Stock Market Data Analysis & Visualization

A comprehensive R script that fetches daily adjusted closing prices for a selection of major tickers from Yahoo Finance and generates a suite of exploratory and analytical plots, including moving averages, returns, volatility, cumulative performance, and comparative normalized charts.

Author: David Nguyen

Table of Contents

1. Project Overview
2. Features & Plots
3. Prerequisites
4. Installation
5. Usage
6. Script Breakdown
7. Selected Visualizations
8. Interpreting the Outputs
9. Extending & Customizing
10. Data Source & Citations
11. License

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Project Overview

This repository provides a single R script (Stock_Market_Analysis.R) which:

• Automatically installs required packages (quantmod, PerformanceAnalytics, zoo) if missing
• Downloads daily adjusted closing prices for tickers (AAPL, MSFT, GOOG, AMZN, TSLA, NFLX, NVDA)
• Computes 20-day & 50-day simple moving averages, daily log returns, 20-day rolling volatility, and monthly returns
• Renders 43 separate plots, each saved as plot-<n>.png in the working directory
• Performs a final comparative normalized performance chart (base = 100)

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Features & Plots

1. Price Chart with SMAs
2. Daily Log Returns Time Series
3. Histogram of Daily Returns
4. 20-Day Rolling Volatility
5. Cumulative Returns
6. Monthly Returns Boxplot
7. Comparative Normalized Performance

Each ticker produces six individual plots, plus one comparative chart, for a total of 43 PNG files.

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Prerequisites

• R (version ≥ 4.0)
• Internet connection to fetch data from Yahoo Finance

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Installation

1. Clone this repository:

   git clone https://github.com/yourusername/stock-market-analysis.git
   cd stock-market-analysis

2. Open R or RStudio in this folder.

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Usage

In an R console or RStudio:

# Source the analysis script
source("Stock_Market_Analysis.R")

The script will:

1. Install any missing packages.
2. Download and process each ticker’s data.
3. Display each plot interactively (with a white background).
4. Save each plot as plot-1.png … plot-43.png in the project directory.
5. Print summary statistics for each ticker’s returns.

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Script Breakdown

1. Setup

   • Defines and installs package list
   • Loads libraries

2. Data Fetch & Preparation

   • Defines tickers & date range
   • Downloads via quantmod::getSymbols()
   • Extracts adjusted close, SMAs, returns, volatility, monthly returns

3. Plotting Sections (per ticker)

   • Price + SMAs
   • Daily Returns
   • Histogram of Returns
   • Rolling Volatility
   • Cumulative Returns
   • Monthly Returns Boxplot

4. Comparative Performance

   • Merges all adjusted series
   • Normalizes each to 100 at the first date
   • Plots on a single chart with legend

5. Summary Statistics

   • Prints mean, SD, skewness, kurtosis, etc., via PerformanceAnalytics::table.Stats()

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Selected Visualizations

Below are some of the 43 plots generated to visualize the stock market data better:

Apple Stock (AAPL)

Microsoft Stock (MSFT)

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Interpreting the Outputs

• SMAs vs. Price: Identify trends and potential crossovers
• Returns Series & Histogram: Assess volatility clustering & distribution shape
• Rolling Volatility: Track risk over time (annualized)
• Cumulative Returns: Evaluate total growth from start date
• Monthly Boxplot: Compare variability across months
• Normalized Chart: Compare relative performance on a common scale

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Extending & Customizing

• Add tickers: Update tickers <- c(...)
• Change SMAs: Modify n in SMA() calls
• Alternative returns: Use type = "arithmetic" in dailyReturn()
• Additional analytics: Compute drawdowns, Sharpe ratios via PerformanceAnalytics
• Export formats: Switch png() to pdf() or svg()

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Data Source & Citations

• Yahoo Finance (via quantmod::getSymbols)

• R & Packages:

  • R Core Team (2023). R: A language and environment for statistical computing.
  • Jeffrey Ryan and Jim Lemon et al., quantmod package.
  • Brian Peterson, PerformanceAnalytics package.

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License

Distributed under the MIT License. See LICENSE for details.