Badger Infrastructure Solutions stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Context

The stock and financial market is of great importance to many. News about the stock market can provide an interesting overview of how companies of current events are percieved. With this dataset, you could build a classifier that can differentiate between positive, neutral or bad stock news. Please be aware that this dataset is only meant for educational purposes and does not intent to be investment advice in any way.

Content

The dataset is strucktured as follows: - headline: Headline of an article about stocks or a company - label: Either Positive, Neutral or Negative

Acknowledgements

The stock news were gathered via the website finviz.com.

Inspiration

Are there any errors in this dataset? What would you do with a stock news classifier?

Badger Infrastructure Solutions reported $11.7M in Stock for its fiscal quarter ending in December of 2024. Data for Badger Infrastructure Solutions | BAD - Stock including historical, tables and charts were last updated by Trading Economics this last December in 2025.

AI-powered price forecasts for BAD stock across different timeframes including weekly, monthly, yearly, and multi-year predictions.

Full historical data for the S&P 500 (ticker ^GSPC), sourced from Yahoo Finance (https://finance.yahoo.com/).

Including Open, High, Low and Close prices in USD + daily volumes.

Info about S&P 500: https://en.wikipedia.org/wiki/S%26P_500

Today´s connected world allows people to gather information in shorter intervals than ever before, widely monitored by massive online data sources. As a dramatic economic event, recent financial crisis increased public interest for large companies considerably. In this paper, we exploit this change in information gathering behavior by utilizing Google query volumes as a "bad news" indicator for each corporation listed in the Standard and Poor´s 100 index. Our results provide not only an investment strategy that gains particularly in times of financial turmoil and extensive losses by other market participants, but reveal new sectoral patterns between mass online behavior and (bearish) stock market movements. Based on collective attention shifts in search queries for individual companies, hence, these findings can help to identify early warning signs of financial systemic risk. However, our disaggregated data also illustrate the need for further efforts to understand the influence of collective attention shifts on financial behavior in times of regular market activities with less tremendous changes in search volumes.

Graph and download economic data for Index of All Common Stock Prices, Cowles Commission and Standard and Poor's Corporation for United States (M1125AUSM343NNBR) from Jan 1871 to Dec 1956 about stock market, corporate, indexes, and USA.

I was able to scrape data from stockanalysis.com. Initially i requested around 7000 different tickers but only got around 1600. I'm thinking this is to my codes bad handling of errors while concatenating all the tables on a page or stockanalysis started getting less friendly with my IP after so many requests. I'll share the code for this dataset some time soon and you'll probable have better luck with it. The dataset doesn't contain data on many famous tickers like AAPL, MSFT, or NVDA unfortunately 😞.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

The Mediterranean Sea is classified as a “data-poor” region in fisheries due to its low number of assessed stocks given its biodiversity and number of exploited species. In this study, the CMSY method was applied to assess the status and exploitation levels of 54 commercial fish and invertebrate stocks belonging to 34 species fished by Turkish fleets in the Eastern Mediterranean (Levantine) and Black Seas, by using catch data and resilience indices. Most of these marine taxa currently lack formal stock assessments. The CMSY method uses a surplus production model (SPM), based on official catch statistics and an abundance index derived from scientific surveys. The SPM estimates maximum sustainable yield (MSY), fishing mortality (F), biomass (B), fishing mortality to achieve sustainable catches (Fmsy), and the biomass to support sustainable catches (Bmsy). Our results show the estimated biomass values for 94% of the stocks were lower than the required amount to support sustainable fisheries (Bmsy). Of the 54 stocks, 85% of them can be deemed as overfished; two stocks were not subject to overfishing (Sardina pilchardus and Trachurus mediterraneus in the Marmara Sea) while only one stock (Sprattus sprattus in the Black Sea) is healthy and capable of producing MSY. Annual values of the stock status indicators, F/Fmsy and B/Bmsy, had opposing trends in all regions, suggesting higher stock biomasses could only be achieved if fishing mortality is drastically reduced. Recovery times and levels were then explored under four varying F/Fmsy scenarios. Under the best-case scenario (i.e., F = 0.5Fmsy), over 60% of the stocks could be rebuilt by 2032. By contrast, if normal fishing practices continue as usual, all stocks will soon be depleted if not already, whose recover may be impossible at later dates. The results of this study are supported by previous regional assessments confirming the overexploitation of Turkish fisheries is driving the near-total collapse of these marine wild fisheries. Hence, the need to urgently rebuild Turkey’s marine fisheries ought to be prioritized to ensure their future viability.

Badger Infrastructure Solutions reported $2.5B in Market Capitalization this December of 2025, considering the latest stock price and the number of outstanding shares.Data for Badger Infrastructure Solutions | BAD - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last December in 2025.

In the world, more than 80% of the fisheries by numbers and about half of the catches have not been formally analyzed and evaluated due to limited data. It has led to the fast growth of data-poor evaluation methods. There have been various studies carried out on the comparative performance of data-poor and data-moderate methods in evaluating fishery exploitation status. However, most studies to date have focused on coastal fish stocks with simple data sources. It is important to pay attention to high sea fisheries because they are exploited by multiple countries, fishing gears and data may be divrsified and inconsistent. Furthermore, a comparison of the performance of catch-based, length-based, and abundance-based methods to estimate fishery status is needed. This study is the first attempt to apply catch-based, length-based, and abundance-based data-poor methods to stock assessment for an oceanic tuna fishery and to compare the performance with a data-moderate model. Results showed that the three data-poor methods with various types of data did not produce an entirely consistent stock status of the southern Atlantic albacore (Thunnus alalunga) fishery in 2005, as the estimated B2005/BMSY ranged from 0.688 to 1.3 and F2005/FMSY ranged from 0.708 to 1.6. The Monte Carlo Catch maximum sustainable yield model (CMSY) produced a similar time series of B/BMSY and F/FMSY and stock status (recovering) to the Bayesian state-space Schaefer model (BSM). The abundance-based method (AMSY) gave the most conservative condition (overfished) of this fishery. Sensitivity analysis showed the results of the length-based Bayesian biomass estimation method (LBB) are sensitive to Linf settings, and the results with higher Linf were similar to those of other models. However, the LBB results with setting Linf at lower levels produced more optimistic conditions (healthy). Our results highlight that attention should be paid to the settings of model parameter priors and different trends implied in various types of data when using these data-poor methods.

A daily data ranging from January 2014 until December 2018 is employed. The period between January, 1, 2014 until November 7, 2016 refers to the pre-election period. The period ranging from November 8, 2016, until December, 31 2018 defines the post-election period. Four U.S stock price indices are retrieved from DataStream: The standard and Poor’s 500 index (S&P 500) covers the performance of 500 largest capitalization stocks. The Dow Jones Industrial Average (DJIA) index tracks the prices of the top 30 US companies. The NASDAQ 100 measures the performance of the 100 largest non-financial stocks traded on NASDAQ. The Russell 2000 index covers the performance of 2.000 lowest capitalization stocks. A daily political risk index is calculated for each period using Google trends and the principal component analysis.

The Standard & Poor’s (S&P) 500 Index is an index of 500 leading publicly traded companies in the United States. In 2021, the index value closed at ******** points, which was the second highest value on record despite the economic effects of the global coronavirus (COVID-19) pandemic. In 2023, the index values closed at ********, the highest value ever recorded. What is the S&P 500? The S&P 500 was established in 1860 and expanded to its present form of 500 stocks in 1957. It tracks the price of stocks on the major stock exchanges in the United States, distilling their performance down to a single number that investors can use as a snapshot of the economy’s performance at a given moment. This snapshot can be explored further. For example, the index can be examined by industry sector, which gives a more detailed illustration of the economy. Other measures Being a stock market index, the S&P 500 only measures equities performance. In addition to other stock market indices, analysts will look to other indicators such as GDP growth, unemployment rates, and projected inflation. Similarly, since these indicators say something about the economic future, stock market investors will use these indicators to speculate on the stocks in the S&P 500.

Non-traditional data signals from social media and employment platforms for BAD stock analysis

Badger Infrastructure Solutions reported $34.34M in Outstanding Shares in January of 2025. Data for Badger Infrastructure Solutions | BAD - Outstanding Shares including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Stock Market News Data in Portuguese

The Financial Phrase Bank is a dataset originally developed for the paper Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts, made available by researchers from Aalto University and the Indian Institute of Management. The dataset allows for a useful benchmark for fine-tuning Language Models on Sentiment Analysis Tasks.

As the amount of annotated text data (especially about the financial market) in Portuguese, I went ahead and translated the entire dataset for people to try out Sentiment Analysis tasks in Portuguese.

Content

The dataset originally contains about 4840 manually annotated financial news in English and consists of three columns: 1. y: the annotated label for the sentiment of the news text (neutral, positive, negative); 2. text: the original text for each record; 3. text_pt: the translated and that I manually validated version of the original record;

Acknowledgments

[1] Malo, P., Sinha, A., Korhonen, P., Wallenius, J., & Takala, P. (2014). Good debt or bad debt: Detecting semantic orientations in economic texts. Journal of the Association for Information Science and Technology, 65(4), 782-796.

Photo by Markus Winkler on Unsplash

Monthly and long-term Sweden Stock Market data: historical series and analyst forecasts curated by FocusEconomics.

This is the data of Saudi stock market companies since 2000-01-01. It was collected from Saudi Stock Exchange (Tadawul) https://www.tadawul.com.sa/wps/portal/tadawul/home/

Content

Each row in the database represents the price of a specific stock at a specific date:

• symbol (Integer): The symbol or the reference number of the company

• name(String) Name of the company

• trading_name (String): The trading name of the company

• sectoer (String): The sector in which the company operates

• date (Date): The date of the stock price

• open (Decimal): The opening price

• high (Decimal): The highest price of the stock at that day

• low (Decimal): The lowest price of the stock at that day

• close (Decimal): The closing price

• change (Decimal): The change in price from the last day

• perc_Change (Decimal): The percentage of the change

• volume_traded (Decimal): The volume of the trades for the day

• value_traded (Decimal): The value of the trades for the day

• no_trades (Decimal): The number of trades for the day

Problem Statement

Using data science in the stock market is not new, but that doesn't apply for Saudi Stock Exchange (Tadawul), It needs to be explored and studied deeply, so we can cluster companies based on its behavior during the good and bad days. Also we can identify the days with a very large number of trades and try to understand the reason behind it. Finally we can predict the stocks prices.

Graph and download economic data for International Migrant Stock, Total for Heavily Indebted Poor Countries (SMPOPTOTLHPC) from 1960 to 2015 about migration, World, and 5-year.