whole 1

Report

Date of Submission: 21-05-2012

“The Polyurethane Company-Cash and Marketable Securities: The Miller-Orr Model”

Working Capital ManagementCourse Code: Fin- 4208

Date of Submission: 21-05-2012

Jagannath University, Dhaka

→“The Polyurethane Company-Cash and Marketable Securities: The Miller-Orr Model” 2

“The Polyurethane Company-Cash and Marketable Securities: The Miller-Orr Model”

Prepared To: Md. Monzur Morshed BhuiyaAssociate ProfessorDept. of FinanceJagannath UniversityDhaka-1100

Prepared by:Group No.: 04BBA-Fin, 2nd Batch4th year, 2nd SemesterDepartment of FinanceJagannath UniversityDhaka

Succession of Learners

Name Roll CGPAWork Load (%)

Case write

up (6)

Presenta-tion(4)

Total Obtained

Mohammad Fayez Uddin 07882684 3.82 13

Md. Maruf Hasan 07882758 3.60 12

Sharmin Mannan 07882722 3.80 12

Nur-A-Afsana 07882701 3.88 13

Md. Abdul Mumen 07882687 3.54 13

Mohammad Alamgir Hossain 07882742 3.52 13

Bubli Rani Saha 07882754 3.59 12

Rifat Islam 07882682 3.79 12


Letter of Transmittal

May 21, 2012Md. Monzur Morshed BhuiyaAssociate ProfessorDepartment of FinanceJagannath University, Dhaka

Subject: Submission of report on the case study “The Polyurethane Company-Cash and Marketable Securities: The Miller-Orr Model”.

Dear Sir,Here is the report on the case study “The Polyurethane Company-Cash and Marketable Securities: The Miller-Orr Model” that you assigned to prepare last Marsh 20, 2012.The report is prepared on the basis of primary & secondary data collected from different sources such as book, Internet etc. Experts of different areas of cash management and researching department have been consulted for their opinions by publishing their journal. Data are classified and tabulated with objective judgment. Findings and conclusion are only subjective judgment of ours.

We sincerely hope that the report and the recommendations would help you making effective decisions. We are truly appreciate this assignment and enjoyed it very much.

Sincerely yours,

…………………………Mohammad Fayez UddinOn behalf ofGroup-04B.B.A-Fin-2nd Batch4th Year, 2nd SemesterDepartment of Finance.Jagannath University, Dhaka


Acknowledgement

At the beginning we desire to express our deepest sense of gratitude of almighty Allah. With profound regard we gratefully acknowledge our respected course teacher Md. Monzur Morshed Bhuiya, Associate Professor, Department of Finance, Jagannath University, Dhaka for his generous help and day to day suggestions during the preparation of this report.

We like to give thanks especially to our friends and many individuals, for their enthusiastic

encouragements and helps during the preparation of this report us by sharing ideas regarding

this subject and for their assistance in typing and proof reading this manuscript.


Executive Summary

The Polyurethane is a largest division of a middle sized chemical company and primarily manufacturer of the chemicals use in the production of Polyurethane form. Polyurethane is made by mixing chemicals together under controlled conditions polyurethane. The controlled conditions of Polyurethane chemicals are used in exact amount to determine the consistency of the Polyurethane foam; hard or soft, with large of small bubbles is known as controlled conditions. The uses of different kinds of Polyurethane foam for insulating materials; shoe soles. The urethane also sometimes is used in paint and rubber production. However the most common use of urethane foam is in cushions and mattresses. Mr. Thomas Charles is a manager who performs many functions in the polyurethane company like, Assisting in credit management, Managing accounts receivables, Reporting cash, Forecasting cash, Assisting the accounting department in computing break even points, Performing accounting functions, Helping the firm’s planning department with statistical analysis. Mr. Charles had fallen in problem with cash flow forecasting so, after getting suggestions from his friend he applied a new approach which is called Miller Orr Model for managing daily cash flows. The Miller and Orr model of cash management is one of the various cash management models in operation. It is an important cash management model as well. It helps the present day companies to manage their cash while taking into consideration the fluctuations in daily cash flow. He then decided to test the applicability of this new approach through non-normality testing and correlation coefficient then he found the new approach can be an appropriate for his firm to manage daily cash flows.


Table of ContentsName Page no.

Letter of Transmittal 04Acknowledgement 05

Executive Summary 06

Chapter-01

Introduction1.1 1.1: Introduction 09

1.2: Rationale of the Study 091.3: Objective of the Report 091.4: Scope of the Study 101.5: Methodology of the Study 101.6: Limitations of the Study 10

Questions and Answers

Chapter-02

Company2.1: What type of company, the Polyurethane is? 112.2: What is size of polyurethane company? 112.3: What are the differences between polyurethane company, the middle sized company to large sized company?

11

Chapter-03

Management of Company3.1: What is educational background of Mr. Thomas Charles? 113.2: What are the functions that Mr. Thomas Charles performs in the polyurethane company?

11

3.3: What was the remark of Charles after moving to the new building of finance department?

12

Chapter-04

Company’s Products and Services4.1: How polyurethane is made? 124.2: What are the controlled conditions of Polyurethane? 124.3: What are the uses of different kinds of Polyurethane foams? 124.4: What is the most common use of urethane foams? 124.5: How are mattresses produced? 12-134.6: What the amount was of sold of Polyurethane in the early 1980s?

13

Chapter 05

Cash flows & Securities management: Old approaches5.1: What were the positive and negative sides of Charles cash forecasting of the firm?

13

5.2: What were the decisions of Mr. Charles for daily and monthly cash forecasting?

13-14

5.3: How Mr. Charles examined the daily and monthly cash forecasting?

14-15

5.4: Whom Mr. Charles told about his cash flow problem and what was the solution given by him?

15

Chapter-06

Cash flows & Securities management: New approach6.1: What is the new approach? 166.2: Enumerate The Miller and Orr Model? 16-176.3: State the assumptions of Miller Orr Model? 17

Testing new approach using statistical tools


Chapter-07

7.1: How Mr. Charles tested the Miller Orr Model as an Appropriate Strategy?

i) Calculating Sample mean and standard deviationii) Test for non-normality for net cash flowiii) Calculation for first order correlation coefficient for net

cash flows

18-22

Chapter-08Application of new approach

8.1: The application of Miller Orr Model 23-24Recommendation 24

conclusion 25Reference 26Appendix 27-31

Contents of Table

Table no. Name Page no.

7.1 The Polyurethane Company changes in cleared balances 18-19

7.2 Calculating Sample mean and standard deviation 19-20

7.3 Test for non-normality for net cash flow 20

7.4 Calculation for first order correlation coefficient for net cash flows 21-22

A-1 Standard Normal Distribution 27-29

A-2 The Chi-Square Distribution 29-30

A-3 the Student t Distribution 30-31

Contents of Graphs

Graph no.

Name Page no.

5.1 Cash flow patterns 14

5.2 Changes in Cleared balances 15

6.1 The Miller-Orr Model 17

8.1 Cash and marketable securities management- Miller Orr Model 24

Chapter-01: Introduction


1.1 IntroductionThe Miller Orr model (Miller and Orr, 1966) for cash management is the

improvement on Baumol’s economic order quality model (EOQ) methodology in significant ways. Miller and Orr start with the assumption that the firm has two forms of assets: cash and marketable securities. The model allows for cash balance movement in both positive and negative directions and its can state the optimal cash balance as a range of values, rather than a single point estimate. This makes the model especially useful for firms with unpredictable day to day sash inflows and outflows. While the Miller-Orr Model is an important over the EOQ model, it too makes some assumptions. The most important is that cash flows are random, which is many cases is not completely valid, Under certain circumstances and particular times of the year consecutive periods cash flows may be dependent upon one another, the volatility of net cash flows may sharply increase, Orr cash balances may demonstrate a definite trend. The frequency and extent of these events will affect the Miller-Orr model’s effectiveness. Actual tests using daily cash flows for various firms indicate that the model minimized cash holding costs as wells as or better than the intuitive decisions of these firm’s financial managers. However, other studies have shown than simple rules of thumb have performed just as well. Still, the Miller Orr Model is valuable because of the insight to offers concerning the forces that influence a firm’s optimal cash balance.

1.2 Rationale of the study

The report is assigned by our course teacher Md. Monzur Morshed Bhuiya as a part of our Working Capital Management. The topic of the report is “The Polyurethane Company-Cash and Marketable Securities: The Miller-Orr Model”. By conducting this study we can enhance our knowledge and skill to apply various research methods in professional life or higher educational life. The report has given us a chance to raise our quality in developing research instrument and its applications. By doing so, we can boost our acceptability in job market and develop our real life knowledge.

1.3 Objective of Our Study

The report has the following objectives:1. To understand inherent matters of Miller Orr Model. 2. To understand the impact of Cash flows of a firm.3. To show how the model works.4. To know how it affects the firms decisions.5. To be meaningful, every work must have to formulate the objectives of the study.

1.4 Scope


There were huge scopes to work in the arena of the case study. Considering the deadline, the scope and expose of the paper has been wide-ganging. The study on “The Polyurethane Company-Cash and Marketable Securities: The Miller-Orr Model” has covered overall cash flows activities of a firm and these are related to the decision has been shown in this report. By preparing this report it becomes more understandable about the practices and the activities of the cash flow and marketable securities and the importance of its through proper channel.

1.5 Methodology

We have collected all the required data for this paper from different primary & secondary sources. The primary data are collected by conducting cash flow and marketable security manager through face to face interview and the secondary data are collected from Internet, Books & Journals etc. The data are presented in The Text and table format adding with graphs.

1.6 Limitations of the Study

Although we have completed our studies on “The Polyurethane Company-Cash and Marketable Securities: The Miller-Orr Model”, we have faced some problems or limitations during our studies. These limitations are given below:

As we collected our data from secondary sources so there may be chance of mistake. Different scientific terms were difficult to understand. The respondents were limited (respondents or samples) in terms of size and

composition. Because of time shortage many related area cannot be focused in depth. The data collection was restricted only within the cash study, which may fail to

represent the actual scenario of cash and marketable security management through Miller Orr Model.

If we were more efficient then we would provide more data and better research. Unknown errors that occur in this paper are also our limitation.

Chapter: 02- Company


Question-2.1: What type of company, the Polyurethane is?Answer

The largest division of a middle sized chemical company and primarily manufacturer of the chemicals use in the production of Polyurethane form.

Question-2.2: What is size of polyurethane company?Answer

The largest division of a middle sized chemical company

Question-2.3: What are the differences between polyurethane company, the middle sized company to large sixed company?Answer

i) The managers of polyurethane company wear many hats but large sized company managers don’t.

ii) The managers do several jobs on Polyurethane Company but these works are performed by specialist in large sized company.

Chapter: 03 – Management of Company

Question-3.1: What is educational background of Mr. Thomas Charles?Answer

He completed undergraduate degree and a MBA from a large land granted university.

Question-3.2: What are the functions that Mr. Thomas Charles performs in the polyurethane company?Answer

i) Assisting in credit managementii) Managing accounts receivables iii) Reporting cashiv) Forecasting cashv) Assisting the accounting department in computing break even pointsvi) Performing accounting functionsvii) Helping the firm’s planning department with statistical analysis.

Question-3.3: What was the remark of Charles after moving to the new building of finance department?


AnswerHe remarked, “I hope my new office isn’t near the boiler; they will have me shoveling

coal too!”

Chapter-04: Company’s Products and Services

Question-4.1: How is polyurethane is made?Answer

By mixing chemicals together under controlled conditions polyurethane is made.

Question-4.2: What are the controlled conditions of Polyurethane?Answer

Chemicals are used in exact amount to determine the consistency of the Polyurethane foam; hard or soft, with large of small bubbles is known as controlled conditions.

Question-4.3: What are the uses of different kinds of Polyurethane foams?

AnswerHard, the large bubbles urethane foams are used I for insulating materials; soften, the

small bubbled foams are used in shoe soles. The urethane also sometimes is used in paint and rubber production. However the most common sue of urethane foam is in cushions and mattresses.

Question-4.4: What is the most common use of urethane foams?

AnswerThe most common use of urethane is in cushions and mattresses.

Question-4.5: How are mattresses produced?

Answer 1 st step

80/20 toluene diisocyanate, the largest selling urethane chemical (80/20 indicates the mix of monomers) is mixed with another chemicals and extruded into long, rectangular ‘buns’2 nd step and finally

These buns are then cut into mattresses sized by means of a hot wire system. The foam produced for this application is large bubbled and soft.


Question-4.6: What the amount was of sold of Polyurethane in the early 1980s?

AnswerIn the early 1980s the Polyurethane Company sold about $200 millions of 80/20

toluene diisocyanate and similar chemicals annually.

Chapter -05: Cash flows & Securities management: Old approach

Question-5.1: What were the positive and negative sides of Charles cash forecasting of the form?

AnswerPositivei) His cash forecasting was increasingly important for the company.

Negativei) The polyurethane company was a largest division firm.ii) His forecasts were critical to the process of planning short term investments

and financings.iii) He was unhappy with the accuracy of his forecasts of daily cash flows.iv) The manager might to alter investment of borrowing plan.v) The alternation was inconvenientvi) Alternation was expensive

Question-5.2: What were the decisions of Mr. Charles for daily and monthly cash forecasting?

AnswerMr. Charles found the inaccuracy of the daily forecasts to be quite perplexing; while

the company sales were somewhat seasonal, by using appropriate estimates of firm’s collections rates, he could make fairly accurate estimates of month cash flows. The difficulty was in forecasting daily flows within the monthly periods. Mr. Charles had been distributing the monthly forecasts to daily forecasts based on his feel for the patterns of cash flow within the month. His initial efforts to improve his daily forecasts centered on the statistical estimation of week of the month and day of the week effects. It was his plan to use these estimates in his cash flow distribution system.


Question-5.3: How Mr. Charles examined the daily and monthly cash forecasting?

Answer

FirstHe first examined the firm’s cash flow pattern near the tenth and twenty-fifth of a month. He knew that, in the past, some customers had tended to concentrate their payments around these dated. However, he found no statistically significant differences between the cash flow patterns around these dates and other times during the month.

Graph: 5.1

4 5 6 7 8 11 12 13 14 15 18 19 20 21 22 25 26 27 28 29

-600

-400

-200

0

200

400

600

Cash flow patterns (monthly)

Cash flow patterns

SecondHe then tested cash flow patterns for days early in the week versus those later in the week. He had suspected that there would be heavier cleared cash flows at some point during the week since the firm’s bank credited checks received in the mail on Saturday to Monday to the company’s account on Monday. Again, he found no statistically significant differences in cleared balances.

Graph: 5.2


4 5 6 7 8 11 12 13 14 15 18 19 20 21 22 25 26 27 28 29

-445

-137

376

-34

445

-479

274

-96

294

501

239

34171

308

-342

-205-103 -117

616

-137

Changes in cash flows (Weekly)Changes in Cleared balances

Finally

He ran regressions looking for daily and weekly forecasting tools, but the explanatory power of these regressions was minimal.

Question-5.4: Whom Mr. Charles told about his cash flow problem and what was the solution given by him?

AnswerMr. Charles told the problem to his friend and golfing partner, David Feldstein.

“Tom,” Mr. Feldstein had said, “has it occurred to you that there may be no time pattern in these net cash flows at all? If there is not really a time pattern, all this churning of cash and investments on the basis of your daily forecasts is counterproductive. May be what you need is a new approach to the daily cash management problem.”

Chapter-06: Cash flows & Securities management: New approach→“The Polyurethane Company-Cash and Marketable Securities: The Miller-Orr Model” 15

Question-6.1: What is the new approach?

AnswerThe Miller and Orr Model is the new approach.

Question-6.2: Enumerate The Miller and Orr Model?

AnswerThe Miller and Orr model of cash management is one of the various cash

management models in operation. It is an important cash management model as well. It helps the present day companies to manage their cash while taking into consideration the fluctuations in daily cash flow.

As per the Miller and Orr model of cash management the companies let their cash balance move within two limits - the upper limit and the lower limit. The companies buy or sell the marketable securities only if the cash balance is equal to any one of these.

When the cash balances of a company touches the upper limit it purchases a certain number of saleable securities that helps them to come back to the desired level. If the cash balance of the company reaches the lower level then the company trades its saleable securities and gathers enough cash to fix the problem.

The Miller-Orr Model provides a formula for determining the optimum cash balance (Z), the point at which to sell securities to raise cash (lower limit L) and when to invest excess cash by buying securities and lowering cash holdings (upper limit H).

Depends on:

– transaction costs of buying or selling securities

– variability of daily cash (incorporates uncertainty)

– return on short-term investments

Graph: 6.1→“The Polyurethane Company-Cash and Marketable Securities: The Miller-Orr Model” 16

The Miller - Orr Model

Question-6.3: State the assumptions of Miller Orr Model?

Assumptions of Miller Orr Modeli) Firm has minimum required cash balance.ii) Cash flows are normally distributed.iii) The expected cash flow is zero.iv) There is no auto correlation in cash flows.v) The standard deviation of cash flows does not change over time.


Days of the Month

Lower Limit

Upper Limit

Z

Sell Securities

Buy SecuritiesH

L

Chapter-07: Testing new approach using statistical tools

Question-7.1: How Mr. Charles tested the Miller Orr Model as an Appropriate Strategy?

Answer

The Polyurethane Company changes in cleared balances (Cleared daily net cash flows)Month of January 1982(Business days only; Rounded thousands of dollars)

(Table: 7.1 )Date Day Change in cleared balances

4 1 -445

5 2 -137

6 3 376

7 4 -34

8 5 445

11 6 -479

12 7 274

13 8 -96

14 9 294

15 10 501

18 11 239

19 12 34

20 13 171

21 14 308

22 15 -342

25 16 -205


26 17 -103

27 18 -117

28 19 616

29 20 -137

26 17 -103

27 18 -117

28 19 616

29 20 -137

Calculating Sample mean and standard deviation (Table 7.2 in thousand dollars)

Date Day X X-X (X−X )2

4 1 -445 -445-58.15 (-445-58.15)2 =253160

5 2 -137 -137-58.15 (-137-58.15) 2 =58084

6 3 376 376-58.15 (376-58.15) 2 =101029

7 4 -34 -34-58.15 (-34-58.15) 2 =8492

8 5 445 445-58.15 (445-58.15) 2 =149653

11 6 -479 -479-58.15 (-479-58.15) 2 =288530

12 7 274 274-58.15 (274-58.15) 2 =46591

13 8 -96 -96-58.15 (-96-58.15) 2 =23,762

14 9 294 294-58.15 (294-58.15) 2 =55,625

15 10 501 501-58.15 (501-58.15) 2 =196,116

18 11 239 239-58.15 (239-58.15) 2 =32,707

19 12 34 34-58.15 (34-58.15) 2 =583

20 13 171 171-58.15 (171-58.15) 2 =12,735

21 14 308 308-58.15 (308-58.15) 2 =62,425

22 15 -342 -342-58.15 (-342-58.15) 2 =160,120

25 16 -205 -205-58.15 (-205-58.15) 2 =69,248


26 17 -103 -103-58.15 (-103-58.15) 2 =25,969

27 18 -117 -117-58.15 (-117-58.15) 2 =30,677

28 19 616 616-58.15 (616-58.15) 2 =311,197

29 20 -137 -137-58.15 (-137-58.15) 2 =38,084

∑X =1163

∑(X−X)2 =924,787

Sample mean X= ∑XN

= 116320

= 58.15

Standard deviation σ = √ 92478720

= 310.22

Upper cutoff of

range

Z for upper cutoff

Probability for upper

cutoff

Probability for range

Expected number of

range

Actual number in range

Cont. to chi-

square

-400 -1.29 0.09853 0.09853 0.9853 1 0.239

-100 -0.32 0.3745 0.27597 2.756 3 0.0238

100 0.32 0.6255 0.251 2.51 2 0.4064

350 1.13 0.8708 0.2453 2.453 2 0.3702

600 1.93 0.9732 0.1024 1.024 1 0.2681

+infinity +Infinity 1 0.0268 0.268 1 0.20008

1 10 10 1.5083


Test for non-normality for net cash flow (Table: 7.3 in thousand dollars)

Total chi-squared statistic measures the difference between the actual and expected frequencies, and the distribution of this statistic is known. The number of degrees of freedom is the number of ranges minus three, so in this case the number of degrees of freedom is 3. From the table of critical chi-square values, at the 90 percent confidence level with three degree of freedom the chi square of greater than 6.25 is required to accept the hypothesis. Since the calculation chi-square statistic is 1.5083, the hypothesis of non-normality is not supported at the 90 percent confidence level.

Calculation for first order correlation coefficient for net cash flows (Table 7.4 in thousand dollars)

DayDay’s

cleared net cash flow

Prior day’s cleared net cash flow

Day’s squared

deviations from zero

Prior day’s squared deviation from zero

Product of deviations from zero

1 -445

2 -137 -445 18,769 198,025 60,965

3 376 -137 1,41,376 18,769 -51,512

4 -34 376 1156 141,376 -12,784

5 445 -34 1,98,025 1156 -15,130

6 -479 445 2,29,441 198,025 -213,155

7 274 -479 75,076 229,441 -131,246

8 -96 274 9,216 75,076 -26,304

9 294 -96 86,436 9,216 -28,224

10 501 294 2,51,001 86,436 147,294

11 239 501 57,121 251,001 119,739

12 34 239 1,156 57,121 8,126

13 171 34 29,241 1156 5,814

14 308 171 94,864 29,241 52,668


15 -342 308 1,16,964 94,864 -105,336

16 -205 -342 42,025 116,964 70,110

17 -103 -205 10,609 42,025 21,115

18 -117 -103 13,689 10,609 12,051

19 616 -117 3,79,456 13,689 -72,072

20 -137 616 18,769 379,456 -84,392

Sums of squared deviations 17,74,390 19,53,646

Sum of product of deviations -242,273

Correlation coefficient -0.130

Correlation Coefficient=

∑All t

(NCF t )(NCF t−1)

(SSNCFt SSNCF t−1)

12

= −242273

(1774390∗1953646)12

=- 0.130

The calculated value for the correlation coefficient is – 0.130. To test whether this is significantly different from zero, we use a student’s t-test. The formula for converting a correlation coefficient into a t-test is:

Student’s t-test = r [ (N−2)1−r2 ]

12

= −0.130 [ (20−2 )1−(−0.130 )2 ]

12

= - 488

The assumptions of the Miller Orr Model are violated if the correlation coefficient is significantly deferent from zero in a positive or negative direction, so a two-tailed test is required. With 17 degree of freedom, 90 percent of the student’s distribution is contained between t statistics of -1.740 and +1.740. Since the calculated statistics is within this range,


the hypothesis of significant autocorrelation is not accepted at the 90 percent confidence level. Since the data ate neither significantly non-normal or significantly auto correlated, the firm might consider applying the Miller Orr Model as part of its management strategy for cash and temporary investments.

Chapter-08: Application of New Approach

Question-8.1: The followings problem is related to Miller Orr Model

ABC co. can earn 0.05% per day from its investment. Based on historic data, the firm has estimated the SD of its. Daily cash flows at $50000 and its transaction csots of investment and disinvestment $40. It wants to maintain a miimum cash balance of $100000 at all times. From this information you are required to calculate:

i) R statistic valueii) Return point in dollariii) UCL (Upper control limit)iv) Formulate the optimal strategy

Solution:

i) R = ( 3av4 i

)13

= (3∗40∗(50,000)2

4∗(.0005))

13

= $53,133

ii) Return point in dollar= R+L = 53,133+1, 00,000 = $1, 53,133


iii) UCL = 3R+L = 3(53,133) + 1, 00,000 = $2, 59,399

iv) Formulate the optimal strategy

3R + L = $2, 59,399 R + L = $1, 53,133 L = $1, 00,000

Decision

So, more than the lower limit then the investment and less than lower limit then no investment is made.

Graph-8.1

0

50000

100000

150000

200000

250000

300000

Cash and marketable securities management- Miller Orr Model

LCLPeturn pointUCL

Recommendation

Mr. Charles had to face frequent problem concerning cash inflows, outflows and decision making due to the seasonal sales and cash flows. He forecasted cash inflows and outflows monthly, weekly and daily but he failed to gratify the firms’ decisions. After getting suggestions from his friends to execute the new approach called the Miller-Orr model, he tested it using different statistical tools. Finally he implemented the new approach for his firm’s daily cash investment and disinvestment. Although Miller-Orr model was decisive


importance after developing Stone model its application had been abridged in some extent. But it’s yet widely used in numerous firms. The reasons for such alluring are allows for net cash flows occurring in a random fashion, transfers can take place at any time and are instantaneous with a fixed transfer cost, produces control limits which can be used as basis for balance management. The management may face some difficulties with Miller-Orr model like difficult to calculate, monitoring needs to be continuous for the organization to benefit. The difficulties which can be removed and new approach can be a strategic tool for the firm.

Conclusion

This model’s practical usefulness is limited by the assumptions it rests on. For example, few managers would agree that cash inflows and outflows are entirely unpredictable, as Miller and Orr assume. The manager of a toy store knows that there will be substantial cash inflows around the holidays. Financial managers know when dividends will be paid and when income taxes will be due. We described how firms forecast cash inflows and outflows and how they arrange short-term investment and financing decisions to supply cash when needed and put cash to work earning interest when it is not needed. This kind of short-term financial plan is usually designed to produce a cash balance that is stable at some lower limit. But there are always fluctuations that financial managers cannot plan for, certainly not on a day-to-day basis. You can think of the Miller-Orr policies as responding to the cash inflows and outflows which cannot be predicted, or which are not worth predicting. Trying to predict all cash flows would chew up enormous amounts of management time. The Miller-Orr model has been tested on daily cash-flow data for several firms. It preformed as well as better than the intuitive policies followed by these firms’ cash managers. However, the model was not an unqualified success; in particular, simple rules of thumb seem to perform just as well. The Miller-Orr model may improve our understanding of the problem of cash management, but it probably will not yield substantial savings compared with policies based on a manager’s judgment; providing of course that the manager understands the trade-offs we have discussed.


Reference

Books

i) Miller, R. H., and D. Orr. “A Model of Demand for Money by Firms,” Quarlerly Journal of Economics (August 1966), pp. 413-435.

ii) Stone, Bernell, “The Use of Forecasts and Smoothing in Control-Limit Models for Cash Management,” Financial Management (Spring 1972), pp. 72-84.

iii) Emery, Gary, “Some Empirical Evidence on the Properties of Daily Cash Flow,” Financial Management (Spring 1981), pp. 21-28.

Websites

i) www.investorwords.comii) www.en.wikipedia.orgiii) www.abinomics.comiv) www.invetopedia.com


Appendix

Terminologies

Polyurethane A type of plastic used for making things such as paint and rubber.

Toluene diisocyanate (TDI) It is an organic compound. It exists in two isomers, 2, 4-TDI and 2, 6-TDI. 2, 4-TDI is

produced in the pure state, but TDI is often marketed as 80/20 and 65/35 mixtures of the 2, 4 and 2, 6 isomers respectively. It is produced on a large scale.

Exothermic

An exothermic reaction is a chemical reaction that releases energy in the form of light or heat. It is the opposite of an endothermic reaction.

Expressed in a chemical equation: reactants → products + energy

Monomer

Monomer (from Greek mono "one"and meros "part") is a molecule that may bind chemically to other molecules to form a polymer. The term "mo nomeric protein" may also be used to describe one of the proteins making up a multiprotein complex. The most common natural monomer is glucose, which is linked by glycosidic bonds into polymers such as cellulose and starch, and is over 77% of the mass of all plant matter. Most often the term monomer refers to the organic molecules which form synthetic polymers, such as, for example, vinyl chloride, which is used to produce the polymer polyvinyl chloride (PVC).


http://en.wikipedia.org/wiki/Polyvinyl_chloride

http://en.wikipedia.org/wiki/Vinyl_chloride

http://en.wikipedia.org/wiki/Synthetic_polymers

http://en.wikipedia.org/wiki/Starch

http://en.wikipedia.org/wiki/Cellulose

http://en.wikipedia.org/wiki/Glycosidic_bonds

http://en.wikipedia.org/wiki/Glucose

http://en.wikipedia.org/wiki/Multiprotein_complex

http://en.wikipedia.org/wiki/Polymer

http://en.wikipedia.org/wiki/Chemical_bonding

http://en.wikipedia.org/wiki/Molecule

Table of the Standard Normal Distribution (A-1)

z .00 .01 .02 .03 .04 .05 .06 .07 .08 .09

.0 .5000 .5040 .5080 .5120 .5160 .5190 .5239 .5279 .5319 .5359

.1 .5398 .5438 .5478 .5517 .5557 .5596 .5636 .5675 .5714 .5753

.2 .5793 .5832 .5871 .5910 .5948 .5987 .6026 .6064 .6103 .6141

.3 .6179 .6217 .6255 .6293 .6331 .6368 .6406 .6443 .6480 .6517

.4 .6554 .6591 .6628 .6664 .6700 .6736 .6772 .6808 .6844 6879

.5 .6915 .6950 .6985 .7019 .7054 .7088 .7123 .7157 .7190 .7224

.6 .7257 .7291 .7324 .7357 .7389 .7422 .7454 .7486 .7157 .7549

.7 .7580 .7611 .7642 .7673 .7704 .7734 .7764 .7794 .7823 .7852

.8 .7881 .7910 .7939 .7969 .7995 .8023 .8051 .8078 .8106 .8133

.9 .8159 .8186 .8212 .8238 .8264 .8289 .8315 .8340 .8365 .8389

1.0 .8413 .8438 .8461 .8485 .8508 .8513 .8554 .8577 .8529 .8621

1.1 .8643 .8665 .8686 .8708 .8729 .8749 .8770 .8790 .8810 .8830

1.2 .8849 .8869 .8888 .8907 .8925 .8944 .8962 .8980 .8997 .9015

1.3 .9032 .9049 .9066 .9082 .9099 .9115 .9131 .9147 .9162 .9177

1.4 .9192 .9207 .9222 .9236 .9215 .9265 .9279 .9292 .9306 .9319

1.5 .9332 .9345 .9357 .9370 .9382 .9394 .9406 .9418 .9492 .9441

1.6 .9452 .9463 .9474 .9484 .9495 .9505 .9515 .9525 .9535 .9545

1.7 .9554 .9564 .9573 .9582 .9591 .9599 .9608 .9616 .9625 .9633

1.8 .9641 .9649 .9656 .9664 .9671 .9678 .9686 .9693 .9699 .9706

1.9 .9713 .9719 .9726 .9732 .9738 .9744 .9750 .9756 .9761 .9767

2.0 .9772 .9778 .9783 .9788 .9793 .9798 .9803 .9808 .9812 .9817

2.1 .9821 .9826 .9830 .9834 .9838 .9842 .9846 .9850 .9854 .9857

2.2 .9861 .9864 .9868 .9871 .9875 .9878 .9881 .9884 .9887 .9890

2.3 .9893 .9896 .9898 .9901 .9904 .9906 .9909 .9911 .9913 .9916


2.4 .9918 .9920 .9922 .9925 .9927 .9929 .9931 .9932 .9934 .9936

2.5 .9938 .9940 .9941 .9943 .9945 .9946 .9948 .9949 .9951 .9952

2.6 .9953 .9955 .9956 .9957 .9959 .9960 .9961 .9962 .9963 .9964

2.7 .9965 .9966 .9967 .9968 .9969 .9970 .9971 .9972 .9973 .9974

2.8 .9974 .9975 .9976 .9977 .9977 .9978 .9979 .9979 .9980 .9981

2.9 .9981 .9982 .9982 .9983 .9984 .9984 .9985 .9985 .9986 .9986

3.0 .9987 .9987 .9987 .9988 .9988 .9989 .9989 .9989 .9990 .9990

3.1 .9990 .9991 .9991 .9991 .9992 .9992 .9992 .9992 .9993 .9993

3.2 .9993 .9993 .9994 .9994 .9994 .9994 .9994 .9995 .9995 .9995

3.3 .9995 .9995 .9995 .9996 .9996 .9996 .9996 .9996 .9996 .9997

3.4 .9997 .9997 .9997 .9997 .9997 .9997 .9997 .9997 .9997 .9998

Table of the Chi-Square Distribution (A-2)

df \p .005 .01 .025 .05 .10 .90 .95 .975 .99 .995

1 .00004 .00016 .00098 .0039 .0158 2.71 3.84 5.02 6.63 7.88

2 .0100 .0201 .0506 .1026 .2107 4.61 5.99 7.38 9.21 10.60

3 .0717 .115 .216 .352 .584 6.25 7.81 9.35 11.34 12.84

4 .207 .297 .484 .711 1.064 7.78 9.49 11.14 13.28 14.86

5 .412 .554 .831 1.15 1.61 9.24 11.07 12.83 15.09 16.75

.6 .676 .872 1.24 1.64 2.20 10.64 12.59 14.45 16.81 18.55

7 .989 1.24 1.69 2.17 2.83 12.02 14.07 16.01 18.48 20.28

8 1.34 1.65 2.18 2.73 3.49 13.36 15.51 17.53 20.09 21.96

9 1.73 2.09 2.70 3.33 4.17 14.68 16.92 19.02 21.67 23.59

10 2.16 2.56 3.25 3.94 4.87 15.99 18.31 20.48 23.21 25.19

11 2.60 3.05 3.82 4.57 5.58 17.28 19.68 21.92 24.73 26.76

12 3.07 3.57 4.40 5.23 6.30 18.55 21.03 23.34 26.22 28.30


13 3.57 4.11 5.01 5.89 7.04 19.81 22.36 24.74 27.69 29.82

14 4.07 4.66 5.63 6.57 7.79 21.06 23.68 26.12 29.14 31.32

15 4.6 5.23 6.26 7.26 8.55 22.31 25 27.49 30.58 32.80

16 5.14 5.81 6.91 7.96 9.31 23.54 26.30 28.85 32.00 34.27

18 6.26 7.01 8.23 9.39 10.86 25.99 28.87 31.53 34.81 37.16

20 7.43 8.26 9.59 10.85 12.44 28.41 31.41 34.17 37.57 40.00

24 9.89 10.86 12.40 13.85 15.66 33.20 36.42 39.36 42.98 45.56

30 13.79 14.95 16.79 18.49 20.60 40.26 43.77 46.98 50.89 53.67

40 20.71 22.16 24.43 26.51 29.05 51.81 55.76 59.34 63.69 66.77

60 35.53 37.48 40.48 43.19 46.46 74.40 79.08 83.30 88.38 91.95

120 83.85 86.92 91.58 95.70 100.62 140.23 146.57 152.21 158.95 163.64

Table of the Student t Distribution (A-3)

df \ p .60 .70 .80 .90 .95 .975 .99 .995

1 .325 .727 1.367 3.078 6.314 12.706 31.821 63.657

2 .289 .617 1.061 1.886 2.920 4.303 6.965 9.925

3 .277 .584 .978 1.638 2.353 3.182 4.541 5.841

4 .271 .569 .941 1.533 2.132 2.776 3.747 4.604

5 .267 .559 .920 1.476 2.015 2.571 3.365 4.032

6 .265 .553 .906 1.440 1.943 2.447 3.143 3.707

7 .263 .549 .896 1.415 1.895 2.365 2.998 3.499

8 .262 .546 .889 1.397 1.860 2.306 2.896 3.355

9 .261 .543 .883 1.383 1.833 2.262 2.821 3.250

10 .260 .542 .879 1.372 1.812 2.228 2.764 3.169

11 .260 .540 .876 1.363 1.796 2.201 2.718 3.106


12 .259 .539 .873 1.356 1.782 2.179 2.681 3.055

13 .259 .538 .870 1.350 1.771 2.160 2.650 3.012

14 .258 .537 .868 1.345 1.761 2.145 2.624 2.977

15 .258 .536 .866 1.341 1.753 2.131 2.602 2.947

16 .258 .535 .865 1.337 1.746 2.120 2.583 2.921

17 .257 .534 .863 1.333 1.740 2.110 2.567 2.898

18 .257 .534 .862 1.330 1.734 2.101 2.552 2.878

19 .257 .533 .861 1.328 1.729 2.093 2.539 2.861

20 .257 .533 .860 1.325 1.725 2.086 2.528 2.845

21 .257 .532 .859 1.323 1.721 2.080 2.518 2.831

22 .256 .532 .858 1.321 1.717 2.074 2.508 2.819

23 .256 .532 .858 1.319 1.714 2.069 2.500 2.807

24 .256 .531 .857 1.316 1.708 2.060 2.485 2.787

25 .256 .531 .856 1.316 1.708 2.060 2.485 2.787

26 .256 .531 .856 1.315 1.706 2.056 2.479 2.779

27 .256 .531 .855 1.314 1.703 2.052 2.473 2.771

28 .256 .530 .855 1.313 1.701 2.048 2.467 2.763

29 .256 .530 .854 1.310 1.697 2.042 2.457 2.750

30 .256 .530 .854 1.310 1.697 2.042 2.457 2.750

40 .255 .529 .851 1.303 1.684 2.021 2.423 2.704

60 .254 .527 .848 1.296 1.671 2.000 2.390 2.660

120 .254 .526 .845 1.289 1.658 1.980 2.358 2.617

infinity .253 .524 .842 1.282 1.645 1.960 2.326 2.576


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