ПРИЛОЖЕНИЕ A

Список событий утечки информации, используемый в исследовании

Название компании	Тикер	Дата утечки	Объем утечки	Отрасль	Характер утечки	Источник
Apple, Inc.	NYSE: AAPL	10.09.2012	12,367,232	ИТ	случайно опубликовано
Barnes & Noble	NYSE: BKS	23.10.2012	63 магазина	ритейл	взлом
Blizzard Entertainment	NYSE: ATVI	09.08.2012	14,000,000	ИТ	взлом
Global Payments	NYSE: GPN	30.03.2012	7,000,000	финансовая	взлом
KT Corporation	NYSE: KT	29.07.2012	8,700,000	телекоммуникации	взлом
New York State Electric & Gas	NYSE: IBDRY	23.01.2012	1,800,000	энергетика	инсайдер
TD Bank	NYSE: TD	09.10.2012	260,000	финансовая	взлом
Adobe Systems	NASDAQ: ADBE	03.10.2013	152,000,000	ИТ	взлом
Facebook	NASDAQ: FB	21.06.2013	6,000,000	ИТ	случайно опубликовано
Kroll Background America	NYSE: MMC	26.09.2013	1,000,000	ИТ	взлом
Vodafone	NASDAQ: VOD	12.09.2013	2,000,000	телекоммуникации	инсайдер
Yahoo Japan	NASDAQ:YAHOY	20.05.2013	22,000,000	ИТ	взлом
AOL	NYSE: AOL	22.04.2014	2,400,000	ИТ	взлом
Community Health Systems	NYSE: CYH	18.08.2014	4,500,000	здравоохранение	взлом
eBay	NASDAQ: EBAY	21.04.2014	145,000,000	ИТ	взлом
Gmail	NASDAQ: GOOGL|GOOG	10.09.2014	5,000,000	ИТ	взлом
Home Depot	NYSE: HD	08.09.2014	56,000,000	ритейл	взлом
JP Morgan Chase	NYSE: JPM	02.10.2014	76,000,000	финансовая	взлом
Sony Pictures	NYSE: SNE	08.12.2014	100 Тб	медиа	взлом
Supervalu	NYSE: SVU	15.08.2014 24.09.2014	200 магазинов	ритейл	взлом
Target Corporation	NYSE: TGT	19.12.2013	70,000,000	ритейл	взлом
UPS	NYSE: UPS	20.08.2014	51 филиал	ритейл	взлом
Anthem Inc.	NYSE: ANTM	04.02.2015	80,000,000	здравоохранение	взлом
CVS	NYSE: CVS	17.07.2015	миллионы	ритейл	взлом
T-Mobile US	NASDAQ: TMUS	01.10.2015	15,000,000	телекоммуникации	взлом
Natural Grocers	NYSE: NGVC	02.03.2015	93 магазина	ритейл	взлом
Hyatt, Sheraton, Mariott, Westin Hotels	NASDAQ: MAR	15.08.2016	54 филиала	отели	взлом
Twitch.tv	NASDAQ: AMZN	23.03.2015	неизвестен	ИТ	взлом
Walmart	NYSE: WMT	17.07.2015	миллионы	ритейл	взлом
Wendy's	NASDAQ: WEN	07.07.2016	неизвестен	ресторанная	взлом
Taobao	NYSE: BABA	03.02.2016	20,000,000	ритейл	взлом
Verizon Communications	NYSE: VZ	24.03.2016	1,500,000	телекоммуникации	взлом
Equifax	NYSE: EFX	2017	143,000,000	финансовая	взлом
Centene Corporation	NYSE: CNC	25.01.2016	950,000	здравоохранение	утеряны носители



ПРИЛОЖЕНИЕ Б

Исходный код программы.

library("lubridate")

library("devtools")

library("plm")

library("sandwich")

library("lmtest")

library("dplyr")

library("broom")

library("ggplot2")

library("Ecdat")

library("Quandl")

library("memisc")

# Загрузка данных в R

estimation_data <- read.table("JPM.csv",header=TRUE,sep=",")

event_data <- read.table("JPM_1.csv",header=TRUE,sep=",")

# Проведение регресионного анализа по модели Фама-Френча

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

# Прогнозирование цены акций на исследумом периоде

JPM <- predict(ffregression,newdata = event_data,se.fit = TRUE)

help(predict)

# Расчёт дисперсии

JPM$VAR <- JPM$se.fit^2

# Расчёт свердоходности

JPM$AR <- event_data$Event_stock_price - JPM$fit

# Расчёт кумулятивной сверхдоходности

JPM$CAR <- sum(JPM$AR)

# Расчёт кумулятивной дисперсии

JPM$VAR_CAR <- sum(JPM$VAR)

# Экспорт полученных данных

write.csv(JPM,"JPM_2.csv")

# Расчёт данных для остальных компаний

estimation_data <- read.table("AMZN.csv",header=TRUE,sep=",")

event_data <- read.table("AMZN_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

AMZN <- predict(ffregression,newdata = event_data,se.fit = TRUE)

AMZN$VAR <- JPM$se.fit^2

AMZN$AR <- event_data$Event_stock_price - JPM$fit

AMZN$CAR <- sum(AMZN$AR)

AMZN$VAR_CAR <- sum(AMZN$VAR)

write.csv(AMZN,"AMZN_2.csv")

estimation_data <- read.table("AAPL.csv",header=TRUE,sep=",")

event_data <- read.table("AAPL_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

AAPL <- predict(ffregression,newdata = event_data,se.fit = TRUE)

AAPL$VAR <- AAPL$se.fit^2

AAPL$AR <- event_data$Event_stock_price - AAPL$fit

AAPL$CAR <- sum(AAPL$AR)

AAPL$VAR_CAR <- sum(AAPL$VAR)

write.csv(AAPL,"AAPL_2.csv")

estimation_data <- read.table("ADBE.csv",header=TRUE,sep=",")

event_data <- read.table("ADBE_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

ADBE <- predict(ffregression,newdata = event_data,se.fit = TRUE)

ADBE$VAR <- ADBE$se.fit^2

ADBE$AR <- event_data$Event_stock_price - ADBE$fit

ADBE$CAR <- sum(ADBE$AR)

ADBE$VAR_CAR <- sum(ADBE$VAR)

write.csv(ADBE,"ADBE_2.csv")

estimation_data <- read.table("ANTM.csv",header=TRUE,sep=",")

event_data <- read.table("ANTM_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

ANTM <- predict(ffregression,newdata = event_data,se.fit = TRUE)

ANTM$VAR <- ANTM$se.fit^2

ANTM$AR <- event_data$Event_stock_price - ANTM$fit

ANTM$CAR <- sum(ANTM$AR)

ANTM$VAR_CAR <- sum(ANTM$VAR)

write.csv(ANTM,"ANTM_2.csv")

estimation_data <- read.table("AOL.csv",header=TRUE,sep=",")

event_data <- read.table("AOL_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

AOL <- predict(ffregression,newdata = event_data,se.fit = TRUE)

AOL$VAR <- AOL$se.fit^2

AOL$AR <- event_data$Event_stock_price - AOL$fit

AOL$CAR <- sum(AOL$AR)

AOL$VAR_CAR <- sum(AOL$VAR)

write.csv(AOL,"AOL_2.csv")

estimation_data <- read.table("ATVI.csv",header=TRUE,sep=",")

event_data <- read.table("ATVI_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

ATVI <- predict(ffregression,newdata = event_data,se.fit = TRUE)

ATVI$VAR <- ATVI$se.fit^2

ATVI$AR <- event_data$Event_stock_price - ATVI$fit

ATVI$CAR <- sum(ATVI$AR)

ATVI$VAR_CAR <- sum(ATVI$VAR)

write.csv(ATVI,"ATVI_2.csv")

estimation_data <- read.table("BABA.csv",header=TRUE,sep=",")

event_data <- read.table("BABA_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

BABA <- predict(ffregression,newdata = event_data,se.fit = TRUE)

BABA$VAR <- BABA$se.fit^2

BABA$AR <- event_data$Event_stock_price - BABA$fit

BABA$CAR <- sum(BABA$AR)

BABA$VAR_CAR <- sum(BABA$VAR)

write.csv(BABA,"BABA_2.csv")

estimation_data <- read.table("BKS.csv",header=TRUE,sep=",")

event_data <- read.table("BKS_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

BKS <- predict(ffregression,newdata = event_data,se.fit = TRUE)

BKS$VAR <- BKS$se.fit^2

BKS$AR <- event_data$Event_stock_price - BKS$fit

BKS$CAR <- sum(BKS$AR)

BKS$VAR_CAR <- sum(BKS$VAR)

write.csv(BKS,"BKS_2.csv")

estimation_data <- read.table("CNC.csv",header=TRUE,sep=",")

event_data <- read.table("CNC_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

CNC <- predict(ffregression,newdata = event_data,se.fit = TRUE)

CNC$VAR <- CNC$se.fit^2

CNC$AR <- event_data$Event_stock_price - CNC$fit

CNC$CAR <- sum(CNC$AR)

CNC$VAR_CAR <- sum(CNC$VAR)

write.csv(CNC,"CNC_2.csv")

estimation_data <- read.table("CVS.csv",header=TRUE,sep=",")

event_data <- read.table("CVS_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

CVS <- predict(ffregression,newdata = event_data,se.fit = TRUE)

CVS$VAR <- CVS$se.fit^2

CVS$AR <- event_data$Event_stock_price - CVS$fit

CVS$CAR <- sum(CVS$AR)

CVS$VAR_CAR <- sum(CVS$VAR)

write.csv(CVS,"CVS_2.csv")

estimation_data <- read.table("CYH.csv",header=TRUE,sep=",")

event_data <- read.table("CYH_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

CYH <- predict(ffregression,newdata = event_data,se.fit = TRUE)

CYH$VAR <- CYH$se.fit^2

CYH$AR <- event_data$Event_stock_price - CYH$fit

CYH$CAR <- sum(CYH$AR)

CYH$VAR_CAR <- sum(CYH$VAR)

write.csv(CYH,"CYH_2.csv")

estimation_data <- read.table("EBAY.csv",header=TRUE,sep=",")

event_data <- read.table("EBAY_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

EBAY <- predict(ffregression,newdata = event_data,se.fit = TRUE)

EBAY$VAR <- EBAY$se.fit^2

EBAY$AR <- event_data$Event_stock_price - EBAY$fit

EBAY$CAR <- sum(EBAY$AR)

EBAY$VAR_CAR <- sum(EBAY$VAR)

write.csv(EBAY,"EBAY_2.csv")

estimation_data <- read.table("EFX.csv",header=TRUE,sep=",")

event_data <- read.table("EFX_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

EFX <- predict(ffregression,newdata = event_data,se.fit = TRUE)

EFX$VAR <- EFX$se.fit^2

EFX$AR <- event_data$Event_stock_price - EFX$fit

EFX$CAR <- sum(EFX$AR)

EFX$VAR_CAR <- sum(EFX$VAR)

write.csv(EFX,"EFX_2.csv")

estimation_data <- read.table("FB.csv",header=TRUE,sep=",")

event_data <- read.table("FB_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

FB <- predict(ffregression,newdata = event_data,se.fit = TRUE)

FB$VAR <- FB$se.fit^2

FB$AR <- event_data$Event_stock_price - FB$fit

FB$CAR <- sum(FB$AR)

FB$VAR_CAR <- sum(FB$VAR)

write.csv(FB,"F_2.csv")

estimation_data <- read.table("GOOGL.csv",header=TRUE,sep=",")

event_data <- read.table("GOOGL_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

GOOGL <- predict(ffregression,newdata = event_data,se.fit = TRUE)

GOOGL$VAR <- GOOGL$se.fit^2

GOOGL$AR <- event_data$Event_stock_price - GOOGL$fit

GOOGL$CAR <- sum(GOOGL$AR)

GOOGL$VAR_CAR <- sum(GOOGL$VAR)

write.csv(GOOGL,"GOOGL_2.csv")

estimation_data <- read.table("GPN.csv",header=TRUE,sep=",")

event_data <- read.table("GPN_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

GPN <- predict(ffregression,newdata = event_data,se.fit = TRUE)

GPN$VAR <- GPN$se.fit^2

GPN$AR <- event_data$Event_stock_price - GPN$fit

GPN$CAR <- sum(GPN$AR)

GPN$VAR_CAR <- sum(GPN$VAR)

write.csv(GPN,"GPN_2.csv")

estimation_data <- read.table("HD.csv",header=TRUE,sep=",")

event_data <- read.table("HD_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

HD <- predict(ffregression,newdata = event_data,se.fit = TRUE)

HD$VAR <- HD$se.fit^2

HD$AR <- event_data$Event_stock_price - HD$fit

HD$CAR <- sum(HD$AR)

HD$VAR_CAR <- sum(HD$VAR)

write.csv(HD,"HD_2.csv")

estimation_data <- read.table("IBDRY.csv",header=TRUE,sep=",")

event_data <- read.table("IBDRY_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

IBDRY <- predict(ffregression,newdata = event_data,se.fit = TRUE)

IBDRY$VAR <- IBDRY$se.fit^2

IBDRY$AR <- event_data$Event_stock_price - IBDRY$fit

IBDRY$CAR <- sum(IBDRY$AR)

IBDRY$VAR_CAR <- sum(IBDRY$VAR)

write.csv(IBDRY,"IBDRY_2.csv")

estimation_data <- read.table("KT.csv",header=TRUE,sep=",")

event_data <- read.table("KT_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

KT <- predict(ffregression,newdata = event_data,se.fit = TRUE)

KT$VAR <- KT$se.fit^2

KT$AR <- event_data$Event_stock_price - KT$fit

KT$CAR <- sum(KT$AR)

KT$VAR_CAR <- sum(KT$VAR)

write.csv(KT,"KT_2.csv")

estimation_data <- read.table("MAR.csv",header=TRUE,sep=",")

event_data <- read.table("MAR_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

MAR <- predict(ffregression,newdata = event_data,se.fit = TRUE)

MAR$VAR <- MAR$se.fit^2

MAR$AR <- event_data$Event_stock_price - MAR$fit

MAR$CAR <- sum(MAR$AR)

MAR$VAR_CAR <- sum(MAR$VAR)

write.csv(MAR,"MAR_2.csv")

estimation_data <- read.table("MMC.csv",header=TRUE,sep=",")

event_data <- read.table("MMC_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

MMC <- predict(ffregression,newdata = event_data,se.fit = TRUE)

MMC$VAR <- MMC$se.fit^2

MMC$AR <- event_data$Event_stock_price - MMC$fit

MMC$CAR <- sum(MMC$AR)

MMC$VAR_CAR <- sum(MMC$VAR)

write.csv(MMC,"MMC_2.csv")

estimation_data <- read.table("NGVC.csv",header=TRUE,sep=",")

event_data <- read.table("NGVC_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

NGVC <- predict(ffregression,newdata = event_data,se.fit = TRUE)

NGVC$VAR <- NGVC$se.fit^2

NGVC$AR <- event_data$Event_stock_price - NGVC$fit

NGVC$CAR <- sum(NGVC$AR)

NGVC$VAR_CAR <- sum(NGVC$VAR)

write.csv(NGVC,"NGVC_2.csv")

estimation_data <- read.table("SNE.csv",header=TRUE,sep=",")

event_data <- read.table("SNE_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

SNE <- predict(ffregression,newdata = event_data,se.fit = TRUE)

SNE$VAR <- SNE$se.fit^2

SNE$AR <- event_data$Event_stock_price - SNE$fit

SNE$CAR <- sum(SNE$AR)

SNE$VAR_CAR <- sum(SNE$VAR)

write.csv(SNE,"SNE_2.csv")

estimation_data <- read.table("SVU.csv",header=TRUE,sep=",")

event_data <- read.table("SVU_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

SVU <- predict(ffregression,newdata = event_data,se.fit = TRUE)

SVU$VAR <- SVU$se.fit^2

SVU$AR <- event_data$Event_stock_price - SVU$fit

SVU$CAR <- sum(SVU$AR)

SVU$VAR_CAR <- sum(SVU$VAR)

write.csv(SVU,"SVU_2.csv")

estimation_data <- read.table("TD.csv",header=TRUE,sep=",")

event_data <- read.table("TD_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

TD <- predict(ffregression,newdata = event_data,se.fit = TRUE)

TD$VAR <- TD$se.fit^2

TD$AR <- event_data$Event_stock_price - TD$fit

TD$CAR <- sum(TD$AR)

TD$VAR_CAR <- sum(TD$VAR)

write.csv(TD,"TD_2.csv")

estimation_data <- read.table("TGT.csv",header=TRUE,sep=",")

event_data <- read.table("TGT_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

TGT <- predict(ffregression,newdata = event_data,se.fit = TRUE)

TGT$VAR <- TGT$se.fit^2

TGT$AR <- event_data$Event_stock_price - TGT$fit

TGT$CAR <- sum(TGT$AR)

TGT$VAR_CAR <- sum(TGT$VAR)

write.csv(TGT,"TGT_2.csv")

estimation_data <- read.table("TMUS.csv",header=TRUE,sep=",")

event_data <- read.table("TMUS_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

TMUS <- predict(ffregression,newdata = event_data,se.fit = TRUE)

TMUS$VAR <- TMUS$se.fit^2

TMUS$AR <- event_data$Event_stock_price - TMUS$fit

TMUS$CAR <- sum(TMUS$AR)

TMUS$VAR_CAR <- sum(TMUS$VAR)

write.csv(TMUS,"TMUS_2.csv")

estimation_data <- read.table("UPS.csv",header=TRUE,sep=",")

event_data <- read.table("UPS_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

UPS <- predict(ffregression,newdata = event_data,se.fit = TRUE)

UPS$VAR <- UPS$se.fit^2

UPS$AR <- event_data$Event_stock_price - UPS$fit

UPS$CAR <- sum(UPS$AR)

UPS$VAR_CAR <- sum(UPS$VAR)

write.csv(UPS,"UPS_2.csv")

estimation_data <- read.table("VOD.csv",header=TRUE,sep=",")

event_data <- read.table("VOD_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

VOD <- predict(ffregression,newdata = event_data,se.fit = TRUE)

VOD$VAR <- VOD$se.fit^2

VOD$AR <- event_data$Event_stock_price - VOD$fit

VOD$CAR <- sum(VOD$AR)

VOD$VAR_CAR <- sum(VOD$VAR)

write.csv(VOD,"VOD_2.csv")

estimation_data <- read.table("VZ.csv",header=TRUE,sep=",")

event_data <- read.table("VZ_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

VZ <- predict(ffregression,newdata = event_data,se.fit = TRUE)

VZ$VAR <- VZ$se.fit^2

VZ$AR <- event_data$Event_stock_price - VZ$fit

VZ$CAR <- sum(VZ$AR)

VZ$VAR_CAR <- sum(VZ$VAR)

write.csv(VZ,"VZ_2.csv")

estimation_data <- read.table("WEN.csv",header=TRUE,sep=",")

event_data <- read.table("WEN_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

WEN <- predict(ffregression,newdata = event_data,se.fit = TRUE)

WEN$VAR <- WEN$se.fit^2

WEN$AR <- event_data$Event_stock_price - WEN$fit

WEN$CAR <- sum(WEN$AR)

WEN$VAR_CAR <- sum(WEN$VAR)

write.csv(WEN,"WEN_2.csv")

estimation_data <- read.table("WMT.csv",header=TRUE,sep=",")

event_data <- read.table("WMT_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

WMT <- predict(ffregression,newdata = event_data,se.fit = TRUE)

WMT$VAR <- WMT$se.fit^2

WMT$AR <- event_data$Event_stock_price - WMT$fit

WMT$CAR <- sum(WMT$AR)

WMT$VAR_CAR <- sum(WMT$VAR)

write.csv(WMT,"WMT_2.csv")

estimation_data <- read.table("YAHOY.csv",header=TRUE,sep=",")

event_data <- read.table("YAHOY_1.csv",header=TRUE,sep=",")

ffregression <- lm(data=estimation_data, Historical_stock_price ~ RF + (Mkt.RF-RF) + SMB + HML)

YAHOY <- predict(ffregression,newdata = event_data,se.fit = TRUE)

YAHOY$VAR <- YAHOY$se.fit^2

YAHOY$AR <- event_data$Event_stock_price - YAHOY$fit

YAHOY$CAR <- sum(YAHOY$AR)

YAHOY$VAR_CAR <- sum(YAHOY$VAR)

write.csv(YAHOY,"YAHOY_2.csv")

# Расчёт

CAAR <- (sum(JPM$CAR + AMZN$CAR + AAPL$CAR + ADBE$CAR + ANTM$CAR + AOL$CAR + ATVI$CAR + BABA$CAR + BKS$CAR + CNC$CAR + CVS$CAR + CYH$CAR +

EBAY$CAR + EFX$CAR + FB$CAR + GOOGL$CAR + GPN$CAR + HD$CAR + IBDRY$CAR + KT$CAR + MAR$CAR + MMC$CAR + NGVC$CAR + SNE$CAR +

SVU$CAR + TD$CAR + TGT$CAR + TMUS$CAR + UPS$CAR + VOD$CAR + VZ$CAR + WEN$CAR + WMT$CAR + YAHOY$CAR))/34

VAAR <- (sum(JPM$VAR_CAR + AMZN$VAR_CAR + AAPL$VAR_CAR + ADBE$VAR_CAR + ANTM$VAR_CAR + AOL$VAR_CAR + ATVI$VAR_CAR + BABA$VAR_CAR + BKS$VAR_CAR + CNC$VAR_CAR + CVS$VAR_CAR + CYH$VAR_CAR +

EBAY$VAR_CAR + EFX$VAR_CAR + FB$VAR_CAR + GOOGL$VAR_CAR + GPN$VAR_CAR + HD$VAR_CAR + IBDRY$VAR_CAR + KT$VAR_CAR + MAR$VAR_CAR + MMC$VAR_CAR + NGVC$VAR_CAR + SNE$VAR_CAR +

SVU$VAR_CAR + TD$VAR_CAR + TGT$VAR_CAR + TMUS$VAR_CAR + UPS$VAR_CAR + VOD$VAR_CAR + VZ$VAR_CAR + WEN$VAR_CAR + WMT$VAR_CAR + YAHOY$VAR_CAR))/(34*34)

# Проверка гипотез

t_test <- CAAR/(sqrt(VAAR))

crit_t <- qt(0.95,37)

write.csv(rbind(CAAR, VAAR, t_test, crit_t), "CAAR.csv")

# Расчёт влияния по дням

AR <- (sum(JPM$AR[1] + AMZN$AR[1] + AAPL$AR[1] + ADBE$AR[1] + ANTM$AR[1] + AOL$AR[1] + ATVI$AR[1] + BABA$AR[1] + BKS$AR[1] + CNC$AR[1] + CVS$AR[1] + CYH$AR[1] +

EBAY$AR[1] + EFX$AR[1] + FB$AR[1] + GOOGL$AR[1] + GPN$AR[1] + HD$AR[1] + IBDRY$AR[1] + KT$AR[1] + MAR$AR[1] + MMC$AR[1] + NGVC$AR[1] + SNE$AR[1] +

SVU$AR[1] + TD$AR[1] + TGT$AR[1] + TMUS$AR[1] + UPS$AR[1] + VOD$AR[1] + VZ$AR[1] + WEN$AR[1] + WMT$AR[1] + YAHOY$AR[1]))/34

VAR <- (sum(JPM$VAR[1] + AMZN$VAR[1] + AAPL$VAR[1] + ADBE$VAR[1] + ANTM$VAR[1] + AOL$VAR[1] + ATVI$VAR[1] + BABA$VAR[1] + BKS$VAR[1] + CNC$VAR[1] + CVS$VAR[1] + CYH$VAR[1] +

EBAY$VAR[1] + EFX$VAR[1] + FB$VAR[1] + GOOGL$VAR[1] + GPN$VAR[1] + HD$VAR[1] + IBDRY$VAR[1] + KT$VAR[1] + MAR$VAR[1] + MMC$VAR[1] + NGVC$VAR[1] + SNE$VAR[1] +

SVU$VAR[1] + TD$VAR[1] + TGT$VAR[1] + TMUS$VAR[1] + UPS$VAR[1] + VOD$VAR[1] + VZ$VAR[1] + WEN$VAR[1] + WMT$VAR[1] + YAHOY$VAR[1]))/(34*34)

t_test <- AR/(sqrt(VAR))

crit_t <- qt(0.95,37)

write.csv(rbind(AR, VAR, t_test, crit_t), "Day_0.csv")

AR[2] <- (sum(JPM$AR[2] + AMZN$AR[2] + AAPL$AR[2] + ADBE$AR[2] + ANTM$AR[2] + AOL$AR[2] + ATVI$AR[2] + BABA$AR[2] + BKS$AR[2] + CNC$AR[2] + CVS$AR[2] + CYH$AR[2] +

EBAY$AR[2] + EFX$AR[2] + FB$AR[2] + GOOGL$AR[2] + GPN$AR[2] + HD$AR[2] + IBDRY$AR[2] + KT$AR[2] + MAR$AR[2] + MMC$AR[2] + NGVC$AR[2] + SNE$AR[2] +

SVU$AR[2] + TD$AR[2] + TGT$AR[2] + TMUS$AR[2] + UPS$AR[2] + VOD$AR[2] + VZ$AR[2] + WEN$AR[2] + WMT$AR[2] + YAHOY$AR[2]))/34

VAR[2] <- (sum(JPM$VAR[2] + AMZN$VAR[2] + AAPL$VAR[2] + ADBE$VAR[2] + ANTM$VAR[2] + AOL$VAR[2] + ATVI$VAR[2] + BABA$VAR[2] + BKS$VAR[2] + CNC$VAR[2] + CVS$VAR[2] + CYH$VAR[2] +

EBAY$VAR[2] + EFX$VAR[2] + FB$VAR[2] + GOOGL$VAR[2] + GPN$VAR[2] + HD$VAR[2] + IBDRY$VAR[2] + KT$VAR[2] + MAR$VAR[2] + MMC$VAR[2] + NGVC$VAR[2] + SNE$VAR[2] +

SVU$VAR[2] + TD$VAR[2] + TGT$VAR[2] + TMUS$VAR[2] + UPS$VAR[2] + VOD$VAR[2] + VZ$VAR[2] + WEN$VAR[2] + WMT$VAR[2] + YAHOY$VAR[2]))/(34*34)

t_test <- AR[2]/(sqrt(VAR[2]))

crit_t <- qt(0.95,37)

write.csv(rbind(AR[2], VAR[2], t_test, crit_t), "Day_1.csv")

AR[3] <- (sum(JPM$AR[3] + AMZN$AR[3] + AAPL$AR[3] + ADBE$AR[3] + ANTM$AR[3] + AOL$AR[3] + ATVI$AR[3] + BABA$AR[3] + BKS$AR[3] + CNC$AR[3] + CVS$AR[3] + CYH$AR[3] +

EBAY$AR[3] + EFX$AR[3] + FB$AR[3] + GOOGL$AR[3] + GPN$AR[3] + HD$AR[3] + IBDRY$AR[3] + KT$AR[3] + MAR$AR[3] + MMC$AR[3] + NGVC$AR[3] + SNE$AR[3] +

SVU$AR[3] + TD$AR[3] + TGT$AR[3] + TMUS$AR[3] + UPS$AR[3] + VOD$AR[3] + VZ$AR[3] + WEN$AR[3] + WMT$AR[3] + YAHOY$AR[3]))/34

VAR[3] <- (sum(JPM$VAR[3] + AMZN$VAR[3] + AAPL$VAR[3] + ADBE$VAR[3] + ANTM$VAR[3] + AOL$VAR[3] + ATVI$VAR[3] + BABA$VAR[3] + BKS$VAR[3] + CNC$VAR[3] + CVS$VAR[3] + CYH$VAR[3] +

EBAY$VAR[3] + EFX$VAR[3] + FB$VAR[3] + GOOGL$VAR[3] + GPN$VAR[3] + HD$VAR[3] + IBDRY$VAR[3] + KT$VAR[3] + MAR$VAR[3] + MMC$VAR[3] + NGVC$VAR[3] + SNE$VAR[3] +

SVU$VAR[3] + TD$VAR[3] + TGT$VAR[3] + TMUS$VAR[3] + UPS$VAR[3] + VOD$VAR[3] + VZ$VAR[3] + WEN$VAR[3] + WMT$VAR[3] + YAHOY$VAR[3]))/(34*34)

t_test <- AR[3]/(sqrt(VAR[3]))

crit_t <- qt(0.95,37)

write.csv(rbind(AR[3], VAR[3], t_test, crit_t), "Day_2.csv")

AR[4] <- (sum(JPM$AR[4] + AMZN$AR[4] + AAPL$AR[4] + ADBE$AR[4] + ANTM$AR[4] + AOL$AR[4] + ATVI$AR[4] + BABA$AR[4] + BKS$AR[4] + CNC$AR[4] + CVS$AR[4] + CYH$AR[4] +

EBAY$AR[4] + EFX$AR[4] + FB$AR[4] + GOOGL$AR[4] + GPN$AR[4] + HD$AR[4] + IBDRY$AR[4] + KT$AR[4] + MAR$AR[4] + MMC$AR[4] + NGVC$AR[4] + SNE$AR[4] +

SVU$AR[4] + TD$AR[4] + TGT$AR[4] + TMUS$AR[4] + UPS$AR[4] + VOD$AR[4] + VZ$AR[4] + WEN$AR[4] + WMT$AR[4] + YAHOY$AR[4]))/34

VAR[4] <- (sum(JPM$VAR[4] + AMZN$VAR[4] + AAPL$VAR[4] + ADBE$VAR[4] + ANTM$VAR[4] + AOL$VAR[4] + ATVI$VAR[4] + BABA$VAR[4] + BKS$VAR[4] + CNC$VAR[4] + CVS$VAR[4] + CYH$VAR[4] +

EBAY$VAR[4] + EFX$VAR[4] + FB$VAR[4] + GOOGL$VAR[4] + GPN$VAR[4] + HD$VAR[4] + IBDRY$VAR[4] + KT$VAR[4] + MAR$VAR[4] + MMC$VAR[4] + NGVC$VAR[4] + SNE$VAR[4] +

SVU$VAR[4] + TD$VAR[4] + TGT$VAR[4] + TMUS$VAR[4] + UPS$VAR[4] + VOD$VAR[4] + VZ$VAR[4] + WEN$VAR[4] + WMT$VAR[4] + YAHOY$VAR[4]))/(34*34)

t_test <- AR[4]/(sqrt(VAR[4]))

crit_t <- qt(0.95,37)

write.csv(rbind(AR[4], VAR[4], t_test, crit_t), "Day_3.csv")

# ит отрасль

AR_IT <- (sum(AAPL$AR[1]+ATVI$AR[1]+ADBE$AR[1]+FB$AR[1]+MMC$AR[1]+YAHOY$AR[1]+EBAY$AR[1]+GOOGL$AR[1]+AMZN$AR[1]+AOL$AR[1]))/10

VAR_IT <- (sum(AAPL$VAR[1]+ATVI$VAR[1]+ADBE$VAR[1]+FB$VAR[1]+MMC$VAR[1]+YAHOY$VAR[1]+EBAY$VAR[1]+GOOGL$VAR[1]+AMZN$VAR[1]+AOL$VAR[1]))/(10*10)

t_test <- AR_IT[1]/(sqrt(VAR_IT[1]))

crit_t <- qt(0.95,9)

write.csv(rbind(AR_IT, VAR_IT, t_test, crit_t), "AR_IT_Day_0.csv")

AR_IT[2] <- (sum(AAPL$AR[2]+ATVI$AR[2]+ADBE$AR[2]+FB$AR[2]+MMC$AR[2]+YAHOY$AR[2]+EBAY$AR[2]+GOOGL$AR[2]+AMZN$AR[2]+AOL$AR[2]))/10

VAR_IT[2] <- (sum(AAPL$VAR[2]+ATVI$VAR[2]+ADBE$VAR[2]+FB$VAR[2]+MMC$VAR[2]+YAHOY$VAR[2]+EBAY$VAR[2]+GOOGL$VAR[2]+AMZN$VAR[2]+AOL$VAR[2]))/(10*10)

t_test <- AR_IT[2]/(sqrt(VAR_IT[2]))

crit_t <- qt(0.95,9)

write.csv(rbind(AR_IT[2], VAR_IT[2], t_test, crit_t), "AR_IT_Day_1.csv")

AR_IT[3] <- (sum(AAPL$AR[3]+ATVI$AR[3]+ADBE$AR[3]+FB$AR[3]+MMC$AR[3]+YAHOY$AR[3]+EBAY$AR[3]+GOOGL$AR[3]+AMZN$AR[3]+AOL$AR[3]))/10

VAR_IT[3] <- (sum(AAPL$VAR[3]+ATVI$VAR[3]+ADBE$VAR[3]+FB$VAR[3]+MMC$VAR[3]+YAHOY$VAR[3]+EBAY$VAR[3]+GOOGL$VAR[3]+AMZN$VAR[3]+AOL$VAR[3]))/(10*10)

t_test <- AR_IT[3]/(sqrt(VAR_IT[3]))

crit_t <- qt(0.95,9)

write.csv(rbind(AR_IT[3], VAR_IT[3], t_test, crit_t), "AR_IT_Day_2.csv")

AR_IT[4] <- (sum(AAPL$AR[4]+ATVI$AR[4]+ADBE$AR[4]+FB$AR[4]+MMC$AR[4]+YAHOY$AR[4]+EBAY$AR[4]+GOOGL$AR[4]+AMZN$AR[4]+AOL$AR[4]))/10

VAR_IT[4] <- (sum(AAPL$VAR[4]+ATVI$VAR[4]+ADBE$VAR[4]+FB$VAR[4]+MMC$VAR[4]+YAHOY$VAR[4]+EBAY$VAR[4]+GOOGL$VAR[4]+AMZN$VAR[4]+AOL$VAR[4]))/(10*10)

t_test <- AR_IT[4]/(sqrt(VAR_IT[4]))

crit_t <- qt(0.95,9)

write.csv(rbind(AR_IT[4], VAR_IT[4], t_test, crit_t), "AR_IT_Day_3.csv")

CAAR_IT <- sum(AR_IT)

VAAR_IT <- sum(VAR_IT)

t_test <- CAAR_IT/(sqrt(VAAR_IT))

crit_t <- qt(0.95,9)

write.csv(rbind(CAAR_IT, VAAR_IT, t_test, crit_t), "AR_IT.csv")

# финансовая отрасль

AR_FIN <- (sum(GPN$AR[1]+TD$AR[1]+JPM$AR[1]+EFX$AR[1]))/4

VAR_FIN <- (sum(GPN$VAR[1]+TD$VAR[1]+JPM$VAR[1]+EFX$VAR[1]))/(4*4)

t_test <- AR_FIN[1]/(sqrt(VAR_FIN[1]))

crit_t <- qt(0.95,3)

write.csv(rbind(AR_FIN, VAR_FIN, t_test, crit_t), "AR_FIN_Day_0.csv")

AR_FIN[2] <- (sum(GPN$AR[2]+TD$AR[2]+JPM$AR[2]+EFX$AR[2]))/4

VAR_FIN[2] <- (sum(GPN$VAR[2]+TD$VAR[2]+JPM$VAR[2]+EFX$VAR[2]))/(4*4)

t_test <- AR_FIN[2]/(sqrt(VAR_FIN[2]))

crit_t <- qt(0.95,3)

write.csv(rbind(AR_FIN[2], VAR_FIN[2], t_test, crit_t), "AR_FIN_Day_1.csv")

AR_FIN[3] <- (sum(GPN$AR[3]+TD$AR[3]+JPM$AR[3]+EFX$AR[3]))/4

VAR_FIN[3] <- (sum(GPN$VAR[3]+TD$VAR[3]+JPM$VAR[3]+EFX$VAR[3]))/(4*4)

t_test <- AR_FIN[3]/(sqrt(VAR_FIN[3]))

crit_t <- qt(0.95,3)

write.csv(rbind(AR_FIN[3], VAR_FIN[3], t_test, crit_t), "AR_FIN_Day_2.csv")

AR_FIN[4] <- (sum(GPN$AR[4]+TD$AR[4]+JPM$AR[4]+EFX$AR[4]))/4

VAR_FIN[4] <- (sum(GPN$VAR[4]+TD$VAR[4]+JPM$VAR[4]+EFX$VAR[4]))/(4*4)

t_test <- AR_FIN[4]/(sqrt(VAR_FIN[4]))

crit_t <- qt(0.95,3)

write.csv(rbind(AR_FIN[4], VAR_FIN[4], t_test, crit_t), "AR_FIN_Day_3.csv")

CAAR_FIN <- sum(AR_FIN)

VAAR_FIN <- sum(VAR_FIN)

t_test <- CAAR_FIN/(sqrt(VAAR_FIN))

crit_t <- qt(0.95,3)

write.csv(rbind(CAAR_FIN, VAAR_FIN, t_test, crit_t), "AR_FIN.csv")

# без учёта Equifax

AR_FIN <- (sum(GPN$AR[1]+TD$AR[1]+JPM$AR[1]))/3

VAR_FIN <- (sum(GPN$VAR[1]+TD$VAR[1]+JPM$VAR[1]))/(3*3)

t_test <- AR_FIN[1]/(sqrt(VAR_FIN[1]))

crit_t <- qt(0.95,2)

write.csv(rbind(AR_FIN, VAR_FIN, t_test, crit_t), "AR_FIN_Day_0_no_EFX.csv")

AR_FIN[2] <- (sum(GPN$AR[2]+TD$AR[2]+JPM$AR[2]))/3

VAR_FIN[2] <- (sum(GPN$VAR[2]+TD$VAR[2]+JPM$VAR[2]+EFX$VAR[2]))/(3*3)

t_test <- AR_FIN[2]/(sqrt(VAR_FIN[2]))

crit_t <- qt(0.95,2)

write.csv(rbind(AR_FIN[2], VAR_FIN[2], t_test, crit_t), "AR_FIN_Day_1_no_EFX.csv")

AR_FIN[3] <- (sum(GPN$AR[3]+TD$AR[3]+JPM$AR[3]))/3

VAR_FIN[3] <- (sum(GPN$VAR[3]+TD$VAR[3]+JPM$VAR[3]))/(3*3)

t_test <- AR_FIN[3]/(sqrt(VAR_FIN[3]))

crit_t <- qt(0.95,2)

write.csv(rbind(AR_FIN[3], VAR_FIN[3], t_test, crit_t), "AR_FIN_Day_2_no_EFX.csv")

AR_FIN[4] <- (sum(GPN$AR[4]+TD$AR[4]+JPM$AR[4]))/3

VAR_FIN[4] <- (sum(GPN$VAR[4]+TD$VAR[4]+JPM$VAR[4]))/(3*3)

t_test <- AR_FIN[4]/(sqrt(VAR_FIN[4]))

crit_t <- qt(0.95,2)

write.csv(rbind(AR_FIN[4], VAR_FIN[4], t_test, crit_t), "AR_FIN_Day_3_no_EFX.csv")

CAAR_FIN <- sum(AR_FIN)

VAAR_FIN <- sum(VAR_FIN)

t_test <- CAAR_FIN/(sqrt(VAAR_FIN))

crit_t <- qt(0.95,2)

write.csv(rbind(CAAR_FIN, VAAR_FIN, t_test, crit_t), "AR_FIN_no_EFX.csv")

# ритейл

AR_RET <- (sum(BKS$AR[1]+HD$AR[1]+SVU$AR[1]+TGT$AR[1]+UPS$AR[1]+CVS$AR[1]+NGVC$AR[1]+WMT$AR[1]+BABA$AR[1]))/9

VAR_RET<- (sum(BKS$VAR[1]+HD$VAR[1]+SVU$VAR[1]+TGT$VAR[1]+UPS$VAR[1]+CVS$VAR[1]+NGVC$VAR[1]+WMT$VAR[1]+BABA$VAR[1]))/(9*9)

t_test <- AR_RET[1]/(sqrt(VAR_RET[1]))

crit_t <- qt(0.95,8)

write.csv(rbind(AR_RET, VAR_RET, t_test, crit_t), "AR_RET_Day_0.csv")

AR_RET[2] <- (sum(BKS$AR[2]+HD$AR[2]+SVU$AR[2]+TGT$AR[2]+UPS$AR[2]+CVS$AR[2]+NGVC$AR[2]+WMT$AR[2]+BABA$AR[2]))/9

VAR_RET[2]<- (sum(BKS$VAR[2]+HD$VAR[2]+SVU$VAR[2]+TGT$VAR[2]+UPS$VAR[2]+CVS$VAR[2]+NGVC$VAR[2]+WMT$VAR[2]+BABA$VAR[2]))/(9*9)

t_test <- AR_RET[2]/(sqrt(VAR_RET[2]))

crit_t <- qt(0.95,8)

write.csv(rbind(AR_RET[2], VAR_RET[2], t_test, crit_t), "AR_RET_Day_1.csv")

AR_RET[3] <- (sum(BKS$AR[3]+HD$AR[3]+SVU$AR[3]+TGT$AR[3]+UPS$AR[3]+CVS$AR[3]+NGVC$AR[3]+WMT$AR[3]+BABA$AR[3]))/9

VAR_RET[3]<- (sum(BKS$VAR[3]+HD$VAR[3]+SVU$VAR[3]+TGT$VAR[3]+UPS$VAR[3]+CVS$VAR[3]+NGVC$VAR[3]+WMT$VAR[3]+BABA$VAR[3]))/(9*9)

t_test <- AR_RET[3]/(sqrt(VAR_RET[3]))

crit_t <- qt(0.95,8)

write.csv(rbind(AR_RET[3], VAR_RET[3], t_test, crit_t), "AR_RET_Day_2.csv")

AR_RET[4] <- (sum(BKS$AR[4]+HD$AR[4]+SVU$AR[4]+TGT$AR[4]+UPS$AR[4]+CVS$AR[4]+NGVC$AR[4]+WMT$AR[4]+BABA$AR[4]))/9

VAR_RET[4]<- (sum(BKS$VAR[4]+HD$VAR[4]+SVU$VAR[4]+TGT$VAR[4]+UPS$VAR[4]+CVS$VAR[4]+NGVC$VAR[4]+WMT$VAR[4]+BABA$VAR[4]))/(9*9)

t_test <- AR_RET[4]/(sqrt(VAR_RET[4]))

crit_t <- qt(0.95,8)

write.csv(rbind(AR_RET[4], VAR_RET[4], t_test, crit_t), "AR_RET_Day_3.csv")

CAAR_RET <- sum(AR_RET)

VAAR_RET <- sum(VAR_RET)

t_test <- CAAR_RET/(sqrt(VAAR_RET))

crit_t <- qt(0.95,8)

write.csv(rbind(CAAR_RET, VAAR_RET, t_test, crit_t), "AR_RET.csv")

# телекомуникационная

AR_TEL <- (sum(KT$AR[1]+VOD$AR[1]+TMUS$AR[1]+VZ$AR[1]))/4

VAR_TEL<- (sum(KT$VAR[1]+VOD$VAR[1]+TMUS$VAR[1]+VZ$VAR[1]))/(4*4)

t_test <- AR_TEL[1]/(sqrt(VAR_TEL[1]))

crit_t <- qt(0.95,3)

write.csv(rbind(AR_TEL, VAR_TEL, t_test, crit_t), "AR_TEL_Day_0.csv")

AR_TEL[2] <- (sum(KT$AR[2]+VOD$AR[2]+TMUS$AR[2]+VZ$AR[2]))/4

VAR_TEL[2]<- (sum(KT$VAR[2]+VOD$VAR[2]+TMUS$VAR[2]+VZ$VAR[2]))/(4*4)

t_test <- AR_TEL[2]/(sqrt(VAR_TEL[2]))

crit_t <- qt(0.95,3)

write.csv(rbind(AR_TEL[2], VAR_TEL[2], t_test, crit_t), "AR_TEL_Day_1.csv")

AR_TEL[3] <- (sum(KT$AR[3]+VOD$AR[3]+TMUS$AR[3]+VZ$AR[3]))/4

VAR_TEL[3]<- (sum(KT$VAR[3]+VOD$VAR[3]+TMUS$VAR[3]+VZ$VAR[3]))/(4*4)

t_test <- AR_TEL[3]/(sqrt(VAR_TEL[3]))

crit_t <- qt(0.95,3)

write.csv(rbind(AR_TEL[3], VAR_TEL[3], t_test, crit_t), "AR_TEL_Day_2.csv")

AR_TEL[4] <- (sum(KT$AR[4]+VOD$AR[4]+TMUS$AR[4]+VZ$AR[4]))/4

VAR_TEL[4]<- (sum(KT$VAR[4]+VOD$VAR[4]+TMUS$VAR[4]+VZ$VAR[4]))/(4*4)

t_test <- AR_TEL[4]/(sqrt(VAR_TEL[4]))

crit_t <- qt(0.95,3)

write.csv(rbind(AR_TEL[4], VAR_TEL[4], t_test, crit_t), "AR_TEL_Day_3.csv")

CAAR_TEL <- sum(AR_TEL)

VAAR_TEL <- sum(VAR_TEL)

t_test <- CAAR_TEL/(sqrt(VAAR_TEL))

crit_t <- qt(0.95,3)

write.csv(rbind(CAAR_TEL, VAAR_TEL, t_test, crit_t), "AR_TEL.csv")

Размещено на Allbest.ru

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