Managing Bank Liquidity Risk:
How Deposit-Loan Synergies Vary with Market Conditions
Evan Gatev
Boston College
Til Schuermann
Federal Reserve Bank of New York and Wharton Financial Institutions Center Philip E.Strahan
Boston College,Wharton Financial Institutions Center,and National Bureau of Economic Research
Liquidity risk in banking has been attributed to transactions deposits and their potential to spark runs or panics.We show instead that transactions deposits help banks hedge liquidity risk from unused loan commitments.Bank stock-return volatility increases with unused commitments,but only for banks with low levels of transactions deposits.This deposit-lending hedge becomes more powerful during periods of tight liquidity,when nervous investors move funds into their banks.Our results reverse the standard notion of liquidity risk at banks,where runs from depositors had been seen as the cause of trouble.(JEL G18,G21) Much theory attempts to?nd dimensions of“bank specialness,”typically from a synergy of combining deposits with loans.Banks’role as delegated moni-tor explains why they tend to hold large and well-diversi?ed loan portfolios, and why they tend to fund themselves mostly with debt(Diamond,1984). Deposits—banks’main source of debt—tend to be short term and subject to a“sequential service constraint,”meaning that priority of payment is on a ?rst-come,?rst-served basis.This unique capital structure stems from bank loan opaqueness.Loans make“bad”collateral because outsiders cannot value them;by subjecting themselves to the possibility of a run,banks increase their borrowing capacity against their loans(Calomiris and Kahn,1991;Flannery, 1994;Diamond and Rajan,2001).
We would like to thank the FDIC Center for Financial Research for?nancial support,as well as for helpful comments on the research.We would also like to thank seminar participants at Boston College,Ohio University, Tilburg University,and the University of Amsterdam,and participants at the2nd FIRS(Financial Intermediation Research Society)Conference in Shanghai and the China International Conference in Finance(CICF)in Xian,as well as Robert de Young,Paul Kupiec,Tobias Moskowitz(the editor),and an anonymous referee.Kristin Wilson assisted with preparation of the data.Any views expressed represent those of the authors only and not necessarily those of the Federal Reserve Bank of New York or the Federal Reserve System.Send correspondence to P.E. Strahan,Boston College,140Commonwealth Avenue,Chestnut Hill,MA02467,United States,telephone:(617) 552-6430.E-mail:philip.strahan@https://www.doczj.com/doc/9314886050.html,.
C The Author2007.Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved.For Permissions,please e-mail:journals.permissions@https://www.doczj.com/doc/9314886050.html,.
doi:10.1093/rfs/hhm060Advance Access publication November20,2007
The Review of Financial Studies/v22n32009
Fama(1985)argues that liquidity production helps explain what is different about banks.Private information from the business checking account gives banks an advantage in lending over other intermediaries.1Banks provide liq-uidity not only to demand depositors,however,but also to borrowers via lines of credit and undrawn loan commitments(we use these terms interchangeably).2 Both contracts allow customers to receive cash on demand.The liquidity in-surance role of banks exposes them to the risk that they will have insuf?cient cash to meet random demands from their depositors or borrowers.This article shows that banks reduce their liquidity risk by combining transactions deposits with loan commitments,thus helping to explain a key feature of the industry.
Our point of departure is the model presented by Kashyap,Rajan,and Stein (2002),hereafter KRS,who explain why banks combine transactions deposits with loan commitments using a risk-management motivation:as long as the demand for liquidity from depositors is not highly correlated with liquidity demands from borrowers,an intermediary can reduce its need to hold cash by serving both customers.Thus,their model yields a diversi?cation synergy between demand deposits(or transactions deposits more generally)and loan commitments.As evidence,KRS report a positive correlation across banks between unused loan commitments and transactions deposits.The correlation is robust to variations in the de?nition of loan commitments and to bank size, and is also consistent across time.KRS do not,however,test the key implication of their model—the idea that by exposing themselves to asset-side and liability-side liquidity risks simultaneously,banks can enjoy a diversi?cation,or risk-reducing synergy.
We test the basic premise of the KRS model—that liquidity risks stemming from the two fundamental businesses of banking yield a diversi?cation bene?t. The results suggest that bank risk,measured by stock-return volatility,increases with unused loan commitments,re?ecting asset-side liquidity risk exposure. This increase,however,is mitigated by transactions deposits.In fact,risk does not increase with loan commitments for banks with high levels of transactions deposits.
Table1illustrates our key?nding in a simple and intuitive way.The table reports our measure of risk(average stock-return volatility,equal to the annu-alized return standard deviation)for large,publicly traded banks,along with 1Mester,Nakamura,and Renault(2006)offer some empirical evidence for this notion.Gatev(2006)argues that banks also have unique information about systematic(as opposed to?rm-speci?c)liquidity shocks,allowing them to lend to opaque?rms such as hedge funds.
2Early literature attempts to understand how banks’role in liquidity production leads to fragility.Diamond and Dybvig(1983)argue that by pooling their funds in an intermediary,agents can insure against idiosyncratic liquidity shocks while still investing most of their wealth in high-return but illiquid projects.This structure leads to the potential for a self-ful?lling bank run and sets up a policy rationale for deposit insurance.More recent theoretical and empirical studies focus on liquidity risk from the asset side.For example,Berger and Bouwman (2006)document the importance of banks in liquidity production on both sides of bank balance sheets,and show that this role has grown sharply over time.There is also a growing literature showing that liquidity-risk management or liquidity shocks to banks affect loan supply.See Paravisini(2006),Khwaja and Mian(2005), Loutskina(2005),and Loutskina and Strahan(2006).
996
Managing Bank Liquidity Risk
Table1
Mean bank characteristics,by transactions deposits and unused commitments
Transactions deposits/total deposits(TD)
TD<33rd33rd pct.≥TD<67th pct.≤
percentile67th pct.TD
(1)(2)(3)
Unused commitments/(commitments+loans)(LC)
LC<33rd percentile
Stock-return volatility0.280.290.32 (Cash+securities)/assets0.320.310.33 Assets(billions of$s)10.5810.207.14 Fed funds purchased/assets0.070.080.07 Equity/assets0.080.080.11 33rd percentile≤LC<67th percentile
Stock-return volatility0.290.290.30 (Cash+securities)/assets0.320.310.33 Assets(billions of$s)17.8521.6416.53 Fed funds purchased/assets0.090.100.11 Equity/assets0.080.080.07 67th percentile≤LC
Stock-return volatility0.360.320.31 (Cash+securities)/assets0.340.300.33 Assets(billions of$s)34.6389.1083.36 Fed funds purchased/assets0.070.100.10 Equity/assets0.080.080.08 Mean commitments ratio0.300.310.37 Average bank characteristics and stock-return volatility,equal to the annualized return standard deviation,for large,publicly traded banks.Data on unused commitments,transactions deposits,as well as the other bank characteristics,come from the most recent quarter of the Reports of Income and Condition prior to the time at which we measure the stock-return variability.The observations are sorted vertically by the percentiles of the distribution of level of loan commitments(unused commitments divided by unused commitments plus loans) and horizontally by the level of transactions deposits relative to total deposits.Each of the nine cells reports the simple average for all observations in that part of the distribution.
several bank characteristics,including a liquidity ratio(cash+securities to assets),total assets,Fed funds purchased to assets,and the ratio of equity to assets.We divide the data into nine cells,based on the level of loan commit-ments(unused commitments divided by unused commitments plus loans)and the level of transactions deposits(relative to total deposits).Each cell reports the simple average for all observations in that part of the distribution.Stock-return volatility(our measure of risk)clearly increases with loan commitments for banks with low and moderate levels of transactions deposits(columns1 and2).Risk does not increase for banks with high levels of transactions de-posits(column3).Loan commitments do not expose banks with high levels of transactions deposits to much risk.
Table1also shows that banks with high levels of unused commitments tend to be larger than average.Banks exposed to commitments that operate without the bene?t of high levels of transactions deposits also hold more cash than other banks(last row of column1).3Thus,the high balance-sheet liquidity held by these banks does not fully offset the risk from liquidity exposure associated 3The cash ratio is not monotonically increasing in liquidity exposure from transactions deposits.
997
The Review of Financial Studies/v22n32009
with high levels of unused commitments.Banks with unused commitments seem to conserve on cash by funding themselves with transactions deposits.
Figure1paints a similar picture.We scatter bank stock-return volatility (with one time-averaged observation per bank)against unused commitments.
For banks with below-median levels of transactions deposits,risk increases with unused commitments(Panel A).The slope coef?cient in this simple regression equals0.28(t=5.55),meaning that moving from the25th to the75th percentile of the distribution of unused commitments comes with an increase in volatility of about10%.Banks with high levels of transactions deposits face no increase in risk with asset-side liquidity exposure(Panel B).If anything,risk falls slightly with commitments for these banks.
In the empirical analysis next,we show that the simple results in Table1 and Figure1hold up in multivariate models,even after controlling for bank size(which is correlated with unused commitments),as well as measures of market and credit risk exposure.The result is robust to alternative measures of stock-return volatility,to different statistical modeling approaches,as well as to variations in the set of control variables included in the model.
We then extend the benchmark results by testing how the deposit-lending diversi?cation synergy varies with market conditions.We show that the hedg-ing synergy is stronger when market-wide liquidity supply is low,which we proxy with the Commercial Paper–Treasury Bill spread(wide spreads indi-cate low liquidity).The hedge strengthens because greater takedown demand from borrowers occurs just as new funds?ow into bank transactions deposit accounts(Gatev and Strahan,2006).In?ows and out?ows of liquidity offset, thus mitigating the risk.Bank ex ante exposure to liquidity shocks,however, does not increase during these short-term episodes.We can,therefore,rule out reverse causality—the possibility that safer banks choose higher exposures to both asset-side and liability-side liquidity risk.
Our results show that transactions deposits play a critically important role in allowing banks to manage their liquidity risk,especially during periods of market pullbacks.The?ndings strengthen the KRS theoretical argument,and help explain the robust positive correlation across banks between transactions deposits and loan commitments.
1.Background and Previous Research
The growth of the commercial paper market and subsequently of the junk bond market in the1980s and1990s has reduced banks’role as credit suppliers for large corporations(Mishkin and Strahan,1998).Other innovations,such as mortgage and credit card securitization,have also expanded the role of securities markets for households.This evolution toward the securities markets, however,has not rendered banks irrelevant(see Boyd and Gertler,1994).They remain important to large?rms as providers of backup https://www.doczj.com/doc/9314886050.html,mercial 998
Managing Bank Liquidity Risk
Figure1a
Stock Return V olatility for Low Transactions Deposit Banks,1990-2002
Figure1b
Stock Return V olatility for High Transactions Deposit Banks,1990-2002
999
The Review of Financial Studies/v22n32009
paper issuers regularly support their program with backup lines from banks.4 In addition,while most?rms do not issue commercial paper,almost all secure a line of credit from a bank.Su?(2006)?nds that at least75%of Compustat?rms disclose a bank line of credit and that these lines relax liquidity constraints. Households also receive liquidity from banks through credit cards and home equity lines.Banks,of course,continue to provide liquidity through their role as issuers of transactions deposits.
Why do banks provide liquidity to both borrowers and depositors?KRS present a model in which a risk-management motive explains the combination of transactions deposits and loan commitments.They argue that as long as liquidity demand from depositors and borrowers is not too correlated,an inter-mediary will reduce its cash buffer by serving both customers.Holding cash raises costs for both agency and tax reasons(Myers and Rajan,1998).Thus, their model yields a diversi?cation synergy between transactions deposits and unused loan commitments.
This synergy helps explain one of the basic features of banking.KRS show that banks offering more transactions deposits tend also to make more loan commitments.In Gatev and Strahan(2006),we suggest a stronger hypothesis, supported by our?ndings that the correlation is not only low but is often negative.Transactions deposits can be viewed as a natural hedge because?ows into these accounts offset the systematic liquidity risk exposure stemming from issuing loan commitments and lines of credit.
Our previous work extends KRS by considering the possibility that liquid-ity production could expose banks to systematic liquidity risk.A bank with many open credit lines may face a problem if systematic increases in liquid-ity demand occur periodically.5Gatev and Strahan(2006)show that funding to banks increases when market liquidity declines,meaning that liquidity de-mands become negatively correlated in tight markets.Why do banks enjoy funding in?ows when liquidity dries up?First,the banking system has explicit guarantees of its liabilities.6Second,banks have access to emergency liquidity 4Banks began offering liquidity insurance for large?rms early in the development of the commercial paper market. In1970,Penn Central Transportation Company?led for bankruptcy with more than$80million in commercial paper outstanding.As a result of their default,investors lost con?dence in other issuers,making it dif?cult for some of these?rms to re?nance their paper as it matured.The Federal Reserve responded to the Penn Central crisis by lending aggressively to banks through the discount window and encouraging them,in turn,to provide liquidity to their large borrowers.In response to this dif?culty,commercial paper issuers began purchasing backup lines of credit from banks to insure against future funding disruptions(Kane,1974;Calomiris,1994;and Calomiris,1989).
5For example,during the?rst weeks of October1998,following the coordinated restructuring of the hedge fund Long-Term Capital Management,spreads between safe treasury securities and risky commercial paper rose dramatically.Many large?rms were consequently unable to roll over their commercial paper as it came due, leading to a sharp reduction in the amount of commercial paper outstanding and a corresponding increase in takedowns on preexisting lines of credit(Saidenberg and Strahan,1999).As a result of this market pullback, banks faced a spike in demand for cash as many of their largest customers drew funds from preexisting backup lines of credit.
6Deposit insurance limits have recently been expanded for the?rst time since1980.In addition,some small banks have begun to avoid binding limits on deposit insurance by splitting very large deposits across multiple 1000
Managing Bank Liquidity Risk
support from the central bank.Third,large banks such as Continental Illinois have been supported in the face of?nancial distress(O’Hara and Shaw,1990). Thus,funding in?ows occur because banks are rationally viewed as a safe haven for funds.Consistent with this notion,Pennacchi(2006)?nds that during the years before the introduction of federal deposit insurance,bank funding supply did not increase when spreads tightened.
In a case study,Gatev,Schuermann,and Strahan(2006)focus on the behavior of deposit?ows across banks during the1998crisis.During the three months leading up to the crisis,bank stock prices were buffetted by news of the Russian default,followed by the demise of LTCM in late September,and?nally by the drying up of the commercial paper market in the?rst weeks of October.7 Figure2shows the aggregate effects of the1998crisis on stock-return volatility. We plot bank stock volatility and volatility of the S&P500,measured as the conditional volatility from an EGARCH(1,1)model.The?gure highlights the difference between bank risk exposures in different business conditions.During the crisis,bank stock prices fell more than the stock prices of non-?nancial?rms (Panel A),and return volatility became higher than overall general market volatility(in contrast to the“normal”precrisis period—Panel B).8
To understand how banks weathered the1998storm,in Gatev,Schuermann, and Strahan(2006)we explore the cross-sectional patterns in deposit?ows. We?nd?rst that investors moved funds from markets into banks;second,that banks with higher levels of transactions deposits before the crisis had the largest ?ows of new money during the crisis;and third,that all of the?ows of new money were concentrated in bank demand deposits.So,banks structured to bear increased demands for liquidity from borrowers(i.e.,banks with transactions deposits)could meet those demands easily(because money?owed into those accounts).Thus,government safety nets can explain why banks generally receive funding during crises,but the evidence from Gatev,Schuermann,and Strahan(2006)and Kashyap,Rajan,and Stein(2002)suggests that the structure of banks matters too.
Before the introduction of government safety nets,transactions deposits could sometimes expose banks to liquidity risk when consumers together re-moved deposits,either to increase consumption or because they had lost con-?dence in the banking system.This bank-run problem has traditionally been institutions.For a broad discussion of deposit insurance and policy rami?cations,see Kroszner and Strahan (2005).
7For policy discussion on LTCM,see Edwards(1999).For a discussion of bank exposure to hedge funds,see Kho,Lee,and Stulz(2000)and Fur?ne(2006).
8Chava and Purnanandam(2006)provide evidence that the CP market ceased to function at the beginning of October by comparing abnormal returns for?rms with and without access to this market.They show?rst that stock prices of CP issuers fell much less than other?rms when bank?nancial condition deteriorated during September1998(while markets continued to function).During the?rst two weeks of October,however,the stock prices of all?rms,regardless of their ability to access the CP market,fell equally.Thus,all?rms became bank dependent—even CP issuers—during those weeks because CP markets ceased to function and even large corporations relied on banks for liquidity.
1001
The Review of Financial Studies/v22n32009
Figure2a
Bank Stock-Price Index.
Figure2b
Bank Conditional Stock-Return V olatility.
viewed as the primary source of liquidity risk and creates a public policy ra-tionale for FDIC insurance as well as for reserve requirements for demand deposits(Diamond and Dybvig,1983).In crisis,investors now run to banks, not away from them(at least they do in the US),and banks funded with 1002
Managing Bank Liquidity Risk
transactions accounts receive the in?ow.Thus,rather than open banks to liq-uidity risk,transactions deposits today help banks hedge that risk,which now stems more from the lending side.We now turn to our direct tests of this idea.
2.Empirical Methods
2.1Sample
We start by identifying the100largest publicly traded domestic banks(based on market capitalization)at the beginning of each year from1990to2002.9 We focus on large banks for several reasons.First,large-bank stocks trade frequently,so daily stock returns are available and reliable.For smaller banks, lack of active trading every day poses problems in estimating the conditional volatility model(see the following text).Second,large banks hold the vast majority of the banking system’s assets.For example,about60%of bank assets were held by the100largest banks during our sample.Third,large banks are more actively engaged in commitment lending than small banks.
After identifying the top100,we drop all banks that engaged in a merger or acquisition(M&A)during the year of the deal itself(but not in other years).
For example,15of the large banks were involved in M&A in1990,leaving 85in our sample.10Next,we construct the weekly conditional volatility for stock returns for these banks(details follow).For our purpose,a week begins and ends on Wednesday,as this is the weekday with the fewest public holidays which might close the markets.We repeat this sampling procedure for every year between1990and2002.Note that it is important to drop both acquirers and targets around M&A announcements because speculation about such deals generates a large amount of stock-price volatility having nothing to do with the basic risks banks face(market,credit,liquidity,etc.).So,for example,we drop both JP Morgan and Chase during the year of their merger,but these two banks are included as two separate banks in the years prior to the merger,and as a single bank in the year after the merger.As a result,the maximum number of banks in any year is98(2002),and the minimum is68(1996).The sample-generating procedure leaves us with170banks,and almost50,000bank-week observations overall.
2.2Conditional mean volatility
We construct our conditional stock-return volatilities by?rst?tting daily returns to a GARCH(1,1)model for each bank-year,as follows.Let r i,t+1be the return 9We begin in1990because that is the?rst year when unused retail loan commitments are available,which we shall control for as a robustness check.Prior to1990,only total commitments are reported.
10Traded equity re?ects the pro?ts and risk of the entire bank holding company,so our use of the term“bank”refers to the whole holding company.In collecting characteristics,we use data for the lead bank within the holding company.
1003
The Review of Financial Studies /v 22n 32009
for bank i from t to t +1.Then,bank returns may be characterized by
r i ,t +1=μi +εi ,t +1σ2i ,t +1=ω+αε2i ,t +βσ2i ,t ,ω,α,β>0,(1)
where μi is a nonzero drift.For estimation,the innovation εi ,t +1is assumed to be conditionally normally distributed with time-varying GARCH(1,1)volatility as speci?ed in the second equation of (1).
Based on the coef?cients estimated in the GARCH model,we construct daily conditional volatility,and then we aggregate up the daily volatilities to weekly frequency.11Table 1reports the simple average level of these conditional volatilities,normalized to represent the annualized standard deviation of the stock return.We split the data into nine cells,based on the joint distribution of the level of the ratio of transactions deposits to total deposits and the level of unused loan commitments to total commitments plus total loans (our measure of asset-side liquidity exposure).This admittedly simple table illustrates the main hedging idea of our research:banks with high levels of transactions deposits have similar levels of risk regardless of their loan-liquidity exposure (column 3).In contrast,risk increases with loan liquidity risk (unused loan commitments)for banks at the low end of the transactions deposits distributions (column 1).Increasing unused loan commitments comes with an increase in risk of nearly 30%for these banks (from 0.28to 0.36).The same patterns show up in the medians within each cell (not reported).The deposit base,therefore,seems to act as a natural hedge against liquidity exposure from loan commitments.12
2.3Regression speci?cation
To demonstrate these results more systematically,we estimate an empirical model of the conditional volatility as a function of bank liquidity exposure,deposits,and other market-level and bank-level characteristics,as follows:
Log(σit )=β0+β1LoanCommitments i ,t ?1+β2DepositBase i ,t ?1
+β3(LoanCommitments i ,t ?1×DepositBase i ,t ?1)
+Bank-Level and Market-Level Control Variables +u i ,t ,(2)
where σit is the conditional stock-return volatility for bank i at time t from the ?rst-stage GARCH (1,1)model in Equation (1);LoanCommitments i ,t ?1is the ratio of unused loan commitments to commitments plus loans (measured in the previous quarter);and DepositBase i ,t ?1is the ratio of transactions deposits 11To aggregate volatilities from daily to lower frequencies,say weekly,we take the average over that week and scale by √5,allowing for the possibility of missing days due to,for instance,holidays.
12
Notice that the hedge seems to go both ways.That is,for banks with low levels of loan commitments (?rst row),higher transactions deposits come with higher risk (from 0.28to 0.32).On the other hand,for banks with high levels of commitments,higher transactions deposits come with lower risk (from 0.36to 0.31).1004
Managing Bank Liquidity Risk
to total deposits(again,from the prior quarter).If deposits help banks hedge asset-side liquidity risk,as suggested by Table1,thenβ3<0.13
2.4Variable de?nitions and data sources
As time-varying controls in the regression,we include the contemporaneous stock-return volatility for the S&P500as a whole,estimated with a GARCH (1,1)model in the same fashion as the bank-speci?c volatilities;the3-month treasury-bill rate;and the spread between the high-grade3-month commercial paper rate and the3-month treasury-bill rate.To be consistent with the condi-tional volatilities,the interest rate data are taken for the Wednesday of a given week.
For bank-level controls,we include the following:the log of assets(size control),the ratio of cash plus securities to total assets(liquid asset measure),the ratio of capital to assets(capital adequacy measure),and the ratio of Fed funds purchased to assets(a measure of access to the Fed funds market,a liquidity pool).Controlling for size is particularly important given its correlation with loan commitments(recall Table1).We also consider a second loan commitment variable as a robustness test that removes retail commitments(e.g.,credit card lines)from both the numerator and the denominator of LoanCommitments i,t?1. Unused retail commitments may be less likely to expose banks to risk compared to business-loan commitments,where takedown demand is both less predictable and more likely to have a systematic component such as the one observed in the fall of1998.
Data on unused commitments,transactions deposits,as well as the other bank characteristics,come from the most recent quarter of the Reports of Income and Condition(“call reports”)prior to the time at which we measure the stock-return variability.For example,all weeks in the second quarter of 1990are matched to call report data for the?rst quarter of1990.Stock return data come from the Center for Research in Securities Prices(CRSP),inclusive of dividend payments and adjusted to account for stock splits.Data on interest rates are available daily from the Federal Reserve Board of Governors.Note that since the regulatory data is available only at quarterly intervals,the bank characteristics remain unchanged for all weeks within a given quarter.
2.5Summary statistics
Table2reports the summary statistics for all of the variables reported in the regressions.Bank-stock volatility averages about30%per year,well above the 16%for the S&P500index.We would expect,of course,index volatility to be lower due to portfolio effects.In our sample,the mean loan commitment ratio is about0.32and the mean level of transactions deposits to total deposits equals about0.26.As in KRS,banks with high levels of loan commitment also 13We have also estimated regressions like(1)using the level of volatility as well as the square of volatility(variance).
These results are similar in terms of statistical and economic signi?cance to those reported here.
1005
The Review of Financial Studies/v22n32009
Table2
Summary statistics for variables included in regression models
Mean Standard deviation
(1)(2)
Dependent variable
Stock-return volatility0.300.13
(annualized return standard deviation)
Commitments and deposits
Commitments/(commitments+loans)0.320.15
(Commitments?retail commitments)/0.240.13
(commitments?retail commitments+loans)
Transactions deposits/total deposits0.260.11
Controls for market conditions
V olatility of S&P5000.160.06
(annualized return standard deviation)
Commercial paper–treasury bill yield spread(%pts)0.400.21
Yield on3-month treasury bill(%pts) 4.52 1.55
Controls for bank characteristics
Assets(billions of$s)34.4769.89
(Cash+securities)/assets0.320.12
Fed funds purchased/assets0.090.06
Equity/assets0.080.04
Summary statistics for all of the variables used in regressions next.Stock-return volatility is equal to the annualized return standard deviation.Data on unused commitments and transactions deposits,as well as the other bank characteristics,come from the most recent quarter of the Reports of Income and Condition prior to the time at which we measure the stock-return variability.
tend to focus on transactions deposits.For instance,46%of the observations in Table1line up on the diagonal(relative to33%if the data were evenly distributed).Most of our control variables are ratios that lie between zero and one(interest rates are in percents).In the case of assets,we take the log of the variable.Hence,there is no concern about outliers driving the results.
3.Results
3.1Main?ndings
Table3reports the benchmark set of?ndings.We report the regressions from Equation(2)considered previously using all of our data.The table reports four speci?cations,two based on the total commitments ratio,and two that exclude retail commitments.For each of these,we report a simple model with just log of assets as a bank control,and a more complex model that adds the liquid assets ratio(cash+securities),the ratio of Fed funds purchased to assets,and the capital-asset ratio.Note that we have a very large sample(almost50,000 bank-week observations),but we cluster the data by bank to avoid assuming independence over time for each bank.This clustering raises the standard errors by a factor of about10relative to the OLS standard errors.
The results in Table3support the idea that loan commitment risk(liquidity risk)can be hedged with transactions deposits.The coef?cient on the interaction term(β3)is negative and highly statistically signi?cant,ranging from?1.60to ?1.75across the four speci?cations.For a bank with transactions deposits at 1006
Managing Bank Liquidity Risk
Table3
Regressions of bank stock-return volatility on liquidity exposure and transactions deposits ratio
Dependent variable:
Log of weekly bank stock-return volatility
predicted volatility from GARCH(1,1)
(1)(2)(3)(4) Unused commitments and deposits
Commitments/(commitments+loans)0.7880.82--
(7.19)??(6.94)??--
(Commitments?retail commitments)/--0.951 1.005 (commitments?retail commitments+loans)--(3.55)??(3.63)??Transactions deposits/total deposits0.7010.7720.4850.497
(4.66)??(3.37)??(1.90)(1.80)
Commitments/(commitments+loans)??1.604?1.749--
Transactions deposits/total deposits(4.46)??(3.52)??--
(commitments?RC)/(commitments?RC+loans)?--?1.702?1.709 transactions deposits/total deposits--(2.19)?(2.07)?Controls for market conditions
Log of volatility of S&P5000.4940.4990.4640.46
(21.43)??(20.82)??(16.80)??(16.43)??
Paper-bill spread0.0850.0840.1030.103
(3.79)??(3.80)??(4.53)??(4.51)??
Yield on3-month treasury bill0.0310.030.0310.03
(6.96)??(6.44)??(6.76)??(6.44)??Controls for bank characteristics
Log of assets?0.001?0.003?0.008?0.012
(0.06)(0.23)(0.57)(0.85)
(Cash+securities)/assets-?0.03-?0.108
-(0.25)-(0.83) Fed funds purchased/assets-0.067-?0.098
-(0.26)-(0.37) Equity/assets-?0.174-0.095
-(0.41)-(0.22) Observations49,52749,52749,52749,527 Number of independent clusters(banks)170170170170
R228.77%28.80%27.86%28.02% Regressions from Equation(2)using all data.The dependent variable equals the GARCH(1,1)estimate of conditional stock-return volatility.There are four speci?cations,two based on the total commitments ratio, and two that exclude retail commitments.One of each speci?cation is a simple model with just log of assets as a bank control,and the other is a more complex model that adds the liquid assets ratio(cash+securities), the ratio of Fed funds purchased to assets,and the capital-asset ratio.Parentheses report absolute value of t-statistics,which are based on robust standard errors clustered by bank,with?denoting signi?cance at5%;??signi?cance at1%.All regressions include an intercept.
the10th percentile of its distribution(0.12),the coef?cients suggest that loan commitments expose banks to risk.For such a bank,a one-standard-deviation increase in the loan commitment ratio would come with an increase in stock-return volatility of about2.5%points(relative to a sample standard deviation of about13%points).For a bank with transactions deposits at the90th percentile (0.38),however,the same increase in loan commitments comes with an increase in stock-return volatility of just0.6%points.
3.2Robustness tests
Tables4–7report four sets of robustness tests.We?rst replace the GARCH (1,1)estimate of conditional stock-return volatility with realized volatility,
1007
The Review of Financial Studies/v22n32009
Table4
Regressions of bank stock-return volatility on liquidity exposure and transactions deposits ratio
Dependent variable:
Log of weekly bank stock-return volatility
Realized volatility
(1)(2)(3)(4) Unused commitments and deposits
Commitments/(commitments+loans)0.8030.859--
(7.77)??(7.05)??--(Commitments?retail commitments)/--0.9040.991 (commitments?retail commitments+loans)--(3.33)??(3.31)??Transactions deposits/total deposits0.7540.8990.5450.621
(6.04)??(3.53)??(2.37)?(2.03)?Commitments/(commitments+loans)??1.541?1.811--Transactions deposits/total deposits(5.00)??(3.36)??--(Commitments?RC)/(commitments?RC+loans)?--?1.633?1.782 Transactions deposits/total deposits--(2.20)?(1.98)?Controls for Market Conditions
Log of volatility of S&P5000.5110.5220.4810.483
(22.01)??(20.62)??(17.35)??(15.84)??Paper-bill spread0.2340.2310.2530.25
(8.59)??(8.54)??(9.50)??(9.38)??Yield on3-month treasury bill0.0140.0130.0140.013
(3.17)??(2.61)??(2.98)??(2.64)??Controls for bank characteristics
Log of assets0.001?0.003?0.005?0.009
(0.03)(0.24)(0.32)(0.60) (Cash+securities)/assets-?0.039-?0.1
-(0.30)-(0.70) Fed funds purchased/assets-?0.016-?0.187
-(0.06)-(0.66) Equity/assets-?0.379-?0.113
-(0.78)-(0.23) Observations49,52749,52749,52749,527 Number of independent clusters(banks)170170170170
R212.37%12.41%11.80%11.87% Regressions from Equation(2)using all data.The dependent variable equals the log of average-squared daily returns over1week.There are four speci?cations,two based on the total commitments ratio,and two that exclude retail commitments.One of each two speci?cations is a simple model with just log of assets as a bank control, and the other is a more complex model that adds the liquid assets ratio(cash+securities),the ratio of Fed funds purchased to assets,and the capital-asset ratio.Parentheses report absolute value of t-statistics,which are based on robust standard errors clustered by bank,with?denoting signi?cance at5%;??signi?cance at1%.All regressions include an intercept.
equal to the weekly average of squared returns(Table4).Next,we remove three systematic risk factors from returns before constructing the weekly average of squared returns(Table5).Table6focuses on the statistical assumptions by separating time series from cross-sectional https://www.doczj.com/doc/9314886050.html,st,Table7focuses on the potential for omitted variables bias.
Table4shows that the precise modeling technique for return volatility has little impact on the coef?cients of interest.The effects of transactions deposits and unused commitments,and,most important,the interaction or hedging term, are similar using both realized volatility and the GARCH volatility.We do?nd, however,that the effect of market conditions(particularly the paper-bill spread) more than doubles and becomes much more statistically powerful.This makes
1008
Managing Bank Liquidity Risk
Table5
Regressions of bank stock-return volatility on liquidity exposure and transactions deposits ratio
Dependent variable:
Log of weekly bank stock-return volatility
realized residual volatility
(1)(2)(3)(4) Unused commitments and deposits
Commitments/(commitments+loans)0.6410.771--
(6.55)??(6.84)??--
(Commitments?retail commitments)/--0.829 1.012 (commitments?retail commitments+loans)--(3.27)??(3.59)??Transactions deposits/total deposits0.6550.9920.5080.766
(5.88)??(5.08)??(3.33)??(3.44)??
Commitments/(commitments+loans)??1.217?1.897--
Transactions deposits/total deposits(4.29)??(4.53)??--
(Commitments-RC)/(commitments-RC+loans)?--?1.424?2.033 Transactions deposits/total deposits--(2.20)?(1.98)??Controls for market conditions
Log of volatility of S&P5000.3680.3920.3410.359
(16.25)??(16.53)??(13.52)??(13.52)??
Paper-bill spread0.2310.2270.2470.242
(9.33)??(9.13)??(10.22)??(10.00)??
Yield on3-month treasury bill0.040.0360.040.036
(8.33)??(7.00)??(8.14)??(6.93)??Controls for bank characteristics
Log of assets?0.041?0.046?0.048?0.057
(3.71)??(3.79)??(3.92)??(4.09)??
(Cash+securities)/assets-?0.042-?0.127
-(0.31)-(0.90) Fed funds purchased/assets-0.086-?0.062
-(0.32)-(0.23) Equity/assets-?0.907-?0.682
-(2.38)?-(1.62)?Observations49,52749,52749,52749,527 Number of independent clusters(banks)170170170170
R29.05%9.27%8.93%9.09% Regressions from Equation(2)using all data.The dependent variable equals the log of the average-squared residual return from the factor model from Equation(3),removing market,credit,and interest rate factors.There are four speci?cations,two based on the total commitments ratio,and two that exclude retail commitments. One of each speci?cation is a simple model with just log of assets as a bank control,and the other is a more complex model that adds the liquid assets ratio(cash+securities),the ratio of Fed funds purchased to assets, and the capital-asset ratio.Parentheses report absolute value of t-statistics,which are based on robust standard errors clustered by bank,with?denoting signi?cance at5%;??signi?cance at1%.All regressions include an intercept.
sense because the realized volatilities will adjust immediately to changing market conditions,while the GARCH estimates will only do so gradually.The goodness of?t of the model also declines sharply,presumably because,in contrast to the GARCH estimates,the realized volatilities are not smoothed over time.
In Table5,we model the realized residual volatility,where we?rst remove three systematic factors from the returns,as follows:
r i,t=αi+β1,i r m,t+β2,i (Baa-Aaa)t+β3,i (3-Month T-Bill)t+εi,t.(3)
1009
The Review of Financial Studies/v22n32009
Table6
Regressions of bank stock-return volatility on liquidity exposure and transactions deposits
ratio—between versus within estimator
Dependent variable:
Log of weekly bank stock-return volatility
Between estimator Within estimator
(1)(2)(3)(4)
Unused commitments and deposits
Commitments/(commitments+loans)0.7620.8050.2020.082
(4.58)??(3.94)??(1.41)(0.60)
Transactions deposits/total deposits0.815 1.0130.2810.237
(3.55)??(2.98)??(0.98)(0.85)
Commitments/(commitments+loans)??1.384?1.739?1.668?1.492
Transactions deposits/total deposits(2.00)?(2.08)?(2.64)??(2.53)?
Controls for market conditions
Log of volatility of S&P5000.5490.5690.4470.452
(6.52)??(6.47)??(24.61)??(25.01)??
Paper-bill spread?1.84?1.7830.1630.162
(4.48)??(4.22)??(8.63)??(8.60)??
Yield on3-month treasury bill0.2040.1950.0240.024
(5.39)??(4.85)??(5.11)??(5.15)??
Controls for bank characteristics
Log of assets?0.014?0.014?0.0140.011
(0.83)(0.72)(0.59)(0.41)
(Cash+securities)/assets-0.057-0.225
-(0.38)-(1.59) Fed funds purchased/assets-?0.013-?0.458
-(0.04)-(1.93) Equity/assets-?0.527-?1.125
-(0.84)-(2.22)?Observations49,52749,52749,52749,527
Number of independent clusters(banks)170170170170
R239.81%40.15%29.73%30.28%
Regressions from Equation(2)using all data.There are four speci?cations:columns1and2report
the“between”estimator from regressions using bank-level time-series averages,and columns3
and4report the“within”estimator,which allows each bank to have its own intercept in the
regressions.One of each two speci?cations is a simple model with just log of assets as a bank
control,and the other is a more complex model that adds the liquid assets ratio(cash+securities),
the ratio of Fed funds purchased to assets,and the capital-asset ratio.Parentheses report absolute
value of t-statistics,which are based on robust standard errors clustered by bank,with?denoting
signi?cance at5%;??signi?cance at1%.All regressions include an intercept.
We estimate this?rst-stage factor model separately for each bank-year,and then compute the average squared residuals to build the realized residual volatility for each bank during each week in the year.In Equation(3),r m,t represents the return on the market portfolio,proxied by the return on the S&P500; (Baa-Aaa)t represents a default-risk or credit-risk factor,equal to the change in the yield on Baa-rated corporate bonds relative to Aaa-rated bonds;and (3-Month Treasury-Bill)t represents an interest rate risk factor, equal to the change in yield on short-term treasury bills.Data for the mar-ket return are from CRSP,and the interest rate series come from the Federal Reserve.
Because we have removed factors related to bank interest rate risk,market risk,and credit risk,the residual volatility is more likely to represent pure
1010
Managing Bank Liquidity Risk
Table7
Regressions of bank stock-return volatility on liquidity exposure and transactions deposits ratio—add controls for credit risk,market risk,and time dummies
Dependent variable:
Log of weekly bank stock-return volatility
(1)(2)(3)(4)
Unused commitments and deposits
Commitments/(commitments+loans)0.6100.5070.6000.501
(5.28)??(4.31)??(5.76)??(4.67)??
Transactions deposits/total deposits0.2790.2510.4270.418
(1.55)(1.51)(2.53)??(2.61)??
Commitments/(commitments+loans)*?1.067?0.884?0.921?0.800
Transactions deposits/total deposits(2.45)?(2.21)?(-2.38)?(2.18)?
Controls for market conditions
Log of volatility of S&P5000.5070.498--
(22.95)??(22.32)??--
Paper-bill spread0.1000.111--
(4.35)??(4.76)??--
Yield on3-month treasury bill0.0290.027--
(5.67)??(5.63)??--
Controls for bank characteristics
Log of assets?0.012?0.007?0.015?0.012
(0.96)(0.64)(1.21)(1.04)
(Cash+securities)/assets0.2460.3320.1250.213
(1.73)(2.36)?(0.96)(1.68)
Fed funds purchased/assets0.1410.1440.1630.166
(0.58)(0.60)(0.72)(0.74)
Equity/assets0.5840.6890.5520.594
(2.37)?(3.04)??(2.30)*(2.46)?
Controls for credit and market risks
C&I loans/assets0.3690.4170.2300.287
(2.29)?(2.66)??(1.33)(1.71)
Commercial real estate loans/assets?0.337?0.179?0.454?0.321
(1.31)(0.71)(1.70)(1.24)
Nonperforming loans/assets10.8327.8598.890 6.476
(5.66)??(4.37)??(4.33)??(3.63)??
Trading assets/assets0.3450.4550.3200.415
(2.00)?(2.79)??(2.08)*(2.81)??
AAA-rated indicator?0.142?0.147?0.259?0.250
(2.58)??(2.78)??(4.42)??(4.44)??
AA-rated indicator?0.075?0.075?0.103?0.098
(1.74)(1.78)(2.29)?(2.27)?
A-rated indicator?0.095?0.095?0.096?0.094
(3.70)??(3.85)??(3.72)??(3.80)??
Unrated indicator?0.032?0.031?0.047?0.043
(1.05)(1.05)(1.56)(1.48)
Loan loss provisions/assets?22.923?20.173
-(5.14)??-(4.92)??Net charge-offs/assets-14.411-15.645
-(1.98)?-(2.30)?Includes weekly indicators?No No Yes Yes
Observations49,52749,52749,52749,527
Number of independent clusters(banks)170170170170
R237.47%38.52%50.46%51.31%
Regressions from Equation(2)using all data and including control variables for market conditions, bank characteristics,and credit and market risks.There are four speci?cations:those in columns1and 2use time-varying control variables(S&P500volatility,the level of interest rates,and the paper-bill spread),while those in columns3and4use a full set of time-indicator variables(i.e.,a separate intercept for each week).The speci?cations in columns2and4include the ratio of net charge-offs to assets,and the ratio of loan loss provisions to assets as additional control variables.Parentheses report absolute value of t-statistics,which are based on robust standard errors clustered by bank,with ?denoting signi?cance at5%;??signi?cance at1%.All regressions include an intercept.
1011
The Review of Financial Studies/v22n32009
liquidity risks,rather than other risks such as default risk or some broader macrofactor(absorbed by the market return in the factor model).And,as shown in Table5,the coef?cients on deposits,unused commitments,and their interaction—the hedging term—remain quite similar to those reported earlier. In fact,if we model the systematic or explained variation from the factor model, we?nd much smaller coef?cients across all three of these variables,and these coef?cients are not statistically signi?cant in three of the four speci?cations (not reported).14
Table6decomposes the results into their time-series and cross-section com-ponents.In columns1and2we report the“between”estimator—that is,the regressions are run using bank-level time-series averages.These coef?cients are driven by pure cross-sectional variation.We?nd that the hedging coef?-cient remains similar to the results in Table3in terms of magnitudes.Levels of statistical signi?cance fall slightly because the between estimator has just 170separate banks.We report the“within”estimator in columns3and4. The within estimator,also sometimes called the?xed-effects estimator,allows each bank to have its own intercept in the regressions.Because the bank-level intercept strips out all cross-sectional variation before estimating the slope coef?cients,the results are driven solely by within-bank variation over time. Again,we continue to?nd strong evidence of the transactions deposits-loan commitment diversi?cation synergy.The results in Table6suggest that the hedging effects of combining loan commitments with transactions deposits are present with approximately equal magnitude in both the cross-sectional and time-series dimensions.
The results in Table5,based on residual volatility,suggest that standard risk factors cannot explain our?ndings.Another way to rule out these alternative explanations is to control for bank exposure to them directly in modeling overall volatility,which we report in Table7.Credit and market risk exposures are, of course,two of the primary risks faced by banks(and,not coincidentally, these are the risks that the Basel Capital Accord focuses on).We,therefore, include the ratio of commercial and industrial loans to assets and the ratio of commercial real estate loans to assets as ex ante proxies for bank credit risk exposure.15We also include the ratio of nonperforming loans to assets as a measure of ex post risk,and,in some speci?cations,we also add the ratio of net charge-offs to assets,and the ratio of loan loss provisions to assets.16To control for cross-bank variation in market risk exposure,we include the ratio of trading account assets to total assets.And,as a?nal catch-all control,we include three 14Consistent with Demsetz and Strahan(1997),Table5also reports a signi?cant negative effect of log of bank assets,re?ecting size-related diversi?cation.
15In general,business loans are the part of the bank loan portfolio with the most credit risk(Demsetz and Strahan, 1997;Stiroh,2006).Loan losses tend to be higher for credit card loans,but these losses are much more predictable than losses associated with business lending.
16Nonperforming loans are de?ned as loans more than30days past due plus loans no longer accruing interest but not yet charged off the balance sheet.
1012
Managing Bank Liquidity Risk
indicators based on the bank’s credit rating,along with an indicator equal to one for unrated banks.
The results in Table7suggest that while our measures for both credit and market risk are correlated with stock-return volatility in the expected ways, the liquidity-risk-offsetting in?uence of deposits is robust to these other risks (columns1and2).The coef?cients on C&I loans and trading account assets enter with positive signs(signi?cant in two of the four models).Even more striking,both nonperforming loans and loan loss provisions have substantial power to explain stock-return volatility,as do the ratings indicators.But,the key result remains:the coef?cient on the interaction term remains negative and signi?cant,and,more generally,the results are similar to those reported before.17
The last two columns of Table7repeat these regressions,but now we replace the time-varying control variables(S&P500volatility,the level of interest rates,and the spread)with a full set of time-indicator variables(i.e.,a separate intercept for each week).The market-level variables are not identi?ed in this model because the time effects sweep out all common shocks to bank-stock volatility.The advantage of this approach is that time effects remove any missing common factors that may move bank-stock volatility around,such as changes in regulations,macroeconomic shocks,changes in monetary policy,and so on.18The effects of interest,again,remain robust in these speci?cations.19 3.3Interpreting our results
We do not explicitly model the trade-offs associated with a bank’s choice of balance sheet liquidity(cash and securities)versus transactions deposits.20Our results(and the KRS model)suggest,however,that banks?ush with transac-tions deposits will supply liquidity to borrowers via loan commitments,and that banks with strong demand for loan commitments will optimally fund them-selves with transactions deposits.Why,then,do we ever observe banks exposed to unused commitments having low levels of transactions deposits(and vice versa)?KRS take bank deposits as exogenous,arguing implicitly that variation depends on supply factors like demographics or banking market concentration, rather than as a hedge against asset-side liquidity risk.Some banks in concen-trated markets,for example,may enjoy a large supply of low-cost transactions 17To the extent that rating agencies account for liquidity risk,adding the credit rating may be absorbing some of the effects that we are trying to measure,thus biasing the coef?cients of interest toward zero.And,in fact,the coef?cients of interest fall slightly relative to the results without these variables.
18For example,passage of the Financial Modernization Act in1999may have increased bank stock-return volatility temporarily by increasing speculation about merger activity among?nancial companies.
19We have also estimated our model for above-and below-median-sized banks.For both samples,we?nd similar results for the coef?cients on the loan commitments and transactions deposits variables and their interaction, both in terms of magnitudes and levels of signi?cance.
20Banks presumably choose all of these jointly,along with other capital structure policies such as leverage and debt maturity.A full analysis of the substitutability across these?nancial policies would require a set of identifying instruments,an exercise well beyond the scope of this paper.We focus,instead,on the covariation between these endogenous?nancial variables and our outcome measure,the bank’s stock-return volatility.
1013
The Review of Financial Studies/v22n32009
deposits but have relatively little demand for loan commitments.These banks are represented by the upper right-hand corner of Table1.The average size of banks in this cell is the smallest in our sample(just over$7billion in assets),and small banks typically have a bank-dependent,small-business clientele.Small ?rms generally receive less liquidity insurance from banks relative to larger ?rms.21Other banks located where transactions deposits supply is low may have borrowers with an unusually high demand for liquidity(represented in the lower left corner of Table1).
Given this discussion,we must consider the possibility of reverse causality. The concern is that risk management drives bank choices of transactions de-posits and loan commitments,rather than the other way around.Perhaps safe banks,for example,choose to expose themselves to greater liquidity risk on both sides of the balance sheet.We have already seen that the hedging effects are robust to varying the set of observable risk measures.Another approach, which we turn to next,is to test how results vary with market conditions.During “normal”times,the diversi?cation synergy comes from reducing the effects of idiosyncratic liquidity demands.When market liquidity becomes scarce,how-ever,borrowers systematically draw funds from preexisting loan commitments at the same time that funding?ows into bank transactions deposits accounts. Since liquidity demands become negatively correlated in tight markets,the hedging term ought to become larger then.Exposure to liquidity shocks(the potentially endogenous choice by the bank)does not vary at high frequency.In fact,our measure is taken from the preceding quarter and has no within-quarter variation at all,so an increase in the hedging term in tight markets is unlikely to re?ect reverse causality.
3.4Varying market conditions
To test this notion,we?rst reestimate Figure1,now scattering stock-return volatility against unused loan commitments during the commercial paper crisis of1998(Figure3).In the?rst weeks of October,commercial paper spreads rose to over100basis points(in the99th percentile of the spread distribution), liquidity in the market dried up,and issuers were forced to draw funds from their back-up lines.The two panels again show an increasing relationship between risk and unused commitments for banks with low levels of transactions deposits(Panel A),but no relationship for banks with high levels of transactions deposits(Panel B).Most important,the slope of the relationship for the low-transactions deposits banks almost doubles from what we see unconditionally (rising from0.28in Figure1,Panel A,to0.46here).Comparing magnitudes, moving from the25th to the75th percentile of the unconditional distribution of unused commitments comes with a3%point increase in volatility(10%of the unconditional mean);during the1998crisis,the same relative increase in 21There is a strong positive correlation between?rm size and the importance of lines of credit,both among small, private?rms as well as among large,Compustat?rms(see Bitler,Robb,and Wolken,2001;Su?,2006).
1014
英雄联盟LOL全攻略英雄出装介绍 打法教学 超级菜鸟玩《英雄联盟》心得 本文由英雄联盟LOL QQ军团魔鬼(31 ***56) 独家撰写首发转载请注明作者与出处。 文章开篇之前，先讲讲我所认为的英雄联盟新手是什么样的。 Λ没有玩过美服的英雄联盟; 2、没玩过DOTA,魔兽争霸，魔兽世界?星际争霸等竞技类游戏； 3、曾经尝试过接触这些游戏但是因为无法理解游戏内容上不了手而不得不放弃； 4、懂基本的电脑基础和有其他类网络游戏经历。
如果你不属于新手，不属于上面几条内，请果断 绕过此文。 我就属于这样一个新手，一直以来，曾无数次的 尝试过玩星际，魔兽。对于我而言，最大的原因不 是语言障碍,而是对这类游戏背景、操作、玩法等内 容的态度和观念。我玩的网游基本上都是象传奇、 奇迹这种练级-装备-PK的RPG游戏。 市面上大多数网游也是这种模式。以前有朋友告诉我，玩匹和传奇一样的都是练级打怪买装备PK O每次按照传统网游那样去体验LoL兽，第一感觉是：好多角色，我该怎么选？这些角色怎么分类？什么样的是战士型的，什么样的是法师型的？而且名字都是外国人的名字，似乎天生对这种名字有一种莫名的抵触。好不容易选了一个角色，上去玩了几下，只是打小兵玩而已?于是顿生无趣，颓然放弃。 直到现在坚持玩了LOL之后，我才发现，我 一直在被这种想法禁锢着，如果想用玩传奇的想法来玩LOL的话，那绝对是大错特错。 如果想上手LOL,我觉得要从以下几点出发: 1：多学习。
现在网络上很多的LOL的新手教程或者游戏攻略，视频，心情等。都是前辈高手们总结出的很重要的经验,对新手有莫大的帮助。任何时候, 想要发挥的更好，基础的知识的一定不能漏掉的，比如每个英雄擅长的技能，该买什么样的装
四大会计师事务所（北京）比较（待遇，职业发展等） 我很多同学在四大会计师事务所，自己也在2家北京四大待过。其实总体差别都不太，除了德勤因为总部所在地在日本，而不是纽约，损失掉部分美国上市客户而保留咨询部门外。 1：人员规模：普华永道〉安永〉毕马威〉德勤 据说北京财富中心的普华永道光审计人员就已经2000人了，而且应届生的offer发得最多． ２：专业方面：普华永道专业最没有限制，和我面试的对外经济贸易大学学中文和别的学文秘专业的都录取为审计人员或者税务人员了，安永对专业限制最为严格，周边几乎都是清一色的会计，财务，金融，经济类专业，从安永的summer leadership program 也可以看出，邀请的多为会计知名的大学的会计或者财经类毕业生．安永的TSRS部门倒是给清华工科本科和硕士发了很多OFFER，毕马威和德勤对专业的限制也不多。 3：从客户方面： 普华永道：中国银行，中国石油，TOM网络，诺基亚，
爱立信，中国华能，中国铝业，国家电网，中国人寿 毕马威：渣打银行，花旗银行，汇丰银行，中国国际金融公司，德意志银行，西门子，宝马，中国电信，中国移动， 建设银行，等等。 安永：高盛银行，瑞士银行（UBS), 雷曼兄弟，中国工商银行，中国人保财险，浦东银行，深证发展银行中国海油，百度在线，思科，ABB,奔驰，上海电气，马鞍山钢铁集团，百盛集团，孟牛乳液，中国国际航空公司，中铁建，中国南车集团，国美电器，等等。 德勤：中国农业银行， 从工业审计领域：毕马威〉普华永道〉安永〉德勤从金融银行领域：安永〉毕马威〉普华永道〉 德勤 从IT领域：德勤〉普华永道〉安永〉毕马威 从零售领域：普华永道〉毕马威〉安永〉 德勤 从税务领域：德勤〉普华永道〉安永〉毕马威 从内部控制（重大错报风险评估领域）安永〉普华永道〉 毕马威〉德勤
《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
山东焦化集团有限公司 项目管理信息化初步设计方案 济南普华春天应用软件有限公司 中国 济南 二零一四年四月 文件编号：SHPOWER-DOC-201404-014
快速导览
目录 第一章背景综述 (7) 1.1.公司简介 (7) 1.2.项目管理系统的现状与发展趋势 (10) 1.3.集团级项目管理信息化的建设思路 (12) 第二章系统总体设计思路 (15) 2.1.系统总体目标 (15) 2.2.项目管理框架体系规划 (15) 2.3.业务处理思路 (16) 2.4.信息和数据收集、处理及流程控制 (16) 第三章系统应用设计方案 (18) 3.1.系统总体架构设计 (18) 3.2.系统技术架构设计 (19) 3.3.系统应用框架设计 (21) 3.3.1.项目与组织 (21) 3.3.2.集团总部应用方案 (23) 3.3.3.项目公司应用方案 (31) 第四章系统业务方案 (44) 4.1.多层级、多项目并行管理 (44) 4.1.1.统一的项目管理体系 (44) 4.1.2.统一的责任范围体系 (44) 4.1.3.统一的计划协调体系 (44) 4.1.4.统一的流程管理体系 (44) 4.1.5.统一的信息发布与交流体系 (45) 4.1.6.统一的知识管理体系 (45) 4.2.项目管理业务功能 (45) 4.2.1.项目与组织 (46) 4.2.1.1项目体系 (46) 4.2.1.2组织体系 (46) 4.2.1.3责任体系 (46) 4.2.2.战略规划 (47) 4.2.2.1规划修编 (47) 4.2.2.2潜在项目库 (48) 4.2.3.前期管理 (48) 4.2.3.1前期准备 (49)
6月中旬，TGA在北京GTV直播间举行。与前两年不同，英雄联盟项目，已经从一个腾讯游戏的“小弟弟”，成长为真正的大哥大了。无论是比赛奖金，观众支持，现场表演，无不展现出一款世界级竞技游戏的风范。最后是由国内最为知名的队伍WE，经过一系列的战斗，以全胜的姿态取得了冠军并抱走了20W的巨额奖金。可以说，WE在国内英雄联盟界的地位，已经不可动摇。 而7月底，由游戏风云在CHINAJOY主办的s2世界总决赛选拔赛，也是画下了一个圆满的句号。形势如出一辙，重新组建的IG战队，同样的以全胜的战绩夺得了桂冠。 本文将重点分析下，这2届比赛笔者所看到的，国内英雄联盟职业界的变化。通过2届比赛的整体阵容选择，对线英雄克制，到战术细节变化，用笔者在英雄联盟这款游戏上的经验，帮助读者们理解在当前的英雄联盟界队伍的实力对比，以及游戏技战术的更新换代。 首先介绍一下TGA的队伍们。 WE WE.国内的成绩不用多讲，而在国际比赛中，WE也曾在IEM广州站上力克世界顶尖强队CLG取得冠军，加上韩国OGN小组赛成功出线，国际上也是有了不错的名气。
中单若风既是队长也是队中元老，实力强劲发挥稳定，正在朝心目中世界最强apc的位置奋力前进。 队伍王牌ADC微笑，被以前CLG打野Saintvious为代表的部分外国选手也公认为最强的ADC。 辅助IF与微笑配合默契娴熟，且最大优点是性格老实沉稳，从Dota时期就以擅长辅助著称。上单草莓是前国服路人王，电一SOLO排位分数长期保持前列，加入WE后从ADC位置换到了上单位置，进步相当的明显，如今已经成为了队伍信任而不可或缺的一份战力。 打野夜尽天明外号刀哥，打法稳健保守，对于坦克类打野的理解尤为出众。 EHMOE EHOME.国内传统三强之一，成立很早，曾经拿到第一届TGA英雄联盟项目的冠军。但之后因为人员不断变更等因素，往往在大赛中未能杀人决赛。 中单梦梦贝利亚是国服高分中单玩家，特点是擅长的英雄不算最热门，比如凤凰，在当时的TGA只有他是把凤凰拿上了比赛并取得了不俗的效果。
四大会计师事务所 道可道，普华永道（PWC）： I、溯源： 普华永道PriceWaterhouseCoopers 由普华PriceWaterhouse 和永道Coopers 最终于1998 年合并组成。 PriceWaterhouse 部分： 1849 年普华公司创始人Samuel Lowell Price 在英国伦敦创立自己的会计公司 1965 年Samuel Lowell Price 结识了William Hopkins Holyland 和Edwin Waterhouse，三人合伙经营 1871 年Holyland 因病退出 1874 年公司改名为普华PriceWaterhouse & Co. 1945 年普华国际公司Price Waterhouse International Firm 建立 1982 年普华全球公司Price Waterhouse World Firm 建立 Coopers 部分： 1854 年William Cooper 在伦敦建立自己的公司 1861 年公司改名为Cooper Brothers 1898 年William Lybrand 与Adam Ross 及其兄弟Edward Ross 还有Robert Montgomery 在美国费城成立了Lybrand Ross Bros. & Montgomeny #我是应届生 1957 年Cooper Bros ( 英), McDonald Currie (加拿大) Lybrand Ross Bros & Montgomery(美) ，三家成立松散性的永道国际 1973 年各地机构统一更名为永道国际Coopers & Lybrand 1998 年最终两家合并成立PricewaterhouseCooper II、简介： 普华永道是全球最具规模的专业服务机构。在全球一百四十二个国家拥有超过十二万五千名专业人士。普华永道融合他们所具备的渊博知识与丰富经验，以最高的职业操守为客户提供高质量的服务。普华永道为PricewaterhouseCoopers国际网络成员公司。 每一家PricewaterhouseCoopers国际网络成员公司都是独立运作的法律实体。 普华永道在中国 普华永道是在中国大陆、香港及澳门处于领先地位的专业服务机构，在中国大陆、香港及澳门共拥有员工约六千人，其中包括二百三十名合伙人，并在北京、重庆、大连、广州、上海、深圳、苏州、天津及西安等内地城市设立办事处。 III、点睛： 最大、工作环境最好！
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。
治疗宝珠+5生命回复/5秒巨人腰带 +400生命值 红水晶 +180生命值 布甲 +15护甲 锁子甲 +40护甲 抗魔斗篷 +20魔法抗性 多兰之盾+100生命值 +5护甲 +5生命回复/5秒唯一被动：格挡6点来自敌方英雄的普攻伤害。 多兰之刃+80生命值 +10攻击力被动：你的普通攻击在每次命中敌人后，都会为你回复5点生命值。
多兰之戒+80生命值 +15法术强度 +3法力回复/5秒被动：每击杀一名敌方单位，你的法力值就会回复5点。 负极斗篷 +45魔法抗性 勘探者之刃+20攻击力 +5%生命偷取唯一被动—勘探者：+200生命值。（同名的唯一被动效果不叠加。） 勘探者之戒+40法术强度 +10法力回复/5秒唯一被动—勘探者：+200生命值（同名的唯一被动效果不叠加。） 精魄之石+14生命回复/5秒 +7法力回复/5秒唯一被动—屠夫：对野怪造成的伤害提升20%。唯一被动—撕裂：普通攻击会对野怪造成10点额外真实伤害。（同名的唯一被动效果不叠加。） 坚韧合剂点击使用：增加120—235生命值（基于英雄等级），和15攻击力，持续3分钟。装备栏已满后购买将自动使用。 水晶瓶唯一主动：消耗一个充能，以在10秒里持续地回复总共100生命值和40法力值。唯一被动：有3个初始充能，并且每当你靠近商店时，都会重新将充能数充满。
洞察红宝石+300生命值唯一被动—守卫刷新：初始就有5个充能，并且会在每次你造访商店时充满。唯一主动—幽灵守卫：消耗一层充能来放置一个隐形的守卫，持续3分钟。你一次最多能放置3个来自该物品的守卫。 洞察之石+100生命值唯一被动—守卫刷新：初始就有4个充能，并且会在每次你造访商店时充满。唯一主动—幽灵守卫：消耗一层充能来放置一个隐形的守卫，持续3分钟。你一次最多能放置2个来自该物品的守卫。 深渊权杖+70法术强度 +45魔法抗性唯一光环：降低周围敌人20魔法抗性。 阿塔玛之戟+45护甲 +15%暴击几率唯一被动：增加相当于你最大生命值1.5%的攻击力 催化神石+200生命值 +300法力值唯一被动—成长之力：升级时，英雄会在8秒的持续时间里回复总共150点生命值和200点法力值。（同名的唯一被动效果不叠加。） 冰霜之锤+700生命值 +20攻击力唯一被动—钻石星辰击：物理攻击将降低目标40%的移动速度，持续1.5秒（远程攻击的减速效果为30%）。 双生暗影+50法术强度 +30魔法抗性 +5%移动速度唯一主动：狩猎召唤2个免疫伤害、持续6秒的幽灵，来寻找最近的2名敌方英雄。如果它们触碰到了一名敌方英雄，那么会将该英雄的移动速度减少40%，并使他暴露2.5秒。冷却时间120秒。
四大会计师事务所发展中遇到的问题 一，“四大”在西方审计市场。品牌维护方面，四大必须要提供较高的审计质量，以维护其品牌形象，因而会存在因其审计质量的原因而影响其品牌形象的问题。审计市场也与其他产品(服务)市场一样，品牌的创立都要依靠过硬的质量和优质的配套服务。会计师事务所品牌的创立可能受很多因素的影响，但归根结底要靠质量。审计质量的不可直接观测性使审计质量的评价成本太高，为降低评价成本，市场需要寻找质量评价的替代品。会计师事务所的规模便是市场选择的低成本的替代品之一。那么，大规模的事务所是否一定高质量？高质量是否能够高收费？ 二，“四大”受到监管。国际性事务所在进入其他国家市场时会遇到当地政府法律法规的影响，受到当地的监管，当地政府采取保护本土事务所的政策，会对其发展受到影响。 三，“四大”管理难题。四大会计师事务所规模太大，分所太多，管理上会存在问题，质量控制较难。 四，会计师事务所的诚信危机。马威国际会计公司、德勤会计师事务所、普华永道会计师事务所相继惹上了麻烦，一直是中国会计行业领头羊的四大会计师事务所遭遇全面诚信危机。德勤近期也卷入了科龙公司的乱局。格林柯尔自入主科龙三年来一直由德勤为其审计，由于格林柯尔及科龙在财务报表上有很多明显漏洞，德勤对科龙2003年年报出具了“非标准无保留意见”，这遭到业内普遍质疑。不久前，毕马威也因涉嫌妨碍司法公正和非法销售避税产品，美国司法部开始对其调查并在讨论是否对其起诉。毕马威正在与美国司法部协商，希望能够避免司法部对它的指控。业内人士猜测，如果司法部对毕马威进行控告，则有可能导致毕马威的消失，从而使“四大会计师事务所”变成“三大会计师事务所”。事实上，四大会计师事务所的信誉已经开始出现危机。据中国注册会计师协会对2004年更换会计师事务所的统计，共有104家上市公司变更了审计师。其中有一个现象引人注目，就是有27家上市公司炒掉了四大会计师事务所，只有6家上市公司改聘四大会计师事务所。 “四大”在中国市场上面临的问题 一，源于中国市场内的竞争压力。四大会计师事务所在中国的市场上，还是要面临巨大的压力。国内会计师事务所发展越来越多，普遍收费低，“同一个审计项目，四大所的收费高出国内所2-5倍很正常。”上海一家大型企业的财务人员说。业务发展越来越多，给四大会计师事务所造成很大的竞争压力 二，客户流失方面。签字注册会计师会发生带走客户的情况。由于客户具有一定的流动性，客户的控制权是归属于会计师事务所还是签字注册会计师，两类所表现出不同的特征。国内所的客户控制权普遍掌握在签字注册会计师手中，之所以发生签字注册会计师跳槽带走客户，是因为上市公司直接与签字注册会计师打交道而不是与会计师事务所。 三，业务模式方面。目前会计事事务所主要从事传统的审计业务，业务单一，目前许多事务发展其他业务，如：管理咨询、税务服务、投资咨询等，四大在审计业务方面具有很强的竞争优势，但对于新业务优势不明显。 四大会计师事务所全球丑闻（截止2003年） 毕马威会计师事务所
XXXXXXXXXXXXXXX投资集团基建项目管理信息平台 上海普华科技发展有限公司
目录 第三部分技术部分 ............................................................................................................. 错误！未定义书签。 第一章具体技术方案 (3) 2.1 综述 (3) 2.1.1硬件与软件建议 (4) 2.2公司本部解决方案 (5) 2.2.1 项目与组织 (5) 2.2.2 模板与标准 (8) 2.2.3 数据汇总 (10) 2.2.4前期管理 (11) 2.2.5 质量控制 (11) 2.2.6 HSE管理 (14) 2.2.7 合同管理 (19) 2.2.8 调试管理 (20) 2.2.9 招投标管理 (24) 2.2.10 个人中心 (25) 2.2.11 工程项目综合信息查询 (25) 2.2.12 决策支持 (31) 2.2.13 系统管理 (36) 2.3基建项目单位解决方案 (40) 2.3.1 OA办公管理 (40) 2.3.1.1 协同办公 (41) 2.3.1.2沟通管理 (51) 2.3.2 费用控制管理 (56) 2.3.2.1 功能组成及其描述 (56) 2.3.2.2 概算管理 (58) 2.3.2.3 合同管理 (66) 2.3.2.4 投资计划管理 (75) 2.3.2.5 统计管理 (78) 2.3.3 工程管理 (80) 2.3.3.1 功能组成及其描述 (80) 2.3.3.2 工程进度管理 (80) 2.3.3.3 工程质量管理 (90) 2.3.3.4 工程安全管理 (104) 2.3.3.5工程信息管理 (113) 2.3.3.6资料档案管理 (113) 2.3.4 物资管理 (122) 2.3.4.1 设备管理 (122) 2.3.4.2 材料管理 (138) 2.3.5 系统维护管理与综合查询 (159) 2.3.5.1 维护管理 (159) 2.3.5.2 决策分析 (168) 2.3.5.3项目部信息网站 (172) 2.3.6 生产准备管理 (173) 2.3.6.1 调试管理 (173)
《英雄联盟(LOL》游戏数据计算大全 1.物品与光环 Q: 物品的叠加原理是怎么样的? A: 所有物品增加的属性都可以叠加,除了唯一被动的效果。比如当你拥有2把Infinity Edge时, 那么一把的效果是: +80 伤害 +20% 暴击 唯一被动: 暴击现在为250%的伤害,而不是200% 。 2把的效果就是: +160 伤害 +40% 暴击 唯一被动: 暴击现在为250%的伤害,而不是200% (这个不叠加 Q: 光环是如何作用的? A: 环效果是最多可以拥有2个的。比如如果你有一个Mana Manipulator(唯一被动:对友军恢复 7.2 Mana每 5 秒,然后你的盟友也合了一个。那么你和盟友自己将获得2个光环的效果!而没有合成的人则只拥有一个光环的效果。但是同样的光环效果最多不超过2个。 Q: 减少cd的物品如何叠加。
A: 此类物品直接叠加。如果你有一个减少9%cd的物品,又合了一个减少 10%cd的物品,那么你将减少19%的cd。但是上限为40%。 2.游戏计算公式 Q: 躲闪是如何计算的。 A: 躲闪增加的效果递减。(这个计算方式和war3一样的就相当于算概率,A发生B 不发生 A不发生B发生 AB一起发生。比如你有10%的躲闪了,又合了忍者鞋11%的躲闪。那么你的躲闪将是10%+11%- 10%*11*=19.9% Q: 可以躲闪掉魔法么? A: 不可以躲闪魔法,唯一的办法是通过某些特定物品。比如- Banshee's Veil (每30秒低档一次魔法攻击 Q: 吸血如何计算。 A: 吸血受护甲的影响。比如你造成100伤害,对方护甲有20%减成,那么你的攻击将造成80伤害。如果此时你的吸血效果为10%,那么你将吸取8点血。吸血效果可以叠加,并且没有上限没,也就是说你可能会获得超过100%的吸血。 Q: 护甲和魔防是如何计算的? A:每一点护甲意味着对方要杀死你将需要多造成你生命1%的攻击。比如当你有100护甲时,对方需要多造成100%伤害才能杀死你,也就是说你减少了一半的攻击。再比如你有300护甲时,对方需要多打300%,也就是总共需要造成400%你生命值的攻击力,也就是你只承受了25%伤害并且这种关系没有收益递减。也就是说为线性关系。如果你有1000血,那么每点护甲可以看成给你多提供了10点血。 魔法防御也是同样的道理。要注意的是魔防只防御魔法,物力防御只防御物力(废话?呵呵
《英雄联盟（LOL）》游戏数据计算大全 1.物品与光环 Q: 物品的叠加原理是怎么样的？ A: 所有物品增加的属性都可以叠加，除了唯一被动的效果。比如当你拥有2把Infinity Edge时， 那么一把的效果是： +80 伤害 +20% 暴击 唯一被动: 暴击现在为250%的伤害，而不是200% 。 2把的效果就是： +160 伤害 +40% 暴击 唯一被动: 暴击现在为250%的伤害，而不是200% (这个不叠加) Q: 光环是如何作用的？ A: 环效果是最多可以拥有2个的。比如如果你有一个Mana Manipulator(唯一被动：对友军恢复 7.2 Mana每 5 秒)，然后你的盟友也合了一个。那么你和盟友自己将获得2个光环的效果！而没有合成的人则只拥有一个光环的效果。但是同样的光环效果最多不超过2个。 Q: 减少cd的物品如何叠加。 A: 此类物品直接叠加。如果你有一个减少9%cd的物品，又合了一个减少10%cd的物品，那么你将减少19%的cd。但是上限为40%。 2.游戏计算公式 Q: 躲闪是如何计算的。 A: 躲闪增加的效果递减。(这个计算方式和war3一样的)就相当于算概率，A发生B 不发生 A不发生B发生 AB一起发生。比如你有10%的躲闪了，又合了忍者鞋11%的躲闪。那么你的躲闪将是10%+11%- 10%*11*=19.9% Q: 可以躲闪掉魔法么？ A: 不可以躲闪魔法，唯一的办法是通过某些特定物品。比如- Banshee's Veil (每30秒低档一次魔法攻击) Q: 吸血如何计算。
A: 吸血受护甲的影响。比如你造成100伤害，对方护甲有20%减成，那么你的攻击将造成80伤害。如果此时你的吸血效果为10%，那么你将吸取8点血。吸血效果可以叠加，并且没有上限没，也就是说你可能会获得超过100%的吸血。 Q: 护甲和魔防是如何计算的？ A:每一点护甲意味着对方要杀死你将需要多造成你生命1%的攻击。比如当你有100护甲时，对方需要多造成100%伤害才能杀死你，也就是说你减少了一半的攻击。再比如你有300护甲时，对方需要多打300%，也就是总共需要造成400%你生命值的攻击力，也就是你只承受了25%伤害并且这种关系没有收益递减。也就是说为线性关系。如果你有1000血，那么每点护甲可以看成给你多提供了10点血。 魔法防御也是同样的道理。要注意的是魔防只防御魔法，物力防御只防御物力(废话？呵呵) Q: 我怎么看到我人物的属性。 A: 在游戏里，左下角可以看见人物的攻击，AP，移动速度，护甲，魔防。如果要看到其他属性，请按Ｃ键，或者点击人物头像。 3. 移动速度 Q: 不同的鞋子增加了多少移动速度 A: 1级增加50，2级70，3级90。 Q: 移动速度如果计算？ A: 首先每个英雄有一个初始移动值，假如为300。当你穿上2级鞋子，那么你拥有370的移动效果。如果此时你获得一个+35%移动效果的buff。那么就是370 * 1.35,你将有499.5的移动速度。要注意的是这里只是理论竖直，移动效果将符合收益递减规则(下面一个问题会详细解释)这里我们先忽略。现在在前面的例子上如果你中了ashe的减速箭，将减少38%的移动速度。本来是499.5，现在将为499.5 *(1-38%) =309.69 Q: 移动速度的收益递减如何计算 A: 当你人物移动速度超过490后，每一点增加将只收益50%。所以假如你最后理论移动数值为748，那么你的速度为：490+(748-490)*0.5= 604的移动速度。当速度超过415 时候，每点收益为80%。所以假如你的人物速度在所有调整后为445，那么你实际的速度为，415+(445-415 )* 0.8=439。 Q: 减速叠加么？ A:不叠加。当你受到2个减速时候，减速减的多的那个将起作用。如果这个效果结束了，并且你还有另一个减速效果，那么另一个将继续起作用。 比如你受到2个debuff：5秒减少30%速度，3秒减少50%。那么前3秒你身上的减速效果为50% ，最后2秒为30% 。
中国四大会计师事务所 篇一：中国四大会计师事务所 如题，中国四大会计师事务所是哪些中国四大会计师事务所是哪些 篇二：中国四大会计师事务所 都说四大，四大，具体哪几个? 答： 四大会计师事务所指世界上著名的四个会计师事务所：普华永道(PwC)、德勤(DTT)、毕马威(KPMG)、安永(EY)。 【更多相关内容推荐】 四大会计师事务所招聘日语会计吗 注册会计师薪水怎么样 会计师事务所应届生应聘技巧 四大会计师事务所薪水 外资银行与四大会计师事务所工作比较 中国四大会计师事务所是哪些？介绍一下
篇三：四大会计师事务所 四大会计师事务所特点： 1.普华永道：“四大”之首，全球500强中有32%是其客户，而内地在H股上市的企业中有40%都信赖PWC为其审计。 2.毕马威：在中国唯一有资格与PWC抗衡的公司，在金融业上优势明显，相对比较本土化，有很多local manager，且是四大中最多金的一个。 3.德勤：全球百强中30％是其客户，在发展日本客户方面有优势，但相对而在华规模较小、每年招聘人数远少于其他几家。 4.安永：在中国的业务逊于其他几家，本地化步伐较慢，以香港经理居多。 关于四大会计师事务所的优势比较 1.四大会计师事务所的人员规模：普华永道安永毕马威德勤
2.四大会计师事务所对专业的要求 普华永道对专业最没有限制； 安永对专业限制最为严格，几乎都是清一色的会计，财务，金融，经济类专业，多为会计知名的大学的会计或者财经类毕业生。安永的TSRS部门给清华工科本科和硕士发OFFER。 毕马威和德勤对专业的限制不多。 3.四大会计师事务所的客户 1.普华永道：中国银行，中国石油，TOM网络，诺基亚，爱立信，中国华能，中国铝业，国家电网，中国人寿 主要国际客户有：埃克森、IBM、日本电报电话公司、强生公司、美国电报电话公司、英国电信、戴尔电脑、福特汽车、雪佛莱、康柏电脑和诺基亚等。 2.毕马威：渣打银行，花旗银行，汇丰银行，中国国际金融公司，德意志银行，西门子，宝马，中国电信，中国移动，建设银行，
富大集团物料主数据模块 : MM 作者: XX 日期 : 2002/1/16 业务蓝图确认签署 该文件定义了未来富大如何利用SAP R/3系统实现本业务操作。 项目小组参与讨论人员 参与部门参与人签字 信息系统管理部(IA) XXX 信息系统管理部(IA) XXX 信息系统开发部XXX 正大工业(IT) XXX 富大XXX 普华永道 ______________ 流程确认富大集团 签署: ___________________________________________________ 项目管理富大集团普华永道 项目经理项目经理 签署: __________________ ____________________
富大集团物料主数据模块 : MM 作者: XX 日期 : 2002/1/16 参考文件: 作者日期备注张碧波2002/1/16 创建
富大集团物料主数据模块 : MM 作者: XX 日期 : 2002/1/16 流程说明： 1.富大集团目前需进入MM管理的物料分以下几种: a.原材料 b.包装物 c.备品备件 d.固定资产 e.产成品 f.半成品 2.富大集团的成品分类有以下几种，新的SAP系统的编码应包含以下信 息，以方便信息的使用和汇总. a.料别：粉料、颗粒料、膨化料等 b.畜禽别：猪料、鸡料、鱼料等 c.配方别：预混料、浓缩料、全价料等 d.品牌别：富大牌等 e.包装别：40Kg、20Kg、10Kg、5x8=40Kg、1x4=4Kg等. 类别的精确名称以富大以后确认的为准. 实施要点： 1.每种原料设一个料号(即使原料的成分含量不同)收货时由品管部在批 号中记录其属性。料号的编码位数待定，具体结构由富大集团跟品管 部商定后确认，确认后的详细资料将由富大交给普华永道的顾问. 2.今后的报表提供和汇总是基于批次的属性而做的. 3.物料管理模块中按以上成品分类的字段名和属性分别加以定义，再进 行组合，然后利用属性生成汇总报表. 4.财务负责维护和协调备件的物料主数维护及相应的协调工作. 5.QA部门负责维护和协调生产用材料的物料主数维护及相应的协调工作. 6.备件分两类, 一类是生产燃料, 另一类是生产燃料以外的备件.
新手推荐攻略：英雄联盟攻击装备全解析 本心得不包括一些比较酱油的物品如燃烧宝石 本心得是写给那些对LOL了解还十分浅的新手玩家，加深他们对物品的理解。 BF！绝大多数人都会用到的配件，合成几件强力AD装备都要用到他。一般来说有钱出一把会是不错的选择，如果你第一次回家和笔者一样有1800块钱不出BF而出草鞋+3把多兰，我只能说我很欣赏你。 不过有人觉得优势就叠BF，其实不是很明智的选择，因为虽然BF的性价比不算太低，但是他升级后才能真正让你的输出变得耀眼起来。 LOL代练，刷金币，代练排位,走淘宝交易++QQ : 2924394866讓妳超神让五 杀不是梦╔★═ ═★══╗承诺：新号定级赛包到黄金段位240元2天完成，定级赛包到铂金段 位600元4天，定级赛包到钻石段位1200元7天，S3黄金段位的S4定位赛20 元一局60%的胜率，不到全额退款 无尽！最强的输出装！傲人的价格使得他对你的攻击力提升上了一个档次，被动效果更是能让你作为一名AD英雄也时不时有了爆发 但是要注意的是无尽加的只有AD和暴击，不是非常适合一些没有攻速加成的DPS英雄作为第一件大件（虽然这种远程DPS比较少，不过最近很火的vayne 就是一个） 或是在逆风打钱难的话，先出黑切或是嗜血也是不错的选择
旗子！虽然是很虎的装备，对团队的帮助也很大，但是渐渐的队里只有一个AD，对旗子的需求就没有那么的急切了，但仍然是一件非常好的装备但是缺点在于出的时机比较难选择，第一件出缺攻击力，如果第一件出了无尽，有暴击又加攻速的红叉更加合适。 所以个人感觉比较适合第一件出了黑切/嗜血的同志 黄叉！少数加移速的装备之一。有攻速有暴击，但是都相少了一点，不过出了后不论是合成三相还是红叉都是很好用的。 有时在觉得比较难憋BF的局也能先当一件中件来出，毕竟那时候一般比较逆风，移速的作用常常就会发挥出来。 这件叫反曲弓的装备虽然很常用，不过好像的确是没什么昵称…… 这件装备给你最大的一个优势就是你感觉你出了这装备以后，你的攻速就一下子老母鸡变鸭了，也的确是能升级成不少很有用的装备。 感觉自己缺攻速又有1000块钱的时候就和黄叉自己取舍吧
四大会计师事务所薪酬体系 不仅仅是安永，四大会计师事务所(下称“四大”)里的普华永道会计师事务所、毕马威会计师事务所和德勤会计师事务所也有类似的裁员情况。虽然“四大”都否认裁员，但实际上，近期“四大”陆陆续续有人离开——被告知的理由多是考评不及格等。 “现在办公室人人自危，间或看到有人早上被找来谈话，谈完话电脑就不让使用，立马封存了。”毕马威一位在职会计师告诉记者，“现在快速消费品、房地产组裁的人比较多，金融组、税务部门的还比较少，有些部门原来20个人的，现在变成8个了。” 而前两年又是另外一番景象——“四大”每年的增员比例超过20%，以安永为例，其2006年新招募的人员就在500人左右。 一手裁人，一手招人 安达信网(“四大”员工经常登陆的一个网站)一位名叫Saad的网友在论坛上说，毕马威的裁员以“劝退“为借口，基本集中在去年表现得4分的人(毕马威用5分制，但实际给5分的人很少)，并不只是裁掉“小朋友”，而是高级经理以下的各个级别均有，且以助理经理居多。 安永一位前员工告诉记者，这次裁员的命令是香港那边下达的，都有具体指标，除了合伙人和刚招聘报到一周的，每个级别都会裁，至于裁谁一般没有十分硬性的标准，多是几个大老板讨论出来的。 安永在8月就曾有一批小规模裁员。“第一批据说为2%，虽然有些同事的确不适合继续做审计。其实往年也有一些人走,只是这次人数多了一点。” 一位早前从安永离职的知情人告诉记者，但随着经济继续恶化，IPO市场基本暂停，老板们开始坐不住了，于是有了上述所谓的第二批。 德勤与安永和毕马威不同的是，不是选择走人，而是选择减薪的方式渡过难关。 不过，尽管“四大”一边在裁员，但也一边在招聘，只是招聘数量跟以前比将有所减少。对此，普华永道公关处陈颖表示，普华永道考虑到经济恢复后将需要大量人才，虽然目前市场状况不好，但今年9月对外公布的明年招聘2000名新员工的计划仍不会变。 “四大”的五层薪酬体系 在业内，“四大”虽然劳动强度高闻名，但是其报酬也很高。
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
主动技能类 里格尔的灯笼总价1600 组件麦瑞德的拳刃（长剑+布甲）+吸血鬼权杖 +23攻击力+30护甲+18%生命偷取 唯一被动：攻击小兵时20%几率对其造成500点伤害 唯一主动：放置一个隐形的守卫，拥有1100视野（假眼）持续3分钟，冷却3分钟 黑水弯刀总价1825 组件长柄战斧+吸血鬼节杖 +35物理攻击+15%生命偷取 唯一主动：为目标英雄造成150点法伤，并减缓其50%移动速度，持续3秒施法距离400，冷却60秒 海克斯科技枪刃总价3625 组件黑水弯刀+科技左轮 +60物理攻击+75法术强度+25%生命偷取+20%法术吸血 唯一主动：对目标造成300点法术伤害减少其50%移动速度，持续3秒施法距离700，冷却60秒 兰顿之兆总价3075 组件：黄金之心（红宝石）典狱官之铠（无袖链甲+活力宝珠）布甲 +300HP +53护甲+25HP/5秒 唯一被动：减少5%技能冷却、被攻击时20%几率减少攻击者20%移动速度和35%攻击速度，持续3秒 唯一主动：减少周围单位35%攻击速度和移动速度，持续2秒，每100点护甲或魔法抗性增加0.5秒持续时间，60秒冷却 妖梦之灵总价2687 组件贪婪之刃（恶徒手套）无情之力（长剑*2） +30攻击力+15%暴击几率 唯一被动：+20护甲穿透、减少15%英雄技能冷却时间 唯一主动：增加移动速度20%，攻击速度50%，持续4秒每次近战攻击延长2秒，最多增加8秒，冷却60秒 冥火之拥总价2610 组件凯奇的幸运之选（增幅手册）恶魔法典（增幅手册+梅齐垂饰） +60法术伤害+10MP/5秒 唯一被动：减少15%技能冷却 唯一主动：造成目标当前血量30%（每100点法伤+3.5%）的魔法伤害最低200点，冷却1分钟
四大会计师事务所 四大会计师事务所指世界上著名的四间会计师事务所：普华永道(PWC)、德勤(DTT)、毕马威(KPMG)、和安永(EY) 目录 普华永道（Price Waterhouse Coopers） 德勤(Deloitte Touche Tohmatsu） 毕马威（KPMG） 安永(Ernest & Young) 2008金融危机下的“四大”裁员潮现 “四大”的五层薪酬体系 四大会计师事务所会否在华裁员降薪 普华永道（Price Waterhouse Coopers） 德勤(Deloitte Touche Tohmatsu） 毕马威（KPMG） 安永(Ernest & Young) 2008金融危机下的“四大”裁员潮现 “四大”的五层薪酬体系 四大会计师事务所会否在华裁员降薪 展开 编辑本段 普华永道（Price Waterhouse Coopers） 基本情况 原来的普华国际会计公司（Price Waterhouse）和永道国际会计公司 普华永道logo （Coopers ＆Lybrand）于1998年7月1日合并而成，2008财年的收入为281亿美元，比2007财年增长14.0%。现全球共有员工155000人。中国大陆，香港地区和新加坡总共有460多名合伙人和12000多名员工。
主要国际客户 埃克森、IBM、日本电报电话公司、强生公司、美国电报电话公司、英国电信、戴尔电脑、福特汽车、雪佛莱、康柏电脑和诺基亚等。 中国业务 到1998年底为止，在中国大陆、香港和澳门共拥有员工8,000人，其中包括接近330名合伙人，并在内地城市设立办事处，包括北京、重庆、大连、广州、青岛、上海、深圳、苏州、天津、宁波、厦门及西安。 在中国大陆的经营实体名字为普华永道中天会计师事务所。 编辑本段 德勤(Deloitte Touche Tohmatsu） 基本情况 德勤全球2008财年的收入为274亿美元，比2007财年增长18.6%，在中国拥有员工8000多人。 德勤 早在1917年，德勤已认识到中国的商机，在上海成立办事处，成为首家在这个动感及繁荣的大城市开设分支机构的外国会计师事务所。 自1972年，德勤在香港特别行政区拥有了办事机构，这是几次成功并购的结果。在1989年，Deloitte Haskins & Sells International 和在1975年与日本的审计公司Tohmatsu Awoki & Sanwa 联合的Touche Ross International 合并，形成了Deloitte Touche Tohmatsu，即德勤全球。Spicer & Oppenheim 于1991年加入了我们在香港特别行政区和英国的国际网络。1997年，德勤与香港特别行政区最大的华人会计师事务所- 关黄陈方会计师事务所- 合并。 主要国际客户 微软公司（Microsoft）、宝洁（P&G）、美国通用汽车公司（General Motors）、沃德芬公司（Vodafone）、克莱斯勒公司（Chrysler）等。