1 Economcs Letters 76 (2002) 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 of Central Florda, Orlando, FL, USA b Unversty of Central Florda, Chapman Unversty, Orlando, FL, USA c Department of Agrcultural & Resource Economcs, Unversty of Maryland, 220 Symons Hall, College Park, MD , USA Receved 6 May 2001; accepted 1 October 2001 Abstract Usng data from a natonal survey of nearly 2000 mutual fund nvestors, we nvestgate whether nvestor gender s related to rsk takng as revealed n mutual fund nvestment decsons. Consonant wth the receved lterature, we fnd that women exhbt less rsk-takng than men n ther most recent, largest, and rskest mutual fund nvestment decsons. More mportantly, we fnd that the mpact of gender on rsk takng s sgnfcantly weakened when nvestor knowledge of fnancal markets and nvestments s controlled n the regresson equaton. Ths result suggests that the greater level of rsk averson among women that s frequently documented n the lterature can be substantally, but not completely, explaned by knowledge dspartes Elsever Scence B.V. All rghts reserved. Keywords: Rsk takng; Gender dfferences JEL classfcaton: D81; G11; J16 1. Introducton In recent years, the fnancal press has ssued frequent warnngs that women are ll-prepared for retrement years due n part to ther selected nvestment programs. Academc lterature has largely confrmed ths anecdotal evdence by suggestng that women are less lkely than men to nvest n rsker, but hgher returnng, assets (see, e.g., McDonald, 1997; Kahn, 1996; Rchardson, 1996). One partcularly nterestng lne of nqury addresses the relatonshp between gender and revealed *Correspondng author. Tel.: ; fax: E-mal addresses: (J.A. Lst), jlst/ (J.A. Lst) / 02/ $ see front matter 2002 Elsever Scence B.V. All rghts reserved. PII: S (02)
2 152 P.D. Dwyer et al. / Economcs Letters 76 (2002) fnancal rsk preferences usng large-scale survey data. For example, Janakoplos and Bernasek (1998) examne total holdngs of rsky assets, and fnd that sngle whte women generally hold lower proportons of rsky assets than other groups. Although these sorts of studes provde sgnfcant nsghts nto the nvestment decson across gender types, one crtcal shortcomng n the lterature s that large-scale survey studes offer poor control over potental gender dfferences n knowledge sets n fact, we are aware of no prevous large-scale study of actual nvestment practces that has measured and analyzed the effects of context-specfc knowledge on the nvestment decson. In ths paper, we have a unque opportunty to advance the lterature by emprcally examnng rsk-takng n mutual fund nvestments across gender types, whle controllng for nvestor-specfc fnancal nvestment knowledge. Usng mutual fund nvestment data s a natural extenson of the extant lterature gven that mutual fund nvestment s at record levels, contnues to grow, and s wdely dscussed n the popular press, makng t a relatvely unambguous decson context for both men and women. In addton, our data provde a natural test of the Hudgens and Fatkn (1985) conjecture that gender dfferences occur only n stuatons where the probablty of success s low. 2. Data In 1995 the Offce of the Comptroller of the Currency and the Securtes Exchange Commsson jontly conducted a survey of 2000 randomly selected mutual fund nvestors. In addton to basc demographc nformaton, the survey asked respondents about the types of mutual funds they owned and the channels through whch these funds were purchased. Respondents were also asked a seres of questons n order to determne ther understandng of basc fnancal concepts. Because one purpose of ths study s to examne whether the rsk-takng behavor of mutual fund nvestors s correlated wth gender, we focused on three peces of nformaton pertanng to the type of fund owned. Specfcally, we examned the types of mutual funds that respondents had purchased for ther LARGEST sngle nvestment, ther most recent (LAST) nvestment, and ther RISKIEST nvestment. The RISKIEST measure s a composte varable created by selectng the rskest mutual fund type reported across all nvestment channels. In order to examne the level of rsk wthn mutual fund selectons, the rskness of the fund type was coded usng an ordnal rankng system. Money market and muncpal money market funds were coded 0, muncpal bond funds were coded 1, bond funds were coded 2, mxed/balanced funds were coded 3, and stock funds were coded 4. The 0 4 rankngs correspond to the rsk level (typcally measured as the varance of returns) assocated wth each category, where 4 s consdered the rskest opton. The upper panel of Table 1 contans means and standard devatons for each mutual fund type, as well as the proporton of respondents that nvested n each fund type. The rght-most column of Table 1 contans large sample t-statstcs testng the null hypothess of homogenous nvestment decsons across men and women. For the LARGEST nvestment category, the bond fund response was not well-represented for men or women, leadng us to omt ths response from our analyss and truncate the remanng responses, resultng n a 0 3 rsk scale (.e. money market, muncpal bond, mxed, and stock) for ths nvestment category. A strkng fndng s that across the three nvestment categores women appear to take less rsk than men. Ths general observaton s supported va t-tests, whch n each case reject the null at the P, 0.01 level. We should note that nonparametrc sgn tests support all of the results of the parametrc t-tests.
3 P.D. Dwyer et al. / Economcs Letters 76 (2002) Table 1 Descrptve statstcs Gender Mean (S.D.) Probabltes of each rsk category t-test Money Muncpal Bond Mxed Stock of means market P(0) P(1) P(2) P(3) P(4) LARGEST Female 2.09 (1.21) Male 2.33 (1.13) LAST Female 2.55 (1.63) Male 2.85 (1.56) RISKIEST Female 3.19 (1.35) Male 3.47 (1.14) Age Female 2.49 (1.26) 0.18 Male 2.48 (1.21) Educaton Female 4.30 (1.38) Male 4.53 (1.39) Income Female 3.23 (1.16) Male 3.38 (1.05) Investment knowledge Female 6.20 (2.22) Male 7.67 (2.35) Means are for the ndvduals that nvested nonzero amounts n that category. Large sample t-statstcs presented n rght-most column. LARGEST, type of fund n whch respondents had the largest nvestment; LAST, type of fund n whch respondents made the most recent nvestment; RISKIEST, rskest type of fund n whch respondents held an nvestment. Investment knowledge, summed response to a 12-tem scale. Even though the descrptve statstcs n the upper panel of Table 1 suggest that women take less rsk than men, t s napproprate to draw such a concluson from uncondtonal dfferences. Theory and prevous emprcal fndngs suggest that other factors ncludng age, educaton, and ncome nfluence rsk takng. We also gather data on these attrbutes, and present descrptve statstcs n the 1 lower panel of Table 1. Large sample t-tests of means ndcate that populatons of men and women n 1 Age was coded 1 for years, 2 for years, 3 for years, 4 for 55 64, and 5 for 65 and older. Educaton was coded 1 for some hgh school or less, 2 for completed hgh school, 3 for trade school past hgh school, 4 for some college, 5 for completed college, and 6 for attended graduate school. Income was coded 1 for less than $15,000 per year, 2 for $15,000 $35,000, 3 for $35,000 $75,000, 4 for $75,000 $150,000 and 5 for $150,000 and over. Gender s measured as a dchotomous varable, coded 1 for males and 0 for females. Of our subjects, 41.6% are female and 54.6% are college graduates (26.5% had some graduate school). Almost two-thrds (66.3%) had purchased ther frst mutual fund pror to The average respondent owned slghtly more than three dfferent funds, and 39.6% owned four or more funds whle only 23.3% owned a sngle fund.
4 154 P.D. Dwyer et al. / Economcs Letters 76 (2002) the sample dd not dffer n age. However, men reported ncome and educaton levels that exceeded levels reported by women (P, 0.01). Whle we have a rough measure of general educaton va the Educaton varable, ntuton suggests that specfc knowledge of nvestment practce s related to nvestment rsk preferences. We measure nvestment knowledge by usng the summed responses to a 12-tem scale that had potental scores rangng from 0 to 12. Sx of the scale tems requred responses that could be compared to a known answer. An example of ths type of tem s: Can a stock mutual fund lose money? Correct answers to these sx tems were coded 1; ncorrect answers were coded 0. The remanng sx tems n the scale 2 measured self-reported understandng of the meanng of selected fnancal and nvestment terms. An example of ths type of tem s: Do you know what a redempton s? Yes answers to these tems were coded as 1; no answers were coded as 0. The lower panel of Table 1 ndcates that the average nvestment knowledge score s 6.20 and 7.67 for women and men. A t-test strongly suggests that ths dfference s sgnfcant (t ), mplyng that men and women have dfferent knowledge sets concernng nvestment decsons. Ths fndng suggests that the uncondtonal fndngs must be vewed wth cauton, snce subjects may be revealng dfferences n ther specfc nvestment knowledge rather than dsplayng any underlyng dfferences n rsk preferences. 3. The emprcal model To supplement our uncondtonal fndngs n Table 1, we use a smple emprcal model that controls 3 for other mportant factors that may affect nvestment choce. Gven that the survey responses are coded 0, 1, 2, 3, and 4 for those that nvested, a lnear regresson model s napproprate. Estmaton of the model va ordnary least squares would treat the dfference between 0 and 1 dentcal as that between 3 and 4. In fact, the responses represent a rankng and therefore one-unt changes are not drectly comparable n ths manner. To amend ths shortcomng, we buld a model around a latent regresson of the form: Y * 5 X9 b 1, (1) where Y * s unobserved, X s a vector of person-specfc exogenous varables, b s the estmated response coeffcent vector, and s the well-behaved random error component. Although we do not drectly observe Y *, we do observe an approxmaton of Y *: Y5 0fY * # 0; 5 1f0, Y * # f 1; 5 2ff 1, Y * # f 2; 5 3ff 2, Y * # f 3; 5 4ff, Y * # f. (2) 3 4 The f are unknown parameters that are estmated jontly wth b; Y * s unknown snce the questonnare requests the survey respondents to select the answer that most closely represents ther 2 In some cases, the psychology lterature has shown that there are gender dfferences n self-reports of knowledge and ablty, such that men tend to overestmate relatve to women. For ths reason, we performed a senstvty analyss usng an nvestment knowledge measure that contaned only the sx tems for whch there s a known answer. The results of these alternatve analyses were not qualtatvely dfferent than those presented n Tables 2 and 3. 3 Sample szes are generally less than 2000 due to ncomplete observatons.
5 P.D. Dwyer et al. / Economcs Letters 76 (2002) true random varable value. As such, we obtan threshold levels of rsk takng by measurng how exogenous varable vector X, whch ncludes gender, age, ncome, educaton, and nvestment 4 knowledge, affects ranked responses, Y *. 4. Emprcal results Table 2 contans ordered probt estmates for each of the three dependent varables (LARGEST, LAST, and RISKIEST nvestment), calculated wth and wthout a control for nvestor knowledge. An 2 mportant frst fndng s that each of our models performs reasonably well: examnaton of the x statstcs suggests that the models explan a sgnfcant amount of the varaton n the dependent varable. Parameter estmates n Table 2 provde evdence of the control factors that affect rsk takng. Estmated coeffcents of ncome are postve and sgnfcantly dfferent from zero n four of the models, and the coeffcents of educaton are postve and sgnfcant n every model. Ths result Table 2 Ordered probt estmaton results Varable Model LARGEST LARGEST LAST LAST RISKIEST RISKIEST Constant (1.24) (20.20) (20.75) (22.96) (2.40) (20.20) Gender (3.21) (1.93) (3.41) (1.34) (4.66) (2.33) Age (21.17) (21.54) (20.50) (21.02) (20.89) (21.51) Educaton (5.48) (3.82) (4.64) (2.02) (6.50) (3.50) Income (0.54) (0.20) (5.76) (5.18) (4.79) (4.06) Investment knowledge (4.32) (7.32) (8.13) 2 x (d.f.) 49.3 (4) 67.8 (5) 88.5 (4) (5) (4) (5) n Gender s a dchotomous varable that equals 1 for males, 0 for females. t-ratos are reported n parentheses beneath coeffcent estmates. Estmates of f are avalable upon request. LARGEST, type of mutual fund n whch respondents had the largest nvestment; LAST, type of mutual fund n whch respondents made the most recent nvestment; RISKIEST, rskest type of fund n whch respondents held an nvestment. Investment knowledge, summed responses to a 12-tem scale. 4 A few aspects of our estmaton procedure mert further consderaton. Frstly, snce the fs are free parameters, there s no sgnfcance to the unt dstance between the set of observed values of Y, thus avodng symmetrc treatment of one-unt changes n the dependent varable. Secondly, estmates of the margnal effects n the ordered probablty model are qute nvolved because there s no meanngful condtonal mean functon. We therefore compute the effects of changes n the covarates on the j probabltes: Prob[cell j]/ X 5 [ f(f 2 X9b ) 2 f(f 2 X9 j21 j b )]*b; where f(? ) s the standard normal densty, and other varables are defned above. By defnton, these effects must sum to zero snce the probabltes sum to one. Thrdly, our choce of regressors follows theory and prevous emprcal fndngs, whch suggest that age, educaton, nvestment knowledge, and ncome are assocated wth rsk takng.
6 156 P.D. Dwyer et al. / Economcs Letters 76 (2002) mples that wealther, more educated nvestors tend to take on more rsk than ther less educated, less wealthy counterparts. When nvestor knowledge s ncluded, ts effect s postve and hghly sgnfcant for all three measures, ndcatng that t contrbutes to rsk takng above and beyond the contrbuton made by general educaton. Estmated coeffcents of the gender varable n Table 2 provde nterestng nformaton. We should frst note that, n the models that exclude the measure of nvestor knowledge, the coeffcent of the gender varable s postve and sgnfcantly dfferent from zero (P,0.05) n all cases. Ths suggests that men take on more rsk than women when selectng mutual funds. However, when nvestor knowledge s ncluded, gender s sgnfcant at P,0.05 for only the RISKIEST nvestment. In contrast, ts sgnfcance drops to P,0.054 n the LARGEST nvestment model and s below conventonal levels for the LAST nvestment model (P50.18). Interestngly, ths change n the level of sgnfcance s drven by the reducton of the coeffcent estmate rather than mprecson. Comparson of the gender coeffcents reveals that for each model type the estmate decreases by nearly 50% when nvestor knowledge s ncluded. Although these coeffcent estmates provde nsghts nto how the gender effect changes when an ndvdual-specfc knowledge regressor s ncluded, not much nformaton beyond ther statstcal sgnfcance can be used snce they are not margnal effects. To amend ths stuaton, we present margnal effects from the models that nclude the nvestment knowledge regressor. The estmates n Table 3, panel A, correspondng to respondents LARGEST mutual fund nvestments, can be read as follows: men are 5% more lkely than women to be n cell 4 (stock fund). Alternatvely, men are 3.4% less lkely to be n cell 0 (money market fund). Ths fndng s consstent across each of the nvestment categores and serves to enhance the results dscussed above. Gven that these effects are robust across nvestment category, t s nterestng to understand how they change when the nvestment knowledge regressor s excluded from the specfcaton. Margnal effects estmates from these models are drectly beneath the margnal effects estmates from the models that nclude the nvestment knowledge varable and support the general observatons of the coeffcent estmates n Table 2. For example, n the LARGEST nvestment category, men are 8.3% more lkely than women to be n cell 4 (stock fund) an ncrease of nearly 60% compared to the margnal effect estmates when nvestor knowledge s ncluded. Lkewse, the margnal effects ncrease by 140% and 95% when the nvestor knowledge varable s excluded from the other models. These results confrm our fndngs n Table 2 and suggest that the gender effect s greatly attenuated when one properly controls for nvestor knowledge. 5. Concludng remarks Usng data from nearly 2000 mutual fund nvestors, we fnd evdence that suggests women take less rsk than men n ther mutual fund nvestments. We fnd, however, that the observed dfference n rsk takng s sgnfcantly attenuated when we nclude a fnancal nvestment knowledge control varable n the regresson model, suggestng that the gender effect found n prevous studes that employ less specfc knowledge controls may be based upward. Our fndngs have several practcal mplcatons. Frstly, our results are contrary to Hudgens and Fatkn (1985), who conjecture that gender dfferences occur only n stuatons where the probablty of success s low. Accordngly, the prevalence of educatonal nvestment marketng efforts that target women s understandable. Secondly, our fndngs
7 P.D. Dwyer et al. / Economcs Letters 76 (2002) Table 3 Margnal effects estmates Varable Money Muncpal Bond Mxed Stock market P(0) P(1) P(2) P(3) P(4) LARGEST Gender w/ o knowledge Age Educaton Income Investment knowledge LAST Gender w/ o knowledge Age Educaton Income Investment knowledge RISKIEST Gender w/ o knowledge Age Educaton Income Investment knowledge Gender s a dchotomous varable that equals 1 for males, 0 for females. Margnal effects are calculated as changes n the covarates on the j probabltes: Prob[cell j]/ X 5 [ f(f 2 X9b ) 2 f(f 2 X9 j21 j b )]*b. LARGEST, type of mutual fund n whch respondents had the largest nvestment; LAST, type of mutual fund n whch respondents made the most recent nvestment; RISKIEST, rskest type of fund n whch respondents held an nvestment. Investment knowledge, summed responses to a 12-tem scale. may be relevant to the current dscusson regardng the prvatzaton of the US socal securty system. Proponents of prvatzaton have suggested that women would beneft from the rght to manage ther own retrement nvestments (Anonymous, 1999). However, our fndngs rase the concern that prvatzaton could further magnfy exstng gaps between men s and women s retrement savngs. Fnally, our fndngs may help to explan the paucty of women n professons that requre a penchant for rsk-takng behavor (Chevaler and Ellson, 1999). Acknowledgements We thank Peter Ngro at the Offce of the Comptroller of the Currency for provdng the survey data examned n ths paper.
8 158 P.D. Dwyer et al. / Economcs Letters 76 (2002) References Anonymous, Women and socal securty: are prvate accounts the answer? Journal of Accountancy 187 (6), Chevaler, J., Ellson, G., Are some mutual fund managers better than others. Cross-sectonal patterns n behavor and performance. Journal of Fnance 54 (3), Hudgens, G.A., Fatkn, L.T., Sex dfferences n rsk takng: repeated sessons on a computer smulated task. Journal of Psychology 119 (3), Janakoplos, N.A., Bernasek, A., Are women more rsk averse? Economc Inqury 36, Kahn, V.M., Learnng to love rsk. Workng Woman 21 (9), McDonald, K.S., No guts, no glory. Workng Woman 22 (4), Rchardson, P., Gong after the gender gap. Insttutonal Investor 30 (4), 141.
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An emprcal study for credt card approvals n the Greek bankng sector Mara Mavr George Ioannou Bergamo, Italy 17-21 May 2004 Management Scences Laboratory Department of Management Scence & Technology Athens
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,
Kel Insttute for World Economcs Duesternbrooker Weg 45 Kel (Germany) Kel Workng Paper No. Path Dependences n enture Captal Markets by Andrea Schertler July The responsblty for the contents of the workng
Evaluatng the Effects of FUNDEF on Wages and Test Scores n Brazl * Naérco Menezes-Flho Elane Pazello Unversty of São Paulo Abstract In ths paper we nvestgate the effects of the 1998 reform n the fundng
ILRRevew Volume 65 Number 2 Artcle 10 2012 The Wllngness to Pay for Job Amentes: Evdence from Mothers' Return to Chrstna Felfe Unversty of St. Gallen, email@example.com The Wllngness to Pay for Job
Two Faces of Intra-Industry Informaton Transfers: Evdence from Management Earnngs and Revenue Forecasts Yongtae Km Leavey School of Busness Santa Clara Unversty Santa Clara, CA 95053-0380 TEL: (408) 554-4667,
Analyss of Subscrpton Demand for Pay-TV Manabu Shshkura * Norhro Kasuga ** Ako Tor *** Abstract In ths paper, we wll conduct an analyss from an emprcal perspectve concernng broadcastng demand behavor and
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
Heterogeneous Paths Through College: Detaled Patterns and Relatonshps wth Graduaton and Earnngs Rodney J. Andrews The Unversty of Texas at Dallas and the Texas Schools Project Jng L The Unversty of Tulsa
Mortgage Default and Prepayment Rsks among Moderate and Low Income Households Roberto G. Querca Unversty of North Carolna at Chapel Hll firstname.lastname@example.org Anthony Pennngton-Cross Marquette Unversty email@example.com
DISCUSSION PAPER SERIES IZA DP No. 4212 Mltary Conscrpton and Unversty Enrolment: Evdence from Italy Gorgo D Petro June 2009 Forschungsnsttut zur Zukunft der Arbet Insttute for the Study of Labor Mltary
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.
Day-of-the-Week Tradng Patterns of Indvdual and Instutonal Investors Joel N. Morse, Hoang Nguyen, and Hao M. Quach Ths study examnes the day-of-the-week tradng patterns of ndvdual and nstutonal nvestors.
The Economc Impacts of Cgarette Tax Reductons on Youth Smokng n Canada Dane P. Dupont and Anthony J. Ward Economcs, Brock Unversty December 2002 Abstract Cgarettes are the most commonly consumed recreatonal
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
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.
A Multstage Model of Loans and the Role of Relatonshps Sugato Chakravarty, Purdue Unversty, and Tansel Ylmazer, Purdue Unversty Abstract The goal of ths paper s to further our understandng of how relatonshps
Hedge Fund Investng n the Aftermath of the Crss: Where dd the Money Go? Gudo Bollger, Ivan Gudott, Florent Pochon Ths verson: July 2010 Abstract Ths paper nvestgates the determnants of hedge fund flows
Research n Hgher Educaton Journal Abstract Hot and easy n Florda: The case of economcs professors Olver Schnusenberg The Unversty of North Florda Cheryl Froehlch The Unversty of North Florda We nvestgate
Journal of mathematcs and computer Scence 5 (05) 54-58 Testng Adverse Selecton Usng Frank Copula Approach n Iran Insurance Markets Had Safar Katesar,, Behrouz Fath Vajargah Departmet of Statstcs, Shahd
Corporate Real Estate Sales and Agency Costs of Manageral Dscreton Mng-Long Lee * Department of Fnance Natonal Yunln Unversty of Scence & Technology Yunln, Tawan Mng-Te Lee Department of Accountng Tamkang
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
Survve Then Thrve: Determnng Success n the Economcs Ph.D. Program Wayne A. Grove Le Moyne College, Economcs Department Donald H. Dutkowsky Syracuse Unversty, Economcs Department Andrew Grodner East Carolna
Dscount Rate for Workout Recoveres: An Emprcal Study* Brooks Brady Amercan Express Peter Chang Standard & Poor s Peter Mu** McMaster Unversty Boge Ozdemr Standard & Poor s Davd Schwartz Federal Reserve
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
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
The tmng ablty of hybrd funds of funds Javer Rodríguez* Graduate School of Busness Admnstraton Unversty of Puerto Rco PO 23332 San Juan, PR 00931 Abstract Hybrd mutual funds are funds that nvest n a combnaton
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
Insurance Markets and Companes: Analyses and Actuaral Computatons, Volume 1, Issue 2, 2010 José Antono Ordaz (Span), María del Carmen Melgar (Span) Covarate-based prcng of automoble nsurance Abstract Ths
THE DETERMINANTS OF THE TUNISIAN BANKING INDUSTRY PROFITABILITY: PANEL EVIDENCE Samy Ben Naceur ERF Research Fellow Department of Fnance Unversté Lbre de Tuns Avenue Khéreddne Pacha, 002 Tuns Emal : firstname.lastname@example.org
Portfolo Loss Dstrbuton Rsky assets n loan ortfolo hghly llqud assets hold-to-maturty n the bank s balance sheet Outstandngs The orton of the bank asset that has already been extended to borrowers. Commtment
The Impact of Resdental Densty on Vehcle Usage and Energy Consumpton * September 26, 2008 Forthcomng n the Journal of Urban Economcs Davd Brownstone (correspondng author) Department of Economcs 3151 Socal
Forecastng the Drecton and Strength of Stock Market Movement Jngwe Chen Mng Chen Nan Ye email@example.com firstname.lastname@example.org email@example.com Abstract - Stock market s one of the most complcated systems
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,
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
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