Introdution

In this data analysis case study, I will utilize R as my data analytics tool of choice. My task is to analyze and compare the performance of four prominent companies in the stock market - Apple, Microsoft, Netflix, and Google. We will be working with historical stock price data for these companies over the past three months.

The primary objective of this analysis is to gain insights into the trends and patterns exhibited by the stock prices of these companies. I will examine their price movements, calculate moving averages and assess volatility between the different stock prices.

Data Source

The dataset utilized in this case study, was provided by Kaggle user Amir Motefaker. It encompasses a comprehensive set of data for each company, including the date, opening price, highest price, lowest price, closing price, adjusted closing price and volume.These data points will serve as the foundation for my analysis.

Preparation For The Analysis Phase

Commencing the preparation stage for the analysis phase by loading the required libraries, uploading the necessary dataset, and performing data cleaning.

Loading essential libraries needed for the analysis process

library(tidyverse) 
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library(dplyr)
library(lubridate)
library(plotly)
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library(tidyquant)
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library(TTR)

Load the CSV dataset into the working environment.

#importing csv file 
stocks <- read.csv("stocks.csv")

Explore the dataset to determine the dimensions, data types, and overall structure of the data.

#look through the dataset
glimpse(stocks)
## Rows: 248
## Columns: 8
## $ Ticker    <chr> "AAPL", "AAPL", "AAPL", "AAPL", "AAPL", "AAPL", "AAPL", "AAP…
## $ Date      <chr> "2023-02-07", "2023-02-08", "2023-02-09", "2023-02-10", "202…
## $ Open      <dbl> 150.64, 153.88, 153.78, 149.46, 150.95, 152.12, 153.11, 153.…
## $ High      <dbl> 155.23, 154.58, 154.33, 151.34, 154.26, 153.77, 155.50, 156.…
## $ Low       <dbl> 150.64, 151.17, 150.42, 149.22, 150.92, 150.86, 152.88, 153.…
## $ Close     <dbl> 154.65, 151.92, 150.87, 151.01, 153.85, 153.20, 155.33, 153.…
## $ Adj.Close <dbl> 154.4142, 151.6884, 150.6400, 151.0100, 153.8500, 153.2000, …
## $ Volume    <int> 83322600, 64120100, 56007100, 57450700, 62199000, 61707600, …

Upon examination, it was observed that the “Date” column in the dataset is being interpreted as a character data type. Given the historical nature of the dataset, it is essential to convert the data type of the “Date” column to the appropriate format, namely Date.

#convert data type from character to date
stocks$Date <- ymd(stocks$Date)

Verify that the data type has been successfully updated.

#confirm that the data type has been updated
class(stocks$Date)
## [1] "Date"

Modify the column name “Ticker” to “Company” for improved clarity and comprehension.

#rename column "Ticker" to "Company"
stocks <- stocks %>% 
  rename(Company = Ticker)

Confirm that the column name has been successfully updated, it is necessary to verify the changes.

#confirm that the column name has been updated
colnames(stocks)
## [1] "Company"   "Date"      "Open"      "High"      "Low"       "Close"    
## [7] "Adj.Close" "Volume"

Verifying that the data frame does not contains any missing values and eliminate any duplicated entries if found.

#check if data frame contains any missing values and delete any duplicates 
unique (unlist (lapply (stocks, function (x) which (is.na (x))))) 
## integer(0)

Inspect the data frame to identify the unique company names listed in the “Company” column.

#list distinct values in the Company column
unique(stocks$Company)
## [1] "AAPL" "MSFT" "NFLX" "GOOG"

Analysis

Entering into the analysis phase, where the focus will be on investigating the movement of stock prices, computing moving averages, and evaluating volatility.

Generate visualizations depicting the daily stock price movement for each company.

#Price Movement in Daily stock Prices of each company
p <- ggplot(data = stocks) +
  geom_line(aes(x = Date , y = Close, color = Company)) +
  labs(title = "Stock Market Movement within the Last Three Months", subtitle = "Apple, Google, Microsoft and Netflix") +
       ylab("Closing Price") +
  scale_color_manual(values = c("red", "green", "black", "darkviolet")) +
  theme_bw()

ggplotly(p) %>% 
  layout(title = list(text = paste0("Stock Market Movement Within The Last Three Months",
                                    '<br>',
                                    '<sup>',
                                    "Apple, Google, Microsoft and Netflix",
                                    '</sup>')))

Calculate the average volume for each company and create visualizations to depict the results.

#calculate average volume of stock for each company
average_volume <- stocks %>%
  group_by(Company) %>% 
  summarise(Volume = mean(Volume))
#plot  average volume of stock
ggplot(data = average_volume) +
  geom_col(aes(x = Company, y = Volume, fill = Company)) +
  labs(title = "Average Volume of Stock", subtitle = "Apple, Google, Microsoft and Netflix") +
  scale_y_continuous(labels = scales::comma_format()) +
  scale_fill_manual(values = c("red", "green", "black", "darkviolet")) +
  theme_bw()

Calculate the 10-day and 20-day moving averages, and create corresponding plots to visualize their trends.

#Calculate 10 and 20 day moving average 
selected_data <- stocks[,c("Company", "Date", "Close")]
selected_data <- selected_data %>%
  group_by(Company) %>%
  mutate(MA_10 = SMA(Close, n = 10)) %>% 
  mutate(MA_20 = SMA(Close, n = 20))
#Visualize moving Average of all companies 
m <- ggplot(data = selected_data) +
  geom_line(aes(x = Date, y = Close, color = Company), linetype = "solid") +
  geom_line(aes(x = Date, y = MA_10, color = Company), linetype = "dashed") +
  geom_line(aes(x = Date, y = MA_20, color = Company), linetype = "dotted") +
  labs(x = "Date", y = "Price", title = "Moving Average Graph") +
  scale_color_manual(values = c("red", "green", "black", "darkviolet")) + 
  theme_bw()


ggplotly(m)

Calculate and visualize the volatility of all four companies

#Calculate Volatility for all companies
volatility_data <- stocks %>% 
  group_by(Company) %>% 
  mutate(volatility = volatility(Close))
#plot volatilty 
v <- ggplot(data = volatility_data) +
  geom_line(aes(x = Date, y = volatility, color = Company)) +
  scale_color_manual(values = c("red", "green", "black", "darkviolet")) + 
  labs(title = "Volatilty of All Companies") +
  theme_bw()

ggplotly(v)

Key Takeaways

These key takeaways provide valuable insights into the stock performance, volume, volatility, and potential trends of the examined companies. They serve as a basis for making informed decisions and formulating investment strategies.

Recommendations

It is important to note that these recommendations are based on the insights derived from this analysis, but individual investment goals, risk preferences, and financial circumstances should also be taken into account when making investment decisions. It is advisable to conduct further research and consult with a financial advisor before making any investment choices.