数据与实证研究

数

据

与

实

证

研

究高

宁

博

士国泰安信息技术有限公司常务副总裁西安交通大学教授香港浸会大学商学院Honorary

Associate香港浸会大学公司管制与金融政策研究中心Research

Fellow什么是实证研究？––

以事实、实际情况和收集到的数据为对象，通过分析、计算、实验、研究，解释和预测会计金融实

务，回答“实际是什么”的问题。◎实证研究要求客观、准确、理性的描述现实◎实证研究以解释现实为目的，认为存在就是事实◎实证研究采用客观中立的立场◎目前，在国际上，实证研究方法广泛的应用在经济、金融、会计等社会学科的研究中实证研究的发展与趋势----实证经济学1953弗里德曼《实证经济学方法论》发展历程----实证会计学1968

Ball，R.J.,P.Brown《An

Empirical

Evaluation

of

AccountingIncome

Numbers》《Journal

of

Accounting

Research》1986

Watts，Zimmerman《实证会计理论》趋势由于金融市场每天都产生海量的数据，这些数据又是从真实的交易

过程中产生的,这一特性使实证研究成为现代金融研究的主流话语”――Ross20世纪80年代《Accounting

Review》上实证性研究的论文占半数以上，有的年份还高达81％。现在实证研究已成为会计，金融研究的主流。推动实证研究发展的因素（William

Beaver)推动实证研究发展的因素（William

Beaver)财务和经济学的发展1证券市场在经济中的地位2政府对证券市场的积极监管，不断推出新的课题3机构投资者占股权比重的增大4计算机技术和数据库的发展56学术刊物受重视程度的增强实

证

论

文

篇

数实证论文篇数类型1994199519961997199819992000200120022003200420052006实证研究论文13616120532538154764183011531751248235665043经济类实证论文8272115168188236276335432697102613991979实

证

的

要

素实证的要素结论推理检验假设模型数据实证的要素数据：反映客观状况的数字材料。模型：刻画客观现象的数学形式。假设：对所研究问题的结果或状态的◆一种预期。检验：利用数据，使用统计学知识对假设的统计显著性作出判断。推理：基于知识和经验对假设检验结果进行推理。结论：利用假设检验的结果，通过合情的逻辑推理得出的结论，观点。实证研究方法步骤确立研究课题实

证

研

究

方

法

步

骤寻找相关理论提出命题假设设计研究方案搜集事实数据分析数据检验命题得出研究结论金

融

实

证

研

究的

主

要

领

域投资组合选择和资产定价–包括现代投资组合理论、资本资产定价理论、套利定价模型、期权定价模型、有效边界、资本市场线、证券市场线等。资金成本和资本结构理论–包括资金成本传统理论、净利理论和营业净利理论、权衡理论和融资偏好次序等。市场微观结构–研究交易价格发现过程与交易运作机制，包括价格发现的模型和市场结构与设计。行为金融学–研究投资者的心理、个人特征等因素与其交易行为之间的关系，包括个人信仰(过度自信、乐观主义、代表性、保守主义、确认偏误、定位、记忆偏误),个人偏好(展望理论、模糊规避)会

计

实

证

研

究的

主

要

领

域会计制度的选择–研究企业会计制度的选择与企业营运绩效之间的关系盈余管理–研究企业管理当局借助会计政策的选择和会计估计的变更，寻求对自己有利结果的行为及其影响会计舞弊–研究公司采取伪造、掩饰的手法编造假账损害股东权益、影响投资者做出正确投资决策的行为财务预测–研究如何根据财务活动的历史资料和现实情况对企业未来财务活动进行科学的预计和测算会计信息披露效应–研究上市公司会计信息披露与公司股票价格之间的关系财务困境–研究企业陷于财务困境的特征及影响因素主要包括财务困境企业与非财务困境企业之间财务项目的分析会计信息的价值相关性–研究会计信息价值相关性对于会计准则制证券市场监管和投资者进行决策的作用CSMAR

实

证论

文

举

例文章研究了中国上市公司盈余公告时间选择对股票交易量和未预期收益的影响。研究发现，与较晚月份公告盈余的公司相比，较早月份进行年度盈余公告的公司具有较强的股票交易量反应。文章认为愿意早些公告盈余的公司往往拥有利好的信息，并且这些较早的盈余公告含有更大的信息量,带来较大的交易量增幅和未预期收益；较晚公告盈余的公司则往往拥有利差的信息，而且更容易被市场预期,因而带来的交易量增幅和未预期收益也较小。作

者发表刊物摘

要题

目

InformationContent

and

Timing

ofEarnings

Announcements陈工孟

高

宁 郑子云（香港理工大学）Journal

of

Business

Finance

and

Accounting,

January

2005,

Vol

3Iss.

1-

2,

Pg.

65－95数

据

样

本以1995年至2002年间发行A股或同时发行A，B股，在时间区间内发表年度盈余公告的上市公司为研究样本。样本容量为3802。年份样本数１月（％）２月（％）３月（％）4月（％）19952656(2.26)9(3.40)81(30.57)169(63.77)19962941(0.34)6(2.04)33(11.22)254(86.40)19973504(1.14)10(2.86)52(14.86)284(81.14)19985904(0.68)45(7.63)269(45.59)272(46.10)19993508(2.28)9(2.57)87(24.86)246(70.29)200053145(8.47)50(9.42)188(35.41)248(46.70)200166313(1.96)108(16.29)299(45.10)243(36.65)200275915(1.98)84(11.07)277(36.50)383(50.45)Total380296(2.52)321(8.45)1286(33.82)2099(55.21)CSMAR

总

体样

本

容

量CSMAR

总

体

样

本

容

量文

献

回

顾和

假

设为什么选交易量而不是价格Bamber,

Barron

and

Stober

(1997)

suggest

that

trading

volume

is

relateto

the

magnitude

of

the

disagreement

among

investors

about

a

firm’searnings.Kim

and

Verrecchia

(1991a)

argue

that

price

changes

reflect

the

averagchange

in

the

aggregate

market’s

average

beliefs,

while

trading

volumis

the

sum

of

all

individual

investors’

trades,

which

also

depends

onprevailing

information

asymmetry

level

before

disclosure.

They

suggethat

although

all

investors

have

equal

access

to

public

pre-disclosureinformation,

they

acquire

private

pre-disclosure

information

withdifferent

degrees

of

precision.为什么选交易量而不是价格Atiase

and

Bamber

(1994)

and

Kross

et

al.

(1994)suggest

that

trading

volumeincreasing

function

of

the

degree

of

divergent

pre-disclosure

expectatiBamber

and

Cheon

(1995)

argue

that

the

reason

for

different

reactions

is

threactions

reflect

the

average

belief

revision,

while

trading

volume

ariindividual

investors

make

differential

belief

revisions.更

进

一

步

的

分

析Kim

and

Verrecchia

(1994)

suggest

that

there

may

be

more

information

asymmeat

the

time

of

an

announcement

than

in

a

non-announcement

period.

This

isbecauseearnings

announcements

provide

information

that

allows

certain

tradersjudgements

about

a

firm’s

performance

that

are

superior

to

the

judgemenothertraders.Lobo

and

Tung

(1997)

find

that

the

trading

volume

around

quarterly

earningsannouncements

is

related

to

the

level

of

pre-disclosure

information

asymForfirms

with

a

high

level

of

pre-disclosure

information

asymmetry,

the

travolumeis

low

prior

to

and

after

the

announcement,

but

high

during

the

announceme更

进

一

步

的

分

析Bamber(1986)

employs

the

divergence

of

earnings

forecasts

from

analysts’

forecasts

as

a

proxy

forinformation

asymmetry.

She

finds

thatthe

higher

the

information

asymmetry,

the

greater

the

abnormalreaction.In

this

study,

we

first

use

unexpected

earnings

as

a

control

variable

for

information

asymmetry.Earlier

announcements

should

generate

a

greater

surprise

in

the

market

because

it

is

more

difficult

toearlier

announcements

than

later

announcements.

Chambers

and

Penman

(1984)

argue

that

longerreportinglags

provide

the

opportunity

for

more

of

the

report’s

information

to

be

supplied

by

other

sourcethrough

search

activity

by

investors,

through

other

voluntary

disclosures

by

firms,

or

through

prthatare

supplied

in

the

earnings

releases

of

earlier

reporting

firms.Haw

et

al.

(1999)

study

the

Chinese

stock

market

and

findthat

firms

withgoodnews

publicize

their

annreports

earlier

thanthose

withbad

news,

and

loss-making

firms

are

the

lastto

release

their

annual

reThey

define

the

reporting

lag

as

the

number

of

days

from

the

fiscal

year-end

to

the

report

announcementEarlier

announcements

should

generate

a

greater

surprise

in

the

market

because

it

is

more

difficult

to

predict

earlier

announcements

than

later

announcements.

Chambersand

Penman

(1984)

argue

that

longer

reporting

lags

provide

the

opportunity

for

more

ofthe

report’s

information

to

be

supplied

by

other

sources,

either

through

search

actiby

investors,

through

other

voluntary

disclosures

by

firms,

or

through

predictions

tharesupplied

in

the

earnings

releases

of

earlier

reporting

firms.Haw

et

al.

(1999)

study

the

Chinese

stock

market

and

find

that

firms

with

good

newspublicize

theirannual

reports

earlier

than

those

with

bad

news,

and

loss-making

fi

are

the

last

to

release

theirannual

reports.

They

define

the

reporting

lag

as

thenumber

of

days

from

the

fiscal

year-end

to

thereport

announcement

date.更

进

一

步

的

分

析1.

First,

normally

due

to

potential

insitrading

and

information

leakage,

it

ispossible

that

the

market

reaction

stalong

before

the

actual

announcements.Consequently,

we

employ

[-20,

2]and

[-20,

-3]

to

capture

the

possible

pevent

reaction.2.

Second,

in

the

relatively

efficient

markannouncement

effects

shouldnot

exist

inlong

event

window.

Therefore,

we

use

fourshort

symmetrical

event

windows

to

capturannouncement

effects.They

are

[-1,

+1],+2],

[-5,

+5],

and

[-7,

+7].时间窗口的确定[-20,

2][-20,

-3][-1,

+1][-2,

+2][-5,

+5][-7,

+7]共6个250

trading

days

from

day

–280

to

day

–31.A

time

gap

between

the

end

of

the

estimation

window

and

the

begiof

the

event

window

(i.e.

from

day

–30

to

day

–21)

is

employedusing

unusualpriceor

volume

data

(due

to

information

leaka-gemodel

estimation.d比较期间（beta期间）To

focus

our

analysis

on

the

number

of

tradable

days,

we

define

the

reporting

lagthe

number

of

working

days

from

the

fiscal

year-end

to

the

annual

release

date.–

1.

a

continuous

variable,

Announcement

Timing

Index

(ATI),

to

proxy

the

reporting

lag,which

isdefined

as

ATI

=

n/N,

where

n

is

the

nth

working

day

from

January

1

on

whichthe

earnings

announcement

is

made.N

is

the

total

number

of

working

days

in

the

periodfrom

January

1

to

April

30

inthe

event

year.三个不同的时间变量(TEA)定义三个不同的时间变量(TEA)定义the

unexpected

ATI

(UATI),

a

proxy

for

the

unexpected

reporting

lag,

is

def

as

the

difference

between

the

actual

and

expected

ATI

(the

expected

ATI

of

the

current

year

should

be

the

same

as

the

ATI

of

the

previous

year),

UATI

=

ATIt

–

ATIt-1.The

final

TEA

is

a

dummy

variable,

called

MAD,

with

a

value

of

1

for

Marchand

April

announcements

and

0

otherwise.Null

Hypothesis:

Firms

with

earlier

and

laterearnings

announcements

should

receive

similarabnormal

market

reaction.简单的假设Alternative

Hypothesis:

Firms

with

earlierearnings

announcements

should

receive

a

higherabnormalmaket

reaction.主

要

模

型■主

要

模

型tt异常交易量的决定因素多变量回归模型CATV

(CAR)

=

0

+

1UEA

(UERW,

UEGM)+

2SIZE

+

3POWN

+

4

TEAt(UATI,

ATI,

MAD)+

5EXCH

+

iYEARi-5

+

jINDj-12

+18FORCATVPOWNUEAEXCHINDSIZETEAYEARFORCAR累积异常交易量累积异常收益率未预期盈余的绝对值人民币计价的总资产的自然流通股所占百分比盈余公告时间交易所哑变量公告年的哑元变量行业哑变量外资股的哑变量Abnormal

Trading

Volume

around

EarningsAnnouncement

by

bi-monthly

sampleJanuaryand

February

(#

Obs

=

417)March

and

April

(#

Obs

=

3385)DayATVt-valueATVt-value-70.00151.640.00071.63-60.00242.39*0.00102.12*-50.00242.25*0.00091.86-40.00443.40**0.00071.61-30.00453.59**0.00112.38*-20.00554.41**0.00102.05*-10.00926.25**0.00193.78**00.01347.87**0.007112.02**+10.01297.63**0.007112.24**+

20.00915.62**0.00366.95**+

30.00554.05**0.00183.61**+

40.00322.64**0.00081.67+

50.00301.860.00061.31+

60.00181.440.00091.89+

70.00201.580.00102.02*IntervalCATVz-valueCATVz-value[-20,2]0.0841a13.32

**0.0380a15.67**[-20,-3]0.0340b7.57**0.0173b8.98**[-7,7]0.0808c14.62**0.0302c14.76**[-5,5]0.0731d14.94**0.0266d14.92**[-2,

2]0.0501e14.21**0.0207e16.57**[-1,

1]0.0355f12.56**0.0161f16.19**Abnormal

Trading

Volume

around

EarningsAnnouncement

by

bi-monthly

sample◎

Most

of

the

ATVs

for

all

monthly

samplesaresignificant,

whindicates

that

the

announcements

do

provide

information

tomarket.◎

The

magnitudesofthe

ATVs

and

CATVs

for

the

January

andFebruary

sample

are

much

greater than

those

for

the

March

anApril

sample.Lowest

40%

ofATI

SampleHighest

40%of

ATI

SampleDifferenceMean

CATVCATV30.02530.01410.0112cdCATV50.03370.01820.0155cdCATV110.04780.02290.0249cdCATV150.05450.02580.0287abCATV180.02650.0298-0.0033CATV230.06020.04790.0123Panel

A

:

Between

the

Lowest

40%

of

the

ATI

Sampleand

Highest

40%

of

the

ATI

SamplePositive

UATSampleNegative

UATSampleDifferenceMean

CATVCATV30.01060.0290-0.0184cCATV50.01320.0413-0.0281cCATV110.01600.0631-0.0471cCATV150.01660.0755-0.0589cCATV180.01100.0407-0.0297aCATV230.02420.0820-0.0578cPanel

B:

Between

the

Positive

UATI

Sampleand

Negative

UATI

SampleThe

lowest

40%

of

ATI

samples

demonstrates

a

significantly

greatvolumereaction

than

those

of

the

highest

40%

of

ATI

samples.The

negative

UATI

samples

demonstrate

a

significantly

greatervolume

reaction

than

those

of

the

positive

UATI

samples.earlier

announcements

provide

more

information

content

to

themarket

than

later

announcements

do.CATV3CATV5CATV11CATV15Intercept0

.15900

.24800

.47600

.7070(4

.12

)**(4

.28

)**(4

.40

)**(5

.16

)**UERW0

.00050

.00100

.00180

.0023(2

.42

)*(3

.19

)**(3

.25

)*(3

.21

)**SIZE-0

.0068-0

.0110-0

.0228-0

.0341(-3

.31

)**(-3

.57

)**(-3

.96

)**(-4

.67

)**POWN-0

.0052-0

.0105-0

.0085-0

.0362(-0

.43

)(-0

.57

)(-0

.25

)(-0

.83

)UATI-0

.0282-0

.0384-0

.0568-0

.0596(-3

.47

)**(-3

.14

)**(-2

.48

)*(-2

.06

)*EXCH0

.00820

.01590

.03360

.0392(2

.37

)*(3

.05

)**(3

.45

)**(3

.19

)**YEAR2-0

.0410-0

.0623-0

.1090-0

.1420(-5

.69

)**(-5

.74

)**(-5

.36

)**(-5

.53

)**YEAR30

.01170

.01070

.00780

.0057(1

.72

)(1

.04

)(0

.41

)(0

.24

)YEAR4-0

.0596-0

.0941-0

.1800-0

.2580–

Results

of

Regression

Model

for

CATVYEAR

5-0.0397-0.0604-0.1050-0.1550(-3.52)**(-3.56)**(-3.31)**(-3.88)**YEAR

6-0.0599-0.0893-0.1560-0.2180(-5.59)**(-5.54)**(-5.16)**(-5.71)**YEAR

7-0.0592-0.0877-0.1590-0.2230(-5.73)**(-5.63)**(-5.46)**(-6.07)**IND

10.05490.07530.15400.2190(2.84)**(2.59)**(2.83)**(3.19)**IND

20.0000-0.00190.0010-0.0011(-0.01)(-0.20)(0.06)(-0.04)IN