Assessing the Fairness of a Firm s Allocation of Shares in Initial Public Offerings: Adapting a Measure from Biostatistics

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Assessing the Fairness of a Firm s Allocation of Shares in Initial Public Offerings: Adapting a Measure from Biostatistics"

Transcription

1 Assessng the Farness of a Frm s Allocaton of Shares n Intal Publc Offerngs: Adaptng a Measure from Bostatstcs by Efstatha Bura and Joseph L. Gastwrth Department of Statstcs The George Washngton Unversty Introducton and Outlne In 1980, Professor Gastwrth was a vstng professor at MIT and gave a semnar on statstcs n law. Two Harvard law students attended the course. One of them went on to become a partner n a premer New York law frm specalzng n securtes law. Realzng that the case he was workng on nvolved a sgnfcant statstcal component, he contacted hs former Professor to be hs statstcal expert. Durng the hgh-tech boom of the late 90 s, ndvduals or frms who receved shares at the ntal offerng prce were often makng substantal proft by sellng ther shares shortly after they bought them. Some nsttutons, e.g. hedge funds, tred to ncrease the number of shares they were allocated by sharng ther profts wth ther brokers or nvestment frms. Ths practce s prohbted by law as the Wall Street frms should serve members of the publc farly. Indeed several major frms, e.g. Credt Susse Frst Boston, pad substantal penaltes to the Securtes and Exchange Commsson (SEC). In ths case, the clent was a relatvely small nvestment frm that was accused of sharng profts wth some customers, prmarly because these customers pad so-called nflated or excessve commssons on other trades made through the frm on or near the days they receved IPO (Intal Publc Offerng) shares. The case nvolved both legal and statstcal ssues. Pror to the complant, a commsson was deemed to be excessve f t exceeded fve percent. Although the publshed rule does state that commssons less than 5% mght also be judged excessve, no alternatve gudelne s provded. Snce almost all the so called nflated or excessve commssons were less than 5%, ndeed, less than 3%, the proprety of the regulators changng the crtera for commssons to be deemed excessve wthout conductng the standard rule-makng procedures was a major legal ssue. A key statstcal ssue concerned the farness of the IPO allocaton process used at the frm. If there were a set of proft sharng customers, one would expect that they would be favored n the allocaton process. Thus, one would expect that ther IPO share allocatons would be greater than ther expected or far share relatve to those of other customers whch would usually be less. Ths problem has analoges n 1

2 epdemology and equal employment. In the former one studes whether the members of a populaton that were exposed to a partcular health rsk dffers sgnfcantly from the unexposed populaton. In dscrmnaton cases, the fracton of jobs or promotons a mnorty group receved s compared to ther percentage of qualfed applcants. In both contexts, the Cochran-Mantel-Haenszel (CMH) test s a wdely used measure of dsparty between two classes of subjects. In our applcaton, the two groups were the alleged proft sharers and other smlar customers who requested IPO shares. The CMH measure was adapted n order to provde an nterpretaton of the dfference between the two groups n terms of a rato that could be easly conveyed to the hearng panel. A major ssue we faced was the defnton of farness n the allocaton of IPO shares. Ths was needed, as our CMH-based measure requres a determnaton of the expected share of customers under far allocaton practces. Arthur Levtt, the Charman of the SEC from 1993 to 2001, has stated that t s reasonable for a frm to allocate shares to ts best customers, especally those who had gven t substantal busness pror to the market and IPO boom. A measure of the farness of the allocatons of a partcular IPO needs to ncorporate the prevous busness of the customers who requested shares n that IPO n order to determne whch ones receved less than ther approprate share and whch customers were favored. The measure of the farness of the allocatons of IPO shares we proposed compares the actual number of shares the alleged proft sharers receved to the number they would have been expected to receve based on ther fracton of the busness all requestng customers gave the frm durng the prevous twelve months. The ssue of a group of subjects beng favored over another s central n dscrmnaton law cases. We expressed our measure n terms of the selecton rato used to evaluate an employment practce for possble dsparate mpact. It wll be seen that the group of alleged proft sharers were not favored. Indeed, the selecton rato measure ndcates that the proft sharers were actually dsadvantaged n the allocaton process. Background: Adaptng the Cochran-Mantel-Haenszel (CMH) Test Statstc No specfc SEC rules govern the process of allocatng shares n a securtes offerng. NASD (Natonal Assocaton of Securtes Dealers) Conduct Rule 2110 requres member frms to observe just and equtable prncples of trade. SEC or NASD rules pror to ths case precluded brokerage frms from chargng commssons n excess of 5%. Unlke many brokerage houses, ths frm dd not have a schedule for commssons for trades of varous szes. From ts ncepton the frm allowed ts customers to place ther own commssons on ther trades. As a consequence of the deregulaton of the securtes ndustry, ths practce was legal and was not unque to the frm. In the case, the regulators examned about two and half years of data and dentfed several groups of accounts as proft sharers at dfferent tmes durng the nvestgaton. The fnal set of accounts was selected by a new crteron for excessve commssons and the alleged volatons were clamed to have occurred n a sx month perod. A stock transacton was categorzed as an nflated rate commsson f the trade 2

3 nvolved 10,000 or more shares wth a commsson of 20 or more cents per share. Also, for the accounts wth such trades, trades of 1,000 or more shares wth commsson of 75 or more cents per share were also consdered nflated. These crtera were met by about 30 nsttutonal accounts of the frm. The regulator s expert decded to drop several of these because ther tradng pattern was not smlar to that of the other members of ths group. Ths fnal set of allegedly favored customers wll be referred to as the APS (Alleged Proft Sharers) group n ths paper. The Cochran-Mantel-Haenszel (CMH) test compares two groups on a bnary response, stratfyng the data nto K subgroups or strata wth smlar levels of other relevant covarates. Agrest provdes a full dscusson of the test and ts applcatons. The data are gven by a seres of K 2x2 contngency tables. Tradtonally, n each table the rows correspond to the "Treatment group" values (e.g "Placebo", "Drug A") and the columns to the "Response" values (e.g "No change," "Improvement"). The value of the test statstc s gven by CMH = K = 1 ( observed K = 1 expected ), V where V s the varance of the usual test of the equalty of proportons n the th stratum. The CMH test statstc s used to test the null hypothess that the response s condtonally ndependent of the treatment. If we consder the APS group as the treatment group and a comparable set of other customers as the control, then for every IPO a 2x2 table contanng the number of requests each group made and ther success rate can be formed. The data for each IPO defne a separate stratum. Ths analyss was carred out after we notced that the aggregate success rates of retal (non-nsttutonal) customers exceeded those of the APS customers. Ths was qute surprsng as nsttutonal customers typcally gve nvestment frms much more busness than retal customers. The CMH test for the 2x2 tables across all IPO allocatons obtaned that ther success rates were not statstcally sgnfcantly dfferent. The success rate analyss does not account for volume dfferences. That s, the fact that a customer was allocated IPO shares does not contan nformaton as to the sze of the allocaton wth respect to the total number of IPO shares the frm had avalable and the number of shares allocated to other customers. Thus, n addton to the CMH formal test, t s useful to have a measure of the magntude of the dfference observed expected ) relatve to the total expected value of the response. We use ( 3

4 K = 1 ( observed K = 1 expected ) expected to measure the dsparty between the actual and expected allocatons as a percent of the expected number of shares. The numerator of ths measure s the numerator of the CMH statstc whle the denomnator reflects ther expected total shares under a far system. Postve values of the rato ndcate an allocaton exceedng ther expected number whereas negatve values ndcate allocatons below expected. Ths measure s smlar to the attrbutable rsk type measures used n epdemology. To mplement ths measure one needs to determne the expected number of shares a customer would receve under a far allocaton system. Expectancy Analyss: Was the allocaton far? As already stated, there were no specfc crtera or rules governng the allocaton process set by the SEC, NASD or the frm. In our search for understandng allocaton practces n the street, we came across statements of two SEC commssoners that t s reasonable for a frm to consder ts busness relatonshp wth a customer n allocatng IPO shares. The problem s to translate the factors a frm could approprately take nto account n the calculaton of a far expected number of shares for each customer who requests shares n a partcular IPO. Some measurable factors that could be used n evaluatng a customer s busness relatonshp wth the frm are: (1) how long the customer has been wth the frm, (2) the amount of busness an account generated for the frm over a perod of tme, and (3) how actve the account has been. Whle the potental of a customer to generate future busness s also a reasonable factor, t s qute subjectve and brokers are usually not asked to record such predctons at the tme they allocate shares. Data on the length of tme a customer had been wth the frm was not as accurate as we frst thought. Some customers came wth a newly hred broker so ther assocaton wth the broker was longer than would be ndcated by the frm s records. Also, several major customers had substantal other busness relatonshp wth the frm. The only hghly relable data avalable to us were the tradng data of all customers over a perod of two years from whch we could easly extract the commsson busness each customer gave the frm. Snce an nvestment frm would naturally allocate more shares to customers who had gven t more busness than to other customers, the relatonshp between the allocatons of shares n all IPO deals and the prevous year s commsson busness customers gave the frm was studed. We focused on one year s pror busness snce t would account for seasonal varatons n the tradng patterns of dfferent customers. The frm also thought that ts brokers would have a relatvely accurate pcture of the busness ther customers gave them over the last year. 4

5 Frst, we nvestgated whether aggregate hstorcal commsson busness would be a good predctor of IPO allocatons especally n comparson to the new cents per share crteron used by the regulator. We compared the fracton of IPO shares allocated to customers requestng those shares to the twelve month average of the cents per share they put on trades and the correspondng average of ther total commsson busness. As the total commsson the customers gave over the year pror to the IPO was not normally dstrbuted the Spearman correlaton coeffcent was used to assess the strength of the relatonshp between the number of shares allocated and total commsson. The average Spearman correlaton between IPO shares allocated and total commssons n all offerngs was.58 and was hghly sgnfcant (p-value <.0001). In contrast, the Spearman correlaton coeffcent between IPO shares allocated and cents per share was only.04, a non-sgnfcant result. Ths calculaton was made because the regulator defned a commsson to be nflated n terms of cents per share. It showed that a customer s allocaton was not related to ther average commsson n that metrc. We consdered an allocaton of IPO shares to a set of requestng customers to be far f the fracton of the total number of shares a customer receved was proportonal to ther share of pror busness. In detal, for each offerng the expected number of shares a customer who expressed nterest would receve was determned by ts fracton of the total pror busness gven the frm amongst the customers who were nterested. Once ths expected fracton, F, was determned, the expected number of shares the customer should receve s just F tmes the number of shares the frm had to allocate. For example, f the frm had 20,000 shares to allocate and a customer s fracton F of pror busness gven by all requestng customers over the last year was.25, the customer would be expected to receve 5,000 shares. To assess whether the APS customers receved more than ther far share, for each offerng we summed the expected fractons (Fs) of the APS customers who requested shares to determne ther total, say FT. The expected number of shares the APS customers should receve, assumng an allocaton proportonal to pror busness, s FT tmes the number of shares the frm had to allocate. To contnue the example, f FT were.60, e.g., three APS customers wth F s of.25,.20 and.15 asked for shares, they would be expected to receve.6 x 20,000 = 12,000 shares of the offerng. By comparng the actual number of shares to the expected number of shares we can assess whether the APS customers receved more or less than ther far share. Ths s accomplshed by calculatng the dfference between the actual and expected numbers of shares for each offerng and summng over all IPO deals. The results can be summarzed by comparng the actual fracton of shares receved by the APS customers n the offerngs of nterest to ther fracton expected. The few new customers,.e. the customers who had not executed any trades n the year pror to the month of the offerng, were excluded from the computatons, as they would be expected to receve zero shares snce they had no busness n the pror twelve months. The effect of ths was qute small as about 96% of all IPO shares went to customers wth pror busness. Of course, some of these customers mght have had an account wth the frm but had not been actve n the year precedng the month of an IPO deal. 5

6 To further llustrate how the calculatons are carred out, we present the followng hypothetcal example. Suppose the frm had a total of 1,000 shares of an IPO to dstrbute to the 10 customers who requested shares. If we assume that two of these customers were new to the frm and were allocated a total of 100 shares, there reman 900 shares to allocate to the remanng 8 customers. Also assume that among the 8 customers, the alleged proft sharers fracton of the pror twelve-month busness s.60 and they were allocated 500 shares. Table 1 below summarzes the calculatons. Table 1: Hypothetcal Example to Illustrate the Calculaton of Expected Number of Shares Allocated to APS Customers and Comparson wth Actual Number Number of Receved Fracton of Aggregate Expected Number of IPO Dfference (Receved IPO Pror Year Shares Expected) Shares Busness APS *0.6= All Other *0.4= Accounts Total Table 2 reports the number of shares n all IPO deals the APS customers receved and the number of shares they would have been expected to receve on the bass of ther fracton of the commssons receved by the frm n the prevous 12 months. Table 2: Comparson of Actual and Expected Number of Shares Allocated to the APS Customers Actual Expected Dfference Number Number 57 IPOs 1,279,775 1,806, ,811 As the total number of IPO shares allocated to all customers wth pror busness was 3,118,025, the actual and expected percentages of these offerngs allocated to these accounts are gven n a bar-plot n Fgure 1. The APS customers receved 41.04% of all IPO shares but on the bass of ther pror commssons they would have been expected to receve 57.94% so they only were allocated 41.04/57.94=.7083 or 70.83% of ther expected number. Thus, n relatve terms, the APS receved 70.83% of the IPO shares they would have been expected to receve had shares been allocated n proporton to a customer s commssons durng the prevous twelve months. 6

7 Fgure 1. Comparson of Actual and Expected Percent of Shares Allocated to APS Customers To confrm that these results reflectng the allocaton process of IPO shares were not unque to the sx-month perod specfc to the law case, a smlar expectancy analyss was conducted for the subsequent sx months. In that perod the APS receved about 66.15% of ther expected number of shares or equvalently, 33.85% fewer shares than would be expected on the bass of ther busness durng the prevous twelve months. A Senstvty Analyss: Demonstratng the Robustness of the Results When presentng the results of a statstcal analyss n court t s useful to provde a senstvty analyss showng that the man nference remans unaffected by the nevtable devatons of the data from the theoretcal deal. Several such analyses are descrbed n our related paper lsted n the references. Here we summarze an analyss answerng an nterestng non-statstcal queston posed by the lawyers. 7

8 For purposes of the regulators analyss, an nflated rate transacton was a stock trade nvolvng: () commssons of $.20 per share or more on 10,000 shares or more, or () commssons of $.75 or more on 1,000 shares or more. What effect would these two crtera have on the number of shares the APS customers would be expected to receve had these crtera been n exstence at the tme and enforced by the frm? To answer ths queston we adjusted the pror busness for all customers who had executed such trades n the twelve-month perod pror to the revew perod as well as durng the revew perod as follows. All trades satsfyng crteron () were assgned commssons calculated by settng the cents per share commsson rate to $.19. All trades satsfyng crteron () were assgned commssons by settng cents per share to $.74. All other commssons were left ntact. Then the expected number of shares for each IPO was calculated from these adjusted data, whch removed the alleged excessve component of commssons gven by the APS customers from the analyss. Note that the pror busness of the APS customers s reduced and, consequently, ther expected fracton of IPO shares becomes smaller. Table 3 reports the number of IPO shares the APS accounts receved and the number of shares they would have been expected to receve on the bass of ther fracton of the adjusted commssons they would have had gven the frm n the prevous twelve months. For each offerng the expected number of shares for the APS customers s determned from ther fracton of the adjusted total commsson busness durng the prevous twelve months of all customers requestng that specfc offerng as descrbed n the man expectancy analyss. Table 3: Comparson of Actual and Adjusted Expected Number of Shares Allocated to the APS Customers Actual Number Expected Number Dfference 57 IPOs 1,279,775 1,542, , Fgure 2 dsplays a bar-plot wth the percentages of actual and expected number of shares for the APS group. The actual remans the same as n Fgure 1. The new expected value, after the adjustment, s now 49.47%. In relatve terms, even after subtractng from ther pror busness the allegedly excessve component of ther commsson, the APS customers receved 17.04% fewer IPO shares they would have been expected to receve had shares been allocated n proporton to a customer s adjusted commssons durng the prevous twelve months. 8

9 Fgure 2. Comparson of Actual and Adjusted Expected Percent of Shares Allocated to the APS Customers Drawng on an Analog n Equal Employment: Selecton Rato Analyss To further apprecate the magntude and mportance of the shortfall n the number of IPO shares receved by the APS customers dentfed as proft sharers by the regulator, ths shortfall can be nterpreted as a selecton rato used n equal employment cases. It s the rato of the pass rate of a mnorty group of job applcants to the pass rate of job applcants from the majorty group and s used to assess whether a job requrement (e.g., attanng a certan score on a test) has a dsparate mpact on mnorty applcants. In that applcaton, the government gudelne s that selecton ratos less than.80 or four-ffths (e.g., mnorty applcants receve passng scores on a test at a rate less than 80%, or four-ffths, the rate of Caucasans) ndcate a dsparate mpact so that the specfed job requrement needs to be shown to be job-related. Although the selecton rato s defned for the comparson of two success rates, Karys et al. and Gastwrth and Greenhouse have translated t to our stuaton. Suppose the fracton π of job applcants s mnorty and they receve the fracton p (<π) of the jobs. 9

10 Assume there s a total of N applcants of whch n are hred. Then the mnorty success rate s np/πn and the majorty success rate s n(1-p)/(1-π)n. The selecton rato then s gven by np πn n(1 p) (1 π ) N p 1 p = π 1 π Notce that the selecton rato s the rato of the odds a hre s mnorty to the odds an applcant s mnorty. In our applcaton the fracton of shares the APS receved s p and ther fracton of pror busness s π. The selecton rato for the APS customers calculated from the data n Fgure 1 s.5053 well below the value of the government gudelne,.80. Ths ndcates that on the bass of ther pror commsson busness the APS customers were dsadvantaged relatve to other customers n the IPO allocaton process used at the frm. The selecton rato for the data n Fgure 2, where the alleged excessve or nflated commssons were adjusted downward to comply wth the new crteron, s.711, stll below the.80 threshold. Thus nterpretng the results of the expectancy analyss n terms of the gudelnes used n equal employment cases shows that the selecton rato of the APS group ndcates they were dsfavored rather than favored by the frm. A Fnal Note Whle the IPO frenzy durng the late 1990 s was unusual, there remans a substantal IPO market as new companes grow and decde to go publc. The methodology descrbed n ths paper should be useful both to regulators and ndvdual frms who desre to check the farness of ther IPO allocatons. In partcular, a frm can readly montor ts ndvdual brokers to ensure that none of them s favorng a few customers by gvng them notceably more shares than ther pror busness merts. If a customer has an unusually hgh success rate or s often allocated more shares than expected, the frm could then examne the pattern of commsson busness the customer gves to see whether t s concentrated near the tme the customer receved shares n an IPO and s related to the proft potental of the IPO. The pattern that the regulatory body n the case motvatng ths research used really does not capture proft sharng actvty around the days of an IPO. Recall that only trades of 10,000 shares or more wth commssons of 20 cents or more per share or trades of at least 1,000 shares wth commssons of 75 cents per share or more were consdered nflated or excessve. Thus, a customer who traded 5,000 shares and placed a commsson of 50 cents per share would go undetected even though he/she gave a commsson of $2,500, exceedng a $2,000 commsson on a trade of 10,000 at 20 cents per share. It would seem more approprate to consder the total commsson busness near the recept of an IPO and examne the rato of these commssons to ther projected proft from the IPO shares they were allocated. If there had been a formal or nformal qud pro 10

11 quo arrangement or understandng these ratos would be expected to concentrate around the understood share. As far as the authors know, ths s the frst use of concepts orgnally developed n bostatstcs n a securtes law case. The factors used to determne the expected number of shares a customer would receve under a far system rely on practces consdered approprate n the ndustry. In another case, other factors mght be ncorporated provded relable data on them are avalable. References and Further Readng Agrest, A. (1990). Categorcal Data Analyss. New York: Wley. Gel, Y. Mao, W. and Gastwrth, J. L. (2005). The Importance of Checkng the Assumptons Underlyng Statstcal Analyss: Graphcal Methods for Assessng Normalty. Jurmetrcs Journal, 46, Gastwrth, J. L. (1988). Statstcal Reasonng n Law and Publc Polcy. Volume 1 Statstcal Concepts and Issues of Farness. San Dego: Academc Press. Gastwrth,, J. L., Bura, E. and Modarres, R. (2005). Statstcal Methods for Assessng the Farness of the Allocaton of Shares n Intal Publc Offerngs. Law, Probablty and Rsk. 4, Gastwrth, J. L., Modarres, R. and Bura, E. (2005). The Use of the Lorenz Curve, Gn Index and Related Measures of Relatve Inequalty and Unformty n Securtes Law. Metron. In press. Gastwrth, J. L. and Greenhouse, S. W. (1987). Estmatng a common relatve rsk: Applcaton n equal employment. Journal of the Amercan Statstcal Assocaton, 82, Karys, D., Kadane, J. B., and Lehoczky, J. P. (1997). Jury Representatveness: A Mandate for Multple Source Pools. Calforna Law Revew, 65,

An Alternative Way to Measure Private Equity Performance

An 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 information

DEFINING %COMPLETE IN MICROSOFT PROJECT

DEFINING %COMPLETE IN MICROSOFT PROJECT CelersSystems DEFINING %COMPLETE IN MICROSOFT PROJECT PREPARED BY James E Aksel, PMP, PMI-SP, MVP For Addtonal Informaton about Earned Value Management Systems and reportng, please contact: CelersSystems,

More information

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ). REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or

More information

Can Auto Liability Insurance Purchases Signal Risk Attitude?

Can Auto Liability Insurance Purchases Signal Risk Attitude? Internatonal Journal of Busness and Economcs, 2011, Vol. 10, No. 2, 159-164 Can Auto Lablty Insurance Purchases Sgnal Rsk Atttude? Chu-Shu L Department of Internatonal Busness, Asa Unversty, Tawan Sheng-Chang

More information

Course outline. Financial Time Series Analysis. Overview. Data analysis. Predictive signal. Trading strategy

Course outline. Financial Time Series Analysis. Overview. Data analysis. Predictive signal. Trading strategy Fnancal Tme Seres Analyss Patrck McSharry patrck@mcsharry.net www.mcsharry.net Trnty Term 2014 Mathematcal Insttute Unversty of Oxford Course outlne 1. Data analyss, probablty, correlatons, vsualsaton

More information

CHAPTER 14 MORE ABOUT REGRESSION

CHAPTER 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 information

Analysis of Premium Liabilities for Australian Lines of Business

Analysis 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 information

1. Measuring association using correlation and regression

1. 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 information

Inequality and The Accounting Period. Quentin Wodon and Shlomo Yitzhaki. World Bank and Hebrew University. September 2001.

Inequality 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 information

1.1 The University may award Higher Doctorate degrees as specified from time-to-time in UPR AS11 1.

1.1 The University may award Higher Doctorate degrees as specified from time-to-time in UPR AS11 1. HIGHER DOCTORATE DEGREES SUMMARY OF PRINCIPAL CHANGES General changes None Secton 3.2 Refer to text (Amendments to verson 03.0, UPR AS02 are shown n talcs.) 1 INTRODUCTION 1.1 The Unversty may award Hgher

More information

Forecasting the Direction and Strength of Stock Market Movement

Forecasting 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 information

Staff Paper. Farm Savings Accounts: Examining Income Variability, Eligibility, and Benefits. Brent Gloy, Eddy LaDue, and Charles Cuykendall

Staff Paper. Farm Savings Accounts: Examining Income Variability, Eligibility, and Benefits. Brent Gloy, Eddy LaDue, and Charles Cuykendall SP 2005-02 August 2005 Staff Paper Department of Appled Economcs and Management Cornell Unversty, Ithaca, New York 14853-7801 USA Farm Savngs Accounts: Examnng Income Varablty, Elgblty, and Benefts Brent

More information

DO LOSS FIRMS MANAGE EARNINGS AROUND SEASONED EQUITY OFFERINGS?

DO 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 information

PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 12

PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 12 14 The Ch-squared dstrbuton PSYCHOLOGICAL RESEARCH (PYC 304-C) 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 information

The covariance is the two variable analog to the variance. The formula for the covariance between two variables is

The 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 information

Financial Mathemetics

Financial Mathemetics Fnancal Mathemetcs 15 Mathematcs Grade 12 Teacher Gude Fnancal Maths Seres Overvew In ths seres we am to show how Mathematcs can be used to support personal fnancal decsons. In ths seres we jon Tebogo,

More information

Analysis of Covariance

Analysis 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 information

The Current Employment Statistics (CES) survey,

The Current Employment Statistics (CES) survey, Busness Brths and Deaths Impact of busness brths and deaths n the payroll survey The CES probablty-based sample redesgn accounts for most busness brth employment through the mputaton of busness deaths,

More information

STATISTICAL DATA ANALYSIS IN EXCEL

STATISTICAL DATA ANALYSIS IN EXCEL Mcroarray Center STATISTICAL DATA ANALYSIS IN EXCEL Lecture 6 Some Advanced Topcs Dr. Petr Nazarov 14-01-013 petr.nazarov@crp-sante.lu Statstcal data analyss n Ecel. 6. Some advanced topcs Correcton for

More information

Hollinger Canadian Publishing Holdings Co. ( HCPH ) proceeding under the Companies Creditors Arrangement Act ( CCAA )

Hollinger Canadian Publishing Holdings Co. ( HCPH ) proceeding under the Companies Creditors Arrangement Act ( CCAA ) February 17, 2011 Andrew J. Hatnay ahatnay@kmlaw.ca Dear Sr/Madam: Re: Re: Hollnger Canadan Publshng Holdngs Co. ( HCPH ) proceedng under the Companes Credtors Arrangement Act ( CCAA ) Update on CCAA Proceedngs

More information

Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 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 information

Section 5.4 Annuities, Present Value, and Amortization

Section 5.4 Annuities, Present Value, and Amortization Secton 5.4 Annutes, Present Value, and Amortzaton Present Value In Secton 5.2, we saw that the present value of A dollars at nterest rate per perod for n perods s the amount that must be deposted today

More information

Multiple-Period Attribution: Residuals and Compounding

Multiple-Period Attribution: Residuals and Compounding Multple-Perod 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 information

The OC Curve of Attribute Acceptance Plans

The 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 information

THE DISTRIBUTION OF LOAN PORTFOLIO VALUE * Oldrich Alfons Vasicek

THE 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 information

Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting

Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting Causal, Explanatory Forecastng Assumes cause-and-effect relatonshp between system nputs and ts output Forecastng wth Regresson Analyss Rchard S. Barr Inputs System Cause + Effect Relatonshp The job of

More information

Chapter 11 Practice Problems Answers

Chapter 11 Practice Problems Answers Chapter 11 Practce Problems Answers 1. Would you be more wllng to lend to a frend f she put all of her lfe savngs nto her busness than you would f she had not done so? Why? Ths problem s ntended to make

More information

Fixed income risk attribution

Fixed income risk attribution 5 Fxed ncome rsk attrbuton Chthra Krshnamurth RskMetrcs Group chthra.krshnamurth@rskmetrcs.com We compare the rsk of the actve portfolo wth that of the benchmark and segment the dfference between the two

More information

Proceedings of the Annual Meeting of the American Statistical Association, August 5-9, 2001

Proceedings of the Annual Meeting of the American Statistical Association, August 5-9, 2001 Proceedngs of the Annual Meetng of the Amercan Statstcal Assocaton, August 5-9, 2001 LIST-ASSISTED SAMPLING: THE EFFECT OF TELEPHONE SYSTEM CHANGES ON DESIGN 1 Clyde Tucker, Bureau of Labor Statstcs James

More information

Understanding the Impact of Marketing Actions in Traditional Channels on the Internet: Evidence from a Large Scale Field Experiment

Understanding the Impact of Marketing Actions in Traditional Channels on the Internet: Evidence from a Large Scale Field Experiment A research and educaton ntatve at the MT Sloan School of Management Understandng the mpact of Marketng Actons n Tradtonal Channels on the nternet: Evdence from a Large Scale Feld Experment Paper 216 Erc

More information

CHOLESTEROL REFERENCE METHOD LABORATORY NETWORK. Sample Stability Protocol

CHOLESTEROL 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 information

Time Value of Money Module

Time Value of Money Module Tme Value of Money Module O BJECTIVES After readng ths Module, you wll be able to: Understand smple nterest and compound nterest. 2 Compute and use the future value of a sngle sum. 3 Compute and use the

More information

Calculation of Sampling Weights

Calculation of Sampling Weights Perre Foy Statstcs Canada 4 Calculaton of Samplng Weghts 4.1 OVERVIEW The basc sample desgn used n TIMSS Populatons 1 and 2 was a two-stage stratfed cluster desgn. 1 The frst stage conssted of a sample

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

Number of Levels Cumulative Annual operating Income per year construction costs costs ($) ($) ($) 1 600,000 35,000 100,000 2 2,200,000 60,000 350,000

Number of Levels Cumulative Annual operating Income per year construction costs costs ($) ($) ($) 1 600,000 35,000 100,000 2 2,200,000 60,000 350,000 Problem Set 5 Solutons 1 MIT s consderng buldng a new car park near Kendall Square. o unversty funds are avalable (overhead rates are under pressure and the new faclty would have to pay for tself from

More information

Statistical Methods to Develop Rating Models

Statistical Methods to Develop Rating Models Statstcal Methods to Develop Ratng Models [Evelyn Hayden and Danel Porath, Österrechsche Natonalbank and Unversty of Appled Scences at Manz] Source: The Basel II Rsk Parameters Estmaton, Valdaton, and

More information

Return decomposing of absolute-performance multi-asset class portfolios. Working Paper - Nummer: 16

Return decomposing of absolute-performance multi-asset class portfolios. Working Paper - Nummer: 16 Return decomposng of absolute-performance mult-asset class portfolos Workng Paper - Nummer: 16 2007 by Dr. Stefan J. Illmer und Wolfgang Marty; n: Fnancal Markets and Portfolo Management; March 2007; Volume

More information

STAMP DUTY ON SHARES AND ITS EFFECT ON SHARE PRICES

STAMP DUTY ON SHARES AND ITS EFFECT ON SHARE PRICES STAMP UTY ON SHARES AN ITS EFFECT ON SHARE PRICES Steve Bond Mke Hawkns Alexander Klemm THE INSTITUTE FOR FISCAL STUIES WP04/11 STAMP UTY ON SHARES AN ITS EFFECT ON SHARE PRICES Steve Bond (IFS and Unversty

More information

Customer Lifetime Value Modeling and Its Use for Customer Retention Planning

Customer Lifetime Value Modeling and Its Use for Customer Retention Planning Customer Lfetme Value Modelng and Its Use for Customer Retenton Plannng Saharon Rosset Enat Neumann Ur Eck Nurt Vatnk Yzhak Idan Amdocs Ltd. 8 Hapnna St. Ra anana 43, Israel {saharonr, enatn, ureck, nurtv,

More information

Transition Matrix Models of Consumer Credit Ratings

Transition Matrix Models of Consumer Credit Ratings Transton Matrx Models of Consumer Credt Ratngs Abstract Although the corporate credt rsk lterature has many studes modellng the change n the credt rsk of corporate bonds over tme, there s far less analyss

More information

How Much to Bet on Video Poker

How Much to Bet on Video Poker How Much to Bet on Vdeo Poker Trstan Barnett A queston that arses whenever a gae s favorable to the player s how uch to wager on each event? Whle conservatve play (or nu bet nzes large fluctuatons, t lacks

More information

To manage leave, meeting institutional requirements and treating individual staff members fairly and consistently.

To manage leave, meeting institutional requirements and treating individual staff members fairly and consistently. Corporate Polces & Procedures Human Resources - Document CPP216 Leave Management Frst Produced: Current Verson: Past Revsons: Revew Cycle: Apples From: 09/09/09 26/10/12 09/09/09 3 years Immedately Authorsaton:

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

High Correlation between Net Promoter Score and the Development of Consumers' Willingness to Pay (Empirical Evidence from European Mobile Markets)

High Correlation between Net Promoter Score and the Development of Consumers' Willingness to Pay (Empirical Evidence from European Mobile Markets) Hgh Correlaton between et Promoter Score and the Development of Consumers' Wllngness to Pay (Emprcal Evdence from European Moble Marets Ths paper shows that the correlaton between the et Promoter Score

More information

Answer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy

Answer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy 4.02 Quz Solutons Fall 2004 Multple-Choce Questons (30/00 ponts) Please, crcle the correct answer for each of the followng 0 multple-choce questons. For each queston, only one of the answers s correct.

More information

The impact of hard discount control mechanism on the discount volatility of UK closed-end funds

The impact of hard discount control mechanism on the discount volatility of UK closed-end funds Investment Management and Fnancal Innovatons, Volume 10, Issue 3, 2013 Ahmed F. Salhn (Egypt) The mpact of hard dscount control mechansm on the dscount volatlty of UK closed-end funds Abstract The mpact

More information

Vasicek s Model of Distribution of Losses in a Large, Homogeneous Portfolio

Vasicek s Model of Distribution of Losses in a Large, Homogeneous Portfolio Vascek s Model of Dstrbuton of Losses n a Large, Homogeneous Portfolo Stephen M Schaefer London Busness School Credt Rsk Electve Summer 2012 Vascek s Model Important method for calculatng dstrbuton of

More information

Underwriting Risk. Glenn Meyers. Insurance Services Office, Inc.

Underwriting 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 information

Study on CET4 Marks in China s Graded English Teaching

Study 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 information

Statistical algorithms in Review Manager 5

Statistical algorithms in Review Manager 5 Statstcal algorthms n Reve Manager 5 Jonathan J Deeks and Julan PT Hggns on behalf of the Statstcal Methods Group of The Cochrane Collaboraton August 00 Data structure Consder a meta-analyss of k studes

More information

Exhaustive Regression. An Exploration of Regression-Based Data Mining Techniques Using Super Computation

Exhaustive Regression. An Exploration of Regression-Based Data Mining Techniques Using Super Computation Exhaustve Regresson An Exploraton of Regresson-Based Data Mnng Technques Usng Super Computaton Antony Daves, Ph.D. Assocate Professor of Economcs Duquesne Unversty Pttsburgh, PA 58 Research Fellow The

More information

Intra-year Cash Flow Patterns: A Simple Solution for an Unnecessary Appraisal Error

Intra-year Cash Flow Patterns: A Simple Solution for an Unnecessary Appraisal Error Intra-year Cash Flow Patterns: A Smple Soluton for an Unnecessary Apprasal Error By C. Donald Wggns (Professor of Accountng and Fnance, the Unversty of North Florda), B. Perry Woodsde (Assocate Professor

More information

SPECIALIZED DAY TRADING - A NEW VIEW ON AN OLD GAME

SPECIALIZED DAY TRADING - A NEW VIEW ON AN OLD GAME August 7 - August 12, 2006 n Baden-Baden, Germany SPECIALIZED DAY TRADING - A NEW VIEW ON AN OLD GAME Vladmr Šmovć 1, and Vladmr Šmovć 2, PhD 1 Faculty of Electrcal Engneerng and Computng, Unska 3, 10000

More information

Latent Class Regression. Statistics for Psychosocial Research II: Structural Models December 4 and 6, 2006

Latent 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 information

A Model of Private Equity Fund Compensation

A Model of Private Equity Fund Compensation A Model of Prvate Equty Fund Compensaton Wonho Wlson Cho Andrew Metrck Ayako Yasuda KAIST Yale School of Management Unversty of Calforna at Davs June 26, 2011 Abstract: Ths paper analyzes the economcs

More information

CHAPTER 5 RELATIONSHIPS BETWEEN QUANTITATIVE VARIABLES

CHAPTER 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 information

What is Candidate Sampling

What 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 information

Stress test for measuring insurance risks in non-life insurance

Stress test for measuring insurance risks in non-life insurance PROMEMORIA Datum June 01 Fnansnspektonen Författare Bengt von Bahr, Younes Elonq and Erk Elvers Stress test for measurng nsurance rsks n non-lfe nsurance Summary Ths memo descrbes stress testng of nsurance

More information

Brigid Mullany, Ph.D University of North Carolina, Charlotte

Brigid Mullany, Ph.D University of North Carolina, Charlotte Evaluaton And Comparson Of The Dfferent Standards Used To Defne The Postonal Accuracy And Repeatablty Of Numercally Controlled Machnng Center Axes Brgd Mullany, Ph.D Unversty of North Carolna, Charlotte

More information

Morningstar After-Tax Return Methodology

Morningstar After-Tax Return Methodology Mornngstar After-Tax Return Methodology Mornngstar Research Report March 1, 2013 2013 Mornngstar, Inc. All rghts reserved. The nformaton n ths document s the property of Mornngstar, Inc. Reproducton or

More information

Reporting Forms ARF 113.0A, ARF 113.0B, ARF 113.0C and ARF 113.0D FIRB Corporate (including SME Corporate), Sovereign and Bank Instruction Guide

Reporting Forms ARF 113.0A, ARF 113.0B, ARF 113.0C and ARF 113.0D FIRB Corporate (including SME Corporate), Sovereign and Bank Instruction Guide Reportng Forms ARF 113.0A, ARF 113.0B, ARF 113.0C and ARF 113.0D FIRB Corporate (ncludng SME Corporate), Soveregn and Bank Instructon Gude Ths nstructon gude s desgned to assst n the completon of the FIRB

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

1. Math 210 Finite Mathematics

1. Math 210 Finite Mathematics 1. ath 210 Fnte athematcs Chapter 5.2 and 5.3 Annutes ortgages Amortzaton Professor Rchard Blecksmth Dept. of athematcal Scences Northern Illnos Unversty ath 210 Webste: http://math.nu.edu/courses/math210

More information

Section 5.3 Annuities, Future Value, and Sinking Funds

Section 5.3 Annuities, Future Value, and Sinking Funds Secton 5.3 Annutes, Future Value, and Snkng Funds Ordnary Annutes A sequence of equal payments made at equal perods of tme s called an annuty. The tme between payments s the payment perod, and the tme

More information

A Simplified Framework for Return Accountability

A Simplified Framework for Return Accountability Reprnted wth permsson from Fnancal Analysts Journal, May/June 1991. Copyrght 1991. Assocaton for Investment Management and Research, Charlottesvlle, VA. All rghts reserved. by Gary P. Brnson, Bran D. Snger

More information

LIFETIME INCOME OPTIONS

LIFETIME INCOME OPTIONS LIFETIME INCOME OPTIONS May 2011 by: Marca S. Wagner, Esq. The Wagner Law Group A Professonal Corporaton 99 Summer Street, 13 th Floor Boston, MA 02110 Tel: (617) 357-5200 Fax: (617) 357-5250 www.ersa-lawyers.com

More information

Thursday, December 10, 2009 Noon - 1:50 pm Faraday 143

Thursday, December 10, 2009 Noon - 1:50 pm Faraday 143 1. ath 210 Fnte athematcs Chapter 5.2 and 4.3 Annutes ortgages Amortzaton Professor Rchard Blecksmth Dept. of athematcal Scences Northern Illnos Unversty ath 210 Webste: http://math.nu.edu/courses/math210

More information

Nordea G10 Alpha Carry Index

Nordea G10 Alpha Carry Index Nordea G10 Alpha Carry Index Index Rules v1.1 Verson as of 10/10/2013 1 (6) Page 1 Index Descrpton The G10 Alpha Carry Index, the Index, follows the development of a rule based strategy whch nvests and

More information

Evaluating credit risk models: A critique and a new proposal

Evaluating credit risk models: A critique and a new proposal Evaluatng credt rsk models: A crtque and a new proposal Hergen Frerchs* Gunter Löffler Unversty of Frankfurt (Man) February 14, 2001 Abstract Evaluatng the qualty of credt portfolo rsk models s an mportant

More information

HARVARD John M. Olin Center for Law, Economics, and Business

HARVARD John M. Olin Center for Law, Economics, and Business HARVARD John M. Oln Center for Law, Economcs, and Busness ISSN 1045-6333 ASYMMETRIC INFORMATION AND LEARNING IN THE AUTOMOBILE INSURANCE MARKET Alma Cohen Dscusson Paper No. 371 6/2002 Harvard Law School

More information

IDENTIFICATION AND CORRECTION OF A COMMON ERROR IN GENERAL ANNUITY CALCULATIONS

IDENTIFICATION AND CORRECTION OF A COMMON ERROR IN GENERAL ANNUITY CALCULATIONS IDENTIFICATION AND CORRECTION OF A COMMON ERROR IN GENERAL ANNUITY CALCULATIONS Chrs Deeley* Last revsed: September 22, 200 * Chrs Deeley s a Senor Lecturer n the School of Accountng, Charles Sturt Unversty,

More information

The Analysis of Covariance. ERSH 8310 Keppel and Wickens Chapter 15

The 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 information

Management Quality and Equity Issue Characteristics: A Comparison of SEOs and IPOs

Management Quality and Equity Issue Characteristics: A Comparison of SEOs and IPOs Management Qualty and Equty Issue Characterstcs: A Comparson of SEOs and IPOs Thomas J. Chemmanur * Imants Paegls ** and Karen Smonyan *** Current verson: November 2009 (Accepted, Fnancal Management, February

More information

Lecture 3: Annuity. Study annuities whose payments form a geometric progression or a arithmetic progression.

Lecture 3: Annuity. Study annuities whose payments form a geometric progression or a arithmetic progression. Lecture 3: Annuty Goals: Learn contnuous annuty and perpetuty. Study annutes whose payments form a geometrc progresson or a arthmetc progresson. Dscuss yeld rates. Introduce Amortzaton Suggested Textbook

More information

A Probabilistic Theory of Coherence

A Probabilistic Theory of Coherence A Probablstc Theory of Coherence BRANDEN FITELSON. The Coherence Measure C Let E be a set of n propostons E,..., E n. We seek a probablstc measure C(E) of the degree of coherence of E. Intutvely, we want

More information

On the Optimal Control of a Cascade of Hydro-Electric Power Stations

On the Optimal Control of a Cascade of Hydro-Electric Power Stations On the Optmal Control of a Cascade of Hydro-Electrc Power Statons M.C.M. Guedes a, A.F. Rbero a, G.V. Smrnov b and S. Vlela c a Department of Mathematcs, School of Scences, Unversty of Porto, Portugal;

More information

Account Transfer and Direct Rollover

Account Transfer and Direct Rollover CED0105 Amerprse Fnancal Servces, Inc. 70100 Amerprse Fnancal Center Mnneapols, MN 55474 Account Transfer and Drect Rollover Important: Before fnal submsson to the Home Offce you wll need a Reference Number.

More information

Demographic and Health Surveys Methodology

Demographic and Health Surveys Methodology samplng and household lstng manual Demographc and Health Surveys Methodology Ths document s part of the Demographc and Health Survey s DHS Toolkt of methodology for the MEASURE DHS Phase III project, mplemented

More information

Construction Rules for Morningstar Canada Target Dividend Index SM

Construction Rules for Morningstar Canada Target Dividend Index SM Constructon Rules for Mornngstar Canada Target Dvdend Index SM Mornngstar Methodology Paper October 2014 Verson 1.2 2014 Mornngstar, Inc. All rghts reserved. The nformaton n ths document s the property

More information

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis The Development of Web Log Mnng Based on Improve-K-Means Clusterng Analyss TngZhong Wang * College of Informaton Technology, Luoyang Normal Unversty, Luoyang, 471022, Chna wangtngzhong2@sna.cn Abstract.

More information

Feature selection for intrusion detection. Slobodan Petrović NISlab, Gjøvik University College

Feature selection for intrusion detection. Slobodan Petrović NISlab, Gjøvik University College Feature selecton for ntruson detecton Slobodan Petrovć NISlab, Gjøvk Unversty College Contents The feature selecton problem Intruson detecton Traffc features relevant for IDS The CFS measure The mrmr measure

More information

APPLICATION OF PROBE DATA COLLECTED VIA INFRARED BEACONS TO TRAFFIC MANEGEMENT

APPLICATION OF PROBE DATA COLLECTED VIA INFRARED BEACONS TO TRAFFIC MANEGEMENT APPLICATION OF PROBE DATA COLLECTED VIA INFRARED BEACONS TO TRAFFIC MANEGEMENT Toshhko Oda (1), Kochro Iwaoka (2) (1), (2) Infrastructure Systems Busness Unt, Panasonc System Networks Co., Ltd. Saedo-cho

More information

Traffic-light a stress test for life insurance provisions

Traffic-light a stress test for life insurance provisions MEMORANDUM Date 006-09-7 Authors Bengt von Bahr, Göran Ronge Traffc-lght a stress test for lfe nsurance provsons Fnansnspetonen P.O. Box 6750 SE-113 85 Stocholm [Sveavägen 167] Tel +46 8 787 80 00 Fax

More information

A DYNAMIC CRASHING METHOD FOR PROJECT MANAGEMENT USING SIMULATION-BASED OPTIMIZATION. Michael E. Kuhl Radhamés A. Tolentino-Peña

A DYNAMIC CRASHING METHOD FOR PROJECT MANAGEMENT USING SIMULATION-BASED OPTIMIZATION. Michael E. Kuhl Radhamés A. Tolentino-Peña Proceedngs of the 2008 Wnter Smulaton Conference S. J. Mason, R. R. Hll, L. Mönch, O. Rose, T. Jefferson, J. W. Fowler eds. A DYNAMIC CRASHING METHOD FOR PROJECT MANAGEMENT USING SIMULATION-BASED OPTIMIZATION

More information

Beating the Odds: Arbitrage and Wining Strategies in the Football Betting Market

Beating the Odds: Arbitrage and Wining Strategies in the Football Betting Market Beatng the Odds: Arbtrage and Wnng Strateges n the Football Bettng Market NIKOLAOS VLASTAKIS, GEORGE DOTSIS and RAPHAEL N. MARKELLOS* ABSTRACT We examne the potental for generatng postve returns from wagerng

More information

ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING

ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING Matthew J. Lberatore, Department of Management and Operatons, Vllanova Unversty, Vllanova, PA 19085, 610-519-4390,

More information

Bond futures. Bond futures contracts are futures contracts that allow investor to buy in the

Bond futures. Bond futures contracts are futures contracts that allow investor to buy in the Bond futures INRODUCION Bond futures contracts are futures contracts that allow nvestor to buy n the future a theoretcal government notonal bond at a gven prce at a specfc date n a gven quantty. Compared

More information

Control Charts for Means (Simulation)

Control Charts for Means (Simulation) Chapter 290 Control Charts for Means (Smulaton) Introducton Ths procedure allows you to study the run length dstrbuton of Shewhart (Xbar), Cusum, FIR Cusum, and EWMA process control charts for means usng

More information

Outsourcing inventory management decisions in healthcare: Models and application

Outsourcing inventory management decisions in healthcare: Models and application European Journal of Operatonal Research 154 (24) 271 29 O.R. Applcatons Outsourcng nventory management decsons n healthcare: Models and applcaton www.elsever.com/locate/dsw Lawrence Ncholson a, Asoo J.

More information

Tuition Fee Loan application notes

Tuition Fee Loan application notes Tuton Fee Loan applcaton notes for new part-tme EU students 2012/13 About these notes These notes should be read along wth your Tuton Fee Loan applcaton form. The notes are splt nto three parts: Part 1

More information

Institute of Informatics, Faculty of Business and Management, Brno University of Technology,Czech Republic

Institute 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 information

ADVERSE SELECTION IN INSURANCE MARKETS: POLICYHOLDER EVIDENCE FROM THE U.K. ANNUITY MARKET *

ADVERSE SELECTION IN INSURANCE MARKETS: POLICYHOLDER EVIDENCE FROM THE U.K. ANNUITY MARKET * ADVERSE SELECTION IN INSURANCE MARKETS: POLICYHOLDER EVIDENCE FROM THE U.K. ANNUITY MARKET * Amy Fnkelsten Harvard Unversty and NBER James Poterba MIT and NBER * We are grateful to Jeffrey Brown, Perre-Andre

More information

THE IMPLIED VOLATILITY OF ETF AND INDEX OPTIONS

THE IMPLIED VOLATILITY OF ETF AND INDEX OPTIONS The Internatonal Journal of Busness and Fnance Research Volume 5 Number 4 2011 THE IMPLIED VOLATILITY OF ETF AND INDEX OPTIONS Stoyu I. Ivanov, San Jose State Unversty Jeff Whtworth, Unversty of Houston-Clear

More information

Solution: Let i = 10% and d = 5%. By definition, the respective forces of interest on funds A and B are. i 1 + it. S A (t) = d (1 dt) 2 1. = d 1 dt.

Solution: Let i = 10% and d = 5%. By definition, the respective forces of interest on funds A and B are. i 1 + it. S A (t) = d (1 dt) 2 1. = d 1 dt. Chapter 9 Revew problems 9.1 Interest rate measurement Example 9.1. Fund A accumulates at a smple nterest rate of 10%. Fund B accumulates at a smple dscount rate of 5%. Fnd the pont n tme at whch the forces

More information

A Novel Methodology of Working Capital Management for Large. Public Constructions by Using Fuzzy S-curve Regression

A Novel Methodology of Working Capital Management for Large. Public Constructions by Using Fuzzy S-curve Regression Novel Methodology of Workng Captal Management for Large Publc Constructons by Usng Fuzzy S-curve Regresson Cheng-Wu Chen, Morrs H. L. Wang and Tng-Ya Hseh Department of Cvl Engneerng, Natonal Central Unversty,

More information

Communication Networks II Contents

Communication Networks II Contents 8 / 1 -- Communcaton Networs II (Görg) -- www.comnets.un-bremen.de Communcaton Networs II Contents 1 Fundamentals of probablty theory 2 Traffc n communcaton networs 3 Stochastc & Marovan Processes (SP

More information

7 ANALYSIS OF VARIANCE (ANOVA)

7 ANALYSIS OF VARIANCE (ANOVA) 7 ANALYSIS OF VARIANCE (ANOVA) Chapter 7 Analyss of Varance (Anova) Objectves After studyng ths chapter you should apprecate the need for analysng data from more than two samples; understand the underlyng

More information

FINANCIAL MATHEMATICS. A Practical Guide for Actuaries. and other Business Professionals

FINANCIAL MATHEMATICS. A Practical Guide for Actuaries. and other Business Professionals FINANCIAL MATHEMATICS A Practcal Gude for Actuares and other Busness Professonals Second Edton CHRIS RUCKMAN, FSA, MAAA JOE FRANCIS, FSA, MAAA, CFA Study Notes Prepared by Kevn Shand, FSA, FCIA Assstant

More information

Sample Design in TIMSS and PIRLS

Sample Design in TIMSS and PIRLS Sample Desgn n TIMSS and PIRLS Introducton Marc Joncas Perre Foy TIMSS and PIRLS are desgned to provde vald and relable measurement of trends n student achevement n countres around the world, whle keepng

More information

PRIVATE SCHOOL CHOICE: THE EFFECTS OF RELIGIOUS AFFILIATION AND PARTICIPATION

PRIVATE SCHOOL CHOICE: THE EFFECTS OF RELIGIOUS AFFILIATION AND PARTICIPATION PRIVATE SCHOOL CHOICE: THE EFFECTS OF RELIIOUS AFFILIATION AND PARTICIPATION Danny Cohen-Zada Department of Economcs, Ben-uron Unversty, Beer-Sheva 84105, Israel Wllam Sander Department of Economcs, DePaul

More information