of the relationship between time and the value of money.

Size: px
Start display at page:

Download "of the relationship between time and the value of money."

Transcription

1 TIME AND THE VALUE OF MONEY Most agrbusess maagers are famlar wth the terms compoudg, dscoutg, auty, ad captalzato. That s, most agrbusess maagers have a tutve uderstadg that each term mples some relatoshp betwee the value of moey ad the passage of tme, but few would be able to accurately descrbe that relatoshp. For example, you probably recogze that the preset value of a $100 promssory ote payable oe year hece s somethg less tha the ote s face value. You also probably recogze that the preset value of a seres of twelve mothly reveues of $10 to be receved the future s somethg less tha the sum total of those reveues. However, are you able to provde a detaled explaato of the relatoshp betwee the value of moey ad the passage of tme? Probably ot-except to state your awareess of the exstece of such a relatoshp. Every agrbusess maager s faced wth makg decsos cocerg alteratve uses of avalable captal. A farm supply busess maager may have to decde whether to elarge hs curret product le or elarge hs facltes. A frut warehouse maager may have to decde whether to vest fuds ew, more effcet equpmet wth a mmedate retur or expaded C.A. facltes whch may ot geerate a et retur for several years. Or, a gra elevator maager may have to decde whether to sell some used gra trucks ow at a stated prce, or wat a few more years ad sell at some ukow prce. Regardless, all three maagers are faced wth a decso, whch requres a uderstadg of the relatoshp betwee tme ad the value of moey. Exactly what s ths tme-value relatoshp ad what should a agrbusess maager kow about t to make better decsos? Actually, there exsts o sgle tme-value relatoshp. Istead there are several, cludg: a) future value of a preset sum, b) preset value of a future sum, c) preset value of a aual reveue, d) preset value of a auty for a gve tme, ad e) future value of a auty for a gve tme. Every agrbusess maager should have at least a workg kowledge of these fve smple relatoshps. Through a seres of bref llustratos, t s hoped that ths paper wll provde such kowledge. of a Preset Sum Let s assume that you are the maager of a fertlzer supply frm, whch specalzes, aeral applcatos. At the preset tme your frm s major vestmet s arcraft wth a remag useful lfe of about fve years. I order to facltate the purchase of ew arcraft, you decde to place $20,000 a specal bak accout yeldg sx percet terest per aum. Your plas are to wthdraw the $20,000, plus terest fve years ad use ths amout to help buy the ew arcraft. Allowg the terest to accumulate over the etre perod, how much moey wll you be able to wthdraw fve years? Ths future value of a preset sum ca be determed by usg the followg formula: A= P(1 + ) 1 WASHINGTON STATE UNIVERSITY & U.S. DEPARTMENT OF AGRICULTURE COOPERATING

2 where: A = future value P = preset sum = terest rate (per coverso perod) = umber of coverso perods I our llustrato, we see that the preset sum ($20,000) s coverted (perod for whch terest s pad) fve tmes at a terest rate of sx percet. Moreover, the formula becomes: = $20,000 (1.06) 5 = $20,000 (1.3382) = $26,764 The factor (1.06) 5 ca be had calculated, but t ca be more easly obtaed from a compoud terest table foud the appedx of most mathematcs or statstcs books, e.g., accordg to the approprate table values for ad, (1.06) 5 equals As show above, $26,764 could be wthdraw from the specal accout at the ed of the fve-year perod. of a Future Sum Suppose a dary cooperatve purchases ts supples from a large federated supply cooperatve. Ths year, the dary cooperatve receved a certfcate of equty for $20,000, whch represets the cooperatve s share of the federato s eargs. At the preset, the federato s payg ts certfcates (revolvg ts equtes) fve years. Let s assume that you are the maager of the dary cooperatve ad that your board of drectors has asked you to select a represetatve dscout rate ad determe the preset value of the $20,000 certfcate. Selectg a dscout rate of sx percet, you could use the followg formula to calculate the preset value: P = A ( 1+ ) where: P = preset value A = future sum = dscout rate (per coverso perod) = umber of coverso perods I our llustrato, we see that the future sum of $20,000 s beg dscouted at sx percet for fve coverso perods ad our formula becomes: $20,000 P = ( 1.06) 5 $20,000 = (1.3382) The factor (1.06) 5 ca be take from the compoud terest table oted earler. Ofte, however, t wll be easer to solve the formula usg a dfferet table -- oe called the dscout table, showg the preset values for varous values of ad, ad also foud most mathematcs books. It ca be show that the followg mathematcal relatoshp exsts: 1 ( 1+ ) ( 1 ) = + Substtutg to our formula we fd that: P = A(1 + ) - = $20,000 (.7473) = $14,946 The preset value of the certfcate s $14,946 ad ca be expressed two ways: a) you have cocluded that you would be just as well off to receve $14,949 ow as to receve $20,000 fve years from ow, or b) you are cocludg that t would take $14,946 vested today at sx percet terest per aum to yeld $20,000 fve years. 2

3 Iterest or Dscout So far we have used the terms terest ad dscout. Oe should be able to dstgush betwee the two. I our frst llustrato, we started wth a preset sum ad moved to the future to determe the value of that sum for a future tme. I fact, whe movg from a earler tme to a future tme, terest s always volved. I our secod llustrato, the movemet was reversed,.e., we started wth a future sum ad determed the value of the sum at the preset tme. Therefore, whe movg from ay future tme to a earler tme, dscoutg s always volved. of Costat Aual Reveue Now let s assume that you are the maager of a frut warehouse. Your frm s stackg ts loose frut boxes o a oe-acre plot just to the rear of your warehouse. Had your frm ot owed ths plot of lad, t would probably have had to pay a aual ret of $200 for the prvlege of stackg ts boxes there. Cosderg the fact that owershp of ths acre allows your frm to avod ths retal fee, ths acre s earg your frm a equvalet of $200 per year. If you expect all frm assets to create a rate of retur of at least te percet, what s the captalzed (preset) value of the oe-acre storage area? To aswer ths questo, oe should use the followg formula: P = R where P = preset (or captalzed) value R = reveue (per coverso perod) = dscout rate (per coverso perod) I our llustrato, we see that: $200 P = = $2, I other words, accordg to your stated mmum rate of retur (dscout), the preset value of your storage area s $2,000. Moreover, ths ca be descrbed two ways: a) t s the preset value of a aual reveue of $200 for a defte tme, or b) t s the sum you would have to vest at te percet terest order to obta a aual retur of $200 for a defte umber of years. of a Auty for a Gve Tme Let s assume that as the maager of a truckg frm you are cosderg the purchase of a ew two-to truck. Accordg to your records, you ca expect the ew truck to produce aual et reveue of $1000 for ts useful lfe of te years, after whch o salvage value exsts. As our earler example, you have establshed a mmum requremet of a te percet rate of retur before you wll make ay ew vestmets. You ow wsh to determe the maxmum amout you could pay for the ew truck ad stll acheve your expected rate of retur. To solve ths problem, oe should use the followg formula: ( 1 ) 1 + P = R where: P = preset value R = reveue (per coverso perod; = terest rate (per coverso perod) = umber of coverso perods I our llustrato, the formula becomes: ( ) P = $1, = 1, = $6,145 3

4 Based o your expectatos ad your requred rate of retur, you could pay up to $6,145 for the ew truck ad stll have a satsfactory vestmet. Actually, ths aswer could be terpreted two ways: a) t s the preset value of te aual reveues of $1,000 each, wth reveues receved at the ed of each year ad wth a te percet dscout rate, ad b) t also represets the dollar amout, whch f vested at te percet terest, wll be completely exhausted after wthdrawals of $1,000 at the ed of each of te cosecutve years. of a Auty for a Gve Tme Let s assume that you have bee the maager of a cooperatve farm supply frm for twety years ad ow expect to retre fve years. Your board of drectors has just formed you that, apprecato of your may years of servce, they have decded to establsh a specal retremet fud for you. Each year, $2,000 s to be vested ths specal fud, whch wll receve a aual terest rate of te percet. At the tme of your retremet, fve years hece, the fud wll be gve to you as a gft. What wll be the total amout of moey receved at the tme of your retremet? To aswer ths questo, the followg formula should be used: R = ( 1 + ) 1 where: A= future value R= reveue (per coverso perod) = terest rate (per coverso) = umber of coverso perods I our llustrato, the formula becomes: A = $2, = $2, = $12,210 ( ) 5 The result of your frm vestg $2,000 each of fve cosecutve years at te percet terest, the reveues accumulatg alog wth terest, wll provde a retremet fud of $12,210. Rates ad Relatoshps I our llustratos, we used varyg terest ad dscout rates. What effect does the selected rate have o the partcular tme-value relatoshp? I fact, the effect may be postve or egatve, depedg o the partcular relatoshp to whch we are referrg. Table 1 llustrates the varyg effects. You wll otce Table 1 that creasg terest rates has a postve effect o the relatoshps cocered wth future values, ad creasg dscout rates has a egatve effect o the relatoshp s cocered wth preset values. Ths s, of course, completely logcal as a creased rate smply speeds up the compoudg or dscoutg process, the frst creasg the future value ad the latter decreasg the preset value. Tme ad Relatoshps I our llustratos, we also chose to use varyg tme perods. What effect does the selected tme perod legth have o the partcular tme-value relatoshp? Aga, the effect vares. I determg the preset 4

5 Table 1 The Effects of Rate Chages o Tme-Value Relatoshps Tme-Value Relatoshp If Iterest or (Dscout) Rate: The: Effect o Future or (Preset) Value of Preset Sum Postve of Future Sum () (Negatve) of Aual Reveue () (Negatve) of a Auty () (Negatve) of a Auty Postve value of costat aual reveue, tme perod legth has zero effect because the aual reveues are expected for a defte perod of tme. The other four tme-value relatoshps, however, are affected dfferetly by chages the tme perod legth selected. These varyg effects are show Table 2. (See top of ext page) You wll otce Table 2 that creasg tme perod legth has a postve effect o all relatoshps except for the preset value of a future sum. Aga, ths s completely logcal because as a gve sum s to be receved oly after a loger perod of tme, oe would expect ts preset value to decrease. Dverse Perods So far our dscusso, the coverso perod the llustratos has bee detcal wth the reveue perod,.e., occurrg oce at the ed of each year. For some agrbusess stuatos ths may ot be true, e.g., terest s ofte compouded semaually or mothly whle reveues (or paymets) occur aually, or reveues (or paymets) occur mothly whle terest s compouded quarterly. Whe terest (or dscoutg) ad reveues (or paymets) are based o detcal perods, our calculatos are relatvely smple, as show. Whe they are based o dverse perods, however, the calculatos become slghtly more complex. Two adjustmets must be made. Frst, the terest or dscout rate must be stated terms of a rate per coverso perod,.e., wth te percet terest compouded semaually, ths must be adjusted to fve percet per coverso perod. Secod, the tme perod volved must be stated terms of umber of coverso perods,.e., f terest s compouded semaually for fve years, the umber of coverso perods wll be te. The frst three relatoshps dscussed ths paper ca be adjusted to dverse perods usg 5

6 Table 2 The Effects of Tme Perod Legth Chages o Tme-Value Relatoshps Tme-Value Relatoshp If tme Perod Legth: The: Effect o Future or (Preset) Value of Preset Sum Postve of Future Sum (Negatve) of a Auty (Postve) of a Auty Postve the two-step coverso just descrbed above. The two auty relatoshps, however, requre a thrd coverso before they become adaptable to dverse perod. The auty formulae used earler cluded the term whch was defed as the umber of coverso perods. Uder dverse perods, the coverso perod must be cosdered as the base perod,.e., the perodc reveue (or paymet), the umber of paymets, ad the terest or dscout rate must be stated terms of the coverso perod. Now we must add a ew factor to our two auty formulae,.e., e s defed as the umber of perodc reveues (or paymets) each coverso perod. For example, f reveue s receved mothly ad dscoutg occurs semaually, ad the e s sx. R was defed earler as the reveue (paymet) per coverso perod. Uder dverse perods, where reveue ad coverso perods dffer, we see that R ow equals e multpled by the dollar reveue (or paymet). Whe determg the preset value of a auty uder dverse perods, we use the same formula show for detcal perods except that we sert a correcto factor whch we shall deote as as follows: ( 1 ) 1 + P = R where s selected from a coverso factor table for the approprate values of e ad. Usg our earler llustrato o the purchase of a ew two-to truck, suppose we ow chage the percet aual dscout rate to a sem-aual rate. The coverso perod s ow sx moths ad our formula becomes: 20 1 (1.05) P = $500 (.9756) = $500 (.9756).05 = $6,080 6

7 where: P = preset value R = reveue (per coverso perod) = $500 = ½ of $1,000 receved aually = correcto factor (table value for, e) = 5 percet dscout rate (per coverso perod) e = ½ = umber of reveues (per coverso perod) = 20 = umber coverso perods (durg te years) If the dscout rate had remaed at te percet aually, but the $1,000 aual reveue were chaged to $500 receved semaually, the we see that R = $1,000, = 10, e = 2, = 10, ad would be selected from the coverso table for the approprate values of ad e. If the future value of a auty s to be determed wth dverse perods, the correcto factor would aga be serted to our earler formula to form: A = R Note: ( 1 + ) 1 I workg wth autes, we defed R as the reveue (or paymet) per coverso perod. I workg wth terest or dscout rates, we referred to the future or preset value of a sum. The llustratos used were descrbed mostly as vestmets. Had we bee referrg to costs, rather tha reveues (or paymets), the calculatos would ot have chaged, for a sum ca be receved as a come, or pad as a cost. Summary Fve dfferet relatoshps betwee tme ad the value of moey have bee descrbed. Each relatoshp s desged to ft a dfferet stuato so far as t apples to preset or future value ad dscout or terest rates. Every agrbusess maager must make decsos cocerg the vestmet of avalable captal. Before such decsos ca be made tellgetly, the maager must recogze, uderstad ad apply umerous tme-value relatoshps. Ths paper presets several tme-value llustratos a attempt to assst the maager developg ths recogto, uderstadg, ad ablty. The effects of chages rates, tme perod legths, ad dverse perods o tme-value relatoshps were also cosdered. Those relatoshps descrbed were: a) future value of a preset sum, b) preset value of a future sum, c) preset value of a costat aual reveue, d) preset value of a auty, ad e) future value of a auty. For a more detaled dscusso of the cocepts oted ths paper, the reader s ecouraged to wrte to the Supertedet of Documets, U. S. Govermet Prtg Offce, Washgto, D.C for a copy of Agrcultural Hadbook No. 349, The Evaluato of Ivestmet Opportutes by Arthur J. Wabrath ad W. L. Gbso, Jr., February 1968, U.S.D.A. Ke D. Duft Exteso Marketg Ecoomst 7

10.5 Future Value and Present Value of a General Annuity Due

10.5 Future Value and Present Value of a General Annuity Due Chapter 10 Autes 371 5. Thomas leases a car worth $4,000 at.99% compouded mothly. He agrees to make 36 lease paymets of $330 each at the begg of every moth. What s the buyout prce (resdual value of the

More information

1. The Time Value of Money

1. The Time Value of Money Corporate Face [00-0345]. The Tme Value of Moey. Compoudg ad Dscoutg Captalzato (compoudg, fdg future values) s a process of movg a value forward tme. It yelds the future value gve the relevat compoudg

More information

Classic Problems at a Glance using the TVM Solver

Classic Problems at a Glance using the TVM Solver C H A P T E R 2 Classc Problems at a Glace usg the TVM Solver The table below llustrates the most commo types of classc face problems. The formulas are gve for each calculato. A bref troducto to usg the

More information

Chapter 3 0.06 = 3000 ( 1.015 ( 1 ) Present Value of an Annuity. Section 4 Present Value of an Annuity; Amortization

Chapter 3 0.06 = 3000 ( 1.015 ( 1 ) Present Value of an Annuity. Section 4 Present Value of an Annuity; Amortization Chapter 3 Mathematcs of Face Secto 4 Preset Value of a Auty; Amortzato Preset Value of a Auty I ths secto, we wll address the problem of determg the amout that should be deposted to a accout ow at a gve

More information

The Time Value of Money

The Time Value of Money The Tme Value of Moey 1 Iversemet Optos Year: 1624 Property Traded: Mahatta Islad Prce : $24.00, FV of $24 @ 6%: FV = $24 (1+0.06) 388 = $158.08 bllo Opto 1 0 1 2 3 4 5 t ($519.37) 0 0 0 0 $1,000 Opto

More information

FINANCIAL MATHEMATICS 12 MARCH 2014

FINANCIAL MATHEMATICS 12 MARCH 2014 FINNCIL MTHEMTICS 12 MRCH 2014 I ths lesso we: Lesso Descrpto Make use of logarthms to calculate the value of, the tme perod, the equato P1 or P1. Solve problems volvg preset value ad future value autes.

More information

Chapter 3. AMORTIZATION OF LOAN. SINKING FUNDS R =

Chapter 3. AMORTIZATION OF LOAN. SINKING FUNDS R = Chapter 3. AMORTIZATION OF LOAN. SINKING FUNDS Objectves of the Topc: Beg able to formalse ad solve practcal ad mathematcal problems, whch the subjects of loa amortsato ad maagemet of cumulatve fuds are

More information

CHAPTER 2. Time Value of Money 6-1

CHAPTER 2. Time Value of Money 6-1 CHAPTER 2 Tme Value of Moey 6- Tme Value of Moey (TVM) Tme Les Future value & Preset value Rates of retur Autes & Perpetutes Ueve cash Flow Streams Amortzato 6-2 Tme les 0 2 3 % CF 0 CF CF 2 CF 3 Show

More information

10/19/2011. Financial Mathematics. Lecture 24 Annuities. Ana NoraEvans 403 Kerchof AnaNEvans@virginia.edu http://people.virginia.

10/19/2011. Financial Mathematics. Lecture 24 Annuities. Ana NoraEvans 403 Kerchof AnaNEvans@virginia.edu http://people.virginia. Math 40 Lecture 24 Autes Facal Mathematcs How ready do you feel for the quz o Frday: A) Brg t o B) I wll be by Frday C) I eed aother week D) I eed aother moth Aa NoraEvas 403 Kerchof AaNEvas@vrga.edu http://people.vrga.edu/~as5k/

More information

The analysis of annuities relies on the formula for geometric sums: r k = rn+1 1 r 1. (2.1) k=0

The analysis of annuities relies on the formula for geometric sums: r k = rn+1 1 r 1. (2.1) k=0 Chapter 2 Autes ad loas A auty s a sequece of paymets wth fxed frequecy. The term auty orgally referred to aual paymets (hece the ame), but t s ow also used for paymets wth ay frequecy. Autes appear may

More information

Time Value of Money. (1) Calculate future value or present value or annuity? (2) Future value = PV * (1+ i) n

Time Value of Money. (1) Calculate future value or present value or annuity? (2) Future value = PV * (1+ i) n Problem 1 Happy Harry has just bought a scratch lottery tcket ad wo 10,000. He wats to face the future study of hs ewly bor daughter ad vests ths moey a fud wth a maturty of 18 years offerg a promsg yearly

More information

Mathematics of Finance

Mathematics of Finance CATE Mathematcs of ace.. TODUCTO ths chapter we wll dscuss mathematcal methods ad formulae whch are helpful busess ad persoal face. Oe of the fudametal cocepts the mathematcs of face s the tme value of

More information

T = 1/freq, T = 2/freq, T = i/freq, T = n (number of cash flows = freq n) are :

T = 1/freq, T = 2/freq, T = i/freq, T = n (number of cash flows = freq n) are : Bullets bods Let s descrbe frst a fxed rate bod wthout amortzg a more geeral way : Let s ote : C the aual fxed rate t s a percetage N the otoal freq ( 2 4 ) the umber of coupo per year R the redempto of

More information

FINANCIAL FORMULAE. Amount of One or Future Value of One ($1, 1, 1, etc.)... 2. Present Value (or Present Worth) of One ($1, 1, 1, etc.)...

FINANCIAL FORMULAE. Amount of One or Future Value of One ($1, 1, 1, etc.)... 2. Present Value (or Present Worth) of One ($1, 1, 1, etc.)... Amout of Oe or Future Value of Oe ($,,, etc.)... 2 Preset Value (or Preset Worth) of Oe ($,,, etc.)... 2 Amout of Oe per Perod... 3 or Future Value of Oe per Perod Preset Value (or Preset Worth) of Oe

More information

Abraham Zaks. Technion I.I.T. Haifa ISRAEL. and. University of Haifa, Haifa ISRAEL. Abstract

Abraham Zaks. Technion I.I.T. Haifa ISRAEL. and. University of Haifa, Haifa ISRAEL. Abstract Preset Value of Autes Uder Radom Rates of Iterest By Abraham Zas Techo I.I.T. Hafa ISRAEL ad Uversty of Hafa, Hafa ISRAEL Abstract Some attempts were made to evaluate the future value (FV) of the expected

More information

How To Value An Annuity

How To Value An Annuity Future Value of a Auty After payg all your blls, you have $200 left each payday (at the ed of each moth) that you wll put to savgs order to save up a dow paymet for a house. If you vest ths moey at 5%

More information

Average Price Ratios

Average Price Ratios Average Prce Ratos Morgstar Methodology Paper August 3, 2005 2005 Morgstar, Ic. All rghts reserved. The formato ths documet s the property of Morgstar, Ic. Reproducto or trascrpto by ay meas, whole or

More information

Banking (Early Repayment of Housing Loans) Order, 5762 2002 1

Banking (Early Repayment of Housing Loans) Order, 5762 2002 1 akg (Early Repaymet of Housg Loas) Order, 5762 2002 y vrtue of the power vested me uder Secto 3 of the akg Ordace 94 (hereafter, the Ordace ), followg cosultato wth the Commttee, ad wth the approval of

More information

ANOVA Notes Page 1. Analysis of Variance for a One-Way Classification of Data

ANOVA Notes Page 1. Analysis of Variance for a One-Way Classification of Data ANOVA Notes Page Aalss of Varace for a Oe-Wa Classfcato of Data Cosder a sgle factor or treatmet doe at levels (e, there are,, 3, dfferet varatos o the prescrbed treatmet) Wth a gve treatmet level there

More information

APPENDIX III THE ENVELOPE PROPERTY

APPENDIX III THE ENVELOPE PROPERTY Apped III APPENDIX III THE ENVELOPE PROPERTY Optmzato mposes a very strog structure o the problem cosdered Ths s the reaso why eoclasscal ecoomcs whch assumes optmzg behavour has bee the most successful

More information

ANNEX 77 FINANCE MANAGEMENT. (Working material) Chief Actuary Prof. Gaida Pettere BTA INSURANCE COMPANY SE

ANNEX 77 FINANCE MANAGEMENT. (Working material) Chief Actuary Prof. Gaida Pettere BTA INSURANCE COMPANY SE ANNEX 77 FINANCE MANAGEMENT (Workg materal) Chef Actuary Prof. Gada Pettere BTA INSURANCE COMPANY SE 1 FUNDAMENTALS of INVESTMENT I THEORY OF INTEREST RATES 1.1 ACCUMULATION Iterest may be regarded as

More information

ECONOMIC CHOICE OF OPTIMUM FEEDER CABLE CONSIDERING RISK ANALYSIS. University of Brasilia (UnB) and The Brazilian Regulatory Agency (ANEEL), Brazil

ECONOMIC CHOICE OF OPTIMUM FEEDER CABLE CONSIDERING RISK ANALYSIS. University of Brasilia (UnB) and The Brazilian Regulatory Agency (ANEEL), Brazil ECONOMIC CHOICE OF OPTIMUM FEEDER CABE CONSIDERING RISK ANAYSIS I Camargo, F Fgueredo, M De Olvera Uversty of Brasla (UB) ad The Brazla Regulatory Agecy (ANEE), Brazl The choce of the approprate cable

More information

Report 52 Fixed Maturity EUR Industrial Bond Funds

Report 52 Fixed Maturity EUR Industrial Bond Funds Rep52, Computed & Prted: 17/06/2015 11:53 Report 52 Fxed Maturty EUR Idustral Bod Fuds From Dec 2008 to Dec 2014 31/12/2008 31 December 1999 31/12/2014 Bechmark Noe Defto of the frm ad geeral formato:

More information

The Present Value of an Annuity

The Present Value of an Annuity Module 4.4 Page 492 of 944. Module 4.4: The Preset Value of a Auty Here we wll lear about a very mportat formula: the preset value of a auty. Ths formula s used wheever there s a seres of detcal paymets

More information

Online Appendix: Measured Aggregate Gains from International Trade

Online Appendix: Measured Aggregate Gains from International Trade Ole Appedx: Measured Aggregate Gas from Iteratoal Trade Arel Burste UCLA ad NBER Javer Cravo Uversty of Mchga March 3, 2014 I ths ole appedx we derve addtoal results dscussed the paper. I the frst secto,

More information

6.7 Network analysis. 6.7.1 Introduction. References - Network analysis. Topological analysis

6.7 Network analysis. 6.7.1 Introduction. References - Network analysis. Topological analysis 6.7 Network aalyss Le data that explctly store topologcal formato are called etwork data. Besdes spatal operatos, several methods of spatal aalyss are applcable to etwork data. Fgure: Network data Refereces

More information

Commercial Pension Insurance Program Design and Estimated of Tax Incentives---- Based on Analysis of Enterprise Annuity Tax Incentives

Commercial Pension Insurance Program Design and Estimated of Tax Incentives---- Based on Analysis of Enterprise Annuity Tax Incentives Iteratoal Joural of Busess ad Socal Scece Vol 5, No ; October 204 Commercal Peso Isurace Program Desg ad Estmated of Tax Icetves---- Based o Aalyss of Eterprse Auty Tax Icetves Huag Xue, Lu Yatg School

More information

Learning objectives. Duc K. Nguyen - Corporate Finance 21/10/2014

Learning objectives. Duc K. Nguyen - Corporate Finance 21/10/2014 1 Lecture 3 Time Value of Moey ad Project Valuatio The timelie Three rules of time travels NPV of a stream of cash flows Perpetuities, auities ad other special cases Learig objectives 2 Uderstad the time-value

More information

Present Value Factor To bring one dollar in the future back to present, one uses the Present Value Factor (PVF): Concept 9: Present Value

Present Value Factor To bring one dollar in the future back to present, one uses the Present Value Factor (PVF): Concept 9: Present Value Cocept 9: Preset Value Is the value of a dollar received today the same as received a year from today? A dollar today is worth more tha a dollar tomorrow because of iflatio, opportuity cost, ad risk Brigig

More information

Report 19 Euroland Corporate Bonds

Report 19 Euroland Corporate Bonds Rep19, Computed & Prted: 17/06/2015 11:38 Report 19 Eurolad Corporate Bods From Dec 1999 to Dec 2014 31/12/1999 31 December 1999 31/12/2014 Bechmark 100% IBOXX Euro Corp All Mats. TR Defto of the frm ad

More information

Performance Attribution. Methodology Overview

Performance Attribution. Methodology Overview erformace Attrbuto Methodology Overvew Faba SUAREZ March 2004 erformace Attrbuto Methodology 1.1 Itroducto erformace Attrbuto s a set of techques that performace aalysts use to expla why a portfolo's performace

More information

An Effectiveness of Integrated Portfolio in Bancassurance

An Effectiveness of Integrated Portfolio in Bancassurance A Effectveess of Itegrated Portfolo Bacassurace Taea Karya Research Ceter for Facal Egeerg Isttute of Ecoomc Research Kyoto versty Sayouu Kyoto 606-850 Japa arya@eryoto-uacp Itroducto As s well ow the

More information

CHAPTER 3 THE TIME VALUE OF MONEY

CHAPTER 3 THE TIME VALUE OF MONEY CHAPTER 3 THE TIME VALUE OF MONEY OVERVIEW A dollar i the had today is worth more tha a dollar to be received i the future because, if you had it ow, you could ivest that dollar ad ear iterest. Of all

More information

Optimal replacement and overhaul decisions with imperfect maintenance and warranty contracts

Optimal replacement and overhaul decisions with imperfect maintenance and warranty contracts Optmal replacemet ad overhaul decsos wth mperfect mateace ad warraty cotracts R. Pascual Departmet of Mechacal Egeerg, Uversdad de Chle, Caslla 2777, Satago, Chle Phoe: +56-2-6784591 Fax:+56-2-689657 rpascual@g.uchle.cl

More information

IDENTIFICATION OF THE DYNAMICS OF THE GOOGLE S RANKING ALGORITHM. A. Khaki Sedigh, Mehdi Roudaki

IDENTIFICATION OF THE DYNAMICS OF THE GOOGLE S RANKING ALGORITHM. A. Khaki Sedigh, Mehdi Roudaki IDENIFICAION OF HE DYNAMICS OF HE GOOGLE S RANKING ALGORIHM A. Khak Sedgh, Mehd Roudak Cotrol Dvso, Departmet of Electrcal Egeerg, K.N.oos Uversty of echology P. O. Box: 16315-1355, ehra, Ira sedgh@eetd.ktu.ac.r,

More information

The Gompertz-Makeham distribution. Fredrik Norström. Supervisor: Yuri Belyaev

The Gompertz-Makeham distribution. Fredrik Norström. Supervisor: Yuri Belyaev The Gompertz-Makeham dstrbuto by Fredrk Norström Master s thess Mathematcal Statstcs, Umeå Uversty, 997 Supervsor: Yur Belyaev Abstract Ths work s about the Gompertz-Makeham dstrbuto. The dstrbuto has

More information

FI A CIAL MATHEMATICS

FI A CIAL MATHEMATICS CHAPTER 7 FI A CIAL MATHEMATICS Page Cotets 7.1 Compoud Value 117 7.2 Compoud Value of a Auity 118 7.3 Sikig Fuds 119 7.4 Preset Value 122 7.5 Preset Value of a Auity 122 7.6 Term Loas ad Amortizatio 123

More information

Beta. A Statistical Analysis of a Stock s Volatility. Courtney Wahlstrom. Iowa State University, Master of School Mathematics. Creative Component

Beta. A Statistical Analysis of a Stock s Volatility. Courtney Wahlstrom. Iowa State University, Master of School Mathematics. Creative Component Beta A Statstcal Aalyss of a Stock s Volatlty Courtey Wahlstrom Iowa State Uversty, Master of School Mathematcs Creatve Compoet Fall 008 Amy Froelch, Major Professor Heather Bolles, Commttee Member Travs

More information

.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth

.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth Questio 1: What is a ordiary auity? Let s look at a ordiary auity that is certai ad simple. By this, we mea a auity over a fixed term whose paymet period matches the iterest coversio period. Additioally,

More information

5.4 Amortization. Question 1: How do you find the present value of an annuity? Question 2: How is a loan amortized?

5.4 Amortization. Question 1: How do you find the present value of an annuity? Question 2: How is a loan amortized? 5.4 Amortizatio Questio 1: How do you fid the preset value of a auity? Questio 2: How is a loa amortized? Questio 3: How do you make a amortizatio table? Oe of the most commo fiacial istrumets a perso

More information

Reinsurance and the distribution of term insurance claims

Reinsurance and the distribution of term insurance claims Resurace ad the dstrbuto of term surace clams By Rchard Bruyel FIAA, FNZSA Preseted to the NZ Socety of Actuares Coferece Queestow - November 006 1 1 Itroducto Ths paper vestgates the effect of resurace

More information

Simple Annuities Present Value.

Simple Annuities Present Value. Simple Auities Preset Value. OBJECTIVES (i) To uderstad the uderlyig priciple of a preset value auity. (ii) To use a CASIO CFX-9850GB PLUS to efficietly compute values associated with preset value auities.

More information

Simple Linear Regression

Simple Linear Regression Smple Lear Regresso Regresso equato a equato that descrbes the average relatoshp betwee a respose (depedet) ad a eplaator (depedet) varable. 6 8 Slope-tercept equato for a le m b (,6) slope. (,) 6 6 8

More information

Report 06 Global High Yield Bonds

Report 06 Global High Yield Bonds Rep06, Computed & Prted: 17/06/2015 11:25 Report 06 Global Hgh Yeld Bods From Dec 2000 to Dec 2014 31/12/2000 31 December 1999 31/12/2014 New Bechmark (01/01/13) 80% Barclays Euro HY Ex Facals 3% Capped

More information

Curve Fitting and Solution of Equation

Curve Fitting and Solution of Equation UNIT V Curve Fttg ad Soluto of Equato 5. CURVE FITTING I ma braches of appled mathematcs ad egeerg sceces we come across epermets ad problems, whch volve two varables. For eample, t s kow that the speed

More information

A New Bayesian Network Method for Computing Bottom Event's Structural Importance Degree using Jointree

A New Bayesian Network Method for Computing Bottom Event's Structural Importance Degree using Jointree , pp.277-288 http://dx.do.org/10.14257/juesst.2015.8.1.25 A New Bayesa Network Method for Computg Bottom Evet's Structural Importace Degree usg Jotree Wag Yao ad Su Q School of Aeroautcs, Northwester Polytechcal

More information

ADAPTATION OF SHAPIRO-WILK TEST TO THE CASE OF KNOWN MEAN

ADAPTATION OF SHAPIRO-WILK TEST TO THE CASE OF KNOWN MEAN Colloquum Bometrcum 4 ADAPTATION OF SHAPIRO-WILK TEST TO THE CASE OF KNOWN MEAN Zofa Hausz, Joaa Tarasńska Departmet of Appled Mathematcs ad Computer Scece Uversty of Lfe Sceces Lubl Akademcka 3, -95 Lubl

More information

Report 05 Global Fixed Income

Report 05 Global Fixed Income Report 05 Global Fxed Icome From Dec 1999 to Dec 2014 31/12/1999 31 December 1999 31/12/2014 Rep05, Computed & Prted: 17/06/2015 11:24 New Performace Idcator (01/01/12) 100% Barclays Aggregate Global Credt

More information

Models for Selecting an ERP System with Intuitionistic Trapezoidal Fuzzy Information

Models for Selecting an ERP System with Intuitionistic Trapezoidal Fuzzy Information JOURNAL OF SOFWARE, VOL 5, NO 3, MARCH 00 75 Models for Selectg a ERP System wth Itutostc rapezodal Fuzzy Iformato Guwu We, Ru L Departmet of Ecoomcs ad Maagemet, Chogqg Uversty of Arts ad Sceces, Yogchua,

More information

Using Series to Analyze Financial Situations: Present Value

Using Series to Analyze Financial Situations: Present Value 2.8 Usng Seres to Analyze Fnancal Stuatons: Present Value In the prevous secton, you learned how to calculate the amount, or future value, of an ordnary smple annuty. The amount s the sum of the accumulated

More information

On Error Detection with Block Codes

On Error Detection with Block Codes BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 9, No 3 Sofa 2009 O Error Detecto wth Block Codes Rostza Doduekova Chalmers Uversty of Techology ad the Uversty of Gotheburg,

More information

FM4 CREDIT AND BORROWING

FM4 CREDIT AND BORROWING FM4 CREDIT AND BORROWING Whe you purchase big ticket items such as cars, boats, televisios ad the like, retailers ad fiacial istitutios have various terms ad coditios that are implemeted for the cosumer

More information

The paper presents Constant Rebalanced Portfolio first introduced by Thomas

The paper presents Constant Rebalanced Portfolio first introduced by Thomas Itroducto The paper presets Costat Rebalaced Portfolo frst troduced by Thomas Cover. There are several weakesses of ths approach. Oe s that t s extremely hard to fd the optmal weghts ad the secod weakess

More information

Green Master based on MapReduce Cluster

Green Master based on MapReduce Cluster Gree Master based o MapReduce Cluster Mg-Zh Wu, Yu-Chag L, We-Tsog Lee, Yu-Su L, Fog-Hao Lu Dept of Electrcal Egeerg Tamkag Uversty, Tawa, ROC Dept of Electrcal Egeerg Tamkag Uversty, Tawa, ROC Dept of

More information

Chapter Eight. f : R R

Chapter Eight. f : R R Chapter Eght f : R R 8. Itroducto We shall ow tur our atteto to the very mportat specal case of fuctos that are real, or scalar, valued. These are sometmes called scalar felds. I the very, but mportat,

More information

Credibility Premium Calculation in Motor Third-Party Liability Insurance

Credibility Premium Calculation in Motor Third-Party Liability Insurance Advaces Mathematcal ad Computatoal Methods Credblty remum Calculato Motor Thrd-arty Lablty Isurace BOHA LIA, JAA KUBAOVÁ epartmet of Mathematcs ad Quattatve Methods Uversty of ardubce Studetská 95, 53

More information

RUSSIAN ROULETTE AND PARTICLE SPLITTING

RUSSIAN ROULETTE AND PARTICLE SPLITTING RUSSAN ROULETTE AND PARTCLE SPLTTNG M. Ragheb 3/7/203 NTRODUCTON To stuatos are ecoutered partcle trasport smulatos:. a multplyg medum, a partcle such as a eutro a cosmc ray partcle or a photo may geerate

More information

Numerical Methods with MS Excel

Numerical Methods with MS Excel TMME, vol4, o.1, p.84 Numercal Methods wth MS Excel M. El-Gebely & B. Yushau 1 Departmet of Mathematcal Sceces Kg Fahd Uversty of Petroleum & Merals. Dhahra, Saud Araba. Abstract: I ths ote we show how

More information

Terminology for Bonds and Loans

Terminology for Bonds and Loans ³ ² ± Termiology for Bods ad Loas Pricipal give to borrower whe loa is made Simple loa: pricipal plus iterest repaid at oe date Fixed-paymet loa: series of (ofte equal) repaymets Bod is issued at some

More information

The Analysis of Development of Insurance Contract Premiums of General Liability Insurance in the Business Insurance Risk

The Analysis of Development of Insurance Contract Premiums of General Liability Insurance in the Business Insurance Risk The Aalyss of Developmet of Isurace Cotract Premums of Geeral Lablty Isurace the Busess Isurace Rsk the Frame of the Czech Isurace Market 1998 011 Scetfc Coferece Jue, 10. - 14. 013 Pavla Kubová Departmet

More information

Section 2.3 Present Value of an Annuity; Amortization

Section 2.3 Present Value of an Annuity; Amortization Secton 2.3 Present Value of an Annuty; Amortzaton Prncpal Intal Value PV s the present value or present sum of the payments. PMT s the perodc payments. Gven r = 6% semannually, n order to wthdraw $1,000.00

More information

Agent-based modeling and simulation of multiproject

Agent-based modeling and simulation of multiproject Aget-based modelg ad smulato of multproject schedulg José Alberto Araúzo, Javer Pajares, Adolfo Lopez- Paredes Socal Systems Egeerg Cetre (INSISOC) Uversty of Valladold Valladold (Spa) {arauzo,pajares,adolfo}ssoc.es

More information

2 Time Value of Money

2 Time Value of Money 2 Time Value of Moey BASIC CONCEPTS AND FORMULAE 1. Time Value of Moey It meas moey has time value. A rupee today is more valuable tha a rupee a year hece. We use rate of iterest to express the time value

More information

Preprocess a planar map S. Given a query point p, report the face of S containing p. Goal: O(n)-size data structure that enables O(log n) query time.

Preprocess a planar map S. Given a query point p, report the face of S containing p. Goal: O(n)-size data structure that enables O(log n) query time. Computatoal Geometry Chapter 6 Pot Locato 1 Problem Defto Preprocess a plaar map S. Gve a query pot p, report the face of S cotag p. S Goal: O()-sze data structure that eables O(log ) query tme. C p E

More information

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ  1 STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS Recall Assumpto E(Y x) η 0 + η x (lear codtoal mea fucto) Data (x, y ), (x 2, y 2 ),, (x, y ) Least squares estmator ˆ E (Y x) ˆ " 0 + ˆ " x, where ˆ

More information

AP Statistics 2006 Free-Response Questions Form B

AP Statistics 2006 Free-Response Questions Form B AP Statstcs 006 Free-Respose Questos Form B The College Board: Coectg Studets to College Success The College Board s a ot-for-proft membershp assocato whose msso s to coect studets to college success ad

More information

SHAPIRO-WILK TEST FOR NORMALITY WITH KNOWN MEAN

SHAPIRO-WILK TEST FOR NORMALITY WITH KNOWN MEAN SHAPIRO-WILK TEST FOR NORMALITY WITH KNOWN MEAN Wojcech Zelńsk Departmet of Ecoometrcs ad Statstcs Warsaw Uversty of Lfe Sceces Nowoursyowska 66, -787 Warszawa e-mal: wojtekzelsk@statystykafo Zofa Hausz,

More information

1,30 1,20 1,10 1,00 0,90 0,80 / S EV / OIBDA P / E

1,30 1,20 1,10 1,00 0,90 0,80 / S EV / OIBDA P / E Ivestmet compay Research July 30, 2008 CTC Meda Busess expads Russa, Moscow, 123610, Krasopreseskaya ab. 12, 7 th gate, 18 th fl. Tel.: 7 (495) 258 1988 Fax: 7 (495) 258 1989 Aalyst: E-mal: Recommedato

More information

DECISION MAKING WITH THE OWA OPERATOR IN SPORT MANAGEMENT

DECISION MAKING WITH THE OWA OPERATOR IN SPORT MANAGEMENT ESTYLF08, Cuecas Meras (Meres - Lagreo), 7-9 de Septembre de 2008 DECISION MAKING WITH THE OWA OPERATOR IN SPORT MANAGEMENT José M. Mergó Aa M. Gl-Lafuete Departmet of Busess Admstrato, Uversty of Barceloa

More information

Fix or Evict? Loan Modifications Return More Value Than Foreclosures

Fix or Evict? Loan Modifications Return More Value Than Foreclosures Fx or Evct? Loa Modfcatos etur More Value Tha Foreclosures We L ad Soa arrso March, 0 www.resposbleledg.org Fx or Evct? Loa Modfcatos etur More Value Tha Foreclosures We L ad Soa arrso Ceter for esposble

More information

MDM 4U PRACTICE EXAMINATION

MDM 4U PRACTICE EXAMINATION MDM 4U RCTICE EXMINTION Ths s a ractce eam. It does ot cover all the materal ths course ad should ot be the oly revew that you do rearato for your fal eam. Your eam may cota questos that do ot aear o ths

More information

Capacitated Production Planning and Inventory Control when Demand is Unpredictable for Most Items: The No B/C Strategy

Capacitated Production Planning and Inventory Control when Demand is Unpredictable for Most Items: The No B/C Strategy SCHOOL OF OPERATIONS RESEARCH AND INDUSTRIAL ENGINEERING COLLEGE OF ENGINEERING CORNELL UNIVERSITY ITHACA, NY 4853-380 TECHNICAL REPORT Jue 200 Capactated Producto Plag ad Ivetory Cotrol whe Demad s Upredctable

More information

CHAPTER 13. Simple Linear Regression LEARNING OBJECTIVES. USING STATISTICS @ Sunflowers Apparel

CHAPTER 13. Simple Linear Regression LEARNING OBJECTIVES. USING STATISTICS @ Sunflowers Apparel CHAPTER 3 Smple Lear Regresso USING STATISTICS @ Suflowers Apparel 3 TYPES OF REGRESSION MODELS 3 DETERMINING THE SIMPLE LINEAR REGRESSION EQUATION The Least-Squares Method Vsual Exploratos: Explorg Smple

More information

MMQ Problems Solutions with Calculators. Managerial Finance

MMQ Problems Solutions with Calculators. Managerial Finance MMQ Problems Solutios with Calculators Maagerial Fiace 2008 Adrew Hall. MMQ Solutios With Calculators. Page 1 MMQ 1: Suppose Newma s spi lads o the prize of $100 to be collected i exactly 2 years, but

More information

Time Value of Money. First some technical stuff. HP10B II users

Time Value of Money. First some technical stuff. HP10B II users Time Value of Moey Basis for the course Power of compoud iterest $3,600 each year ito a 401(k) pla yields $2,390,000 i 40 years First some techical stuff You will use your fiacial calculator i every sigle

More information

RQM: A new rate-based active queue management algorithm

RQM: A new rate-based active queue management algorithm : A ew rate-based actve queue maagemet algorthm Jeff Edmods, Suprakash Datta, Patrck Dymod, Kashf Al Computer Scece ad Egeerg Departmet, York Uversty, Toroto, Caada Abstract I ths paper, we propose a ew

More information

n. We know that the sum of squares of p independent standard normal variables has a chi square distribution with p degrees of freedom.

n. We know that the sum of squares of p independent standard normal variables has a chi square distribution with p degrees of freedom. UMEÅ UNIVERSITET Matematsk-statstska sttutoe Multvarat dataaalys för tekologer MSTB0 PA TENTAMEN 004-0-9 LÖSNINGSFÖRSLAG TILL TENTAMEN I MATEMATISK STATISTIK Multvarat dataaalys för tekologer B, 5 poäg.

More information

We present a new approach to pricing American-style derivatives that is applicable to any Markovian setting

We present a new approach to pricing American-style derivatives that is applicable to any Markovian setting MANAGEMENT SCIENCE Vol. 52, No., Jauary 26, pp. 95 ss 25-99 ess 526-55 6 52 95 forms do.287/msc.5.447 26 INFORMS Prcg Amerca-Style Dervatves wth Europea Call Optos Scott B. Laprse BAE Systems, Advaced

More information

Chapter 5 Unit 1. IET 350 Engineering Economics. Learning Objectives Chapter 5. Learning Objectives Unit 1. Annual Amount and Gradient Functions

Chapter 5 Unit 1. IET 350 Engineering Economics. Learning Objectives Chapter 5. Learning Objectives Unit 1. Annual Amount and Gradient Functions Chapter 5 Uit Aual Amout ad Gradiet Fuctios IET 350 Egieerig Ecoomics Learig Objectives Chapter 5 Upo completio of this chapter you should uderstad: Calculatig future values from aual amouts. Calculatig

More information

Australian Climate Change Adaptation Network for Settlements and Infrastructure. Discussion Paper February 2010

Australian Climate Change Adaptation Network for Settlements and Infrastructure. Discussion Paper February 2010 Australa Clmate Chage Adaptato Network for Settlemets ad Ifrastructure Dscusso Paper February 2010 The corporato of ucertaty assocated wth clmate chage to frastructure vestmet apprasal Davd G. Carmchael

More information

Speeding up k-means Clustering by Bootstrap Averaging

Speeding up k-means Clustering by Bootstrap Averaging Speedg up -meas Clusterg by Bootstrap Averagg Ia Davdso ad Ashw Satyaarayaa Computer Scece Dept, SUNY Albay, NY, USA,. {davdso, ashw}@cs.albay.edu Abstract K-meas clusterg s oe of the most popular clusterg

More information

How To Make A Supply Chain System Work

How To Make A Supply Chain System Work Iteratoal Joural of Iformato Techology ad Kowledge Maagemet July-December 200, Volume 2, No. 2, pp. 3-35 LATERAL TRANSHIPMENT-A TECHNIQUE FOR INVENTORY CONTROL IN MULTI RETAILER SUPPLY CHAIN SYSTEM Dharamvr

More information

The simple linear Regression Model

The simple linear Regression Model The smple lear Regresso Model Correlato coeffcet s o-parametrc ad just dcates that two varables are assocated wth oe aother, but t does ot gve a deas of the kd of relatoshp. Regresso models help vestgatg

More information

Statistical Pattern Recognition (CE-725) Department of Computer Engineering Sharif University of Technology

Statistical Pattern Recognition (CE-725) Department of Computer Engineering Sharif University of Technology I The Name of God, The Compassoate, The ercful Name: Problems' eys Studet ID#:. Statstcal Patter Recogto (CE-725) Departmet of Computer Egeerg Sharf Uversty of Techology Fal Exam Soluto - Sprg 202 (50

More information

Study on prediction of network security situation based on fuzzy neutral network

Study on prediction of network security situation based on fuzzy neutral network Avalable ole www.ocpr.com Joural of Chemcal ad Pharmaceutcal Research, 04, 6(6):00-06 Research Artcle ISS : 0975-7384 CODE(USA) : JCPRC5 Study o predcto of etwork securty stuato based o fuzzy eutral etwork

More information

7.5. Present Value of an Annuity. Investigate

7.5. Present Value of an Annuity. Investigate 7.5 Present Value of an Annuty Owen and Anna are approachng retrement and are puttng ther fnances n order. They have worked hard and nvested ther earnngs so that they now have a large amount of money on

More information

Optimization Model in Human Resource Management for Job Allocation in ICT Project

Optimization Model in Human Resource Management for Job Allocation in ICT Project Optmzato Model Huma Resource Maagemet for Job Allocato ICT Project Optmzato Model Huma Resource Maagemet for Job Allocato ICT Project Saghamtra Mohaty Malaya Kumar Nayak 2 2 Professor ad Head Research

More information

Time Value of Money, NPV and IRR equation solving with the TI-86

Time Value of Money, NPV and IRR equation solving with the TI-86 Time Value of Moey NPV ad IRR Equatio Solvig with the TI-86 (may work with TI-85) (similar process works with TI-83, TI-83 Plus ad may work with TI-82) Time Value of Moey, NPV ad IRR equatio solvig with

More information

An Application of Graph Theory in the Process of Mutual Debt Compensation

An Application of Graph Theory in the Process of Mutual Debt Compensation Acta Poltechca Hugarca ol. 12 No. 3 2015 A Applcato of Graph Theor the Process of Mutual Debt Compesato ladmír Gazda Des Horváth Marcel Rešovský Techcal Uverst of Košce Facult of Ecoomcs; Němcove 32 040

More information

USEFULNESS OF BOOTSTRAPPING IN PORTFOLIO MANAGEMENT

USEFULNESS OF BOOTSTRAPPING IN PORTFOLIO MANAGEMENT USEFULNESS OF BOOTSTRAPPING IN PORTFOLIO MANAGEMENT Radovaov Bors Faculty of Ecoomcs Subotca Segedsk put 9-11 Subotca 24000 E-mal: radovaovb@ef.us.ac.rs Marckć Aleksadra Faculty of Ecoomcs Subotca Segedsk

More information

On Savings Accounts in Semimartingale Term Structure Models

On Savings Accounts in Semimartingale Term Structure Models O Savgs Accouts Semmartgale Term Structure Models Frak Döberle Mart Schwezer moeyshelf.com Techsche Uverstät Berl Bockehemer Ladstraße 55 Fachberech Mathematk, MA 7 4 D 6325 Frakfurt am Ma Straße des 17.

More information

BENEFIT-COST ANALYSIS Financial and Economic Appraisal using Spreadsheets

BENEFIT-COST ANALYSIS Financial and Economic Appraisal using Spreadsheets BENEIT-CST ANALYSIS iacial ad Ecoomic Appraisal usig Spreadsheets Ch. 2: Ivestmet Appraisal - Priciples Harry Campbell & Richard Brow School of Ecoomics The Uiversity of Queeslad Review of basic cocepts

More information

Simple Interest Loans (Section 5.1) :

Simple Interest Loans (Section 5.1) : Chapter 5 Fnance The frst part of ths revew wll explan the dfferent nterest and nvestment equatons you learned n secton 5.1 through 5.4 of your textbook and go through several examples. The second part

More information

An Approach to Evaluating the Computer Network Security with Hesitant Fuzzy Information

An Approach to Evaluating the Computer Network Security with Hesitant Fuzzy Information A Approach to Evaluatg the Computer Network Securty wth Hestat Fuzzy Iformato Jafeg Dog A Approach to Evaluatg the Computer Network Securty wth Hestat Fuzzy Iformato Jafeg Dog, Frst ad Correspodg Author

More information

I apply to subscribe for a Stocks & Shares ISA for the tax year 20 /20 and each subsequent year until further notice.

I apply to subscribe for a Stocks & Shares ISA for the tax year 20 /20 and each subsequent year until further notice. IFSL Brooks Macdoald Fud Stocks & Shares ISA Trasfer Applicatio Form IFSL Brooks Macdoald Fud Stocks & Shares ISA Trasfer Applicatio Form Please complete usig BLOCK CAPITALS ad retur the completed form

More information

An Evaluation of Naïve Bayesian Anti-Spam Filtering Techniques

An Evaluation of Naïve Bayesian Anti-Spam Filtering Techniques Proceedgs of the 2007 IEEE Workshop o Iformato Assurace Uted tates Mltary Academy, West Pot, Y 20-22 Jue 2007 A Evaluato of aïve Bayesa At-pam Flterg Techques Vkas P. Deshpade, Robert F. Erbacher, ad Chrs

More information

Integrating Production Scheduling and Maintenance: Practical Implications

Integrating Production Scheduling and Maintenance: Practical Implications Proceedgs of the 2012 Iteratoal Coferece o Idustral Egeerg ad Operatos Maagemet Istabul, Turkey, uly 3 6, 2012 Itegratg Producto Schedulg ad Mateace: Practcal Implcatos Lath A. Hadd ad Umar M. Al-Turk

More information

Suspicious Transaction Detection for Anti-Money Laundering

Suspicious Transaction Detection for Anti-Money Laundering Vol.8, No. (014), pp.157-166 http://dx.do.org/10.1457/jsa.014.8..16 Suspcous Trasacto Detecto for At-Moey Lauderg Xgrog Luo Vocatoal ad techcal college Esh Esh, Hube, Cha es_lxr@16.com Abstract Moey lauderg

More information

Managing Interdependent Information Security Risks: Cyberinsurance, Managed Security Services, and Risk Pooling Arrangements

Managing Interdependent Information Security Risks: Cyberinsurance, Managed Security Services, and Risk Pooling Arrangements Maagg Iterdepedet Iformato Securty Rsks: Cybersurace, Maaged Securty Servces, ad Rsk Poolg Arragemets Xa Zhao Assstat Professor Departmet of Iformato Systems ad Supply Cha Maagemet Brya School of Busess

More information

ANALYTICAL MODEL FOR TCP FILE TRANSFERS OVER UMTS. Janne Peisa Ericsson Research 02420 Jorvas, Finland. Michael Meyer Ericsson Research, Germany

ANALYTICAL MODEL FOR TCP FILE TRANSFERS OVER UMTS. Janne Peisa Ericsson Research 02420 Jorvas, Finland. Michael Meyer Ericsson Research, Germany ANALYTICAL MODEL FOR TCP FILE TRANSFERS OVER UMTS Jae Pesa Erco Research 4 Jorvas, Flad Mchael Meyer Erco Research, Germay Abstract Ths paper proposes a farly complex model to aalyze the performace of

More information