The main stock market index of United States, the US500, rose to 6271 points on July 14, 2025, gaining 0.19% from the previous session. Over the past month, the index has climbed 3.94% and is up 11.36% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on July of 2025.

Graph and download economic data for Share Prices: All Shares/Broad: Total for China (SPASTT01CNQ657N) from Q2 1999 to Q1 2025 about stock market and China.

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

Graph and download economic data for Share Prices: All Shares/Broad: Total for Germany (SPASTT01DEQ657N) from Q2 1960 to Q1 2025 about Germany and stock market.

TT: Index: Share Price (End of Period) data was reported at 148.565 2010=100 in Jun 2018. This records a decrease from the previous number of 152.017 2010=100 for Mar 2018. TT: Index: Share Price (End of Period) data is updated quarterly, averaging 96.534 2010=100 from Mar 1991 (Median) to Jun 2018, with 110 observations. The data reached an all-time high of 152.308 2010=100 in Dec 2017 and a record low of 7.108 2010=100 in Mar 1993. TT: Index: Share Price (End of Period) data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Trinidad and Tobago – Table TT.IMF.IFS: Share Price Index: Quarterly.

AT: Index: Share Price (End of Period) data was reported at 90.152 2010=100 in 2016. This records an increase from the previous number of 82.526 2010=100 for 2015. AT: Index: Share Price (End of Period) data is updated yearly, averaging 19.792 2010=100 from Dec 1957 (Median) to 2016, with 60 observations. The data reached an all-time high of 155.380 2010=100 in 2007 and a record low of 3.952 2010=100 in 1957. AT: Index: Share Price (End of Period) data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Austria – Table AT.IMF.IFS: Share Price Index: Annual.

Graph and download economic data for Financial Market: Share Prices for Euro Area (19 Countries) (SPASTT01EZQ661N) from Q1 1987 to Q1 2025 about Euro Area, stock market, and Europe.

LB: Index: Share Price (End of Period) data was reported at 44.278 2010=100 in 2013. This records a decrease from the previous number of 49.778 2010=100 for 2012. LB: Index: Share Price (End of Period) data is updated yearly, averaging 41.971 2010=100 from Dec 1996 (Median) to 2013, with 18 observations. The data reached an all-time high of 133.338 2010=100 in 2008 and a record low of 6.453 2010=100 in 2002. LB: Index: Share Price (End of Period) data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Lebanon – Table LB.IMF.IFS: Share Price Index: Annual.

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 Share Prices: All Shares/Broad: Total for United Kingdom (SPASTT01GBQ657N) from Q2 1958 to Q1 2025 about stock market and United Kingdom.

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

South African monthly The FTSE/JSE All Share Index data was procured from Bloomberg and the nominal effective exchange rate (NEER) from South African Reserve Bank (SARB) database, where the data has been seasonally adjusted specifying 2015 as the base year. Volatility measures in these markets are generated through a multivaraite EGARCH model in the WinRATS software. South African monthly consumer price index (CPI) data was procured from the International Monetary Fund’s International Financial Statistics (IFS) database, where the data has been seasonally adjusted, specifying 2010 as the base year. The inflation rate is constructed by taking the year-on-year changes in the monthly CPI figures. Inflation uncertainty was generated through the GARCH model in Eviews software. The following South African macroeconomic variables were procured from the SARB: real industrial production (IP), which is used as a proxy for real GDP, real investment (I), real consumption (C), inflation (CPI), broad money (M3), the 3-month treasury bill rate (TB3) and the policy rate (R), a measure of U.S. EPU developed by Baker et al. (2016) to account for global developments available at http://www.policyuncertainty.com/us_monthly.html.

IS: Index: Share Price data was reported at 213.618 2010=100 in 2017. This records a decrease from the previous number of 216.957 2010=100 for 2016. IS: Index: Share Price data is updated yearly, averaging 213.618 2010=100 from Dec 2003 (Median) to 2017, with 15 observations. The data reached an all-time high of 2,307.538 2010=100 in 2007 and a record low of 84.257 2010=100 in 2009. IS: Index: Share Price data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Iceland – Table IS.IMF.IFS: Share Price Index: Annual.

LT: Index: Share Price data was reported at 155.847 2010=100 in 2016. This records an increase from the previous number of 145.365 2010=100 for 2015. LT: Index: Share Price data is updated yearly, averaging 113.090 2010=100 from Dec 2001 (Median) to 2016, with 16 observations. The data reached an all-time high of 156.412 2010=100 in 2007 and a record low of 24.098 2010=100 in 2001. LT: Index: Share Price data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Lithuania – Table LT.IMF.IFS: Share Price Index: Annual.

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.

BOT 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

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.

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.

Mortgage Rates: Hot Economic Conjecture Puts the Squeeze on Homebuyers

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

CH: Index: Share Price (End of Period) data was reported at 126.989 2010=100 in 2016. This records a decrease from the previous number of 137.152 2010=100 for 2015. CH: Index: Share Price (End of Period) data is updated yearly, averaging 95.614 2010=100 from Dec 1989 (Median) to 2016, with 28 observations. The data reached an all-time high of 140.263 2010=100 in 2007 and a record low of 25.021 2010=100 in 1990. CH: Index: Share Price (End of Period) data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Switzerland – Table CH.IMF.IFS: Share Price Index: Annual.

IHS predictions include continued revenue growth due to strong demand for its products and services, particularly in the healthcare industry. However, the stock may face risks from increased competition, regulatory changes, and economic headwinds, leading to volatility in share price and potential underperformance relative to the broader market.