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.

Comprehensive 27+ years of daily stock market data for Indian indices (SENSEX & NIFTY 50) and all their constituent companies. This dataset includes OHLCV data along with pre-calculated technical indicators, making it perfect for time series analysis, algorithmic trading strategies, and machine learning applications.

Total Records: 400,000+
Companies: 80 stocks (30 SENSEX + 50 NIFTY 50)
Features: 21 columns per record

Use Cases:

Machine Learning & Prediction:

• Stock price forecasting using LSTM, GRU, or Transformers
• Next-day close price prediction
• Multi-stock portfolio prediction
• Market regime detection (bull/bear markets)

Technical Analysis:

• Backtest trading strategies (RSI, MACD, Moving Average crossovers)
• Identify support/resistance levels
• Bollinger Band squeeze patterns
• Golden Cross / Death Cross detection

Statistical Analysis:

-Correlation analysis between stocks - Volatility clustering analysis - Market crash impact studies (2008 financial crisis, 2020 COVID) - Sectoral performance comparison

Portfolio Optimization:

• Modern Portfolio Theory implementation
• Risk-return optimization
• Diversification analysis
• Sharpe ratio calculations

Education:

• Financial markets course projects
• Time series analysis tutorials
• Data science portfolio projects
• Algorithmic trading education

Company List:

SENSEX 30 Companies:

Adani Enterprises, Asian Paints, Axis Bank, Bajaj Finance, Bajaj Finserv, Bharti Airtel, HDFC Bank, HCL Technologies, Hindustan Unilever, ICICI Bank, IndusInd Bank, Infosys, ITC, Kotak Mahindra Bank, Larsen & Toubro, Mahindra & Mahindra, Maruti Suzuki, Nestle India, NTPC, ONGC, Power Grid Corporation, Reliance Industries, State Bank of India, Sun Pharmaceutical, Tata Consultancy Services, Tata Motors, Tata Steel, Tech Mahindra, Titan Company, UltraTech Cement, Wipro

NIFTY 50 Companies:

All SENSEX 30 companies plus: Adani Ports, Apollo Hospitals, Bajaj Auto, Bharat Petroleum, Britannia Industries, Cipla, Coal India, Divi's Laboratories, Dr. Reddy's Laboratories, Eicher Motors, Grasim Industries, Hero MotoCorp, Hindalco Industries, Hindustan Zinc, JSW Steel, LTIMindtree, Shriram Finance, Tata Consumer Products, Trent

Ticker Conventions: - .BO suffix = Bombay Stock Exchange (BSE) - .NS suffix = National Stock Exchange (NSE)

Citation Policy:

If you use this dataset in your research, please cite:

Indian Stock Market Historical Data - SENSEX & NIFTY 50 (1997-2024)
Kaggle Dataset, November 2024
URL: https://www.kaggle.com/datasets/rockyt07/stock-market-sensex-nifty-all-time-dataset

A daily data ranging from January 2014 until December 2018 is employed. The period between January, 1, 2014 until November 7, 2016 refers to the pre-election period. The period ranging from November 8, 2016, until December, 31 2018 defines the post-election period. Four U.S stock price indices are retrieved from DataStream: The standard and Poor’s 500 index (S&P 500) covers the performance of 500 largest capitalization stocks. The Dow Jones Industrial Average (DJIA) index tracks the prices of the top 30 US companies. The NASDAQ 100 measures the performance of the 100 largest non-financial stocks traded on NASDAQ. The Russell 2000 index covers the performance of 2.000 lowest capitalization stocks. A daily political risk index is calculated for each period using Google trends and the principal component analysis.

Sources:

German Central Bank (ed.), 1975: Deutsches Geld- und Bankwesen in Zahlen 1876 – 1975. (German monetary system and banking system in numbers 1876 – 1975) German Central Bank (ed.), different years: monthly reports of the German Central Bank, statistical part, interest rates German Central Bank (ed.), different years: Supplementary statistical booklets for the monthly reports of the German Central Bank 1959 – 1992, security statistics Reich Statistical Office (ed.), different years: Statistical yearbook of the German empire Statistical Office (ed.), 1985: Geld und Kredit. Index der Aktienkurse (Money and Credit. Index of share prices) – Lange Reihe; Fachserie 9, Reihe 2. Statistical Office (ed.), 1987: Entwicklung der Nahrungsmittelpreise von 1800 – 1880 in Deutschland. (Development of food prices in Germany 1800 – 1880) Statistical Office (ed.), 1987: Entwicklung der Verbraucherpreise (Development of consumer prices) seit 1881 in Deutschland. (Development of consumer prices since 1881 in Germany) Statistical Office (ed.), different years: Fachserie 17, Reihe 7, Preisindex für die Lebenshaltung (price index for costs of living) Donner, 1934: Kursbildung am Aktienmarkt; Grundlagen zur Konjunkturbeobachtung an den Effektenmärkten. (Prices on the stock market; groundwork for observation of economic cycles on the stock market) Homburger, 1905: Die Entwicklung des Zinsfusses in Deutschland von 1870 – 1903. (Development of the interest flow in Germany, 1870 – 1903) Voye, 1902: Über die Höhe der verschiedenen Zinsarten und ihre wechselseitige Abhängigkeit.(On the values of different types of interests and their interdependence).

Last Update - 9th FEB 2025

Disclaimer!!! Data uploaded here are collected from the internet and some google drive. The sole purposes of uploading these data are to provide this Kaggle community with a good source of data for analysis and research. I don't own these datasets and am also not responsible for them legally by any means. I am not charging anything (either money or any favor) for this dataset. RESEARCH PURPOSE ONLY

THIS IS THE LARGEST DATASET ON NIFTY 100 STOCKS WITH EACH MINUTES AND DAILY DATA (2015 to 2025)

The NIFTY 50 is a benchmark Indian stock market index that represents the weighted average of 50 of the largest Indian companies listed on the National Stock Exchange. It is one of the two main stock indices used in India, the other being the BSE SENSEX.

Nifty 50 is owned and managed by NSE Indices (previously known as India Index Services & Products Limited), which is a wholly-owned subsidiary of the NSE Strategic Investment Corporation Limited.NSE Indices had a marketing and licensing agreement with Standard & Poor's for co-branding equity indices until 2013. The Nifty 50 index was launched on 22 April 1996, and is one of the many stock indices of Nifty.

The NIFTY 50 index is a free-float market capitalization-weighted index. The index was initially calculated on a full market capitalization methodology. On 26 June 2009, the computation was changed to a free-float methodology. The base period for the NIFTY 50 index is 3 November 1995, which marked the completion of one year of operations of the National Stock Exchange Equity Market Segment. The base value of the index has been set at 1000 and a base capital of ₹ 2.06 trillion.

Content This dataset contains Nifty 100 historical daily prices. The historical data are retrieved from the NSE India website. Each stock in this Nifty 500 and are of 1 minute itraday data.

Every dataset contains the following fields. Open - Open price of the stock High - High price of the stock Low - Low price of the stock Close - Close price of the stock Volume - Volume traded of the stock in this time frame

Inspiration

• Data is uploaded for Research and Educational purposes.
• The data scientists and researchers can download and can build EDA, find Correlations, and perform Regression analysis on it.
• Quant researchers can build strategies and backtest their strategies with this dataset.

Stock Names

| ACC | ADANIENT | ADANIGREEN | ADANIPORTS | AMBUJACEM | | -- | -- | -- | -- | -- | | APOLLOHOSP | ASIANPAINT | AUROPHARMA | AXISBANK | BAJAJ-AUTO | | BAJAJFINSV | BAJAJHLDNG | BAJFINANCE | BANDHANBNK | BANKBARODA | | BERGEPAINT | BHARTIARTL | BIOCON | BOSCHLTD | BPCL | | BRITANNIA | CADILAHC | CHOLAFIN | CIPLA | COALINDIA | | COLPAL | DABUR | DIVISLAB | DLF | DMART | | DRREDDY | EICHERMOT | GAIL | GLAND | GODREJCP | | GRASIM | HAVELLS | HCLTECH | HDFC | HDFCAMC | | HDFCBANK | HDFCLIFE | HEROMOTOCO | HINDALCO | HINDPETRO | | HINDUNILVR | ICICIBANK | ICICIGI | ICICIPRULI | IGL | | INDIGO | INDUSINDBK | INDUSTOWER | INFY | IOC | | ITC | JINDALSTEL | JSWSTEEL | JUBLFOOD | KOTAKBANK | | LICI | LT | LTI | LUPIN | M&M | | MARICO | MARUTI | MCDOWELL-N | MUTHOOTFIN | NAUKRI | | NESTLEIND | NIFTY 50 | NIFTY BANK | NMDC | NTPC | | ONGC | PEL | PGHH | PIDILITIND | PIIND | | PNB | POWERGRID | RELIANCE | SAIL | SBICARD | | SBILIFE | SBIN | SHREECEM | SIEMENS | SUNPHARMA | | TATACONSUM | TATAMOTORS | TATASTEEL | TCS | TECHM | | TITAN | TORNTPHARM | ULTRACEMCO | UPL | VEDL | | WIPRO | YESBANK | | | |

This table contains figures on capital stock. The capital stock of different branches and sectors is presented here. The capital stock is broken down by type of capital good.

Figures of the sectors households and non-profit institutions serving households (NPISH) are from reporting year 2013 onwards no longer separately published. Only their aggregate will be published. The reason for this change is that reliable estimates for NPISH for recent years are no longer available.

Data available from: 1995 Status of the figures: The figures for the most recent reporting year 2016 are provisional. The status of the figures for 2015 is final.

Changes as from 13 July 2017: Provisional figures on the reporting year 2016 have been added. Using old data has led to incorrect figures for investments of the households and NPISHs for the reporting years 2001-2010. Adjusting for these errors results in different figures on investments and the statistical discrepancies. Smaller differences due to rounding occur for the capital stock opening and closing balance sheet, the depreciation and the revaluation. Data on the years 1995-2000 have been added.

Changes as from 25 October 2016: A number of corrections have been applied as a result of mistakes in the calculations for the years 2002, 2003, 2004, 2009 and 2015. These mistakes did not result in any changes in the totals for the closing balance sheet, but led to incorrect aggregations of sectors/branches or type of capital good.

Furthermore the calculation method of the volume indices have been harmonised for the capital stock and non-financial balance sheets. Moreover, the volume index will now be calculated on the basis of rounded figures. Because of these changes in method a maximum difference of 109.5 percent points occurs for series of less than 100 mln. A maximum difference of 2.1 percent points occurs for series larger than 100 mln. Volume indices of series which contain 0 mln of capital stock every year are set at 100, rather than hidden.

When will new figures be published? Provisional data are published 6 months after the end of the reporting year. Final data are released 18 months after the end of the reporting year. Since the end of June 2016 the release and revision policy of the national accounts have been changed. References to additional information about these changes can be found in section 3.

Snapshot img

Mutual Funds Market Size 2025-2029

The mutual funds market size is valued to increase USD 85.5 trillion, at a CAGR of 9.9% from 2024 to 2029. Market liquidity will drive the mutual funds market.

Major Market Trends & Insights

North America dominated the market and accounted for a 52% growth during the forecast period.
By Type - Stock funds segment was valued at USD 50.80 trillion in 2023
By Distribution Channel - Advice channel segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 151.38 trillion
Market Future Opportunities: USD 85.50 trillion
CAGR : 9.9%
North America: Largest market in 2023

Market Summary

The market represents a dynamic and ever-evolving financial landscape, characterized by continuous growth and innovation. With core technologies such as artificial intelligence and machine learning increasingly shaping investment strategies, mutual funds have become a preferred choice for individual and institutional investors alike. According to recent reports, mutual fund assets under management globally reached an impressive 61.8 trillion USD as of 2021, underscoring the market's substantial size and influence. However, the market is not without challenges. Transaction risks, regulatory compliance, and competition from alternative investment vehicles remain significant hurdles.
Despite these challenges, opportunities abound, particularly in developing nations where mutual fund adoption rates have been on the rise. For instance, mutual fund assets in Asia Pacific grew by 15.3% in 2020, outpacing the global average. As market liquidity continues to improve and regulatory frameworks evolve, the market is poised for further expansion and transformation.

What will be the Size of the Mutual Funds Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Mutual Funds Market Segmented and what are the key trends of market segmentation?

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

Type

  Stock funds
  Bond funds
  Money market funds
  Hybrid funds


Distribution Channel

  Advice channel
  Retirement plan channel
  Institutional channel
  Direct channel
  Supermarket channel


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    Spain
    UK


  APAC

    Australia
    China
    India


  Rest of World (ROW)

By Type Insights

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

Mutual funds, specifically those investing in stocks, constitute a significant segment of the financial market. These funds exhibit diverse characteristics, catering to various investor preferences. For instance, growth funds prioritize stocks with high growth potential, while income funds focus on securities yielding regular dividends. Index funds mirror a specific market index, such as the S&P 500, and sector funds zero in on a particular industry sector. Share classes within mutual funds differ based on the share of investment. For example, large-cap funds allocate a minimum of 80% of their assets to large-cap companies, which represent the top 100 firms in terms of market capitalization.

Investors can opt for dividend reinvestment plans, enabling them to reinvest their dividends to maximize returns. Tax-efficient investing strategies, such as tax-loss harvesting, help minimize tax liabilities. Bond fund yields and currency exchange risk are essential considerations for investors in bond funds. Risk management strategies, including diversification and asset allocation models, play a crucial role in mitigating potential losses. Fund manager expertise and regulatory compliance frameworks are essential factors for investors. Hedge fund strategies, financial statement audits, actively managed funds, and passive investment strategies all contribute to the evolving mutual fund landscape. Expense ratios, asset allocation models, capital gains distributions, and portfolio rebalancing techniques are essential metrics for evaluating mutual fund performance.

Inflation-adjusted returns and equity fund volatility are crucial for long-term investment planning. Alternative investment funds and exchange-traded funds (ETFs) offer additional investment opportunities, with global diversification benefits and passive investment strategies gaining popularity. Nav calculation methods and passive investment strategies further broaden the scope of mutual fund investments. According to recent studies, stock mutual fund adoption stands at 35%, with expectations of a 21% increase in industry participation over the next five years. Meanwhil

This table contains figures on non-financial balance sheets. The balance sheets show the market value of non-financial assets. Changes in the value of non-financial assets are also presented in this table. These changes are, for example, the result of price changes or the result of purchases minus sales of non-financial assets. Non-financial balance sheets are part of the National Accounts. The balance sheets are presented by different types of assets for the economy as a whole and for the different institutional sectors in the Dutch economy. Figures of the sectors households and non-profit institutions serving households (NPISH) are from reporting year 2013 onwards no longer separately published. Only their aggregate will be published. The reason for this change is that reliable estimates for NPISH for recent years are no longer available. Data available from: 1995 Status of the figures: The figures for the most recent reporting year 2016 are provisional. The status of the figures for other years is final. Changes as from 11 August 2017: Provisional figures on the reporting year 2016 have been added. - The volume-indices of the inventories of the general government were mistakenly displayed with a dot (.). This has now been corrected and the volume-indices for the base year are now set to 100. - Due to data on the reporting years 1995-2001 becoming available, this table has been expanded. The years 1995-2000 are completely new, for the year 2001 only the closing balance sheet was displayed. Now figures on opening balance sheet, revaluation, capital formation, other changes in volume and statistical discrepancy are added for this reporting year. Changes as from 25 October 2016: A number of corrections have been applied as a result of mistakes in the calculations for the years 2002, 2003, 2004, 2009, 2011, 2012 and 2015. These mistakes did not result in any changes in the totals for the closing balance sheet, but led to incorrect aggregations of sectors or type of non-financial asset. Furthermore the calculation method of the volume indices have been harmonised for the capital stock and non-financial balance sheets. Moreover, the volume index will now be calculated on the basis of rounded figures. Because of these changes in method a maximum difference of 85.6 percentage points occurs for series of less than 100 mln. A maximum difference of 16.2 percentage points occurs for series larger than 100 mln. Volume indices of series which contain 0 mln of non-financial assets every year are set at 100, rather than hidden. The calculation method of consumer durables has been changed as well, to account for the purchase of lease cars by consumers. Correction as of 5 February 2016: As a result of a mistake in the calculation the opening balance sheet of 2006 is not equal to the closing balance sheet of 2005. The mistake has been corrected. Correction as of 4 November 2015: The volume-indices of total of non-financial assets 2010-2014 have been changed because consumer durables do not belong to this category and were previously included. When will new figures be published? Provisional data are published 6 months after the end of the reporting year. Final data are released 18 months after the end of the reporting year. Since the end of June 2016 the release and revision policy of the national accounts have been changed. References to additional information about these changes can be found in section 3.

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.

Liberty Formula 1 (Series C) assigned short-term Ba1 & long-term B1 forecasted stock rating.

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

This table contains figures on non-financial balance sheets. The balance sheets show the market value of non-financial assets. Changes in the value of non-financial assets are also presented in this table. These changes are, for example, the result of price changes or the result of purchases minus sales of non-financial assets.

Non-financial balance sheets are part of the National Accounts. The balance sheets are presented by different types of assets for the economy as a whole and for the different institutional sectors in the Dutch economy.

Figures of the sectors households and non-profit institutions serving households (NPISH) are from reporting year 2013 onwards no longer separately published. Only their aggregate will be published. The reason for this change is that reliable estimates for NPISH for recent years are no longer available.

Data available from: 1995

Status of the figures: The figures for the most recent reporting year 2016 are provisional. The status of the figures for other years is final.

Changes as from 11 August 2017: Provisional figures on the reporting year 2016 have been added. - The volume-indices of the inventories of the general government were mistakenly displayed with a dot (.). This has now been corrected and the volume-indices for the base year are now set to 100. - Due to data on the reporting years 1995-2001 becoming available, this table has been expanded. The years 1995-2000 are completely new, for the year 2001 only the closing balance sheet was displayed. Now figures on opening balance sheet, revaluation, capital formation, other changes in volume and statistical discrepancy are added for this reporting year.

Changes as from 25 October 2016: A number of corrections have been applied as a result of mistakes in the calculations for the years 2002, 2003, 2004, 2009, 2011, 2012 and 2015. These mistakes did not result in any changes in the totals for the closing balance sheet, but led to incorrect aggregations of sectors or type of non-financial asset.

Furthermore the calculation method of the volume indices have been harmonised for the capital stock and non-financial balance sheets. Moreover, the volume index will now be calculated on the basis of rounded figures. Because of these changes in method a maximum difference of 85.6 percentage points occurs for series of less than 100 mln. A maximum difference of 16.2 percentage points occurs for series larger than 100 mln. Volume indices of series which contain 0 mln of non-financial assets every year are set at 100, rather than hidden.

The calculation method of consumer durables has been changed as well, to account for the purchase of lease cars by consumers.

Correction as of 5 February 2016: As a result of a mistake in the calculation the opening balance sheet of 2006 is not equal to the closing balance sheet of 2005. The mistake has been corrected.

Correction as of 4 November 2015: The volume-indices of total of non-financial assets 2010-2014 have been changed because consumer durables do not belong to this category and were previously included.

When will new figures be published? Provisional data are published 6 months after the end of the reporting year. Final data are released 18 months after the end of the reporting year. Since the end of June 2016 the release and revision policy of the national accounts have been changed. References to additional information about these changes can be found in section 3.

The data for this study consist of the daily opening, highest, lowest and closing prices of 10 industry indices, including the energy industry index (EII), raw material industry index (RMII), industrial sector index (ISI), optional consumer industry index (OCII), major consumer industry index (MCII), medical and health industry index (MHII), financial real estate industry index (FEII), information technology industry index (ITII), telecom business industry index (TBII) and utilities industry index (UII) of the Shanghai stock exchange (SSE). The Shanghai Stock Exchange Industry Index can reflect the overall performance of the stocks of companies in different sectors of the Shanghai stock market and provide a target for the development of indexed investment products, especially ETF. The base period was December 31, 2013 with a base point of 1000, which was started in January 9, 2009. The sample period is January 9, 2009 to June 29, 2018 and includes a total of 2303 groups of daily data. These data sets were extracted from the Wind information database. The rates of returns are calculated from yesterday’s and today’s closing prices in the form of a logarithmic expression. The realized range fluctuation rates are calculated using the range estimation method based on the stochastic volatility model.

The export market share is calculated by dividing the exports of the country by the total exports of the region/world (expressed as percentage in the database). The indicator measures the degree of importance of a country within the total exports of the region/world. For the calculation at current prices, the market share refers to the world trade (world export market share). Data on the values of exports of goods and services are compiled as part of the Balance of Payments of each country. The indicator is calculated as 1 years % change. A country might lose shares of export market not only if exports decline but most importantly if its exports do not grow at the same rate of world exports and its relative position at the global level deteriorates. Source of total world data used as denominator: International Monetary Fund (IMF).

The export market share is calculated by dividing the exports of the country by the total exports of the region/world (expressed as percentage in the database). The indicator measures the degree of importance of a country within the total exports of the region/world. For the calculation at current prices, the market share refers to the world trade (world export market share). Data on the values of exports of goods and services are compiled as part of the Balance of Payments of each country. The indicator is calculated as % of world total. Source of total world data used as denominator: International Monetary Fund (IMF).

This document was prepared to outline the methods used and citations for the calculation of the map layers considered to be the City of Vancouver's Equity Index, created by Kobel Solutions.

The dataset contains a total of 25,161 rows, each row representing the stock market data for a specific company on a given date. The information collected through web scraping from www.nasdaq.com includes the stock prices and trading volumes for the companies listed, such as Apple, Starbucks, Microsoft, Cisco Systems, Qualcomm, Meta, Amazon.com, Tesla, Advanced Micro Devices, and Netflix.

Data Analysis Tasks:

1) Exploratory Data Analysis (EDA): Analyze the distribution of stock prices and volumes for each company over time. Visualize trends, seasonality, and patterns in the stock market data using line charts, bar plots, and heatmaps.

2)Correlation Analysis: Investigate the correlations between the closing prices of different companies to identify potential relationships. Calculate correlation coefficients and visualize correlation matrices.

3)Top Performers Identification: Identify the top-performing companies based on their stock price growth and trading volumes over a specific time period.

4)Market Sentiment Analysis: Perform sentiment analysis using Natural Language Processing (NLP) techniques on news headlines related to each company. Determine whether positive or negative news impacts the stock prices and volumes.

5)Volatility Analysis: Calculate the volatility of each company's stock prices using metrics like Standard Deviation or Bollinger Bands. Analyze how volatile stocks are in comparison to others.

Machine Learning Tasks:

1)Stock Price Prediction: Use time-series forecasting models like ARIMA, SARIMA, or Prophet to predict future stock prices for a particular company. Evaluate the models' performance using metrics like Mean Squared Error (MSE) or Root Mean Squared Error (RMSE).

2)Classification of Stock Movements: Create a binary classification model to predict whether a stock will rise or fall on the next trading day. Utilize features like historical price changes, volumes, and technical indicators for the predictions. Implement classifiers such as Logistic Regression, Random Forest, or Support Vector Machines (SVM).

3)Clustering Analysis: Cluster companies based on their historical stock performance using unsupervised learning algorithms like K-means clustering. Explore if companies with similar stock price patterns belong to specific industry sectors.

4)Anomaly Detection: Detect anomalies in stock prices or trading volumes that deviate significantly from the historical trends. Use techniques like Isolation Forest or One-Class SVM for anomaly detection.

5)Reinforcement Learning for Portfolio Optimization: Formulate the stock market data as a reinforcement learning problem to optimize a portfolio's performance. Apply algorithms like Q-Learning or Deep Q-Networks (DQN) to learn the optimal trading strategy.

The dataset provided on Kaggle, titled "Stock Market Stars: Historical Data of Top 10 Companies," is intended for learning purposes only. The data has been gathered from public sources, specifically from web scraping www.nasdaq.com, and is presented in good faith to facilitate educational and research endeavors related to stock market analysis and data science.

It is essential to acknowledge that while we have taken reasonable measures to ensure the accuracy and reliability of the data, we do not guarantee its completeness or correctness. The information provided in this dataset may contain errors, inaccuracies, or omissions. Users are advised to use this dataset at their own risk and are responsible for verifying the data's integrity for their specific applications.

This dataset is not intended for any commercial or legal use, and any reliance on the data for financial or investment decisions is not recommended. We disclaim any responsibility or liability for any damages, losses, or consequences arising from the use of this dataset.

By accessing and utilizing this dataset on Kaggle, you agree to abide by these terms and conditions and understand that it is solely intended for educational and research purposes.

Please note that the dataset's contents, including the stock market data and company names, are subject to copyright and other proprietary rights of the respective sources. Users are advised to adhere to all applicable laws and regulations related to data usage, intellectual property, and any other relevant legal obligations.

In summary, this dataset is provided "as is" for learning purposes, without any warranties or guarantees, and users should exercise due diligence and judgment when using the data for any purpose.

The indicator is calculated by subtracting the world exports volume growth rate from the country exports volume growth rate. Eurostat's National accounts volumes for exports (as for all transactions concerning goods and services) are estimated in previous year prices to eliminate the influence of inflation. The volume growth rate is always calculated by comparing the year Y volume in previous year prices with the year Y-1 figure in current prices. The indicator is expressed as percentage change comparing year Y with year Y-1.

Data sources: Eurostat, online data code: nama_10_gdp (country's exports of goods and services) and IMF, World Economic Outlook (WEO) – world exports of goods and services.

This dataset contains quarterly, weekly, and monthly return data for the Nifty 50 index, one of India's leading stock market indices. The Nifty 50 index represents the weighted average of 50 of the largest and most liquid Indian companies listed on the National Stock Exchange (NSE).

The returns are calculated based on the price movements of the constituent stocks within the Nifty 50 index over specific time intervals, including quarterly, weekly, and monthly periods. These returns provide valuable insights into the performance of the Indian stock market and can be used by investors, analysts, and researchers for various financial analyses, including trend analysis, volatility assessment, and portfolio optimization.

The export market share is calculated by dividing the exports of the country by the total exports of the region/world. The indicator measures the degree of importance of a country within the total exports of the region/world. For the calculation at current prices, the market share refers to the world trade (world export market share). Data on the values of exports of goods and services are compiled as part of the Balance of Payments of each country. To capture the structural losses in competitiveness that can accumulate over longer time periods, the indicator is calculated as 3 years % change - comparing year Y with year Y-3. A country might lose shares of export market not only if exports decline but most importantly if its exports do not grow at the same rate of world exports and its relative position at the global level deteriorates. The MIP auxiliary indicator is the percentage change of export market shares (of goods and services) over three years. The formula is: [[(EXPc,t/EXPworld,t)-(EXPc,t-3/EXPworld,t-3)]/(EXPc,t-3/EXPworld,t-3)]*100 Source of total world data used as denominator: International Monetary Fund (IMF).

Overview of NSE - Nifty 50 data

The NIFTY 50 is a benchmark Indian stock market index that represents the weighted average of 50 of the largest Indian companies listed on the National Stock Exchange. It is one of the two main stock indices used in India, the other being the BSE SENSEX.

Nifty 50 is owned and managed by NSE Indices (previously known as India Index Services & Products Limited), which is a wholly-owned subsidiary of the NSE Strategic Investment Corporation Limited.NSE Indices had a marketing and licensing agreement with Standard & Poor's for co-branding equity indices until 2013. The Nifty 50 index was launched on 22 April 1996, and is one of the many stock indices of Nifty.

The NIFTY 50 index is a free-float market capitalization-weighted index. The index was initially calculated on a full market capitalization methodology. On 26 June 2009, the computation was changed to a free-float methodology. The base period for the NIFTY 50 index is 3 November 1995, which marked the completion of one year of operations of the National Stock Exchange Equity Market Segment. The base value of the index has been set at 1000 and a base capital of ₹ 2.06 trillion.

Content

This dataset contains Nifty 50 historical daily prices. The historical data are retrieved from the NSE India website. Along with OHLC (Open, High, Low and Close price), many technical indicator data are calculated and published here. A list of all technical indicators is given below with a description.

"Nifty 50 data" contains the below columns: 1. Date - Trading date 2. Open - Day's open price 3. High - Day's high price 4. Low - Day's low price 5. Close - Day's close price 6. sma5 7. sma10 8. sma15 9. sma20 10. ema5 11. ema10 12. ema15 13. ema20 15. upperband 15. middleband 16. lowerband 17. HT_TRENDLINE 18. KAMA10 19. KAMA20 20. KAMA30 21. SAR 22. TRIMA5 23. TRIMA10 24. TRIMA20 25. ADX5 26. ADX10 27. ADX20 28. APO 29. CCI5 30. CCI10 31. CCI15 32. macd510 33. macd520 34. macd1020 35. macd1520 36. macd1226 37. MOM10 38. MOM15 39. MOM20 40. ROC5 41. ROC10 42. ROC20 43. PPO 44. RSI14 45. RSI8 46. slowk 47. slowd 48. fastk 49. fastd 50. fastksr 51. fastdsr 52. ULTOSC 53. WILLR 54. ATR 55. Trange 56. TYPPRICE 57. HT_DCPERIOD 58. BETA

Stock prices for various companies are obtained from Google Finance through the utilization of the googlefinance() function, and are stored in an .xlsx file format. The stock data is classified and categorized into individual sheets, which correspond to a specific company. The table contains data for each day from the beginning of data collection up to March 2023, including the opening, high, low, and closing prices for the stock, as well as the volume of trades. The prices are denominated in the local currency of the respective country. Drive Stocks file link: https://docs.google.com/spreadsheets/d/1ElCXYXv-NjAmMKy7fQ1bjI05q1xij5hZ2DCLrJs0A5w/edit?usp=share_link

Alongside the stock data, two other files are used: the Inflation consumer prices (annual %) and the Wholesale price index (2010 = 100).

• The Wholesale price index is a measure of the average price of a basket of goods and services in a given economy, including both agricultural and industrial goods at various stages of production and distribution, and may also include import duties. The Laspeyres formula is typically used to calculate the index.

• The Inflation consumer prices (annual %) file measures the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services. The basket may be fixed or changed at specified intervals, such as yearly. The Laspeyres formula is typically used to calculate the index. Both of these files provide valuable context for understanding the performance of the stock market and the broader economic conditions that may be affecting it.

Wholesale price index and Inflation consumer prices are uncleared on propose. The cleaned version of the financial data is also included.