Part 3 Exam

The objective is to perform a time series analysis for Netflix stock price.

# Uploading libraries
library(xts)

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library(ggplot2)

data <- read.csv("C:\\Users\\naila\\OneDrive\\Documentos\\1 TEC\\5 QUINTO SEMESTRE\\Econometrics\\Exam\\entretainment_stocks.csv") 
str(data)

## 'data.frame':    192 obs. of  7 variables:
##  $ Date               : chr  "1/1/2007" "2/1/2007" "3/1/2007" "4/1/2007" ...
##  $ Disney_Adj_Close   : num  29.1 28.4 28.5 29 29.3 ...
##  $ Netflix_Adj_Close  : num  3.26 3.22 3.31 3.17 3.13 2.77 2.46 2.5 2.96 3.78 ...
##  $ Nintendo_Adj_Close : num  37.1 33.1 36.3 40 43.6 ...
##  $ WBD_Adj_Close      : num  8.47 8.21 9.78 11.11 11.95 ...
##  $ EA_Adj_Close       : num  49.5 49.9 49.8 49.9 48.4 ...
##  $ Paramount_Adj_Close: num  22.1 21.5 21.6 22.6 23.7 ...

a) Visualization

sum(is.na(data))

## [1] 0

data$Date <- as.Date(data$Date,"%m/%d/%Y")
Nxts<-xts(data$Netflix_Adj_Close,order.by=data$Date)
plot(Nxts)

Netflixts<-ts(data$Netflix_Adj_Close,start=c(2007,1),end=c(2022,12),frequency=12)
Netflixdec<-decompose(Netflixts)
plot(Netflixdec)

b) Estimation

adf.test(data$Netflix_Adj_Close)

## 
##  Augmented Dickey-Fuller Test
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## data:  data$Netflix_Adj_Close
## Dickey-Fuller = -2.9019, Lag order = 5, p-value = 0.1987
## alternative hypothesis: stationary

non-stationary

acf(data$Netflix_Adj_Close,main="Significant Autocorrelations")

## ARMA with log

summary(Netflix_ARMA<-arma(log(data$Netflix_Adj_Close),order=c(1,1)))

## 
## Call:
## arma(x = log(data$Netflix_Adj_Close), order = c(1, 1))
## 
## Model:
## ARMA(1,1)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.73942 -0.07764  0.01083  0.08273  0.52979 
## 
## Coefficient(s):
##            Estimate  Std. Error  t value Pr(>|t|)    
## ar1        0.990021    0.007347  134.746   <2e-16 ***
## ma1        0.125387    0.068350    1.834   0.0666 .  
## intercept  0.063487    0.031922    1.989   0.0467 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Fit:
## sigma^2 estimated as 0.02343,  Conditional Sum-of-Squares = 4.45,  AIC = -169.81

Netflix_ARMA$fitted.values <- na.omit(data$fitted.values)
Netflix_ARMA$fitted.values

## NULL

#Netflix_estimated_sp<-exp(Netflix_ARMA$fitted.values)

c) Evaluation

d) Forecast