3 datasets found
  1. Precious Metals: Data & News (2000-Present)

    • kaggle.com
    Updated Apr 17, 2025
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    Roman (2025). Precious Metals: Data & News (2000-Present) [Dataset]. https://www.kaggle.com/datasets/romanfonel/precious-metals-history-since-2000-with-news
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 17, 2025
    Dataset provided by
    Kaggle
    Authors
    Roman
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    This dataset offers a rich combination of quantitative financial market data and qualitative news text data on a daily basis, specifically focusing on the precious metals market (gold, silver, platinum and palladium using Gold Futures 'GC=F' as the primary example) from August 2000, up to April 2025. It is designed for researchers, analysts, and data scientists interested in exploring the interplay between financial news and market dynamics.

  2. Wall Street Journal subscriber numbers 2018-2024

    • statista.com
    Updated Sep 23, 2024
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    Statista (2024). Wall Street Journal subscriber numbers 2018-2024 [Dataset]. https://www.statista.com/statistics/193788/average-paid-circulation-of-the-wall-street-journal/
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    Dataset updated
    Sep 23, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    As of June 2024, The Wall Street Journal had over 4.3 million paying subscribers. The majority were online-only subscribers, whilst print readers continued to fall. The Wall Street Journal The Wall Street Journal is a well-respected international newspaper that focuses on business, economics, and politics. The publication is generally seen as a trustworthy source of news and information, with about twice as many people deeming it trustworthy as those that consider it untrustworthy. While measures of trustworthiness can suffer from bias associated with political leanings, accuracy is generally more easily verifiable and thus arguably a better metric for assessing publications of any type. In terms of accuracy, the Wall Street journal ranks extremely high with only around ten percent of people finding it to be inaccurate. Newspaper circulation The Wall Street Journal, as well as The NYTimes, have both successfully managed to cater to both print and digitally focused consumers by becoming multiplatform publications. This is an undoubtedly clever (and perhaps necessary) move in an era where print popularity has waned significantly, as digital readership takes over. The accessibility of smartphone news apps and online news publications have made it difficult for physical newspapers to compete, and although the majority of newspaper circulation revenue still comes from print offerings, companies that wish to continue in the market have been forced to adapt their business strategies to accommodate online-only readers.

  3. c

    Global Trading Software Market Report 2025 Edition, Market Size, Share,...

    • cognitivemarketresearch.com
    pdf,excel,csv,ppt
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    Cognitive Market Research, Global Trading Software Market Report 2025 Edition, Market Size, Share, CAGR, Forecast, Revenue [Dataset]. https://www.cognitivemarketresearch.com/trading-software-market-report
    Explore at:
    pdf,excel,csv,pptAvailable download formats
    Dataset authored and provided by
    Cognitive Market Research
    License

    https://www.cognitivemarketresearch.com/privacy-policyhttps://www.cognitivemarketresearch.com/privacy-policy

    Time period covered
    2021 - 2033
    Area covered
    Global
    Description

    According to Cognitive Market Research, the Trading Software market size will be USD XX million in 2024 and will expand at a compound annual growth rate (CAGR) of XX% from 2024 to 2033. Market Dynamics Key Driver

    Surge in retail investor participation is fueling the trading software market 
    

    The rise in retail investor participation in the market is driving the demand for trading software. The driving forces behind the Retail Investor Boom are technology and accessibility, commission free trading software, social media influence, and financial literacy and awareness. The rising demand for trading software is mainly driven by the increasing demand for higher returns. • For instance, the number of demat accounts in India has seen a significant surge, reaching 179 million by October 2024, driven by increased awareness and adoption of equity investments, particularly among young investors. • In the last two years, approximately 30 million new retail investors opened brokerage accounts in United States. The percent of households with stock holdings increased to an all-time high of 58 percent as of 2022, according to the Federal Reserve’s Survey of Consumer Finances, up from 49 percent in 2013. Social media platforms have further fueled the demand for the trading software market. Retail investors are influenced by the Platforms such as Twitter, Reddit, and YouTube have created a community-driven investing culture, where retail traders share strategies, discuss stocks, and even coordinate market moves. (source- https://www.wsj.com/finance/stocks/stocks-americans-own-most-ever-9f6fd963) Technical Innovation is driving the market for trading software Advancements in technology have transformed trading software, enabling automation, real- time analytics and enhanced security. AI-powered insights have democratized finance, allowing anyone with a smartphone to participate in the stock market. Technology has enabled algorithmic trading systems to execute trades at high speeds leveraging automation to place orders, monitor markets, and execute complex strategies within milliseconds. For example, u Trade Algos provides users with capability to access their strategy ‘s historical performance accurately through precise historical data. Prior to engaging in live market trading, traders can conduct backtesting of their strategies to know their hypothetical performance. • For instance, the fusion of AI with crypto trading has given rise to AI crypto trading bots, which currently make up 60% of trading volumes on major exchanges. • AI-powered platforms like DeepSeek in China are being used to predict market movements and enhance decision-making.
    The increase in technological advancement has enabled trading systems to execute trades in milliseconds. This speed has reduced transaction costs and enhanced market liquidity which has led to increase in demand for trading software. (source-https://www.debutinfotech.com/blog/what-are-ai-crypto-trading-bots)

    Restraints

    Cyber security Risks and Data Breaches
    

    The increasing reliance on digital trading platforms has made them prime target for cyberattacks as these platforms handle large volumes of transactions and store sensitive financial data, breaches can lead to financial losses, identify theft, and loss of investor trust. The rising cases of data breaches are one of the prominent restraints in the market. Cybercriminals target trading platforms to steal funds, manipulate markets and disrupt services. • For instance, the collapse of FTX, a major trading platform, was partly due to internal mismanagement and security lapses that led to billions in investors losses. • In October 2022, the Binance exchange experienced hack after an unauthorized third party discovered a vulnerability in the cross-chain bride of system. By exploiting the flaw, the hacker was able to create and withdraw an extra two million Binance coins(BNB). Traders often fall victim to fake trading apps, scam emails, or fraudulent brokerages. Algorithmic trading all over the world has increased with increased penetration, low-cost trading platforms. Algorithmic trading and decentralised finance (DeFi) platforms rely on APIs and smart contracts, which can b...

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Roman (2025). Precious Metals: Data & News (2000-Present) [Dataset]. https://www.kaggle.com/datasets/romanfonel/precious-metals-history-since-2000-with-news
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Precious Metals: Data & News (2000-Present)

Daily market history (OHLCV) and WSJ news headlines since 2000

Explore at:
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Apr 17, 2025
Dataset provided by
Kaggle
Authors
Roman
License

MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically

Description

This dataset offers a rich combination of quantitative financial market data and qualitative news text data on a daily basis, specifically focusing on the precious metals market (gold, silver, platinum and palladium using Gold Futures 'GC=F' as the primary example) from August 2000, up to April 2025. It is designed for researchers, analysts, and data scientists interested in exploring the interplay between financial news and market dynamics.

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