This is the name of the 38 global main stock indexes in the world. We collected from Yahoo! Finance. For the convenience of expression and computation later, we numbered it. For each item, the front is its serial number, followed by the corresponding stock index.

DAX

Analysis of ‘Time Series Forecasting with Yahoo Stock Price ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/arashnic/time-series-forecasting-with-yahoo-stock-price on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Stocks and financial instrument trading is a lucrative proposition. Stock markets across the world facilitate such trades and thus wealth exchanges hands. Stock prices move up and down all the time and having ability to predict its movement has immense potential to make one rich. Stock price prediction has kept people interested from a long time. There are hypothesis like the Efficient Market Hypothesis, which says that it is almost impossible to beat the market consistently and there are others which disagree with it.

There are a number of known approaches and new research going on to find the magic formula to make you rich. One of the traditional methods is the time series forecasting. Fundamental analysis is another method where numerous performance ratios are analyzed to assess a given stock. On the emerging front, there are neural networks, genetic algorithms, and ensembling techniques.

Another challenging problem in stock price prediction is Black Swan Event, unpredictable events that cause stock market turbulence. These are events that occur from time to time, are unpredictable and often come with little or no warning.

A black swan event is an event that is completely unexpected and cannot be predicted. Unexpected events are generally referred to as black swans when they have significant consequences, though an event with few consequences might also be a black swan event. It may or may not be possible to provide explanations for the occurrence after the fact – but not before. In complex systems, like economies, markets and weather systems, there are often several causes. After such an event, many of the explanations for its occurrence will be overly simplistic.

#
#

https://www.visualcapitalist.com/wp-content/uploads/2020/03/mm3_black_swan_events_shareable.jpg"> #
#
New bleeding age state-of-the-art deep learning models stock predictions is overcoming such obstacles e.g. "Transformer and Time Embeddings". An objectives are to apply these novel models to forecast stock price.

Content

Stock price prediction is the task of forecasting the future value of a given stock. Given the historical daily close price for S&P 500 Index, prepare and compare forecasting solutions. S&P 500 or Standard and Poor's 500 index is an index comprising of 500 stocks from different sectors of US economy and is an indicator of US equities. Other such indices are the Dow 30, NIFTY 50, Nikkei 225, etc. For the purpose of understanding, we are utilizing S&P500 index, concepts, and knowledge can be applied to other stocks as well.

Dataset

The historical stock price information is also publicly available. For our current use case, we will utilize the pandas_datareader library to get the required S&P 500 index history using Yahoo Finance databases. We utilize the closing price information from the dataset available though other information such as opening price, adjusted closing price, etc., are also available. We prepare a utility function get_raw_data() to extract required information in a pandas dataframe. The function takes index ticker name as input. For S&P 500 index, the ticker name is ^GSPC. The following snippet uses the utility function to get the required data.(See Simple LSTM Regression)

Features and Terminology: In stock trading, the high and low refer to the maximum and minimum prices in a given time period. Open and close are the prices at which a stock began and ended trading in the same period. Volume is the total amount of trading activity. Adjusted values factor in corporate actions such as dividends, stock splits, and new share issuance.

Starter Kernel(s)

• Simple LSTM Regression

Acknowledgements

Mining and updating of this dateset will depend upon Yahoo Finance .

Inspiration

Sort of variation of sequence modeling and bleeding age e.g. attention can be applied for research and forecasting

Some Readings

• Applications of deep learning in stock market prediction: recent progress
• Stock predictions with state-of-the-art Transformer and Time Embeddings
• https://arxiv.org/pdf/1712.02136.pdf
• https://catanacapital.com/blog/unpredictable-black-swan-event-stock-market/
• https://par.nsf.gov/servlets/purl/10149715
• https://arxiv.org/pdf/1712.02136.pdf

*If you download and find the data useful your upvote is an explicit feedback for future works*

--- Original source retains full ownership of the source dataset ---

################################################################################################## THE FILES HAVE BEEN CREATED BY SZILÁRD ERHART FOR A RESEARCH: ERHART (2021): ESG RATINGS OF GENERAL # STOCK EXCHANGE INDICES, INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS# USERS OF THE FILES AGREE TO QUOTE THE ABOVE PAPER# THE PYTHON SCRIPT (PYTHONESG_ERHART.TXT) HELPS USERS TO GET TICKERS BY STOCK EXCHANGES AND EXTRACT ESG SCORES FOR THE UNDERLYING STOCKS FROM YAHOO FINANCE.# THE R SCRIPT (ESG_UA.TXT) HELPS TO REPLICATE THE MONTE CARLO EXPERIMENT DETAILED IN THE STUDY.# THE EXPORT_ALL CSV CONTAINS THE DOWNLOADED ESG DATA (SCORES, CONTROVERSIES, ETC) ORGANIZED BY STOCKS AND EXCHANGES.############################################################################################################################################################################################################### DISCLAIMER # The author takes no responsibility for the timeliness, accuracy, completeness or quality of the information provided. # The author is in no event liable for damages of any kind incurred or suffered as a result of the use or non-use of the # information presented or the use of defective or incomplete information. # The contents are subject to confirmation and not binding. # The author expressly reserves the right to alter, amend, whole and in part, # without prior notice or to discontinue publication for a period of time or even completely. ###########################################################################################################################################READ ME############################################################# BEFORE USING THE MONTE CARLO SIMULATIONS SCRIPT: # (1) COPY THE goascores.csv and goalscores_alt.csv FILES ONTO YOUR ON COMPUTER DRIVE. THE TWO FILES ARE IDENTICAL.# (2) SET THE EXACT FILE LOCATION INFORMATION IN THE 'Read in data' SECTION OF THE MONTE CARLO SCRIPT AND FOR THE OUTPUT FILES AT THE END OF THE SCRIPT# (3) LOAD MISC TOOLS AND MATRIXSTATS IN YOUR R APPLICATION# (4) RUN THE CODE.####################################READ ME

Full historical data for the S&P 500 (ticker ^GSPC), sourced from Yahoo Finance (https://finance.yahoo.com/).

Including Open, High, Low and Close prices in USD + daily volumes.

Info about S&P 500: https://en.wikipedia.org/wiki/S%26P_500

A collective dataset derived from Yahoo Finance for:

1. Stock prices
2. Raw material prices
3. Volatility indices

For multiple historical scenarios.

Context

In this dataset you can find the Top 100 companies in the technology sector. You can also find 5 of the most important and used indices in the financial market as well as a list of all the companies in the S&P 500 index and in the technology sector.

The Global Industry Classification Standard also known as GICS is the primary financial industry standard for defining sector classifications. The Global Industry Classification Standard was developed by index providers MSCI and Standard and Poor’s. Its hierarchy begins with 11 sectors which can be further delineated to 24 industry groups, 69 industries, and 158 sub-industries.

You can read the definition of each sector here.

The 11 broad GICS sectors commonly used for sector breakdown reporting include the following: Energy, Materials, Industrials, Consumer Discretionary, Consumer Staples, Health Care, Financials, Information Technology, Telecommunication Services, Utilities and Real Estate.

In this case we will focuse in the Technology Sector. You can see all the sectors and industry groups here.

To determine which companies, correspond to the technology sector, we use Yahoo Finance, where we rank the companies according to their “Market Cap”. After having the list of the Top 100 best valued companies in the sector, we proceeded to download the historical data of each of the companies using the NASDAQ website.

Regarding to the indices, we searched various sources to find out which were the most used and determined that the 5 most frequently used indices are: Dow Jones Industrial Average (DJI), S&P 500 (SPX), NASDAQ Composite (IXIC), Wilshire 5000 Total Market Inde (W5000) and to specifically view the technology sector SPDR Select Sector Fund - Technology (XLK). Historical data for these indices was also obtained from the NASDQ website.

Content

In total there are 107 files in csv format. They are composed as follows:

• 100 files contain the historical data of tech companies.
• 5 files contain the historical data of the most used indices.
• 1 file contain the list of all the companies in the S&P 500 index.
• 1 file contain the list of all the companies in the technology sector.

Column Description

Every company and index file has the same structure with the same columns:

Date: It is the date on which the prices were recorded. High: Is the highest price at which a stock traded during the course of the trading day. Low: Is the lowest price at which a stock traded during the course of the trading day. Open: Is the price at which a stock started trading when the opening bell rang. Close: Is the last price at which a stock trades during a regular trading session. Volume: Is the number of shares that changed hands during a given day. Adj Close: The adjusted closing price factors in corporate actions, such as stock splits, dividends, and rights offerings.

The two other files have different columns names:

List of S&P 500 companies

Symbol: Ticker symbol of the company. Name: Name of the company. Sector: The sector to which the company belongs.

Technology Sector Companies List

Symbol: Ticker symbol of the company. Name: Name of the company. Price: Current price at which a stock can be purchased or sold. (11/24/20) Change: Net change is the difference between closing prices from one day to the next. % Change: Is the difference between closing prices from one day to the next in percentage. Volume: Is the number of shares that changed hands during a given day. Avg Vol: Is the daily average of the cumulative trading volume during the last three months. Market Cap (Billions): Is the total value of a company’s shares outstanding at a given moment in time. It is calculated by multiplying the number of shares outstanding by the price of a single share. PE Ratio: Is the ratio of a company's share (stock) price to the company's earnings per share. The ratio is used for valuing companies and to find out whether they are overvalued or undervalued.

Acknowledgements

SEC EDGAR | Company Filings NASDAQ | Historical Quotes Yahoo Finance | Technology Sector Wikipedia | List of S&P 500 companies S&P Dow Jones Indices | S&P 500 [S&P Dow Jones Indices | DJI](https://www.spglobal.com/spdji/en/i...

Ukraine's main stock market index, the PFTS, closed flat at 464 points on July 29, 2025. Over the past month, the index has declined 5.81% and is down 8.46% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Ukraine. Ukraine Stock Market (PFTS) - values, historical data, forecasts and news - updated on July of 2025.

The data files contain seven low-dimensional financial research data (in .txt format) and two high-dimensional daily stock prices data (in .csv format). The low-dimensional data sets are provided by Lorenzo Garlappi on his website, while the high-dimensional data sets are downloaded from Yahoo!Finance by the contributor's own effort. The description of the low-dimensional data sets can be found in DeMiguel et al. (2009, RFS). The two high-dimensional data sets contain daily adjusted close prices (from Jan 1, 2013 to Dec 31, 2014) of the stocks, which are in the index components list (as of Jan 7, 2015) of S&P 500 and Russell 2000 indices, respectively.

This data series of stock market indices includes FTSE 100(FTSE), AEX Index(AEX), DAX(GDAXI) and Straits Times Index(STI), from January 2007 to December 2017. And all these data is from a third party, downloaded with R software from 'Yahoo finance'.

This report analyses movements in the Chicago Board Options Exchange (CBOE) Volatility Index. Known by its ticker symbol VIX, the CBOE Volatility Index is a real-time market index that indicates the stock market's expectation of volatility and is derived from the price inputs of the S&P 500 Index options - the S&P 500 is a US stock market index based on the market capitalisation of 500 large companies having common stock listed on the New York Stock Exchange (NYSE), the Nasdaq Stock Market (NASDAQ), or the Cboe BZX Exchange. Effectively, the VIX measures the degree of variation in S&P 500 stocks' trading price observed over a period of time. The data is sourced from Yahoo Finance, which ultimately derives from the CBOE, in addition to estimates by IBISWorld. The figures represent the average daily unadjusted close value of the index over the UK financial year (i.e. April through March).

This report analyses the movements of the Financial Times Stock Exchange (FTSE) 100 Index. The FTSE 100 is a share index of the 100 companies listed on the London Stock Exchange (LSE) with the highest market capitalisation (i.e. the market value of a publicly-traded company's outstanding shares). Constituents listed in the FTSE 100 are subject to change, whereby a publicly-traded entity can be demoted or promoted to or from the FTSE 250 index - this consists of the 101st to the 350th largest companies listed on the LSE by market capitalisation - when a quarterly reshuffle occurs in March, June, September and December of each calendar year. Movements in the FTSE 100 index are responsive to the weighted average movements of the constituents' stocks, which are ranked according to market capitalisation value. The data is sourced from Yahoo Finance, which ultimately derives from the LSE, and represents the closing price of the FTSE 100 index on the last day of each financial year (i.e. the close price on 31 March).

Context

Time Series Analysis is an important part in Data science toolkit. This dataset was created from Yahoo Finance with the help of their official API yfinance.

Content

This dataset contains closing price of Top 4 indexes recorded over daily frame from 1994 to 2021 October (27 years).

Experience a decade of NASDAQ market dynamics with this comprehensive historical price dataset from 2014 to 2024.

The NASDAQ Composite is a benchmark index representing the performance of more than 2,500 stocks listed on the NASDAQ stock exchange, encompassing various sectors including technology, healthcare, and finance. This dataset, sourced meticulously from Yahoo Finance, offers daily insights into the index's opening, highest, lowest, and closing prices, along with adjusted close prices and daily volume.

This report analyses the All Ordinaries index. The All Ordinaries index is a share price index, which comprises the 500 largest companies listed on the Australian Securities Exchange. Companies are ranked by market capitalisation, which is the only requirement for inclusion in the index. The All Ordinaries is a non-float adjusted, market capitalisation weighted, price index. The data for this report is sourced from Yahoo Finance and is represented by an average of the daily index points at close over each financial year.

Nigeria's main stock market index, the NSE-All Share, rose to 139354 points on July 31, 2025, gaining 0.05% from the previous session. Over the past month, the index has climbed 16.38% and is up 43.13% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Nigeria. Nigeria Stock Market NSE - values, historical data, forecasts and news - updated on July of 2025.

Xiong et al. (2021) use data on daily stock returns from 25 major publicly listed firms from China and eight major pork-exporting countries to provide the first systematic analysis of the firm-level economic impacts of the 2018 African Swine Fever (ASF) outbreaks. We find that, on average, announcements of ASF outbreaks have led to positive and significant stock returns for both Chinese and international hog firms. China’s hog firms, on average, enjoyed 10%–40% cumulative abnormal returns during the 2019 Chinese Spring Festival, when investors saw signs of a near-20% inventory loss during a peak demand season for pork.

Several databases are necessary to conduct the analyses. ASF announcements in China from August 2018 to September 2019 came from China Ministry of Agriculture and Rural Affairs. The ASF announcements detailed the release date, the county-level location and specific site (i.e., pig farm, slaughterhouse, or transport vehicle) of event detection, the number of hogs in inventory, and the number of infected and dead pigs. Daily market indices and stock price data for China’s top 10 publicly listed hog firms and 15 foreign public listed hog firms from eight countries were downloaded from Yahoo Finance.

The daily firm-level stock price data contain important information that evaluates firms’ performance in the market. In addition to economic drivers, stock prices often also reflect the effect of non-economic shocks. The ASF announcements dataset put together in Xiong et al. (2021) allows researchers to explore different aspects of the economic consequences as a result of the ASF outbreaks in China other than for the financial market. The daily stock price data for Chinese and international hog firms allow others to study the effect of any economic and non-economic events that also occurred during the same sample period that might impact stock price movements.

Morocco's main stock market index, the CFG 25, rose to 19446 points on July 29, 2025, gaining 0.69% from the previous session. Over the past month, the index has climbed 6.28% and is up 39.06% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Morocco. Morocco Stock Market MASI - values, historical data, forecasts and news - updated on July of 2025.

The data set is collected for a quantile regression analysis testing the predictive ability of investor sentiment on bitcoin return and volatility. The data is obtained from several online sources including Google trends, Wikipedia, Twitter, News headlines, Bitcointalk.org, and market indexes available at yahoo finance. the dataset includes daily values from mid-2015 to the end of 2020.

In our study, we used data from the NSE-NIFTY 50 (National Stock Exchange) indices. We collected data from two websites for the long run; covering most of the recession from January 1, 2008, to December 2, 2021. The different websites used to collect the data of Indices are www.nse.com and www.yahoofinance.com.