How do bookmakers (or FdJ 1 ) ALWAYS manage to win?
|
|
|
- Denis Bruce
- 10 years ago
- Views:
Transcription
1 How do bookakers (or FdJ ALWAYS aage to w? Itroducto otatos & varables Bookaker's beeft eected value 4 4 Bookaker's strateges5 4 The hoest bookaker 6 4 "real lfe" bookaker 6 4 La FdJ 8 5 How ca we estate the bookaker's beeft?8 5 Marg estato 8 5 Eales (etracted fro actual odds 9 FdJ stads for Fraçase des Jeu, whch s the oly bookaker authorsed Frace Ths coay rus a gae ("Cote & Match" whch retty looks lke bookakers gaes
2 Itroducto We suose of course that the uesto relates to soccer lay betwee two teas, ad that outcoes are ossble : hoe w, draw or away w These results are usually referred to as,, (frech otato whch stads for,, teratoal otato I the les to coe, we wll suose that we address OE secal atch Ths assuto s ecessary to ake the elaatos ore sle (though stll tough, but the reasog etraolates easly to ay atch We wll thus derve the aalyss that bookakers ake (or should ake our sese to always w agast uters To aswer the uesto, we eed varous forato Frst, the bookaker ust have hs ow dea of results robabltes : utg s after all a cotest betwee the bookaker (who wats to r off layers ad the layers who do ot agree wth that urose, cosder theselves sarter tha the bookaker (or try to be, ad of course wat to get ther oey back at least ad eve ore f ossble Secod, t s ecessary to kow the bets reartto aog,, ossbltes ot oly the uber of the (ths s of course obvous but also the aout of oey reartto For eale, 50% of layers ay ut sall oey o a hoe w, whereas 0% ut bg oey o a dfferet result ( or Fally, ths last oe wll gather the ost oey, ad that's what effectvely couts for the bookaker whe t coes to the evaluato of hs eargs chaces Ths beg sad, we wll ow address the core uesto whch s bookaker trck to fool layers It wll reure soe atheatcal develoets (we do't kow how to ake t sler So the webaster would wat to war readers that fro ow o the tet ay be aful for soe of the Most courageous of you ca kee o readg (ake sure to have oe asr tube at had
3 otatos & varables Kow otatos eag By layers by bookakers by FdJ Total uber of uters o yes o uber of bets o result (, ou Average euro bet o result o yes o o yes o Bookaker's odds yes yes yes M α Probablty of result estated by the bookaker Probablty of result estated by the uters Total aout of euros bet o the atch Average bet er uter over all results (,, The bookaker's ta o bets (hs arg! o yes yes dffcult yes o o yes o o yes o o yes yes Recall : the bookaker odds are the ubers used to ultly the uter's bet to calculate uter's eargs f he redcted the good result Relatos betwee varables : varable Is eual to varable Is eual to M
4 4 coets : To decde f a varable s kow or ot recedg table, we looked for each kd of layer (bookaker, uter, FdJ f they have a ea, DURIG BETTIG PERIOD OF TIME, to kow ths varable It haes that both layer ad FdJ who ake ther bets or f ther odds before the bettg erod are soewhat eve The uter eve has a slght advatage over FdJ : f he s sart, he wll wat for the last utes of bettg erod to ake hs bet order to have as uch forato as ossble Bookakers o the cotrary ca eraetly adjust ther odds utl the bettg erod edg So they have a lot of very foratve data (esecally the aouts of bets er each result whch gves the a huge advatage over uters FdJ however has access to all data after bettg erod ad ca thus use ths eerece for et bettg days The gae s thus uch ufar tha t could see at frst sght FdJ has also the ossblty to cacel the bet o ay atch f t aears that ts rsks are too hgh (sae for bookakers, but of course deed to uters Accordg to ths frst aalyss o access to bettg data, bookakers have already ay eas to bas the gae ther favor But that's ot the oly ea they have avalable They have above all the oortuty to decde the odds levels, ad ths s a treedous weao to ake the balace shft ther way That's what we try to deostrate et aragrahs Bookaker's beeft eected value Ths value corresods to the average beeft the bookaker could ake f the atch cosdered was layed ay tes I fact, the atch s oly layed oce, but as there are ay atches, the coutato of the "eected value" s evertheless relevat Ths "eected value" s by coveto wrtte E (, where s the robablstc varable we look for, aely the bookaker's beeft here For stace, the eected value of the result of a roll of a 6 faces dce s 5; ths does't ea you'll get 5 whe you roll the dce (of course, or you have really werd dce but that you'll get ths average result f you roll the dce ay tes Frst, let's look at bookaker's eargs for each gae result : Match result Bookaker's earg (evetually egatve, e loss ( ( ( As the bookaker's redcto for,, results are,,, hs beeft eected value s fally :
5 5 ( ( ( ( ( ( ( E Ths ca be rewrtte usg, total uber of bets o the atch : ( ( ( ( E ( ( ( [ ] ( ( ( ( E ( E ad fally, as ( E [] Ths s the ost geeral forula for bookaker's beeft eected value, as t does ot clude aroatos or hyothess of ay kd 4 Bookaker's strateges I order to slfy the deostrato, we wll assue that average aouts of euros bet o each result are the sae The bookaker ca have the actual data, s ot oblged to go through ths aroato, ad ca udate the followg reasog as ofte as he wats We thus have : The relato [] becoes the a lttle sler : ( E [']
6 6 (because, as before, The bookaker, wth ths relato, ca ow develo hs strateges 4 The hoest bookaker Of course, ths varety of bookaker does ot est, but hs fcttous estece wll hel us ela further how "real lfe bookakers" always aage to w So for ths good ol' vrtual hlathrost, othg atters but akg the gae betwee h ad uters far To reach ths goal, he eeds : ( E 0 There's oly oe way to obta ths result, whch s to choose the odds sartly Whe lookg to relato ['], our ave bookaker ca observe that he has two obvous choces : or I the frst case, the relato ['] becoes fact : E( 0 whch s the objectve that our bookaker has fed to hself I the secod case, the result s the sae because et act syetrcally relato ['] But the frst soluto s uch ore terestg for the bookaker because he does't eve have to ake hs ow bets Effectvely, whatever the bookaker's values, eve f they are very badly estated, whe choosg the fal earg wll be the sae (w or loss eual to zero 4 "real lfe" bookaker Ths bookaker, that everyoe kows, has two secal characterstcs that ake h dffer fro the revous sece : he ust f hs odds BEFORE kowg the uters bets (so he does't kow the ad ust estate the he strogly whshes to have a ostve beeft "eected value" To solve hs frst roble, he as ot uch alteratves : all he ca do s to suose the uters to be as sart (or clueless as he s, ad assue that at the ed of bettg erod he wll have
7 7 (evertheless, he wll have the oortuty to sca the evoluto of ['] utl the ed, ad to udate the so as to have E( rea ostve Wth ths assuto, hs beeft "eected value" ['] becoes : E ( [ ] ad f he was the "hoest" bookaker, he would set But he wll ot, because two thgs bother h uch : Frst, he has o guaratee that uters wll have the sae redctos as h Ths gves brth to a very ebarrassg ucertaty o, because t ca ake the relato [''] ot relevat, ad relato ['] (whch s to be accouted for the ca be egatve Hece uleasat loss vew! Secod he s ot retty sure of hs redctos ether, ad would lke to decrease ths rsk So he wll "work" hs odds so as to guaratee a ostve earg Whch eas he wll odfy the order to get a arg o E( Fro ow o, the true bookakers ethods ca oly be guesses They ca, for eale, odfy the three odds o (,, the sae way ad calculate : α [] ( where α s chose as a fucto of the arg the bookakers wats for hself He wll have the oortuty durg the bettg erod to verfy that the devate too uch fro each other If they do, he wll be able to "re-coute" the odds accordg to α [ ] ( ad the do ot order to lower hs rsks (ths s the case, because the are kow eactly ad recsely Ths wll effectvely guaratee h a ostve beeft wth u rsk
8 8 4 Beeft "eected value" uder hyothess [] : ( E α α ( ( E( α α ( E ( α where α s the bookaker's ta o uters' bets [] 4 Beeft "eected value" uder hyothess ['] The bookaker ca greatly reduce hs rsks, as the are erfectly kow Hs beeft "eected value" ca the be calculated wth relato ['] whch does ot ake ay assuto o uters' bets ad s thus uch ore recse Ths eected value the wrtes : ( E α ( whch gves the sae eresso [] as for hyothess [], BUT wthout ay hyothess o Ths cosderably reduces the bookaker's rsk I fact, the bookaker ca refe eve ore hs strategy by usg relato [] whch DOES OT IMPLY AY HYPOTHESIS AT ALL He ca thus coute hs α args wth o error ad roose very attractve odds 4 La FdJ Ths bookaker ever udate hs odds, ad s oblged to lay accordg to odel [] Hs rsks are hgher, whch elas for a art why the odds are less attractve tha other ole bookakers 5 How ca we estate the bookaker's beeft? 5 Marg estato Let's suose that the bookaker uses relato [] As we kow hs odds, we ca easly fer the "bet ta ercetage" (ad thus access to hs "beeft eected value"
9 9 To do so, we ust aga assue a ufor average bet over all results (,, Fro [] we deduce α ad fro, we the get the forula to α calculate (ukow fro the (kow : α [4] 5 Eales (etracted fro actual odds Odds Frace Lgue, Jauary d to rd 005 FdJ : teas Metz Marselle 7 65 St Etee PSG Basta ce Cae Auerre 7 65 Istres Strasbourg Moaco Les 5 5 Rees Ajacco Sochau Bordeau Toulouse ates Llle Lyo 4 55 Bookaker : teas Metz Marselle St Etee PSG Basta ce Cae Auerre Istres Strasbourg Moaco Les Rees Ajacco Sochau Bordeau Toulouse ates 8 45 Llle Lyo We use relato [4] to get each bookaker's beeft estatos, whch gves atch er atch the two followg tables:
10 0 FdJ : Metz Marselle 99% St Etee PSG 99% Basta ce 99% Cae Auerre 99% Istres Strasbourg 99% Moaco Les 00% Rees Ajacco 00% Sochau Bordeau 97% Toulouse ates 99% Llle Lyo 96% Bookaker Metz Marselle 97% St Etee PSG 97% Basta ce 99% Cae Auerre 0% Istres Strasbourg 96% Moaco Les 99% Rees Ajacco 00% Sochau Bordeau 99% Toulouse ates 99% Llle Lyo 0% We have wth ths eale a guess : of the arg that ole bookakers ake o uters bets (>0%, whch s eorous seakg of reveue o oey you do't ow of the "ucertaty bous" that FdJ grats to herself (0% wrt bookaker, whch eas a cofortable 0%!!!
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
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
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
A Study of Unrelated Parallel-Machine Scheduling with Deteriorating Maintenance Activities to Minimize the Total Completion Time
Joural of Na Ka, Vol. 0, No., pp.5-9 (20) 5 A Study of Urelated Parallel-Mache Schedulg wth Deteroratg Mateace Actvtes to Mze the Total Copleto Te Suh-Jeq Yag, Ja-Yuar Guo, Hs-Tao Lee Departet of Idustral
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
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
Measuring the Quality of Credit Scoring Models
Measur the Qualty of Credt cor Models Mart Řezáč Dept. of Matheatcs ad tatstcs, Faculty of cece, Masaryk Uversty CCC XI, Edurh Auust 009 Cotet. Itroducto 3. Good/ad clet defto 4 3. Measur the qualty 6
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
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
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
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 [email protected],
A Fair Non-repudiation Protocol without TTP on Conic Curve over Ring
Far No-reudato Protocol wthout TTP o Coc Curve over Rg Z L Zhahu, Fa Ka, 3L Hu, Zheg Ya Far No-reudato Protocol wthout TTP o Coc Curve over Rg Z 1 L Zhahu, Fa Ka, 3 L Hu, 4 Zheg Ya 1State Key Laboratory
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
Numerical Comparisons of Quality Control Charts for Variables
Global Vrtual Coferece Aprl, 8. - 2. 203 Nuercal Coparsos of Qualty Cotrol Charts for Varables J.F. Muñoz-Rosas, M.N. Pérez-Aróstegu Uversty of Graada Facultad de Cecas Ecoócas y Epresarales Graada, pa
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
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
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
Fuzzy Task Assignment Model of Web Services Supplier in Collaborative Development Environment
, pp.199-210 http://dx.do.org/10.14257/uesst.2015.8.6.19 Fuzzy Task Assget Model of Web Servces Suppler Collaboratve Developet Evroet Su Ja 1,2, Peg Xu-ya 1, *, Xu Yg 1,3, Wag Pe-e 2 ad Ma Na- 4,2 1. College
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,
CHAPTER 4: NET PRESENT VALUE
EMBA 807 Corporate Fiace Dr. Rodey Boehe CHAPTER 4: NET PRESENT VALUE (Assiged probles are, 2, 7, 8,, 6, 23, 25, 28, 29, 3, 33, 36, 4, 42, 46, 50, ad 52) The title of this chapter ay be Net Preset Value,
Polyphase Filters. Section 12.4 Porat 1/39
Polyphase Flters Secto.4 Porat /39 .4 Polyphase Flters Polyphase s a way of dog saplg-rate coverso that leads to very effcet pleetatos. But ore tha that, t leads to very geeral vewpots that are useful
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
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
10/19/2011. Financial Mathematics. Lecture 24 Annuities. Ana NoraEvans 403 Kerchof [email protected] 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 [email protected] http://people.vrga.edu/~as5k/
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
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
of the relationship between time and the value of money.
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
Maintenance Scheduling of Distribution System with Optimal Economy and Reliability
Egeerg, 203, 5, 4-8 http://dx.do.org/0.4236/eg.203.59b003 Publshed Ole September 203 (http://www.scrp.org/joural/eg) Mateace Schedulg of Dstrbuto System wth Optmal Ecoomy ad Relablty Syua Hog, Hafeg L,
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
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
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
OPTIMAL KNOWLEDGE FLOW ON THE INTERNET
İstabul Tcaret Üverstes Fe Blmler Dergs Yıl: 5 Sayı:0 Güz 006/ s. - OPTIMAL KNOWLEDGE FLOW ON THE INTERNET Bura ORDİN *, Urfat NURİYEV ** ABSTRACT The flow roblem ad the mmum sag tree roblem are both fudametal
Checking Out the Doght Stadard Odors in Polygamy
Cosstey Test o Mass Calbrato of Set of Weghts Class ad Lowers Lus Oar Beerra, Igao Herádez, Jorge Nava, Fél Pezet Natoal Ceter of Metrology (CNAM) Querétaro, Meo Abstrat: O weghts albrato oe by oe there
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
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
Security Analysis of RAPP: An RFID Authentication Protocol based on Permutation
Securty Aalyss of RAPP: A RFID Authetcato Protocol based o Permutato Wag Shao-hu,,, Ha Zhje,, Lu Sujua,, Che Da-we, {College of Computer, Najg Uversty of Posts ad Telecommucatos, Najg 004, Cha Jagsu Hgh
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
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,
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 [email protected]
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
Approximation Algorithms for Scheduling with Rejection on Two Unrelated Parallel Machines
(ICS) Iteratoal oural of dvaced Comuter Scece ad lcatos Vol 6 No 05 romato lgorthms for Schedulg wth eecto o wo Urelated Parallel aches Feg Xahao Zhag Zega Ca College of Scece y Uversty y Shadog Cha 76005
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
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
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%
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
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
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
Constrained Cubic Spline Interpolation for Chemical Engineering Applications
Costraed Cubc Sple Iterpolato or Chemcal Egeerg Applcatos b CJC Kruger Summar Cubc sple terpolato s a useul techque to terpolate betwee kow data pots due to ts stable ad smooth characterstcs. Uortuatel
A Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining
A Fast Clusterg Algorth to Cluster Very Large Categorcal Data Sets Data Mg Zhexue Huag * Cooperatve Research Cetre for Advaced Coputatoal Systes CSIRO Matheatcal ad Iforato Sceces GPO Box 664, Caberra
CSSE463: Image Recognition Day 27
CSSE463: Image Recogto Da 27 Ths week Toda: Alcatos of PCA Suda ght: roject las ad relm work due Questos? Prcal Comoets Aalss weght grth c ( )( ) ( )( ( )( ) ) heght sze Gve a set of samles, fd the drecto(s)
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
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
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
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,
Lecture 7. Norms and Condition Numbers
Lecture 7 Norms ad Codto Numbers To dscuss the errors umerca probems vovg vectors, t s usefu to empo orms. Vector Norm O a vector space V, a orm s a fucto from V to the set of o-egatve reas that obes three
Relaxation Methods for Iterative Solution to Linear Systems of Equations
Relaxato Methods for Iteratve Soluto to Lear Systems of Equatos Gerald Recktewald Portlad State Uversty Mechacal Egeerg Departmet [email protected] Prmary Topcs Basc Cocepts Statoary Methods a.k.a. Relaxato
Applications of Support Vector Machine Based on Boolean Kernel to Spam Filtering
Moder Appled Scece October, 2009 Applcatos of Support Vector Mache Based o Boolea Kerel to Spam Flterg Shugag Lu & Keb Cu School of Computer scece ad techology, North Cha Electrc Power Uversty Hebe 071003,
Optimal multi-degree reduction of Bézier curves with constraints of endpoints continuity
Computer Aded Geometrc Desg 19 (2002 365 377 wwwelsevercom/locate/comad Optmal mult-degree reducto of Bézer curves wth costrats of edpots cotuty Guo-Dog Che, Guo-J Wag State Key Laboratory of CAD&CG, Isttute
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
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.
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
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
16. Mean Square Estimation
6 Me Sque stmto Gve some fomto tht s elted to uow qutty of teest the poblem s to obt good estmte fo the uow tems of the obseved dt Suppose epeset sequece of dom vbles bout whom oe set of obsevtos e vlble
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
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
Key players and activities across the ERP life cycle: A temporal perspective
126 Revsta Iformatca Ecoomcă, r. 4 (44)/2007 Key layers ad actvtes across the ERP lfe cycle: A temoral ersectve Iulaa SCORŢA, Bucharest, Romaa Eterrse Resource Plag (ERP) systems are eterrse wde systems
Questions? Ask Prof. Herz, [email protected]. General Classification of adsorption
Questos? Ask rof. Herz, [email protected] Geeral Classfcato of adsorpto hyscal adsorpto - physsorpto - dsperso forces - Va der Waals forces - weak - oly get hgh fractoal coerage of surface at low temperatures
Project 3 Weight analysis
The Faculty of Power ad Aeroautcal Egeerg Arcraft Desg Departet Project 3 Weght aalyss Ths project cossts of two parts. Frst part cludes fuselage teror (cockpt) coceptual desg. Secod part cludes etoed
Load Balancing via Random Local Search in Closed and Open systems
Load Balacg va Rado Local Search Closed ad Ope systes A. Gaesh Dept. of Matheatcs Uversty of Brstol, UK [email protected] A. Proutere Mcrosoft Research Cabrdge, UK [email protected] S. Llethal Stats
Multi-Channel Pricing for Financial Services
0-7695-435-9/0 $7.00 (c) 00 IEEE Proceedgs of the 35th Aual Hawa Iteratoal Coferece o yste ceces (HIC-35 0) 0-7695-435-9/0 $7.00 00 IEEE Proceedgs of the 35th Hawa Iteratoal Coferece o yste ceces - 00
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.
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
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
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:
Determining the sample size
Determiig the sample size Oe of the most commo questios ay statisticia gets asked is How large a sample size do I eed? Researchers are ofte surprised to fid out that the aswer depeds o a umber of factors
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
Automated Event Registration System in Corporation
teratoal Joural of Advaces Computer Scece ad Techology JACST), Vol., No., Pages : 0-0 0) Specal ssue of CACST 0 - Held durg 09-0 May, 0 Malaysa Automated Evet Regstrato System Corporato Zafer Al-Makhadmee
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
A probabilistic part-of-speech tagger for Swedish
A probablstc part-of-speech tagger for Swedsh eter Nlsso Departmet of Computer Scece Uversty of Lud Lud, Swede [email protected] Abstract Ths paper presets a project for mplemetg ad evaluatg a probablstc
Session 4: Descriptive statistics and exporting Stata results
Itrduct t Stata Jrd Muñz (UAB) Sess 4: Descrptve statstcs ad exprtg Stata results I ths sess we are gg t wrk wth descrptve statstcs Stata. Frst, we preset a shrt trduct t the very basc statstcal ctets
Fault Tree Analysis of Software Reliability Allocation
Fault Tree Aalyss of Software Relablty Allocato Jawe XIANG, Kokch FUTATSUGI School of Iformato Scece, Japa Advaced Isttute of Scece ad Techology - Asahda, Tatsuokuch, Ishkawa, 92-292 Japa ad Yaxag HE Computer
Sequences and Series
Secto 9. Sequeces d Seres You c thk of sequece s fucto whose dom s the set of postve tegers. f ( ), f (), f (),... f ( ),... Defto of Sequece A fte sequece s fucto whose dom s the set of postve tegers.
Efficient Compensation for Regulatory Takings. and Oregon s Measure 37
Effcet Compesato for Regulatory Takgs ad Orego s Measure 37 Jack Scheffer Ph.D. Studet Dept. of Agrcultural, Evrometal ad Developmet Ecoomcs The Oho State Uversty 2120 Fyffe Road Columbus, OH 43210-1067
A DISTRIBUTED REPUTATION BROKER FRAMEWORK FOR WEB SERVICE APPLICATIONS
L et al.: A Dstrbuted Reputato Broker Framework for Web Servce Applcatos A DISTRIBUTED REPUTATION BROKER FRAMEWORK FOR WEB SERVICE APPLICATIONS Kwe-Jay L Departmet of Electrcal Egeerg ad Computer Scece
How to use what you OWN to reduce what you OWE
How to use what you OWN to reduce what you OWE Maulife Oe A Overview Most Caadias maage their fiaces by doig two thigs: 1. Depositig their icome ad other short-term assets ito chequig ad savigs accouts.
