The benchmark interest rate in the United States was last recorded at 4.50 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The benchmark interest rate in Sweden was last recorded at 2 percent. This dataset provides the latest reported value for - Sweden Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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.

S&P 500: A Bull or a Bear?

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 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.

Cloudflare (NET) Navigates the Web of Growth

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

The benchmark interest rate In the Euro Area was last recorded at 2.15 percent. This dataset provides - Euro Area Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Japan was last recorded at 0.50 percent. This dataset provides - Japan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

The benchmark interest rate in India was last recorded at 5.50 percent. This dataset provides - India Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Access to finance of small and medium enterprises. Topics: development of the following indicators in the last six months: turnover, profit, profit margin, level of debt, cash flow, investment, level of exports, research and development, market share; existence of a development plan for the next three years; most important element to ensure the company’s development: better qualified people on the market, social and fiscal regulations more suited to the sector of activity, greater production capacity, easy access to means of financing, stricter regulation regarding competition from outside the EU, advice and support service for the development of the company; use of selected types of financing in the past: overdraft, leasing or renting, discount or factoring, increase in capital dedicated to venture capital funds or to private individuals, loans shorter or longer than a 3-year term, public subsidies; approximate amount of last loan; recent request for a loan less than 25000 €; needs to be met by this loan; assessment of the difficulties to obtain a loan less than 25000 € compared to other forms of company’s financing; most important elements to resort a loan less than 25000 €: lower interest rates, simpler procedures for granting loans, less demanding on guarantee requirements, shorter delays for granting loans; assessment of the current financing of the company as sufficient; institutions contacted to obtain financing: banks, public institutions, private financing companies, leasing or renting companies, venture capital companies, private investors; expectations regarding the increase of the company’s capital within the next years; measures to increase the company’s capital: opening-up capital to private individual investors or to venture capital companies, management buy-out, going on the stock exchange, opening-up capital to the company’s employees; assessment of the access to bank loans as easy; assessment of the development of the impediments to access bank loans compared to a few years ago; reasons that impede obtaining a bank loan compared to a few years ago: interest rates are too high, banks request too much information, loan granting procedures are too long, administrative side of the loan application is very demanding; approval of the following statements: loan is needed to conclude projects, unsuitable offers from banks, risk-averseness of banks, banker understands specifics of the company’s sector, banker sufficiently supports the company in terms of its financing; assessment how the company’s needs regarding financial management are met internally; preferred sources of information on financing. Demography: information about the company: number of employees, development of the number of employees since 2004; company size; main activity of the company; year of company establishment; shareholding of the company; turnover of the company in the own country in the last fiscal year. Additionally coded was: country; respondent ID; language of the interview; weighting factor. Unternehmensfinanzierung. Nutzung von Krediten. Schwierigkeiten bei Kreditaufnahme. Vorgehen von Kreditinstituten in Bezug auf Finanzierungsmöglichkeiten. Finanzierungsberatung. Themen: Finanzielle Situation des Unternehmens; 3-Jahres Plan; wichtigste Maßnahmen zur Festigung des Unternehmens: qualifizierte Mitarbeiter, der Branche angepasstere soziale und steuerliche Bestimmungen, größere Produktionskapazität, einfacher Zugang zu Finanzierungsmitteln, strengere Regulierung der Konkurrenz aus Nicht-EU-Ländern, Beratung und Unterstützung für die Unternehmensentwicklung; Inanspruchnahme finanzieller Leistungen (Dispositionskredit, Leasing/Mieten, Diskont/Factoring, Kapitalerhöhung für Wagniskapitalfonds und für Privatpersonen, (Kurz-)Darlehen, öffentliche Fördermittel); Höhe des letzten Kreditantrags; Verwendungsabsicht für den Kredit; Schwierigkeiten einen Kredit unter 250.000 Euro zu bekommen im Vergleich zu anderen Finanzierungsformen; Gründe für Kreditaufnahme (niedrigere Zinssätze, einfacherer Bewilligungsvorgang, geringere Anforderung auf Kreditsicherheit, kürzere Bearbeitungszeit für Kreditbewilligung); Einschätzung der Unternehmensfinanzierung als ausreichend für Projektrealisierung; primäre Anlaufstellen für den Erhalt von Finanzmitteln; Erschließungsmöglichkeiten für Kapital um finanzielle Bedürfnisse des Unternehmens zu erfüllen; Wege für die Kapitalerschließung des Unternehmens; Einschätzung der Schwierigkeit heutzutage einen Kredit bei Banken zu bekommen im Vergleich zu früher; Gründe, warum es heutzutage schwieriger ist einen Kredit bei einer Bank zu bekommen; Einstellung zu: Kreditabhängigkeit bei Durchführung von Projekten, nicht auf die Belange des Unternehmens zugeschnittene Angebote der Banken; geringe Risikobereitschaft von Banken bei Kreditvergabe; Verständnis für die spezifischen Belange der eigenen Branche durch den zuständigen Bankangestellten; ausreichende Unterstützung bei der Finanzierung durch die Bank; Beurteilung des firmeninternen Finanzmanagements; primäre Anlaufstelle für Finanzierungsberatung. Demographie: Angaben zum Unternehmen: Anzahl der Mitarbeiter, Entwicklung der Anzahl der Beschäftigten seit 2004, Unternehmensgröße, Hauptgeschäftsfeld des Unternehmens, Gründungsjahr, Aktienanteil des Unternehmens; Jahresumsatz des Unternehmens im letzten Geschäftsjahr. Zusätzlich verkodet wurde: Land; Befragten-ID; Interviewsprache; Gewichtungsfaktor.

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.

Context

Microsoft is an American company that develops and distributes software and services such as: a search engine (Bing), cloud solutions and the computer operating system Windows.

Market cap

Market capitalization of Microsoft (MSFT)

Market cap: $3.085 Trillion USD

As of February 2025 Microsoft has a market cap of $3.085 Trillion USD. This makes Microsoft the world's 2nd most valuable company by market cap according to our data. The market capitalization, commonly called market cap, is the total market value of a publicly traded company's outstanding shares and is commonly used to measure how much a company is worth.

Revenue

Revenue for Microsoft (MSFT)
Revenue in 2024 (TTM): $254.19 Billion USD

According to Microsoft's latest financial reports the company's current revenue (TTM ) is $254.19 Billion USD. In 2023 the company made a revenue of $227.58 Billion USD an increase over the revenue in the year 2022 that were of $204.09 Billion USD. The revenue is the total amount of income that a company generates by the sale of goods or services. Unlike with the earnings no expenses are subtracted.

Earnings

Earnings for Microsoft (MSFT)
Earnings in 2024 (TTM): $110.77 Billion USD

According to Microsoft's latest financial reports the company's current earnings are $254.19 Billion USD. In 2023 the company made an earning of $101.21 Billion USD, an increase over its 2022 earnings that were of $82.58 Billion USD. The earnings displayed on this page are the earnings before interest and taxes or simply EBIT.

End of Day market cap according to different sources On Feb 2nd, 2025 the market cap of Microsoft was reported to be:

• $3.085 Trillion USD by Nasdaq

• $3.085 Trillion USD by CompaniesMarketCap

• $3.085 Trillion USD by Yahoo Finance

Content

Geography: USA

Time period: March 1986- February 2025

Unit of analysis: Microsoft Stock Data 2025

Variables

Acknowledgements

This dataset belongs to me. I’m sharing it here for free. You may do with it as you wish.

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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.

Machine Learning stock prediction: HD Stock Prediction

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

The benchmark interest rate in China was last recorded at 3 percent. This dataset provides the latest reported value for - China Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

30 Year Mortgage Rate in the United States decreased to 6.72 percent in July 31 from 6.74 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.

The benchmark interest rate in the United Kingdom was last recorded at 4.25 percent. This dataset provides - United Kingdom Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Indonesia was last recorded at 5.25 percent. This dataset provides - Indonesia Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

Machine Learning Predicts QQQ to Increase in Value by 5% in the Next 3 Months

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 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.

China A50 - A Window into the Future of Chinese Equities?

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

The benchmark interest rate in Mexico was last recorded at 8 percent. This dataset provides - Mexico Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

Affirm (AFRM) - Buy Now, Pay Later: Growth and Profitability on the Horizon

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