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Historical Working Papers Cox School of Business
1-1-1982
Teaching a Financial Planning Language as the Principal Teaching a Financial Planning Language as the Principal
Computer Language for MBA's Computer Language for MBA's
Thomas E. Perkins Southern Methodist University
Paul Gray Southern Methodist University
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Southern Methodist University Dallas, Texas 75275
TEACHING A FINANCIAL PLANNING LANGUAGE AS THE PRINCIPAL COMPUTER LANGUAGE FOR MBA'S
Working Paper 82-301*
by
Thomas E. Perkins
and
Paul Gray
Thomas E. Perkins Assistant Professor of Management Science and Computers
Edwin L. Cox School of Business Southern Methodist University
Dallas, Texas 75275
Paul Gray Professor of Management Science and Computers
Edwin L. Cox School of Business Southern Methodist University
Dallas, Texas 75275
*This paper represents a draft of work in progress by the authors and is being sent to you for information and review. Responsibility for the contents rests solely with the authors. This working paper may not be reproduced or distributed without the written consent of the authors. Please address all correspondence to Thomas E. Perkins.
TEACHING A FINANCIAL PLANNING LANGUAGE AS THE PRINCIPAL COMPUTER LANGUAGE FOR MBA'S
Thomas E. Perkins and Paul Gray Edwin L. Cox School of Business
Southern Methodist University Dallas, TX 75275
ABSTRACT
This paper de,scribes t,~e experiences at SMU in using a financial planning
language ( specific$l.lX, the Interactive Financial Planning System (IFPS)) as ' · ~ ,-~
the pri~ry computer language t_aught to MBA's. The language was introduced to
extend the students' ability to solve business problems. Experiences with
teaching the language, the extent of its use, and the results of a student
survey are discussed.
INTRODUCTION
For a number of years, incoming MBA students at Southern Methodist Uni-
versity (SMU) had been taught to program in BASIC and to use a sophisticated
calculator (such as the TI-58) as part of their first semester introductory
management science and computer course. Part of the reason for the use of
BASIC was that the SMU MBA program attracts large numbers of students with hu-
manities and social science backgrounds. When a new chairman was brought in
from outside the University at the end of the 1970's, he took the view that it
was no longer appropriate to give graduate credit for learning BASIC at a time
'\ when many were learning the language in high school. The problem then was to
redesign the computer portion of the course in a meaningful way.
It was, of course, clear that MBA students would be living in a computer
environment after graduation and that they had to be prepared for it. It was
also clear that the students should know more about computing than merely
2
putting data into canned programs. Teaching a more complex language than
BASIC (e.g., Pascal) would accomplish little since it was not our objective
nor that of the students to create professional programmers. What we wanted
was a way of getting students to use the computer routinely as a natural means
of solving complex problems and aiding in decision making.
The solution to our problem was to introduce a financial planning lan
guage into the introductory course and to propagate its use throughout the MBA
program. In the following sections we describe the language chosen (the In
teractive Financial Planning System, known by the acronym IFPS), the pedagogi
cal advantages of using such a language, how the language was introduced into
the curriculum, and the student acceptance of this approach.
WHAT IFPS IS
IFPS is a commercial software package designed by Execucom, an Austin,
Texas firm, as a modeling language for financial planning. The original goal
of the designers of IFPS was to provide a package that would allow firms to do
probabilistic risk analysis. However, as the package took form, it became
clear that its capabilities are much broader. The basic structure of IFPS is
shown in Figure 1. From the user's point of view, IFPS is a package that op
erates on stored files which are written in the modeling language. Each file
contains a collection of models, reports, and data files that can be re
trieved, changed and, in the case of models, solved under a variety of condi
tions. Models represent programs, whereas reports contain formats for print
out results. Datafiles can be called in the course of solving models.
Among the features of IFPS which are generally not available in conven
tional compiler languages are:
3
""' n&turll language. Otdlnary English names can be used with almost no
rur:r·fot:to'H•fon name lengths •
. . .... ',\' ' ' ;.,: ,'order d~il not matte-r. IFPS is a non-procedural language. Such lan-
guagu are no~ n•w (e.g., DYNAMO). The only restriction is that each variable
has to appea~ once on the left . side of a relation.
spread sheet format. The user perceives the internal world of IFPS as
consisting of a two-dimensiotlal mat:rix. The rows of this matrix are the vari-
ab les in the model; the columns represent the time pet"iods. IFPS places a
value in each cell to represent the value of the row variable during the col-
umn's time period.
forward and backward movement in time. Commands are available that
allow the use of values from previous and future time periods to be used in
the computation of values for individual cells.
"what if" capability. Built-in commands allow asking "what if" ques-
tions. These can be in the form of substituting alternative values or rela-
tions, sensitivity analysis, and goal seeking. The user thereby can quickly
explore the implications of decision alternatives.
simulation analysis. IFPS can be run in simulation mode, with vari-
ables being defined in terms of probability distributions. Thus, because of
its column structure, IFPS is also a fixed-time interval simulation language.
built-in functions. IFPS relieves the user of programming many of the
tedious, repetitive calculations of finance and statistics by containing
built-in functions for net present value, internal rate of return, deprecia-
tion, trend extrapolation, and many more.
Figure 2 shows a simple deterministic IFPS model and its solution. The
decision problem being modeled involves introduction of a new product which
will require an initial investment and will have certain variable costs
4
associated with it. Estimates of the market, the firm's market share, and the
growth of these two quantities drive the values in the model. The financial
functions express the measures of effectiveness. (The model shown in Figure 2
serves as the example used to introduce our students to IFPS.)
WHY IFPS WAS INTRODUCED
As stated in the introduction of this paper, we were seeking a way of
getting students, particularly computer-naive students, to use the computer
routinely as a natural means of solving problems and aiding in decision mak
ing. We therefore sought a language which:
1. is as close to the language of business as possible,
2. is easy for the student to learn quickly and to achieve succes in
rapidly,
3. allows students to see and understand the underlying assumptions of
their models, and
4. is likely to be related to the languages they would see after
graduation.
We will now discuss each of these points in turn.
1. The many financial planning languages developed commercially during
the 1970's were aimed at financial analysts and other corporate staff people.
~ese people were interested in having the computer essentially speak the same
language that they do. The designers of the successful financial planning
languages understood that the style, format, and conventions of compiler lan
guages such as BASIC introduced a psychological barrier to the use of the com
puter and had to be eliminated as much as possible. By making the planning
languages conform to English and to the spread-sheets familiar from accounting
practice, they gained rapid and widespread acceptance. Since, in our opinion,
5
a large portion of the business school experience is learning the language of
business, financial planning languages are a natural choice to meet the first
criterion.
2. From an instructional point of view, one of the primary attractions
of the financial planning languages is the simplicity of the underlying con
ceptual model. This is particularly true for the financial planning languages
that are non-procedural. Since the student can write down expressions one af
ter the other in the order in which they occur in their thinking process, the
models become easy to formulate and write. The simplicity of the language
makes it possible for the student to write and solve a meaningful model after
a single class period. Students, particularly computer-naive students, gain
success quickly and, as a result, develop faith in their own ability to cope
with the computer. The ability of students to learn the language quickly
means that more class time can be devoted to the principles of modeling and to
applications, thereby increasing the content of the course for the students.
3. Financial planning languages allow students to express their modeling
assumptions clearly and explcitly. By stating their assumptions about input
values and relationships in a clear form in the model, students can understand
what the assumptions are. As G. R. Wagner states, they then "own" their as
sumptions (Ref.l).
4. Having decided on a financial planning language, we next sought to
find one that was being widely used in business and was available to univer
sities at reasonable cost with good support. We chose IFPS because it has a
very large users group (over 1,000 organizations) and is available on a time
sharing network commercially, as well as being used in over 60 universities
around the country.
6
HOW IFPS WAS INTRODUCED
IFPS became operational on the SMU CDC-6600 in February 1980. Because of
our MBA program's trimester schedule we were then in the middle of the second
semester. We therefore used IFPS initially as a supplementary language. The
130 full-time MBA students (in 4 sections) were taking the production and op
erations management course. We developed a full 2-hour lecture that intro
duced the deterministic features of the language including the "what if" capa
bilities. This initial lecture centered around the example shown in Figure 2.
Students were told to put this model up on the computer and to run a series of
"what if" cases. Without exception, students were able to do this without
difficulty. This lecture and assignment are now standard and give students
the initial success they need with a new language.
We followed up this initial assignment with one requiring the students to
write their own model. Toward the end of the semester, we brought IFPS in
again, this time as a way of teaching simulation, which is part of the produc
tion and operations management course. IFPS proved to be an efficient way of
getting students to write their own simulation models. One of the examples
used is shown in Figure 3. A limitation, of course, is that the IFPS struc
ture is set up for fixed time intervals and hence IFPS is not suitable for
modeling next event situations such as are encountered in queuing situations.
(But then, one can't have everything.)
Our initial success led the engineering school to introduce the language
in their engineering economy courses. It also encouraged several students to
undertake projects involving the use of the language and/or to do their home
work in other courses, particularly finance, in IFPS.
In the Summer 1980 trimester we introduced IFPS into the first (introduc
tory) course for the Executive MBA class and then into the first course for
7
the full-time group in the fall of 1980. These students received reinforce-
ment in the language throughout the semester and then through the following
semester as they took production and operations management. At this point, as
in any changeover, we continued to teach both BASIC and IFPS. However, the
student response we were receiving to IFPS was so enthusiastic that starting
in the summer of 1981, we dropped BASIC and offered IFPS only. (As a backup,
students were offered a separate 8--hour BASIC short course. Some 55 students
paid $25 to take it.)
Of particular help was the creation of the SMU Student's Guide to IFPS.
The IFPS Tutorial supplied by the vendor is designed more for commercial users
than students. We felt we needed a simple "how-to" set of instructions that
begin with how to get on the computer and obtain IFPS, how to write a simple
-model, how to use the editor to correct mistakes, how to get out of IFPS, et:c.
This initial portion of the wide is essentially a life-support system for the
beginning student. The second part of the Guide deals with the useful fea-
tures of the systems (what if, built-in functions and subroutines, and Monte
Carlo simulation). The third and final part covers special features (includ-
ing data files, formatted reports and some specialized examples) designed for
the students who wante~ to go into the language more deeply. Figure 4 shows
the table of contents of the Student Guide. Figure 5 shows the extremely sim-
ple model that we used as an example throughout the Guide and ties the various
pieces together.
Today, all 350 MBA's (full-time, part-time, and executive) are taught on-
ly IFPS. At the undergraduate level, it is included in most management sci-
ence electives. In engineering it is used in all engineering economy and pro-
duction courses. During Fall 1981, IFPS was used for the first time in an
economics course.
8
In summary, we have moved from providing the language as a supplement to
relying on it as the primary way of teaching MBA students how to use the
computer.
SURVEY RESULTS
To evaluate the use of IFPS as a teaching tool, full-time and Executive
MBA students were surveyed at the end of the Fall 1981 semester. The ques
tionnaire used is shown in Figure 6. It asks students about such factors as
the student's background experience with computers prior to entering the MBA
program, the difficulty they experienced in learning IFPS, and how well they
had accepted IFPS as a tool which they could use outside the classes in which
it was taught.
Two groups of students were surveyed, each with a different amount of ex
posure to IFPS. The first group (117 students) consisted of four sections of
full-time and one section of part-time students who had just completed the
course in which the language was introduced. The second group (19 students)
were a section of Executive MBA students who had used the language for two
semesters, the same introductory course and the follow-on production course.
Student Background
As anticipated, the survey showed that an appreciable number of the stu
dents entering the MBA program have some experience with a computer language.
Of all the students surveyed, 60% indicated they had received instruction in
at least one computer language prior to the course in which IFPS was intro
duced. Of these, 81% had programmed in BASIC, 46% had programmed in FORTRAN,
and 11% had programmed in COBOL. Of the students with no prior programming
experience, over half (52%) recalled experiencing "computer anxiety" prior to
starting the program.
9
The !~~~~ng Experience ,. :,~!,'} ':~ i .
As· the students began to use IFPS as a modeling tool, 34% rated it "easy"
to l earn; 46% rated it "somewhat hard" to learn. Only 20% found it "hard" or
"very hard" to learn.
A common problem in the teaching of computer languages is that students
tend to become attached to the first language they learn and resist efforts to
introduce a second language. However, this phenomenon did not occur with the
students surveyed. Of the 99 students who were able to compare their learning
experience in IFPS with other languages, 6 7 indicated that IFPS was easier to
learn. TWenty-eight found the learning experiences comparable, and only 4
found IFPS more difficult to learn.
As we expected, students in the advanced section tended to perceive them-
selves as mc{re proficient in the language. In the group with two semesters of
experience with IFPS, 47% rated themselves as having "intermediate" language
skills; the remainder of this group rated themselves as a "novice. No stu-
dent in either group felt that their skills had yet reached the "expert" lev-
el. It is interesting to note that in the group with only one semesters of
exposure to IFPS, almost 25% described their IFPS language fluency level as
"intermediate. "
Most students (61%) felt that the IFPS Student Guide in its present form
provided sufficient support for IFPS learning outside the classroom. Others
expressed a need for more examples of the language features, possibly includ-
ing cases and related IFPS models as an appendix. Another common suggestion
was for more explanation of "what to do when you make a mistake." Some stu-
dents commented that the availability of an "IFPS expert" teaching assistant
in the terminal lab area would have been helpful.
IFPS Use Outside the Classroom
The survey showed that students with two semesters of exposure to IFPS
tended to use it more as a personal tool, both for other courses and outside
the classroom. In the group for which IFPS had just been introduced, only 9%
reported using IFPS for homework in another course. In the more advanced
group, however, over 52% used IFPS for other classes and projects.
A similar pattern was found in the students' use of IFPS as a tool for
personal calculations or in a work environment. Of the students in the intro
ductory course, 8% had used the language for personal use or on the job. In
t:he Executive MBA section, however, over 68% indicated they had used IFPS for
calculations other than classroom work.
IFPS versus Hand-held Calculators
When asked whether they felt instruction in the use of hand....;held calcula
tors or instruction in IFPS would be worth more t .o them for their short-term
MBA experience, the students responded overwhelmingly (85%) in favor of the
hand-held calculator instruction. We believe this to be a reflection of the
methods. of teaching in other courses in the program. When asked a similar
question regarding the anticipated relative value of the two tools for the
long term (in the next five years), 42% responded they felt the IFPS instruc
tion would prove more valuable.
Perceived Future Computer Use
Most of the students anticipate using the computer as an everyday tool
in the future. The survey showed that 80% plan to make use of the computer in
business once they graduate.
It is interesting to note the students' perception of computer availabil
ity. Rather than the difficult-to-get-to computing power of an isolated
11
main-frame environment, almost two-trirds of the students surveyed said they
plan to own a personal computer after graduation.
CONCLUSIONS AND FUTURE DIRECTIONS
This paper has reported the qualitative experiences with using a finan
cial planning language as the principal language for MBA's. Survey data indi
cates overall satisfaction with the language after the first semester, and the
ability of at least some students to use the language outside the course in
which it is taught.
Based on our experience, IFPS and similar financial planning languages
present a viable approach around which to .structure MBA student interaction
with the computer. Even students with no prior computer experience found the
language easy to learn and apply to a wide range of problems. Since students
can readily grasp the syntax of the language, instructional time can be better
spent dealing with the principles of business problem analysis and solution.
Less time is needed to describe how to represent the problem in a computer
language.
In addition to being easy to learn, financial planning languages provide
the student with a skill that he can apply directly upon graduation. Since
very few of our students plan to become systems analysts or professional pro
grammers, they generally have little need for procedural programming lan
guages.
In the future, we plan to broaden the base of courses at SMU which ·use
IFPS, including the required management science courses at the undergraduate
level. Within the introductory MBA course we plan to reinforce IFPS instruc
tion by including additional IFPS examples in the statistics and forecasting
portions of the course.
12
We plan to survey the full-time MBA student group again at the end of the
Spring semester and prior to graduation at the end of the summer semester. We
anticipate finding a larger and larger percentage of students making more use
of the computer as a problem-solving tool, both inside and outside of the
classroom.
In general, our experiences with IFPS have been positive. The develop
ment of the Student Guide combined with documentation provided by Execucom
(the software's creator) have made this a well-documented system. Students
grasped the language quickly; we met very little resistan~e, even from stu
dents who were "programming experts" in languages such as FORTRAN or COBOL.
Even that prodigious bastion of resistance to pedagogical change, the faculty,
had its foundations shaken. Instructors actually be~ame enthusiastic about
using the language in their classes.
13
REFERENCES
l. Wagner, G. R., "Decision Support Systems: Computerized Mind Support for Executive Problems," Managerial Planning, September/October , 1980.
ENTER IFPS
FILES
EXECUTIVE COMMANDS
I I RETRIEVE .MANIPULATE SOLVE QUIT
I I , MODELS REPORTS DATAFILES ~
FI GURE 1. Basic structure of IFPS
MODEL DECIDE VERSION OF 07/l A/81 l0:28 10 COLUMNS 1-8 20 * 30 * 40 50 *
~10DEL
6 0 SAJ .. ES V •NARKE'r*~·lAPSET SHJ\RE 7 0 i-lAF.KDJ'rill I PREVIous MARKET* l . 0 3 3 0 r-1ARI<Iil'lt · .. ~!fARE•O. 15 I PREVIOUS MP .. RKET SHARE +0. 0 0 5 9 0 GROSS S~LES =SAL.SS VOLU~E *U'HT PRICE l 0 0 D;·Yt'T P:~ICE =2. 5 FOR 4, 2. 7 5 FOR -1 110 OP ~~A'l'ING COS'l' =SALES VOLU~·m *(PRODUCTION C'OST + OVERHE.l\D) 120 PRODUC'l'IO!I COST =1.25 FOR 4,1.35 1 30 OV!!:RHEAD = PRODUCTION COST *0.80 1.35 ~ET INCOME =GROSS SALES -OPERATING rOST 140 INVESTMENT =2500,0 150 RATE OF RETURN = IRR(NET INCm·1E, INVES'..l'[ •lE~lT)
lfO NS T PRESEN':t VALUE=NPVC(l!:ST Il'Wm·IE,DISCOUn'I' RATE,INVEST~lEHT) 170 DISCOUNT PA'1'E=.08 130 PERIODS .1
EI·m 0F ¥()DEL ? ~o1. ve MODEL DECIDE VERSION OF 07/14/81 10:28 -- 8 COLUMNS 13 VARIABLES ENTER SOLVE OPTIONS ? 2.:_1
~J E~~; PPODUCT 'vtODEL
SALES VOLU.l-1£ '1ARK:ST ~ ·IARI\ET SHl\RE GROSS Si\LES U~J I'=' PRICE OPERATING COST PRODUCTION COST OVL:RHEAD 'l'.TP 1r l. .. .J-..J .4. INCOME I ~'NES Tr-1ENT RATE OF RETURN NET PRESENT Vi\LUE DISCOUNT RATE
NEW PRODUCT MODEL
SALES VOLU:,lE ~ARKET
1
1200 8000
. l5 00 3000
2.500 2700
l .2SO l
300 2500
-22 0 6 .0800
7
1719 9552
3 4 5
1 .., ... .., .1- ~ I I 1358 }_442 1531 3~ ~ 0 3487 8742 9004
.1550 .1600 . lf.SO .1700 3193 3395 3606 4209
2.SOO 2.500 2.500 2.750 2874 3055 3245 3720
l. 2SO 1.250 l. 250 l_. 3 50 1 1 l l. 080 ·'-
31. 9. 3 339.5 360 . 6 .189 .8 0 0 0 0
- _1_898 - lS78 -12~14 -799.2 .0300 .0800 .0800 .0300
8
1820 983 9
1.623 ?'274
.1750 4463
2.750 3944
1. 350 L 080 519.4
0
-336.5 .0800
MARKET SHARE GROSS SALES
.1800 4728
.1850 5006
Figure: 2. ]introductory Example
Ul'i IT PRICE OP2P.ATING COST PRODUCTION COST OVERHEAD NET INCO~'lE
I NVES 1'~1ENT RA 'lE OF PETUR.N N~'i ' PR F: ::;F.N'1' V,li..T.flF.
2.750 4178
l. 350 1.080 550.2
0 .137? 1 4 4- 4
2.750 Ll423
l.3r..o J . • 030 582.5
0 .3 056 F t.l_ 8
- ----------------------------------
\ 1QD2L \10NTS VERSION 0!." 12 / '.5/80 H:22 1 o -:oL mms ;_:; Bl- ;_ 9 s s 2C * 3 0 *R!S K A~lALYS IS ':OD.SL 'TS I~iG S I :~ULATI0l·i
40 * 50 ':::clGI?-lF.ERI~!G DESIG~i='!'!U?_l\Nn ( 50000,60000, 80000 ) . 0 6 0 c·•.:;.RKET SH!I.P.E=~ORP.AND (.!., . 0 J. ) 7 0 SALES PRICE=UNIP.A~:D ( 8. 00, R. i 5) 80 TOTAL ~ARKET=tOOOOOO,PREVIODS TOTAL UARKET*l.l qo SALES VOLU''E=!-!?.RKET SHAP.E*~O'!'AL '·1ARKE': 100 G?.OSS INCO:'<lE=SALl:S 1/0LU:'E"'SALES PRFE l t O ?RODDC'!'IO~ COST=7.00 fOR 2,7.25 l2 0~~=~ E ·ICO: lE =GROSS E!CO!••E-SALES VOLUt'.E * PRCDUr.T I C:1 ~as T- E:·:G I:!F.ERING ::JES IG!·! l '-'0 I<<v'EST~·'EN7=1.00000, 0 150 DISCC'UNT RATE=O . 15 :. ;: 0 P.?ESENT '·'OR~H=::PVC (!:E7 INC0!-':7.:, DISCOUNT HATE, !)NES'I':\ENT) 170 ?ATE OF RETUP.!·: =D.R(li:::T I!'1CO~!E, IYV!::S'INENT) 1 ?0 ':'ER"!El~ . .L VALUE.=NTV(:·iET INC01'!E,0.08, INV!':ST;'-lE~:?) 190 ::!ENEFI'i' 'l'O COST ?.A'I'IO=GC?.ATIO(NET I;:oJC0~·1E,DISCOUclT :l..A,'I'E,INVES'i.' :-lE?~T) i::ND 'JF "O:JEL ? sol_ve 1100SI. ~10:1TE VERSION OF l::'/15 / 30 16:22 -- 5 CC!..l'~·!!I!S l~ VARIABLES SNTEP. SOLVE OP'l'Im1S 7 rnnnt• ca~lo 300 ENT!::R : !ONTE CARLO OPTIONS 7 f'r••:r,~ist nP.t i.ncome,freo: p~P.s•nt ·-roocth,none
FREOUENCY TABLE
PPCBAP. ISI'!'Y OF VALUE BEINC GREATER TEA~! D!DIC.l\TED
30 70 'iO 50 30 20
NET nco~~E
1.0
1985 ll9~65 l31BBC 141705 l~l450 l62l14 ' 72028 135957 201775 215354
PRESEN'l' lvORTil 1985 2~3807 143031 ~76531 2?6640 329297 359703 391085 437677 477330
SN·lPLE ST.l\TISTICS
~!EAN S?D DEV SKm·l'NESS KURTOSIS 10PC CONF :"'EAN ')QPC
NET 1985
H!CO~!E
165433
PRESENT r.;QRTH 1')85 337968
36647 • 2 2. 3
102044 • 3 ~. 4
HISTOGRAN :'OR COLUM~l 1.985 OF NET InC0'\2 31- 33 • 23- ?0 * 25- 27 * * *
* * * * * * * * * * * ~ • * * * * * * * * * * * *
* *
* *
* * * * * * * *
22- 24 l?- 21 16- 18 13- JS 10- 12 * * * * * ******* *
* * * * * * * * * * * * * * * * 7- 9 4- 6 * * * * * * * * * * * * * * * * * * * l- 3 • * * * * * * * * * * * * * * * * * * *
l '!.. 1. "' ?
8 3 ,:; 8 1_ 1 '5 7 2 8 3 :l 2 '
., 7 2 7 7. -5 5 5 5 5 s 5 0 IJ 0 () 0 0 •J
START 82000.0 S':'OP 252000.0 SIZE ~"'? I N .. rERVJ;.I~
162725 HBl-11
330427 3'\550'3
FIGURE 3. IFPS Simulation ~!odel
8500 . 00
MODEL REPLACE 'lERSION OF 01/26/ 8 1 2l:31 10 COLUMNS 1-20 20 * 30 CASH OUTFLO,'lS=NEiq PRICE +NE;v INS TALL- {OLD SALVAGE'* { 1-TAX RATE)) 40 CASH INFLOWS = {NEW OPEP-ATING SAVINGS-DEPRECIATION) *{1-TAX ~ATE) +NEW SALVAGE + SOSTLINE DEPR { INVESTI1ENT ,SALVAGE VALUE FRACTION, LIFE, DEPRECIATION, 300KVALUE) 60 .. 70 !?RESENT VALUE=NPVC{CASH INFLOivS, .10 ,CASH OUTFLOHS) 80 RATE OF RETU~< = IRR(CASH INFLOWS,CASH OUTFLOWS) 90 * 100*---INPUT DATA----110* 120 NEW PRICE=40000,0 130 NEW INSTALL=3000,0 135 !NVESTMENT=NEH PRICE -+-NEW INSTALL 140 OLD SALVAGE=1000,0 l50 TAX RATE = .50 160 NE1ol OPERATING SAVINGS•9000 180 NE>•T SALVAGE=O FOR l 9, 80011: VALUE 1~0 SALVAGE VALUE FRACTION•l /43 20 0 LIFE=20 END OF ~10DEL ? solve ~!ODEL REPLACE VERSION ENTER SOLVE OPTIONS
OF 01 /2 6/ 81 21:31 -- 20 COLUMNS 15 VARIABLES
? all
CASH OUTFLOWS <::ASH INFLOWS DEPRECIATION BOOKVALUE
PRESENT VALUE RATE OF RETURN
---INPUT DATA----
NEN PRICE ~:EH INSTALL INVESTMENT OLD SALVAGE TAX PJI.TE NEN OPERI\TH!G SAVINGS NE,•T SALVAGE SALVAGE VALUE FRACTION LIFE
CASH OUTFLOWS CASH INFLOWS DEPRECIATION BOOKVALUE
PRESENT VALUE RATE OF P-ETURN
---INPUT DATA----
NEN PRICE NE~·r INSTALL INVESTilENT OLD SALVAGE TAX P-ATE NEH OPERATING SAVINGS NEW SALVAGE SALVAGE VALUE FRACTION LIFE
CASH OUTFLOI-TS CASH INFLO:-lS DEPRECIAT!Ot-: 300KVALUE
PRESENT VALUE RATE OF RETURN
1
42500 5550 2100
40900
-37455
4000 0 3000
43000 1000
.5000 9000
0 .0233
20
7
0 555 0 21 00
2830 0
-15480
0 0 0 0
. 5000 9000
0 .0233
20
13
0 3550 210 0
15700
-307<i .0858
2
0 5550 2100
38800
-32868
0 0 0 0
. 5000 9000
0 .0233
20
8
0 5550 210 0
26200
-12891 .0098
0 0 0 0
.5000 9000
0 .0 233
20
l4
0 5550 2100
1360 0
-1615 .093 0
3 4 5
0 0 0 5550 5550 5550 2100 2100 2100
36700 34600 32500
-28698 -24907 -21461
0 0 0 0 0 0 0 0 0 0 0 0
.5000 .5000 .50 00 9000 9000 9000
0 0 0 .0233 .0233 . 0233
20 20 20
9 1 0 11
0 0 0 5550 5550 5550 2100 2100 2100
24100 22000 19900
-10537 -8398 -6452 .0336 . 0517 .0658
0 0 0 0 0 0 0 0 0 0 0 0
.5000 . 5000 . 5000 9000 9000 9000
0 0 I)
.0233 .0233 .0233 20 20 20
1 5 16 17
0 0 0 5550 5550 5550 210 0 2100 210 0
11500 94 00 73 00
-2S S. J 921 . 6 20 2 0 . 0988 .1035 . 1076
6
0 5550 2100
30400
-18328
0 0 0 0
.5000 900 0
0 .0233
20
12
0 5550 2100
17800
-4684 .0769
0 0 0 0
.5000 90 0 0
0 .0 233
20
18
0 5550 2100 5 200
3018 .1 : 09
---E-lPUT DATA----
NEi; PRICE 0 NEW INSTALL 0 INVESTMENT 0 OLD 3ALVAGE 0 T."<X RATE .5000 NEW OP::RATING SAVINGS 9000 NEH SALVAGE 0 SALVAGE IJALUE FRACTION .0233 LIFE 20
19
CASH OUTFLONS 0 CASH INFLOt-TS '5550 DEPRECIATION 2~00
BOOKVALUE 3100
PRESENT VALUE 3925 RATE OF RETURN .1137
---INPUT DATA----
NEW ?RICE 0 NEI•T INSTALL 0 INVESTMENT 0 OLD SALVAGE 0 TAX RATE .5000 NEW OPERATING SAVINGS 9000 NEW SALVAGE 0 SALVAGE VALUE FRACTION .0233 LIFE 20
ENTER SOLVE OPTIONS ?
moCe.!. monte READY FOR EDIT, LAST LINE IS 110 ? list
0 0 0 0
.5000 9000
0 .0233
20
20
0 6550 2100 1000
4899 .1165
0 0 0 0
.5000 9000 1000
.0233 20
MODEL 'IONTE VERSION OF 12/29/80 14:55 10 COLUMNS l-6
0 0 0 0 0 0 0 0
.5000 .5000 9000 9000
0 0 .0233 .0233
20 20
20 SALES=INITIAL SALES,PREVIOUS SALES* SALES GROWTH RATE
0 0 0 0 0 0 0 0
.5000 .5000 9000 9000
a 0 .0233 . 0233
20 20
• 30 SXPENSESs75, PREVIOUS EXPENSES + 25 +FIXED EXPENSE GROWTH +VARIABLE EXPENSE GRO\·; 4 0 NET INC0~1E=SALES-EXPENSES
50 * 60 * 70* 80 INITIAL SALES=TRIRAND(90,100,120) ,0 90 SALES GRO~'ITH RATE=l.25*UN!RANOR(0.8,1.2) 100 FIXED EXPENSE GRONTH =UNIRAND(-2,4) 110 VARIABLE EXPENSE GRONTHcUNIRANDR(-3, 1) END OF ~IODEL
? solve 1·!0DEL ~10NTE VERSION OF 12/29/80 14:55 -- 6 COLUMNS 7 VARIABLES ENTER SOLVE OPTIONS ? all_
SALES EXPENSES NET INCOME
INITIAL SALES SALES GROWTH RATE
FIXED EXPENSE GRO~ITH VARIABLE EXPENSE GRO>•ITH
ENTER SOL'JE OPTIO~TS ? monte carlo 300 ENTER ~ONTE CARLO OPTIONS ? default fr~a,all ENTER MONTE CARLO OPTIONS ? net income,none
1
103.3 75
28.33
103.3 1. 250
-l
2
129. 2 100
29.17
0 1. 250
1 -1
3
161.5 t25
36.46
0 1. 250
1 -l
201. B :!.SO
51.82
0 1.250
-l
5
252.3 175
77.28
0 l. 250
l -l
s
315.3 200
115.3
0 l. 250
:!. -l
FREQUENCY TABLE
PROBABILITY OF VALUE BEING GREATER THA~l !NDICATED
90 80 70 60
NET INCOME l 20.12 22.37 24.25 25.93 2 3.9 3 1.3 4 -l.7 5 .6 6 10.3
MEAN
NET INCOME 1 28.13 2 27.63 3 34.34 4 48.65 5 73.81 6 111.4
11.6 16.5 22.1 10.0 17.0 23.5 11.2 22.9 34.1 22.0 34.1 49.0 36.2 58.3 79,5
SAMPLE STATISTICS
STD OEV SKE~·INESS
• 4 . 2 .4 • 6 .6 • 7
50 40 30
27. 23 29.03 31.01 26.8 32.4 37.2 30.9 39.1 48.5 46.1 56.0 69.0 o58.6 82.2 102.0 98.3 120.9 148.6
KURTOSIS lOPC CONF
2.5 2.4 2.9 3.6 3.2 3.3
27.66 26.35 32.31 45.58 69. 4l 105.2
ENTER POOL OR MODELING ? solve
6.352 17.41 27.38 41.61 59.60 85.09 LANGUAGE COMMAND
ENTER SOLVE OPTIONS ? monte carlo 300 ENTER MONTE CARLO OPTIONS ? hist net income,none
FREQUENCY TABLE
PROBABILITY OF VALUE BEING GREATER THAN INDICATED
20 10
33.58 37.63 41.9 52.2 58.1 72.3 83.9 102.3
125.2 150.7 181.6 219.8
MEAN 90PC
28.60 28 . 92 36.36 51.73 78.22 117.7
90 80 70 60 50 40 30 20 10
NET INCOME 6 16.4 43.7 55.5 75.3 98.1 114.5 144.1 170.0 207.4
SAMPLE STATISTICS
MEAN STD OEV SKE"..rNESS KURTOSIS 10PC CONF MEAN 90PC
NET INCO!'!E 6 106.7 79.59 • 8 4.1
HISTOGRAM 6 OF NET INCOME FOR COLUMN 43- 45 40- 42 37- 39 34- 36 31- 33 28- 30
* * * * * *
* * * * *
* * * * * * *
25- 27 * * * * * 22- 24 * * * * * * * 19- 21 * * * * * * * * 16- 18 * * * * * * * * LJ- 15 * * * * * * * * 10- 12 * * * * * * * * * * 7- 9 •• * •• * • * • * * 4- 6 •• * • * •• * • * ••• 1- 3 • * •• * * • * • * ••• * • * •• *
1 2 3 3 5 t 8 6 3 l 8 8 6 9 3 6 0 3
START -70.0 STOP 420.0 SIZE OF INTERVAL ENTER POOL OR MODELING LAHGUAGE COMNAND ?
100.8 _112. 6
24.50
CONTENTS
PART I. BASIC ELEMENTS OF IFPS
I. Introduction
II. Organization of IFPS
III. How to Obtain IFPS at Your Terminal and Begin Work
IV. A Simple IFPS Model
- Problem - IFPS Model - What You Type at the Terminal - Listing Your Program - Obtaining Answers (The use of SOLVE)
V. Getting out of IFPS (the QUIT Command)
VI. Correcting Mistakes (The EDIT Commands)
Misspellings While Typing Error in Complete Line Using the C Edit Command Retyping a Line Deleting a Line Inserting a Line Obtaining a Listing of a Single Line
VII. Obtaining a Listing on the Line Printer at the Computer Center
PAGE
1
2
5
8
8 9
10 11 12
13
14
14
15 16 17 17 17
(The RECORD Comand) 18
PART II. USEFUL FEATURES OF IFPS
VIII. Using IFPS to Ask Questions and Make Decisions (WHAT IF, SENSITIVITY, GOAL SEEKING, ANALY2E)
What If Sensitivity Goal Seeking Analyze
IX. Built-In Functions (Financial, Forecasting, Mathematical)
Financial Functions The Periods Statement Forecasting Functions Mathematical Functions
i
FIGURE if. TABLE OF CvNTENTS OF STUDENT GUIDE
20
20 22 24 24
25
26 28 28 30
PART II. CONT.
X. Built-in Subroutines (Depreciation, Amortization)
XI. Monte Carlo Simulation (Risk Analysis)
Built-In Probability Distributions (UNIRAND, TRIRAND, NORRAND)
Defining Your Own Probability Distribution Choosing the Same Random Number for All Periods
or a New Value for Each Period A Simple Monte Carlo Program Deterministic Solution of Monte Carlo Models (Best
Case/Worst Case/Most Likely Case) MONTE CARLO Simulation The STOP Command (What to do if you make an options
mistake) Interpreting Monte Carlo Results The POOL Command
PART III. SPECIAL CAPABILITIES AND SAMPLE PROBLEMS
XII. Solve Options
XIII. Plotting Results Graphically
XIV. Data Files
The Dummy Model The Data File Obtaining Answers Creating and Saving a New Data File Changing a Data File Storing the Output of a Model for Future Use as Data
XV. Generating Formatted Reports
Creating a New Report A Simple Example
XVI. Miscellaneous Features and Examples
Using Line Numbers Rather than Variable Names SUM Function IF Statements in IFPS Example: Using IFPS for Exponential Smoothing Example: Series Decomposition of Trends Example: Computing the Values in the Tables in Your
Finance Text
ii
PAGE
31
34
35 36
37 38
39 41
42 42 45
46
48
50
so so 51 51 52 52
53
53 54
59
59 60 60 62 62
63
10 COLU~lNS l-6 20 SALES = lOO,PREVIOUS SALES *1.75 30 EXPENSES = 7S,J.OO,l~S,l50,175 40 NBT INCOME = SALES -EXPENSES 50 ir::
60 * RA'l'IO Ai·LI\LYSIS 70 *PERCENT OF SALES 80 EXPE~lSE PATIO = EXPENSES/S.?\LES 90 NET IN~OnE P-..t-\TIO = NET INC0~1E/S.I\L~•:s
? solve iv!ODEL GRAY VERSION OF 02/06/82 "f. :01 -- 6 COLm.·mS 5 VARIABLES
SOLVE 0PTimJS ? ""1 l
E:<PENSES t·!E'l IL,iCt)!_.E
PERCENT OF Si\LES EXP2NSE P .. A'I'IO PET INCO~<•E RATIO
ENTER SOLVE OPTIONS ?
1
1. 00
?.
125 100
'25
RATIO Al:!ALYSIS
.7500
.2500 .8000 .2000
FIGURE j. Example Used In Student Guide
") . )
]_56. 3 1 ') c:: .i .• :. -·
31.25
.8000
.2000
4
:!..95.3 150
4 5. 11
.7630
.2320
5
~ !', 4. l 175
69.1~
. 71J8
.2332
6
30:).2 175
130.~
.:J73~
.42fih
Figure 6.
IFPS QUESTIONNAIRE
t.Te are surveying the t1BA class about its responses to the use of the IFPS in the MBA program. We would appreciate your filling out this brief questionnaire. Thanks for your help.
T.E. Perkins and P. Gray
Do you consider yourself a novice ____ intermediate ____ expert ____ in IFPS?
Did you find it easy ____ somewhat hard hard ____ very hard ____ to learn IFPS?
Was the IFPS Student Guide sufficient support for learning the language? Yes No
If not, please expl~on the back of this page.
Have you used IFPS 1n any course other than MS&C (e.g., homework, class project, case analysis)? Yes ___ NO ____ _
If yes, please describe on the back of this page what you used IFPS for and how often you have used it.
Have you ever created an IFPS model for personal use {e.g., personal finances, taxes) or as part of your job or as a research assistant to a professor? Yes ____ No __ __
!£ yes, please describe on the back of this page.
Have you received instruction in another computer language either at SMU or elsewhere (include BASIC short course)? Yes ___ No ___ I£ yes, which languages: BASIC __ FORTRAN __ SPSS __ OTHER(specify)
Did you find IFPS easier same as ____ harder ____ to learn than other computer languages? ----
Do you expect to be using a computer after you graduate? Yess ____ No __ __
Do you expect to own your own personal computer? Yes ____ No
As part of your MS&C course ~rk you received instruction in both the use of the MBA calculator and modeling in IFPS. Which of these do you believe is ~rth more to you:
in the short term (while at 51111) MBA IFPS in the long term {during next S years-)---MBA -IFPS ____ _
Answer the following questions only 1f you did not have computer experience prior to coming to SMU:
·otd you have "fear of computer" prior to coming to SMU? Yes ___ No __ __
If yes, have you overcome that fear? Yes ____ No __ __
Demographic Data:
Male ___ Female
Year of Bachelor.'s Degree'-------No. of units of mathematics/statistics as undergraduate ______ _ MBA concentration:
Acc:ounting ___ Finance ____ International _____ Market:f.ng ___ MS&C __ _ Organizational Behavior _____ Real Estate _____ Other __________ __
Pro-fessor: Aronofsky _____ Barr ___ Perkins ___ Sobol ____ _
The following papers are currently available in the Edwin L. Cox School of Business ~iorking Paper Series.
79-100
79-101
79-103
80-100
80-101
80-102
80-103
80-104
80-200
80-300
80-301
80-405
80-500
80-600
80-601
80-800
80-801
80-900
80-902
"Microdata File Merging Through Large-Scale Network Technology," by RichardS. Barr and J. Scott Turner
"Perceived Environmental Uncertainty: An Individual or Environmental Attribute," by Peter Lorenzi, Henry P. Sims, Jr., and John W. Slocum, Jr.
"A Typology for Integrating Technology, Organization and Job Design," by John W. Slocum, Jr., and Henry P. Sims, Jr.
"Implementing the Portfolio (SBU) Concept," by Richard A. Bettis and William K. Hall
"Assessing Organizational Change Approaches: Towards a Comparative Typology," by Don Hellriegel and John w. Slocum, Jr.
"Constructing a Theory of Accounting--An Axiomatic Approach," by Marvin L. Carlson and James W. Lamb
"Mentors & Managers," by Michael E. McGill
"Budgeting Capital for R&D: An Application of Option Pricing," by John W. Kensinger
"Financial Terms of Sale and Control of Marketing Channel Conflict," by Michael Levy and Dwight Grant
"Toward An Optimal Customer Service Package," by Michael Levy
"Controlling the Performance of People in Organizations," by Steven Kerr and John W. Slocum, Jr.
"The Effects of Racial Composition on Neighborhood Succession," by Kerry D. Vandell
"Strategies of Growth: Forms, Characteristics and Returns," by Richard D. Miller
"Organization Roles, Cognitive Roles, and Problem-Solving Styles," by Richard Lee Steckroth, John W. Slocum, Jr., and Henry P. Sims, Jr.
"New Efficient Equations to Compute the Present Value of Mortgage Interest Payments and Accelerated Depreciation Tax Benefits," by Elbert B. Greynolds, Jr.
"Mortgage Quality and the Two-Earner Family: Issues and Estimates," by Kerry D. Vandell
, "Comparison of the EEOCC Four-Fifths Rule and A One, TWo or Three a Binomial Criterion," by Marion Gross Sobol and Paul Ellard
"Bank Portfolio Management: The Role of Financial Futures," by Dwight M. Grant and George Hempel
"Hedging Uncertain Foreign Exchange Positions," by Mark R. Eaker and Dwight M. Grant
90""-j . "Strategic Portfolio Management in the Multibusiness Firm: An , :ij::f;A Implementation Status Report," by Richard A. Bettis and William
::'" K. Hall
SQ;.;t;l,,J,.l "Sources of Performance Differences in Related and Unrelated Diversified Firms," by Richard A. Bettis
SO•ll2 "The Information Needs of Business With Special Application to Managerial Decision Making," by Paul Gray
90•113 "Diversification Strategy, Accounting Determined Risk, and Accounting Determined Return," by Richard A. Bettis and William K. Hall
80-114 "Toward Analytically Precise Definitions of Market Value and Highest and Best Use," by Kerry D. Vandell
80-115 "Person-Situation Interaction: An Exploration of Competing Models of Fit," by William F. Joyce, John W. Slocum, Jr., and Mary Ann Von Glinow
80-116 "Correlates of Climate Discrepancy," by William F. Joyce and John Slocum
80-117 "Alternative Perspectives on Neighborhood Decline," by Arthur P. Solomon and Kerry D. Vandell
80-121
80-122
80-..123
81-100
81- 200
81-201
81-300
81-400
81-500
"Project Abandonment as a Put Option: Investment Decision and Operating Risk Theory," by John W. Kensinger
Dealing with the Capital Using Option Pricing
"The Interrelationships Between Banking Returns and Risks," by George H. Hempel
"The Environment For Funds Management Decis i ons In Corning Years," by George H. Hempel
"A Test of Gouldner's Norm of Reciprocity In A Commercial Mark eting Research Setting," by Roger Kerin, Thomas Barry, and Alan Dubinsky
" Solution Strategies and Algorithm Behavi or in Large-Scale Network Codes, " by Richard S. Barr
"The SMU De cision Room Pro j ect," by Paul Gray, Juli us Arono fsk y , Nancy W. Berry, Olaf Helmer, Gerald R. Kane, and Thomas E. Perkins
"Cash Discounts To Retail Customers: An Alternative To Credit Ca r d Performance," by Michael Levy and Charles I ngene
"Merchandising Decis i ons: A Ne w View o f Planning a nd Measuri ng Per f orma nce ," b y Michael Levy and Charle; A. I ngene
"A Meth~dology For The Formulation a nd Ev a l uati on o f Energy Goals And ~ol~cy Alternatives For I s r ael," by Juli us Aronofsky, Reuven Karn~ , a nd Ha r ry Tankin
81-501 "Job Redesign: Improving The Quality of Working Life," by John W. Slocum, Jr.
81-600 "Managerial Uncertainty and Performance," by H. Kirk Downey and John W. Slocum, Jr.
81-601 "Compensating Balance, Rationality, and Optimality," by Chun H. Lam and Kenneth J. Boudreaux
81-700 "Federal Income Taxes, Inflation and Holding Periods For IncomeProducing Property," by William B. Brueggeman, Jeffrey D. Fisher, and Jerrold J. Stern
81-800 "The Chinese-U.S. Symposium On Systems Analysis," by Paul Gray and Burton V. Dean
81-801 "The Sensitivity of Policy Elasticities to the Time Period Examined in the St. Louis Equation and Other Tests," by Frank J. Bonello and William R. Reichenstein
81-900 "Forecasting Industrial Bond Rating Changes: A Multivariate Model," by John W. Peavy, III
81-110 "Improving Gap Management As A Technique For Reducing Interest Rate Risk," by Donald G. Simonson and George H. Hempel
81-lll "The Visible and Invisible Hand: Source Allocation in the Industrial Sector," by Richard A. Bettis and C. K. Prahalad
81-112 "The Significance of Price-Earnings Ratios on Portfolio Returns," by John W. Peavy, III and David A. Goodman
81-113 "Further Evaluation of Financing Costs for Multinational Subsidiaries," by Catherine J. Bruno and Mark R. Eaker
81-114 "Seven Key Rules For Successful Stock Market Speculation," by David Goodman
81-115 "The Price-Earnings Relative As An Indicator of Investment Returns," by David Goodman
81-116 "Strategic Management for Wholesalers: An Environmental Management Perspective," b y William L. Cron a n d Valari e A. Zeithaml
81-117 "Sequential Information Diss emination and Relative Market Efficiency," by Christopher B. Barry and Robert H. Jennings
81-118 "Modeling Earnings Be havior," by Mic hael F. van Breda
81-119 "The Dime n sions o f Se l f -Manageme nt, " by David Goodma n and Leland M. Wooton
81-120 "The Pric e-Earnings Relatives- A New Twist To The Low-Multiple Strategy," by David A. Goodman and John W. Peavy , III .
82:..100 "Risk Considerations in Modeling Corporate Strategy," by Richard A. Bettis
82-101 "Modern Financial Theory, Corporate Strategy, and Public Policy: Three Conundrums," by Richard A. Bettis
82-102 "Children's Advertising: The Differential Impact of Appeal Strategy," by Thomas E. Barry and Richard F. Gunst
82-103 "A Typology of Small Businesses: Hypothesis and Preliminary Study," by Neil C. Churchill and Virginia L. Lewis
82-104 "Imperfect Information, Uncertainty, and Credit Rationing: A Comment and Extension," by Kerry D. Vandell
82-200 "Equilibrium in a Futures Market," by Jerome Baesel and Dwight Grant
82-201 "A Market Index Futures Contract and Portfolio Selection," by Dwight Grant
82-202 "Selecting Optimal Portfolios with a Futures Market in a Stock Index," by Dwight Grant
82-203 "Market Index Futures Contracts: Some Thoughts on Delivery Dates," by Dwight Grant
82-204 "Optimal Sequential Futures Trading," by Jerome Baesel and Dwight Grant
82-300 "The Hypothesized Effects of Ability in the Turnover Process," by Ellen F. Jackofsky and Lawrence H. Peters
82-301 "Teaching A Financial Planning Language As The Principal Computer Language For MBA's," by Thomas E. Perkins and Paul Gray