View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

Analysis of ‘NIFTY-50 Stocks Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/iamsouravbanerjee/nifty50-stocks-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

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 has shaped up to be the largest single financial product in India, with an ecosystem consisting of exchange-traded funds (onshore and offshore), exchange-traded options at NSE, and futures and options abroad at the SGX. NIFTY 50 is the world's most actively traded contract. WFE, IOM, and FIA surveys endorse NSE's leadership position.

The NIFTY 50 index covers 13 sectors (as of 30 April 2021) of the Indian economy and offers investment managers exposure to the Indian market in one portfolio. Between 2008 & 2012, the NIFTY 50 index's share of NSE's market capitalization fell from 65% to 29% due to the rise of sectoral indices like NIFTY Bank, NIFTY IT, NIFTY Pharma, NIFTY SERV SECTOR, NIFTY Next 50, etc. The NIFTY 50 Index gives a weightage of 39.47% to financial services, 15.31% to Energy, 13.01% to IT, 12.38% to consumer goods, 6.11% to Automobiles a and 0% to the agricultural sector.

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

In this Dataset, we have records of all the NIFTY-50 stocks along with various parameters.

Important Note

• % change is calculated with respect to adjusted price on ex-date for Dividend, Bonus, Rights & Face Value Split.
• 52 weeks high & 52 week low prices are adjusted for Bonus, Split & Rights Corporate actions.
• 365 days % Change and 30 days % Change values are adjusted With respect to corporate actions.

Acknowledgements

For more, you can visit the website of the National Stock Exchange of India Limited (NSE): https://www1.nseindia.com/

--- Original source retains full ownership of the source dataset ---

While the global coronavirus (COVID-19) pandemic caused all major stock market indices to fall sharply in March 2020, both the extent of the decline at this time, and the shape of the subsequent recovery, have varied greatly. For example, on March 15, 2020, major European markets and traditional stocks in the United States had shed around ** percent of their value compared to January *, 2020. However, Asian markets and the NASDAQ Composite Index only shed around ** to ** percent of their value. A similar story can be seen with the post-coronavirus recovery. As of November 14, 2021 the NASDAQ composite index value was around ** percent higher than in January 2020, while most other markets were only between ** and ** percent higher. Why did the NASDAQ recover the quickest? Based in New York City, the NASDAQ is famously considered a proxy for the technology industry as many of the world’s largest technology industries choose to list there. And it just so happens that technology was the sector to perform the best during the coronavirus pandemic. Accordingly, many of the largest companies who benefitted the most from the pandemic such as Amazon, PayPal and Netflix, are listed on the NADSAQ, helping it to recover the fastest of the major stock exchanges worldwide. Which markets suffered the most? The energy sector was the worst hit by the global COVID-19 pandemic. In particular, oil companies share prices suffered large declines over 2020 as demand for oil plummeted while workers found themselves no longer needing to commute, and the tourism industry ground to a halt. In addition, overall share prices in two major stock exchanges – the London Stock Exchange (as represented by the FTSE 100 index) and Hong Kong (as represented by the Hang Seng index) – have notably recovered slower than other major exchanges. However, in both these, the underlying issue behind the slower recovery likely has more to do with political events unrelated to the coronavirus than it does with the pandemic – namely Brexit and general political unrest, respectively.

New York Stock Exchange: Index: S&P Consumer Staples Select Sector Index data was reported at 826.910 NA in Apr 2025. This records an increase from the previous number of 825.980 NA for Mar 2025. New York Stock Exchange: Index: S&P Consumer Staples Select Sector Index data is updated monthly, averaging 581.670 NA from Aug 2013 (Median) to Apr 2025, with 141 observations. The data reached an all-time high of 840.110 NA in Sep 2024 and a record low of 395.070 NA in Aug 2013. New York Stock Exchange: Index: S&P Consumer Staples Select Sector Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: New York Stock Exchange: S&P: Monthly.

India's main stock market index, the SENSEX, fell to 80600 points on August 1, 2025, losing 0.72% from the previous session. Over the past month, the index has declined 3.37% and is down 0.47% 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 August of 2025.

United States New York Stock Exchange: Index: S&P Communication Services Select Sector Index data was reported at 499.250 NA in Apr 2025. This records a decrease from the previous number of 504.840 NA for Mar 2025. United States New York Stock Exchange: Index: S&P Communication Services Select Sector Index data is updated monthly, averaging 256.660 NA from Aug 2013 (Median) to Apr 2025, with 141 observations. The data reached an all-time high of 535.040 NA in Jan 2025 and a record low of 128.980 NA in Aug 2013. United States New York Stock Exchange: Index: S&P Communication Services Select Sector Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: New York Stock Exchange: S&P: Monthly.

China's main stock market index, the SHANGHAI, fell to 3560 points on August 1, 2025, losing 0.37% from the previous session. Over the past month, the index has climbed 3.04% and is up 22.53% 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 August of 2025.

Graph and download economic data for Total Quantity Indexes for Real GDP for Sioux Falls, SD (MSA) (QGMP43620) from 2001 to 2023 about Sioux Falls, quantity index, SD, real, industry, GDP, and USA.

Japan JP: S&P Global Equity Indices: Annual % Change data was reported at 19.099 % in 2017. This records an increase from the previous number of 0.424 % for 2016. Japan JP: S&P Global Equity Indices: Annual % Change data is updated yearly, averaging 4.737 % from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 56.720 % in 2013 and a record low of -34.861 % in 2000. Japan JP: S&P Global Equity Indices: Annual % Change data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Japan – Table JP.World Bank.WDI: Financial Sector. S&P Global Equity Indices measure the U.S. dollar price change in the stock markets covered by the S&P/IFCI and S&P/Frontier BMI country indices.; ; Standard & Poor's, Global Stock Markets Factbook and supplemental S&P data.; ;

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.

Tech Sector Poised for Moderate Growth, Analysts Predict for Dow Jones U.S. Technology Index.

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

Total Wage Index, Private Sectors & Public Sector by Region according to Institut Maklumat & Analisis Pasaran Buruh (ILMIA) No. of Views : 45

In general, the stock prices of the same industry have a similar trend, but those of different industries do not. When investing in stocks of different industries, one should select the optimal model from lots of trading models for each industry because any model may not be suitable for capturing the stock trends of all industries. However, the study has not been carried out at present. In this paper, firstly we select 424 S&P 500 index component stocks (SPICS) and 185 CSI 300 index component stocks (CSICS) as the research objects from 2010 to 2017, divide them into 9 industries such as finance and energy respectively. Secondly, we apply 12 widely used machine learning algorithms to generate stock trading signals in different industries and execute the back-testing based on the trading signals. Thirdly, we use a non-parametric statistical test to evaluate whether there are significant differences among the trading performance evaluation indicators (PEI) of different models in the same industry. Finally, we propose a series of rules to select the optimal models for stock investment of every industry. The analytical results on SPICS and CSICS show that we can find the optimal trading models for each industry based on the statistical tests and the rules. Most importantly, the PEI of the best algorithms can be significantly better than that of the benchmark index and “Buy and Hold” strategy. Therefore, the algorithms can be used for making profits from industry stock trading.

Contains a range of producer price indexes. Firstly, economy-wide indexes are presented within a Stage of Production (SOP) framework, followed by a set of partial, stand-alone measures relating to specific industry sectors of the economy (selected manufacturing, construction, mining and service industries).

Turkey TR: S&P Global Equity Indices: Annual % Change data was reported at 33.787 % in 2017. This records an increase from the previous number of -9.240 % for 2016. Turkey TR: S&P Global Equity Indices: Annual % Change data is updated yearly, averaging 17.888 % from Dec 1996 (Median) to 2017, with 22 observations. The data reached an all-time high of 254.500 % in 1999 and a record low of -62.401 % in 2008. Turkey TR: S&P Global Equity Indices: Annual % Change data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Turkey – Table TR.World Bank.WDI: Financial Sector. S&P Global Equity Indices measure the U.S. dollar price change in the stock markets covered by the S&P/IFCI and S&P/Frontier BMI country indices.; ; Standard & Poor's, Global Stock Markets Factbook and supplemental S&P data.; ;

🇸🇰 슬로바키아

LV: S&P Global Equity Indices: Annual % Change data was reported at 40.609 % in 2017. This records an increase from the previous number of 1.811 % for 2016. LV: S&P Global Equity Indices: Annual % Change data is updated yearly, averaging 5.604 % from Dec 1998 (Median) to 2017, with 20 observations. The data reached an all-time high of 62.590 % in 2003 and a record low of -67.400 % in 1998. LV: S&P Global Equity Indices: Annual % Change data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Latvia – Table LV.World Bank.WDI: Financial Sector. S&P Global Equity Indices measure the U.S. dollar price change in the stock markets covered by the S&P/IFCI and S&P/Frontier BMI country indices.; ; Standard & Poor's, Global Stock Markets Factbook and supplemental S&P data.; ;

Romania RO: S&P Global Equity Indices: Annual % Change data was reported at 22.127 % in 2017. This records an increase from the previous number of -1.115 % for 2016. Romania RO: S&P Global Equity Indices: Annual % Change data is updated yearly, averaging 4.343 % from Dec 1998 (Median) to 2017, with 20 observations. The data reached an all-time high of 99.300 % in 2004 and a record low of -72.177 % in 2008. Romania RO: S&P Global Equity Indices: Annual % Change data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Romania – Table RO.World Bank.WDI: Financial Sector. S&P Global Equity Indices measure the U.S. dollar price change in the stock markets covered by the S&P/IFCI and S&P/Frontier BMI country indices.; ; Standard & Poor's, Global Stock Markets Factbook and supplemental S&P data.; ;

Saudi Arabia SA: S&P Global Equity Indices: Annual % Change data was reported at -72.061 % in 2017. This records a decrease from the previous number of 4.001 % for 2016. Saudi Arabia SA: S&P Global Equity Indices: Annual % Change data is updated yearly, averaging 4.194 % from Dec 1998 (Median) to 2017, with 18 observations. The data reached an all-time high of 110.989 % in 2005 and a record low of -72.061 % in 2017. Saudi Arabia SA: S&P Global Equity Indices: Annual % Change data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Saudi Arabia – Table SA.World Bank: Financial Sector. S&P Global Equity Indices measure the U.S. dollar price change in the stock markets covered by the S&P/IFCI and S&P/Frontier BMI country indices.; ; Standard & Poor's, Global Stock Markets Factbook and supplemental S&P data.; ;

NASDAQ: Index: NASDAQ 100 Technology Sector Index data was reported at 9,723.190 NA in Apr 2025. This records an increase from the previous number of 9,472.590 NA for Mar 2025. NASDAQ: Index: NASDAQ 100 Technology Sector Index data is updated monthly, averaging 4,219.390 NA from Jan 2012 (Median) to Apr 2025, with 160 observations. The data reached an all-time high of 10,862.950 NA in Jan 2025 and a record low of 1,306.370 NA in May 2012. NASDAQ: Index: NASDAQ 100 Technology Sector Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: NASDAQ: Monthly.

Bangladesh CSE: Sector Index: Cement data was reported at 3,432.399 30Dec1999=1000 in Apr 2025. This records a decrease from the previous number of 3,469.317 30Dec1999=1000 for Mar 2025. Bangladesh CSE: Sector Index: Cement data is updated monthly, averaging 4,343.588 30Dec1999=1000 from Mar 2005 (Median) to Apr 2025, with 241 observations. The data reached an all-time high of 10,177.349 30Dec1999=1000 in Sep 2014 and a record low of 1,501.257 30Dec1999=1000 in Apr 2006. Bangladesh CSE: Sector Index: Cement data remains active status in CEIC and is reported by Chittagong Stock Exchange. The data is categorized under Global Database’s Bangladesh – Table BD.Z: Chittagong Stock Exchange: Index.