About the Google Stock Price Dataset

The Google Stock Price Dataset consists of two CSV (Comma Separated Values) files containing historical stock price data for training and evaluation. Each row in the dataset represents a trading day, and the columns provide various information related to Google's stock for that day.

Columns:

Date: The date of the trading day in the format "YYYY-MM-DD."

Open: The opening price of Google's stock on that trading day.

High: The highest price reached during the trading day.

Low: The lowest price reached during the trading day.

Close: The closing price of Google's stock on that trading day.

Adj Close: The adjusted closing price, accounting for any corporate actions (e.g., stock splits, dividends) that may affect the stock's value.

Volume: The trading volume, representing the number of shares traded on that trading day.

Time Period: The train dataset spans from January 1, 2010, to December 31, 2022, providing twelve years of daily stock price information for model training. The test dataset spans from January 1, 2023, to July 30, 2023, providing seven month of daily stock price data for model evaluation.

Data Source:

The dataset was collected from Yahoo Finance (finance.yahoo.com), a reputable and widely-used financial data platform.

Use Case:

The Google Stock Price Dataset can be utilized for various purposes, such as predicting future stock prices, analyzing historical stock trends, and building machine learning models for financial forecasting.

Potential Applications:

Time Series Analysis: Explore stock price patterns and seasonality. Financial Modeling: Develop predictive models to forecast stock prices. Algorithmic Trading: Create trading strategies based on historical stock data. Risk Management: Assess potential risks and volatilities in the stock market.

Citation:

If you use this dataset in your research or analysis, please provide proper attribution and citation to acknowledge the source.

License: This dataset is provided under the Creative Commons CC0 1.0 Universal (CC0 1.0) Public Domain Dedication, making it freely available for use without any restrictions or attribution requirements.

The art of forecasting stock prices has been a difficult task for many of the researchers and analysts. In fact, investors are highly interested in the research area of stock price prediction. For a good and successful investment, many investors are keen on knowing the future situation of the stock market. Good and effective prediction systems for the stock market help traders, investors, and analyst by providing supportive information like the future direction of the stock market. In this work, we present a recurrent neural network (RNN) and Long Short-Term Memory (LSTM) approach to predict stock market indices.

🇺🇸 Alphabet Inc. (GOOGL) Comprehensive Financial Dataset

📌 Overview

Welcome to the GOOGL Financial Dataset! This dataset provides clear and easy-to-use quarterly financial statements (income statement, balance sheet, and cash flow) along with daily historical stock prices.

As a data engineer double majored with economics, I'll personally analyze and provide constructive feedback on all your work using this dataset. Let's dive in and explore Google's financial journey together!

🗃 Files Included

• googl_daily_prices.csv: Historical daily stock prices.
• googl_income_statement.csv: Quarterly income statements.
• googl_balance_sheet.csv: Quarterly balance sheets.
• googl_cash_flow_statement.csv: Quarterly cash flow statements.

📘 About This Dataset

This dataset offers a unique blend of long-term market performance and detailed financial metrics:

• Time Series of Daily Prices: Track the historical performance of GOOGL stock from its early days up until now.
• Quarterly Financial Statements: Dive into the income statements, balance sheets, and cash flow statements that reflect the company’s financial evolution.
• Integrated Insights: Ideal for comprehensive financial analyses, forecasting, model building, and exploring the dynamic interplay between market performance and underlying business operations.

Whether you're building predictive models, performing deep-dive financial analysis, or exploring the evolution of one of the world's most innovative tech giants, this dataset is your go-to resource for clean, well-organized, and rich financial data.

💡 Tips for Using the Dataset

• Visualize Stock Trends: Plot daily prices to quickly understand stock movements.
• Financial Analysis: Compare income, balance sheet, and cash flow data to spot financial trends and health.
• Predictive Modeling: Use this dataset to build forecasting models and predict future performance.
• Combine Data: Merge price data with financial statements to analyze relationships and uncover deeper insights.

🔗 Works Great with My GOOGL News Dataset!

For a more comprehensive financial analysis, pair this dataset with my other Kaggle dataset:
👉 Google (Alphabet Inc.) Daily News — 2000 to 2025

That dataset includes:

• Daily news articles from Finnhub
• Headlines, summaries, sources, and timestamps
• Covering GOOGL from 2000 to 2025

Combining both datasets unlocks powerful analysis such as:

• Correlating news sentiment with stock price movements
• Studying the impact of earnings reports and product launches
• Developing event-driven forecasting models

Together, they give you everything you need for news + financial signal modeling.

📝 Column Descriptions

📈 googl_daily_prices.csv

• date: Trading date.
• 1. open: Opening stock price on the trading day.
• 2. high: Highest stock price on the trading day.
• 3. low: Lowest stock price on the trading day.
• 4. close: Closing stock price on the trading day.
• 5. volume: Number of shares traded on the day.

📊 googl_income_statement.csv

• fiscalDateEnding: Date marking the end of fiscal quarter.
• reportedCurrency: Currency used in reporting (USD).
• grossProfit: Revenue minus the cost of goods sold.
• totalRevenue: Total income generated from operations.
• costOfRevenue: Direct costs attributable to the production of goods.
• costofGoodsAndServicesSold: Costs directly associated with goods sold.
• operatingIncome: Earnings after operating expenses deducted.
• sellingGeneralAndAdministrative: Administrative and general sales costs.
• researchAndDevelopment: Expenses related to research and innovation.
• operatingExpenses: Total operational costs.
• investmentIncomeNet: Net income from investments.
• netInterestIncome: Income earned from interest after deducting interest paid.
• interestIncome: Income generated from interest-bearing investments.
• interestExpense: Expenses from interest payments.
• nonInterestIncome: Income from non-interest-bearing activities.
• otherNonOperatingIncome: Additional income outside regular operations.
• depreciation: Reduction in value of assets over time.
• depreciationAndAmortization: Combined depreciation and amortization costs.
• incomeBeforeTax: Income before taxation.
• incomeTaxExpense: Taxes paid on earnings.
• interestAndDebtExpense: Interest paid on debts.
• netIncomeFromContinuingOperations: Profit from ongoing operations.
• comprehensiveIncomeNetOfTax: Income after comprehensive expenses.
• ebit: Earnings before interest and taxes.
• ebitda: Earnings before interest, taxes, depreciation, and...

Dataset

This dataset was created by Sandeep K

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This dataset was created by Ramchandra

Released under MIT

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Dataset

This dataset was created by R.Sriram

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