Stock Price Analysis: Tech Stocks (2016-2024)
Your Name
2025-12-01
Introduction
This analysis examines the stock price trends of major technology companies from 2016 to 2024. The stocks analyzed include:
- 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.
- TWTR: Twitter Inc. (now X Corp)
Load Required Libraries
library(tidyverse)
library(tidyquant)
library(lubridate)Data Collection
We use the tidyquant package to download historical
stock price data from Yahoo Finance.
# Define stock symbols
stock_symbols <- c("AAPL", "AMD", "MU", "NIO", "NVDA", "PRPL", "SNAP", "TSLA", "TWTR")
# Download stock prices
stock_prices <- tq_get(
stock_symbols,
from = "2016-01-01",
to = "2024-12-31",
get = "stock.prices"
)
# Display data structure
glimpse(stock_prices)## Rows: 17,134
## Columns: 8
## $ symbol <chr> "AAPL", "AAPL", "AAPL", "AAPL", "AAPL", "AAPL", "AAPL", "AAPL…
## $ date <date> 2016-01-04, 2016-01-05, 2016-01-06, 2016-01-07, 2016-01-08, …
## $ open <dbl> 25.6525, 26.4375, 25.1400, 24.6700, 24.6375, 24.7425, 25.1375…
## $ high <dbl> 26.3425, 26.4625, 25.5925, 25.0325, 24.7775, 24.7650, 25.1725…
## $ low <dbl> 25.5000, 25.6025, 24.9675, 24.1075, 24.1900, 24.3350, 24.7100…
## $ close <dbl> 26.3375, 25.6775, 25.1750, 24.1125, 24.2400, 24.6325, 24.9900…
## $ volume <dbl> 270597600, 223164000, 273829600, 324377600, 283192000, 198957…
## $ adjusted <dbl> 23.75315, 23.15792, 22.70472, 21.74648, 21.86147, 22.21545, 2…Exploratory Data Analysis
Summary Statistics
stock_summary <- stock_prices %>%
group_by(symbol) %>%
summarise(
Start_Date = min(date),
End_Date = max(date),
Min_Price = min(close),
Max_Price = max(close),
Avg_Price = mean(close),
Total_Days = n()
) %>%
arrange(desc(Avg_Price))
knitr::kable(stock_summary,
caption = "Summary Statistics by Stock",
digits = 2)| symbol | Start_Date | End_Date | Min_Price | Max_Price | Avg_Price | Total_Days |
|---|---|---|---|---|---|---|
| TSLA | 2016-01-04 | 2024-12-30 | 9.58 | 479.86 | 126.73 | 2263 |
| AAPL | 2016-01-04 | 2024-12-30 | 22.58 | 259.02 | 103.68 | 2263 |
| AMD | 2016-01-04 | 2024-12-30 | 1.80 | 211.38 | 64.77 | 2263 |
| MU | 2016-01-04 | 2024-12-30 | 9.56 | 153.45 | 56.34 | 2263 |
| NVDA | 2016-01-04 | 2024-12-30 | 0.63 | 148.88 | 23.02 | 2263 |
| SNAP | 2017-03-02 | 2024-12-30 | 4.99 | 83.11 | 21.52 | 1971 |
| NIO | 2018-09-12 | 2024-12-30 | 1.32 | 62.84 | 15.37 | 1585 |
| PRPL | 2016-01-04 | 2024-12-30 | 0.56 | 40.05 | 9.82 | 2263 |
Visualization
Individual Stock Trends (Faceted Plot)
This visualization shows each stock’s closing price in separate panels, with independent y-axis scales to highlight individual trends.
plot1 <- stock_prices %>%
ggplot(aes(x = date, y = close)) +
geom_line(color = "darkblue", size = 0.5) +
facet_wrap(~ symbol, scales = "free_y", ncol = 3) +
labs(
x = "Date",
y = "Closing prices"
) +
theme_minimal() +
theme(
strip.background = element_rect(fill = "lightgray", color = "lightgray"),
strip.text = element_text(face = "bold", size = 11),
panel.grid.minor = element_blank()
)
print(plot1)
Normalized Stock Prices
This chart normalizes all stock prices to start at $1, allowing for direct comparison of relative performance across different stocks.
# Normalize prices to start at $1
stock_normalized <- stock_prices %>%
group_by(symbol) %>%
mutate(
first_price = first(close),
normalized_price = close / first_price
) %>%
ungroup()
plot2 <- stock_normalized %>%
ggplot(aes(x = date, y = normalized_price, color = symbol)) +
geom_line(size = 0.7) +
labs(
title = "Normalized Stock Prices (2016-2024)",
x = "Date",
y = "Normalized price (base = $1)",
color = "Stock"
) +
theme_minimal() +
theme(
plot.title = element_text(hjust = 0.5, face = "bold", size = 14),
legend.position = "right",
panel.grid.minor = element_blank()
) +
scale_color_manual(values = c(
"AAPL" = "red",
"AMD" = "blue",
"MU" = "green",
"NIO" = "purple",
"NVDA" = "orange",
"PRPL" = "yellow3",
"SNAP" = "brown",
"TSLA" = "pink",
"TWTR" = "gray30"
))
print(plot2)
Performance Analysis
Total Return by Stock
stock_returns <- stock_normalized %>%
group_by(symbol) %>%
summarise(
Initial_Price = first(close),
Final_Price = last(close),
Total_Return = ((Final_Price - Initial_Price) / Initial_Price) * 100,
Normalized_Growth = last(normalized_price)
) %>%
arrange(desc(Total_Return))
knitr::kable(stock_returns,
caption = "Total Returns (2016-2024)",
digits = 2)| symbol | Initial_Price | Final_Price | Total_Return | Normalized_Growth |
|---|---|---|---|---|
| NVDA | 0.81 | 137.49 | 16889.81 | 169.90 |
| AMD | 2.77 | 122.44 | 4320.22 | 44.20 |
| TSLA | 14.89 | 417.41 | 2702.54 | 28.03 |
| AAPL | 26.34 | 252.20 | 857.57 | 9.58 |
| MU | 14.33 | 85.31 | 495.32 | 5.95 |
| NIO | 6.60 | 4.38 | -33.64 | 0.66 |
| SNAP | 24.48 | 10.86 | -55.64 | 0.44 |
| PRPL | 9.66 | 0.83 | -91.36 | 0.09 |
Top Performers
top_3 <- stock_returns %>%
slice_max(Total_Return, n = 3)
cat("Top 3 Performing Stocks:\n")## Top 3 Performing Stocks:for(i in 1:nrow(top_3)) {
cat(sprintf("%d. %s: %.2f%%\n",
i,
top_3$symbol[i],
top_3$Total_Return[i]))
}## 1. NVDA: 16889.81%
## 2. AMD: 4320.22%
## 3. TSLA: 2702.54%Key Insights
Based on the normalized price chart:
- NVDA (NVIDIA) shows exceptional growth, particularly accelerating in 2023-2024
- AMD demonstrates strong performance with significant volatility
- TSLA experienced dramatic growth followed by correction
- AAPL shows steady, consistent growth over the period
- NIO and SNAP show more volatile patterns with recent declines
Conclusion
This analysis reveals significant variation in stock performance across the technology sector from 2016 to 2024. The semiconductor stocks (NVDA, AMD) showed particularly strong performance, likely driven by AI and technology infrastructure demand.
Session Information
sessionInfo()## R version 4.5.1 (2025-06-13 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 11 x64 (build 26200)
##
## Matrix products: default
## LAPACK version 3.12.1
##
## locale:
## [1] LC_COLLATE=English_United States.utf8
## [2] LC_CTYPE=English_United States.utf8
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.utf8
##
## time zone: Asia/Taipei
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] PerformanceAnalytics_2.0.8 quantmod_0.4.28
## [3] TTR_0.24.4 xts_0.14.1
## [5] zoo_1.8-14 tidyquant_1.0.11
## [7] lubridate_1.9.4 forcats_1.0.1
## [9] stringr_1.5.2 dplyr_1.1.4
## [11] purrr_1.1.0 readr_2.1.5
## [13] tidyr_1.3.1 tibble_3.3.0
## [15] ggplot2_4.0.0 tidyverse_2.0.0
##
## loaded via a namespace (and not attached):
## [1] gtable_0.3.6 xfun_0.53 bslib_0.9.0
## [4] recipes_1.3.1 lattice_0.22-7 tzdb_0.5.0
## [7] quadprog_1.5-8 vctrs_0.6.5 tools_4.5.1
## [10] generics_0.1.4 parallel_4.5.1 curl_7.0.0
## [13] RobStatTM_1.0.11 pkgconfig_2.0.3 Matrix_1.7-3
## [16] data.table_1.17.8 RColorBrewer_1.1-3 S7_0.2.0
## [19] lifecycle_1.0.4 compiler_4.5.1 farver_2.1.2
## [22] codetools_0.2-20 htmltools_0.5.8.1 class_7.3-23
## [25] sass_0.4.10 yaml_2.3.10 prodlim_2025.04.28
## [28] furrr_0.3.1 pillar_1.11.1 jquerylib_0.1.4
## [31] MASS_7.3-65 cachem_1.1.0 gower_1.0.2
## [34] rpart_4.1.24 parallelly_1.45.1 lava_1.8.2
## [37] tidyselect_1.2.1 digest_0.6.37 future_1.67.0
## [40] stringi_1.8.7 listenv_0.9.1 labeling_0.4.3
## [43] splines_4.5.1 fastmap_1.2.0 grid_4.5.1
## [46] cli_3.6.5 magrittr_2.0.3 survival_3.8-3
## [49] future.apply_1.20.0 withr_3.0.2 scales_1.4.0
## [52] timechange_0.3.0 rmarkdown_2.29 globals_0.18.0
## [55] nnet_7.3-20 timeDate_4051.111 hms_1.1.3
## [58] evaluate_1.0.5 knitr_1.50 timetk_2.9.1
## [61] hardhat_1.4.2 rsample_1.3.1 rlang_1.1.6
## [64] Rcpp_1.1.0 glue_1.8.0 ipred_0.9-15
## [67] rstudioapi_0.17.1 jsonlite_2.0.0 R6_2.6.1