Stock Analysis Software Market Outlook

The global stock analysis software market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 3.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5% during the forecast period. The growth of this market is driven by the increasing adoption of advanced analytics tools by individual investors and financial institutions to make informed investment decisions. The rising demand for automated trading systems and the integration of artificial intelligence (AI) and machine learning (ML) in stock analysis software are significant growth factors contributing to the market expansion.

One of the primary growth factors for the stock analysis software market is the increasing complexity and volume of financial data. With the exponential growth of data from various sources such as social media, news articles, and financial statements, investors and financial analysts require sophisticated tools to process and interpret this information accurately. Stock analysis software equipped with AI and ML algorithms can analyze vast datasets in real-time, providing valuable insights and predictive analytics that enhance investment strategies. Moreover, the growing trend of algorithmic trading, which relies heavily on high-speed data processing and automated decision-making, is further propelling the market growth.

Another crucial growth driver is the rising awareness and adoption of stock analysis software among individual investors. As more individuals seek to actively manage their investment portfolios, there is a growing demand for user-friendly and cost-effective stock analysis tools that offer comprehensive market analysis, technical indicators, and personalized investment recommendations. The proliferation of mobile applications and the increasing accessibility of cloud-based stock analysis solutions have made it easier for retail investors to access advanced analytical tools, thereby contributing to market expansion.

The integration of innovative technologies such as natural language processing (NLP) and sentiment analysis into stock analysis software is also a significant growth factor. These technologies enable the software to interpret and analyze unstructured data from news articles, social media, and other textual sources to gauge market sentiment and predict stock price movements. This capability is particularly valuable in today's fast-paced financial markets, where sentiment and news events can have a substantial impact on stock prices. The continuous advancements in AI and NLP technologies are expected to drive further innovations and improvements in stock analysis software, thereby boosting market growth.

In the evolving landscape of financial technology, Investor Relations Tools have become indispensable for companies seeking to maintain transparent and effective communication with their stakeholders. These tools facilitate seamless interaction between companies and their investors, providing real-time updates, financial reports, and strategic insights. By leveraging these tools, companies can enhance their investor engagement strategies, build trust, and foster long-term relationships with their shareholders. The integration of advanced analytics and AI-driven insights into Investor Relations Tools further empowers companies to tailor their communication strategies, ensuring that they meet the diverse needs of their investor base. As the demand for transparency and accountability in financial markets continues to grow, the adoption of sophisticated Investor Relations Tools is expected to rise, playing a crucial role in the broader ecosystem of stock analysis software.

From a regional perspective, North America is anticipated to hold the largest market share due to the high concentration of financial institutions, brokerage firms, and individual investors in the region. The presence of key market players and the early adoption of advanced technologies also contribute to the dominant position of North America in the global stock analysis software market. Additionally, the Asia Pacific region is expected to witness significant growth during the forecast period, driven by the increasing number of retail investors, rapid economic development, and the growing financial markets in countries such as China and India.

Component Analysis

This dataset consists of five CSV files that provide detailed data on a stock portfolio and related market performance over the last 5 years. It includes portfolio positions, stock prices, and major U.S. market indices (NASDAQ, S&P 500, and Dow Jones). The data is essential for conducting portfolio analysis, financial modeling, and performance tracking.

1. Portfolio

This file contains the portfolio composition with details about individual stock positions, including the quantity of shares, sector, and their respective weights in the portfolio. The data also includes the stock's closing price.

• Columns:
  • Ticker: The stock symbol (e.g., AAPL, TSLA)
  • Quantity: The number of shares in the portfolio
  • Sector: The sector the stock belongs to (e.g., Technology, Healthcare)
  • Close: The closing price of the stock
  • Weight: The weight of the stock in the portfolio (as a percentage of total portfolio)

2. Portfolio Prices

This file contains historical pricing data for the stocks in the portfolio. It includes daily open, high, low, close prices, adjusted close prices, returns, and volume of traded stocks.

• Columns:
  • Date: The date of the data point
  • Ticker: The stock symbol
  • Open: The opening price of the stock on that day
  • High: The highest price reached on that day
  • Low: The lowest price reached on that day
  • Close: The closing price of the stock
  • Adjusted: The adjusted closing price after stock splits and dividends
  • Returns: Daily percentage return based on close prices
  • Volume: The volume of shares traded that day

3. NASDAQ

This file contains historical pricing data for the NASDAQ Composite index, providing similar data as in the Portfolio Prices file, but for the NASDAQ market index.

• Columns:
  • Date: The date of the data point
  • Ticker: The stock symbol (for NASDAQ index, this will be "IXIC")
  • Open: The opening price of the index
  • High: The highest value reached on that day
  • Low: The lowest value reached on that day
  • Close: The closing value of the index
  • Adjusted: The adjusted closing value after any corporate actions
  • Returns: Daily percentage return based on close values
  • Volume: The volume of shares traded

4. S&P 500

This file contains similar historical pricing data, but for the S&P 500 index, providing insights into the performance of the top 500 U.S. companies.

• Columns:
  • Date: The date of the data point
  • Ticker: The stock symbol (for S&P 500 index, this will be "SPX")
  • Open: The opening price of the index
  • High: The highest value reached on that day
  • Low: The lowest value reached on that day
  • Close: The closing value of the index
  • Adjusted: The adjusted closing value after any corporate actions
  • Returns: Daily percentage return based on close values
  • Volume: The volume of shares traded

5. Dow Jones

This file contains similar historical pricing data for the Dow Jones Industrial Average, providing insights into one of the most widely followed stock market indices in the world.

• Columns:
  • Date: The date of the data point
  • Ticker: The stock symbol (for Dow Jones index, this will be "DJI")
  • Open: The opening price of the index
  • High: The highest value reached on that day
  • Low: The lowest value reached on that day
  • Close: The closing value of the index
  • Adjusted: The adjusted closing value after any corporate actions
  • Returns: Daily percentage return based on close values
  • Volume: The volume of shares traded

Personal Portfolio Data

This data is received using a custom framework that fetches real-time and historical stock data from Yahoo Finance. It provides the portfolio’s data based on user-specific stock holdings and performance, allowing for personalized analysis. The personal framework ensures the portfolio data is automatically retrieved and updated with the latest stock prices, returns, and performance metrics.

This part of the dataset would typically involve data specific to a particular user’s stock positions, weights, and performance, which can be integrated with the other files for portfolio performance analysis.

Iran's main stock market index, the TEDPIX, closed flat at 2900000 points on October 11, 2025. Over the past month, the index has climbed 7.41% and is up 39.15% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Iran. Iran Tehran Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

Cash-Flow-Per-Share Time Series for Raymond James Financial Inc.. Raymond James Financial, Inc., a diversified financial services company, provides private client group, capital markets, asset management, banking, and other services to individuals, corporations, and municipalities in the United States, Canada, and Europe. The Private Client Group segment offers investment services, portfolio management services, insurance and annuity products, and mutual funds; support to third-party mutual fund and annuity companies, including sales and marketing support, as well as distribution and accounting, and administrative services; margin loans; securities borrowing and lending services; diversification strategies and alternative investment products; and custodial, trade execution, research, and other support and services. The Capital Markets segment provides investment banking services, such as equity and debt underwriting, and merger and acquisition advisory services; and fixed income and equity brokerage services. This segment also offers institutional sales, securities trading, equity research, and the syndication and management of investments in low-income housing funds and funds of a similar nature. The Asset Management segment provides asset management, portfolio management, and related administrative services to retail and institutional clients; and administrative support services, such as record-keeping. The Bank segment offers various types of loans, including securities-based, commercial and industrial, commercial real estate and construction, real estate investment trust, residential mortgage, and tax-exempt loans; insured deposit accounts; retail and corporate deposit; and liquidity management products and services. The Other segment engages in the private equity investments comprising invests in third-party funds. Raymond James Financial, Inc. was founded in 1962 and is headquartered in Saint Petersburg, Florida.

Share-of-Periods-With-Dividend-Payments-In-Percent Time Series for Morgan Stanley. Morgan Stanley, a financial holding company, provides various financial products and services to governments, financial institutions, and individuals in the Americas, Asia, Europe, Middle East, and Africa. The company operates through Institutional Securities, Wealth Management, and Investment Management segments. It offers capital raising and financial advisory services, including services related to the underwriting of debt, equity, and other securities, as well as advice on mergers and acquisitions, restructurings, and project finance. It also provides equity and fixed income products comprising sales, financing, prime brokerage, and market-making services; Asia wealth management; business-related investments services; originating corporate and commercial real estate loans, secured lending facilities, and extending securities; and research. In addition, the company offers financial advisor-led brokerage, custody, and administrative and investment advisory services; self-directed brokerage services; financial and wealth planning services; stock plan administration; securities-based lending, residential and commercial real estate loans, and other lending products; banking; and retirement plan services. Further, it provides equity, fixed income, alternatives and solutions, and liquidity and overlay services to benefit/defined contribution plans, foundations, endowments, government entities, sovereign wealth funds, insurance companies, third-party fund sponsors, corporations, and individuals. The company was founded in 1924 and is headquartered in New York, New York.

The Survey of Consumer Finances (SCF) is normally a triennial cross-sectional survey of U.S. families. The survey data include information on families' balance sheets, pensions, income, and demographic characteristics.

Data Description

The dataset provided includes information about various companies, their stock symbols, financial metrics such as price-to-book ratio and share price, as well as details about their origin countries. Additionally, the dataset contains frequency distribution information for certain ranges of price-to-book ratios and share prices.

About Data

The dataset appears to be a compilation of financial data for different companies, likely for investment analysis or comparison purposes. It includes the following key components:

• Rank: Rank of the company based on some criteria (not explicitly mentioned).
• Company: Name of the company.
• Stock Symbol: Symbol used to identify the company's stock in trading.
• Price to Book Ratio: Financial metric indicating the relationship between a company's market value and its book value.
• Share Price (USD): Price of a single share of the company's stock in US dollars.
• Company Origin: Country where the company is based.
• Label Count: Frequency distribution information for certain ranges of price-to-book ratios and share prices.

This dataset can be utilized for various financial analyses such as company valuation, comparison of financial metrics across companies, and investment decision-making.

Share-of-Periods-With-Dividend-Payments-In-Percent Time Series for Choice Properties Real Estate Investment Trust. Choice Properties is a leading Real Estate Investment Trust that creates enduring value through places where people thrive. We are more than a national owner, operator and developer of high-quality commercial and residential real estate. We believe in creating spaces that enhance how our tenants and communities come together to live, work, and connect. This includes our industry leadership in integrating environmental, social and economic sustainability practices into all aspects of our business. In everything we do, we are guided by a shared set of values grounded in Care, Ownership, Respect and Excellence.

Global Stock Dataset

Overview

This dataset contains market data from various countries, organized into a hierarchical structure. It includes information such as share prices, trading volumes, market capitalization, and industry classifications.

Structure

The dataset is organized as follows:

• List of market data
  • Canada
  • China
  • India
  • Japan
  • Middle East
  • USA

Each country folder likely contains specific market data for companies within that region.

Data Fields

The dataset includes the following fields:

1. Share Price (CAD): The stock price in Canadian Dollars.
2. Volume: The trading volume of the stock.
3. Market Capitalization (CAD): The total market value of the company's outstanding shares in Canadian Dollars.
4. Industry: The sector or industry classification of the company.

Features

• Sorting: The data can be sorted by share price, volume, and market capitalization.
• Grid View: A 3x3 grid view is available for data visualization.
• Text Formatting: Volume and Market Capitalization data are formatted for easy reading.

Version Information

• Current Version: 1
• File Size: 1.72 MB

Usage

This dataset can be used for various purposes, including: - Market analysis - Comparative studies across different countries - Industry sector analysis - Investment research

Note

Please ensure you have the necessary permissions and comply with all relevant data usage regulations when using this dataset.

Updates

For the latest version and updates to this dataset, please check the source regularly.

Share-of-Periods-With-Dividend-Payments-In-Percent Time Series for Washington Trust Bancorp Inc. Washington Trust Bancorp, Inc. operates as the bank holding company for The Washington Trust Company, of Westerly that provides various banking and financial services to individuals and businesses. The company operates in two segments, Commercial Banking and Wealth Management Services. The Commercial Banking segment offers deposit accounts, including interest-bearing and noninterest-bearing demand deposits, NOW and savings accounts, money market and retirement deposit accounts, and time deposits; various commercial and retail lending products, such as commercial real estate loans, including commercial mortgages, and construction and development loans; commercial and industrial loans comprising working capital, equipment financing, and financing for other business-related purposes; residential real estate loans that consist of mortgage and homeowner construction loans; and consumer loans comprising home equity loans and lines of credit, personal installment loans, and loans to individuals secured by general aviation aircraft. This segment also provides debit cards; automated teller machines (ATMs); telephone banking, internet banking, mobile banking, remote deposit capture, and other cash management services; and investment portfolio and wholesale funding services. The Wealth Management Services segment offers investment management; financial planning; personal trust and estate services, such as trustee, personal representative, custodian, and guardian; and settlement of decedents' estates, as well as institutional trust services comprising custody and fiduciary services for personal and institutional clients. The company was founded in 1800 and is headquartered in Westerly, Rhode Island.

The Finance & Economics Dataset provides daily financial and macroeconomic data, including stock market prices, GDP growth, inflation, interest rates, consumer spending, exchange rates, and more. It is designed for use in:

✔ Financial Market Analysis – Track stock index movements and trading volumes. ✔ Macroeconomic Research – Study economic trends, including inflation and GDP growth. ✔ Investment Decision Making – Evaluate interest rates, corporate profits, and consumer confidence. ✔ Machine Learning & Predictive Analytics – Develop forecasting models for economic indicators.

This dataset is valuable for economists, investors, data scientists, researchers, and policymakers.

📁 Dataset 3: Microsoft Corp. (MSFT)

💻 Microsoft Corp. (MSFT) Stock Price Data – Last 5 Years

Description:
This dataset contains daily historical stock price data for Microsoft Corporation (Ticker: MSFT) over the past 5 years. It is sourced from reliable financial market data providers and is well-suited for:

• 📈 Time series forecasting and trend analysis
• 📊 Financial dashboards and visualizations
• 🧠 Machine learning applications in finance
• 💼 Investment research and strategy backtesting

Each entry corresponds to a single trading day and includes various price indicators and trading volume.

Columns Included:

• Date: The calendar date of the trading session (YYYY-MM-DD).
• Open: The price of the stock at the beginning of the trading day.
• High: The highest price reached during the trading day.
• Low: The lowest price recorded during the trading day.
• Close: The price of the stock at the end of the trading session.
• Adj Close: The closing price adjusted for splits and dividend distributions.
• Volume: The total number of shares traded during that day.

🔍 Beginner-Friendly Analysis Techniques:

If you're new to data analysis or finance, here are some simple but powerful techniques you can apply:

• Line Plots: Use line charts to visualize how the stock price has changed over time.
• Moving Averages: Calculate simple or exponential moving averages to identify trends and smooth out short-term fluctuations.
• Daily Returns: Compute percentage change between closing prices to analyze daily gains or losses.
• Volatility Analysis: Use rolling standard deviation to understand how volatile the stock has been.
• Volume Trends: Track trading volume to identify periods of high market activity.
• Candlestick Charts (intermediate): Create candlestick charts to represent daily open-high-low-close patterns visually.

Use Cases:
This dataset can be used to evaluate stock performance trends, calculate technical indicators, simulate investment strategies, or train predictive models on financial data.

High-Tech Companies on NASDAQ

Market Capitalization and Performance Metrics

By [source]

About this dataset

This dataset offers an insightful look into the performance of high-tech companies listed on the NASDAQ exchange in the United States. With information pertaining to over 8,000 companies in the electronics, computers, telecommunications, and biotechnology sectors, this is an incredibly useful source of insight for researchers, traders, investors and data scientists interested in acquiring information about these firms.

The dataset includes detailed variables such as stock symbols and names to provide quick identification of individual companies along with pricing changes and percentages from the previous day’s value as well as sector and industry breakdowns for comprehensive analysis. Other metrics like market capitalization values help to assess a firm’s relative size compared to competitors while share volume data can give a glimpse into how actively traded each company is. Additionally provided numbers include earnings per share breakdowns to gauge profits along with dividend pay date symbols for yield calculation purposes as well as beta values that further inform risk levels associated with investing in particular firms within this high-tech sector. Finally this dataset also collects any potential errors found amongst such extensive scrapes of company performance data giving users valuable reassurance no sensitive areas are missed when assessing various firms on an individual basis or all together as part of an overarching system

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For more datasets, click here.

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How to use the dataset

This dataset is invaluable for researchers, traders, investors and data scientists who want to obtain the latest information about high-tech companies listed on the NASDAQ exchange in the United States. It contains data on more than 8,000 companies from a wide range of sectors such as electronics, computers, telecommunications, biotechnology and many more. In this guide we will learn how to use this dataset effectively.

Basics: The basics of working with this dataset include understanding various columns like symbol, name, price,pricing_changes, pricing_percentage_changes,sector,industry,market_cap,share_volume,earnings_per_share. Each column is further described below: - Symbol: This column gives you the stock symbol of the company. (String) - Name: This column gives you the name of the company. (String)
- Price: The current price of each stock given by symbol is mentioned here.(Float) - Pricing Changes: This represents change in stock price from previous day.(Float) - Pricing Percentage Changes :This provides percentage change in stock prices from previous day.(Float) - Sector : It give information about sector in which company belongs .(String). - Industry : Describe industry in which company lies.(string). - Market Capitalization : Give market capitalization .(String). - Share Volume : It refers to number share traded last 24 hrs.(Integer). - Earnings Per Share : It refer to earnings per share per Stock yearly divided by Dividend Yield ,Symbol Yield and Beta .It also involves Errors related with Data Set so errors specified here proviedes details regarding same if any errors occured while collecting data set or manipulation on it.. (float/string )

Advanced Use Cases: Now that we understand what each individual feature stands for it's time to delve deeper into optimizing returns using this data set as basis for our decision making processes such as selecting right portfolio formation techniques or selecting stocks wisely contrarian investment style etc. We can do a comparison using multiple factors like Current Price followed by Price Change percentage or Earnings feedback loop which would help us identify Potentially Undervalued investments both Short Term & Long Term ones at same time and We could dive into analysis showing Relationship between Price & Volumne across Sectors and

Research Ideas

• Analyzing stock trends - The dataset enables users to make informed decisions by tracking and analyzing changes in indicators such as price, sector, industry or market capitalization trends over time.
• Exploring correlations between different factors - By exploring the correlation between different factors such as pricing changes, earning per share or beta etc., it enables us to get a better understanding of how these elements influence each other and what implications it may have on our investments

Acknowledgements

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The dataset contains information on shares listed on the Casablanca Stock Exchange. Each column represents a specific share, identified by its ticker symbol, and each row represents a market session (trading day) Dataset columns:

1. Séance : Market session date.
2. Stocks in the Banking Sector : ATW , CIH, BCI, BOA
3. Stocks in the Insurance sector : AFM, SAH, WAA
4. Stock in the Agri-food and Production sector : CSR
5. Stock in the Building and Construction Materials sector :COL
6. Stock in the Beverages sector : SBM
7. Chemicals sector stock : SNP
8. Distributor sector stocks : LBV, NEJ
9. Electricity sector stock : TQM
10. Pharmaceutical industry sector stock : SOT
11. Stocks in the Hardware, Software and IT Services sector : DWY, HPS, S2M
12. Stocks in the Oil & Gas sector : GAZ, TMA
13. Stock in the transport and logistics sector : MSA
14. Transport sector stock : CTM
15. Stock in the Telecommunications sector : IAM
16. Stock in the Mines sector : MNG
17. Financing sector stock: EQD
18. Stock in Real Estate sectors : ADI
19. Treasury Bonds : OATWQ : 28DEC2017 3.59% 7A 100K ATW. OCAMG : 01NOV2018 4.10% 10A 100K CAM. OCIHA : 09JAN2015 4.75% 10A 100K CIH. OLYDA : 14JUL2010 5.62% 15A 100K LYD. OOCPC : 14MAI2018 4.03% IND 100K OCP. OOCFC : 20OCT2011 5.11% 15A 100K OCF.

short explanation: The "28DEC2017 3.59% 7A 100K ATW" bond is a bond issued on December 28, 2017, with an annual interest rate of 3.59%, a 7-year maturity, a nominal value of 100,000 MAD, and linked to Attijariwafa Bank (ATW). Dataset characteristics Frequency: Daily, each line represents one market session. Period: The coverage period is almost 3 years. Data Type: The values in the columns are the closing share prices for each market session.

Dataset usage This dataset can be used for various financial analyses, such as : - Analysis of stock price trends. - Calculation of stock returns and volatility. - Construction and optimization of investment portfolios. - Modeling and forecasting stock prices using statistical and machine learning methods

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 2200 companies listed on the Australian Securities Exchange* (XASX) in Australia. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

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Top 5 used data fields in the End-of-Day Pricing Dataset for Australia:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

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Top 5 financial instruments with End-of-Day Pricing Data in Australia:

S&P/ASX 200 Index: The S&P/ASX 200 is the benchmark stock market index in Australia. It tracks the performance of the 200 largest publicly listed companies on the Australian Securities Exchange (ASX) and is widely used as a measure of the Australian stock market's overall performance.

Australian Dollar (AUD): The Australian Dollar is the official currency of Australia and is commonly abbreviated as AUD. It is one of the most traded currencies in the world and is used for both domestic and international transactions.

Reserve Bank of Australia (RBA): The central bank of Australia responsible for monetary policy, issuing currency, and maintaining financial stability. The RBA's decisions on interest rates and monetary policy have a significant impact on the Australian economy.

Australian Securities Exchange (ASX): The ASX is the primary stock exchange in Australia, where domestic and international companies are listed and traded. It plays a crucial role in facilitating capital raising and investment in Australia's financial markets.

Australian Government Bonds: These are debt securities issued by the Australian government to fund government operations and infrastructure projects. Australian Government Bonds are considered safe investments and are used as benchmarks for interest rates and economic sentiment.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Australia, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Australia ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Australia?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Australia exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, d...

This dataset includes various economic indicators such as stock market performance, inflation rates, GDP, interest rates, employment data, and housing index, all of which are crucial for understanding the state of the economy. By analysing this dataset, one can gain insights into the causes and effects of past recessions in the US, which can inform investment decisions and policy-making.

There are 20 columns and 343 rows spanning 1990-04 to 2022-10

The columns are:

1. Price: Price column refers to the S&P 500 lot price over the years. The S&P 500 is a stock market index that measures the performance of 500 large companies listed on stock exchanges in the United States. This variable represents the value of the S&P 500 index from 1980 to present. Industrial Production: This variable measures the output of industrial establishments in the manufacturing, mining, and utilities sectors. It reflects the overall health of the manufacturing industry, which is a key component of the US economy.

2. INDPRO: Industrial production measures the output of the manufacturing, mining, and utility sectors of the economy. It provides insights into the overall health of the economy, as a decline in industrial production can indicate a slowdown in economic activity. This data can be used by policymakers and investors to assess the state of the economy and make informed decisions.

3. CPI: CPI stands for Consumer Price Index, which measures the change in the prices of a basket of goods and services that consumers purchase. CPI inflation represents the rate at which the prices of goods and services in the economy are increasing.

4. Treasure Bill rate (3 month to 30 Years): Treasury bills (T-bills) are short-term debt securities issued by the US government. This variable represents the interest rates on T-bills with maturities ranging from 3 months to 30 years. It reflects the cost of borrowing money for the government and provides an indication of the overall level of interest rates in the economy.

5. GDP: GDP stands for Gross Domestic Product, which is the value of all goods and services produced in a country. This dataset is taking into account only the Nominal GDP values. Nominal GDP represents the total value of goods and services produced in the US economy without accounting for inflation.

6. Rate: The Federal Funds Rate is the interest rate at which depository institutions lend reserve balances to other depository institutions overnight. It is set by the Federal Reserve and is used as a tool to regulate the money supply in the economy.

7. BBK_Index: The BBKI are maintained and produced by the Indiana Business Research Center at the Kelley School of Business at Indiana University. The BBK Coincident and Leading Indexes and Monthly GDP Growth for the U.S. are constructed from a collapsed dynamic factor analysis of a panel of 490 monthly measures of real economic activity and quarterly real GDP growth. The BBK Leading Index is the leading subcomponent of the cycle measured in standard deviation units from trend real GDP growth.

8. Housing Index: This variable represents the value of the housing market in the US. It is calculated based on the prices of homes sold in the market and provides an indication of the overall health of the housing market.

9. Recession binary column: This variable is a binary indicator that takes a value of 1 when the US economy is in a recession and 0 otherwise. It is based on the official business cycle dates provided by the National Bureau of Economic Research.

Top Indian Stocks: Financial Metrics Dataset This dataset offers a comprehensive view of key financial indicators for the top-performing stocks in India. With insights into valuation, profitability, and performance, this dataset is a perfect tool for investors, analysts, and data enthusiasts to explore stock market trends.

📊 Columns Overview 1. S.No. 🔹 Description: The serial number for each stock. 💡 Use: Index for easy row reference, not a financial indicator. 2. Name 🔹 Description: The stock's name or ticker symbol. 💡 Use: Identifies each company for further analysis. 3. CMP (Current Market Price) Rs. 💰 Description: The latest trading price of the stock in Indian Rupees (₹). 💡 Use: Critical for assessing the current market valuation of the stock.

1. P/E (Price-to-Earnings Ratio) 📈 Description: Ratio of the company’s stock price to its earnings per share. 💡 Use: A key indicator to determine if a stock is over- or under-valued.

2. MarCap (Market Capitalization) Rs.Cr. 🏢 Description: The company’s total market value, in crores of Indian Rupees. 💡 Use: Helps categorize companies as large-cap, mid-cap, or small-cap.

3. DivYld (Dividend Yield) % 💸 Description: The dividend income as a percentage of the stock price. 💡 Use: Useful for investors seeking steady income through dividends.

4. NPQtr (Net Profit for the Quarter) Rs.Cr. 📊 Description: The company’s net profit for the latest quarter in crores. 💡 Use: A measure of recent profitability and financial health.

5. QtrProfitVar (Quarterly Profit Variation) % 📉 Description: Percentage change in profit from the previous quarter. 💡 Use: Helps evaluate the company’s growth or decline in profitability.

6. SalesQtr (Quarterly Sales) Rs.Cr. 💼 Description: Total revenue generated by the company during the quarter. 💡 Use: Useful to gauge the business's short-term performance.

7. QtrSalesVar (Quarterly Sales Variation) % 📊 Description: Percentage change in sales compared to the previous quarter. 💡 Use: Highlights revenue growth or contraction over time.

8. ROCE (Return on Capital Employed) % ⚙️ Description: Measures the company’s profitability relative to the capital used. 💡 Use: A higher ROCE shows better efficiency in using capital for profits.

9. PATAnn (Profit After Tax for the Year) Rs.Cr. 📅 Description: Net profit after taxes for the entire year. 💡 Use: Key to understanding long-term profitability and financial performance. Why Use This Dataset? With a variety of financial metrics covering stock performance, this dataset is perfect for:

📅 Time-Series Analysis: Forecast stock price movements using historical data. 🔍 Investment Research: Analyze market trends and evaluate stock performance. 🤖 Algorithmic Trading: Develop machine learning models to create automated trading strategies. 📈 Financial Forecasting: Build predictive models to anticipate stock prices and market shifts. This dataset offers rich financial insights and is a must-have for anyone looking to dive deep into India’s stock market landscape. Explore trends, develop predictive models, and take your financial analytics to the next level! 🔥📊

Dataset: Leading Companies in Market Capitalization https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F14839888%2Fe7df5ac47c282cc6288f211b698233c0%2FAdd%20a%20heading%20(5).png?generation=1716157027189074&alt=media" alt="">

Introduction: This dataset provides comprehensive information on the leading companies globally by market capitalization. It includes various key metrics and identifiers for each company, facilitating detailed analysis and comparisons. This dataset is gathered from companies market capital website. below i have given the details of the dataset and columns after that i have given some information about the use cases of this dataset.

About Dataset Columns: Below is a detailed description of each column in the dataset:

1-Rank: -Description: This column shows the ranking number of the company based on its market capitalization. The rankings are in ascending order, with rank 1 representing the company with the highest market capitalization. -Data Type: Integer -Example Values: 1, 2, 3, ...

2-Company: -Description: This column displays the full name of the company. It helps identify the company being analyzed. -Data Type: String -Example Values: "Apple Inc.", "Microsoft Corporation", "Amazon.com Inc."

3-Stock Symbol: -Description: This column contains the stock symbols (ticker symbols) of the companies, which are used for trading on stock exchanges. This is essential for identifying the company's stock in financial markets. -Data Type: String -Example Values: "AAPL", "MSFT", "AMZN"

4-Market Cap (USD): -Description: This column provides the market capitalization of the company in trillion US dollars. Market capitalization is calculated as the share price times the number of outstanding shares, representing the company's total market value. -Data Type: Float (to handle large values with precision) -Example Values: 2.43, 1.87, 1.76

5-Share Price: -Description: This column contains the current share price of the respective company in US dollars. It represents the price at which a single share of the company is traded on the stock market. -Data Type: Float -Example Values: 145.09, 250.35, 3400.50

6-Company Origin: -Description: This column provides the country name where the company is headquartered. It helps in understanding the geographical distribution of the leading companies. -Data Type: String -Example Values: "United States", "China", "Germany

Use Cases of the Leading Companies in Market Capitalization Dataset

This dataset is a treasure of information for anyone interested in the financial world. Here’s how different people and professionals might use it:

1-Investors and Traders: - Stock Picking: Investors can use the dataset to identify top-performing companies by market cap. This helps them make informed decisions about where to put their money. - Comparative Analysis: Traders can compare the share prices and market caps to find potential investment opportunities and trends.

2-Financial Analysts: -Performance Tracking: Analysts can track the performance of leading companies over time, helping them to forecast future trends and provide investment recommendations. -Sector Analysis: By examining the companies and their origins, analysts can identify which sectors and countries are leading the market.

3-Business Students and Educators: -Case Studies: Students can use the dataset for case studies and projects, analyzing the financial health and market position of global giants. -Learning Tool: Educators can use the data to teach about market capitalization, stock markets, and financial metrics.

4-Economists and Researchers: -Economic Indicators: The dataset can help economists understand the economic impact of leading companies on their respective countries and the global market. -Market Dynamics: Researchers can study the market dynamics and how large companies influence economic trends.

5-Journalists and Media: -Reporting: Journalists can use the data to report on the financial health of major companies, industry trends, and economic forecasts. -Insights: Media can provide insights into the rise and fall of company rankings, helping the public stay informed about market changes.

6-Corporate Strategists: -Benchmarking: Companies can benchmark their performance against the leaders in their industry, identifying areas for improvement. -Strategic Planning: Strategists can use the data to develop long-term plans, aiming to enhance their market position.

7-General Public: -Personal Finance: Individuals interested in personal finance can use the dataset to learn more about the companies behind the brands they use daily. -Educational: For anyone curious about how global markets work, this dataset provides a straightforward way to see which companies are at the top and why.

This dataset provides a comprehensive overview of the Brazilian stock market, covering various sectors and industries from 2018 to 2023. Each row in the dataset represents a specific asset, identified by its ticker on B3 (the Brazilian stock exchange). The dataset includes detailed information such as sector, industry, trading dates, opening price, closing price, trading volume, dividends, dividend yield, and P/E (price/earnings) ratio. Additionally, it contains daily historical price data for the assets over time.

Key Columns:

•  Ticker: The asset’s code on the stock exchange.
•  Sector: The sector in which the asset operates.
•  Industry: The specific industry within the sector.
•  Date: The reference date for the data.
•  Open: The asset’s opening price on the specified date.
•  Price: The asset’s closing price on the specified date.
•  Volume: The trading volume of the asset.
•  Dividend: The amount of dividends paid.
•  Dividend Yield: The dividend yield as a percentage of the price.
•  P/E Ratio: The price-to-earnings ratio.

Applications:

This dataset is ideal for stock performance analysis, sector and industry studies, investment strategy development, and machine learning models focused on market predictions. It can be used by investors, market analysts, researchers, and finance professionals interested in examining the behavior of the Brazilian stock market over five years.

3709 Indian Shares With All Financial Details, you can filter out data as per your requirement.

Date: 31st March 2020

Excel Sheet Contain Following Details & Free to use. 1.Current Price. 2. Price To Earning. 3. Market Capitalization. 4. Dividend Yield. 5. Pledge Percentage. 6. PEG Ratio. 7. Down from 52 Week High. 8. Return on Capital Employed. 9. Return on Capital Employed 3 Year Average. 10. Return on Equity. 11. Price to Book Value. 12. Book Value. 13. Debt to Equity Value. 14. Operating Profit Margin 15. Sales. 16. Promoter Holding. 17. Sales Growth 3 Year. 18. Sales Growth 5 Year. 19. Profit Growth 3 Years. 20. Profit Growth 5 Years. 21. Average Return on Equity 5 Years. 22. Average Return on Equity 3 years. 23. Industry PE.