NASDAQ EPS

download data from here.

modify data like below. use   %s/\s/,/g   in vi.

2020-12-31,1.34

2020-09-30,1.58

2020-06-30,1.45

2020-03-31,1.22

2019-12-31,1.21

       <skip>

2005-06-30,0.13

2005-03-31,0.13

store data into csv file. " ~/nasdaqeps.txt" is used in the sample. change end date accordingly!.

w <- as.xts(read.zoo(read.csv("~/nasdaqeps.txt",header=FALSE)))

ndx_eps <- as.xts(as.vector(na.omit(filter(as.vector(w),rep(1,4)))),seq(as.Date("2005-10-01"),as.Date("2020-10-01"),by='quarters'))

when k2007 is as below.

>  k2007

[1] "2007-01-01::2020-12-31"

summary(lm(apply.quarterly(NDX[,4][k2007],mean) ~ ndx_eps[k2007]+apply.quarterly(PA[k2007],mean)+apply.quarterly(UC[k2007],mean)+apply.quarterly(CS[k2007],mean)))

Call:

lm(formula = apply.quarterly(NDX[, 4][k2007], mean) ~ ndx_eps[k2007] + 

    apply.quarterly(PA[k2007], mean) + apply.quarterly(UC[k2007], 

    mean) + apply.quarterly(CS[k2007], mean))

Residuals:

   Min     1Q Median     3Q    Max 

-787.4 -293.1 -105.6  106.1 2281.4 

Coefficients:

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

(Intercept)                      -1.335e+04  8.966e+02 -14.893  < 2e-16 ***

ndx_eps[k2007]                    2.612e+02  7.864e+01   3.321 0.001664 ** 

apply.quarterly(PA[k2007], mean)  7.181e-01  5.575e-02  12.882  < 2e-16 ***

apply.quarterly(UC[k2007], mean) -2.917e+00  9.409e-01  -3.100 0.003144 ** 

apply.quarterly(CS[k2007], mean)  4.479e+01  1.096e+01   4.088 0.000154 ***

---

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

Residual standard error: 532.2 on 51 degrees of freedom

Multiple R-squared:  0.9611, Adjusted R-squared:  0.958 

F-statistic: 314.8 on 4 and 51 DF,  p-value: < 2.2e-16