Real-time price data collected by the Boston Market News Reporter. The NOAA Fisheries' "Fishery Market News" began operations in New York City on February 14, 1938. The primary function of this joint Federal/industry program is to provide accurate and unbiased reports depicting current conditions affecting the trade in fish and fishery products. The Boston and New York Market News Reports are now hosted by the Northeast Fisheries Science Center. Please navigate to the URL below for 2014 and newer data: https://www.nefsc.noaa.gov/read/socialsci/marketNews.php

Live Briefs Investor – US Covering thousands of listed securities and events across 80 news categories, Live Briefs Investor US is specifically designed to keep individual investors and active traders on top of breaking news that is likely to affect their portfolios.

Most of the largest and most respected retail and self-directed brokerage firms in the North America rely on MT Newswires to provide their clients with complete coverage of the financial markets. The Investor service includes timely and insightful commentary on equities, commodities, ETFs, economics, forex, options and fixed income assets throughout the day (6:30 am to 6:30 pm EST).

Every story is ticker-tagged and category-coded to allow for seamless platform integration. US Equities – significant events affecting individual public companies in the US: After-hours and pre-market news, trading activity and technical price level indications; Earnings estimate change alerts; Analyst Rating Changes- the most comprehensive view and coverage of rating changes available anywhere; ETF Power Play – daily trends in ETF trading activity; Mini and detailed sector summaries – pre-market, mid-day, and closing; Market Chatter – real-time coverage of trading desk rumors and breaking news; Zero noise: Only premium, original news and event analysis. Never any fillers (press releases, non-market related news, etc.).

This dataset contains over 19,000+ rows of financial headlines from 2008 to 2024, paired with daily closing prices of the S&P 500 index.

Columns: - date: Trading date (YYYY-MM-DD) - headline: Financial news headline for the day - close: S&P 500 closing price on that date

You can use this dataset to: - Perform sentiment analysis on news vs. market behavior - Correlate sentiment score with price movement - Build predictive models or NLP-based trading strategies

Combine this with financial sentiment lexicons for more accuracy.

If you find this dataset useful, an upvote would mean a lot — it helps others discover it too!

The primary function of the AMS Market News Program is to compile and disseminate information that will aid producers, consumers, and distributors in the sale and purchase of agricultural related products nationally and internationally.

The primary function of the Fruit and Vegetable Market News Division of the Fruit and Vegetable Programs is to provide an exchange of information for growers, shippers, wholesalers, researchers and others on supplies, demand and prices of fresh fruit and vegetables and speciality crops.

Crude oil stock market news is crucial for investors, traders, and industries dependent on oil prices. Factors such as OPEC decisions, global economic trends, geopolitical events, and market speculation influence crude oil prices. Financial news outlets and specialized publications report regular updates on crude oil prices and other relevant factors affecting the market.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Nifty 50: A Journey to 20,000 and Beyond?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This package contains the datasets and source codes used in the PhD thesis entitled Predicting the Brazilian stock market using sentiment analysis, technical indicators and stock prices. The following files are included: File Labeled.zip - financial news labeled in two classes (Positive and Negative), organized to train Sentiment Analysis models. Part of these news were initially presented in [1]. Besides the news in this file, in the related PhD thesis the training dataset was complemented with the labeled news presented in [2]. File Unlabeled.zip - general unlabeled financial news collected during the period 2010-2020 from the following online sources: G1, Folha de São Paulo and Estadão. This file contains news from the Bovespa index and from the following companies: Banco do Brasil, Itau, Gerdau and Ambev. File Stocks.zip - stock prices from the companies Banco do Brasil, Itau, Gerdau, Ambev, and the Bovespa index. The considered period ranges from 2010 to 2020. File Models.zip - contains the source codes of the models used in the PhD thesis (i.e., Multilayer Perceptron, Long Short-Term Memory, Bidirectional Long Short-Term Memory, Convolutional Neural Network, and Support Vector Machines). File Utils.zip - contains the source codes of the preprocessing step designed for the methodology of this work (i.e., load data and generate the word embeddings), alongside with stocks manipulation, and investment evaluation. [1] Carosia, A. E. D. O., Januário, B. A., da Silva, A. E. A., & Coelho, G. P. (2021). Sentiment Analysis Applied to News from the Brazilian Stock Market. IEEE Latin America Transactions, 100. DOI: 10.1109/TLA.2022.9667151 [2] MARTINS, R. F.; PEREIRA, A.; BENEVENUTO, F. An approach to sentiment analysis of web applications in portuguese. Proceedings of the 21st Brazilian Symposium on Multimedia and the Web, ACM, p. 105–112, 2015. DOI: 10.1145/2820426.2820446

This Widget provides access to all FSA Daily Terminal Market Prices information releases. The widget may be embedded into your website or blog with code provided using either Flash or Javascript.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: XML File For complete information, please visit https://data.gov.

Dairy Market News covers the supply, demand, and price situation every week on a regional, national, and international basis for milk, butter, cheese, and dry and fluid products.

Crude Oil rose to 68.75 USD/Bbl on July 11, 2025, up 3.27% from the previous day. Over the past month, Crude Oil's price has risen 1.04%, but it is still 16.37% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Crude Oil - values, historical data, forecasts and news - updated on July of 2025.

Oil prices dropped today due to concerns over the economic impact of the COVID-19 pandemic and the increase in oil supply. However, the approval of vaccines and discussions of production cuts provide some hope for a potential recovery in the future.

WTI price news is reported regularly by financial news outlets, such as Bloomberg, CNBC, and OilPrice.com. These news sources provide analysis and commentary on the factors affecting WTI prices, as well as projections and forecasts for future price movements. Traders and investors closely follow WTI price news to make informed decisions about buying or selling oil futures contracts. They use technical analysis, fundamental analysis, and market sentiment to predict future price movements and capitalize on th

Coffee fell to 288.72 USd/Lbs on July 11, 2025, down 0.41% from the previous day. Over the past month, Coffee's price has fallen 16.63%, but it is still 15.90% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Coffee - values, historical data, forecasts and news - updated on July of 2025.

FNSPID: A Comprehensive Financial News Dataset in Time Series

  Description

FNSPID is a meticulously curated dataset designed to support research and applications in the field of financial news analysis within the context of time-series forecasting. Our dataset encompasses a wide range of financial news articles, providing a rich resource for developing and testing models aimed at understanding market trends, investor sentiment, and other critical financial… See the full description on the dataset page: https://huggingface.co/datasets/Zihan1004/FNSPID.

This survey focused on the stock market. Respondents were asked if they thought the economy was getting better or worse, whether they thought they would be better off financially one year from now, if they planned to spend more or less money than last year at Christmas, and whether stock market prices affected them personally. Additional questions pertained to the recent sharp drop in stock prices and its impact on the respondent, and the respondent's understanding of a number of terms used to describe the economy and the stock market (e.g., the Down Jones Industrial Average, federal budget and trade deficits, liquidity, "buying on margin," and bear and bull markets). The results of the poll were announced on the ABC television program "Nightline." Demographic characteristics of respondents are included.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

if the stock market goes down during a recession, you should sell all of your investments to minimize your losses.

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Discover the contrasting fortunes of zinc and lead in the metal markets, with zinc rallying due to supply constraints and lead pressured by a supply glut.

This dataset is designed for the commodity market, featuring over 10,000 manually annotated news headlines. It aims to provide deep insights into news sentiment and its implications for commodity prices. The headlines were collected from various news sources and evaluated by three subject experts over a period of more than 20 years, from 2000 to 2021. Each news item has been assessed across multiple dimensions, including implied price direction (up, down, or constant), whether the news discusses past or future events, and if it involves asset comparisons. This dataset is particularly valuable for developing machine learning models that can understand commodity news, which can then serve as an additional input for both short-term and long-term price forecasting models. It is also useful for creating news-based indicators for commodities. Researchers focused on text analytics and classification problems will find this dataset beneficial, although some classes are highly imbalanced, which may present challenges for machine learning algorithms.

Columns

The dataset includes the following columns:

• Dates: The date of the news headline.
• URL: The URL where the news headline was published.
• News: The actual news headline text.
• Price Direction Up: A binary indicator (1 for Yes, 0 for No) if the news headline suggests an increase in price.
• Price Direction Constant: A binary indicator (1 for Yes, 0 for No) if the news headline suggests a stable price (no change).
• Price Direction Down: A binary indicator (1 for Yes, 0 for No) if the news headline suggests a decrease in price.
• Asset Comparison: A binary indicator (1 for Yes, 0 for No) if the news headline compares different assets.
• Past Information: A binary indicator (1 for Yes, 0 for No) if the news headline refers to past events.
• Future Information: A binary indicator (1 for Yes, 0 for No) if the news headline refers to future events.
• Price Sentiment: The overall sentiment of the gold commodity price based on the headline, categorised as positive, negative, or other.

Distribution

The dataset contains over 10,000 unique news headlines and corresponding metadata. Data files are typically provided in CSV format. Key distribution statistics for some dimensions are as follows:

• Dates: 3,761 unique values.
• URL: 10,570 unique values.
• News: 10,570 unique values.
• Price Direction Up: 6,158 headlines do not imply up, 4,412 imply up.
• Price Direction Constant: 10,126 headlines do not imply constant, 444 imply constant.
• Price Direction Down: 6,658 headlines do not imply down, 3,912 imply down.
• Asset Comparison: 8,569 headlines do not compare assets, 2,001 compare assets.
• Past Information: 318 headlines do not discuss past information, 10,252 discuss past information.
• Future Information: 10,251 headlines do not discuss future information, 319 discuss future information.
• Price Sentiment: Approximately 42% positive, 36% negative, and 22% other sentiment.

Usage

This dataset is ideally suited for:

• Developing machine learning models that understand commodity news for price forecasting.
• Creating news-based indicators for commodity markets.
• Evaluating text classification models in the context of news analytics.
• Research into the impact of news on commodity market volatility.

Coverage

The dataset has a global regional coverage. It spans a significant time range of over 20 years, from 2000 to 2021, with headlines collected across this period. There are no specific demographic notes beyond the focus on gold commodity news.

License

CC-BY-NC

Who Can Use It

This dataset is primarily intended for:

• Researchers and practitioners specialising in news analytics for commodities, who can leverage it for building predictive models.
• Data scientists and machine learning engineers working on text classification and natural language processing tasks, especially those dealing with imbalanced datasets.
• Financial analysts and market strategists interested in incorporating news sentiment into their commodity market analysis.

Dataset Name Suggestions

• Gold News Sentiment Analysis Dataset
• Commodity Market News Classifier
• Financial News Headline Sentiment
• Gold Price Direction News Data
• Annotated Commodity News for ML

Attributes

Original Data Source: Sentiment Analysis of Commodity News (Gold)

This dataset contains news headlines relevant to key forex pairs: AUDUSD, EURCHF, EURUSD, GBPUSD, and USDJPY. The data was extracted from reputable platforms Forex Live and FXstreet over a period of 86 days, from January to May 2023. The dataset comprises 2,291 unique news headlines. Each headline includes an associated forex pair, timestamp, source, author, URL, and the corresponding article text. Data was collected using web scraping techniques executed via a custom service on a virtual machine. This service periodically retrieves the latest news for a specified forex pair (ticker) from each platform, parsing all available information. The collected data is then processed to extract details such as the article's timestamp, author, and URL. The URL is further used to retrieve the full text of each article. This data acquisition process repeats approximately every 15 minutes.

To ensure the reliability of the dataset, we manually annotated each headline for sentiment. Instead of solely focusing on the textual content, we ascertained sentiment based on the potential short-term impact of the headline on its corresponding forex pair. This method recognizes the currency market's acute sensitivity to economic news, which significantly influences many trading strategies. As such, this dataset could serve as an invaluable resource for fine-tuning sentiment analysis models in the financial realm.

We used three categories for annotation: 'positive', 'negative', and 'neutral', which correspond to bullish, bearish, and hold sentiments, respectively, for the forex pair linked to each headline. The following Table provides examples of annotated headlines along with brief explanations of the assigned sentiment.

Examples of Annotated Headlines


    Forex Pair
    Headline
    Sentiment
    Explanation




    GBPUSD 
    Diminishing bets for a move to 12400 
    Neutral
    Lack of strong sentiment in either direction


    GBPUSD 
    No reasons to dislike Cable in the very near term as long as the Dollar momentum remains soft 
    Positive
    Positive sentiment towards GBPUSD (Cable) in the near term


    GBPUSD 
    When are the UK jobs and how could they affect GBPUSD 
    Neutral
    Poses a question and does not express a clear sentiment


    JPYUSD
    Appropriate to continue monetary easing to achieve 2% inflation target with wage growth 
    Positive
    Monetary easing from Bank of Japan (BoJ) could lead to a weaker JPY in the short term due to increased money supply


    USDJPY
    Dollar rebounds despite US data. Yen gains amid lower yields 
    Neutral
    Since both the USD and JPY are gaining, the effects on the USDJPY forex pair might offset each other


    USDJPY
    USDJPY to reach 124 by Q4 as the likelihood of a BoJ policy shift should accelerate Yen gains 
    Negative
    USDJPY is expected to reach a lower value, with the USD losing value against the JPY


    AUDUSD

    <p>RBA Governor Lowe’s Testimony High inflation is damaging and corrosive </p>

    Positive
    Reserve Bank of Australia (RBA) expresses concerns about inflation. Typically, central banks combat high inflation with higher interest rates, which could strengthen AUD.

Moreover, the dataset includes two columns with the predicted sentiment class and score as predicted by the FinBERT model. Specifically, the FinBERT model outputs a set of probabilities for each sentiment class (positive, negative, and neutral), representing the model's confidence in associating the input headline with each sentiment category. These probabilities are used to determine the predicted class and a sentiment score for each headline. The sentiment score is computed by subtracting the negative class probability from the positive one.