High-Tech Companies On NASDAQ
Market Capitalization and Performance Metrics
@kaggle.thedevastator_high_tech_companies_on_nasdaq
About this Dataset
High-Tech Companies On NASDAQ
High-Tech Companies on NASDAQ
Market Capitalization and Performance Metrics
By [source]
About this dataset
This dataset offers an insightful look into the performance of high-tech companies listed on the NASDAQ exchange in the United States. With information pertaining to over 8,000 companies in the electronics, computers, telecommunications, and biotechnology sectors, this is an incredibly useful source of insight for researchers, traders, investors and data scientists interested in acquiring information about these firms.
The dataset includes detailed variables such as stock symbols and names to provide quick identification of individual companies along with pricing changes and percentages from the previous day’s value as well as sector and industry breakdowns for comprehensive analysis. Other metrics like market capitalization values help to assess a firm’s relative size compared to competitors while share volume data can give a glimpse into how actively traded each company is. Additionally provided numbers include earnings per share breakdowns to gauge profits along with dividend pay date symbols for yield calculation purposes as well as beta values that further inform risk levels associated with investing in particular firms within this high-tech sector. Finally this dataset also collects any potential errors found amongst such extensive scrapes of company performance data giving users valuable reassurance no sensitive areas are missed when assessing various firms on an individual basis or all together as part of an overarching system
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How to use the dataset
This dataset is invaluable for researchers, traders, investors and data scientists who want to obtain the latest information about high-tech companies listed on the NASDAQ exchange in the United States. It contains data on more than 8,000 companies from a wide range of sectors such as electronics, computers, telecommunications, biotechnology and many more. In this guide we will learn how to use this dataset effectively.
Basics: The basics of working with this dataset include understanding various columns like
symbol,name,price,pricing_changes,pricing_percentage_changes,sector,industry,market_cap,share_volume,earnings_per_share. Each column is further described below:
- Symbol: This column gives you the stock symbol of the company. (String)
- Name: This column gives you the name of the company. (String)
- Price: The current price of each stock given by symbol is mentioned here.(Float)
- Pricing Changes: This represents change in stock price from previous day.(Float) - Pricing Percentage Changes :This provides percentage change in stock prices from previous day.(Float) - Sector : It give information about sector in which company belongs .(String). - Industry : Describe industry in which company lies.(string). - Market Capitalization : Give market capitalization .(String). - Share Volume : It refers to number share traded last 24 hrs.(Integer). - Earnings Per Share : It refer to earnings per share per Stock yearly divided by Dividend Yield ,Symbol Yield and Beta .It also involves Errors related with Data Set so errors specified here proviedes details regarding same if any errors occured while collecting data set or manipulation on it.. (float/string )
Advanced Use Cases: Now that we understand what each individual feature stands for it's time to delve deeper into optimizing returns using this data set as basis for our decision making processes such as selecting right portfolio formation techniques or selecting stocks wisely contrarian investment style etc. We can do a comparison using multiple factors like Current Price followed by Price Change percentage or Earnings feedback loop which would help us identify Potentially Undervalued investments both Short Term & Long Term ones at same time and We could dive into analysis showing Relationship between Price & Volumne across Sectors and
Research Ideas
- Analyzing stock trends - The dataset enables users to make informed decisions by tracking and analyzing changes in indicators such as price, sector, industry or market capitalization trends over time.
- Exploring correlations between different factors - By exploring the correlation between different factors such as pricing changes, earning per share or beta etc., it enables us to get a better understanding of how these elements influence each other and what implications it may have on our investments
Acknowledgements
If you use this dataset in your research, please credit the original authors.
Data Source
License
License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.
Columns
File: 2022_03_17_02_06_nasdaq.csv
| Column name | Description |
|---|---|
| symbol | A unique identifier for each company listed on the NASDAQ exchange. (String) |
| name | The name of the company. (String) |
| price | The current price of the company's stock. (Float) |
| pricing_changes | The change in price from day-to-day. (Float) |
| pricing_percentage_changes | The percentage change in price from day-to-day. (Float) |
| sector | The sector the company belongs to. (String) |
| industry | The industry the company belongs to. (String) |
| market_cap | The market capitalization of the company. (Float) |
| share_volume | The amount of shares traded over 24 hours. (Integer) |
| earnings_per_share | A measure of profitability. (Float) |
| symbol_yield | The dividend yield paid by companies over time. (Float) |
| beta | The volatility index. (Float) |
| errors | Any errors associated with data inputed related to these columns. (String) |
Acknowledgements
If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .