We offer historical price data for equity indexes, ETFs and individual stocks in a Open/High/Low/Close (OHLC) format and can add almost any other required metric. We cover all major markets and many minor markets. Available for one-time purchase or with regular updates. Real-time/near-time (usually anything quicker than a 15min delay) requires an additional licence from the respective exchange, anything slower does not.

The main stock market index in the United States (US500) increased 437 points or 9.16% since the beginning of 2024, 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 April of 2024.

Stock price data of the fifty stocks in NIFTY-50 index from NSE India

Graph and download economic data for Index of All Common Stock Prices, Cowles Commission and Standard and Poor's Corporation for United States (M1125AUSM343NNBR) from Jan 1871 to Dec 1956 about stock market, corporate, indexes, and USA.

It include Multi-source data that affect stock prices, such as stock historical trading data and stock forum sentiment indicator.搜索复制

This compilation of historical daily stock market price data relates to the Kenyan Nairobi Securities Exchange (NSE) for year 2022 (Jan-May). This data is valuable for any machine learning algorithm that needs data (training, validation, testing). This compilation develops on an earlier dataset (2008-2012) that was initially compiled as part of a research project to predict next day stock price, based on the previous five days, using Artificial Neural Networks (ANN). This initial research [1],[2] tested 6 stocks [3] using ANN of configuration 5:21:21:1. The data was then enhanced as a new compilation of all stocks for the period 2007-2012 [4].

This new dataset augments the NSE dataset for 2007-2012 [4], 2013 to 2020 [5] and that for 2021 [6]. The method of data compilation has remained as done for all the other datasets by scrapping from a publicly accessible website [7] licensed by NSE. The processing involves exporting the raw web data to spreadsheets, then cleaning up by removal of unnecessary data elements such as HTML tags and other graphics that cannot be converted to text.

Just like the previous compilations, each stock data row has the following 13 data columns (1) Date (2) Stock Code (3) Stock Name (4) 12-month Low price (5) 12-month High price (6) Day's Low price (7) Day's High price (8) Day's Final Price (9) Previous traded price (10) Change in price value (11) Change in price % (12) Volume traded (13) Adjusted price. One additional CSV file is also provided to show market sector that each stock belongs to. The 3 column headings for this additional CSV are: (1) Market sector (2) Stock Code (3) Stock Name.

This additional dataset provides researchers with an even larger dataset (2007-2022) of stocks market data including market sector information for bigger opportunities of data analysis and usage in machine learning research.

List of data files on this dataset: NSE_data_all_stocks_2022_jan_to_may.csv NSE_data_stock_market_sectors_2022.csv

References: [1] Wanjawa, B. W. (2014). A Neural Network Model for Predicting Stock Market Prices at the Nairobi Securities Exchange (Dissertation, University of Nairobi). [2] Wanjawa, B. W., & Muchemi, L. (2014). ANN model to predict stock prices at stock exchange markets. arXiv preprint arXiv:1502.06434. [3] Wanjawa, Barack (2020), “Nairobi Securities Exchange Prices 2008-2012 for 6 selected stocks”, Mendeley Data, v3, http://dx.doi.org/10.17632/95fb84nzcd.3 [4] Wanjawa, Barack (2020), “Nairobi Securities Exchange All Stocks Prices 2007-2012”, Mendeley Data, v1, http://dx.doi.org/10.17632/5hk4zw32f5.1 [5] Wanjawa, Barack (2021), “Nairobi Securities Exchange (NSE) All Stocks Prices 2013-2020”, Mendeley Data, V2, doi: 10.17632/73rb78pmzw.2 [6] Wanjawa, Barack (2022), “Nairobi Securities Exchange (NSE) Kenya - All Stocks Prices 2021”, Mendeley Data, V5, doi: 10.17632/97hkwn5y3x.5 [7] Synergy Systems Ltd. (2020). MyStocks. Retrieved May 31, 2022, from http://live.mystocks.co.ke/

United States Steel stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

PVH stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

American Electric Power stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Stock correlation networks use stock price data to explore the relationship between different stocks listed in the stock market. Currently this relationship is dominantly measured by the Pearson correlation coefficient. However, financial data suggest that nonlinear relationships may exist in the stock prices of different shares. To address this issue, this work uses mutual information to characterize the nonlinear relationship between stocks. Using 280 stocks traded at the Shanghai Stocks Exchange in China during the period of 2014-2016, we first compare the effectiveness of the correlation coefficient and mutual information for measuring stock relationships. Based on these two measures, we then develop two stock networks using the Minimum Spanning Tree method and study the topological properties of these networks, including degree, path length and the power-law distribution. The relationship network based on mutual information has a better distribution of the degree and larger value of the power-law distribution than those using the correlation coefficient. Numerical results show that mutual information is a more effective approach than the correlation coefficient to measure the stock relationship in a stock market that may undergo large fluctuations of stock prices.

This dataset provides a comprehensive collection of financial and macroeconomic data, along with various machine learning features for analysis and prediction. It aims to provide researchers, analysts, and enthusiasts with the necessary resources to explore the potential of machine learning in stock market modeling.

How does the CPI affect Nasdaq?

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

Graph and download economic data for American Railroad Stock Prices, 25 Railroad Stocks for United States (M1105CUSM347NNBR) from Jan 1949 to Dec 1964 about railroad, equity, and USA.

This Dataset contains daily stock prices of some stocks in the Indian market

Stocks of video game retailer GameStop exploded in January 2021, effectively doubling in value on a daily basis. At the close of trading on January 27, GameStop Corporation's stock price reaching 86.88 U.S. dollars per share - or +134 percent compared to the day before. On December 30, 2020, the price was valued at 4.82 U.S. dollars per share. The cause of this dramatic increase is a concerted effort via social media to raise the value of the company's stock, intended to negatively affect professional investors planning to ‘short sell’ GameStop shares. As professional investors started moving away from GameStop the stock price began to fall, stabilizing at around 11-13 U.S. dollars in mid-February. However, stock prices unexpectedly doubled again on February 24, and continued to rise, reaching 66.25 U.S. dollars at the close of trade on March 10. The reasons for this second increase are not fully clear. At the close of trade on April 11, 2024, GameStop shares were trading at 11.29 U.S. dollars. Who are GameStop? GameStop are a retailer of video games and associated merchandise headquartered in a suburbs of Dallas, Texas, but with stores throughout North America, Europe, Australia and New Zealand. As of February 2020 the group maintained just over 5,500 stores, variously under the GameStop, EB Games, ThinkGeek, and Micromania-Zing brands. The company's main revenue source in 2020 was hardware and accessories - a change from 2019, when software sales were the main source of revenue. While the company saw success in the decade up to 2016 (owing to the constant growth of the video game industry), GameStop experienced declining sales since because consumers increasingly purchased video games digitally. It is this continual decline, combined with the effect of the global coronavirus pandemic on traditional retail outlets, that led many institutional investors to see GameStop as a good opportunity for short selling. What is short selling? Short selling is where an investor effectively bets on a the price of a financial asset falling. To do this, an investor borrows shares (or some other asset) via an agreement that the same number of shares be returned at a future date. They can then sell the borrowed shares, and purchase the same number back once the price has fallen to make a profit. Obviously, this strategy only works when the share price does fall – otherwise the borrowed stocks need to be repurchased at a higher price, causing a loss. In the case of GameStop, a deliberate campaign was arranged via social media (particularly Reddit) for individuals to purchase GameStop shares, thus driving the price higher. As a result, some estimates place the loss to institutional investors in January 2021 alone at around 20 billion U.S. dollars. However, once many of these investors had 'closed out' their position by returning the shares they borrowed, demand for GameStop stock fell, leading to the price reduction seen early in early February. A similar dynamic was seen at the same time with the share price of U.S. cinema operator AMC.

Paramount Global stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

South Africa: Stock price volatility, percent: For that indicator, we provide data for South Africa from 1996 to 2021. The average value for South Africa during that period was 18.81 percent with a minimum of 11.07 percent in 1996 and a maximum of 34.38 percent in 2009. The latest value from 2021 is 23.37 percent. For comparison, the world average in 2021 based on 87 countries is 20.14 percent.

Horace Mann Educators stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Ghana: Stock price volatility, percent: For that indicator, we provide data for Ghana from 2011 to 2021. The average value for Ghana during that period was 9.92 percent with a minimum of 6.76 percent in 2016 and a maximum of 14.01 percent in 2021. The latest value from 2021 is 14.01 percent. For comparison, the world average in 2021 based on 87 countries is 20.14 percent.

These data and/or computer programs are part of ICPSR's Publication-Related Archive and are distributed exactly as they arrived from the data depositor. ICPSR has not checked or processed this material. Users should consult the INVESTIGATOR(S) if further information is desired.