India's main stock market index, the SENSEX, rose to 82727 points on July 23, 2025, gaining 0.66% from the previous session. Over the past month, the index has climbed 0.82% and is up 3.22% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from India. BSE SENSEX Stock Market Index - values, historical data, forecasts and news - updated on July of 2025.

About this dataset

India's National Stock Exchange (NSE) has a total market capitalization of more than US$3.4 trillion, making it the world's 10th-largest stock exchange as of August 2021, with a trading volume of ₹8,998,811 crore (US$1.2 trillion) and more 2000 total listings.

NSE's flagship index, the NIFTY 50, is a 50 stock index is used extensively by investors in India and around the world as a barometer of the Indian capital market.

This dataset contains data of all company stocks listed in the NSE, allowing anyone to analyze and make educated choices about their investments, while also contributing to their countries economy.

How to use this dataset

• Create a time series regression model to predict NIFTY-50 value and/or stock prices.
• Explore the most the returns, components and volatility of the stocks.
• Identify high and low performance stocks among the list.

Highlighted Notebooks

• Your kernel can be featured here!
• Related Dataset: S&P 500 Stocks - daily updated
• More datasets

Acknowledgements

License

CC0: Public Domain

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Stonks by unknown memer.

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.

Tweet Sentiment's Impact on Stock Returns

862,231 Labeled Instances

By [source]

About this dataset

This dataset contains 862,231 labeled tweets and associated stock returns, providing a comprehensive look into the impact of social media on company-level stock market performance. For each tweet, researchers have extracted data such as the date of the tweet and its associated stock symbol, along with metrics such as last price and various returns (1-day return, 2-day return, 3-day return, 7-day return). Also recorded are volatility scores for both 10 day intervals and 30 day intervals. Finally, sentiment scores from both Long Short - Term Memory (LSTM) and TextBlob models have been included to quantify the overall tone in which these messages were delivered. With this dataset you will be able to explore how tweets can affect a company's share prices both short term and long term by leveraging all of these data points for analysis!

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How to use the dataset

In order to use this dataset, users can utilize descriptive statistics such as histograms or regression techniques to establish relationships between tweet content & sentiment with corresponding stock return data points such as 1-day & 7-day returns measurements.

The primary fields used for analysis include Tweet Text (TWEET), Stock symbol (STOCK), Date (DATE), Closing Price at the time of Tweet (LAST_PRICE) a range of Volatility measures 10 day Volatility(VOLATILITY_10D)and 30 day Volatility(VOLATILITY_30D ) for each Stock which capture changes in market fluctuation during different periods around when Twitter reactions occur. Additionally Sentiment Polarity analysis undertaken via two Machine learning algorithms LSTM Polarity(LSTM_POLARITY)and Textblob polarity provide insight into whether people are expressing positive or negative sentiments about each company at given times which again could influence thereby potentially influence Stock Prices over shorter term periods like 1-Day Returns(1_DAY_RETURN),2-Day Returns(2_DAY_RETURN)or longer term horizon like 7 Day Returns*7DAY RETURNS*.Finally MENTION field indicates if names/acronyms associated with Companies were specifically mentioned in each Tweet or not which gives extra insight into whether company specific contexts were present within individual Tweets aka “Company Relevancy”

Research Ideas

• Analyzing the degree to which tweets can influence stock prices. By analyzing relationships between variables such as tweet sentiment and stock returns, correlations can be identified that could be used to inform investment decisions.
• Exploring natural language processing (NLP) models for predicting future market trends based on textual data such as tweets. Through testing and evaluating different text-based models using this dataset, better predictive models may emerge that can give investors advance warning of upcoming market shifts due to news or other events.
• Investigating the impact of different types of tweets (positive/negative, factual/opinionated) on stock prices over specific time frames. By studying correlations between the sentiment or nature of a tweet and its effect on stocks, insights may be gained into what sort of news or events have a greater impact on markets in general

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: reduced_dataset-release.csv | Column name | Description | |:----------------------|:-------------------------------------------------------------------------------------------------------| | TWEET | Text of the tweet. (String) | | STOCK | Company's stock mentioned in the tweet. (String) | | DATE | Date the tweet was posted. (Date) | | LAST_PRICE | Company's last price at the time of tweeting. (Float) ...

China's main stock market index, the SHANGHAI, rose to 3606 points on July 24, 2025, gaining 0.65% from the previous session. Over the past month, the index has climbed 4.33% and is up 24.91% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from China. China Shanghai Composite Stock Market Index - values, historical data, forecasts and news - updated on July of 2025.

The data is contained in the winrar file - 'DataSet-AssociationMining-India.rar'

Once you open the above winrar file, you will see the below files & folders:

• File: "IndiaData-ForAssociationMining.xlsx" is the primary data retrieved from 'Refinitiv-Datastream' which was used in the project.

• Folder-1MetricsGT-NSE50 o This folder has MS-Excel macro files used to create return determinant data to be eventually used in the 'Final-Transaction-Table' from which associations would be mined. o This folder also has computed returns for different holding periods for different stocks considered in this study. File: "0_nYrRtnGTNSE50.xlsm" o This folder also has the 'Final-Sheet' used for mining of association rules.

• Folder: 2Analysis-GTNSE50 o This folder has the R-program used to mine associations. It also has the final sheets used in association mining for different holding periods. And the output of the association rules mined is also stored here (File name: RulesRHS_1YrRtnGTNSE50.csv and so on)

• Folder: 3Validation o This folder has data related to the validation carried out in the project. It has 2 sub-folders: § 1-MetricsForValidation: This folder has excel-macro files to compute the metrics required in the Final-Table for validation of the association rules. § 2-BetaCalc-PortRtns: This folder has the Final transaction sheet which will be later used to compute portfolio beta and portfolio returns for each association rule. This also has the computation of portfolio beta & portfolio returns for each of the 10 association rules analyzed in this paper.

• Folder: 4LogitRegression o This folder has the 'R' program used to carry out Logit regression and different model consistency test. It also has the input file for the Logit regression (Filename: India-LogitRegression-csv.csv) o The sub-folder 'Regression_OP' has the output of Logit regression for all association rules for different holding periods.

The Nifty 50 Index data provides a comprehensive overview of the performance of the top 50 actively traded stocks listed on the National Stock Exchange of India (NSE). This dataset encompasses a wide range of industries, including finance, technology, healthcare, and consumer goods, offering insights into the overall health and direction of the Indian stock market.

Included in the data are key metrics such as daily opening and closing prices, high and low prices, trading volume, and percentage changes. These metrics allow analysts and investors to track trends, identify patterns, and make informed decisions regarding investment strategies.

Additionally, the dataset may incorporate historical data, enabling users to conduct thorough analyses over specific time periods and assess the long-term performance of individual stocks or the index as a whole. Whether used for research, financial modeling, or investment decision-making, the Nifty 50 Index data serves as a valuable resource for understanding and navigating the dynamic landscape of the Indian stock market.

The systematic impact of macroeconomic variables on stock market returns makes it crucial to comprehend the link between macroeconomic variables and the stock market. The autoregressive distributed lag (ARDL) model was used in this study to examine the causal links between specific macroeconomic factors and Indian stock prices

This dataset provides information on the stock prices and day highs of various companies across different sectors. It includes data on the company names, stock prices in Indian Rupees (Rs.), and the highest prices reached by the stocks during a specific trading day.

The dataset allows for analyzing the performance of different companies' stocks and identifying potential trading or investment opportunities based on the day highs. It can be useful for conducting research, developing investment strategies, and gaining insights into stock market trends.

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 (JII) Stock: Is the Indian Investment Trust a Winner?

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

The data represent the daily market return of Sensex, a group 30 top-performing companies in Indian financial market, as well as individual company return.

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Venture Capital Investment Market Size 2025-2029

The venture capital investment market size is forecast to increase by USD 2920.2 billion, at a CAGR of 37.9% between 2024 and 2029.

The Venture Capital (VC) investment market is experiencing significant growth, particularly in the biotech sector, driven by advancements in technology and innovation. This trend is fueled by an increasing number of high-net-worth individuals (HNWIs) worldwide, who are seeking to diversify their portfolios and invest in promising startups. However, this market faces challenges, including foreign exchange volatility, which can impact the returns on investments made across borders. As HNWIs continue to invest in VC funds, they bring not only capital but also expertise and industry connections, further enhancing the potential for successful ventures.
Simultaneously, biotech companies, with their innovative solutions, are attracting substantial VC interest, presenting significant opportunities for growth and returns. Navigating foreign exchange risks and identifying promising biotech startups will be crucial for VC firms seeking to capitalize on these trends and outperform their competitors.

What will be the Size of the Venture Capital Investment Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The venture capital (VC) investment market continues to evolve, shaped by dynamic market conditions and diverse sector applications. Dividend yields and capital gains remain key drivers for investors, as they seek to maximize returns. Big data and growth hacking are increasingly integral to investment theses, enabling industry analysis and informed decision-making. Limited partnerships (LPs) and funds collaborate, with GPs overseeing operations and risk management. Deal sourcing and due diligence are essential components of the investment process, ensuring portfolio companies align with the fund's objectives. Revenue growth and marketing strategies are critical for portfolio companies, as they aim to scale and attract investment.

Term sheets outline investment details, while advisory boards provide strategic guidance. Financial modeling and cash flow management are essential for effective fund management. Technology infrastructure, including AI, cloud computing, and blockchain technology, underpins innovation and growth. Joint ventures and technology licensing offer opportunities for collaboration and expansion. Sales strategy and burn rate analysis help optimize portfolio performance. Private equity and data analytics provide valuable insights for investment opportunities. Stock options and Series A and B funding rounds offer potential for significant returns. Legal agreements and intellectual property (IP) rights are crucial for protecting investments and ensuring long-term success.

How is this Venture Capital Investment Industry segmented?

The venture capital investment industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Sector

  Software
  Pharmaceutical and biotechnology
  Media and entertainment
  Medical devices and equipments
  Others


Type

  First-time venture funding
  Follow-on venture funding


Variant

  Institutional Investors
  Corporate venture capital
  Private equity firms
  Angel investors
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    The Netherlands
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Sector Insights

The software segment is estimated to witness significant growth during the forecast period.

The market has witnessed significant activity in the software industry, with a focus on disruptive technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Blockchain technology. VC firms have invested billions of dollars in these areas, with some companies achieving unicorn status. The software sector includes application software, system infrastructure software, software as a service (SaaS), operating systems, database software, and analytics software. The growing number of entrepreneurs and businesses, estimated to be over 450 million and 300 million, respectively, is fueling the growth of the software segment in the market. VC funds have been actively involved in Series A funding, providing capital for early-stage startups, and Series B funding, for growth-stage companies.

Limited partnerships (LPs) have been essential in providing capital for these funds. Risk management is a critical factor in venture capital investment, with due diligence, financial modeling, and market analysis being crucial c

Pakistan's main stock market index, the KSE 100, rose to 139451 points on July 22, 2025, gaining 0.89% from the previous session. Over the past month, the index has climbed 20.04% and is up 76.55% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Pakistan. Pakistan Stock Market (KSE100) - values, historical data, forecasts and news - updated on July of 2025.

Nigeria's main stock market index, the NSE-All Share, rose to 132452 points on July 22, 2025, gaining 0.47% from the previous session. Over the past month, the index has climbed 11.70% and is up 31.81% 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.

Silver fell to 39.18 USD/t.oz on July 23, 2025, down 0.31% from the previous day. Over the past month, Silver's price has risen 9.07%, and is up 35.61% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Silver - values, historical data, forecasts and news - updated on July of 2025.

Platinum fell to 1,464.50 USD/t.oz on July 22, 2025, down 0.91% from the previous day. Over the past month, Platinum's price has risen 13.63%, and is up 55.29% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Platinum - values, historical data, forecasts and news - updated on July of 2025.