onom: 6月 2020

2020年6月13日土曜日

EPS 2020JUN13

eps_year_xts["2020::"]

[,1]

2020-01-01 116.42

2020-04-01 100.84

2020-07-01 93.89

2020-10-01 90.33

2021-01-01 110.84

2021-04-01 125.10

2021-07-01 135.49

2021-10-01 142.90

~$ tac eps.txt

3/31/2019 2834.40 $37.99 $35.02 18.52 21.09 $153.05 $134.39

6/30/2019 2941.76 $40.14 $34.93 19.04 21.75 $154.54 $135.27

9/30/2019 2976.74 $39.81 $33.99 19.46 22.40 $152.97 $132.90

12/31/2019 3230.78 $39.18 $35.53 20.56 23.16 $157.12 $139.47

3/31/2020 (97.5%) 2584.59 $19.70 $11.98 22.42 26.73 $138.83 $116.43

6/30/2020 $23.08 $19.94 25.56 30.68 $121.77 $101.44

9/30/2020 $31.04 $27.52 27.54 32.77 $113.00 $94.97

12/31/2020 $35.89 $32.62 28.37 33.81 $109.71 $92.06

3/31/2021 $37.02 $33.16 24.50 27.49 $127.03 $113.24

6/30/2021 $38.14 $34.09 21.90 24.43 $142.09 $127.39

9/30/2021 $42.05 $37.98 20.33 22.58 $153.10 $137.85

12/31/2021 $44.59 $40.05 19.24 21.42 $161.80 $145.28

~$ tac eps.txt | awk '{gsub("\\$","",$NF);print "eps_year_xts[\"2019::\"]["NR"] <- "$NF}'

eps_year_xts["2019::"][1] <- 134.39

eps_year_xts["2019::"][2] <- 135.27

eps_year_xts["2019::"][3] <- 132.90

eps_year_xts["2019::"][4] <- 139.47

eps_year_xts["2019::"][5] <- 116.43

eps_year_xts["2019::"][6] <- 101.44

eps_year_xts["2019::"][7] <- 94.97

eps_year_xts["2019::"][8] <- 92.06

eps_year_xts["2019::"][9] <- 113.24

eps_year_xts["2019::"][10] <- 127.39

eps_year_xts["2019::"][11] <- 137.85

eps_year_xts["2019::"][12] <- 145.28

Personal Income and GDP PI

Recalculate based upon the past 20 years except 2020.

summary(lm(last(yearlyReturn(PI),20)[-20] ~ last(yearlyReturn(G),20)[-20]))

Call:

lm(formula = last(yearlyReturn(PI), 20)[-20] ~ last(yearlyReturn(G),

20)[-20])

Residuals:

Min 1Q Median 3Q Max

-0.063473 -0.008838 0.000614 0.013313 0.045431

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 0.006467 0.012627 0.512 0.61514

last(yearlyReturn(G), 20)[-20] 0.878119 0.290504 3.023 0.00767 **

---

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.02297 on 17 degrees of freedom

Multiple R-squared: 0.3496, Adjusted R-squared: 0.3113

F-statistic: 9.137 on 1 and 17 DF, p-value: 0.007673

calculate PI according to the formula above on the assumption that 2020 GDPC1 year return is -5% and CPI is 2%.

~~0.006467 + 0.878119 *G["2019-10-01"]*0.97~~

~~GDP~~

~~2019-10-01 18508.34~~

when 2020 GDP growth is forecasted as -0.05, to calculate PI growth rate do this.

0.006467 + 0.878119 *(expected GDP growth rate)

then 2020 PI forecast is as below

(1 + 0.006467 + 0.878119 *-0.05)*PI["2019-10-01"]
PI
2019-10-01 18061.11

plot.default(last(yearlyReturn(G),20)[-20],last(yearlyReturn(PI),20)[-20])

abline(lm(last(yearlyReturn(PI),20)[-20] ~ last(yearlyReturn(G),20)[-20]))

abline(v=0)

Recalculate based upon the past 50 years except 2020.

summary(lm(last(yearlyReturn(PI),50)[-50] ~ last(yearlyReturn(G),50)[-50]))

Call:

lm(formula = last(yearlyReturn(PI), 50)[-50] ~ last(yearlyReturn(G),

50)[-50])

Residuals:

Min 1Q Median 3Q Max

-0.065914 -0.005273 0.000005 0.006657 0.043099

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 0.008346 0.005670 1.472 0.148

last(yearlyReturn(G), 50)[-50] 0.890811 0.080517 11.064 1.11e-14 ***

---

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.01723 on 47 degrees of freedom

Multiple R-squared: 0.7226, Adjusted R-squared: 0.7167

F-statistic: 122.4 on 1 and 47 DF, p-value: 1.11e-14

~~0.008346+0.890811 *G["2019-10-01"]*0.97~~

~~GDP~~

~~2019-10-01 18775.85~~

0.008346+0.890811 *(expected GDP growth rate)

when 2020 is -0.05

0.008346+0.890811 *-0.05
[1] -0.03619455

(1+ 0.008346+0.890811 *-0.05)*PI["2019-12-01"]

PI
2019-12-01 18222.28

2020年6月2日火曜日

.Last function 終了　終了処理

save image files with timestamp file name like "yyyy-mm-dd-hh-mm-ss-00" and .Rdata.

.Last <- function() {

# save.image(file=paste(getwd(),Sys.time(),sep="/"))

# save working image in the parent directory. expected the length of the directory name is fixed.

# adjust value "len" to fit the length of the result "getwd()".

op <- options(digits.secs = 2)

start_pos <- 1

# len <- 23

# change to decide based upon strings length.

last_pos <- nchar(as.character(getwd())) -2

save.image(file=gsub("[ :.]","-",paste(substr(getwd(),start_pos,last_pos),Sys.time(),sep="/")))

cat("time stamp file done...\n")

save.image(file=paste(getwd(),".Rdata",sep="/"))

cat("RData done...\n")

# OR store working image at the working directory

# save.image(file=gsub(" ","-",paste(getwd(),Sys.time(),sep="/")))

cat("bye bye...\n")

}

2020年6月1日月曜日

EPS 2020JUN01

the entries below are wrong.

> eps_year_xts["2020::"]

[,1]

~~2020-01-01 102.44~~

~~2020-04-01 96.80~~

~~2020-07-01 95.16~~

~~2020-10-01 115.77~~

~~2021-01-01 130.19~~

~~2021-04-01 140.44~~

~~2021-07-01 147.68~~

~~2021-10-01 150.31~~

~$ tac eps.txt | awk '{gsub("\\$","",$NF);print "eps_year_xts[\"2019::\"]["NR"] <- "$NF}'

eps_year_xts["2019::"][1] <- 134.39

eps_year_xts["2019::"][2] <- 135.27

eps_year_xts["2019::"][3] <- 132.90

eps_year_xts["2019::"][4] <- 139.47

eps_year_xts["2019::"][5] <- 116.42

eps_year_xts["2019::"][6] <- 100.84

eps_year_xts["2019::"][7] <- 93.89

eps_year_xts["2019::"][8] <- 90.33

eps_year_xts["2019::"][9] <- 110.84

eps_year_xts["2019::"][10] <- 125.10

eps_year_xts["2019::"][11] <- 135.49

eps_year_xts["2019::"][12] <- 142.90