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FNCE 435 – Empirical Finance
创建日期：2024-04-18 15:09:05
所属分类：金融Finance
标签： FNCE 435
Empirical Finance

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FNCE 435 – Empirical Finance 

Part I - Concepts You are examining the relationship between bonus paid to the CEO and its firm’s future performance. Your alternative hypothesis is that the higher the bonus paid, the higher the performance of the firm. For a bonus paid in year t, future performance (PERF) is measured as the firm’s average monthly return in the 3 years following t (that is, t+1 through t+3). To measure bonus, the study defines LBONUS as the natural logarithm of the bonus paid (US$ thousands) to the CEO in year t. The other variables of the study are:  LSIZE: the logarithm of market value of equity (US$ millions) for the firm measured at year t.  BEME: the ratio of book value of equity to market value of equity, both measured at the end of year t  CHAIR: dummy equal to 1 if the CEO is also the Chairman of the Board in year t  TENURE: the number of years since the person started in the CEO position Summary statistics for the data as of t=2006 is shown in Figure 1. For our sample, the 3- year average performance of firms is 2.63%, firm’s average size is $8.4 billion, average book-to-market is 0.52, and average CEO bonus is $738,000. Finally, 18% of the firms in our sample have CEO cumulating the role of Chairman of the Board, and the average CEO tenure is 3.43 years. Figure 1: Summary statistics The results of a SAS execution of the regression explaining future performance, PERFi=β0+ β1*LBONUSi + β2*LSIZEi + β3*BEMEi + β4*CHAIR+ β5*TENURE + εi appear in Figure 2. FNCE 435 Fall 2021 Assignment 8 Page 2 Figure 2: Regression results a) Examine the effect of bonus payments on company’s future performance. Please formulate your hypothesis clearly and how it is being tested in the model above. Do bonus payments matter? How so? b) Define a 95% two-tailed confidence interval for the coefficient on tenure (TENURE). Then conclude whether and how tenure affects the future performance of the firms. c) Interpret the coefficient β0 in the model above. Is it meaningful in this regression d) Your colleague argues that, since you are interested in the relationship between BONUS and future performance, you should be running a simple regression model, as in PERFi=β0+ β1*LBONUSi + εi Your colleague believes that this would give a cleaner measure of association between these measures, and that the introduction of other right-hand side variable can confound the true association between bonus and performance. Please argue which model—your colleague’s model or the extended version used to produce the results in Figure C.2— is more suited to the analysis of the relationship between bonus and performance, and why. e) After running the regression model whose result appears in Figure 2, you were shown the following pattern for the errors in the regression. FNCE 435 Fall 2021 Assignment 8 Page 3 Figure 3: Errors from regression model explaining firm performance What does that illustrate as a problem with respect to the assumptions required to run a regression model? How can you confirm this as a problem, and what measure can you use to remedy the problem? Part II – Empirical Examination of Reactions to Earnings Announcements We will examine reactions to earnings announcements. Companies release earnings numbers every quarter. Depending on the expectations that investors have regarding the soon-to-be-released numbers, these earnings announcements might convey good, bad, or no news to investors. We can then look at market reactions to earnings news to verify the financial implications of earnings numbers. Why this study matters? Not only for academic purposes. There is a huge industry out there that tries to predict where earnings will be. If market do react to earnings news (our hypothesis) and you know how to predict the news, then you have a money machine! See in Figure 5 an article from The New York Times about this. Our sample involves all quarterly earnings announcements for U.S. public firms trading at NYSE or NASDAQ for the first quarter of 2013. The sample is in the dataset “a8_earnings.sas7bdat” file, available on Canvas. Each data point involves an earnings announcement. An example of such data point is show in Figure 4. Figure 4. One data point of the sample of earnings announcements It shows the earnings announcement for the company with PERMNO=10225 (named “BEAM INC”) and for the quarter ending in PENDS=03/31/2013. Earnings per share for that quarter were released in 05/02/2013 (variable ANNDATS). The earnings per share released is stored in the variable VALUE, while the market expectation is stored in the variable MEANEST. So, for the first quarter of 2013, J & J’s earnings number was $0.64, a bit more than the market expectation of $0.54. -15 -10 -5 0 5 10 15 20 25 30 -6 -4 -2 0 2 4 6 Residual Predicted AR permno gvkey comnam pends anndats value meanest car_ea n_buys n_holds n_sells 10225 1408 BEAM INC 31-Mar-13 2-May-13 0.64 0.54 0.0194 1 0 0 FNCE 435 Fall 2021 Assignment 8 Page 4 As for market reaction, we define a variable CAR_EA that measures the cumulative abnormal return for the window [0,1] around the earnings—that is, it adds the abnormal return in the day after the announcement day and the abnormal return at the announcement day. This makes sense since some earnings announcements happen after the close of the market, and so their effects materialize only in the next trading day. For the first quarter of 2013, the abnormal reaction to BEAM INC’s earnings announcement was 1.94% (notice that returns here are stored in decimals). The idea is to examine and quantify the relationship between the market reaction and the surprise in the earnings announcement. We define SURPRISE as the gap between the actual earnings and the analyst’s expectation about the earnings right before the announcement date. In our example above, the surprise was VALUE – MEANEST=0.64– 0.54=$0.10; or, 10 cents per share. Given that earnings were above the market expectations, the announcement amounts to good news! We are interested in a linear relationship between CAR_EA and surprise, as follows: CAR_EA= β0+β1*SURPRISE+ε Earnings surprise might matter for market reactions, but there are other explanations for the reactions to earnings announcements. The same Wall Street analysts that produce earnings forecasts also produce recommendations on the stocks of the firms they follow. (We’ve seen them in assignment 6 already.) Recommendations come with extensive research reports, but in the end a recommendation amounts to a statement about whether someone should buy, hold, or sell the fim’s stock. If recommendations do affect the price of each share, and recommendations are issued around earnings announcements, we may see market reactions to the earnings announcement due to recommendations, rather than (or on top of) the surprise in the announcement. In order to examine this possibility, we also collect, for each earnings announcement, the recommendations issued for the firm in the day right before the earnings announcement. The new variables are: N_BUYS (the number of buy recommendations issued for the firm in the day preceding the earnings announcement for that quarter); (3) N_HOLDS (the number of hold recommendations); and (5) N_SELLS (the number of sell recommendations). Our example in Figure 4 shows that there was one buy recommendation (but no hold, nor sell recommendation) issued for PERMNO=10225 one day before the earnings for the first quarter of 2013 was released. We now have to think about which control variables to adopt to our examination. First, we will control the issuance of recommendations, and thus use the variables N_BUYS, N_HOLDS, and N_SELLS. Our question thus becomes: after controlling for the issuance of recommendations, does surprise in earnings numbers have any say on reactions to earnings? Page 5 Figure 5. Earnings announcements and trading strategies (NYT, 01/27, 2008, Sunday Business Section, Page 6) The second control variable is firm size. We know that size is related to returns. Plus we want to examine that relationship based on rates of change in firm size. We will use two proxies for firm size:  The variable LMVE is defined as the natural logarithm of the firm’s market value of equity in the year before the earnings. Since our data refer to 2013 earnings, you FNCE 435 Fall 2021 Assignment 8 Page 6 will collect market value of equity as of December 2012. Market value of equity is abs(PRC)*SHROUT, where both PRC (stock price) and SHROUT (shares outstanding) are variables available in the CRSP dataset "msf.sas7bdat" (located in "/wrds/crsp/sasdata/a_stock");  The variable LTA is defined as the natural logarithm of firm’s total assets, measured in 2012. Total assets is the variable AT in Compustat dataset FUNDA, located at “/wrds/comp/sasdata/nam”. The baseline model, using LVME as our proxy for firm size, thus becomes: CAR_EA= β0+β1*SURPRISE+β2*LMVE+β3*N_BUYS+β4*N_HOLDS +β5*N_SELLS+ε But, before we run the regression model, generate and show some summary statistics for the basic variables of your model. The summary statistics you should have for each variable are: the number of observations, the average, the standard deviation, the minimum and maximum value. A first look at reactions to earnings announcements: Let’s first test reactions to earnings announcements, conditioned on the type of surprise, in some event studies. You will run 4 event studies. First, test whether reactions to earnings with positive surprises (that is, the sample of earnings with SURPRISE>0) are significantly positive. That is, formally test: Ho: E(CAR_EASurprise>0)=0 Ha: E(CAR_EASurprise>0)>0 Second, you test whether reactions to earnings with negative surprises (that is, the sample of earnings with SURPRISE<0) are significantly negative. Third, you test whether reactions to earnings with no surprises (that is, the sample of earnings with SURPRISE=0) are significantly different from zero. Finally, let’s look at reactions to very small surprises—the ones with positive but up to 1 cent surprise. You test whether reactions to earnings with small positive surprises (that is, the sample of earnings with 0

Ho: E(CAR_EA0Ha: E(CAR_EA00
Here is the output you should produce and show:

Figure 6. Event study results
What can you learn from these event studies?

Sample # obs
Average
CAR_EA t
Earnings with negative surprise 0,000 0.0000 0.00
Earnings with no surprise 0,000 0.0000 0.00
Earnings with positive surprise 0,000 0.0000 0.00
Earnings with small (1 cent) surprise 0,000 0.0000 0.00
FNCE 435 Fall 2021 Assignment 8
Page 7

A first look at the regression results:
Recall that our basic regression model is
CAR_EA= β0+β1*SURPRISE+β2*LMVE+β3*N_BUYS+β4*N_HOLDS +β5*N_SELLS+ε
but there is some kind of misspecification in this model. We know that returns and firm
size are negatively related—that smaller firms tend to have higher returns. You (probably!)
learned from the event studies that reactions to earnings announcements are asymmetrical
and dependent on the surprise: abnormal returns are positive following good news (positive
surprises) and negative following bad news (negative surprises). We may thus see that
reactions to positive surprises are more positive the smaller the firm size (a negative
correlation between CAR_EA and firm size for the sample of positive surprises), but
reactions to negative surprises are more negative the smaller the firm size (a positive
correlation between CAR_EA and firm size for the sample of negative surprises).
In sum, the coefficient on LMVE may vary dependent on the subsample being analyzed.
Let’s thus run the regression model above for three subsamples as described in Figure 7.

Figure 7. Regression results, baseline model
Interpret the effect on CAR_EA, if any, of each of the right-hand side variables. Describe
whether the effect is dependent on which subsample you are employing.
Let’s now examine the robustness of our results to the concerns regarding
heteroscedasticity:
From this point onwards, let us focus on the subsample of earnings with positive
surprises—that is, with SURPRISE>0.
Let’s now examine the robustness of our results to the concerns regarding
heteroscedasticity. Thus, for the model II in Figure 7, generate a plot of residuals vs.
predicted CAR_EA. Does heteroscedasticity stand out visually from the graph?
Then, run the regression with the White test for heteroscedasticity. Does the test reject the
null of homoscedasticity?
If so, rerun the regression asking for t-statistics (and standard errors) that are robust to
heteroscedasticity. Create a new output (as the output from model II in Figure 7) for the
regression results, and comment on whether the inferences are robust to this change.

Intercept Surprise Lmve N_buys N_holds N_Sells # obs Adj-R 2
I All 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0,000 0.0%
[0.00] [0.00] [0.00] [0.00] [0.00] [0.00]
II Surprise>0 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0,000 0.0%
[0.00] [0.00] [0.00] [0.00] [0.00] [0.00]
III Surprise≤0 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0,000 0.0%
[0.00] [0.00] [0.00] [0.00] [0.00] [0.00]
Model
FNCE 435 Fall 2021 Assignment 8
Page 8

We then examine the robustness of our results to the concerns regarding correlation of
residuals:
(Still, use for this analysis only the subsample of earnings with positive surprises—that is,
with SURPRISE>0.)
First, make sure the data is sorted by the announcement date, that is, first run the code
proc sort data=d;
by anndats;
run;
then run the regression asking for the Durbin-Watson test for serial correlation and
comment whether serial correlation seems to be a problem for this regression specification.
Now test for the Durbin-Watson again, but, before running the regression, sort the data by
the firm identifier, that is, run the statement:
proc sort data=d;
by permno;
run;
Comment on the results you get for the Durbin-Watson test. (Hint: this is a test about
correlation of consecutive residuals, so it depends on sorting the data…)
Now exploring some other potential explanatory variables:
(Still, use for this analysis only the subsample of earnings with positive surprises—that is,
with SURPRISE>0.)
Recall you also collected a different proxy for firm size, LTA. Let’s change the model a
bit. Instead of using LMVE as a control variable, use instead LTA,
CAR_EA= β0+β1*SURPRISE+β2*LTA+β3*N_BUYS+β4*N_HOLDS +β5*N_SELLS+ε
Now your research team argues that you should use both LMVE and LTA in the same
model. In sum, you will the three regressions shown in Figure 8 below. (The first model
in Figure 8 you already ran.)That is, run regressions separately with LMVE and LTA, then
add them all in the same specification. Make sure that for all regressions here you use the
standard errors adjusted for heteroscedasticy.

Figure 8. Regression results, using different proxies for size
Intercept Surprise Lmve Lta N_buys N_buys N_sells # obs Adj-R 2
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0,000 0.0%
[0.00] [0.00] [0.00] [0.00] [0.00] [0.00]
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0,000 0.0%
[0.00] [0.00] [0.00] [0.00] [0.00] [0.00]
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0,000 0.0%
[0.00] [0.00] [0.00] [0.00] [0.00] [0.00] [0.00]
FNCE 435 Fall 2021 Assignment 8
Page 9

Do you see any issues with the inferences, regarding the proxies for firm size (LMVE and
LTA)? Can you get a hint of what is going on? You can use a PROC CORR to help you in
your analysis.
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MOEC0021 STA 106 ENGG 1300A ENGG 1300 OLET1139 STAT 3503 STAT 206 FEM11149 ELEN 4720 COMP20003 SOLA1070 CS574 C11CS LINB29H3 MAST90083 ECON7200 STAT 610 LINB29 DSM-5 COMP3161 EEEN40010 MATH 4431 K353 STA 3386 IERG4300 COMS W4167 ENG5298 AMME 2302 CS3910 COMPSCI 320 STATS 369 GR5242 COSC 328 CSCI435 MGTS1301 INFS2200 COMP3900 CIS 375 Module AE03 COMP3121 CS260 HAM503 MGMT5050 INT3075 COSC 285 GEOL0029 GEGE2X01 STAT1201 MATH1061 MMF1941H STAT 5060 CS 520 COMP 4320 MSIN0104 000T FINS3648 ECMT2160 ECON4003 Phys 129L CSE 142 CS 3130 MGEB01 EECS 340 Finance (20443/20444) ECMM149 COMP5310 EEEN60180 CSC 111 AT2 CSCI 4430 STAB57 MA/CSC 427 MATH322 D100 CSCI544 CITS3004 CI6227 CSE 565 APCO 1P01 852L1 ELEC S411F COMP3207 STATS330 ECON7530 FIT5149 CSI4104 CMPT 371 COMP201 SEHS4696 FI4003 MATH6182 CG1 WS MATH401 MGT 205 QTM 110 COMP 0137 MACE12201 FIT5210 ECE 544 BISM7206 HUDM 6055 ECON3203 STATS 210 MSCI 570 MATH6173 COMPSCI4039 Accounting COM3503 AOE2024 ECON213 PSYC10004 Control Data 100/200A PHIL2617 PHYS3116 MIE250 BCPM0008 ECN 620 QBUS6830 ECSE-415 ECON5102 MGMT20005 MAT 653 ECO 480 COMP 5/600 CSSE 5600 ME 571 7CCMMS61 CS 550 COMP2865 MATH3075 STATS 506 QTM 220 ISYS2120 FIT5106 STAT 153 MKTG3112 ECON2102 ELE2018 COMP3031 MSIN0010 SCC461 MATH237 CS246 CSC3065 CSE 101 ICTWEB411 COMP9313 ECOS 2025 FN620 MATH11111 MATH 3018 KE1068 FINA 6112 C++ MISM 6202 MSIN0105 CSC8631 MECO6935 COMP4121 ACCT2011 DPBS1150 EEL 3701C F19PL OPM 661 CSE 310 CS918 BIOL30001 ACCT3563 BANK3600 MET CS622 MATH367 ACCT5907 Continuing COMP5048 COMP61021 CS3334 GSOE9210 CSCI-570 COMP 5322 HW 2 ALY-6020 IEOR 4706 DATA 200 EBUS504 CGE12412 COMP4108 7SSMM800 ECMM171P MATE50002 CS225 RS 6003 BH2274 ELEC3111 MAT6669 ECON-UA 227 PHIL 1012 7SSMM603 TB028A P6 ECS708 CSC 427 CS6476 CSE 250A A1A2 ITS61504 EECS 376 model HW6 UV0010 ECON 6074 MG4F7 CMP-7030Y INFO3008 BFIP013 ECON 5068 MATH6002 CS3483 PSTAT 131 C31FM CSOR W4246 AAEC 5307 DATA ELEC362 COMSM0047 ECN6006 MA20224 BST351 MATH 323 MATH323 AA2021 COMP3811 COMP2013 IB9X60 INVP001 EMATM0051 COMP220 BLAW1002 E20 TCP8001 ECON61001 EART40005 ECONM1022 MATH 365 MATH 367 ELEC6218 COMP 5812M EDUC 5061M ELEC ENG 1101 equation MATH6174 ST334 C4 EC9570 A33648 EBUS603 ECON47815 stata WFMS0001 PEM102 INF6060 ionically ELECTRICAL MATH 375 MMW 12 MKTG860 ECON60401 ALY6010 SOST20131 Introduction COM3524 MME2 GGR305 JNB 538 EECS101 COMM5030 IEOR E4601 2B CSC336 EN2315 LAW 432 CSC263 MATH2001 OLET1143 CSCB63 SIT772 COMM1170 STAT 441 MS930 INFT3050 GLBH0030 Stat 120B MAST10007 MGEC61 Machine GGR 252H1S ECON7030 Financial Marketing MGMT30019 ECON5120 SDSC 8009 CSC165H1S DSCI 551 3081W ACM41000 ECA 5304 MATH 1064 ECSE444 DATA 604 COMP7105 MIE1615 ECON4004 ENV200H1S EC481 MKT 306 EC1B1 MATH08051 ALY6140 CS260C CSE30 MSIN0226 CS4103 CSC3064 AM16 COMP11212 CSE4101 PSTAT 174 Math 380W CNT 5106C STAT 310 FIN2028 CS5014 STAT 425 COMP 4102A ELEC2140 MANF9544 QRM 9 QRM 8 ECON 457 COMPSCI 4091 MATH 523 DNSC-4211 PEAS 6000 MSIN0180 COMP2119 A2A HT 2022 IB93F0 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ACCOUNTING BUSS1040 CHEM 181 MAT136H1 GY 6183 COMP3170 AGRI90089 PHYSICS 1002 ECON10003 IEOR E4004 ACS61012 MATH 40550 MKTG2113 TELE4653 ENSC1004 ECE 232E MK726 MTRX2700 SHEET 3 CSCA08 COMP30027 Math 2XX3 statistic MMAN3200 BISM7213 ECMM109 ECON 2022 FNCE 20005 GR5067 CSCB09 ME 3017 CS 5100 PHYS 2903 COMS 4771 COMP222 COMP 1046 CSC148H1 ISYS2038 FINS2615 OT5206 COMP2048 A6 MECH5770M MA826/20 CMPT 726 STA457 INFS6071 INFS588 integral ECON 216 IRDR 0017 MA323 PA 15213 PSYC2001 ENV222 Statistics ACCT5930 ACCT12 LAND2121 ME 5405 COMM1180 TELE3113 A7 ECE 568 ACCT2019 INFS1200 ENGG1340 CS 1400 ASST3 FINM7409 SWEN30006 INFO2222 FIN20150 SOSS1000 CSSE7231 FINS5513 COMS3200 CECN 702 ECON4060H MN3515 HW2 PSYA02H3 ECON 2112 COMP90051 CS 1652 MA30059 AF1605 MKTG7501 ECO-6003B COM4521 CTM ECON3350 COMP 3804 ELEC9762 BISM 7255 ST22 MATH20014 MNE3122 COMP343 MATH5165 ELE7029 ENGR30002 SEEM3500 ECON7350 RISK5002 FINM7405 COMP90059 MOS 3320B STAT 431 BISM7221 MATH3014 MTH783P EBA 35302 ACS133 BUS2206 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FN1024 MATH0094 MANG 6554 ECOS3003 ELEC6252 C2511C PHY328 EC1060 ECON32202 COMP4002-E1 MATH3063 EFIMM0124 QBUS2310 ECON4411 FUNDAMENTALS ACCT20001 18YX RPM COMP 2711 QBUS6600 BUSS5221 ST3189 FIT1047 MAT344 EGB375 MATH60013 MATH96011 COMP3557 MAST20029 ECM159 EAE1011 ENSCEN 105 SECTION A STA 141C SOSE 2022 PAM006 COMP0048 M40007 ISOM2500 CMPE 142 A06472 MCC150 ACST 8083 GEOG6087 ECON920 FINM7008 ENVENG 4110 COMPSCI 361 ENGRCEE 21 PHYS 1112 ACF2100 ECMM108 COMPS267F MAT00050M MAST30025 CSIT970 MSCI 224 INT202 PUBL0054 MSDM5004 BMAN 21020 BMAN21020 MGMT1002 MATH11150 LAWS7023 ACTL30002 CIVL3340 MATH1002 ECON 8069 COMPSCI 351 IBUS7302 STAT4528 CHEM1101 STAT3925 BUSN 7008 Comp 3120 ECOS2040 SWEN90010 ENGN6528 ECM158 ECON20022 COMP6452 Calculation CITS1401 S1889112 BUS 351 ACTL30008 FIT2091 ECON6002 IBUS6020 PHI 001 ECON30290 MATH7501 ECON10004 MFIN7017 ISYS90043 LAWS7012 ECON30025 COMP30023 COMM1190 ECOM20001 GSBS6009 STAT 337 EMET8002 ECOM90001 ECOM30001 FINC 2012 BUSINESS ENGCV512 ECO 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