Baselight

Historical Stock Price Dataset

Analyzing Trends and Volatility for Major Ticker Symbols

@kaggle.anitarostami_historical_stock_price_dataset

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About this Dataset

Historical Stock Price Dataset

Dataset Description:

This dataset provides historical stock price data for selected ticker symbols ['AAPL', 'MSFT', 'JPM', 'GS', 'AMZN', 'PG', 'KO', 'JNJ', 'XOM', 'CAT'] from January 1, 2014, to December 31, 2023. It contains the daily opening, highest, lowest, closing, adjusted closing prices, and trading volume for each trading day. These tickers represent a diverse range of sectors to allow comprehensive financial analysis.

Purpose and Use Case:

This dataset is ideal for financial analysis, market trend assessments, and investment decision-making. Analysts and researchers can use this dataset to:

  • Analyze price and market trends.
  • Evaluate volatility by analyzing price fluctuations and trading volume.
  • Use historical price movements to forecast and predict future trends.
  • Assess investment opportunities and portfolio performance.

Acknowledgments:

Data was collected using Python and Yahoo Finance. This dataset supports visualization, exploratory data analysis (EDA), and in-depth analysis to develop a predictive model for forecasting stock prices, aiming to gain insights, identify patterns, and improve prediction accuracy.

Potential Research Questions and Inspiration:

  • What is the correlation between stock prices and trading volume over time?
  • How do corporate actions and adjustments affect adjusted closing prices?
  • How does volatility vary across different stocks and sectors?
  • What key factors influence stock price dynamics, such as earnings reports, industry news, regulatory changes, or global economic trends?

Tables

Stockpricedataset

@kaggle.anitarostami_historical_stock_price_dataset.stockpricedataset
  • 930.25 KB
  • 25160 rows
  • 8 columns
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CREATE TABLE stockpricedataset (
  "date" TIMESTAMP,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "adj_close" DOUBLE,
  "volume" BIGINT,
  "ticker" VARCHAR
);

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