Graph and download economic data for Financial Market: Share Prices for India (SPASTT01INM661N) from Jan 1957 to May 2025 about India and stock market.

Using the MSCI emerging markets index, stock markets in emerging economies performed above those of developed economies in 2020, with an annual return of 18.31 percent. This compares to a 2020 annual return of 15.9 percent for the MSCI World Index, which tracks the stock markets of 23 developed economies.

Graph and download economic data for Financial Market: Share Prices for China (SPASTT01CNM661N) from Jan 1999 to May 2025 about stock market and China.

Key information about India Sensitive 30 (Sensex)

• India Sensitive 30 (Sensex) closed at 73,198.1 points in Feb 2025, compared with 77,500.6 points at the previous month end
• India Equity Market Index: Month End: BSE: Sensitive 30 (Sensex) data is updated monthly, available from Apr 1979 to Feb 2025, with an average number of 4,285.0 points
• The data reached an all-time high of 84,299.8 points in Sep 2024 and a record low of 115.6 points in Nov 1979

• In the latest reports, SENSEX recorded a daily P/E ratio of 20.6 in Mar 2025

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.

Sensex Soars to New Record High on Hopes of Strong Economic Growth

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

Stock price, because it is a forward-looking variable, forecasts economic activities. An unexpected increase in stock price reflects that (1) future dividend growth is higher and/or (2) future discount rates are lower than previously anticipated. Therefore, the increase predicts higher output and investment. As well, other studies argue for an important relation between the expected stock market return and investment. In this paper, the author analyses the relative importance of these mechanisms by using Campbell and Shiller's (1988) method to decompose stock market return into three parts: expected return, a shock to the expected future return, and a shock to the expected future dividend growth. Contrary to the conventional wisdom, the author finds that dividend shocks are a rather weak predictor for future economic activities. Moreover, the expected return and shocks to the expected future return display different predictive patterns. The results shown here, collectively, explain why the forecasting power of stock market return is rather limited.

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.

MSCI World: Reflecting Global Economic Trends or Inflated Valuations?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The S&P BSE Sensex index, one of India's two main stock indices, lost almost *********** of its value between the end of February and the end of March 2020, owing to the economic impact of the global coronavirus (COVID-19) pandemic. It has since recovered, surpassing its pre-corona level in *************.The S&P BSE Sensex index includes 30 companies listed on the Bombay Stock Exchange which are representative of various industrial sectors of the Indian economy. It is considered one of the main Indicators of the Indian stock market, along with the CNX Nifty Index (which includes shares from India's other main stock exchange, the National Stock Exchange).

India's main stock market index, the SENSEX, fell to 82253 points on July 14, 2025, losing 0.30% from the previous session. Over the past month, the index has climbed 0.56% and is up 1.97% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from India. BSE SENSEX Stock Market Index - values, historical data, forecasts and news - updated on July of 2025.

China's main stock market index, the SHANGHAI, rose to 3520 points on July 14, 2025, gaining 0.27% from the previous session. Over the past month, the index has climbed 3.86% and is up 18.35% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from China. China Shanghai Composite Stock Market Index - values, historical data, forecasts and news - updated on July of 2025.

Trends in Stock market index. The latest data for over 100 countries around the world.

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.

US Economy to Slow in June as Fed Tightens Monetary Policy

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 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.

The US Economy: A House of Cards?

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 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.

G Stock: Soars on Positive Economic Data

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

Stock Market Expectation: Germany: Decrease data was reported at 27.100 % in Apr 2025. This records a decrease from the previous number of 27.600 % for Mar 2025. Stock Market Expectation: Germany: Decrease data is updated monthly, averaging 15.200 % from Dec 1991 (Median) to Apr 2025, with 401 observations. The data reached an all-time high of 37.000 % in Jun 2020 and a record low of 2.800 % in Feb 2001. Stock Market Expectation: Germany: Decrease data remains active status in CEIC and is reported by Leibniz Centre for European Economic Research. The data is categorized under Global Database’s Germany – Table DE.S001: Indicator of Economic Sentiment: ZEW.

United States CSI: Savings: Stock Market Increase Probability: Next Yr: 100% data was reported at 11.000 % in Oct 2018. This records an increase from the previous number of 10.000 % for Sep 2018. United States CSI: Savings: Stock Market Increase Probability: Next Yr: 100% data is updated monthly, averaging 6.000 % from Jun 2002 (Median) to Oct 2018, with 196 observations. The data reached an all-time high of 13.000 % in Jan 2018 and a record low of 1.000 % in Nov 2011. United States CSI: Savings: Stock Market Increase Probability: Next Yr: 100% data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s United States – Table US.H029: Consumer Sentiment Index: Savings & Retirement. The question was: What do you think the percent change that this one thousand dollar investment will increase in value in the year ahead, so that it is worth more than one thousand dollars one year from now?

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.

The Dow Jones Industrial Average (DJIA) index dropped around ***** points in the four weeks from February 12 to March 11, 2020, but has since recovered and peaked at ********* points as of November 24, 2024. In February 2020 - just prior to the global coronavirus (COVID-19) pandemic, the DJIA index stood at a little over ****** points. U.S. markets suffer as virus spreads The COVID-19 pandemic triggered a turbulent period for stock markets – the S&P 500 and Nasdaq Composite also recorded dramatic drops. At the start of February, some analysts remained optimistic that the outbreak would ease. However, the increased spread of the virus started to hit investor confidence, prompting a record plunge in the stock markets. The Dow dropped by more than ***** points in the week from February 21 to February 28, which was a fall of **** percent – its worst percentage loss in a week since October 2008. Stock markets offer valuable economic insights The Dow Jones Industrial Average is a stock market index that monitors the share prices of the 30 largest companies in the United States. By studying the performance of the listed companies, analysts can gauge the strength of the domestic economy. If investors are confident in a company’s future, they will buy its stocks. The uncertainty of the coronavirus sparked fears of an economic crisis, and many traders decided that investment during the pandemic was too risky.

Panama Stock Exchange: Turnover Value: Stock Market data was reported at 74,612,708.000 USD in Feb 2025. This records an increase from the previous number of 27,853,162.000 USD for Jan 2025. Panama Stock Exchange: Turnover Value: Stock Market data is updated monthly, averaging 8,041,217.200 USD from Jan 1999 (Median) to Feb 2025, with 314 observations. The data reached an all-time high of 226,067,029.240 USD in Oct 2017 and a record low of 265,044.500 USD in Feb 1999. Panama Stock Exchange: Turnover Value: Stock Market data remains active status in CEIC and is reported by Latin American Stock Exchange. The data is categorized under Global Database’s Panama – Table PA. Z001: Panama Stock Exchange: Turnover and Index.

United States FCI-G Index: 1-Yr Lookback: Stock Market data was reported at -0.084 Index in Mar 2025. This records an increase from the previous number of -0.306 Index for Feb 2025. United States FCI-G Index: 1-Yr Lookback: Stock Market data is updated monthly, averaging -0.212 Index from Jan 1990 (Median) to Mar 2025, with 423 observations. The data reached an all-time high of 1.267 Index in Feb 2009 and a record low of -0.864 Index in Mar 2021. United States FCI-G Index: 1-Yr Lookback: Stock Market data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.S021: Financial Conditions Impulse on Growth.