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NFLX Historical Stock Prices
Stock & Market Data
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About
The data contains daily stock market performance records for Netflix (NFLX) between 2002 and 2020. It provides historical pricing and trading volume, allowing users to conduct detailed financial research, observe market trends, and analyse the performance of the company over an extended timeline. It serves as a strong foundation for building predictive models related to market movements.
Columns
- Date: The calendar date corresponding to the stock activity.
- Open: The starting price of the stock at the beginning of the trading day.
- High: The maximum price the stock achieved during that trading day.
- Low: The minimum price the stock reached during that trading day.
- Close: The closing price of the stock at the end of the trading day.
- Adj Close: The daily closing price adjusted to reflect any corporate actions such as stock splits and dividends.
- Volume: The total physical number of shares traded for Netflix stock on that specific day.
Distribution
The data is organised in a standard tabular format, consisting of 7 distinct columns and 4,581 valid daily records. The source file is a CSV, labelled NFLX.csv, with a size of approximately 313.96 kB.
Usage
This resource is ideal for various quantitative applications, including:
- Developing and testing investment strategies and trading algorithms.
- Conducting time-series forecasting, such as applying linear regression models to predict future price changes.
- Academic research focusing on market volatility, growth stocks, or the behaviour of technology sector equities.
- Creating visualisations of long-term financial trends and performance metrics.
Coverage
The temporal scope spans over eighteen years, beginning on 23 May 2002 and concluding on 3 August 2020. The collection captures the entire lifespan of Netflix stock activity during this key period of expansion.
License
CC0: Public Domain
Who Can Use It
- Financial Modellers: To build and validate economic models that forecast potential future stock prices.
- Investors and Traders: For historical analysis necessary for making informed decisions regarding buying and selling.
- Data Scientists: For training machine learning models on real-world financial data for prediction tasks.
- Business Analysts: To understand the relationship between corporate events and market reactions.
Dataset Name Suggestions
- Netflix Daily Market Activity 2002-2020
- NFLX Historical Stock Prices
- FAANG Stock Performance: Netflix
Attributes
Original Data Source: NFLX Historical Stock Prices
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