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.

About:

This dataset encompasses the historical data of major stock indices from around the world, sourced directly from Yahoo Finance. With data reaching back to the early 1920s (where available), it serves as an invaluable repository for academic researchers, financial analysts, and market enthusiasts. Users can delve into trends across decades, evaluate historical market behaviors, or even design and validate predictive financial models.

Photo by Tötös Ádám on Unsplash

Info on CSVs:

1. all_indices_data.csv:

   • Description: A consolidated dataset merging all the stock indices from Yahoo Finance.
   • Columns:
     • date: The date of the data point (formatted as YYYY-MM-DD).
     • open: The opening value of the index on that date.
     • high: The highest value of the index during the trading session.
     • low: The lowest value of the index during the trading session.
     • close: The closing value of the index.
     • volume: The trading volume of the index on that date.
     • ticker: The ticker symbol of the stock index.

2. individual_indices_data/[SYMBOL]_data.csv:

   • Description: Individual datasets for each stock index, where [SYMBOL] denotes the ticker symbol of the respective stock index. Each dataset is curated from Yahoo Finance's historical data archives.
   • Columns:
     • date: The date of the data point (formatted as YYYY-MM-DD).
     • open: The opening value of the index on that date.
     • high: The highest value of the index during the trading session.
     • low: The lowest value of the index during the trading session.
     • close: The closing value of the index.
     • volume: The trading volume of the index on that date.

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.

Explore the intricacies of wheat as a global commodity on Yahoo Finance, offering live price updates, historical data, and market insights. Discover how geopolitical events, weather conditions, and supply chain logistics influence wheat prices and affect various economic sectors. Stay informed with expert analyses and community discussions, providing comprehensive resources for both novice and seasoned investors in the agricultural markets.

20 years of Yahoo Finance Open, High, Low, Close, Adjusted Close, Volume data, plus generated technical features (RSI, SMA) on close to 5000 global equities. Various targets including 20 days raw returns, residual returns, etc. Use to create predictive models on Numerai Signals tournament to stake and earn/burn $NMR.

As of October 2025, Google represented ***** percent of the global online search engine referrals on desktop devices. Despite being much ahead of its competitors, this represents a modest increase from the previous months. Meanwhile, its longtime competitor Bing accounted for ***** percent, as tools like Yahoo and Yandex held shares of over **** percent and **** percent respectively. Google and the global search market Ever since the introduction of Google Search in 1997, the company has dominated the search engine market, while the shares of all other tools has been rather lopsided. The majority of Google revenues are generated through advertising. Its parent corporation, Alphabet, was one of the biggest internet companies worldwide as of 2024, with a market capitalization of **** trillion U.S. dollars. The company has also expanded its services to mail, productivity tools, enterprise products, mobile devices, and other ventures. As a result, Google earned one of the highest tech company revenues in 2024 with roughly ****** billion U.S. dollars. Search engine usage in different countries Google is the most frequently used search engine worldwide. But in some countries, its alternatives are leading or competing with it to some extent. As of the last quarter of 2023, more than ** percent of internet users in Russia used Yandex, whereas Google users represented little over ** percent. Meanwhile, Baidu was the most used search engine in China, despite a strong decrease in the percentage of internet users in the country accessing it. In other countries, like Japan and Mexico, people tend to use Yahoo along with Google. By the end of 2024, nearly half of the respondents in Japan said that they had used Yahoo in the past four weeks. In the same year, over ** percent of users in Mexico said they used Yahoo.

Explore how Yahoo Finance serves as a key resource for tracking soybeans, offering real-time analytics, historical insights, and expert commentary on the global soybean market's trends, supply chain dynamics, and economic impact.

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.

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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*

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In January 2025, Google accounted for 93.82 percent of the global mobile search engine market worldwide. Yandex had 2.5 percent of the global mobile search, while, competitors like Baidu and Yahoo! accounted for less than one percent each on a global scale.

Coffee fell to 408.66 USd/Lbs on December 2, 2025, down 0.95% from the previous day. Over the past month, Coffee's price has risen 0.50%, and is up 38.54% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Coffee - values, historical data, forecasts and news - updated on December of 2025.

Our project involves creating a model using Multiple Linear Regression to analyze and predict the stock prices of Pepsico. Multiple Linear Regression is a statistical technique that allows us to understand the relationship between multiple independent variables and a dependent variable, in this case, the stock price of Pepsico. By considering various factors such as historical stock prices, market trends, and financial indicators, we aim to develop a robust model that can provide valuable insights and predictions for investors and analysts. Through the implementation of this model, we hope to uncover meaningful patterns and correlations within the Pepsico share data, enabling more informed decision-making in the dynamic world of stock market investments.

Google is not only popular in its home country, but is also the dominant internet search provider in many major online markets, frequently generating between ** and ** percent of desktop search traffic. The search engine giant has a market share of over ** percent in India and accounted for the majority of the global search engine market, way ahead of other competitors such as Yahoo, Bing, Yandex, and Baidu. Google’s online dominance All roads lead to Rome, or if you are browsing the internet, all roads lead to Google. It is hard to imagine an online experience without the online behemoth, as the company offers a wide range of online products and services that all seamlessly integrate with each other. Google search and advertising are the core products of the company, accounting for the vast majority of the company revenues. When adding this up with the Chrome browser, Gmail, Google Maps, YouTube, Google’s ownership of the Android mobile operating system, and various other consumer and enterprise services, Google is basically a one-stop shop for online needs. Google anti-trust rulings However, Google’s dominance of the search market is not always welcome and is keenly watched by authorities and industry watchdogs – since 2017, the EU commission has fined Google over ***** billion euros in antitrust fines for abusing its monopoly in online advertising. In March 2019, European Commission found that Google violated antitrust regulations by imposing contractual restrictions on third-party websites in order to make them less competitive and fined the company *** billion euros.

The global Query Engine market is poised for substantial growth, projected to reach an estimated market size of $16,390 million by 2025. This growth is fueled by an impressive Compound Annual Growth Rate (CAGR) of 11% anticipated over the forecast period. The market's expansion is primarily driven by the ever-increasing volume of digital data and the escalating demand for efficient and intelligent methods to access and process this information. Key applications span both personal and commercial sectors, reflecting the ubiquitous nature of information retrieval in modern life. The market is bifurcated into two primary types: Crawler Search Engines, which systematically index the web, and Meta Search Engines, which aggregate results from multiple sources. This dual approach caters to diverse user needs, from broad information discovery to specialized and comprehensive searches. The proliferation of internet-connected devices, the rise of big data analytics, and the continuous innovation in natural language processing and artificial intelligence are significant tailwinds supporting this upward trajectory. As businesses and individuals alike rely more heavily on digital platforms for information, services, and commerce, the demand for sophisticated query engines that can deliver accurate, relevant, and timely results will only intensify. The Query Engine market landscape is characterized by intense competition and continuous innovation from major global players such as Google, Baidu, and Microsoft, alongside specialized companies like DuckDuckGo and Hulbee. These companies are at the forefront of developing advanced algorithms, machine learning capabilities, and user interface enhancements to capture market share. While growth is robust, certain restraints may impact the pace, including evolving privacy regulations, the challenge of filtering misinformation, and the significant investment required for continuous R&D to stay competitive. Geographically, the Asia Pacific region, particularly China and India, is expected to be a significant growth engine due to its massive internet user base and rapid digitalization. North America and Europe will continue to be mature yet vital markets, driven by technological adoption and sophisticated user expectations. The Middle East & Africa and South America are emerging markets with substantial untapped potential, offering future growth opportunities for query engine providers. The overall outlook suggests a dynamic and evolving market where technological prowess, user experience, and data handling capabilities will be paramount for success. This report offers an in-depth analysis of the global Query Engine market, encompassing a Study Period from 2019 to 2033. With a Base Year of 2025 and an Estimated Year also of 2025, the Forecast Period extends from 2025 to 2033, building upon Historical Period data from 2019 to 2024. The market is projected to reach several hundred million dollars by the end of the forecast period, driven by technological advancements and increasing digital integration across personal and commercial applications.

This dataset contains CSV files of all tickers available via the Yahoo Finance API (stocks, currencies, cryptocurrencies, ETFs, etc.) and their associated name, performance, volume and market cap over the past 5/10 years. The 10_year_results.csv and 5_year_results.csv are filtered for assets with current market cap $1B+, decade-old volume $1K+, current volume $100K+, and sorted by top performance.

Baltic Dry rose to 2,600 Index Points on December 2, 2025, up 0.66% from the previous day. Over the past month, Baltic Dry's price has risen 33.68%, and is up 110.19% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Baltic Exchange Dry Index - values, historical data, forecasts and news - updated on December of 2025.

Content

Daily price data for World indices stock exchanges from all over the world (United States, China, Canada, Germany, Japan, and more). The data was all collected from Yahoo Finance, which had several decades of data available for most exchanges. Prices are quoted in terms of the USD currency of where each exchange is located.

Acknowledgement

Data collected from Yahoo Finance.

This statistic gives information on Yahoo!'s net income from 2004 to 2016. In the last reported year, the internet company's GAAP net loss was *** million US dollars, down from a net income of *** billion US dollars in 2014.

Yahoo has had its share of financial troubles, in part due to Google’s almost complete domination of market sectors where Yahoo used to be an important player, such as the search engine market. For example, as of April 2015, just under * percent of worldwide internet users search the web using Yahoo’s service, while more than ** percent use Google Search. But despite its ups and downs, the company has remained one of the most relevant multinational technology companies in the world. In 2014, Yahoo’s net income was a reported *** billion U.S. dollars, up from *** billion in the previous year. That same year, the company’s yearly revenue however was the second-lowest in the past decade – *** billion U.S. dollars. Especially the second quarter of 2014 displays lower than ever revenues for the company, as compared to previous years – just slightly over * billion U.S. dollars. According to the most recent report regarding Yahoo’s quarterly net income, the company generated a **** billion U.S. dollars profit in the third quarter of 2014, as a result the company's sale of Alibaba shares, but also a net loss of ***** million U.S. dollars in the second quarter of 2015. Yahoo was founded in the mid ***** in California, in the midst of the Silicon Valley technological boom. It is mostly known for its search engine, Yahoo Search, and the Yahoo web portal, featuring such services as Yahoo Finance, Yahoo News, Yahoo Answers and most notably Yahoo Mail. The company, which has made a lot of acquisitions since its modest beginnings, also provides advertising services, online mapping and video sharing. Since it acquired Tumblr in 2013, the company has also started to move into the social media sector. As of 2015, Yahoo is the second-most popular website in the United States, after Google, with more than *** million unique visitors per month on all of its properties combined.

As of March 2025, Google continued to dominate the global search engine industry by far, with an 89.62 percent market share. However, this stronghold may be showing signs of erosion, with its share across all devices dipping to its lowest point in over two decades. Bing, Google's closest competitor, currently holds a market share of 4.01 percent across, while Russia-based Yandex hikes to the third place with a share of around 2.51 percent. Competitive landscape and regional variations While Google's overall dominance persists, other search engines carve out niches in various markets and platforms. Bing holds a 12.21 percent market share across desktop devices worldwide, as Yandex and Baidu have found success inside and outside of their home markets. Yandex is used by over 63 percent of Russian internet users, but Baidu has seen its market share significantly in China As regional variations highlight the importance of local players in challenging Google's global supremacy, the company is likely to face more challenges with the AI-powered online search trend and increasing regulatory scrutiny. Search behavior and antitrust concerns Despite facing more competition, Google remains deeply ingrained in users' online habits. In 2024, "Google" itself was the most popular search query on its own platform, followed by "YouTube" - another Google-owned property. This self-reinforcing ecosystem has drawn scrutiny from regulators, with the European Commission imposing millionaire antitrust fines on the company. As its influence extends beyond search into various online services, the company's market position continues to be a subject of debate among industry watchdogs and authorities worldwide.

As of March 2025, Google represented 79.1 percent of the global online search engine market on desktop devices. Despite being much ahead of its competitors, this represents the lowest share ever recorded by the search engine in these devices for over two decades. Meanwhile, its long-time competitor Bing accounted for 12.21 percent, as tools like Yahoo and Yandex held shares of over 2.9 percent each. Google and the global search market Ever since the introduction of Google Search in 1997, the company has dominated the search engine market, while the shares of all other tools has been rather lopsided. The majority of Google revenues are generated through advertising. Its parent corporation, Alphabet, was one of the biggest internet companies worldwide as of 2024, with a market capitalization of 2.02 trillion U.S. dollars. The company has also expanded its services to mail, productivity tools, enterprise products, mobile devices, and other ventures. As a result, Google earned one of the highest tech company revenues in 2024 with roughly 348.16 billion U.S. dollars. Search engine usage in different countries Google is the most frequently used search engine worldwide. But in some countries, its alternatives are leading or competing with it to some extent. As of the last quarter of 2023, more than 63 percent of internet users in Russia used Yandex, whereas Google users represented little over 33 percent. Meanwhile, Baidu was the most used search engine in China, despite a strong decrease in the percentage of internet users in the country accessing it. In other countries, like Japan and Mexico, people tend to use Yahoo along with Google. By the end of 2024, nearly half of the respondents in Japan said that they had used Yahoo in the past four weeks. In the same year, over 21 percent of users in Mexico said they used Yahoo.