The "yahoo_finance_dataset(2018-2023)" dataset is a financial dataset containing daily stock market data for multiple assets such as equities, ETFs, and indexes. It spans from April 1, 2018 to March 31, 2023, and contains 1257 rows and 7 columns. The data was sourced from Yahoo Finance, and the purpose of the dataset is to provide researchers, analysts, and investors with a comprehensive dataset that they can use to analyze stock market trends, identify patterns, and develop investment strategies. The dataset can be used for various tasks, including stock price prediction, trend analysis, portfolio optimization, and risk management. The dataset is provided in XLSX format, which makes it easy to import into various data analysis tools, including Python, R, and Excel.

The dataset includes the following columns:

Date: The date on which the stock market data was recorded. Open: The opening price of the asset on the given date. High: The highest price of the asset on the given date. Low: The lowest price of the asset on the given date. Close*: The closing price of the asset on the given date. Note that this price does not take into account any after-hours trading that may have occurred after the market officially closed. Adj Close**: The adjusted closing price of the asset on the given date. This price takes into account any dividends, stock splits, or other corporate actions that may have occurred, which can affect the stock price. Volume: The total number of shares of the asset that were traded on the given date.

Context:

The provided dataset is extracted from yahoo finance using pandas and yahoo finance library in python. This deals with stock market index of the world best economies. The code generated data from Jan 01, 2003 to Jun 30, 2023 that’s more than 20 years. There are 18 CSV files, dataset is generated for 16 different stock market indices comprising of 7 different countries. Below is the list of countries along with number of indices extracted through yahoo finance library, while two CSV files deals with annualized return and compound annual growth rate (CAGR) has been computed from the extracted data.

Number of Countries & Index:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F90ce8a986761636e3edbb49464b304d8%2FNumber%20of%20Index.JPG?generation=1688490342207096&alt=media" alt="">

Content:

Unit of analysis: Stock Market Index Analysis

This dataset is useful for research purposes, particularly for conducting comparative analyses involving capital market performance and could be used along with other economic indicators.

There are 18 distinct CSV files associated with this dataset. First 16 CSV files deals with number of indices and last two CSV file deals with annualized return of each year and CAGR of each index. If data in any column is blank, it portrays that index was launch in later years, for instance: Bse500 (India), this index launch in 2007, so earlier values are blank, similarly China_Top300 index launch in year 2021 so early fields are blank too.

The extraction process involves applying different criteria, like in 16 CSV files all columns are included, Adj Close is used to calculate annualized return. The algorithm extracts data based on index name (code given by the yahoo finance) according start and end date.

Annualized return and CAGR has been calculated and illustrated in below image along with machine readable file (CSV) attached to that.

To extract the data provided in the attachment, various criteria were applied:

1. Content Filtering: The data was filtered based on several attributes, including the index name, start and end date. This filtering process ensured that only relevant data meeting the specified criteria.

2. Collaborative Filtering: Another filtering technique used was collaborative filtering using yahoo finance, which relies on index similarity. This approach involves finding indices that are similar to other index or extended dataset scope to other countries or economies. By leveraging this method, the algorithm identifies and extracts data based on similarities between indices.

In the last two CSV files, one belongs to annualized return, that was calculated based on the Adj close column and new DataFrame created to store its outcome. Below is the image of annualized returns of all index (if unreadable, machine-readable or CSV format is attached with the dataset).

Annualized Return:

As far as annualised rate of return is concerned, most of the time India stock market indices leading, followed by USA, Canada and Japan stock market indices.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F37645bd90623ea79f3708a958013c098%2FAnnualized%20Return.JPG?generation=1688525901452892&alt=media" alt="">

Compound Annual Growth Rate (CAGR):

The best performing index based on compound growth is Sensex (India) that comprises of top 30 companies is 15.60%, followed by Nifty500 (India) that is 11.34% and Nasdaq (USA) all is 10.60%.

The worst performing index is China top300, however this is launch in 2021 (post pandemic), so would not possible to examine at that stage (due to less data availability). Furthermore, UK and Russia indices are also top 5 in the worst order.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F58ae33f60a8800749f802b46ec1e07e7%2FCAGR.JPG?generation=1688490409606631&alt=media" alt="">

Geography: Stock Market Index of the World Top Economies

Time period: Jan 01, 2003 – June 30, 2023

Variables: Stock Market Index Title, Open, High, Low, Close, Adj Close, Volume, Year, Month, Day, Yearly_Return and CAGR

File Type: CSV file

Inspiration:

• Time series prediction model
• Investment opportunities in world best economies
• Comparative Analysis of past data with other stock market indices or other indices

Disclaimer:

This is not a financial advice; due diligence is required in each investment decision.

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.

Dataset

This dataset was created by Aditya Rajuladevi

Released under CC0: Public Domain

Contents

Cotton futures showed resilience with gains despite early weakness, influenced by dollar and oil trends, CFTC positioning, and ICE stock changes.

Dataset

This dataset was created by Joshua Quah Shou Sing

Contents

The main stock market index of United States, the US500, rose to 6818 points on December 2, 2025, gaining 0.08% from the previous session. Over the past month, the index has declined 0.50%, though it remains 12.70% higher than a year ago, 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 December 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.

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*

Cryptocurrency historical datasets from January 2012 (if available) to October 2021 were obtained and integrated from various sources and Application Programming Interfaces (APIs) including Yahoo Finance, Cryptodownload, CoinMarketCap, various Kaggle datasets, and multiple APIs. While these datasets used various formats of time (e.g., minutes, hours, days), in order to integrate the datasets days format was used for in this research study. The integrated cryptocurrency historical datasets for 80 cryptocurrencies including but not limited to Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Cardano (ADA), Tether (USDT), Ripple (XRP), Solana (SOL), Polkadot (DOT), USD Coin (USDC), Dogecoin (DOGE), Tron (TRX), Bitcoin Cash (BCH), Litecoin (LTC), EOS (EOS), Cosmos (ATOM), Stellar (XLM), Wrapped Bitcoin (WBTC), Uniswap (UNI), Terra (LUNA), SHIBA INU (SHIB), and 60 more cryptocurrencies were uploaded in this online Mendeley data repository. Although the primary attribute of including the mentioned cryptocurrencies was the Market Capitalization, a subject matter expert i.e., a professional trader has also guided the initial selection of the cryptocurrencies by analyzing various indicators such as Relative Strength Index (RSI), Moving Average Convergence/Divergence (MACD), MYC Signals, Bollinger Bands, Fibonacci Retracement, Stochastic Oscillator and Ichimoku Cloud. The primary features of this dataset that were used as the decision-making criteria of the CLUS-MCDA II approach are Timestamps, Open, High, Low, Closed, Volume (Currency), % Change (7 days and 24 hours), Market Cap and Weighted Price values. The available excel and CSV files in this data set are just part of the integrated data and other databases, datasets and API References that was used in this study are as follows: [1] https://finance.yahoo.com/ [2] https://coinmarketcap.com/historical/ [3] https://cryptodatadownload.com/ [4] https://kaggle.com/philmohun/cryptocurrency-financial-data [5] https://kaggle.com/deepshah16/meme-cryptocurrency-historical-data [6] https://kaggle.com/sudalairajkumar/cryptocurrencypricehistory [7] https://min-api.cryptocompare.com/data/price?fsym=BTC&tsyms=USD [8] https://min-api.cryptocompare.com/ [9] https://p.nomics.com/cryptocurrency-bitcoin-api [10] https://www.coinapi.io/ [11] https://www.coingecko.com/en/api [12] https://cryptowat.ch/ [13] https://www.alphavantage.co/ This dataset is part of the CLUS-MCDA (Cluster analysis for improving Multiple Criteria Decision Analysis) and CLUS-MCDAII Project: https://aimaghsoodi.github.io/CLUSMCDA-R-Package/ https://github.com/Aimaghsoodi/CLUS-MCDA-II https://github.com/azadkavian/CLUS-MCDA

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

The dataset reports a collection of earnings call transcripts, the related stock prices, and the sector index In terms of volume, there is a total of 188 transcripts, 11970 stock prices, and 1196 sector index values. Furthermore, all of these data originated in the period 2016-2020 and are related to the NASDAQ stock market. Furthermore, the data collection was made possible by Yahoo Finance and Thomson Reuters Eikon. Specifically, Yahoo Finance enabled the search for stock values and Thomson Reuters Eikon provided the earnings call transcripts. Lastly, the dataset can be used as a benchmark for the evaluation of several NLP techniques to understand their potential for financial applications. Moreover, it is also possible to expand the dataset by extending the period in which the data originated following a similar procedure.

Multifractality is a concept that extends locally the usual ideas of fractality in a system. Nevertheless, the multifractal approaches used lack a multifractal dimension tied to an entropy index like the Shannon index. This paper introduces a generalized Shannon index (GSI) and demonstrates its application in understanding system fluctuations. To this end, traditional multifractality approaches are explained. Then, using the temporal Theil scaling and the diffusive trajectory algorithm, the GSI and its partition function are defined. Next, the multifractal exponent of the GSI is derived from the partition function, establishing a connection between the temporal Theil scaling exponent and the generalized Hurst exponent. Finally, this relationship is verified in a fractional Brownian motion and applied to financial time series. In fact, this leads us to propose an approximation called local fractional Brownian motion approximation, where multifractal systems are viewed as a local superposition of distinct fractional Brownian motions with varying monofractal exponents. Also, we furnish an algorithm for identifying the optimal q-th moment of the probability distribution associated with an empirical time series to enhance the accuracy of generalized Hurst exponent estimation. Methods The data sets used in this research are divided into two parts:

Data from fractional Brownian motion (fBm) simulations: These data are generated using the Wood-Chan algorithm for fractional Gaussian noise. Additionally, to verify the value of the Hurst exponent of each fBm sample, the Multifractal Detrended Fluctuation Analysis (MF-DFA) method implemented in Python by Gorjao et. al. (https://doi.org/10.1016/j.cpc.2021.108254) Is used. Financial time series data: This data is generated by an API designed in Python to directly download financial time series data from Yahoo Finance with the ticker name. From these, the time series of returns, log returns (log-returns), absolute log returns and volatilities of log returns are generated.

Context

If you are satisfied in data and code, please upvote :)👍 The investing is necessary for everyone's future. I think that just knowing the meaning of the variables without interpreting this dataset is enough to study. This data is an Nasdaq index, taken from yahoo finance. Contains multiple financial statements and represents prices over a period of about 10 years(2010-01-01 - 2021-06-30) we can analyze price of stocks by time series with comparing financial statements that it is expected to be good measurement of correlation! Have you fun!🎉

The data format is received as json and can be used as a data frame. The script used can be checked at Github repository and if you want longer time scale data or up-to-date data, please run the script from the link. And also, if you want another list of stock, you should check the link which can analysis like Dow (tickers are 30), S&P500 (ticker are 500).

If you interest this data and code, Pleases see notebooks of strategy :)

I'm still learning Python, so if you find messy code execution or have a better way of doing it, let me know!! and Please contact me :) I think it will be a good study.

Content

• In FS_nasdaq_Value.json(csv) It is presented by price like 'Open', 'Close' and so on.

• In FS_nasdaq_Recent+Value.json(csv) It is presented by recent price (2021-06-30)

All data is presented recently. If you want the statements before, Pleases check and fix below code.

Acknowledgements

I'm studying physics and writing code of python and c++. However I'm not used to it yet and also English :(. Let you know if it is not correctly for code and English :🙏

Inspiration

In interpreting the stock market, there are traditionally low PER and PBR strategies. Prior to this, an ML model using various statements and a price estimation model using time series data have been proposed recently, but we know that they are of little use. This data is highly likely to be used for various analyzes, and it is considered to be basic data for understanding the stock's market. Let's study together and find the best model!

If you are satisfied in data and code, please upvote :)👍

View market daily updates and historical trends for CBOE Equity Put/Call Ratio. from United States. Source: Chicago Board Options Exchange. Track economic…

The SPDR® S&P 500® ETF (ticker: SPY) is an exchange-traded fund that seeks to track the performance of the S&P 500® Index by holding a portfolio of the same large-cap U.S. stocks. It serves as a broad gauge of market sentiment and investor confidence, with movements often reflecting expectations about corporate earnings, economic growth, and financial conditions. Widely used by investors, SPY offers a real-time view of equity market trends. (‘SPDR®’ is a registered trademark of Standard & Poor’s Financial Services LLC and has been licensed for use by State Street Global Advisors. ‘S&P 500®’ is a registered trademark of S&P Dow Jones Indices LLC. This site is not affiliated with, endorsed by, or sponsored by Standard & Poor’s, S&P Dow Jones Indices, or State Street Global Advisors.)

Replication files for "Differences from Differencing: Should Local Projections with Observed Shocks be Estimated in Levels or Differences?" Jeremy Piger and Thomas Stockwell, 2025, Journal of Applied Econometrics.

Matlab Programs

There are three Matlab programs in this repository:

LP_MonteCarlo_Final.m - This is the primary program for conducting the simulations reported in the paper. Using this code, one can replicates Figures 1-2, 4-5, and 7-17

LPIV_MonteCarlo_Final.m - This is the program to conduct simulations using LP-IV. Replicates Figure 19

Application_Jarocinski_Karadi_Shock_Final.m - This is the program to produce results for the application. Replicates Figure 20

Data Files

The Matlab programs collectively use three data files, contained in the “Data” folder:

GDPC1.csv - Data file containing U.S. real GDP, 1947:Q1 - 2024:3, FRED code: GDPC1.

CEE_VAR_Data.csv - contains ten quarterly series necessary to construct the nine variables used to estimate the Christiano, Eichenbaum and Evans (2005) 9-variable VAR:

1) GDPC1 = U.S. real GDP, 1947:Q1 - 2024:Q3, FRED code: GDPC1 2) PCEC96 = U.S. real personal consumption expenditures, 1947:Q1 - 2024:Q3, FRED code: PCEC96 3) GPDEF = U.S. GDP implicit price deflator, 1947:Q1 - 2024:Q3, FRED code: GDPDEF 4) GPDIC1 = U.S. real gross private domestic investment, 1947:Q1 - 2024:Q3, FRED code: GPDIC1 5) COMPNFB = Nonfarm business sector: hourly compensation for all workers, 1947:Q1 - 2024:Q3, FRED code: COMPNFB 6) PRS85006023 = Nonfarm business sector: average weekly hours for all workers, 1947:Q1 - 2024:Q3, FRED code: PRS85006023 7) OPHNFB = Nonfarm business sector: labor productivity (output per hour) for all workers, 1947:Q1 - 2024:Q3, FRED code: OPHNFB 8) FEDFUNDS = Federal funds effective rate, 1954:Q3 - 2024:Q3, FRED code: FEDFUNDS 9) CP = Corporate profits after tax, 1947:Q1 - 2024:Q3, FRED code: CP 10) M2SL = M2 monetary aggregate, 1947:Q1 - 2024:Q3, FRED code: M2SL

application_data.csv - contains seven monthly series used for the application:

1) indpro = U.S. industrial production index, Jan. 1989-Sep. 2024, FRED code: INDPRO 2) CPI = U.S. consumer price index, Jan. 1989-Sep. 2024, FRED code: CPIAUCSL 3) 1yrTreasury = Market Yield on U.S. Treasury Securities at 1-Year Constant Maturity, Jan. 1989-Sep. 2024, FRED code: DGS1 4) SP500 = S&P 500 Index, Jan. 1989-Sep. 2024, Yahoo Finance code GSPC and FRED code: SP500 5) EBP = Gilchrist and Zakrajsek (2012, AER) Excess Bond Premium, Jan 1989-Sep.2024, obtained from the dataset provided for Bauer and Swanson (2023, NBER Macro Annual) on Michael Bauer’s website: https://www.michaeldbauer.com/research/. 6) MP_median = Jarocinski and Karadi (2020, AEJ Macro) U.S. monetary policy shock, Feb. 1990-Sep. 2024, obtained from: https://marekjarocinski.github.io/jkshocks/jkshocks.html 7) CBI_median = Jarocinski and Karadi (2020, AEJ Macro) U.S. central bank information shock, Feb. 1990-Sep. 2024, obtained from: https://marekjarocinski.github.io/jkshocks/jkshocks.html

CRB Index rose to 378.33 Index Points on December 1, 2025, up 0.45% from the previous day. Over the past month, CRB Index's price has fallen 0.80%, but it is still 10.95% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. CRB Commodity Index - values, historical data, forecasts and news - updated on December of 2025.

Comparison of evaluation metrics for different models.

Context

If you are satisfied in data and code, please upvote :)👍 The investing is necessary for everyone's future. I think that just knowing the meaning of the variables without interpreting this dataset is enough to study. This data is an S&p500 index, taken from yahoo finance. Contains multiple financial statements and represents prices over a period of about 10 years(2010-01-01 - 2022-04-18(version 12)) we can analyze price of stocks by time series with comparing financial statements that it is expected to be good measurement of correlation! Have you fun!🎉

The data format is received as json and can be used as a data frame. The script used can be checked at Github repository and if you want longer time scale data or up-to-date data, please run the script from the link. And also, if you want another list of stock, you should check the link which can analysis like Dow (tickers are 30), nasdaq (ticker are about 3000).

If you interest this data and code, Pleases see notebooks of strategy :)

I'm still learning Python, so if you find messy code execution or have a better way of doing it, let me know!! and Please contact me :) I think it will be a good study.

Content

• In FS_sp500_Value.json It is presented by price like 'Open', 'Close' and so on.

• In FS_sp500_RecentValue.json It is presented by Current price.

• In FS_sp500_stats.json. It is summary statement for each ticker.

• In FS_sp500_addstats.json It is fundamental statement not to be presented in stats.

• In FS_sp500_balsheets.json It is presented in balance sheets.

• In FS_sp500_income.json It is presented in income statements.

• In FS_sp500_flow.json It is presented by cash flow.

All data is presented recently. If you want the statements before, Pleases check and fix below code.

Acknowledgements

I'm studying physics and writing code of python and c++. However I'm not used to it yet and also English :(. Let you know if it is not correctly for code and English :🙏

Inspiration

In interpreting the stock market, there are traditionally low PER and PBR strategies. Prior to this, an ML model using various statements and a price estimation model using time series data have been proposed recently, but we know that they are of little use. This data is highly likely to be used for various analyzes, and it is considered to be basic data for understanding the stock's market. Let's study together and find the best model!

If you are satisfied in data and code, please upvote :)👍