MP R A
Munich Personal RePEc Archive
Credit Cycles,Credit Risk,and Prudential Regulation
Jesus,Saurina and Gabriel,Jimenez
UNSPECIFIED
20March2006
Online at http://mpra.ub.uni-muenchen.de/718/ MPRA Paper No.718,posted07.November2007/01:13
Credit Cycles,Credit Risk,and
Prudential Regulation?
Gabriel Jim′e nez and Jes′u s Saurina
Banco de Espa?n a
This paper?nds strong empirical support of a positive, although quite lagged,relationship between rapid credit growth
and loan losses.Moreover,it contains empirical evidence of
more lenient credit standards during boom periods,both in
terms of screening of borrowers and in collateral requirements.
We?nd robust evidence that during upturns,riskier borrowers
get bank loans,while collateralized loans decrease.We develop
a regulatory prudential tool,based on a countercyclical,or
forward-looking,loan loss provision that takes into account
the credit risk pro?le of banks’loan portfolios along the busi-
ness cycle.Such a provision might contribute to reinforce the
soundness and the stability of banking systems.
JEL Codes:E32,G18,G21.
?This paper is the sole responsibility of its authors,and the views represented here do not necessarily re?ect those of the Banco de Espa?n a.We thank R.Repullo and J.M.Rold′a n for very fruitful and lively discussions about prudential banking supervisory devices.We are also grateful for the detailed comments provided by V.Salas;H.Shin,the editor;J.Segura;an anonymous referee;and participants at the BCBS/Oesterreichische Nationalbank Workshop;the Bank of England work-shop on the relationship between?nancial and monetary stability;X Reuni′o n de la Red de Investigadores de Bancos Centrales Iberoamericanos in Per′u;XX Jornadas Anuales de Econom′?a in Uruguay;Foro de Finanzas in Madrid;and FUNCAS Workshop Basilea II y cajas de ahorros in Alicante.In particular,we would like to express our gratitude for the comments of S.Carb′o,X.Freixas, M.Gordy,A.Haldane,P.Hartman,N.Kiyotaki,M.Kwast,https://www.doczj.com/doc/7c13186091.html,rra′?n,J.A. Licandro,I.van Lelyveld,P.Lowe,L.A.Medrano,J.Moore,C.Tsatsaronis, and B.Vale.Any errors that remain are entirely the authors’own.Correspond-ing author:Saurina:C/Alcal′a48,28014Madrid,Spain;Tel+34-91-338-5080; e-mail:jsaurina@bde.es.
65
66International Journal of Central Banking June2006 1.Introduction
Banking supervisors,after many painful experiences,are quite con-vinced that banks’lending mistakes are more prevalent during upturns than in the midst of a recession.1In good times both bor-rowers and lenders are overcon?dent about investment projects and their ability to repay and to recoup their loans and the corresponding fees and interest rates.Banks’overoptimism about borrowers’future prospects,coupled with strong balance sheets(i.e.,capital well above minimum requirements)and increasing competition,brings about more liberal credit policies with lower credit standards.2Thus,banks sometimes?nance negative net present value(NPV)projects only to ?nd later that the loan becomes impaired or the borrower defaults. On the other hand,during recessions—when banks are?ooded with nonperforming loans,high speci?c provisions,and tighter capital bu?ers—banks suddenly turn very conservative and tighten credit standards well beyond positive net present values.Only their best borrowers get new funds;thus,lending during downturns is safer and credit policy mistakes much lower.Across many jurisdictions and at di?erent points in time,bank managers seem to overweight concerns regarding type1lending policy errors(i.e.,good borrowers not getting a loan)during economic booms and underweight type2 errors(i.e.,bad borrowers getting?nanced).The opposite happens during recessions.
Several explanations have appeared in the literature to rational-ize?uctuations in credit policies.First of all,the classic principal-agency problem between bank shareholders and managers can feed excessive volatility into loan growth rates.Once managers obtain a reasonable return on equity for their shareholders,they may engage in other activities that depart from the?rm’s value maximization and focus more on their own rewards.One of these activities might be excessive credit growth in order to increase the social presence of the bank(and its managers)or the power of managers in a con-tinuously enlarging organization(Williamson1963).If managers are 1See,for instance,Caruana(2002),Ferguson(2004),and the numerous joint announcements by U.S.bank regulators in the late nineties warning U.S.banks to tighten credit standards.
2A loose monetary policy can also contribute to overoptimism through excess liquidity provision.
Vol.2No.2Credit Cycles,Credit Risk,and Prudential Regulation67 rewarded more in terms of growth objectives instead of pro?tability targets,incentives to rapid growth may result.This has been doc-umented previously by the expense preference literature and,more recently,by the literature that relates risk and managers’incentives.3 Strong competition among banks or between banks and other ?nancial intermediaries erodes margins as both loan and deposit interest rates get closer to the interbank rate.To compensate for the fall in pro?tability,bank managers might increase loan growth at the expense of the(future)quality of their loan portfolios.Excess capacity in the banking industry is being built up.Nevertheless,that will not impact immediately on problem loans,so it might encourage further loan growth.
In a more formalized framework,Van den Heuvel(2002)shows that the combination of risk-based capital requirements,an imper-fect market for bank equity,and a maturity mismatch in banks’balance sheets gives rise to a bank capital channel of monetary pol-icy.In boom periods,when banks show strong balance sheets and capital bu?ers,they overlend.However,as the expansion heads to its end,the surge in loan portfolios has eroded much of the capital bu?er;at that point,a monetary shock may trigger a decline in bank pro?ts,stringent capital ratios,and a tightening of lending standards and,subsequently,of loans available to?rms and households.4 Herd behavior(Rajan1994)might also help to explain why bank managers?nance negative NPV projects during expansions.Credit mistakes are judged more leniently if they are common to the whole industry.Moreover,a manager whose bank systematically loses mar-ket share and underperforms its competitors in terms of earnings growth increases his or her probability of being?red.Thus,managers have a strong incentive to behave as their peers,which,at an aggre-gate level,enhances lending booms and recessions.Short-term objec-tives are prevalent and might explain why banks?nance projects during expansions that,later on,will become nonperforming loans.
Berger and Udell(2004)have developed the so-called institu-tional memory hypothesis in order to explain the markedly cyclical 3For the former,see(among others)Edwards(1977),Hannan and Mavinga (1980),Akella and Greenbaum(1988),and Mester(1989).For the latter,see Saunders,Strock,and Travlos(1990),Gorton and Rosen(1995),and Esty(1997).
4Ayuso,P′e rez,and Saurina(2004)?nd evidence of this cyclical behavior of capital bu?ers.
68International Journal of Central Banking June2006 pro?le of loans and nonperforming loan losses.It states that as time passes since the last loan bust,loan o?cers become less and less skilled to grant loans to high-risk borrowers.That might be the result of two complementary forces.First,the proportion of loan o?cers that experienced the last bust decreases as the bank hires new,younger employees and the former ones retire.Thus,there is a loss of learning experience.Second,some of the experienced o?cers may forget the lessons of the past,especially as more years go by and the former recession becomes a more distant memory.5 Finally,collateral might also play a role in fueling credit cycles. Usually,loan booms are intertwined with asset booms.6Rapid increases in land,house,or share prices increase the availability of funds for those who can pledge such assets as collateral.At the same time,the bank is more willing to lend since it has an(increasingly worthier)asset to back the loan in case of trouble.On the other hand, it could be possible that the widespread con?dence among bankers results in a decline in credit standards,including the need to pledge collateral.Collateral,as risk premium,can be thought to be a signal of the degree of tightening of individual bank loan policies.7 Despite the theoretical developments and the banking supervi-sors’experiences,the empirical literature providing evidence of the link between rapid credit growth and loan losses is scant.8In this paper we produce clear evidence of a direct,although lagged,rela-tionship between credit cycle and credit risk.9A rapid increase in loan portfolios is positively associated with an increase in nonper-forming loan ratios later on.Moreover,those loans granted during 5Kindleberger(1978)contains the idea of fading bad experiences among eco-nomic agents.
6See Borio and Lowe(2002),Davis and Zhu(2004),and Goodhart,Hofmann, and Segoviano(2005).
7The Federal Reserve Board’s Senior Loan O?cer Opinion Survey on Bank Lending Practices shows the cyclical nature of bank lending standards,loan demand,and loan spreads.Asea and Blomberg(1998)?nd,with bank-level vari-ables,that the probability of collateralization increases during contractions and decreases during expansions in the United States.
8Clair(1992),Keeton(1999),and Salas and Saurina(2002)are a few excep-tions.
9Goodhart,Hofmann,and Segoviano(2005)document that credit over GDP is a good predictor of future defaults.Dell’Ariccia and Marquez(forthcoming) predict that episodes of?nancial distress are more likely in the aftermath of periods of strong credit expansion.
Vol.2No.2Credit Cycles,Credit Risk,and Prudential Regulation69 boom periods have a higher probability of default than those granted during periods of slow credit growth.To our knowledge,this is the ?rst time that such an empirical study,based on loan-by-loan infor-mation,relating credit-cycle phase and future problem loans is being carried out.Finally,we show that in boom periods collateral require-ments are relaxed,while the opposite happens in recessions,which we take as evidence of looser credit standards during expansions.
The three empirical avenues provide similar results:In boom periods,when lending accelerates,the seeds for problem loans are being sown.During recession periods,when banks curtail credit growth,they become much more cautious,both in terms of the quality of the borrowers and the loan conditions.Therefore,bank-ing supervisors’concerns are well rooted both in theoretical and empirical grounds and deserve careful scrutiny and a proper answer by regulators.We call the former?ndings procyclicality of ex ante credit risk,as opposed to the behavior of ex post credit risk(i.e., nonperforming loans),which increases during recessions and declines in good periods.10The issue here is to realize that lending policy mistakes occur in good times;thus,a prudential response from the supervisor might be needed at those times.
We develop a new regulatory devise speci?cally designed to cope with procyclicality of ex ante credit risk.It is a countercycli-cal,or forward-looking,loan loss provision that takes into account the former empirical results.Spain already had a dynamic provi-sion(the so-called statistical provision)with a clear prudential bias (Fern′a ndez de Lis,Mart′?nez Pag′e s,and Saurina2000).The main criticism to that provision(coming from accountants,not from bank-ing supervisors)was that resulting total loan loss provisions were excessively“?at”through an entire economic cycle.Although it shares the prudential concern of the statistical provision,the new proposal does not achieve,by construction,a?at loan loss provision through the cycle.Instead,total loan loss provisions are still higher in recessions,but they are also signi?cant when credit policies are the most lax and therefore credit risk(according to supervisors’expe-riences and our empirical?ndings)is entering at a high speed on bank loan portfolios.By making a concrete proposal,we would like 10A thorough discussion of banking regulatory tools to cope with procyclicality of the?nancial system is in Borio,Fur?ne,and Lowe(2001).
70International Journal of Central Banking June2006 to open a debate on banking regulatory tools that can contribute to dampen business-cycle?uctuations and,thus,to enhance?nancial stability.
The rest of the paper is organized as follows.Section2provides the empirical evidence on credit cycles and credit risk.Section3 explains the rationale and workings of the new regulatory tool through a simulation exercise.Section4contains a policy discussion, and section5concludes.
2.Empirical Evidence on Lending Cycles and
Credit Risk
2.1Problem Loan Ratios and Credit Growth
Salas and Saurina(2002)model problem loan ratios as a function of both macro-and microvariables(i.e.,bank balance sheet variables). They?nd that lagged credit growth has a positive and signi?cant impact on ex post credit risk measures.Here,we follow that paper in order to disentangle the relationship between past credit growth and current problem loans.Although in spirit the methodology is simi-lar,there are some important di?erences worth pointing out.First of all,we use a longer period,which allows us to consider two lend-ing cycles of the Spanish economy.Secondly,we focus more on loan portfolio characteristics(industry and regional concentration and importance of collateralized loans)of the bank rather than on bal-ance sheet variables,which are much more general and di?cult to interpret.For that,we take advantage of the information contained in the Central Credit Register(CCR)database run by Banco de Espa?n a.11The equation we estimate is the following:
NPL it=αNPL it?1+β1GDPG t+β2GDPG t?1+β3RIR t
+β4RIR t?1+δ1LOANG it?2+δ2LOANG it?3
+δ4LOANG it?4+χ1HERFR it+χ2HERFI it
+φ1COLIND it+φ2COLFIR it+ωSIZE it+ηi+εit,(1) 11Any loan above e6,000granted by any bank operating in Spain must be reported to the CCR.A detailed description of the CCR content can be found in Jim′e nez and Saurina(2004)and Jim′e nez,Salas,and Saurina(forthcoming).
Vol.2No.2Credit Cycles,Credit Risk,and Prudential Regulation71 where NPL it is the ratio of nonperforming loans over total loans for bank i in year t.In fact,we estimate the logarithmic transfor-mation of that ratio(i.e.,ln(NPL it/(100?NPL it)))in order to not curtail the range of variation of the endogenous variable.Since prob-lem loans present a lot of persistence,we include the left-hand-side variable in the right-hand side lagged one year.We control for the macroeconomic determinants of credit risk(i.e.,common shocks to all banks)through the real rate of growth of the gross domestic product(GDPG)and the real interest rate(RIR),proxied as the interbank interest rate less the in?ation of the period.Both vari-ables are included contemporaneously as well as lagged one year since some of the impacts might take some time to appear.
Our variable of interest is the loan growth rate,lagged two,three, and four years.A positive and signi?cant parameter for those vari-ables will be empirical evidence supporting the prudential concerns of banking regulators since the swifter the loan growth,the higher the problem loans in the future.
Moreover,we control for risk-diversi?cation strategies of each bank through the inclusion of two Her?ndahl indexes(one for region, HERFR,and the other for industry,HERFI).We also include as a control variable the size of the bank(SIZE)—that is,the market share of the bank in each period of time.Equation(1)also takes into account the specialization of the bank in collateralized loans,distin-guishing between those of?rms(COLFIR)and those of households (COLIND).
Finally,ηi is a bank?xed e?ect to control for idiosyncratic char-acteristics of each bank,constant along time.It might re?ect the risk pro?le of the bank,the way of doing business,etc.εit is a ran-dom error.We estimate model1in?rst di?erences in order to pre-vent from biasing the results due to a possible correlation between unobservable bank characteristics and some of the right-hand-side variables.Given that some of the explanatory variables might be determined at the same time as the left-hand-side variable,we use a GMM estimator(Arellano and Bond1991).
All the information from each individual bank comes from the CCR.Table1contains the descriptive statistics of the variables. The period analyzed covers two credit cycles of the Spanish bank-ing sector(from1984to2002),with an aggregate maximum for NPL around1985and,again,in1993.We focus on commercial
72International Journal of Central Banking June2006
Table1.Descriptive Statistics
Variable Mean St.Dev.Min.Max. NPL it3.945.700.0099.90 GDPG t2.901.51?1.034.83
RIR t4.142.90?0.678.12 LOANG i,t?217.3614.37?17.2971.97 LOANG i,t?317.3713.93?13.8067.82 LOANG i,t?417.5414.09?11.1064.68 HERFR it52.6824.8611.2698.87 HERFI it18.479.827.4570.26 COLIND it19.2516.280.0069.91 COLFIR it20.4712.890.0070.35 SIZE it0.591.050.008.79 Note:NPL it is the nonperforming loan ratio—that is,the quotient between nonperforming loans and total loans.GDPG t is the real rate of growth of gross domestic product.RIR t is the real interest rate,calculated as the interbank interest rate less the in?ation of the period.LOANG it is the rate of the growth of loans for bank i.HERFR it is the Her?ndahl index of bank i in terms of the amount lent to each region.HERFI it is the Her?ndahl index of bank i in terms of the amount lent to each industry.COLIND it is the percentage of fully collateralized loans to households over total loans for bank i.COLFIR it is the percentage of fully collateralized loans to?rms over total loans for bank i. SIZE it is the market share of bank i.All variables are shown in percentage points.i denotes the bank and t denotes the year.
and savings banks,which represent more than95percent of total assets among credit institutions(only small credit cooperatives and specialized?nancial?rms are left aside).Some outliers have been eliminated in order to avoid the possibility that a small number of observations,with a very low relative weight over the total sample, could bias the results.Thus,we have eliminated those extreme loan growth rates(i.e.,banks with a loan growth rate lower or higher than the5th and95th percentile,respectively).
Results appear in the?rst column of table2(labeled“Model1”). As expected,since we take?rst di?erences of equation(1)and εit is white noise,there is?rst-order residual autocorrelation and
Vol.2No.2
Credit Cycles,Credit Risk,and Prudential Regulation
73
T a b l e 2.G M M E s t i m a t i o n R e s u l t s o f E q u a t i o n (1)U s i n g D P D (A r e l l a n o a n d B o n d 1991)
M o d e l 1
M o d e l 2M o d e l 3M o d e l 4
V a r i a b l e s
C o e ?c i e n t
S E C o e ?c i e n t S E C o e ?c i e n t S E C o e ?c i e n t
S E
N P L i ,t ?1
0.55240.0887***0.55200.0889***0.54990.0841***0.54470.0833***
M a c r o e c o n o m i c C h a r a c t e r i s t i c s G D P D t
?0.06310.0135***?0.06540.0137***?0.07090.0131***?0.07160.0134***G D P G t ?1
?0.07710.0217***?0.07700.0220***?0.07500.0212***?0.07770.0209***R I R t
0.07100.0194***0.07030.0193***0.07040.0195***0.07110.0192***R I R t ?10.0295
0.0103***0.02920.0103***0.0262
0.0098***0.02630.0101***
B a n k
C h a r a c t e r i s t i c s L O A N G i ,t ?2
?0.00080.0013?0.00080.0013L O A N G i ,t ?3
0.00180.00120.00180.0012L O A N G i ,t ?4(α)
0.0034
0.0012***0.00290.0012**
|L O A N G i ,t ?2?A V E R A G E L O A N G i |0.00040.0017|L O A N G i ,t ?3?A V E R A G E L O A N G i |?0.00050.0016|L O A N G i ,t ?4?A V E R A G E L O A N G i |(β)
0.00250.0019
L O A N G i ,t ?2?A V E R A G E L O A N G t
0.00070.00120.00110.0013L O A N G i ,t ?3?A V E R A G E L O A N G t
0.00150.00130.00140.0014L O A N G i ,t ?4?A V E R A G E L O A N G t (α)
0.0025
0.0013**0.00200.0013
(c o n t i n u e d )
74
International Journal of Central Banking
June 2006
T a b l e 2(c o n t i n u e d ).G M M E s t i m a t i o n R e s u l t s o f E q u a t i o n (1)U s i n g D P D (A r e l l a n o a n d B o n d 1991)
M o d e l 1
M o d e l 2M o d e l 3M o d e l 4
V a r i a b l e s
C o e ?c i e n t S E C o e ?c i e n t S E C o e ?c i e n t S E C o e ?c i e n t S E
B a n k
C h a r a c t e r i s t i c s (c o n t i n u e d )|L O A N G i ,t ?2?A V E R A G E L O A N G t |?0.00260.0018|L O A N G i ,t ?3?A V E R A G E L O A N G t |0.00170.0017|L O A N G i ,t ?4?A V E R A G E L O A N G t |(β)
0.0029
0.0018
H E R F R i t
0.02120.0096**0.02090.0097**0.02070.0098**0.02180.0099**H E R F I i t
?0.00320.0094?0.00250.0095?0.00380.0098?0.00260.0097C O L F I R i t
0.00340.00630.00340.00630.00340.00650.00460.0065C O L I N D i t
?0.01250.0072*?0.01250.0072*?0.01410.0073*?0.01410.0074*S I Z E i t
0.01990.04820.01530.04860.02130.0475
0.0261
0.0484
T i m e D u m m i e s N o N o N o N o N o .O b s e r v a t i o n s 868868868868T i m e P e r i o d 1984–20021984–20021984–20021984–2002S a r g a n T e s t [χ(2)138]/p -v a l u e 124.760.78125.560.77
123.850.80
122.860.82
F i r s t -O r d e r A u t o c o r r e l a t i o n (m 1)?5.43?5.37?5.36?5.28S e c o n d -O r d e r A u t o c o r r e l a t i o n (m 2)?1.27
?1.4
?1.34
?1.24
T e s t A s y m m e t r i c I m p a c t (p -v a l u e )α+β=0—0.01—0.01α?β=0—0.84
—
0.73
N o t e :S e e n o t e i n t a b l e 1f o r a d e s c r i p t i o n o f t h e v a r i a b l e s .N P L i t ,H E R F R i t ,H E R F I i t ,C O L F I R i t ,a n d C O L I N D i t a r e t r e a t e d a s e n d o g e n o u s u s i n g t h r e e l a g s f o r N P L i t a n d t w o f o r t h e o t h e r s .R o b u s t S E r e p o r t e d .*,**,a n d ***a r e s i g n i ?c a n t a t t h e 10p e r c e n t ,5p e r c e n t ,a n d 1p e r c e n t l e v e l s ,r e s p e c t i v e l y .
Vol.2No.2Credit Cycles,Credit Risk,and Prudential Regulation75 not second order.A Sargan test of validity of instruments is also fully satisfactory.The results of the estimation are robust to heteroskedasticity.
Regarding the explanatory variables,there is persistence in the NPL variable.The macroeconomic control variables are both signif-icant and have the expected signs.Thus,the acceleration of GDP, as well as a decline in real interest rates,brings about a decline in problem loans.The impact of interest rates is much more rapid than that of economic activity.The more concentrated the credit port-folio in a region,the higher the problem loan ratio,while industry concentration is not signi?cant.Collateralized loans to households are less risky(10percent level of signi?cance),mainly because these are mortgages that,in Spain,have the lowest credit risk.The para-meter of the collateralized loans to?rms,although positive,is not signi?cant.The size of the bank does not have a signi?cant impact on the problem loan ratio.
Finally,regarding the variables that are the focus of our paper, the rate of loan growth lagged four years is positive and signi?cant (at the1percent level).The loan growth rate lagged three years is also positive,although not signi?cant.Therefore,rapid credit growth today results in lower credit standards that,eventually,bring about higher problem loans.
The economic impact of the explanatory variables is signi?cant. The long-run elasticity of GDP growth rate,evaluated at the mean of the variables,is?1.19;that is,an increase of1percentage point in the rate of GDP growth(i.e.,GDP grows at3percent instead of at 2percent)decreases the NPL ratio by30.1percent(i.e.,it declines from3.94percent to2.75percent).For interest rates,a100-basis-point increase brings about a rise in the NPL ratio of21.6percent. Regarding loan growth rates,an acceleration of1percent in the growth rate has a long-term impact of a0.7percent higher problem loan ratio.
We have performed numerous robustness tests.Model2(the second column of table2)tests for the asymmetric impact of loan expansions and contractions.We augment model1with the absolute value of the di?erence between the loan credit growth of bank i in year t and its average over time.All model1results hold,but it can be seen that there is some asymmetry:rapid credit growth of a bank (i.e.,above its own average loan growth)increases nonperforming
76International Journal of Central Banking June2006 loans,while slow growth(i.e.,below average)has no signi?cant impact on problem loans.12If instead of focusing on credit growth of bank i(either alone or compared to its average growth rate over time),we look at the relative position of bank i in respect to the rest of the banks at a point in time(i.e.,at each year t),we?nd that the relative loan growth rate lagged four years still has a positive and signi?cant impact on bank i’s NPL ratio(model3,third column of table2).The parameter of relative credit growth lagged three years is positive but not signi?cant.The rest of the variables keep their sign and signi?cance.Model4(the last column of table2)shows that there is asymmetry in the response of nonperforming loans to credit growth.When banks expand their loan portfolios at a speed above the average of the banking sector,future nonperforming loans increase,while there is no signi?cant e?ect if the loan growth is below the average.13Finally,the former results are robust to changes in the macroeconomic control variables(not shown).If we substitute time dummies for the change in the GDP growth rate and for the real interest rate,the loan growth rate is still positive and signi?-cant in lag4(although at the10percent level)and,again,positive (although not signi?cant)in lag3.
All in all,we?nd a robust statistical relationship between rapid credit growth at each bank portfolio and problem loans later on. The lag is around four years,so bank managers and short-term investors(including shareholders)might have incentives to foster credit growth today in order to reap short-term bene?ts to the expense of long-term bank stakeholders,including depositors,the deposit guarantee fund,and banking supervisors.
2.2Probability of Default and Credit Growth
Instead of focusing on bank-aggregated-level credit risk measures,in this section we analyze the probability of default at an individual 12Note that in model1,regression results are the same for the variable rate of growth of loans in bank i at year t as they are for the di?erence between the for-mer variable and the average rate of growth of loans of bank i along time.That is because the latter term is constant over time for each bank and disappears when we take?rst di?erences in equation(1).
13Note that the relevant test here is to test ifα+β(andα?β)is signi?cant, not each of them alone.
Vol.2No.2Credit Cycles,Credit Risk,and Prudential Regulation 77
loan level and its relation to the cyclical position of the bank credit policy.The hypothesis is that,for the reasons explained in section 1above,those loans granted during credit booms are riskier than those granted when the bank is reining in loan growth.That would pro-vide a rigorous empirical microfoundation for prudential regulatory devises aimed at covering the losses embedded in policies regarding rapid credit growth.
In order to test the former hypothesis,we use individual loan data from the CCR.We focus on new loans granted to non?nancial ?rms with a maturity larger than one year and keep track of them the following years.We study only ?nancial loans (i.e.,excluding receivables,leasing,etc.),which are 60percent of the total loans to non?nancial ?rms in the CCR,granted by commercial and savings banks.The equation estimated is
Pr(DEFAULT ijt +k =1)=F (θ+α(LOANG it ?averageLOANG i )
+β LOANG it ?averageLOANG i χLOANCHAR iit
+δ1DREG i +δ2DIND i +δ3BANKCHAR it +?t +ηi ),(2)where we model the probability of default of loan j ,in bank i ,some k years after being granted (i.e.,at t +2,t +3,and t +4)14as a logis-tic function [F (x )=1/(1+exp (?x ))]of the characteristics of that loan (LOANCHAR ),such as its size,maturity (i.e.,between one and three years and more than three years),and collateral (fully collateralized or no collateral);a set of control variables (i.e.,the region where the ?rm operates,DREG ,and the industry to which the borrower pertains,DIND );and the characteristics of the bank that grants the loan (BANKCHAR ),such as its size and type (i.e.,commercial or savings bank).We also control for macroeconomic characteristics,including time dummies (?t ).
We do not consider default immediately after the loan is granted (i.e.,in t +1)because it takes time for a bad borrower to reveal as
14
We consider that a loan is in default when its doubtful part is larger than the 5percent of its total amount.Thus,we exclude from default small arrears,mainly technical,that are sorted out by borrowers in a few days and that,usually,never reach the following month.The level and the evolution of the probability of default (PD)across time and ?rm size in Spain can be seen in Saurina and Trucharte (2004).On average,large ?rms (i.e.,those with annual sales above e 50million)have a PD between four and ?ve times lower than that of small and medium-sized enterprises (i.e.,?rms with annual turnover below e 50million).
78International Journal of Central Banking June2006 such.When granted a loan,a borrower takes the money from the
bank and invests it into the project.As the project develops,the
borrower is either able to repay the loan and the due interest pay-
ments or is not able to pay and defaults.Therefore,it takes time for
the default to occur.
Once we have controlled for loan,bank,and time characteris-
tics,we add the relative loan growth rate of bank i at time t with
respect to?nancial loans granted to non?nancial?rms(LOANG it?averageLOANG i)—that is,the current lending position of each bank
in comparison to its average loan growth.Ifαis positive and sig-
ni?cant,we interpret this as a signal of more credit risk in boom
periods when,probably,credit standards are low.On the contrary,
when credit growth slows,banks become much more careful in scru-
tinizing loan applications;as a result,next-year defaults decrease sig-
ni?cantly.To our knowledge,this is the?rst time that such a direct
test has been run.Additionally,we test for asymmetries in that rela-
tionship,as in the previous section.We have considered only those
banks with a loan growth rate within the5th and95th percentile,
to eliminate outliers.
It is very important to control for the great heterogeneity due to
?rm e?ects,even more because our database does not contain?rm-
related variables(i.e.,balance sheet and pro?t and loss variables).
For this reason,we have controlled for?rm(loan)characteristics
using a random e?ects model,which allows us to take into account
the unobserved heterogeneity(without limiting the sample as the
conditional model does)assuming a zero correlation between the
?rm e?ects and the rest of the characteristics of the?rm.15
Table3shows the estimation results for the pool of all loans
granted.We observe that the faster the growth rate of the bank,the
higher the likelihood to default in the following years.We observe
thatαis positive and signi?cant when we consider defaults three
and four years later,andαis positive,although not signi?cant,for
defaults two years after the loan was granted(table3,columns1,
3,and5).As mentioned before,although not reported in table3,
we control for macroeconomic characteristics,region and industry
15We have also estimated a logit model with?xed e?ects,and the results are quite similar.
Vol.2No.2Credit Cycles,Credit Risk,and Prudential Regulation79 Table3.GMM Estimation Results of Equation(2)Using
a Random E?ect Logit Model(Results for Pool of All
Loans Granted)
(1)(2)
Variables Coe?.SE Coe?.SE
Dependent Variable DEFAULT ijt+2(0/1)DEFAULT ijt+2(0/1)
Bank Characteristics
LOANG it?AVERAGE
LOANG i(α)0.0010.001?0.0010.001*
|LOANG it?AVERAGE
LOANG i(β)——0.0050.001*** Province Dummies Yes Yes
Industry Dummies Yes Yes
No.Observations1,823,6561,823,656
Time Period1985–20041985–2004
Wald Test[χ(2)]/p-value8,9590.009,1210.00
Test Asymmetric Impact
(p-value)
α+β=0—0.00
α?β=0—0.00
(3)(4)
Variables Coe?.SE Coe?.SE Dependent Variable DEFAULT ijt+3(0/1)DEFAULT ijt+3(0/1)
Bank Characteristics
LOANG it?AVERAGE
LOANG i(α)0.0020.001***0.0010.001 |LOANG it?AVERAGE
LOANG i|(β)——0.0010.001 Province Dummies Yes Yes
Industry Dummies Yes Yes
No.Observations1,643,7081,643,708
Time Period1985–20041985–2004
Wald Test[χ(2)]/p-value4,8000.004,8740.00 Test Asymmetric Impact
(p-value)
α+β=0—0.00
α?β=0—0.93
(continued)
80International Journal of Central Banking June2006 Table3(continued).GMM Estimation Results of
Equation(2)Using a Random E?ect Logit Model (Results for Pool of All Loans Granted)
(5)(6)
Variables Coe?.SE Coe?.SE
Dependent Variable DEFAULT ijt+4(0/1)DEFAULT ijt+4(0/1)
Bank Characteristics
LOANG it?AVERAGE0.0020.001**0.0020.002 LOANG i(α)
|LOANG it?AVERAGE——0.0000.002 LOANG i|(β)
Province Dummies Yes Yes
Industry Dummies Yes Yes
No.Observations1,433,0741,433,074
Time Period1985–20041985–2004
Wald Test[χ(2)]/p-value2,9920.003,054
Test Asymmetric Impact
(p-value)
α+β=0—0.04
α?β=0—0.55
Note:DEFAULT is a dummy variable that takes1if the loan is doubtful and0other-wise.LOANG it is the growth rate of all?nancial credits granted to?rms for bank i.We also control for bank size and type(i.e.,commercial or savings)and for loan characteristics (i.e.,size,maturity,and collateral).Region,industry,and time dummies have been included. *,**,and***are signi?cant at the10percent,5percent,and1percent levels,respectively.
of the borrowing?rm,size and type of the bank lender,and,?nally, for size,maturity,and collateral of the loan granted.
We have also investigated if there is an asymmetric impact of loan growth over defaults(columns2,4,and6in table3).In good times,when loan growth of each bank is above its average,we?nd a positive and signi?cant impact on future defaults(two,three,and four years later).However,in bad times,with loan growth below the bank’s average,there is no impact on defaults.Thus,this asymmet-ric e?ect reinforces the conclusions about too-lax lending policies during booms.
To test the robustness of the former results,table4shows the estimation of the model when the loan growth rate of the bank is introduced without any comparison to its average value.The results obtained are exactly the same:there is no e?ect on the probability of
Vol.2No.2
Credit Cycles,Credit Risk,and Prudential Regulation
81
T a b l e 4.G M M E s t i m a t i o n R e s u l t s o f E q u a t i o n (2)U s i n g a R a n d o m E ?e c t L o g i t M o d e l (L o a n G r o w t h R a t e o f B a n k I n t r o d u c e d w i t h o u t C o m p a r i s o n t o I t s A v e r a g e V a l u e )
V a r i a b l e s
C o e ?.
S E C o e ?.S E C o e ?.
S E
D e p e n d e n t V a r i a b l e
D E F A U L T i j t +2(0/1)
D E F A U L T i j t +3(0/1)
D E F A U L T i j t +4(0/1)
B a n k
C h a r a c t e r i s t i c s L O A N G i t
0.001
0.0010.0020.001***
0.0020.001***
P r o v i n c e D u m m i e s Y e s Y e s Y e s I n d u s t r y D u m m i e s Y e s Y e s Y e s N o .O b s e r v a t i o n s 1,823,6561,643,7081,433,074T i m e P e r i o d 1985–20041985–20041985–2004W a l d T e s t [χ(2)]/p -v a l u e
8,9660.004,8020.002,987
0.00
N o t e :D E F A U L T i s a d u m m y v a r i a b l e t h a t t a k e s 1i f t h e l o a n i s d o u b t f u l a n d 0o t h e r w i s e .L O A N G i t i s t h e g r o w t h r a t e o f a l l ?n a n c i a l c r e d i t s g r a n t e d t o ?r m s f o r b a n k i .W e a l s o c o n t r o l f o r b a n k s i z e a n d t y p e (i .e .,c o m m e r c i a l o r s a v i n g s )a n d f o r l o a n c h a r a c t e r i s t i c s (i .e .,s i z e ,m a t u r i t y ,a n d c o l l a t e r a l ).R e g i o n ,i n d u s t r y ,a n d t i m e d u m m i e s h a v e b e e n i n c l u d e d .*,**a n d ***a r e s i g n i ?c a n t a t t h e 10p e r c e n t ,5p e r c e n t ,a n d 1p e r c e n t l e v e l s ,r e s p e c t i v e l y .
82International Journal of Central Banking June2006 default in t+2and a positive and signi?cant one on the likelihood of default in t+3and t+4.
In terms of the economic impact,the semi-elasticity of the credit growth is0.13percent for default in t+3(0.13percent in t+4),16 which means that if a bank grows1percentage point,then the like-lihood of default in t+3is increased by0.13percent(0.13percent in t+4).If a bank was expanding its loan portfolio at one standard deviation above the average rate of growth,the impact would be 1.9percent(1.9percent).Thus,the economic impact estimated is low for the period analyzed and the sample considered,despite the signi?cance of the variables.
All in all,the previous results show that in good times,when credit is growing rapidly,credit risk in bank loan portfolios is also increasing.
2.3Collateral and Credit Growth
This section provides evidence of the behavior of banks in terms of their credit policies along the business cycle.The argument so far has been that too-rapid credit growth comes with lower credit stan-dards and,later on,manifests in a higher number of problem loans. Here,we provide some complementary evidence based on the tight relationship between credit cycles and business cycles.We argue that banks adjust their credit policies depending on the business-cycle position.For instance,in good times,banks relax credit stan-dards and are prepared to be more lenient in collateral requirements. On the other hand,when a recession arrives,banks toughen credit conditions and,in particular,collateral requirements.
If the hypothesis presented in the former paragraph is true,we would have complementary evidence to support prudential regula-tory policies.If it is true that,during boom times,loan portfolios are increasingly loaded with higher expected defaults,then it should also be true that other protective devises for banks,such as collateral,are eroded.17The following equation allows us to test the relationship between collateral and economic cycle.
16The marginal e?ect of the k-variable is computed as ME
k =d[Pr ob(y=1|ˉx)]
dx k
=
Λ(?βˉx)[1?Λ(?βˉx)]?βk.Then,the semi-elasticity is given by ME k/Average Default. 17It might also be the case that,during good times,banks decrease credit risk spreads in their granted loans partially as a result of overoptimism and tight
Vol.2No.2Credit Cycles,Credit Risk,and Prudential Regulation83
Pr(Collateral ijklt=1)=F(θ+αGDPG t?1
+β|GDPG t?1?Average GDP|+Control Variables ijklt)(3)
A full description of model3and its control variables is in Jim′e nez,Salas,and Saurina(forthcoming).Here we only focus on the impact of GDP growth on collateral,controlling for the other determinants of collateral.The variable on the left-hand side takes the value of1if the loan is collateralized and0otherwise. j refers to the loan,i refers to the bank,k refers to the market, l refers to the?rm(borrower),and t refers to the time period(year). We estimate equation(3)using a probit model.As control vari-ables,we use borrower characteristics(i.e.,if they were in default the year before or the year after the loan was granted,their indebt-edness level,and their age as a borrower),bank characteristics(size, type of bank,and its specialization in lending to?rms),character-istics of the borrower-lender relationship(duration and scope),and other control variables(such as the level of competition in the loan market,the size of the loan,and the industry and region of the borrower).18
The database used is the CCR.We focus on all new?nancial loans above e6,000with a maturity of one year or more,granted by any Spanish commercial or savings bank to non?nancial?rms every year during the time period between December1984and December 2002.We exclude commercial loans,leasing,factoring operations, and o?-balance-sheet commitments for homogeneity reasons.
The?rst column in table5shows the results of estimating model3for the pool of loans,nearly two million loans.There is a negative and signi?cant relationship between GDP growth rates and collateral;that is,in good times banks lower collateral require-ments,and they increase them in bad times.In terms of the impact, the semi-elasticity of GDPG is?3.1percent,which means that an competition among banks.The opposite would happen in bad times,when bank managers would tighten credit spreads.Unfortunately,our database does not allow us to test this hypothesis.
18Jim′e nez,Salas,and Saurina(forthcoming)contains a similar analysis on a di?erent sample of loans and using a di?erent estimation procedure(i.e.,?xed e?ects).
本文针对公众投资者信息获取与风险态度等方面的影响问题，根据博雅证券研究所的统计数据，运用主成分分析法建立了个人状况、信息获取方式、媒体信任程和风险态度的相关性的模型，借助Excel 和Matlab 软件定量地分析了这四个方面内容，并通过权重分析了四者间的相关性；最后，结合问题一和问题二向证券监管部门和公众投资者提出合理的建议。针对问题一，据附件中调查问卷和问卷调查统计表定量地分析了个人状况、信息获取方式、媒体信任度和风险态度。在个人状况、信息获取方式和媒体信任度中，选取了部分问题，通过Excel 处理数据，作出饼状图和柱形图得出投资者职业分布广、趋于年轻化；中低收入人群居多、投资者有自己独立判断以及信息获取渠道广等特征；对于风险态度，本文通过给选项赋分的方法，将投资者分为三种类型：风险厌恶型、风险中性和风险偏好型，计算出616 位被调查者的得分，并算出三类投资者的所占比例分别为：18.2、67.5、14.3。针对问题二，根据问卷调查统计表，建立数学模型，分析个人状况、信息获取方式、媒体信任程和风险态度的相关性。本文着重考虑风险态度，用权重来描述相关性，运用主成分分析法和Matlab 软件分别算出年龄、职业和月平均收入对个人状况的权重，知识来源、决策依据和信息获取渠道对信息获取方式的权重，投资者对推荐者的态度、媒体可信度和各类人士的意见对媒体信任度的权重，再根据这些数据，算出个人状况、信息获取方式、媒体信任程对风险态度的权重，得出相关系数分别为0.289、0.386、0.325。针对问题三，结合问题一和问题二的结论，将模型与实际相联系起来，向证券监管部门和公众投资者提出了一些的建议。本文还对模型的误差进行了定性分析，最后，我们通过聚类分析方法运用SPSS 软件将风险态度重新分类和用典型相关性模型直接算出个人状况、信息获取方式、媒体信任程对风险态度的相关系数对模型进行了改进，使结论更加全面可靠；并从数据处理和建模方法方面对模型做出了推广。本文分析思路清晰，分析全面，特色鲜明。关键词：信息获取；风险态度；主成分分析；聚类分析；Matlab；SPSS §1 问题的重述一、背景知识⒈我国的证券市场我国证券市场自1990 年沪深证券交易所开市以来，逐渐发展，已成为我国社会主义市场经济体系中不可或缺的重要组成成分，对于推动国有企业的改革和发展，壮大国有经济的实力，促进市场经济的建立和完善都发挥着显著的积极作用。但是，我国的证券市场也存在着规模偏小、直接融资比重过低、市场层次单一、交易品种和数量不够丰富等问题，且上市公司整体实力有待进一步提高。⒉公众投资者投资现状几十年来，随着我国经济改革的进行，证券市场的发展，我国投资业中公众投资者也不断增加。投资者希望能够充分地把握各种证券信息，但发行方有时却会视公司信息为其独占信息，甚至视之为发行人的商业秘密。证券发行人为了出售证券，虽然不得不向投资者公开某些信息，但信息公开程度系以满足证券销售为最高限度。在采取信息自愿公开的时期，信息公开的范围和方式、信息公开的时间性或者时效性、信息的真实性及准确性等方面，都是站在发行人立场上作出判定的，难免出现损害投资者利益的情形。二、相关数据⒈调查问卷统计表（附件2）⒉通过整理部分问题的调查结果得到的数据（见附录附表1）三、要解决的问题⒈问题一：量化分析公众投资者的个人状况、信息获取方式、媒体信任程、风险态度；⒉问题二：建立数学模型，分析个人状况、信息获取方式、媒体信任程、风险态度之间的相关性；⒊问题：结合问题一和问题二向证券监管部门和公众投资者提供一些建议。§2 问题的分析一、问题的总分析问题要求根据博雅证券研究所所掌握的一些数据资料利用数学建模的方法，提出以量化分析为基础的公众投资者的个人状况、信息获取方式、媒体信任度、风险态度等，并分析他们之间的相关性，同时向证券监管部门和公众投资者提供一些建议。首先，根据个人状况、信息获取方式、媒体信任程、风险态度，选出部分对应的问题，运用Excel软件对调查问卷统计表的数据进行处理并作图分析；其次，根据调查数据，建立主成分分析法模型，借助Matlab 软件编程，依次算出年龄、职业和月平均收入对个人状况的权重，知识来源、决策依据和信息获取渠道对信息获取方式的权重，投资者对推荐者的态度、媒体可信度和各类人士的意见对媒体信任度的权重，
飞航导弹数字化产品定义技术探讨 摘要：在虚拟制造生产方式下实现信息共享的前提是数字化产品定义技术应用，详细阐述了数字化产品定义技术及其关键元件，对飞航导弹数字化产品定义技术进行了初步的探讨，并对现阶段飞航导弹数字化产品定义技术应用提出了建议。 关键词：虚拟制造；PDM；数字化产品定义 1 概述 1.1 问题提出 当前，计算机技术的飞速发展，已经大大影响了我们的生产生活方式。为快速响应市场需求，缩短产品开发周期，产品的生产模式逐渐进入了以虚拟仿真验证为主，物理样机验证为辅的虚拟制造生产方式阶段。这是一种基于敏捷制造、虚拟制造、网络制造、全球制造上的生产模式，它要求从产品设计的一开始就要考虑到全寿命周期中的问题。虚拟制造生产方式的特点是，强调以协同为基础的并行有序的产品开发和上下游共同决策过程，并在产品数据管理( PDM)下进行过程控制与管理，解决设计与产品其它环节脱离的矛盾，为多品种单件小批生产的自动化提供有效途径。在虚拟制造生产方式下，产品设计、制造以及其它环节是建立在协同基础上的，产品信息反馈贯穿于全寿命周期的各个阶段，为减少信息反馈中的出错几率，要求各阶段对于产品的描述尽量保持一致。然而，在实际生产过程各个阶段和环节，由于实际需求和对产品的理解层次上的不同，对于产品信息的描述不尽相同。使得我们在共享产品信息资源的同时，不得不花费大量的时间去整理这些信息，从而影响了产品协同开发与设计的效率。因此，如何在实践中总结出一套适合于本企业的产品信息规X，并采用统一模型设计的工作方法，显得尤为重要。 1.2 解决方案 数字化产品定义的概念正是基于上述问题提出的。数字化产品定义(Digital Product Definition，DPD)是在产品的全寿命周期中对产品进行数字化的描述，其中包括产品全寿命周期中各个阶段的数字化信息描述和各个阶段数字化信息之间相互关系的描述，以便进行产品的异地设计和制造。数字化产品定义是虚拟制造生产方式的重要使能技术。通过数字化产品定义，将规X产品在各专业阶段模型的形式，为不同专业、不同阶段的人员协同设计与交流提供环境基础。 1.3 数字化产品定义技术在国内外发展状况
《危险化学品生产储存装置个人可接受风险标准和社会可接受风险标 准试行》 GE GROUP SyStem OffiCe room [GEIHUA16H-GEIHUA GEIHUA8Q8-
危险化学品生产、储存装置个人可接受风险标准和社会可接受风险标准（试行） —、适用范围 《危险化学品生产、储存装置个人可接受风险标准和社会可接受风险标准（试行）》（以下简称《可接受风险标准》）用于确定陆上危险化学品企业新建、改建、扩建和在役生产、储存装置的外部安全防护距离。 二、个人可接受风险标准 我国个人可接受风险标准值表
三、社会可接受风险标准 我国社会可接受风险标准图附录：1 ?相关术语 2?危险化学品生产、储存装置外部安全防护距离推荐方法
附录1 相关术语 定量风险评价：是对某一装置或作业活动中发生事故频率和后果进行定量分析，并与可接受风险标准比较的系统方法。 风险：是指发生特定危害事件的可能性以及发生事件后果严重性的结合。 个人风险：是指因危险化学品生产、储存装置各种潜在的火灾、爆炸、有毒气体泄漏事故造成区域内某一固定位置人员的个体死亡概率，即单位时间内（通常为一年）的个体死亡率。通常用个人风险等值线表示。 社会风险：是对个人风险的补充，指在个人风险确定的基础上，考虑到危险源周边区域的人口密度，以免发生群死群伤事故的概率超过社会公众的可接受范围。通常用累积频率和死亡人数之间的关系曲线（F-N曲线）表示。 防护目标：指在发生危险化学品事故时，易造成群死群伤的危险化学品单位周边的人员密集场所或敏感场所，包括居民区、村镇、商业中心、公园、学校、医院、影剧院、体育场（馆）、养老院、车站等C 不可接受区：指风险不能被接受。 可接受区：指风险可以被接受，无需采取安全改进措施。 尽可能降低区：指需要尽可能采取安全措施，降低风险。 外部安全防护距离：是指危险化学品生产、储存装置危险源在发生火灾、爆炸、有毒气体泄漏时，为避免事故造成防护目标处人员伤亡而设定的安全防护距离C
私募投资基金投资者风险问卷调查 投资者姓名：____________ 填写日期：_____________ 风险提示：私募基金投资需承担各类风险，本金可能遭受损失。同时，私募基金投资还要考虑市场风险、信用风险、流动性风险、操作风险等各类投资风险。您在基金认购过程中应当注意核对自己的风险识别和风险承受能力，选择与自己风险识别能力和风险承受能力相匹配的私募基金。 以下一系列问题可在您选择合适的私募基金前，协助评估您的风险承受能力、理财方式及投资目标。 请签字承诺您是为自己购买私募基金产品【】 请签字确认您符合以下何种合格投资者财务条件： 符合金融资产不低于300万元（金融资产包括银行存款、股票、债券、基金份额、资产管理计划、银行理财产品、信托计划、保险产品、期货权益等）【】 符合最近三年个人年均收入不低于50万元【】 问卷调查应至少涵盖以下几方面： 一、基本信息 1、您的姓名【】联系方式【】 证件类型【】证件号码【】
2、您的年龄介于 A 18-30岁 B 31-50岁 C 51-65岁 D 高于65岁 3、你的学历 A 高中及以下 B 中专或大专 C 本科 D 硕士及以上 4、您的职业为 A 无固定职业 B 专业技术人员 C 一般企事业单位员工 D 金融行业一般从业人员 二、财务状况 1、您的家庭可支配年收入为（折合人民币）？ A 50万元以下 B 50—100万元 C 100—500万元 D 500—1000万元 E 1000万元以上 2、在您每年的家庭可支配收入中，可用于金融投资（储蓄存款除外）的比例为？ A 小于10%
B 10%至25% C 25%至50% D 大于50% 三、投资知识 1、您的投资知识可描述为： A. 有限：基本没有金融产品方面的知识 B. 一般：对金融产品及其相关风险具有基本的知识和理解 C. 丰富：对金融产品及其相关风险具有丰富的知识和理解 2、您的投资经验可描述为： A、除银行储蓄外，基本没有其他投资经验 B、购买过债券、保险等理财产品 C、参与过股票、基金等产品的交易 D、参与过权证、期货、期权等产品的交易 3、您有多少年投资基金、股票、信托、私募证券或金融衍生产品等风险投资品的经验？ A、没有经验 B、少于2年 C、2至5年 D、5至10年 E、10年以上 四、投资目标 1、您计划的投资期限是多久？ A、1年以下 B、1至3年 C、3至5年
从基于模型的定义（MBD）到基于模型的企业（MBE） 当前，驱动制造业创新和持续发展的动因正在注入新的要素并由此引发其架构的巨大变化，从美国的再工业化到德国的工业4.0再到中国制造2025，从中我们可以发现，制造业是全球经济稳定发展的重要驱动力，世界各国都意在占领未来制造业的制高点。 美国提出以“软”的服务为主，注重软件、网络、大数据等对工业领域服务方式的颠覆；德國通过价值网络实现横向集成、跨越整个价值链的工程端到端数字化集成、垂直集成和网络化的制造系统，来保证德国在传统制造领域的领先地位；中国提出以信息化和工业化深度融合为主线，在智能制造、互联网+的突破等方面来增强国家工业竞争力，带动产业数字化水平和智能化水平的提高。 在这个全球大背景下，智能制造体现着从传统制造向数字制造转型的创新方向——在保持灵活性的同时，兼顾提升生产效率、缩短上
市时间、提高产品质量，其中最重要的是实现从企业管理、产品研发到制造控制的高度互联，其次是在整个价值链中集成IT系统的应用，第三是涵盖设计、生产、物流、市场和销售的生命周期的自动化控制和管理。 全球制造业数字化发展趋势 新一轮工业革命为企业带来新应用，数字化技术贯穿于产品设计、生产规划、生产工程、生产执行、客户服务等的各个环节，以实现虚拟数字世界与现实生产世界的准确映射（图1）。这个演进过程离不 开物联网技术、云计算、大数据、工业互联网等新一代数字技术支持，离不开集成先进的数字工程环境、生产管理系统和现场自动化技术，并以此为平台达到数字技术在企业中的深刻应用，以最小的资源消耗获得最高的生产效率。 从全球视野看，数字制造技术的理念正在发生重大变化。美国国家技术和标准研究院（NIST）提出从MBD（基于模型的定义）到MBE （基于模型的企业）的跃升，其要义是，模型驱动贯穿系统生命周期
危险化学品生产、储存装置个人可接受风险标准和社会可接受风险标准（试行） 一、适用范围 《危险化学品生产、储存装置个人可接受风险标准和社会可接受风险标准（试行）》（以下简称《可接受风险标准》）用于确定陆上危险化学品企业新建、改建、扩建和在役生产、储存装置的外部安全防护距离。 二、个人可接受风险标准 我国个人可接受风险标准值表 防护目标 个人可接受风险标准 （概率值） 新建装置 （每年）≤ 在役装置 （每年）≤ 低密度人员场所（人数<30人）：单个或少 量暴露人员。 1×10-53×10-5居住类高密度场所（30人≤人数＜100人）： 居民区、宾馆、度假村等。 公众聚集类高密度场所（30人≤人数＜100 人）：办公场所、商场、饭店、娱乐场所等。 3×10-61×10-5 高敏感场所：学校、医院、幼儿园、养老院、 监狱等。 重要目标：军事禁区、军事管理区、文物保 护单位等。 特殊高密度场所（人数≥100人）：大型体 育场、交通枢纽、露天市场、居住区、宾馆、 度假村、办公场所、商场、饭店、娱乐场所 等。 3×10-73×10-6
三、社会可接受风险标准 1 10100 10001x10 -9 1x10-8 1x10-7 1x10-6 1x10-5 1x10-4 1x10-31x10-2 1x10 -1 我国社会可接受风险标准图 附录：1.相关术语 2.危险化学品生产、储存装置外部安全防护距离推荐方法 死亡人数N （人） 累积频率F （次/年） 不可接受区 尽可能降低区 可接受区
附录1 相关术语 定量风险评价：是对某一装置或作业活动中发生事故频率和后果进行定量分析，并与可接受风险标准比较的系统方法。 风险：是指发生特定危害事件的可能性以及发生事件后果严重性的结合。 个人风险：是指因危险化学品生产、储存装置各种潜在的火灾、爆炸、有毒气体泄漏事故造成区域内某一固定位置人员的个体死亡概率，即单位时间内（通常为一年）的个体死亡率。通常用个人风险等值线表示。 社会风险：是对个人风险的补充，指在个人风险确定的基础上，考虑到危险源周边区域的人口密度，以免发生群死群伤事故的概率超过社会公众的可接受范围。通常用累积频率和死亡人数之间的关系曲线（F-N曲线）表示。 防护目标：指在发生危险化学品事故时，易造成群死群伤的危险化学品单位周边的人员密集场所或敏感场所，包括居民区、村镇、商业中心、公园、学校、医院、影剧院、体育场（馆）、养老院、车站等。 不可接受区：指风险不能被接受。 可接受区：指风险可以被接受，无需采取安全改进措施。 尽可能降低区：指需要尽可能采取安全措施，降低风险。 外部安全防护距离：是指危险化学品生产、储存装置危险源在发生火灾、爆炸、有毒气体泄漏时，为避免事故造成防护目标处人员伤亡而设定的安全防护距离。
投资者风险测评问卷 (个人版) 投资者姓名：填写日期： 身份证号码：□□□□□□□□□□□□□□□□□□ 尊敬的投资者： 您好！为了给您提供更优质的服务，请您花费几分钟的时间，如实填写以下调查问卷，本问卷可协助评估您的风险承受能力，进而为您挑选更适合的投资产品。衷心感谢您对【涌泉（深圳）资本管理有限公司】的支持与信任。 风险提示：私募基金投资需承担各类风险，本金可能遭受失。同时，还要考虑市场风险、信用风险、流动风险、操作风险等各类投资风险。您在基金认购过程中应当注意核对自己的风险识别和风险承受能力，选择与自己风险识别能力和风险承受能力相匹配的私募基金。 本问卷旨在了解您可承受的风险程度等情况，借此协助您选择合适的私募基金产品类别，以符合您的风险承受能力。 风险承受能力评估是本公司向客户履行适当性职责的一个环节，其目的是使本公司所提供的私募基金产品与您的风险承受能力等级相匹配。 本公司特别提醒您：本公司向客户履行风险承受能力评估等适当性职责，并不能取代您自己的投资判断，也不会降低私募基金产品的固有风险。同时，与私募基金产品相关的投资风险、履约责任以及费用等将由您自行承担。 本公司提示贵您：本公司根据您提供的信息对您进行风险承受能力评估，开展适当性工作。您应当如实提供相关信息及证明材料，并对所提供的信息和证明
材料的真实性、准确性、完整性负责。
本公司建议：当您的各项状况发生重大变化时，需对您所投资的私募基金产品及时进行重新审视，以确保您的投资决定与您可承受的投资风险程度等实际情况一致。 本公司在此承诺：对于您在本问卷中所提供的一切信息，本公司将严格按照法律法规要求承担保密义务。除法律法规规定的有权机关依法定程序进行查询以外，本公司保证不会将涉及您的任何信息提供、泄露给任何第三方，或者将相关信息用于违法、不当用途。 测试题目(所有题目均为单选，请在选定答案上打“√”) 1、您的主要收入来源是： A、工资、劳务报酬 B、生产经营所得 C、利息、股息、转让等金融性资产收入 D、出租、出售房地产等非金融性资产收入 E、无固定收入 2、您的家庭可支配年收入为(折合人民币) ： A、50万元以下 B、50—100万元 C、100—500万元 D、500—1000万元 E、1000万元以上
试述数据模型的概念，数据模型的作用和数据模型的三个要素: 答案： 模型是对现实世界的抽象。在数据库技术中，表示实体类型及实体类型间联系的模型称为“数据模型”。 数据模型是数据库管理的教学形式框架，是用来描述一组数据的概念和定义，包括三个方面： 1、概念数据模型（Conceptual Data Model）：这是面向数据库用户的实现世界的数据模型，主要用来描述世界的概念化结构，它使数据库的设计人员在设计的初始阶段，摆脱计算机系统及DBMS的具体技术问题，集中精力分析数据以及数据之间的联系等，与具体的DBMS 无关。概念数据模型必须换成逻辑数据模型，才能在DBMS中实现。 2、逻辑数据模型（Logixal Data Model）:这是用户从数据库所看到的数据模型，是具体的DBMS所支持的数据模型，如网状数据模型、层次数据模型等等。此模型既要面向拥护，又要面向系统。 3、物理数据模型（Physical Data Model）:这是描述数据在储存介质上的组织结构的数据模型，它不但与具体的DBMS有关，而且还与操作系统和硬件有关。每一种逻辑数据模型在实现时都有起对应的物理数据模型。DBMS为了保证其独立性与可移植性，大部分物理数据模型的实现工作又系统自动完成，而设计者只设计索引、聚集等特殊结构。 数据模型的三要素： 一般而言，数据模型是严格定义的一组概念的集合，这些概念精确地描述了系统的静态特征（数据结构）、动态特征（数据操作）和完整性约束条件，这就是数据模型的三要素。 1。数据结构 数据结构是所研究的对象类型的集合。这些对象是数据库的组成成分，数据结构指对象和对象间联系的表达和实现，是对系统静态特征的描述，包括两个方面： （1）数据本身：类型、内容、性质。例如关系模型中的域、属性、关系等。 （2）数据之间的联系：数据之间是如何相互关联的，例如关系模型中的主码、外码联系等。 2 。数据操作 对数据库中对象的实例允许执行的操作集合，主要指检索和更新（插入、删除、修改）两类操作。数据模型必须定义这些操作的确切含义、操作符号、操作规则（如优先级）以及实现操作的语言。数据操作是对系统动态特性的描述。 3 。数据完整性约束 数据完整性约束是一组完整性规则的集合，规定数据库状态及状态变化所应满足的条件，以保证数据的正确性、有效性和相容性。
危险化学品生产、储存装置个人可接受风险和社会可接受风险基准（征求意见稿） 为落实《危险化学品安全管理条例》（国务院令第591号）有关要求，妥善解决危险化学品新建企业选址、高风险企业搬迁和化工园区规划布局以及现有企业出现的危险化学品生产、储存装置外部安全防护距离不明确的问题。国家安全监管总局在对发达国家和地区土地安全规划、安全距离确定方法进行广泛调研和分析的基础上，结合我国国情，研究提出了我国危险化学品生产、储存装置个人可接受风险和社会可接受风险基准（以下简称可接受风险基准）。 一、我国可接受风险基准制定的必要性 由于危险化学品生产、储存装置自身存在固有风险，会对周边居民、社区和其他公共场所（以下简称防护目标）带来一定的个人风险和社会风险。从风险防范的角度出发，国际上通常采用设定国家可接受风险基准来控制危险源与防护目标的外部安全防护距离，确保防护目标增加的风险在国家设定的可接受风险基准范围内。 为保护人民生命和财产安全，强化风险管理意识，提升我国危险化学品生产、储存企业的本质安全水平，我国急需制定和颁布国家可接受风险基准，以保障人民群众生命安全，合理利用我国有限的土地资源。 二、国际上制定可接受风险基准通行的做法 国外发达国家和地区在确定可接受风险基准时,通常采用个人可
接受风险和社会可接受风险作为确定外部安全防护距离和土地使用规划的依据。 个人可接受风险基准：国际上通常采用国家人口分年龄段死亡率最低值乘以一定的风险可允许增加系数，作为个人可接受风险的基准值。荷兰、英国中国香港等不同国家（地区）具体实例，见表1。 表1 不同国家（地区）所制定的个人可接受风险基准 社会可接受风险基准：国际上通常采用危险化学品生产、储存装置发生火灾、爆炸、中毒事故时，可能造成周边民众和社区群死群伤事故的累积频率来确定，通常由频率（F）/死亡人数（N）曲线体现。荷兰、英国、中国香港等不同国家（地区）具体实例，见图1。 图1 不同国家（地区）制定的社会可接受风险标准
“投资收益权权益” 风险属性评估问卷 (自然人适用) 一、关于这份问卷： 本问卷调查表旨在协助投资人了解自身的投资状况，主要包括投资偏好、风险承受能力及风险认知能力等方面，问卷结果不能完全呈现投资人自身投资状况及在面对投资风险时的真正态度，但是可以向投资人提供一些衡量自身风险属性的指标。 二、问卷题目： 1、您目前的年龄？ a. 小于25岁；（3分） b. 25-40岁之间（5分） c. 41-60岁之间（4分） d. 大于60岁（1分） 2、您当前的资产主要构成情况？ a. 只有银行存款（活期或者定期）；（1分） b. 主要是银行存款，还有少量国债和基金、蓝筹股；（2分） c. 既有股票、基金、股票、理财产品，也有银行存款，前后两者各半；（3分） d. 以期货等金融衍生产品和股票投资为主。（5分） 3、您当前用家庭资产多少比例进行投资？ a. 10%之内的资产；（1分） b. 10-20%之间；（2分） c. 25-50%之间；（3分） d. 超过50%。（5分） 4、如果有个为期1年投资项目可参与，投资成功的概率是50%，一旦成功 可获得4倍收益，一旦失败则本钱全部损失，对这样的投资您愿意投入的资金量是多少？ a. 一分也不投入；（0分） b. 1个月的收入；（2分） c. 3个月的收入；（3分） d. 6个月或超过6个月的收入。（5分）
5、往往高收益的基金或理财产品伴随着高风险，您准备承担多大的风险？ a. 不愿意让我的钱有任何损失；（1分） b. 因为基金有潜在的增值功能，我愿意承担一定的风险；（2分） c. 很愿意通过长期投资使资产增值保值，也愿意在此过程中承担风险；（4分） d. 我愿意投资最具增长潜力的产品，也愿意为了更高的收益承受大幅度的风险变动。（5分） 6、请问您投资价格波动性的产品有多少年的经验？（具有价格波动的产品包括股票、基金、外汇、期权期货等金融产品） a. 7-10年（5分） b. 4-7年（3分） c. 1-3年（2分） d. 从来没有或刚刚开始（1分） 7、通常情况下您一笔投资预备持有的期限是多久？ a. 长期——超过7年；（5分） b. 中期——4年到7年（3分） c. 中短期——1年到3年（2分） d. 短期——1年内（1分） 8、一年中您能最能接受的资产波动范围是？ a. 最多盈利10%，最大亏损5%；（1分） b. 最多盈利20，最大亏损15%；（3分） c. 最多盈利40%，最大亏损30%；（4分） d. 最多盈利60%，最大亏损50%（5分） 9、您此次投资的目的是？ a. 就希望能每年分红或者获得固定的收益；（1分） b. 希望能获得分红并实现资产的部分升值；（2分） c. 两者各半；（3分） d. 主要是为了资产升值；（4分） e. 为了资产大幅升值。（5分） 10、如果您刚刚投资就遇到市场调整，您能承受的损失大概是多少？ a. 5%以内；（1分） b. 6-20%（2分） c. 21-40%（4分） d. 超过40%（5分）
第三篇仿真的定义和分类 计算机仿真技术是以数学理论、相似原理、信息技术、系统技术及其应用领域有关的专业技术为基础，以计算机和各种物理效应设备为工具，利用系统模型对实际的或设想的系统进行试验研究的一门综合性技术。 雷诺（T.H.Naylor）定义：“仿真是在数字计算机上进行试验的数字化技术，它包括数字与逻辑模型的某些模式，这些模型描述某一事件或经济系统（或者它们的某些部分）在若干周期内的特征。” 系统仿真是建立在控制理论、相似理论、信息处理技术和计算技术等理论基础之上的，以计算机和其它专用物理效应设备为工具，利用系统模型对真实或假想的系统进行试验，并借助于专家经验知识、统计数据和信息资料对试验结果进行分析研究，进而做出决策的一门综合性的和试验性的学科。 连续系统仿真及离散事件系统仿真。 系统仿真分为物理仿真、数学仿真及物理--数学仿真（又称半物理仿真或半实物仿真）。 根据国际标准化组织(ISO)标准中的《数据处理词汇》部分的名词解释，“模拟”(Simulation)与“仿真”(Emulation)两词含义分别为：“模拟”即选取一个物理的或抽象的系统的某些行为特征，用另一系统来表示它们的过程。“仿真”即用另一数据处理系统，主要是用硬件来全部或部分地模仿某一数据处理系统，以致于模仿的系统能像被模仿的系统一样接受同样的数据，执行同样的程序，获得同样的结果。鉴于目前实际上已将上述“模拟”和“仿真”两者所含的内容都统归于“仿真”的范畴，而且都用英文Simulation一词来代表。 计算机仿真技术综合集成了计算机、网络技术、图形图像技术、面向对象技术、多媒体、软件工程、信息处理、自动控制等多个高新技术领域的知识。 计算机仿真技术是以数学理论、相似原理、信息技术、系统技术及其应用领域有关的专业技术为基础，以计算机和各种物理效应设备为工具，利用系统模型对实际的或设想的系统进行试验研究的一门综合性技术。 计算机仿真技术的应用已不仅仅限于产品或系统生产集成后的性能测试试验，仿真技术已扩大为可应用于产品型号研制的全过程，包括方案论证、战术技术指标论证、设计分析、生产制造、试验、维护、训练等各个阶段。仿真技术不仅仅应用于简单的单个系统，也应用于由多个系统综合构成的复杂系统。 系统仿真的定义 仿真界专家和学者对仿真下过不少定义。艾伦（A.Alan）在1979年8月出版的“仿真”期刊上对众多的定义进行了综述，其中雷诺（T.H.Naylor）于1966年在其专著中对仿真作了如下定义：“仿真是在数字计算机上进行试验的数字化技术，它包括数字与逻辑模型的某些模式，这些模型描述某一事件或经济系统（或者它们的某些部分）在若干周期内的特征。”其它一些定义只对仿真作一些概括的描述：仿真就是模仿真实系统；仿真就是利用模型来作实验等等。从这些有关仿真的定义中不难看出，要进行仿真试验，系统和系统模型是两个主要因素。同时由于对复杂系统的模型处理和模型求解离不开高性能的信息处理装置，而现代化的计算机又责无旁贷地充当了这一角色，所以系统仿真（尤其是数学仿真）实质上应该包括三个基本要素：系统、系统模型、计算机。而联系这三项要素的基本活动则是：模型建立、仿真模型建立和仿真试验。参见图3.1。
投资者风险态度与羊群行为的理论分析 古远平 (广东证券股份有限公司,广东 510000) 摘要:本文从投资者的期望效用函数出发,建立模型研究了投资者的风险态度变化与其羊群行为的关系。模型假设一部分投资者提前获得内部信息,他们的交易推动股票价格上涨,股票价格上涨通过两个渠道影响没有获得该信息的投资者的风险态度:股票账面价值增加和期望收益率的上升。结合行为金融学的研究成果,模型得出的结论是随着股票账面价值的增加和股票期望收益率的上升,如果未提前获得信息的投资者的风险厌恶程度降低,那么他就会表现出羊群行为。 关键词:羊群行为;风险厌恶;期望效用;投资;股票 中图分类号:F 830 文献标识码:A 文章编号:1004—3926(2005)06—0279—05 收稿日期:2005-03-01 作者简介:古远平(1972-),男,广东五华人,就职于广东证券股份有限公司。 一、引言和基本模型 有效市场假设(e f f i c i e n tm a r k e th y p o t h e s i s ,E M H )认为股价能反映所有的相关信息。即使股价在短期内可能偏离股票的基本价值,但随着时间的流逝,投资者获取的信息越来越全面,因此,股价最终必定回归其基本价值。然而,有关投资者羊群行为的研究成果挑战了这一结论。研究羊群行为的文献证明了理性投资者在时间上前后相互作用可以导致羊群行为,这阻止了投资者了解股票的基本价值,从而使股票价格在长期也可能偏离其基本价值。 A v e r y 和Z e m s k y (1998)把投资者的羊群行为定义为:某个拥有私人信息的投资者在期初作出了不买(不卖)资产的决定,如果在观察到其他投资者买入(卖出)该资产时,他也决定买入(卖出)该资产,那么他就呈现出买方羊群行为;如果在观察到其他投资者卖出(买入)该资产时,他决定买入(卖出)该资产,那么他呈现买方反向行为(c o n t r a r i a nb e h a v -i o r )。S i a s (2004)对羊群行为的定义是一群投资者在一段时间内相互跟着对方买入(或卖出)同一证券。从这两个定义可以看出,羊群行为的本质特征是投资者买入或卖出股票的行为不是对股票基本价值的反应,而是基于市场上其他投资者的交易行为。在本文我们也采用这一定义。 现有研究理性羊群行为的理论文献很多是假设投资者的目标函数是期望收益最大化,而在本文中我们假设投资者是理性的风险厌恶者,其目标函数是期望效用最大化。这样做的好处是我们可以分析 投资者风险态度的变化是怎样导致羊群行为。然而,对投资者风险态度变化的把握已经涉及到了心 理学和社会学,更深入的研究可能要借助试验经济学的方法。 我们的模型与A v e r y 和Z e m s k y (1998)的模型设定的前提条件相似,即股票市场存在两类投资者:H 和L 。H 类投资者拥有非常精确的信息(或者说是“内部信息”),L 类投资者拥有不精确的信息。两类投资者都不知道对方的数量和身份。本文模型中的时间逻辑是:在时期0,H 和L 根据期望效用最大化原则在股票和银行存款上分配资金比例,购买股票的数量由该只股票的期望收益率、投资者资金成本与投资的风险态度三者共同决定。在时期1,H 收到一个有关某只股票基本价值的正面信息①[1] ,其交易行为推动该只股票价格上涨,股票价格并不能准确反应H 的私人信息。在时期2,L 还没有获得该只股票基本价值的信息,但他能观察到股票价格上涨,此时L 做出买入、卖出和不买不卖股票的决定。根据羊群行为定义,如果L 买入该只股票,那么他就表现出羊群行为;如果L 卖出该只股票,那么他就表现出反向行为。 L 的效用函数的设定:L 的效用函数u (w )在给定的定义域内连续,一阶、二阶可微。L 对收入具有不满足性,收入越高,满足程度越高,所以收入的边际效用u '(w )>0,但随着收入的增高,收入的边际效用递减,即u "(w )<0。L 在时期0可用于投资的资金数量为C 。为了分析的简便,假设L 把资金的一部分存于银行,获得无风险利率,另一外部分用
学术论文 RESEARCH [摘要] 基于模型的定义技术是一种新的产品定义技术，它以三维产品模型为基础，集成了三维空间尺寸公差标注和制造要求标注在内的特征信息，并作为设计生产过程中的唯一依据。结合国际先进航空企业的应用基于模型的定义技术的成功经验，总结了基于模型定义技术数据信息的组织与管理及其系统的实现，以及开发工程注释信息的管理系统。 关键词： MBD 零件注释 标准注释 MBD数据集 产品集成标准管理 [ABSTRACT] Model based definition is a new method completely, which integrates feature information including 3D size tolerance annotation and manufacturing requirement annotation on the basis of 3D product model and is the sole basis in design and manufacture. By study-ing the experience of the successful use of MBD techno-logy in advanced aviation industry, a set rules of content and organization for model based de? nition with fully inte-grality and the system implementation of MBD technology are summarized, ? nally the system to manage the engineer information is developed. Keywords: Model based definition Part notes Standard notes MBD data set Integrated product standard management 美国机械工程师协会从1997年1月起发起关于三维模型标注标准的起草工作，以解决图纸与信息系统传输之间的矛盾。此标准于2003年7月被美国机械工程师协会接纳为新标准（ASME Y14.41）[1]。随后，UG、PTC、Dassault等公司将该标准应用于各自的CAD系统中，对三维标注进行了支持。作为该项技术的发起者之一，波音公司在787项目中开始推广使用该项技术，从设计开始，波音公司作为上游企业，全面在合作伙伴中推行基于模型的数字化定义技术即MBD（Model Based Definition）技术。 波音公司采用MBD技术后，在管理和效率上取得了本质的飞跃。鉴于国外先进航空企业采用MBD技术后取得巨大成功，国内的航空企业逐渐开始学习MBD 技术并逐步将MBD应用于现实生产中。但国内航空企业对于MBD技术的学习与应用起步比较晚，现实生产中的应用并不成熟。 本研究结合国际先进航空企业的应用MBD技术的成功经验以及国内航空场所应用MBD技术中遇到的问题，开发了工程信息管理系统。 1 基于模型定义技术 MBD技术是指用集成的三维实体模型来完整表达产品定义信息的方法，MBD技术是将原来定义在二维图纸上的几何形状信息、尺寸与公差以及工艺信息等产品信息，集成定义在三维实体模型中。改变了传统上用三维实体模型描述几何形状信息，而用二维图纸来定义产品的尺寸与公差以及工艺信息的数字化定义方法[2]。 采用MBD技术后，用来集成产品的几何形状信息、尺寸与公差以及工艺信息的三维实体模型被称为MBD 数据集，如图1所示。 基于模型定义的数据组织与系统实现 Data Organization and System Implementation of Model Based De? nition 北京航空航天大学 冯国成 梁 艳 于 勇 范玉青 图1 MBD数据集 Fig.1 MBD data set 2 MBD数据集的内容与表达方式 2.1 零部件MBD数据集的内容 在传统的设计制造过程当中，以二维工程图纸作为 主要的制造依据，而三维实体模型仅仅作为制造过程中 的辅助参考依据。由于MBD技术要求使用三维实体模 型作为生产制造过程中的唯一依据，这样就要求对产品 数字化定义信息按照MBD技术的要求进行分类组织管 理，以便表现出零部件的几何属性、质量检测属性、管理 属性以及工艺属性等信息，来满足制造过程中的各个阶 段对数据的需求。 62航空制造技术·2011 年第 9 期
公众投资者信息获取与风险态度等方面的影响 摘要 本文针对公众投资者信息获取与风险态度等方面的影响问题，根据博雅证券研究所的统计数据，运用主成分分析法建立了个人状况、信息获取方式、媒体信任程和风险态度的相关性的模型，借助Excel和Matlab软件定量地分析了这四个方面内容，并通过权重分析了四者间的相关性；最后，结合问题一和问题二向证券监管部门和公众投资者提出合理的建议。 针对问题一，据附件中调查问卷和问卷调查统计表定量地分析了个人状况、信息获取方式、媒体信任度和风险态度。在个人状况、信息获取方式和媒体信任度中，选取了部分问题，通过Excel处理数据，作出饼状图和柱形图得出投资者职业分布广、趋于年轻化；中低收入人群居多、投资者有自己独立判断以及信息获取渠道广等特征；对于风险态度，本文通过给选项赋分的方法，将投资者分为三种类型：风险厌恶型、风险中性和风险偏好型，计算出616位被调查者的得分，并算出三类投资者的所占比例分别为：18.2%、67.5%、14.3%。 针对问题二，根据问卷调查统计表，建立数学模型，分析个人状况、信息获取方式、媒体信任程和风险态度的相关性。本文着重考虑风险态度，用权重来描述相关性，运用主成分分析法和Matlab软件分别算出年龄、职业和月平均收入对个人状况的权重，知识来源、决策依据和信息获取渠道对信息获取方式的权重，投资者对推荐者的态度、媒体可信度和各类人士的意见对媒体信任度的权重，再根据这些数据，算出个人状况、信息获取方式、媒体信任程对风险态度的权重，得出相关系数分别为0.289、0.386、0.325。 针对问题三，结合问题一和问题二的结论，将模型与实际相联系起来，向证券监管部门和公众投资者提出了一些的建议。 本文还对模型的误差进行了定性分析，最后，我们通过聚类分析方法运用SPSS软件将风险态度重新分类和用典型相关性模型直接算出个人状况、信息获取方式、媒体信任程对风险态度的相关系数对模型进行了改进，使结论更加全面可靠；并从数据处理和建模方法方面对模型做出了推广。本文分析思路清晰，分析全面，特色鲜明。 关键词：信息获取；风险态度；主成分分析；聚类分析；Matlab；SPSS
一、相关术语 定量风险评价：是对某一装置或作业活动中发生事故频率和后果进行定量分析，并与可接受风险标准比较的系统方法。 风险：是指发生特定危害事件的可能性以及发生事件后果严重性的结合。 个人风险：是指因危险化学品生产、储存装置各种潜在的火灾、爆炸、有毒气体泄漏事故造成区域内某一固定位置人员的个体死亡概率，即单位时间内（通常为一年）的个体死亡率。通常用个人风险等值线表示。 社会风险：是对个人风险的补充，指在个人风险确定的基础上，考虑到危险源周边区域的人口密度，以免发生群死群伤事故的概率超过社会公众的可接受范围。通常用累积频率和死亡人数之间的关系曲线（F-N曲线）表示。 二、个人可接受风险标准 个人风险是指因危险化学品重大危险源各种潜在的火灾、爆炸、有毒气体泄漏事故造成区域内某一固定位置人员的个体死亡概率，即单位时间内（通常为年）的个体死亡率(AFR——年死亡率)。通常用个人风险等值线表示。 通过定量风险评价，危险化学品单位周边重要目标和敏感场所承受的个人风险应满足表1中可容许风险标准要求 我国个人可接受风险标准值表
三、社会可接受风险标准 社会风险是事故对整个社会的风险总和，指能够引起大于等于N 人死亡的事故累积频率（F），也即单位时间内（通常为年）的死亡人数。通常用社会风险曲线（F-N曲线）表示。
通过定量风险评价，危险化学品重大危险源产生的社会风险应满足图中可容许社会风险标准要求。 F-N 曲线 110100 10001x10 -9 1x10-8 1x10-7 1x10-6 1x10-5 1x10-4 1x10-3 1x10 -2 1x10-1 k N F a =?（K 改变截距、a 改变斜率） F ：社会风险概率 N ：死亡人数 a ：一般取1—2 四、ALARP 准则 死亡人数N （人） 累积频率F （次/年） 不可接受区 尽可能降低区 可接受区
投资者风险偏好的效用分析 （作者:___________单位: ___________邮编: ___________） 【摘要】风险是一种不确定性状态，不确定条件下的个人行为就表现为不同投资者的风险偏好。本文通过运用预期效用函数对投资者风险偏好行为的分析，结合我国对利息和股息相关征税政策的现实问题进行简要分析，并说明投资者在不同风险偏好下进行投资选择时应以预期效用最大化为标准。 2006年上半年，央行曾在2004年上调利率0.27%的基础上，调整了银行存贷利息，这是继1996年5月1日我国首次降息以来，连续五次降息后的小幅升息调整。这似乎对众多储户来说是个利好信息，但我国最新修订的《个人所得税法》中，对个人的利息、股息、红利等所得要征收个人所得税。我国对利息征税是从1999年起征的，对存款利息征20%的利息税，初衷是刺激人们的消费，减少银行存款余额，缓解房地产市场的投资过热。而却不难发现，在中国资本市场还不健全、没有合适的投资产品进行选择的前提下，人们依然保持了普遍避免风险的偏好，选择将钱存入银行，国家征利息税不仅并未起到刺激投资的预期效果，而且利息税每年占我国整个财政收入的比例还不足1%，对国家增加财政收入没有明显的帮助。对股息征税的情
况就不同了，大多数在资本市场的投资者都希望能获得未来的股息和红利，征税对他们来说虽然减少了获利状况下的收入，却会分散亏损状况下的损失……本文将从风险与效用的理论出发，对投资者的风险偏好进行探讨。 一、风险与效用理论 （一）风险理论 风险是事件的不确定性所引起的，由于对未来结果予以期望所带来的无法符合期望结果的可能性。简言之，风险就是结果差异引起的结果偏离，即期望结果的可能偏离。把风险的概念影射到现实资本市场中，由于信息不对称和资本市场的不完善，投资者在追求超额报酬的同时，必然要承担类似于支付对价的投资风险，这是风险与报酬必然存在的矛盾。从财务风险产生的机理分析：首先，财务风险是实现资本增值的内在障碍，由于财务风险的存在，资本增值难以实现；其次，财务风险失去的是超额增值的外部条件，没有财务风险的存在，资本的增值性又难以充分实现。因此，投资者需要权衡二者的关系，进行风险管理，同时也就产生了投资者的风险偏好。在财务理论中，常用马克维茨的方差模型来衡量风险程度；在微观经济学中则采用预期效用理论来分析人们的偏好及其投资风险。 （二）效用理论