Introduction

This analysis examines the stock price performance of eight major technology stocks from 2016 to 2024:

AAPL: Apple Inc.
AMD: Advanced Micro Devices
MU: Micron Technology
NIO: NIO Inc.
NVDA: NVIDIA Corporation
PRPL: Purple Innovation
SNAP: Snap Inc.
TSLA: Tesla Inc.

Load Required Libraries

library(tidyverse)
library(tidyquant)
library(lubridate)

Data Collection

Download Stock Data

# Define stock symbols
stock_symbols <- c("AAPL", "AMD", "MU", "NIO", "NVDA", "PRPL", "SNAP", "TSLA")

# Download stock data from 2016 to 2024
stock_prices <- tq_get(stock_symbols,
                       from = "2016-01-01",
                       to = "2024-12-31",
                       get = "stock.prices")

# Display summary
cat("Total observations:", nrow(stock_prices), "\n")

## Total observations: 17134

cat("Date range:", min(stock_prices$date), "to", max(stock_prices$date), "\n")

## Date range: 16804 to 20087

Data Preview

# View first few rows
head(stock_prices, 10)

## # A tibble: 10 × 8
##    symbol date        open  high   low close    volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>
##  1 AAPL   2016-01-04  25.7  26.3  25.5  26.3 270597600     23.8
##  2 AAPL   2016-01-05  26.4  26.5  25.6  25.7 223164000     23.2
##  3 AAPL   2016-01-06  25.1  25.6  25.0  25.2 273829600     22.7
##  4 AAPL   2016-01-07  24.7  25.0  24.1  24.1 324377600     21.7
##  5 AAPL   2016-01-08  24.6  24.8  24.2  24.2 283192000     21.9
##  6 AAPL   2016-01-11  24.7  24.8  24.3  24.6 198957600     22.2
##  7 AAPL   2016-01-12  25.1  25.2  24.7  25.0 196616800     22.5
##  8 AAPL   2016-01-13  25.1  25.3  24.3  24.3 249758400     22.0
##  9 AAPL   2016-01-14  24.5  25.1  23.9  24.9 252680400     22.4
## 10 AAPL   2016-01-15  24.0  24.4  23.8  24.3 319335600     21.9

# Summary statistics
summary(stock_prices %>% select(symbol, close, volume))

##     symbol              close             volume         
##  Length:17134       Min.   :  0.564   Min.   :0.000e+00  
##  Class :character   1st Qu.: 10.050   1st Qu.:1.993e+07  
##  Mode  :character   Median : 24.017   Median :5.210e+07  
##                     Mean   : 54.663   Mean   :1.144e+08  
##                     3rd Qu.: 71.667   3rd Qu.:1.121e+08  
##                     Max.   :479.860   Max.   :3.693e+09

Data Transformation

Calculate Normalized Prices

Normalized prices allow us to compare relative performance across stocks with different price levels. We set the base price to $1 at the start date for each stock.

stock_normalized <- stock_prices %>%
  group_by(symbol) %>%
  mutate(normalized_price = adjusted / first(adjusted)) %>%
  ungroup()

# Show normalized price summary
stock_normalized %>%
  group_by(symbol) %>%
  summarise(
    start_price = first(adjusted),
    end_price = last(adjusted),
    normalized_end = last(normalized_price),
    return_pct = (normalized_end - 1) * 100
  ) %>%
  arrange(desc(return_pct))

## # A tibble: 8 × 5
##   symbol start_price end_price normalized_end return_pct
##   <chr>        <dbl>     <dbl>          <dbl>      <dbl>
## 1 NVDA         0.790   137.          174.        17308. 
## 2 AMD          2.77    122.           44.2        4320. 
## 3 TSLA        14.9     417.           28.0        2703. 
## 4 AAPL        23.8     251.           10.6         957. 
## 5 MU          14.0      85.1           6.08        508. 
## 6 NIO          6.60      4.38          0.664       -33.6
## 7 SNAP        24.5      10.9           0.444       -55.6
## 8 PRPL         9.66      0.835         0.0865      -91.4

Visualization

Plot 1: Normalized Stock Prices (All Stocks)

This plot shows the relative performance of all stocks on a single chart with normalized prices.

plot1 <- stock_normalized %>%
  ggplot(aes(x = date, y = normalized_price, color = symbol)) +
  geom_line(linewidth = 0.8) +
  labs(
    title = "Normalized Stock Prices (2016-2024)",
    x = "Date",
    y = "Normalized price (Base = $1)",
    color = "Stock"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, face = "bold", size = 14),
    legend.position = "right",
    panel.grid.minor = element_blank()
  ) +
  scale_y_continuous(breaks = seq(0, 200, 50))

print(plot1)

Key Insights from Plot 1

NVDA shows the most dramatic growth, reaching over 150x its initial value
AMD also demonstrates strong performance, especially in recent years
TSLA shows significant volatility with periods of rapid growth and decline
Traditional stocks like AAPL show more stable, gradual growth

Plot 2: Individual Stock Charts (Faceted)

This plot shows actual closing prices for each stock in separate panels.

# Download stock data for plot 2 (8 currently trading stocks)
stocks_plot2 <- c("AAPL", "AMD", "MU", "NIO", "NVDA", "PRPL", "SNAP", "TSLA")

stock_prices_plot2 <- tq_get(stocks_plot2,
                             from = "2016-01-01",
                             to = "2023-12-31",
                             get = "stock.prices")

# Try to load TWTR data from CSV file (delisted in Oct 2022)
twtr_file <- "twtr_historical_data.csv"  # Adjust path if needed

if (file.exists(twtr_file)) {
  twtr_data <- read_csv(twtr_file, show_col_types = FALSE)
  
  # Ensure date column is in proper format
  if ("date" %in% names(twtr_data)) {
    twtr_data <- twtr_data %>%
      mutate(date = ymd(date))
  }
  
  # Add symbol column if missing
  if (!"symbol" %in% names(twtr_data)) {
    twtr_data <- twtr_data %>%
      mutate(symbol = "TWTR")
  }
  
  # Filter date range to match other stocks
  twtr_data <- twtr_data %>%
    filter(date >= as.Date("2016-01-01"), date <= as.Date("2023-12-31"))
  
  # Combine with other stock data
  stock_prices_plot2 <- bind_rows(stock_prices_plot2, twtr_data)
  message("TWTR data successfully loaded from CSV file")
} else {
  message("Note: TWTR CSV file not found. Continuing with 8 stocks.")
  message("To include TWTR, save the file as: ", twtr_file)
}

plot2 <- stock_prices_plot2 %>%
  ggplot(aes(x = date, y = close)) +
  geom_line(color = "navy", linewidth = 0.6) +
  facet_wrap(~ symbol, scales = "free_y", ncol = 3) +
  labs(
    x = "Date",
    y = "Closing prices"
  ) +
  theme_minimal() +
  theme(
    strip.background = element_rect(fill = "gray85", color = NA),
    strip.text = element_text(face = "bold", size = 11),
    panel.grid.minor = element_blank(),
    axis.text = element_text(size = 9)
  )

print(plot2)

Key Insights from Plot 2

Each stock shows distinct price patterns:

AAPL: Steady upward trend with moderate volatility
AMD: Strong growth, especially after 2020
MU: Cyclical pattern typical of semiconductor industry
NIO: High volatility with recent decline
NVDA: Exponential growth, particularly in 2023-2024
PRPL: Volatile with significant peak around 2021
SNAP: Declining trend after initial public offering
TSLA: Dramatic rise to 2021, followed by correction

Performance Summary

performance_summary <- stock_normalized %>%
  group_by(symbol) %>%
  summarise(
    start_date = min(date),
    end_date = max(date),
    start_price = first(adjusted),
    end_price = last(adjusted),
    total_return = ((end_price / start_price) - 1) * 100,
    max_price = max(adjusted),
    min_price = min(adjusted),
    volatility = sd(adjusted / lag(adjusted) - 1, na.rm = TRUE) * 100
  ) %>%
  arrange(desc(total_return))

knitr::kable(performance_summary, 
             digits = 2,
             caption = "Stock Performance Summary (2016-2024)")

Stock Performance Summary (2016-2024)
symbol	start_date	end_date	start_price	end_price	total_return	max_price	min_price	volatility
NVDA	2016-01-04	2024-12-30	0.79	137.46	17308.12	148.84	0.62	3.15
AMD	2016-01-04	2024-12-30	2.77	122.44	4320.22	211.38	1.80	3.72
TSLA	2016-01-04	2024-12-30	14.89	417.41	2702.54	479.86	9.58	3.70
AAPL	2016-01-04	2024-12-30	23.75	251.06	956.97	257.85	20.60	1.81
MU	2016-01-04	2024-12-30	13.99	85.07	508.16	152.51	9.33	2.92
NIO	2018-09-12	2024-12-30	6.60	4.38	-33.64	62.84	1.32	5.75
SNAP	2017-03-02	2024-12-30	24.48	10.86	-55.64	83.11	4.99	4.65
PRPL	2016-01-04	2024-12-30	9.66	0.83	-91.35	40.04	0.56	4.87

Export Plots

# Save plots as high-resolution PNG files
ggsave("normalized_stock_prices.png", plot1, width = 10, height = 6, dpi = 300)
ggsave("faceted_stock_prices.png", plot2, width = 10, height = 8, dpi = 300)

Conclusion

This analysis demonstrates the varying performance of technology stocks over the 2016-2024 period:

NVDA emerged as the top performer, driven by AI and GPU demand
AMD showed strong recovery and growth in the semiconductor space
TSLA exhibited high volatility characteristic of growth stocks
SNAP and PRPL struggled to maintain momentum after initial gains

The normalized price comparison highlights how different stocks can start at similar points but diverge dramatically based on company performance, market conditions, and sector trends.

Stock Price Analysis (2016-2024)

Enkhjin Narankhuu

2025-12-02