PRACTICE 1: MUTUAL FUNDS EVALUATION USING MATLAB.


 Cornelius Ward
 3 years ago
 Views:
Transcription
1 PRACTICE 1: MUTUAL FUNDS EVALUATION USING MATLAB. INDEX 1. Load data usng the Edtor wndow and mfle 2. Learnng to save results from the Edtor wndow. 3. Computng the Sharpe Rato 4. Obtanng the Treynor Rato 5. The Alpha from the CAPM 6. The Alpha from the Fama and French three factors model. 7. Makng rankng and obtanng conclusons. Jesús Davd Moreno (Assocate Professor Unversty Carlos III) 1/16
2 1. Load data usng the Edtor wndow and mfle In ths practce you wll use a sample of Equty Mutual Funds from one of the most prestgous mutual fund database, the CRSP (Chcago Research Securty Prces). In ths database there are data from 196 untl 26, and for all mutual fund categores (equty, fxed ncome, balanced, etc). However, gven that ths s only an exercse you wll use data from 55 Equty mutual funds n the Aggressve Growth Category and from January 22 untl December 24. The frequency of the data wll be monthly (24 observatons for each fund). All ths data are n a matlab fle name DATAFUNDS.mat Moreover, you wll need some other addtonal data, as Treasure Blls returns, Market returns, or the Fama and French factors. And these addtonal data are n a fle named ADDITIONAL_DATA.mat. Frst, we must mport data usng the Edtor Wndow. Thus, we must open a new MFle Second, wrte some comments at the begnnng that permts you to dentfy ths code. >> %% Ths code has been created to evaluate Equty Mutual Funds  Mater n Fnance >> %% Date: February212 Thrd, we must use the LOAD functon to mport the varable. >> clear all >> load DATAFUNDS.mat >> load ADDITIONAL_DATA.mat Jesús Davd Moreno (Assocate Professor Unversty Carlos III) 2/16
3 Fourth, we run the program to check the data loaded. To get t, clck on the con save and on the con Run The data you have mported are: rmf: Ths contans the returns of all mutual funds. Each mutual fund s represented n a dfferent column. So rmf s a 24 x 55 matrx. rtblls: It s a vector (24x1) wth the monthly rskfree rate. rmk: It contans the returns of a Market ndex. smb: A column vector wth the SmallmnusBg factor to be use n the Fama and Frech model. hml: A column vector wth the HghmnusLow factor for the Fama and French model. wml: A column vector wth the momentum factor for the Carhart model. You can fnd all these factors n the followng webpage: If you want, you could plot each of the varables to check that they are rght or there are not extreme outlers, etc. >> plot(rtblls) >> plot(rmk) >> plot(smb) >> plot(hml) >> for =1:3 >> plot(rmf(:,)) >> end Or you can also use a subplot command. Jesús Davd Moreno (Assocate Professor Unversty Carlos III) 3/16
4 2. Learnng to save results from the Edtor wndow. Frst, to save results you wll use the save command. I you want to save all the varables n the workspace >> save DavdResults.mat If you want to save only a varable n a Matlab fle. >> save DavdResults_X1.mat X1 If you want to save only one varable n Excel (may be to do another operatons wth t), the command wll be >> save DavdResults_X1.txt X1 asc tabs Jesús Davd Moreno (Assocate Professor Unversty Carlos III) 4/16
5 3. Computng the Sharpe Rato Frst, we compute the average return obtaned by each mutual fund, and we can do t wth the functon mean. >>meanrmf=mean(rmf); Remember that the functon "mean" s computng the average or mean for each column n the matrx. When you have the returns of the mutual funds n rows nstead of n columns, you have to transpose the data [mean(rmf')] Second, we compute the average return obtaned by the rskfree asset. >> meanrtblls=mean(rtblls); Thrd, we compute the standard devaton of each mutual funds, usng the functon std. >> stdrmf=std(rmf); Fourth, we can compute the Sharpe Rato as >> Sharpe=(meanrMFmeanrtblls)./ stdrmf; It s must be noted that you are usng./ and not /. Because actually you do not want to do a matrx dvson. Instead you need to dvde each mutual fund excess return by ts standard devaton. Addtonally, you could compute the Average Sharpe Rato (or the medan) for all the mutual funds, or plot the Sharpe Ratos to analyze them. >> fgure >> bar(sharpe) Jesús Davd Moreno (Assocate Professor Unversty Carlos III) 5/16
6 >> ttle('sharpe Rato of Equty Mutual Funds') >> medansharpe=medan(sharpe) >> meansharpe=mean(sharpe).5 Sharpe Rato of Equty Mutual Funds Jesús Davd Moreno (Assocate Professor Unversty Carlos III) 6/16
7 4. Obtanng the Treynor Rato In ths rato we can use some of the varables computed n the prevous performance measure. Because the numerator s equal than n the Sharpe rato, we only need to compute the beta (denomnator of Treynor Rato). Frst, we compute the beta of each mutual fund In ths case we can not use matrx operators but we need to compute the beta for each mutual fund ndependently, thus we need to use the command FOR to repeat some operatons a specfc number of tmes. Moreover, we need excess returns to compute the Beta. >> for j=1:55 >> rmf2(:,j)=rmf(:,j)rtblls; >> rmk2=rmkrtblls; >> varanrmk2=var(rmk2); >> covarmatrx=cov(rmf2(:,j),rmk2); >> covarmf=covarmatrx(1,2); >> Beta(1,j)=covarMF/varanrMk2; >> end Second, we can compute the Treynor Rato. >> Treynor=(meanrMFmeanrtblls)./Beta; Fnally, to analyze the results we can plot t or compute some statstcs. >> meantreynor=mean(treynor); >> medantreynor=medan(treynor); >> fgure >> bar(treynor) >> ttle( Treynor Rato from Equty Mutual Funds ) Jesús Davd Moreno (Assocate Professor Unversty Carlos III) 7/16
8 .3 Treynor Rato of Equty Mutual Funds Jesús Davd Moreno (Assocate Professor Unversty Carlos III) 8/16
9 5. Computng the Alpha from the CAPM We are computng the alpha as a coeffcent estmated n the followng regresson (solved by Ordnary Least Squeares, OLS) r r r M = ˆ α + ˆ β ( r ) + ε R where = = R r M f r f M To estmate that equaton by OLS we could use the functon regress or regstats. But we are usng regstats because ths functon wll gve us more nformaton (tstatstcs, Rsquared, pvalues,...). Moreover, n the regstats functon by default we estmate a model wth a constant or ntercept and ths s the easer for us than n regress, where we have to ntroduce a colum vector of ones to use a constant. [B, BINT]=regress(Y,X) >> help regress REGRESS Multple lnear regresson usng least squares. B = REGRESS(Y,X) returns the vector B of regresson coeffcents n the lnear model Y = X*B. X s an nbyp desgn matrx, wth rows correspondng to observatons and columns to predctor varables. Y s an nby1 vector of response observatons. [B,BINT] = REGRESS(Y,X) returns a matrx BINT of 95% confdence ntervals for B. [STATS]=regstats(Y,X, 'lnear') >> help regstats REGSTATS Regresson dagnostcs for lnear models. STATS = REGSTATS(RESPONSES,DATA,MODEL,WHICHSTATS) creates an output structure STATS contanng the statstcs lsted n WHICHSTATS. WHICHSTATS can be a sngle strng such as 'leverage' or a cell array of strngs such as {'leverage' 'standres' 'studres'}. By default, REGSTATS returns all statstcs. Vald statstc strngs are: Jesús Davd Moreno (Assocate Professor Unversty Carlos III) 9/16
10 Name Meanng 'Q' Q from the QR Decomposton of the desgn matrx 'R' R from the QR Decomposton of the desgn matrx 'beta' Regresson coeffcents 'covb' Covarance of regresson coeffcents 'yhat' Ftted values of the response data 'r' Resduals 'mse' Mean squared error 'rsquare' Rsquare statstc 'adjrsquare' Adjusted Rsquare statstc 'leverage' Leverage 'hatmat' Hat (projecton) matrx 's2_' Delete1 varance 'beta_' Delete1 coeffcents 'standres' Standardzed resduals 'studres' Studentzed resduals 'dfbetas' Scaled change n regresson coeffcents 'dfft' Change n ftted values 'dffts' Scaled change n ftted values 'covrato' Change n covarance 'cookd' Cook's dstance 'tstat' t statstcs for coeffcents 'fstat' F statstc 'dwstat' Durbn Watson statstc 'all' Create all of the above statstcs Frst, we need to crate the Y and X matrxes. Y varable s very easy to compute gven that t s equal to mutual fund excess returns. >> for j=1:55 >> Y=rMF2(:,j); >> X= rmk2; >> stats=regstats(y, X,'lnear'); >> coefc=stats.beta; >> Beta(j,1)=coefc(2) >> alpha_j(j,1)=coefc(1) >> t_jensen(j,1)=stats.tstat.t(1); >> clear Y X coefc stats >> end Second, we can analyze results by plottng them. >> bar(alpha_j) Jesús Davd Moreno (Assocate Professor Unversty Carlos III) 1/16
11 Remember that you can ntroduce ttles wth the command "ttle', and the text n the axes wth the commands "xlabel", "ylabel" >> hst(alpha_j) >> meanalpha=mean(alpha_j);.2 Alpha`s Jensen from Equty Mutual Funds.15.1 alpha number of mutual fund 12 hstogram from alpha`s Jensen Jesús Davd Moreno (Assocate Professor Unversty Carlos III) 11/16
12 6. Computng the Alpha from the FF model We are computng the alpha as a coeffcent estmated n the followng multvarate regresson (solved by Ordnary Least Squeares, OLS) R R R M SMB HML = ˆ α + β ( RM ) + β ( SMB) + β ( HML) ε where = r r = r M f ˆ r f ˆ To estmate that equaton by OLS we use the functon regstats. [stats]=regstats(y,x,'lnear') ˆ Frst, we need to crate the Y and X matrxes. >> for j=1:55 >> Y=rMF2(:,j); >> X= [rmk2, SMB, HML]; >> stats=regstats(y, X,'lnear'); >> coefc=stats.beta; >> alphaff(j,1)=coefc(1) >> t_ff(j,1)=stats.tstat.t(1); >> clear Y X coefc stats >> end Second, we can analyze results by plottng them. >> bar(alphaff) >> hst(alphaff) Jesús Davd Moreno (Assocate Professor Unversty Carlos III) 12/16
13 >> meanalphaff=mean(alphaff);.1 Alpha`s FF model from Equty Mutual Funds.5 alpha FF number of mutual fund 14 hstogram from alpha FF model Jesús Davd Moreno (Assocate Professor Unversty Carlos III) 13/16
14 Thrd, we can analyze the dfferences between usng Jensen s Alpha and Alpha from the FF three factors model. >> Dff_alpha=alphaalphaFF >> fgure >> plot(dff_alpha, sr ) >> ttle( Dfference among Alphas ), grd on.15 Dfference among Alphas Jesús Davd Moreno (Assocate Professor Unversty Carlos III) 14/16
15 7. Makng rankngs and obtanng conclusons. One of the most relevance functons of performance measures s to acheve rankngs of the mutual funds. These rankngs are publshed n newspapers, magaznes, etc. and are used by nvestors to allocate ther funds. Frst, we can sort the mutual funds wth the functon sort. >> help sort SORT order. for vectors, SORT(X) sorts the elements of X n ascendng >> [SharpeRank, SharpeRank2]=sort(Sharpe) You must note that n SharpeRank varable you have the Sharpe Rato values for each mutual fund ranked by Sharpe Rato. But n the varable SharpeRank2 you have the number (from colums n rmf) of the mutual fund n that poston. >> [TreynorRank, TreynorRank2]=sort(Treynor) >> [alpharank, alpharank2]=sort(alpha) >> [alphaffrank, alphaffrank2]=sort(alphaff) Now, we can bult a new matrx wth the ndex of each funds n the rankngs computed prevously. >> TableRank=[SharpeRank2, TreynorRank2, alpharank2, alphaffrank2 ] TableRank = The worst fund Jesús Davd Moreno (Assocate Professor Unversty Carlos III) 15/16
16 The best fund Fnally, we can analyze all performance measures together usng the command subplot >> subplot(2,2,1) >> bar(sharpe) >> subplot(2,2,2) >> bar(treynor)... >>....6 Sharpe.3 Treynor alpha.1 alphaff Jesús Davd Moreno (Assocate Professor Unversty Carlos III) 16/16
Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting
Causal, Explanatory Forecastng Assumes causeandeffect relatonshp between system nputs and ts output Forecastng wth Regresson Analyss Rchard S. Barr Inputs System Cause + Effect Relatonshp The job of
More informationTHE METHOD OF LEAST SQUARES THE METHOD OF LEAST SQUARES
The goal: to measure (determne) an unknown quantty x (the value of a RV X) Realsaton: n results: y 1, y 2,..., y j,..., y n, (the measured values of Y 1, Y 2,..., Y j,..., Y n ) every result s encumbered
More informationThe covariance is the two variable analog to the variance. The formula for the covariance between two variables is
Regresson Lectures So far we have talked only about statstcs that descrbe one varable. What we are gong to be dscussng for much of the remander of the course s relatonshps between two or more varables.
More informationCHAPTER 14 MORE ABOUT REGRESSION
CHAPTER 14 MORE ABOUT REGRESSION We learned n Chapter 5 that often a straght lne descrbes the pattern of a relatonshp between two quanttatve varables. For nstance, n Example 5.1 we explored the relatonshp
More informationOnline Appendix for Forecasting the Equity Risk Premium: The Role of Technical Indicators
Onlne Appendx for Forecastng the Equty Rsk Premum: The Role of Techncal Indcators Chrstopher J. Neely Federal Reserve Bank of St. Lous neely@stls.frb.org Davd E. Rapach Sant Lous Unversty rapachde@slu.edu
More informationForecasting the Direction and Strength of Stock Market Movement
Forecastng the Drecton and Strength of Stock Market Movement Jngwe Chen Mng Chen Nan Ye cjngwe@stanford.edu mchen5@stanford.edu nanye@stanford.edu Abstract  Stock market s one of the most complcated systems
More informationThe Analysis of Covariance. ERSH 8310 Keppel and Wickens Chapter 15
The Analyss of Covarance ERSH 830 Keppel and Wckens Chapter 5 Today s Class Intal Consderatons Covarance and Lnear Regresson The Lnear Regresson Equaton TheAnalyss of Covarance Assumptons Underlyng the
More informationv a 1 b 1 i, a 2 b 2 i,..., a n b n i.
SECTION 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS 455 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS All the vector spaces we have studed thus far n the text are real vector spaces snce the scalars are
More informationLecture 14: Implementing CAPM
Lecture 14: Implementng CAPM Queston: So, how do I apply the CAPM? Current readng: Brealey and Myers, Chapter 9 Reader, Chapter 15 M. Spegel and R. Stanton, 2000 1 Key Results So Far All nvestors should
More informationExamples of Multiple Linear Regression Models
ECON *: Examples of Multple Regresson Models Examples of Multple Lnear Regresson Models Data: Stata tutoral data set n text fle autoraw or autotxt Sample data: A crosssectonal sample of 7 cars sold n
More informationMULTIPLE LINEAR REGRESSION IN MINITAB
MULTIPLE LINEAR REGRESSION IN MINITAB Ths document shows a complcated Mntab multple regresson. It ncludes descrptons of the Mntab commands, and the Mntab output s heavly annotated. Comments n { } are used
More informationPSYCHOLOGICAL RESEARCH (PYC 304C) Lecture 12
14 The Chsquared dstrbuton PSYCHOLOGICAL RESEARCH (PYC 304C) Lecture 1 If a normal varable X, havng mean µ and varance σ, s standardsed, the new varable Z has a mean 0 and varance 1. When ths standardsed
More informationInequality and The Accounting Period. Quentin Wodon and Shlomo Yitzhaki. World Bank and Hebrew University. September 2001.
Inequalty and The Accountng Perod Quentn Wodon and Shlomo Ytzha World Ban and Hebrew Unversty September Abstract Income nequalty typcally declnes wth the length of tme taen nto account for measurement.
More informationLatent Class Regression. Statistics for Psychosocial Research II: Structural Models December 4 and 6, 2006
Latent Class Regresson Statstcs for Psychosocal Research II: Structural Models December 4 and 6, 2006 Latent Class Regresson (LCR) What s t and when do we use t? Recall the standard latent class model
More informationRegression Models for a Binary Response Using EXCEL and JMP
SEMATECH 997 Statstcal Methods Symposum Austn Regresson Models for a Bnary Response Usng EXCEL and JMP Davd C. Trndade, Ph.D. STATTECH Consultng and Tranng n Appled Statstcs San Jose, CA Topcs Practcal
More information) of the Cell class is created containing information about events associated with the cell. Events are added to the Cell instance
Calbraton Method Instances of the Cell class (one nstance for each FMS cell) contan ADC raw data and methods assocated wth each partcular FMS cell. The calbraton method ncludes event selecton (Class Cell
More information8.5 UNITARY AND HERMITIAN MATRICES. The conjugate transpose of a complex matrix A, denoted by A*, is given by
6 CHAPTER 8 COMPLEX VECTOR SPACES 5. Fnd the kernel of the lnear transformaton gven n Exercse 5. In Exercses 55 and 56, fnd the mage of v, for the ndcated composton, where and are gven by the followng
More informationRecurrence. 1 Definitions and main statements
Recurrence 1 Defntons and man statements Let X n, n = 0, 1, 2,... be a MC wth the state space S = (1, 2,...), transton probabltes p j = P {X n+1 = j X n = }, and the transton matrx P = (p j ),j S def.
More informationAnalysis of Premium Liabilities for Australian Lines of Business
Summary of Analyss of Premum Labltes for Australan Lnes of Busness Emly Tao Honours Research Paper, The Unversty of Melbourne Emly Tao Acknowledgements I am grateful to the Australan Prudental Regulaton
More informationThe Analysis of Outliers in Statistical Data
THALES Project No. xxxx The Analyss of Outlers n Statstcal Data Research Team Chrysses Caron, Assocate Professor (P.I.) Vaslk Karot, Doctoral canddate Polychrons Economou, Chrstna Perrakou, Postgraduate
More informationLinear Regression Analysis for STARDEX
Lnear Regresson Analss for STARDEX Malcolm Halock, Clmatc Research Unt The followng document s an overvew of lnear regresson methods for reference b members of STARDEX. Whle t ams to cover the most common
More informationStudy on CET4 Marks in China s Graded English Teaching
Study on CET4 Marks n Chna s Graded Englsh Teachng CHE We College of Foregn Studes, Shandong Insttute of Busness and Technology, P.R.Chna, 264005 Abstract: Ths paper deploys Logt model, and decomposes
More informationWhat is Candidate Sampling
What s Canddate Samplng Say we have a multclass or mult label problem where each tranng example ( x, T ) conssts of a context x a small (mult)set of target classes T out of a large unverse L of possble
More informationMarginal Returns to Education For Teachers
The Onlne Journal of New Horzons n Educaton Volume 4, Issue 3 MargnalReturnstoEducatonForTeachers RamleeIsmal,MarnahAwang ABSTRACT FacultyofManagementand Economcs UnverstPenddkanSultan Idrs ramlee@fpe.ups.edu.my
More informationCHOLESTEROL REFERENCE METHOD LABORATORY NETWORK. Sample Stability Protocol
CHOLESTEROL REFERENCE METHOD LABORATORY NETWORK Sample Stablty Protocol Background The Cholesterol Reference Method Laboratory Network (CRMLN) developed certfcaton protocols for total cholesterol, HDL
More informationGender differences in revealed risk taking: evidence from mutual fund investors
Economcs Letters 76 (2002) 151 158 www.elsever.com/ locate/ econbase Gender dfferences n revealed rsk takng: evdence from mutual fund nvestors a b c, * Peggy D. Dwyer, James H. Glkeson, John A. Lst a Unversty
More informationTHE DISTRIBUTION OF LOAN PORTFOLIO VALUE * Oldrich Alfons Vasicek
HE DISRIBUION OF LOAN PORFOLIO VALUE * Oldrch Alfons Vascek he amount of captal necessary to support a portfolo of debt securtes depends on the probablty dstrbuton of the portfolo loss. Consder a portfolo
More informationEconomic Interpretation of Regression. Theory and Applications
Economc Interpretaton of Regresson Theor and Applcatons Classcal and Baesan Econometrc Methods Applcaton of mathematcal statstcs to economc data for emprcal support Economc theor postulates a qualtatve
More informationCan Auto Liability Insurance Purchases Signal Risk Attitude?
Internatonal Journal of Busness and Economcs, 2011, Vol. 10, No. 2, 159164 Can Auto Lablty Insurance Purchases Sgnal Rsk Atttude? ChuShu L Department of Internatonal Busness, Asa Unversty, Tawan ShengChang
More informationL10: Linear discriminants analysis
L0: Lnear dscrmnants analyss Lnear dscrmnant analyss, two classes Lnear dscrmnant analyss, C classes LDA vs. PCA Lmtatons of LDA Varants of LDA Other dmensonalty reducton methods CSCE 666 Pattern Analyss
More informationQUANTUM MECHANICS, BRAS AND KETS
PH575 SPRING QUANTUM MECHANICS, BRAS AND KETS The followng summares the man relatons and defntons from quantum mechancs that we wll be usng. State of a phscal sstem: The state of a phscal sstem s represented
More informationAn Alternative Way to Measure Private Equity Performance
An Alternatve Way to Measure Prvate Equty Performance Peter Todd Parlux Investment Technology LLC Summary Internal Rate of Return (IRR) s probably the most common way to measure the performance of prvate
More informationModule 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur
Module LOSSLESS IMAGE COMPRESSION SYSTEMS Lesson 3 Lossless Compresson: Huffman Codng Instructonal Objectves At the end of ths lesson, the students should be able to:. Defne and measure source entropy..
More informationFinite Math Chapter 10: Study Guide and Solution to Problems
Fnte Math Chapter 10: Study Gude and Soluton to Problems Basc Formulas and Concepts 10.1 Interest Basc Concepts Interest A fee a bank pays you for money you depost nto a savngs account. Prncpal P The amount
More informationLOOP ANALYSIS. The second systematic technique to determine all currents and voltages in a circuit
LOOP ANALYSS The second systematic technique to determine all currents and voltages in a circuit T S DUAL TO NODE ANALYSS  T FRST DETERMNES ALL CURRENTS N A CRCUT AND THEN T USES OHM S LAW TO COMPUTE
More informationSimple 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 informationMATLAB Workshop 15  Linear Regression in MATLAB
MATLAB: Workshop 15  Lnear Regresson n MATLAB page 1 MATLAB Workshop 15  Lnear Regresson n MATLAB Objectves: Learn how to obtan the coeffcents of a straghtlne ft to data, dsplay the resultng equaton
More informationCHAPTER 5 RELATIONSHIPS BETWEEN QUANTITATIVE VARIABLES
CHAPTER 5 RELATIONSHIPS BETWEEN QUANTITATIVE VARIABLES In ths chapter, we wll learn how to descrbe the relatonshp between two quanttatve varables. Remember (from Chapter 2) that the terms quanttatve varable
More informationx f(x) 1 0.25 1 0.75 x 1 0 1 1 0.04 0.01 0.20 1 0.12 0.03 0.60
BIVARIATE DISTRIBUTIONS Let be a varable that assumes the values { 1,,..., n }. Then, a functon that epresses the relatve frequenc of these values s called a unvarate frequenc functon. It must be true
More informationESTIMATING THE MARKET VALUE OF FRANKING CREDITS: EMPIRICAL EVIDENCE FROM AUSTRALIA
ESTIMATING THE MARKET VALUE OF FRANKING CREDITS: EMPIRICAL EVIDENCE FROM AUSTRALIA Duc Vo Beauden Gellard Stefan Mero Economc Regulaton Authorty 469 Wellngton Street, Perth, WA 6000, Australa Phone: (08)
More informationThe Mathematical Derivation of Least Squares
Pscholog 885 Prof. Federco The Mathematcal Dervaton of Least Squares Back when the powers that e forced ou to learn matr algera and calculus, I et ou all asked ourself the ageold queston: When the hell
More informationIDENTIFICATION AND CONTROL OF A FLEXIBLE TRANSMISSION SYSTEM
Abstract IDENTIFICATION AND CONTROL OF A FLEXIBLE TRANSMISSION SYSTEM Alca Esparza Pedro Dept. Sstemas y Automátca, Unversdad Poltécnca de Valenca, Span alespe@sa.upv.es The dentfcaton and control of a
More informationDO LOSS FIRMS MANAGE EARNINGS AROUND SEASONED EQUITY OFFERINGS?
DO LOSS FIRMS MANAGE EARNINGS AROUND SEASONED EQUITY OFFERINGS? Fernando Comran, Unversty of San Francsco, School of Management, 2130 Fulton Street, CA 94117, Unted States, fcomran@usfca.edu Tatana Fedyk,
More informationMAPP. MERIS level 3 cloud and water vapour products. Issue: 1. Revision: 0. Date: 9.12.1998. Function Name Organisation Signature Date
Ttel: Project: Doc. No.: MERIS level 3 cloud and water vapour products MAPP MAPPATBDClWVL3 Issue: 1 Revson: 0 Date: 9.12.1998 Functon Name Organsaton Sgnature Date Author: Bennartz FUB Preusker FUB Schüller
More informationApproximating Crossvalidatory Predictive Evaluation in Bayesian Latent Variables Models with Integrated IS and WAIC
Approxmatng Crossvaldatory Predctve Evaluaton n Bayesan Latent Varables Models wth Integrated IS and WAIC Longha L Department of Mathematcs and Statstcs Unversty of Saskatchewan Saskatoon, SK, CANADA
More information8 Algorithm for Binary Searching in Trees
8 Algorthm for Bnary Searchng n Trees In ths secton we present our algorthm for bnary searchng n trees. A crucal observaton employed by the algorthm s that ths problem can be effcently solved when the
More information6. EIGENVALUES AND EIGENVECTORS 3 = 3 2
EIGENVALUES AND EIGENVECTORS The Characterstc Polynomal If A s a square matrx and v s a nonzero vector such that Av v we say that v s an egenvector of A and s the correspondng egenvalue Av v Example :
More informationSTATISTICAL DATA ANALYSIS IN EXCEL
Mcroarray Center STATISTICAL DATA ANALYSIS IN EXCEL Lecture 6 Some Advanced Topcs Dr. Petr Nazarov 1401013 petr.nazarov@crpsante.lu Statstcal data analyss n Ecel. 6. Some advanced topcs Correcton for
More informationINVESTIGATION OF VEHICULAR USERS FAIRNESS IN CDMAHDR NETWORKS
21 22 September 2007, BULGARIA 119 Proceedngs of the Internatonal Conference on Informaton Technologes (InfoTech2007) 21 st 22 nd September 2007, Bulgara vol. 2 INVESTIGATION OF VEHICULAR USERS FAIRNESS
More informationHow Sets of Coherent Probabilities May Serve as Models for Degrees of Incoherence
1 st Internatonal Symposum on Imprecse Probabltes and Ther Applcatons, Ghent, Belgum, 29 June 2 July 1999 How Sets of Coherent Probabltes May Serve as Models for Degrees of Incoherence Mar J. Schervsh
More informationScale Dependence of Overconfidence in Stock Market Volatility Forecasts
Scale Dependence of Overconfdence n Stoc Maret Volatlty Forecasts Marus Glaser, Thomas Langer, Jens Reynders, Martn Weber* June 7, 007 Abstract In ths study, we analyze whether volatlty forecasts (judgmental
More informationtotal A A reag total A A r eag
hapter 5 Standardzng nalytcal Methods hapter Overvew 5 nalytcal Standards 5B albratng the Sgnal (S total ) 5 Determnng the Senstvty (k ) 5D Lnear Regresson and albraton urves 5E ompensatng for the Reagent
More informationControl Charts with Supplementary Runs Rules for Monitoring Bivariate Processes
Control Charts wth Supplementary Runs Rules for Montorng varate Processes Marcela. G. Machado *, ntono F.. Costa * * Producton Department, Sao Paulo State Unversty, Campus of Guaratnguetá, 564 Guaratnguetá,
More information5 Multiple regression analysis with qualitative information
5 Multple regresson analyss wth qualtatve nformaton Ezequel Urel Unversty of Valenca Verson: 913 5.1 Introducton of qualtatve nformaton n econometrc models. 1 5. A sngle dummy ndependent varable 5.3 Multple
More informationGRAVITY DATA VALIDATION AND OUTLIER DETECTION USING L 1 NORM
GRAVITY DATA VALIDATION AND OUTLIER DETECTION USING L 1 NORM BARRIOT JeanPerre, SARRAILH Mchel BGI/CNES 18.av.E.Beln 31401 TOULOUSE Cedex 4 (France) Emal: jeanperre.barrot@cnes.fr 1/Introducton The
More informationErrorPropagation.nb 1. Error Propagation
ErrorPropagaton.nb Error Propagaton Suppose that we make observatons of a quantty x that s subject to random fluctuatons or measurement errors. Our best estmate of the true value for ths quantty s then
More informationMacro Factors and Volatility of Treasury Bond Returns
Macro Factors and Volatlty of Treasury Bond Returns Jngzh Huang Department of Fnance Smeal Colleage of Busness Pennsylvana State Unversty Unversty Park, PA 16802, U.S.A. Le Lu School of Fnance Shangha
More informationSIMPLE LINEAR CORRELATION
SIMPLE LINEAR CORRELATION Smple lnear correlaton s a measure of the degree to whch two varables vary together, or a measure of the ntensty of the assocaton between two varables. Correlaton often s abused.
More informationRiskbased Fatigue Estimate of Deep Water Risers  Course Project for EM388F: Fracture Mechanics, Spring 2008
Rskbased Fatgue Estmate of Deep Water Rsers  Course Project for EM388F: Fracture Mechancs, Sprng 2008 Chen Sh Department of Cvl, Archtectural, and Envronmental Engneerng The Unversty of Texas at Austn
More informationThe Application of Fractional Brownian Motion in Option Pricing
Vol. 0, No. (05), pp. 738 http://dx.do.org/0.457/jmue.05.0..6 The Applcaton of Fractonal Brownan Moton n Opton Prcng Qngxn Zhou School of Basc Scence,arbn Unversty of Commerce,arbn zhouqngxn98@6.com
More informationThe OC Curve of Attribute Acceptance Plans
The OC Curve of Attrbute Acceptance Plans The Operatng Characterstc (OC) curve descrbes the probablty of acceptng a lot as a functon of the lot s qualty. Fgure 1 shows a typcal OC Curve. 10 8 6 4 1 3 4
More informationFuzzy Regression and the Term Structure of Interest Rates Revisited
Fuzzy Regresson and the Term Structure of Interest Rates Revsted Arnold F. Shapro Penn State Unversty Smeal College of Busness, Unversty Park, PA 68, USA Phone: 84865396, Fax: 84865684, Emal: afs@psu.edu
More informationPortfolio Loss Distribution
Portfolo Loss Dstrbuton Rsky assets n loan ortfolo hghly llqud assets holdtomaturty n the bank s balance sheet Outstandngs The orton of the bank asset that has already been extended to borrowers. Commtment
More information+ + +   This circuit than can be reduced to a planar circuit
MeshCurrent Method The meshcurrent s analog of the nodeoltage method. We sole for a new set of arables, mesh currents, that automatcally satsfy KCLs. As such, meshcurrent method reduces crcut soluton to
More informationExhaustive Regression. An Exploration of RegressionBased Data Mining Techniques Using Super Computation
Exhaustve Regresson An Exploraton of RegressonBased Data Mnng Technques Usng Super Computaton Antony Daves, Ph.D. Assocate Professor of Economcs Duquesne Unversty Pttsburgh, PA 58 Research Fellow The
More informationUnderwriting Risk. Glenn Meyers. Insurance Services Office, Inc.
Underwrtng Rsk By Glenn Meyers Insurance Servces Offce, Inc. Abstract In a compettve nsurance market, nsurers have lmted nfluence on the premum charged for an nsurance contract. hey must decde whether
More informationAn Evaluation of the Extended Logistic, Simple Logistic, and Gompertz Models for Forecasting Short Lifecycle Products and Services
An Evaluaton of the Extended Logstc, Smple Logstc, and Gompertz Models for Forecastng Short Lfecycle Products and Servces Charles V. Trappey a,1, Hsnyng Wu b a Professor (Management Scence), Natonal Chao
More informationH 1 : at least one is not zero
Chapter 6 More Multple Regresson Model The Ftest Jont Hypothess Tests Consder the lnear regresson equaton: () y = β + βx + βx + β4x4 + e for =,,..., N The tstatstc gve a test of sgnfcance of an ndvdual
More informationSurvival analysis methods in Insurance Applications in car insurance contracts
Survval analyss methods n Insurance Applcatons n car nsurance contracts Abder OULIDI 1 JeanMare MARION 2 Hervé GANACHAUD 3 Abstract In ths wor, we are nterested n survval models and ther applcatons on
More informationUsing 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 information1. Measuring association using correlation and regression
How to measure assocaton I: Correlaton. 1. Measurng assocaton usng correlaton and regresson We often would lke to know how one varable, such as a mother's weght, s related to another varable, such as a
More informationAnalysis of Covariance
Chapter 551 Analyss of Covarance Introducton A common tas n research s to compare the averages of two or more populatons (groups). We mght want to compare the ncome level of two regons, the ntrogen content
More informationThe announcement effect on mean and variance for underwritten and nonunderwritten SEOs
The announcement effect on mean and varance for underwrtten and nonunderwrtten SEOs Bachelor Essay n Fnancal Economcs Department of Economcs Sprng 013 Marcus Wkner and Joel Anehem Ulvenäs Supervsor: Professor
More informationVision Mouse. Saurabh Sarkar a* University of Cincinnati, Cincinnati, USA ABSTRACT 1. INTRODUCTION
Vson Mouse Saurabh Sarkar a* a Unversty of Cncnnat, Cncnnat, USA ABSTRACT The report dscusses a vson based approach towards trackng of eyes and fngers. The report descrbes the process of locatng the possble
More informationBERNSTEIN POLYNOMIALS
OnLne Geometrc Modelng Notes BERNSTEIN POLYNOMIALS Kenneth I. Joy Vsualzaton and Graphcs Research Group Department of Computer Scence Unversty of Calforna, Davs Overvew Polynomals are ncredbly useful
More informationLuby s Alg. for Maximal Independent Sets using Pairwise Independence
Lecture Notes for Randomzed Algorthms Luby s Alg. for Maxmal Independent Sets usng Parwse Independence Last Updated by Erc Vgoda on February, 006 8. Maxmal Independent Sets For a graph G = (V, E), an ndependent
More informationHOUSEHOLDS DEBT BURDEN: AN ANALYSIS BASED ON MICROECONOMIC DATA*
HOUSEHOLDS DEBT BURDEN: AN ANALYSIS BASED ON MICROECONOMIC DATA* Luísa Farnha** 1. INTRODUCTION The rapd growth n Portuguese households ndebtedness n the past few years ncreased the concerns that debt
More informationPAS: A Packet Accounting System to Limit the Effects of DoS & DDoS. Debish Fesehaye & Klara Naherstedt University of IllinoisUrbana Champaign
PAS: A Packet Accountng System to Lmt the Effects of DoS & DDoS Debsh Fesehaye & Klara Naherstedt Unversty of IllnosUrbana Champagn DoS and DDoS DDoS attacks are ncreasng threats to our dgtal world. Exstng
More informationNonlinear data mapping by neural networks
Nonlnear data mappng by neural networks R.P.W. Dun Delft Unversty of Technology, Netherlands Abstract A revew s gven of the use of neural networks for nonlnear mappng of hgh dmensonal data on lower dmensonal
More informationwhere the coordinates are related to those in the old frame as follows.
Chapter 2  Cartesan Vectors and Tensors: Ther Algebra Defnton of a vector Examples of vectors Scalar multplcaton Addton of vectors coplanar vectors Unt vectors A bass of noncoplanar vectors Scalar product
More informationAlthough ordinary leastsquares (OLS) regression
egresson through the Orgn Blackwell Oxford, TEST 014198X 003 5 31000 Orgnal Joseph Teachng G. UK Artcle Publshng Esenhauer through Statstcs the Ltd Trust Orgn 001 KEYWODS: Teachng; egresson; Analyss of
More informationManagement Quality, Financial and Investment Policies, and. Asymmetric Information
Management Qualty, Fnancal and Investment Polces, and Asymmetrc Informaton Thomas J. Chemmanur * Imants Paegls ** and Karen Smonyan *** Current verson: December 2007 * Professor of Fnance, Carroll School
More informationInterIng 2007. INTERDISCIPLINARITY IN ENGINEERING SCIENTIFIC INTERNATIONAL CONFERENCE, TG. MUREŞ ROMÂNIA, 1516 November 2007.
InterIng 2007 INTERDISCIPLINARITY IN ENGINEERING SCIENTIFIC INTERNATIONAL CONFERENCE, TG. MUREŞ ROMÂNIA, 1516 November 2007. UNCERTAINTY REGION SIMULATION FOR A SERIAL ROBOT STRUCTURE MARIUS SEBASTIAN
More informationMultiplePeriod Attribution: Residuals and Compounding
MultplePerod Attrbuton: Resduals and Compoundng Our revewer gave these authors full marks for dealng wth an ssue that performance measurers and vendors often regard as propretary nformaton. In 1994, Dens
More informationThis study examines whether the framing mode (narrow versus broad) influences the stock investment decisions
MANAGEMENT SCIENCE Vol. 54, No. 6, June 2008, pp. 1052 1064 ssn 00251909 essn 15265501 08 5406 1052 nforms do 10.1287/mnsc.1070.0845 2008 INFORMS How Do Decson Frames Influence the Stock Investment Choces
More informationNONPARAMETRIC REGRESSION ESTIMATION FOR DATA WITH EQUAL VALUES
European Scentfc Journal February 24 edton vol., No.4 ISSN: 857 788 (Prnt) e  ISSN 857743 NONPARAMETRIC REGRESSION ESTIMATION FOR DATA WITH EQUAL VALUES N. Alp Erll, PhD Department of Econometrcs, Unversty
More informationSupport Vector Machines
Support Vector Machnes Max Wellng Department of Computer Scence Unversty of Toronto 10 Kng s College Road Toronto, M5S 3G5 Canada wellng@cs.toronto.edu Abstract Ths s a note to explan support vector machnes.
More information1 De nitions and Censoring
De ntons and Censorng. Survval Analyss We begn by consderng smple analyses but we wll lead up to and take a look at regresson on explanatory factors., as n lnear regresson part A. The mportant d erence
More informationInstitute of Informatics, Faculty of Business and Management, Brno University of Technology,Czech Republic
Lagrange Multplers as Quanttatve Indcators n Economcs Ivan Mezník Insttute of Informatcs, Faculty of Busness and Management, Brno Unversty of TechnologCzech Republc Abstract The quanttatve role of Lagrange
More informationThe Development of Web Log Mining Based on ImproveKMeans Clustering Analysis
The Development of Web Log Mnng Based on ImproveKMeans Clusterng Analyss TngZhong Wang * College of Informaton Technology, Luoyang Normal Unversty, Luoyang, 471022, Chna wangtngzhong2@sna.cn Abstract.
More informationTHE EFFECT OF PREPAYMENT PENALTIES ON THE PRICING OF SUBPRIME MORTGAGES
THE EFFECT OF PREPAYMENT PENALTIES ON THE PRICING OF SUBPRIME MORTGAGES Gregory Ellehausen, Fnancal Servces Research Program George Washngton Unversty Mchael E. Staten, Fnancal Servces Research Program
More informationA Novel Methodology of Working Capital Management for Large. Public Constructions by Using Fuzzy Scurve Regression
Novel Methodology of Workng Captal Management for Large Publc Constructons by Usng Fuzzy Scurve Regresson ChengWu Chen, Morrs H. L. Wang and TngYa Hseh Department of Cvl Engneerng, Natonal Central Unversty,
More informationCS 2750 Machine Learning. Lecture 3. Density estimation. CS 2750 Machine Learning. Announcements
Lecture 3 Densty estmaton Mlos Hauskrecht mlos@cs.ptt.edu 5329 Sennott Square Next lecture: Matlab tutoral Announcements Rules for attendng the class: Regstered for credt Regstered for audt (only f there
More informationthe Manual on the global data processing and forecasting system (GDPFS) (WMONo.485; available at http://www.wmo.int/pages/prog/www/manuals.
Gudelne on the exchange and use of EPS verfcaton results Update date: 30 November 202. Introducton World Meteorologcal Organzaton (WMO) CBSXIII (2005) recommended that the general responsbltes for a Lead
More informationProactive Secret Sharing Or: How to Cope With Perpetual Leakage
Proactve Secret Sharng Or: How to Cope Wth Perpetual Leakage Paper by Amr Herzberg Stanslaw Jareck Hugo Krawczyk Mot Yung Presentaton by Davd Zage What s Secret Sharng Basc Idea ((2, 2)threshold scheme):
More informationMulticomponent Distillation
Multcomponent Dstllaton need more than one dstllaton tower, for n components, n1 fractonators are requred Specfcaton Lmtatons The followng are establshed at the begnnng 1. Temperature, pressure, composton,
More informationEE201 Circuit Theory I 2015 Spring. Dr. Yılmaz KALKAN
EE201 Crcut Theory I 2015 Sprng Dr. Yılmaz KALKAN 1. Basc Concepts (Chapter 1 of Nlsson  3 Hrs.) Introducton, Current and Voltage, Power and Energy 2. Basc Laws (Chapter 2&3 of Nlsson  6 Hrs.) Voltage
More informationTrade Adjustment and Productivity in Large Crises. Online Appendix May 2013. Appendix A: Derivation of Equations for Productivity
Trade Adjustment Productvty n Large Crses Gta Gopnath Department of Economcs Harvard Unversty NBER Brent Neman Booth School of Busness Unversty of Chcago NBER Onlne Appendx May 2013 Appendx A: Dervaton
More informationInternational University of Japan Public Management & Policy Analysis Program
Internatonal Unversty of Japan Publc Management & Polcy Analyss Program Practcal Gudes To Panel Data Modelng: A Step by Step Analyss Usng Stata * Hun Myoung Park, Ph.D. kucc65@uj.ac.jp 1. Introducton.
More informationMetaAnalysis of Hazard Ratios
NCSS Statstcal Softare Chapter 458 MetaAnalyss of Hazard Ratos Introducton Ths module performs a metaanalyss on a set of togroup, tme to event (survval), studes n hch some data may be censored. These
More information