Analysis of ‘Fortune 1000’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/winston56/fortune-500-data-2021 on 13 November 2021.

--- Dataset description provided by original source is as follows ---

Context

Every year Fortune, an American Business Magazine, publishes the Fortune 500, which ranks the top 500 corporations by revenue. This dataset includes the entire Fortune 1000, as opposed to just the top 500.

Content

The Fortune 1000 dataset is from the Fortune website, collected by the processes outlined in this notebook. It contains U.S. company data for the year 2021. The dataset is 1000 rows and 18 columns.

Features

• Company - values are the name of the company
• Rank - The 2021 rank established by Fortune (1-1000)
• Rank Change - The change in the rank from 2020 to 2021. There is only a rank change listed if the company is currently in the top 500 and was previously in the top 500.
• Revenue - Revenue of each company in millions. This is the criteria used to rank each company.
• Profit - Profit of each company in millions.
• Num. of Employees - The number of employees each company employs.
• Sector - The sector of the market the company operates in.
• City - The city where the company's headquarters is located.
• State - The state where the company's headquarters is located
• Newcomer - Indicates whether or not the company is new to the top Fortune 500 ("yes" or "no"). No value will be listed for companies outside of the top 500.
• CEO Founder - Indicates whether the CEO of the company is also the founder ("yes" or "no").
• CEO Woman - Indicates whether the CEO of the company is a woman ("yes" or "no").
• Profitable - Indicates whether the company is profitable or not ("yes" or "no").
• Prev. Rank - The 2020 rank of the company, as established by Fortune. There will only be previous rank data for the top 500 companies.
• CEO - The name of the CEO of the company
• Website - The url of the company website
• Ticker - The stock ticker symbol of public companies. Some rows will have empty values because the company is a private corporation.
• Market Cap - The market cap (or value) of the company in millions. Some rows will have empty values because the company is private. Market valuations were determined on January 20, 2021.

Inspiration

This dataset is made to explore the top corporations in the U.S. Answer questions such as: What percentage of companies have women ceo's? How many companies are newcomers? What percentage of companies have ceos who were also founders? What role does profitability play in ranking?

--- Original source retains full ownership of the source dataset ---

This dataset is about books. It has 1 row and is filtered where the book is Multiple directors in top Scottish companies 1904-1956. It features 7 columns including author, publication date, language, and book publisher.

Corporate Profits in the United States decreased to 3203.60 USD Billion in the first quarter of 2025 from 3312 USD Billion in the fourth quarter of 2024. This dataset provides the latest reported value for - United States Corporate Profits - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

RepLab Summarization DatasetThis package contains the dataset generated in the research published in the paper:"Javier Rodríguez-Vidal, Jorge Carrillo-de-Albornoz, Enrique Amigó, Laura Plaza, Julio Gonzalo and Felisa Verdejo. 2019. Automatic Generation of Entity-Oriented Summaries for Reputation Management. Ambient Intelligence & Humanized Computing."The dataset is available for research purpose. If you use it, please, cite us.This README file contains: 1) A brief description of the corpus2) A description of the contents of each directory in this package.1. Description of RepLab Summarization DatasetThe RepLab summarization dataset contains companies data from the RepLab 2013 dataset (http://nlp.uned.es/replab2013/), where users from Twitter talk about different topics of the companies. Each topic consists of a different number of tweets posted by Twitter users.The collection comprises tweets about 31 entities from two domains: automotive and banking. As a result, our subset of RepLab 2013 comprises 71,303 English and Spanish tweetsFor each entity, tweets are groupped in topics and for each topic three different summaries are manually generated: abstractive english, abstractive spanish and extractive.Please see the paper for further details. 2. Description of the contents of this package./entities:This directory includes the information of each organization in order to create a summary. Each .xml file corresponds to an entity and includes the following information: -”Corpus entity”: Id of the entity. -”cluster”: each one of the topics of the entity. -"label": name of the topic. -"priority": level of relevance of the topic: Alert (the highest priority being a reputation alert, i.e., an issue that requires an immediate response from the entity), Midly_important (relevant for the entity, an intermediate priority) or unimportant (the lowest priority). -”tweet”: Information about the tweets. -"id": Id of the tweet. -"date": When the tweet was written. -"followers": Of the author of the tweet. -"polarity": Of the tweet. -"text": Text of the tweet. -"summary": Information about the summary: -"abstract_EN": Abstractive summary in English. -"abstract_ES": Abstractive summary in Spanish. -"tweet": Id of the tweet(s) selected for the extractive summary (if it is not filled, the extractive summary is the one of the tweets in the topic).

The data were collected from the top 100 Malaysian companies, as demonstrated in Table 1, totalling 300 firm-year observations from 2018 to 2020. Observations were based on the companies' disclosures pertaining to environmental and social aspects, primarily extracted from their annual financial statements, sustainability reports, and integrated reports. These documents were publicly available on the websites of Bursa Malaysia or the companies themselves. This study's content analysis allows for a detailed and systematic examination of sustainability disclosures, providing an empirical foundation for the assessment of sustainability strategies across various industries.

Tweets about the Top Companies from 2015 to 2020

This dataset as a part of the paper published in the 2020 IEEE International Conference on Big Data under the 6th Special Session on Intelligent Data Mining track, is created to determine possible speculators and influencers in a stock market. Although we used both tweet data and companies' market data in our project, we thought that it is a better choice to split our datasets into two parts while sharing in Kaggle. This dataset is helpful for those interested in tweets that are written about Amazon, Apple, Google, Microsoft, and Tesla by using their appropriate share tickers.

Note: For those interested in the process of evaluating speculators and influencers in a stock market, the dataset in the following link may be helpful. https://www.kaggle.com/omermetinn/values-of-top-nasdaq-copanies-from-2010-to-2020

Content

This dataset contains over 3 million unique tweets with their information such as tweet id, author of the tweet, post date, the text body of the tweet, and the number of comments, likes, and retweets of tweets matched with the related company.

Acknowledgements

Tweets are collected from Twitter by a parsing script that is based on Selenium. Note 1: For those interested in the script, please visit the following link. https://github.com/omer-metin/TweetCollector

Note 2: For those interested in our paper used this dataset, please visit the following link. https://ieeexplore.ieee.org/document/9378170

Inspiration

Some of the interesting questions (tasks) which can be performed on this dataset -

1) Determining the correlation between the market value of company respect to the public opinion of that company. 2) Sentiment Analysis of the companies with a time series in a graph and reasoning the possible declines and rises. 3) Evaluating troll users who try to occupy the social agenda.

A List of top 30 Listed companies on Nigeria Stock Exchange as at April 2018 with their Capitalization Value and Ranking. We also Include a computation of proportion of the NSE controlled by the NSE 30 Index by dividing the total Market Capitalization for the NSE 30 Index by total market Capitalization for the whole NSE. In addition we compute the the ratio of Non-Financial services companies and Financial services companies as a percentage the whole value of NSE Market Capitalization

Context

The National Stock Exchange of India Ltd. (NSE) is an Indian stock exchange located at Mumbai, Maharashtra, India. National Stock Exchange (NSE) was established in 1992 as a demutualized electronic exchange. It was promoted by leading financial institutions on request of the Government of India. It is India’s largest exchange by turnover. In 1994, it launched electronic screen-based trading. Thereafter, it went on to launch index futures and internet trading in 2000, which were the first of its kind in the country.

With the help of NSE, you can trade in the following segments:

• Equities

• Indices

• Mutual Funds

• Exchange Traded Funds

• Initial Public Offerings

• Security Lending and Borrowing Scheme

https://cdn6.newsnation.in/images/2019/06/24/Sharemarket-164616041_6.jpg" alt="Stock image">

Companies on successful IPOs gets their Stocks traded over different Stock Exchnage platforms. NSE is one important platofrm in India. There are thousands of companies trading their stocks in NSE. But, I have chosen two popular and high rated IT service companies of India; TCS and INFOSYS. and the third one is the benchmark for Indian IT companies , i.e. NIFTY_IT_INDEX .

Content

The dataset contains three csv files. Each resembling to INFOSYS, NIFTY_IT_INDEX, and TCS, respectively. One can easily identify that by the name of CSV files.

Timeline of Data recording : 1-1-2015 to 31-12-2015.

Source of Data : Official NSE website.

Method : We have used the NSEpy api to fetch the data from NSE site. I have also mentioned my approach in this Kernel - "**WebScraper to download data for NSE**". Please go though that to better understand the nature of this dataset.

Shape of Dataset:

INFOSYS - 248 x 15 || NIFTY_IT_INDEX - 248 x 7 || **TCS - 248 x 15

• Colum Descriptors:

• Date: date on which data is recorded

• Symbol: NSE symbol of the stock

• Series: Series of that stock | EQ - Equity

OTHER SERIES' ARE:

EQ: It stands for Equity. In this series intraday trading is possible in addition to delivery.

BE: It stands for Book Entry. Shares falling in the Trade-to-Trade or T-segment are traded in this series and no intraday is allowed. This means trades can only be settled by accepting or giving the delivery of shares.

BL: This series is for facilitating block deals. Block deal is a trade, with a minimum quantity of 5 lakh shares or minimum value of Rs. 5 crore, executed through a single transaction, on the special “Block Deal window”. The window is opened for only 35 minutes in the morning from 9:15 to 9:50AM.

BT: This series provides an exit route to small investors having shares in the physical form with a cap of maximum 500 shares.

GC: This series allows Government Securities and Treasury Bills to be traded under this category.

IL: This series allows only FIIs to trade among themselves. Permissible only in those securities where maximum permissible limit for FIIs is not breached.

• Prev Close: Last day close point

• Open: current day open point

• High: current day highest point

• Low: current day lowest point

• Last: the final quoted trading price for a particular stock, or stock-market index, during the most recent day of trading.

• Close: Closing point for the current day

• VWAP: volume-weighted average price is the ratio of the value traded to total volume traded over a particular time horizon

• Volume: the amount of a security that was traded during a given period of time. For every buyer, there is a seller, and each transaction contributes to the count of total volume.

• Turnover: Total Turnover of the stock till that day

• Trades: Number of buy or Sell of the stock.

• Deliverable: Volumethe quantity of shares which actually move from one set of people (who had those shares in their demat account before today and are selling today) to another set of people (who have purchased those shares and will get those shares by T+2 days in their demat account).

• %Deliverble: percentage deliverables of that stock

Acknowledgements

I woul dlike to acknowledge all my sincere thanks to the brains behind NSEpy api, and in particular SWAPNIL JARIWALA , who is also maintaining an amazing open source github repo for this api.

Inspiration

I have also built a starter kernel for this dataset. You can find that right here .

I am so excited to see your magical approaches for the same dataset.

THANKS!

This study conducts a comparative analysis of how geopolitical risk (GPR) and innovation impact stock returns in the defense industry based on data from 75 defense companies across 17 countries and 4 continents. With daily datasets spanning from January 1, 2014 to March 29, 2024, wavelet coherence and wavelet phase differences were used to conduct the analysis. The results revealed that innovation had a greater and more pronounced impact during the entire analysis period compared with the influence of GPR events. GPRs exerted an uneven and heterogeneous impact on global defense stocks and had a concentrated impact during events that generated uncertainty. Overall, we found significant time-varying dependence across a large number of companies at different time frequencies. The COVID-19 pandemic did not have a major impact on companies in the defense industry. Further, GPR events led to increased volatility during the Russia–Ukraine war, leading to increased uncertainty. In addition to the dominant role they play in the world defense market, US companies served as a robust hedge, especially from 2021 to 2022. Defense companies in the UK are more sensitive to both GPR events and innovation, followed by companies in Germany and France. Comparative analysis of the scalograms of China reveals a greater influence of innovation compared with GPR events. Thus, diversification opportunities have been extended from the defense industry in China, offering investors a promising way to capitalize on refuge opportunities during periods of disruption. To mitigate the global rearmament trend, we suggest alternative investment opportunities for different time horizons.

In 2023, Meta Platforms had a total annual revenue of over 134 billion U.S. dollars, up from 116 billion in 2022. LinkedIn reported its highest annual revenue to date, generating over 15 billion USD, whilst Snapchat reported an annual revenue of 4.6 billion USD.