39 datasets found
  1. S&P 500: A Bull or a Bear? (Forecast)

    • kappasignal.com
    Updated Apr 8, 2024
    + more versions
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    KappaSignal (2024). S&P 500: A Bull or a Bear? (Forecast) [Dataset]. https://www.kappasignal.com/2024/04/s-500-bull-or-bear.html
    Explore at:
    Dataset updated
    Apr 8, 2024
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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

  2. Machine Learning stock prediction: HD Stock Prediction (Forecast)

    • kappasignal.com
    Updated Oct 13, 2022
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    KappaSignal (2022). Machine Learning stock prediction: HD Stock Prediction (Forecast) [Dataset]. https://www.kappasignal.com/2022/10/machine-learning-stock-prediction-hd.html
    Explore at:
    Dataset updated
    Oct 13, 2022
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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

  3. Apple Security Market Data

    • kaggle.com
    Updated Sep 6, 2023
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    Sanket2002 (2023). Apple Security Market Data [Dataset]. https://www.kaggle.com/datasets/sanket2002/apple-security-market-data/data
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 6, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Sanket2002
    Description

    The Apple share market data of 10 years can be used for educational purposes in a variety of ways, such as:

    To learn about the stock market and how it works. By studying the historical price movements of Apple stock, you can learn about the different factors that can affect the stock market, such as economic conditions, interest rates, and company earnings. To develop investment strategies. By analyzing the Apple share market data, you can identify patterns and trends that can help you make better investment decisions. For example, you might notice that Apple stock tends to perform well in certain economic conditions or when the company releases new products. To learn about Apple's business. By tracking the company's stock price, you can get a sense of how investors are viewing Apple's financial performance and future prospects. This information can be helpful for making decisions about whether or not to invest in Apple stock. To conduct research on financial topics. The Apple share market data can be used to support research on a variety of financial topics, such as the impact of inflation on stock prices, the relationship between stock prices and interest rates, and the performance of different investment strategies. In addition to these educational purposes, the Apple share market data can also be used for other purposes, such as:

    To create trading algorithms. Trading algorithms are computer programs that automatically buy and sell stocks based on certain criteria. The Apple share market data can be used to train trading algorithms to identify profitable trading opportunities. To develop risk management strategies. Risk management strategies are used to protect investors from losses. The Apple share market data can be used to identify risks associated with investing in Apple stock and to develop strategies to mitigate those risks. To make corporate decisions. The Apple share market data can be used by companies to make decisions about their business, such as how much to invest in research and development, how to allocate capital, and when to issue new shares. Overall, the Apple share market data is a valuable resource that can be used for a variety of educational and practical purposes. If you are interested in learning more about the stock market or investing, I encourage you to explore the Apple share market data.

  4. F

    S&P 500

    • fred.stlouisfed.org
    json
    Updated Jul 11, 2025
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    (2025). S&P 500 [Dataset]. https://fred.stlouisfed.org/series/SP500
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jul 11, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-pre-approvalhttps://fred.stlouisfed.org/legal/#copyright-pre-approval

    Description

    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.

  5. FRED - Dataset USREC

    • kaggle.com
    Updated Nov 21, 2023
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    Felipe Teti (2023). FRED - Dataset USREC [Dataset]. http://doi.org/10.34740/kaggle/dsv/7014643
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 21, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Felipe Teti
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Inspired by:

    Modeling and predicting U.S. recessions using machine learning techniques

    As variáveis do FRED-MD como preditivas e a USREC como alvo (período de 1979-2019)

    Diversos Modelos: probit, logit, LDA, árvores Naive-Bayes Algumas variáveis tiveram que ser transformadas em mensais (interpolação cúbica)

    128 varibles. Grupos: Output and Income Labor Market Consumption and Orders Orders and Inventories Money and Credit Interest Rates and Exchange Rates Prices Stock Market

  6. U.S. Fed Funds Target Rate

    • kaggle.com
    Updated Nov 16, 2023
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    Aniket Patil (2023). U.S. Fed Funds Target Rate [Dataset]. https://www.kaggle.com/aniketkolte04/u-s-fed-funds-target-rate/discussion
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 16, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Aniket Patil
    License

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

    Description

    Dataset Description

    This dataset contains the actual and predicted federal funds target rate for the United States from 1990 to 2023. The federal funds target rate is the interest rate at which depository institutions lend their excess reserves to each other overnight. It is set by the Federal Open Market Committee (FOMC) and is a key tool used by the Federal Reserve to influence the economy.

    The dataset includes the following five columns:

    Release Date: The date on which the data was released by the Federal Reserve. Time: The time of day at which the data was released. Actual: The actual federal funds target rate. Predicted: The predicted federal funds target rate. Forecast: The forecast federal funds target rate.

    Data Usage

    This dataset can be used for a variety of purposes, including: - Analyzing trends in the federal funds target rate over time. - Forecasting the future path of the federal funds target rate. - Assessing the effectiveness of monetary policy. - Data Quality

    The data for this dataset is of high quality. The Federal Reserve is a reputable source of data and the data is updated regularly.

    Data Limitations

    The data for this dataset is limited to the United States. Additionally, the data does not include information on the factors that influenced the Federal Open Market Committee's decision to set the federal funds target rate.

  7. Cloudflare (NET) Navigates the Web of Growth (Forecast)

    • kappasignal.com
    Updated Sep 26, 2024
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    KappaSignal (2024). Cloudflare (NET) Navigates the Web of Growth (Forecast) [Dataset]. https://www.kappasignal.com/2024/09/cloudflare-net-navigates-web-of-growth.html
    Explore at:
    Dataset updated
    Sep 26, 2024
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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

  8. k

    An In-depth Analysis of the S&P 500 Index: Performance, Composition, and...

    • kappasignal.com
    Updated May 24, 2023
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    KappaSignal (2023). An In-depth Analysis of the S&P 500 Index: Performance, Composition, and Implications (Forecast) [Dataset]. https://www.kappasignal.com/2023/05/an-in-depth-analysis-of-s-500-index.html
    Explore at:
    Dataset updated
    May 24, 2023
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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.

    An In-depth Analysis of the S&P 500 Index: Performance, Composition, and Implications

    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

  9. M

    1 Year LIBOR Rate - Historical Dataset

    • macrotrends.net
    csv
    Updated Jul 6, 2025
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    MACROTRENDS (2025). 1 Year LIBOR Rate - Historical Dataset [Dataset]. https://www.macrotrends.net/2515/1-year-libor-rate-historical-chart
    Explore at:
    csvAvailable download formats
    Dataset updated
    Jul 6, 2025
    Dataset authored and provided by
    MACROTRENDS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    World
    Description

    Historical dataset of the 12 month LIBOR rate back to 1986. The London Interbank Offered Rate is the average interest rate at which leading banks borrow funds from other banks in the London market. LIBOR is the most widely used global "benchmark" or reference rate for short term interest rates.

  10. T

    Pakistan Interest Rate

    • tradingeconomics.com
    • jp.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated May 5, 2025
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    TRADING ECONOMICS (2025). Pakistan Interest Rate [Dataset]. https://tradingeconomics.com/pakistan/interest-rate
    Explore at:
    csv, xml, excel, jsonAvailable download formats
    Dataset updated
    May 5, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Feb 3, 1992 - Jun 16, 2025
    Area covered
    Pakistan
    Description

    The benchmark interest rate in Pakistan was last recorded at 11 percent. This dataset provides - Pakistan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

  11. T

    Philippines Interest Rate

    • tradingeconomics.com
    • fa.tradingeconomics.com
    • +13more
    csv, excel, json, xml
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    TRADING ECONOMICS, Philippines Interest Rate [Dataset]. https://tradingeconomics.com/philippines/interest-rate
    Explore at:
    csv, excel, json, xmlAvailable download formats
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 31, 1985 - Jun 19, 2025
    Area covered
    Philippines
    Description

    The benchmark interest rate in Philippines was last recorded at 5.25 percent. This dataset provides the latest reported value for - Philippines Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

  12. USD-TRY Conversion and Interest Rates 2010-2021

    • kaggle.com
    Updated Nov 4, 2021
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    captainozlem (2021). USD-TRY Conversion and Interest Rates 2010-2021 [Dataset]. https://www.kaggle.com/captainozlem/usdtry-conversion-and-interest-rates-20102021/code
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 4, 2021
    Dataset provided by
    Kaggle
    Authors
    captainozlem
    Description

    Context

    The Turkish Lira is losing its value against U.S. Dollar constantly. As of October 22, 2021, 1 USD = 9.61 Turkish Lira (TRY). On the other hand, interest rates are quite high, especially for the Turkish Lira.

    I set out to investigate if I had $100000 in 2010 and invested this money in different interest rates in both Turkish Lira (TRY) and US Dollar (USD), which investment would bring more gain in 2021.

    Content

    The data has been gathered from Türkiye Cumhuriyeti Merkez Bankasi (TCMB), aka the Turkish FED, website. The data shows the historical interest rates as well as USD/TRY conversion rates between July 2010 and July 2021. The original data’s all column names and relative explanations were Turkish, so the columns are renamed and the data is cleaned.

    There are ten cleaned columns on the dataset: Date, 1-month TRY interest rates, 3 months TRY interest rates, 6 months TRY interest rates, 1-year TRY interest rates, 1 month USD interest rates, 3 months USD interest rates, 6 months USD interest rates, 1 year USD interest rates, USD/TRY Buying Conversion Rate, USD/TRY Selling Conversion Rate.

    ** USD Buying means, the customer is selling USD to the bank/ exchange office ** USD Selling means, the customer is buying USD to the bank/ exchange office

    Inspiration

    Would it be more beneficial if I converted my $100000 in July 2010 to Turkish Lira, which is the equivalent of 153631.36 TRY using July 2010’s rates and invested with Turkish high-interest rates or kept my money as U.S. Dollars and invested with relatively lower U.S. Dollar interest rates until July 2021? $100000 is equivalent to 861294.12 TRY in July 2021.

  13. T

    Vietnam Interest Rate

    • tradingeconomics.com
    • it.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jan 3, 2025
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    TRADING ECONOMICS (2025). Vietnam Interest Rate [Dataset]. https://tradingeconomics.com/vietnam/interest-rate
    Explore at:
    json, excel, csv, xmlAvailable download formats
    Dataset updated
    Jan 3, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Apr 5, 2000 - Jun 30, 2025
    Area covered
    Vietnam
    Description

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

  14. m

    Data for: Impact of consumer confidence on the expected returns of the Tokyo...

    • data.mendeley.com
    Updated Jul 20, 2020
    + more versions
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    Javier Rojo Suárez (2020). Data for: Impact of consumer confidence on the expected returns of the Tokyo Stock Exchange: A comparative analysis of consumption and production-based asset pricing models [Dataset]. http://doi.org/10.17632/vyxt842rzg.1
    Explore at:
    Dataset updated
    Jul 20, 2020
    Authors
    Javier Rojo Suárez
    License

    Attribution-NonCommercial 3.0 (CC BY-NC 3.0)https://creativecommons.org/licenses/by-nc/3.0/
    License information was derived automatically

    Description

    Using all stocks listed in the Tokyo Stock Exchange and macroeconomic data for Japan, the dataset comprises the following series:

    1. Monthly returns for 25 size-book-to-market equity portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
    2. Monthly returns for 20 momentum portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
    3. Monthly returns for 25 price-to-cash flow-dividend yield portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
    4. Fama and French three-factors (RM, SMB and HML), following the Fama and French (1993) methodology. (Raw data source: Datastream database)
    5. Fama and French five-factors (RM, SMB, HML, RMW, and CMA), following the Fama and French (2015) methodology for all factors, except for RMW, which is determined using the return on assets as sorting variable, as in Hou, Xue and Zhang (2014). (Raw data source: Datastream database)
    6. Private final consumption expenditure, in national currency and constant prices, non-seasonally adjusted, for Japan. (Raw data source: OECD)
    7. Consumer Confidence Index (CCI) for Japan. (Raw data source: OECD)
    8. Three-month interest rate of the Treasury Bill for Japan. (Raw data source: OECD)

    We have produced all return series using the following data from Datastream: (i) total return index (RI series), (ii) market value (MV series), (iii) market-to-book equity (PTBV series), (iv) total assets (WC02999 series), (v) return on equity (WC08301 series), (vi) price-to-cash flow ratio (PC series), and (vii) dividend yield (DY series). We have used the generic rules suggested by Griffin, Kelly, & Nardari (2010) for excluding non-common equity securities from Datastream data. We also exclude stocks with less than twelve observations in the period from July 1992 to June 2018. Accordingly, our sample comprises a total number of 5,312 stocks.

    REFERENCES:

    Fama, E. F. and French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3–56. Fama, E. F. and French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116, 1–22. Griffin, J. M., Kelly, P., and Nardari, F. (2010). Do market efficiency measures yield correct inferences? A comparison of developed and emerging markets. Review of Financial Studies, 23, 3225–3277. Hou K, Xue C, Zhang L. (2014). Digesting anomalies: An investment approach. Review of Financial Studies, 28, 650-705.

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

    • kappasignal.com
    Updated Oct 6, 2024
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    KappaSignal (2024). Affirm (AFRM) - Buy Now, Pay Later: Growth and Profitability on the Horizon (Forecast) [Dataset]. https://www.kappasignal.com/2024/10/affirm-afrm-buy-now-pay-later-growth.html
    Explore at:
    Dataset updated
    Oct 6, 2024
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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

  16. Predict the ASX-200

    • kaggle.com
    Updated Aug 25, 2021
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    YasAli (2021). Predict the ASX-200 [Dataset]. https://www.kaggle.com/datasets/yasali/predict-the-asx200/data
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 25, 2021
    Dataset provided by
    Kaggle
    Authors
    YasAli
    Description

    Disclaimer

    All information presented here is for display purpose only, and may not be complete nor accurate. This information does not constitute a financial advice, and should not be used to make any investment decisions or financial transactions. This author rejects any claims for liabilities resulting from the use, misuse, or abuse of this information. Use at your own risk.

    Motivation

    Due to time zone differences between Australia and most of the rest of the world, Australians have the advantage of knowing what happened at markets elsewhere in the world, before the Australian market (ASX) is open in the morning, Sydney time.

    This prior knowledge provides an excellent opportunity for arbitrage. In the hands of a savvy day-trader, or a shrewd long-term investor, this information gives you the advantage of predicting the ASX, and achieve potentially significant financial gains.

    Method

    For the ten years period from 1/7/2010 to 30/6/2020, the daily closing prices for 41 global market indicators are collected from various reliable public-domain sources. We checked the data for error or omissions and normalised all tabulated records in a format that facilitates further analysis and visulaisation.

    Those 41 market indicators are what we consider significant measures of various external factors that may affect the performance of the Australian Stock Market, as represented by the ASX200. Those indicators are:

    • Nine other major stock market indices from the USA, Europe, and Asia.

    • The exchange rate of the $AU against 10 world currencies that are most relevant to Australia's international trade.

    • Official interest rates by the RBA and the US Feds, as indicators of affinity of foreign funds to Australia.

    • Yield rates for governments-issued bonds by 10 countries from Western and Asian economies, as measures of relative availability of credit and cross-border investment. Bonds are grouped into "Short-term" (one year maturity) and "Long-term" (10 to 30 years maturity).

    • Since Australia's economy is mainly an exporter of raw materials, we include prices for commodities that are most traded by Australia, as indicators for potential profitability for various relevant sectors of the ASX.

    We feed relevant data to a machine learning model, which uses this data to extract heuristic parameters that are used to predict the ASX200 on daily basis, before market opens, and validates predictions at market close, with favourable results.

    For more information, please visit the Tableau viz at: https://public.tableau.com/app/profile/yasser.ali.phd/viz/PredictingAustralianStockMarket/Story

  17. m

    Data from: Liquidity, time-varying betas and anomalies. Is the high trading...

    • data.mendeley.com
    Updated Nov 19, 2019
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    Paper authors Paper authors (2019). Liquidity, time-varying betas and anomalies. Is the high trading activity enhancing the validity of the CAPM in the UK equity market? [Dataset]. http://doi.org/10.17632/56n2yxgpcf.1
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    Dataset updated
    Nov 19, 2019
    Authors
    Paper authors Paper authors
    License

    Attribution-NonCommercial 3.0 (CC BY-NC 3.0)https://creativecommons.org/licenses/by-nc/3.0/
    License information was derived automatically

    Area covered
    United Kingdom
    Description

    Using all stocks listed in the London Stock Exchange for the period from January 1989 to December 2018, the dataset comprises the following series:

    1. Annual returns for 20 asset growth portfolios, following Fama and French (1993) methodology.
    2. Annual returns for 25 portfolios size-book to market equity, following Fama and French (1993) methodology.
    3. Annual returns for 62 industry portfolios, using two-digit SIC codes.
    4. Fama and French (1993) factors for their three-factor model (RM, SMB and HML).
    5. Fama and French (2015) factors for their five-factor model (RM, SMB, HML, RMW, and CMA).
    6. Variation of the Amihid illiquidy measure for the London Stock Exchange, following Amihud (2002) methodology.
    7. Three-month interest rate of the Treasury Bill for the United Kingdom, as provided by the OECD database.

    We have produced these series using the following data from Thomson Reuters Datastream: (i) total return index (RI series), (ii) market value (MV series), (iii) market-to-book equity (PTBV series), (iv) total assets (WC02999 series), (v) return on equity (WC08301 series), (vi) tax rate (WC08346 series), (vii) primary SIC codes, (viii) turnover by volume (VO series), and (ix) the market price (P series). Following Griffin et al. (2010), we use the generic rules provided by the authors for excluding non-common equity securities from Datastream data.

    REFERENCES: Amihud, Y. (2002). Illiquidity and stock returns: Cross-section and time-series effects. Journal of Financial Markets, 5, 31–56. Fama, E. F. and French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3–56. Fama, E. F. and French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116, 1–22. Griffin, J. M., Kelly, P., and Nardari, F. (2010). Do market efficiency measures yield correct inferences? A comparison of developed and emerging markets. Review of Financial Studies, 23, 3225–3277.

  18. MSCI World: Reflecting Global Economic Trends or Inflated Valuations?...

    • kappasignal.com
    Updated May 7, 2024
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    KappaSignal (2024). MSCI World: Reflecting Global Economic Trends or Inflated Valuations? (Forecast) [Dataset]. https://www.kappasignal.com/2024/05/msci-world-reflecting-global-economic.html
    Explore at:
    Dataset updated
    May 7, 2024
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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.

    MSCI World: Reflecting Global Economic Trends or Inflated Valuations?

    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

  19. EGPBD: An Event-based Gold Price Benchmark Dataset

    • kaggle.com
    Updated Mar 28, 2025
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    Wael Al Etaiwi (2025). EGPBD: An Event-based Gold Price Benchmark Dataset [Dataset]. https://www.kaggle.com/datasets/waelaletaiwi/egpbd-an-event-based-gold-price-benchmark-dataset/discussion
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 28, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Wael Al Etaiwi
    License

    Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
    License information was derived automatically

    Description

    EGPB - An Event-based Gold Price Benchmark Dataset

    This benchmark dataset consists of 8030 rows and 36 variables sourced from multiple credible economic websites, covering a period from January 2001 to December 2022. This dataset can be utilized to predict gold prices specifically or to aid any economic field that is influenced by the variables in this dataset.

    Key variables & Features include:

    • Previous gold prices

    • Future gold prices with predictions for one day, one week, and one month

    • Oil prices

    • Standard & Poor's 500 Index (S&P 500)

    • Dow Jones Industrial (DJI)

    • US dollar index

    • US treasury

    • Inflation rate

    • Consumer price index (CPI)

    • Federal funds rate

    • Silver prices

    • Copper prices

    • Iron prices

    • Platinum prices

    • Palladium prices

    Additionally, the dataset considers global events that may impact gold prices, which were categorized into groups and collected from three distinct sources: the Al-Jazeera website spanning from 2022 to 2019, the Investing website spanning from 2018 to 2016, and the Yahoo Finance website spanning from 2007 to 2001.

    These events data were then divided into multiple groups:

    • Economic data

    • Politics

    • logistics

    • Oil

    • OPEC

    • Dollar currency

    • Sterling pound currency

    • Russian ruble currency

    • Yen currency

    • Euro currency

    • US stocks

    • Global stocks

    • Inflation

    • Job reports

    • Unemployment rates

    • CPI rate

    • Interest rates

    • Bonds

    These events were encoded using a numeric value, where 0 represented no events, 1 represented low events, 2 represented high events, 3 represented stable events, 4 represented unstable events, and 5 represented events that were observed during the day but had no effect on the dataset.

    Cite this dataset: Farah Mansour and Wael Etaiwi, "EGPBD: An Event-based Gold Price Benchmark Dataset," 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Tenerife, Canary Islands, Spain, 2023, pp. 1-7, doi: 10.1109/ICECCME57830.2023.10252987.

    @INPROCEEDINGS{10252987, author={Mansour, Farah and Etaiwi, Wael}, booktitle={2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)}, title={EGPBD: An Event-based Gold Price Benchmark Dataset}, year={2023}, volume={}, number={}, pages={1-7}, doi={10.1109/ICECCME57830.2023.10252987}}

  20. u

    Key South African Macro-economic variables data

    • zivahub.uct.ac.za
    xlsx
    Updated Jan 28, 2019
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    Alison Olivier (2019). Key South African Macro-economic variables data [Dataset]. http://doi.org/10.25375/uct.7553534.v1
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    xlsxAvailable download formats
    Dataset updated
    Jan 28, 2019
    Dataset provided by
    University of Cape Town
    Authors
    Alison Olivier
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    A monthly and quarterly data set spanning July 1995 to December 2016 of the following macro-economic variables 1. South African stock market 2. South African GDP3. United States GDP 4. South African interest rate 5. US interest rate 6. South African inflation rate 7. US inflation rate 8. South African Money Supply 9. Rand/Dollar Exchange 10. FTSE

Share
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Email
Click to copy link
Link copied
Close
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KappaSignal (2024). S&P 500: A Bull or a Bear? (Forecast) [Dataset]. https://www.kappasignal.com/2024/04/s-500-bull-or-bear.html
Organization logo

S&P 500: A Bull or a Bear? (Forecast)

Explore at:
Dataset updated
Apr 8, 2024
Dataset authored and provided by
KappaSignal
License

https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

Description

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

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