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# Load necessary packages
library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.0.4     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(lmtest)

## Loading required package: zoo
## 
## Attaching package: 'zoo'
## 
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

library(sandwich)

# Set seed for reproducibility
set.seed(123)

# Simulate 200 days of data
n <- 200

# Simulated variables
covid_cases <- round(runif(n, 0, 10000)) # random daily COVID cases
lockdown <- rbinom(n, 1, 0.3)            # 30% chance of lockdown
gdp_growth <- rnorm(n, 2, 1)             # normally distributed around 2%

# Simulate stock returns affected by these variables
stock_return <- 0.05 - 0.00001 * covid_cases - 0.03 * lockdown + 0.02 * gdp_growth + rnorm(n, 0, 1)

# Combine into a dataframe
data <- data.frame(
  stock_return = stock_return,
  covid_cases = covid_cases,
  lockdown = as.factor(lockdown),
  gdp_growth = gdp_growth
)

# View first few rows
head(data)

##   stock_return covid_cases lockdown gdp_growth
## 1   0.03166019        2876        0   4.198810
## 2  -1.16123316        7883        1   3.312413
## 3  -0.59095117        4090        0   1.734855
## 4  -0.01627767        8830        0   2.543194
## 5   0.65835917        9405        0   1.585660
## 6  -1.60463148         456        1   1.523753

# Run regression
model <- lm(stock_return ~ covid_cases + lockdown + gdp_growth, data = data)

# Summary of model
summary(model)

## 
## Call:
## lm(formula = stock_return ~ covid_cases + lockdown + gdp_growth, 
##     data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.64650 -0.58399  0.05316  0.59506  2.24827 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  2.847e-01  2.020e-01   1.410   0.1602  
## covid_cases -8.163e-06  2.499e-05  -0.327   0.7443  
## lockdown1   -3.575e-01  1.487e-01  -2.405   0.0171 *
## gdp_growth  -3.582e-02  6.851e-02  -0.523   0.6017  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9589 on 196 degrees of freedom
## Multiple R-squared:  0.02974,    Adjusted R-squared:  0.01489 
## F-statistic: 2.003 on 3 and 196 DF,  p-value: 0.1149

# With robust standard errors
coeftest(model, vcov = vcovHC(model, type = "HC1"))

## 
## t test of coefficients:
## 
##                Estimate  Std. Error t value Pr(>|t|)  
## (Intercept)  2.8471e-01  2.0657e-01  1.3783   0.1697  
## covid_cases -8.1634e-06  2.3525e-05 -0.3470   0.7290  
## lockdown1   -3.5751e-01  1.6076e-01 -2.2238   0.0273 *
## gdp_growth  -3.5816e-02  6.1567e-02 -0.5817   0.5614  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

ggplot(data, aes(x = covid_cases, y = stock_return)) +
  geom_point(alpha = 0.5) +
  geom_smooth(method = "lm", col = "red") +
  labs(title = "Simulated Stock Returns vs COVID Cases",
       x = "COVID-19 Cases", y = "Stock Return (%)")

## `geom_smooth()` using formula = 'y ~ x'

diplom

2025-04-07

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