measure the general demand for cash in the Malaysian econ-omy.

The study shows no significant statistical evidence to indicate that bankwithdrawals and deposits vary according to the day of the week. This basi-cally indicates that withdrawals in each day are rather similar. This is also


TABLE 7. Summary of ANOVA Test Results for the Time of the Month Effect

on Withdrawals


Between Months 3.91672E+12 2 1.95836E+12 9.077 0

Within Months 6.27814E+13 291 2.15744E+11

Total 6.66981E+13 293

1200000

1000000

800000

600000

400000

200000

0

RM

Beginning Middle End

Time of the Month

Time of the Month Effect on Withdrawals

FIGURE 6


14/17

true in the case of deposits. With regards to the month of the year effect,

both the withdrawals and deposits were found to exhibit similar signifi-cant trends with the largest amount of both withdrawals and deposits oc-curring in May. This coincides with the school holidays which appear inthis month.

Finally, in relation to the time of the month effect, both withdrawalsand deposits appear to be high at the beginning and end of the month.Moreover, these variations were found to be significant for both the casesof withdrawals and deposits. The above findings imply that commercialbanks daily cash flows are not uniform but do indicate systematic andregular fluctuations which can be traced and hence be used for predictivepurposes.

Now, the paper commenced with the assertion that rule of thumb blan-ket vault cash management policies would not be optimal, especially if thecommercial banks withdrawals and deposits are uncertain and exhibitsystematic variations or seasonal effects. In the presence of such seasonalvariations, a more dynamic forecasting model will be required for effi-cient vault cash management. Thus, by analyzing the seasonal variations


TABLE 8. Summary of ANOVA Test Results for the Time of the Month Effect

on Deposits


Between Months 4.57811E+12 2 2.28905E+12 3.86 0.022

Within Months 1.72557E+14 291 5.92978E+11

Total 1.77135E+14 293

1800000

1600000

1400000

1200000

1000000

800000

600000

400000

200000

0

RM

Time of the Month Effect on Deposits

Beginning Middle End

Time of Month

FIGURE 7


15/17

in commercial banks withdrawals and deposits, this study has high-lighted the inadequacy of simple rule of thumb blanket vault cash manage-ment policies.

REFERENCES

1. Allen S. (1998), How Closely do Banks Manage Vault cash? Federal ReserveBank of St. Louis, Vol. 80, Issue 4, July/August, pp. 43-54.

2. Ashford, Duncan (2000), Optimising Cash and Liquidity Management in AsiaPacific, HSBC Global Payment and Cash Management, January 28.

3. BNM (1995), Money and Banking in Malaysia, Bank Negara Malaysia, KualaLumpur.

4. Budhiraja, Sudeep & Abdul Raof Latiff (1998), IT Evoluation in Banking, News

Straits Time Press (Malaysia) Berhad, Malaysia Business, Banking, January 1, pp. 20.5. Daniel, E. and Baxter, B. (1999), Cash Handling Strategies for Banks in Asia.Bankers Journal Malaysia, No. 116, January, pp. 47-52.

6. De La Rue (1999), Cash Handling Costs Asia-Pacific Millions, Research Re-veals, M2 PRESSWIRE, Coventry, February 15, available at http://proquest.umi.com/pqdweb?TS=9529...Fmt=3%Sid=9&Idx=7&Deli=1&RQT=309&Dtp

7. Glaze, Gordon (1999), Cash Demand and Liquidity Risk Issues and Year 2000,Readiness Cash Demand and Liquidity Risk Issues and Year 2000 Readiness, available athttp://ww.aba.com/aba/static/GR_cashY2K.html

8. GMT Press, GMTs CashMaster Increases Profitability by Reducing Cash Bal-ances, available athttp://gmtcorp.com/subpages/p060297.html

9. Hamid, Hamisah (1998), It Takes 21 sen to process every RM1 Cash Transac-tion, News Straits Times Press (Malaysia) Berhad, Business Times (Malaysia), August12, pp.18.

10. Levine, David M., Berenson, Mark L. and Stephan, David (1999), Statistics forManagers using Microsoft Excel. Second Edition, Prentice Hall, United States, pp.604-649.

11. Scarf, Herbert E. (1960), The Optimality of (s,S) policies for the Dynamic Inven-tory Problem. Proceedings of the First Staford Symposium on Mathematical Methods inthe Social sciences, Stanford University Press, United States.

12. Suganthi, Balachandher and Balachandran (2001), Internet Banking Patronage:An Empirical Investigation of Malaysia, Journal of Internet Banking and Commerce,May 2001, vol. 6, no. 1.

13. Tan, Clarissa (1998), Cost of Handling Cash in Singapore Tops $650m A Year,Business Times (Singapore), June 25.

14. Wrase, Jeffrey M. (1998), IstheFedbeing swept out of (Monetary) control?Business Review-Federal Reserve Bank of Philadelphia, November/December, available athttp://proquest.umi.com/pqdweb?TS=9528...Fmt=4&Sid=1&Idx=3&Deli=1&RQT=309&

Dtp

http://proquest.umi/http://ww.aba.com/aba/static/GR_cashY2K.htmlhttp://gmtcorp.com/subpages/p060297.htmlhttp://proquest.umi.com/pqdweb?TS=9528...Fmt=4&Sid=1&Idx=3&Deli=1&RQT=309&http://proquest.umi.com/pqdweb?TS=9528...Fmt=4&Sid=1&Idx=3&Deli=1&RQT=309&http://gmtcorp.com/subpages/p060297.htmlhttp://ww.aba.com/aba/static/GR_cashY2K.htmlhttp://proquest.umi/


16/17


17/17

cash management of malaysian banks_ k. g. balachander and santha

Documents