End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

The main stock market index of United States, the US500, rose to 6256 points on July 3, 2025, gaining 0.46% from the previous session. Over the past month, the index has climbed 4.77% and is up 12.37% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on July of 2025.

This paper utilizes data on subjective probabilities to study the impact of the stock market crash of 2008 on households' expectations about the returns on the stock market index. We use data from the Health and Retirement Study that was fielded in February 2008 through February 2009. The effect of the crash is identified from the date of the interview, which is shown to be exogenous to previous stock market expectations. We estimate the effect of the crash on the population average of expected returns, the population average of the uncertainty about returns (subjective standard deviation), and the cross-sectional heterogeneity in expected returns (disagreement). We show estimates from simple reduced-form regressions on probability answers as well as from a more structural model that focuses on the parameters of interest and separates survey noise from relevant heterogeneity. We find a temporary increase in the population average of expectations and uncertainty right after the crash. The effect on cross-sectional heterogeneity is more significant and longer lasting, which implies substantial long-term increase in disagreement. The increase in disagreement is larger among the stockholders, the more informed, and those with higher cognitive capacity, and disagreement co-moves with trading volume and volatility in the market.

📈 Daily Historical Stock Price Data for American Well Corporation (2020–2025)

A clean, ready-to-use dataset containing daily stock prices for American Well Corporation from 2020-09-17 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: American Well Corporation Ticker Symbol: AMWL Date Range: 2020-09-17 to 2025-05-28 Frequency: Daily Total Records: 1179 rows (one… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-american-well-corporation-20202025.

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.

📈 Daily Historical Stock Price Data for American Resources Corporation (2017–2025)

A clean, ready-to-use dataset containing daily stock prices for American Resources Corporation from 2017-06-30 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: American Resources Corporation Ticker Symbol: AREC Date Range: 2017-06-30 to 2025-05-28 Frequency: Daily Total Records:… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-american-resources-corporation-20172025.

📈 Daily Historical Stock Price Data for American Financial Group, Inc. (1980–2025)

A clean, ready-to-use dataset containing daily stock prices for American Financial Group, Inc. from 1980-03-17 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: American Financial Group, Inc. Ticker Symbol: AFG Date Range: 1980-03-17 to 2025-05-28 Frequency: Daily Total Records:… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-american-financial-group-inc-19802025.

📈 Daily Historical Stock Price Data for First American Financial Corporation (2010–2025)

A clean, ready-to-use dataset containing daily stock prices for First American Financial Corporation from 2010-05-28 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: First American Financial Corporation Ticker Symbol: FAF Date Range: 2010-05-28 to 2025-05-28 Frequency:… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-first-american-financial-corporation-20102025.

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.

SHL Telemedicine Ltd American Depositary Shares is assigned short-term B1 & long-term Ba3 estimated rating.

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.

AEL^A American Equity Investment Life Holding Company Depositary Shares each representing a 1/1000th interest in a share of 5.95% Fixed-Rate Reset Non-Cumulative Preferred Stock Series A

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

📈 Daily Historical Stock Price Data for American Homes 4 Rent (2013–2025)

A clean, ready-to-use dataset containing daily stock prices for American Homes 4 Rent from 2013-08-01 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: American Homes 4 Rent Ticker Symbol: AMH Date Range: 2013-08-01 to 2025-05-28 Frequency: Daily Total Records: 2974 rows (one per trading… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-american-homes-4-rent-20132025.

Corporate Profits in the United States decreased to 3203.60 USD Billion in the first quarter of 2025 from 3312 USD Billion in the fourth quarter of 2024. This dataset provides the latest reported value for - United States Corporate Profits - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This is a high-resolution dataset of building design characteristics, life cycle inventories, and environmental impact assessment results for 292 building projects in the United States and Canada. The dataset contains harmonized and non-aggregated LCA model results across life cycle stages, building elements, and building materials to enable detailed analysis, comparisons, and data reuse. It includes over 90 building design and LCA features to assess distributions and trends of material use and environmental impacts. Uniquely, the data were crowd-sourced from designers conducting LCAs of real-world building projects.The dataset is composed of two files:buildings_metadata.xlsx includes all project metadata and LCA parameters for every project associated with a unique index number to cross-reference across other files. This also includes various calculated summaries of LCI and LCIA totals and intensities per project.full_lca_results.xlsx includes LCI and LCIA results per material and life cycle stage of each building project.data_glossary.xlsx identifies and defines each feature of the dataset including its name, data structure, syntax, units, descriptions, and more.material_definitions.xlsx a full list of material groups, types, and descriptions of what they include.This dataset is documented and described in a Data Descriptor, currently under review with a preprint available:Benke et al. A Harmonized Dataset of High-resolution Whole Building Life Cycle Assessment Results in North America, 07 March 2025, PREPRINT (Version 1) available at Research Square https://doi.org/10.21203/rs.3.rs-6108016/v1When referencing this work, please cite both the Data Descriptor and the most recent dataset version on this Fighshare DOI.The dataset also appears on the Github repository: https://github.com/Life-Cycle-Lab/wblca-benchmark-v2-data. Access to the code used to prepare this dataset is available on an additional Github repository: https://github.com/Life-Cycle-Lab/wblca-benchmark-v2-data-preparation.Release Notes:2025-02-24 - First public release2025-05-05 - Title revised and two supplementary dataset files added: data_glossary.xlsx and material_definitions.xlsx.

📈 Daily Historical Stock Price Data for American Public Education, Inc. (2007–2025)

A clean, ready-to-use dataset containing daily stock prices for American Public Education, Inc. from 2007-11-09 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: American Public Education, Inc. Ticker Symbol: APEI Date Range: 2007-11-09 to 2025-05-28 Frequency: Daily Total… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-american-public-education-inc-20072025.

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.

JPMorgan American Investment Trust (JAM): Questioning the Future

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

In order to avoid missing representative features, we should select a lot of features as far as possible when using machine learning algorithms in stock trading. Meanwhile, these high dimensional features can lead to redundancy of information and reduce the efficiency, and accuracy of learning algorithms. It is worth noting that dimensionality reduction operation (DRO) is one of the main means to deal with stock high-dimensional data. However, there are few studies on whether DRO can significantly improve the trading performance of deep neural network (DNN) algorithms. Therefore, this paper selects large-scale stock datasets in the American market and in the Chinese market as the research objects. For each stock, we firstly apply four most widely used DRO, namely principal component analysis (PCA), least absolute shrinkage and selection operator (LASSO), classification and regression trees (CART), and autoencoder (AE) to deal with original features respectively, and then use the new features as inputs of the most six popular DNN algorithms such as Multilayer Perceptron (MLP), Deep Belief Network (DBN), Stacked Auto-Encoders(SAE), Recurrent Neural Network(RNN), Long Short-Term Memory(LSTM), Gated Recurrent Unit(GRU) to generate trading signals. Finally, we apply the trading signals to conduct a lot of daily trading back-testing and non-parameter statistical testing. The experiments show that LASSO can significantly improve the performance of RNN, LSTM, and GRU. In addition, any DRO mentioned in this paper do not significantly improve trading performance and the speed of generating trading signals of the other DNN algorithms.

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.

General American Investors - Cumulative Preferred Stock (GAM-B): Worth Investing?

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

📈 Daily Historical Stock Price Data for American Realty Investors, Inc. (1982–2025)

A clean, ready-to-use dataset containing daily stock prices for American Realty Investors, Inc. from 1982-04-21 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: American Realty Investors, Inc. Ticker Symbol: ARL Date Range: 1982-04-21 to 2025-05-28 Frequency: Daily Total… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-american-realty-investors-inc-19822025.

Total Housing Inventory in the United States increased to 1540 Thousands in May from 1450 Thousands in April of 2025. This dataset includes a chart with historical data for the United States Total Housing Inventory.

Consolidated last sale, exchange BBO and national BBO across all US equity options exchanges. Includes single name stock options (e.g. TSLA), options on ETFs (e.g. SPY, QQQ), index options (e.g. VIX), and some indices (e.g. SPIKE and VSPKE). This dataset is based on the newer, binary OPRA feed after the migration to SIAC's OPRA Pillar SIP in 2021. OPRA is notable for the size of its data and we recommend users to anticipate several TBs of data per day for the full dataset in its highest granularity (MBP-1).