數(shù)據(jù)和實證研究課件

1、數(shù) 據(jù) 與 實 證 研 究 gtadata 高 寧 博 士國泰安信息技術(shù)有限公司常務(wù)副總裁西安交通大學(xué)教授香港浸會大學(xué)商學(xué)院 Honorary Associate香港浸會大學(xué)公司管制與金融政策研究中心 Research Fellow1不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 什么是實證研究？ 以事實、實際情況和收集到的數(shù)據(jù)為對象，通過分析、計算、實驗、研究，解釋和預(yù)測會計金融實務(wù)，回答“實際是什么”的問題。2不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 實證研究要求客觀、準(zhǔn)確、理性的描述現(xiàn)實 實證研究以解釋現(xiàn)實為目的，認(rèn)為存在就是事實 實證研究采用客觀中立的立場 目前，

2、在國際上，實證研究方法廣泛的應(yīng)用在經(jīng)濟、金融、會計等社會學(xué)科的研究中3不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 實 證 研 究 的發(fā) 展 與 趨 勢4不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 -實證經(jīng)濟學(xué) 1953 弗里德曼實證經(jīng)濟學(xué)方法論發(fā)展歷程-實證會計學(xué) 1968 Ball，R.J., P.Brown An Empirical Evaluation of Accounting Income NumbersJournal of Accounting Research 1986 Watts， Zimmerman 實證會計理論趨勢由于金融市場每天都產(chǎn)生海量的數(shù)據(jù)，這

3、些數(shù)據(jù)又是從真實的交易 過程中產(chǎn)生的, 這一特性使實證研究成為現(xiàn)代金融研究的主流話語” Ross20世紀(jì)80年代Accounting Review上實證性研究的論文占半數(shù)以上，有的年份還高達(dá)81。現(xiàn)在實證研究已成為會計，金融研究的主流。5不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 推動實證研究發(fā)展的因素（William Beaver)6不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 推動實證研究發(fā)展的因素（William Beaver)財務(wù)和經(jīng)濟學(xué)的發(fā)展1證券市場在經(jīng)濟中的地位2政府對證券市場的積極監(jiān)管，不斷推出新的課題3機構(gòu)投資者占股權(quán)比重的增大4計算機技術(shù)和數(shù)據(jù)庫的發(fā)展

4、56學(xué)術(shù)刊物受重視程度的增強7不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 實 證 論 文 篇 數(shù)8不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 實 證 論 文 篇 數(shù)類型1994201920192019201920192000201920192019201920192019實證研 究論文13616120532538154764183011531751248235665043經(jīng)濟類實證論文82721151681882362763354326971026139919799不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 實 證 的 要 素10不積蹞步，無以致千里；不積小

5、流，無以成江海 友友情分享 實 證 的 要 素 結(jié)論推理檢驗 假設(shè)模型 數(shù)據(jù)實證的要素 數(shù)據(jù)：反映客觀狀況的數(shù)字材料。 模型：刻畫客觀現(xiàn)象的數(shù)學(xué)形式。 假設(shè)：對所研究問題的結(jié)果或狀態(tài)的 一種預(yù)期。 檢驗：利用數(shù)據(jù)，使用統(tǒng)計學(xué)知識對假設(shè)的統(tǒng)計顯著性作出判斷。 推理：基于知識和經(jīng)驗對假設(shè)檢驗結(jié)果進行推理。 結(jié)論：利用假設(shè)檢驗的結(jié)果，通過合情的邏輯推理得出的結(jié)論，觀點。11不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 實證研究方法步驟12不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 確立研究課題實 證 研 究 方 法 步 驟尋找相關(guān)理論提出命題假設(shè)設(shè)計研究方案搜集事實數(shù)據(jù)分析數(shù)據(jù)

6、檢驗命題得出研究結(jié)論13不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 金 融 實 證 研 究 的 主 要 領(lǐng) 域14不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 包括現(xiàn)代投資組合理論、資本資產(chǎn)定價理論、套利定價模型、期權(quán)定價模型、有效邊界、資本市場線、證券市場線等。包括資金成本傳統(tǒng)理論、凈利理論和營業(yè)凈利理論、權(quán)衡理論和融資偏好次序等。投資組合選擇和資產(chǎn)定價資金成本和資本結(jié)構(gòu)理論市場微觀結(jié)構(gòu)研究交易價格發(fā)現(xiàn)過程與交易運作機制，包括價格發(fā)現(xiàn)的模型和市場結(jié)構(gòu)與設(shè)計。行為金融學(xué)研究投資者的心理、個人特征等因素與其交易行為之間的關(guān)系，包括 個人信仰(過度自信、 樂觀主義、代表性、保

7、守主義、確認(rèn)偏誤、定位、記憶偏誤), 個人偏好(展望理論、模糊規(guī)避) 15不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 會 計 實 證 研 究 的 主 要 領(lǐng) 域16不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 會計制度的選擇研究企業(yè)會計制度的選擇與企業(yè)營運績效之間的關(guān)系 盈余管理研究企業(yè)管理當(dāng)局借助會計政策的選擇和會計估計的變更，尋求對自己有利結(jié) 果的行為及其影響會計舞弊研究公司采取偽造、掩飾的手法編造假賬損害股東權(quán)益、影響投資者做出正確 投資決策的行為財務(wù)預(yù)測會計信息披露效應(yīng)財務(wù)困境會計信息的價值相關(guān)性研究如何根據(jù)財務(wù)活動的歷史資料和現(xiàn)實情況對企業(yè)未來財務(wù)活動進行科學(xué)的

8、預(yù)計和測算研究上市公司會計信息披露與公司股票價格之間的關(guān)系研究企業(yè)陷于財務(wù)困境的特征及影響因素，主要包括財務(wù)困境企業(yè)與非財務(wù)困境企業(yè)之間財務(wù)項目的分析研究會計信息價值相關(guān)性對于會計準(zhǔn)則制定、證券市場監(jiān)管和投資者進行決策的作用17不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 CSMAR 實 證論 文 舉 例18不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 文章研究了中國上市公司盈余公告時間選擇對股票交易量和未預(yù)期收益的影響。研究發(fā)現(xiàn)，與較晚月份公告盈余的公司相比，較早月份進行年度盈余公告的公司具有較強的股票交易量反應(yīng)。文章認(rèn)為愿意早些公告盈余的公司往往擁有利好的信息，并且這些

9、較早的盈余公告含有更大的信息量,帶來較大的交易量增幅和未預(yù)期收益；較晚公告盈余的公司則往往擁有利差的信息，而且更容易被市場預(yù)期,因而帶來的交易量增幅和未預(yù)期收益也較小。題 目作 者發(fā)表刊物摘 要Information Content and Timing of Earnings Announcements陳工孟 高 寧 鄭子云（香港理工大學(xué)）Journal of Business Finance and Accounting, January 2019, Vol 32, Iss. 1- 2, Pg. 659519不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 數(shù) 據(jù) 樣 本20不積蹞步

10、，無以致千里；不積小流，無以成江海 友友情分享 以2019年至2019年間發(fā)行A股或同時發(fā)行A，B股，在時間區(qū)間內(nèi)發(fā)表年度盈余公告的上市公司為研究樣本。樣本容量為3802。年份樣本數(shù)月（）月（）月（）4月（）20192656(2.26)9(3.40)81(30.57)169(63.77)20192941(0.34)6(2.04)33(11.22)254(86.40)20193504(1.14)10(2.86)52(14.86)284(81.14)20195904(0.68)45(7.63)269(45.59)272(46.10)20193508(2.28)9(2.57)87(24.86)246

11、(70.29)200053145(8.47)50(9.42)188(35.41)248(46.70)201966313(1.96)108(16.29)299(45.10)243(36.65)201975915(1.98)84(11.07)277(36.50)383(50.45)Total380296(2.52)321(8.45)1286(33.82)2099(55.21)21不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 CSMAR 總 體樣 本 容 量22不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 CSMAR 總 體 樣 本 容 量23不積蹞步，無以致千里；不積小流，無以

12、成江海 友友情分享 文 獻(xiàn) 回 顧 和 假 設(shè)24不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 為什么選交易量而不是價格 Bamber, Barron and Stober (2019) suggest that trading volume is related to the magnitude of the disagreement among investors about a firms earnings. Kim and Verrecchia (1991a) argue that price changes reflect the average change in the

13、 aggregate markets average beliefs, while trading volume is the sum of all individual investors trades, which also depends on the prevailing information asymmetry level before disclosure. They suggest that although all investors have equal access to public pre-disclosure information, they acquire pr

14、ivate pre-disclosure information with different degrees of precision.25不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 為什么選交易量而不是價格 Atiase and Bamber (1994) and Kross et al. (1994)suggest that trading volume is an increasing function of the degree of divergent pre-disclosure expectations。 Bamber and Cheon (2019) argue

15、that the reason for different reactions is that price reactions reflect the average belief revision, while trading volume arises when individual investors make differential belief revisions.26不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 更 進 一 步 的 分 析 Kim and Verrecchia (1994) suggest that there may be more informati

16、on asymmetry at the time of an announcement than in a non-announcement period. This is because earnings announcements provide information that allows certain traders to make judgements about a firms performance that are superior to the judgements of other traders. Lobo and Tung (2019) find that the

17、trading volume around quarterly earnings announcements is related to the level of pre-disclosure information asymmetry. For firms with a high level of pre-disclosure information asymmetry, the trading volume is low prior to and after the announcement, but high during the announcement.27不積蹞步，無以致千里；不積

18、小流，無以成江海 友友情分享 更 進 一 步 的 分 析Bamber(1986) employs the divergence of earnings forecasts from analysts forecasts as a proxy for information asymmetry. She finds that the higher the information asymmetry, the greater the abnormal volume reaction. In this study, we first use unexpected earnings as a cont

19、rol variable for information asymmetry.Earlier announcements should generate a greater surprise in the market because it is more difficult to predict earlier announcements than later announcements. Chambers and Penman (1984) argue that longer reporting lags provide the opportunity for more of the re

20、ports information to be supplied by other sources, either through search activity by investors, through other voluntary disclosures by firms, or through predictions that are supplied in the earnings releases of earlier reporting firms.Haw et al. (2019) study the Chinese stock market and find that fi

21、rms with good news publicize their annual reports earlier than those with bad news, and loss-making firms are the last to release their annual reports. They define the reporting lag as the number of days from the fiscal year-end to the report announcement date. 28不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 Earlier

22、announcements should generate a greater surprise in the market because it is more difficult to predict earlier announcements than later announcements. Chambers and Penman (1984) argue that longer reporting lags provide the opportunity for more of the reports information to be supplied by other sourc

23、es, either through search activity by investors, through other voluntary disclosures by firms, or through predictions that are supplied in the earnings releases of earlier reporting firms.Haw et al. (2019) study the Chinese stock market and find that firms with good news publicize their annual repor

24、ts earlier than those with bad news, and loss-making firms are the last to release their annual reports. They define the reporting lag as the number of days from the fiscal year-end to the report announcement date. 更 進 一 步 的 分 析29不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 1. First, normally due to potential inside

25、r trading and information leakage, it is possible that the market reaction starts long before the actual announcements. Consequently, we employ -20, 2 and -20, -3 to capture the possible pre- event reaction. 2. Second, in the relatively efficient market, announcement effects should not exist in the

26、long event window. Therefore, we use four short symmetrical event windows to capture announcement effects. They are -1, +1, -2, +2, -5, +5, and -7, +7. 時 間 窗 口 的 確 定-20, 2-20, -3-1, +1-2, +2-5, +5-7, +7共6個30不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 250 trading days from day 280 to day 31. A time gap between the e

27、nd of the estimation window and the beginning of the event window (i.e. from day 30 to day 21) is employed to avoid using unusual price or volume data (due to information leaka-ge) for model estimation.比 較 期 間（beta 期 間）31不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 To focus our analysis on the number of tradable day

28、s, we define the reporting lag as the 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 is defined as ATI = n/N, where n is the nth working day from January 1 on which the earnings a

29、nnouncement is made. N is the total number of working days in the period from January 1 to April 30 in the event year.三個不同的時間變量(TEA)定義32不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 三個不同的時間變量(TEA)定義2. the unexpected ATI (UATI), a proxy for the unexpected reporting lag, is defined as the difference between the actual

30、and expected ATI (the expected ATI of the current year should be the same as the ATI of the previous year), UATI = ATIt ATIt-1.3. The final TEA is a dummy variable, called MAD, with a value of 1 for March and April announcements and 0 otherwise.33不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 Null Hypothesis: Firms wi

31、th earlier and later earnings announcements should receive similar abnormal market reaction.簡 單 的 假 設(shè)Alternative Hypothesis: Firms with earlier earnings announcements should receive a higher abnormal maket reaction.34不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 主 要 模 型 35不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 主 要 模 型 異常交易量的決定因

32、素多變量回歸模型 CATV (CAR) = 0 + 1UEA (UERW, UEGM)+ 2SIZE + 3POWN + 4 TEA (UATI, ATI, MAD)+ 5EXCH + iYEARi-5 + jINDj-12 + 18 FORCATVPOWNUEAEXCHINDSIZETEAYEARFORCAR累積異常交易量累積異常收益率未預(yù)期盈余的絕對值人民幣計價的總資產(chǎn)的自然流通股所占百分比盈余公告時間交易所啞變量公告年的啞元變量行業(yè)啞變量外資股的啞變量36不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 Abnormal Trading Volume around Earnings

33、 Announcement by bi-monthly sample January and 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.

34、007112.02*37不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 +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-20,20.0841a13.32 *0.0380a15.67*-20,-30.0340b7.57*0.0173b8.98

35、*-7,70.0808c14.62*0.0302c14.76*-5,50.0731d14.94*0.0266d14.92*-2, 20.0501e14.21*0.0207e16.57*-1, 10.0355f12.56*0.0161f16.19*Abnormal Trading Volume around Earnings Announcement by bi-monthly sample 38不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 Most of the ATVs for all monthly samples are significant, which indicates

36、 that the announcements do provide information to the market. The magnitudes of the ATVs and CATVs for the January and February sample are much greater than those for the March and April sample. 39不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 CATV3CATV5CATV11CATV15CATV18CATV23Lowest 40% of ATI Sample0.02530.03370.047

37、80.05450.02650.0602Highest 40% of ATI Sample0.01410.01820.02290.02580.04790.0298Difference in Mean CATV0.0112cd0.0155cd0.0249cd0.0287ab-0.00330.0123 Panel A : Between the Lowest 40% of the ATI Sample and Highest 40% of the ATI Sample 40不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 CATV3CATV5CATV11CATV15CATV18CATV23Po

38、sitive UATISample0.01060.01320.01600.01660.01100.0242Negative UATISample0.02900.04130.06310.07550.08200.0407Difference in Mean CATV-0.0184cd-0.0281cd-0.0471cd-0.0589cd-0.0297a-0.0578cd Panel B: Between the Positive UATI Sample and Negative UATI Sample 41不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 The lowest 40% of

39、ATI samples demonstrates a significantly greater volume reaction than those of the highest 40% of ATI samples. The negative UATI samples demonstrate a significantly greater volume reaction than those of the positive UATI samples. earlier announcements provide more information content to the market t

40、han later announcements do. 42不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 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)

41、(-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)*YEAR30.0117 0.0107 0.00780.0057(1.72)(1.04)(0.41)(0.24)YEAR4-0.0596-0.0941-0.1800 -0.2580(-5.

42、17)*(-5.42)*(-5.55)*(-6.28)* Results of Regression Model for CATV 43不積蹞步，無以致千里；不積小流，無以成江海 友友情分享 YEAR5-0.0397-0.0604-0.1050-0.1550(-3.52)*(-3.56)*(-3.31)*(-3.88)*YEAR6-0.0599-0.0893-0.1560-0.2180(-5.59)*(-5.54)*(-5.16)*(-5.71)*YEAR7-0.0592-0.0877-0.1590-0.2230(-5.73)*(-5.63)*(-5.46)*(-6.07)*IND10.05490.07530.15400.2190(2.84)*(2.59)*(2.83)*(3.19)*IND20.0000-0.00190.0010-0.0011 (-0.01)(-0.20)(0.06)(-0.04)IND30.00100.0023-0.0027-0.0008(0.14)(0.20)(-0.12)(-0.03)IND40.00390.00460.0034-0.0002(0.79)(0.61)(0.24)(-0.01)IND50.00600.01000.01750.0203(1.13)(1.25)(1.18)(1.08)FOR-0.0048-0.0048