Interactive chart of the S&P 500 stock market index since 1927. Historical data is inflation-adjusted using the headline CPI and each data point represents the month-end closing value. The current month is updated on an hourly basis with today's latest value.

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 Standard & Poor’s (S&P) 500 Index is an index of 500 leading publicly traded companies in the United States. In 2021, the index value closed at ******** points, which was the second highest value on record despite the economic effects of the global coronavirus (COVID-19) pandemic. In 2023, the index values closed at ********, the highest value ever recorded. What is the S&P 500? The S&P 500 was established in 1860 and expanded to its present form of 500 stocks in 1957. It tracks the price of stocks on the major stock exchanges in the United States, distilling their performance down to a single number that investors can use as a snapshot of the economy’s performance at a given moment. This snapshot can be explored further. For example, the index can be examined by industry sector, which gives a more detailed illustration of the economy. Other measures Being a stock market index, the S&P 500 only measures equities performance. In addition to other stock market indices, analysts will look to other indicators such as GDP growth, unemployment rates, and projected inflation. Similarly, since these indicators say something about the economic future, stock market investors will use these indicators to speculate on the stocks in the S&P 500.

Interactive chart of the S&P 500 stock market index over the last 10 years. Values shown are daily closing prices. The most recent value is updated on an hourly basis during regular trading hours.

Between March 4 and March 11, 2020, the S&P 500 index declined by ** percent, descending into a bear market. On March 12, 2020, the S&P 500 plunged *** percent, its steepest one-day fall since 1987. The index began to recover at the start of April and reached a peak in December 2021. As of December 29, 2024, the value of the S&P 500 stood at ******** points. Coronavirus sparks stock market chaos Stock markets plunged in the wake of the COVID-19 pandemic, with investors fearing its spread would destroy economic growth. Buoyed by figures that suggested cases were leveling off in China, investors were initially optimistic about the virus being contained. However, confidence in the market started to subside as the number of cases increased worldwide. Investors were deterred from buying stocks, and this was reflected in the markets – the values of the Dow Jones Industrial Average and the Nasdaq Composite also dived during the height of the crisis. What is a bear market? A bear market occurs when the value of a stock market suffers a prolonged decline of more than 20 percent over a period of at least 2 months. The COVID-19 pandemic caused severe concern and sent stock markets on a steep downward spiral. The S&P 500 achieved a record closing high of ***** on February 19, 2020. However, just over 3 weeks later, the market closed on *****, which represented a decline of around ** percent in only 16 sessions.

This statistic presents the returns of the S&P 500 Information Technology Index in the United States from 2007 to 2023. The IT sector had its worst year in 2008, where it lost **** percent of its value. After three years of value gain, it lost **** percent of its value in 2022. On the contrary, 2023 witnessed the second-highest value gain during this period, reaching **** percent.

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.

An In-depth Analysis of the S&P 500 Index: Performance, Composition, and Implications

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 500, an index of 500 publicly traded companies in the United States, closed at 5,881.63 points on the last trading day of December 2024. What is the S&P 500? The S&P 500 is a stock market index that tracks the evolution of 500 companies. In contrast to the Dow Jones Industrial Index, which measures the performance of thirty large U.S. companies, the S&P 500 shows the sentiments in the broader market. Publicly traded companies Companies on the S&P 500 are publicly traded, meaning that anyone can invest in them. A large share of adults in the United States invest in the stock market, though many of these are through a retirement account or mutual fund. While most people make a modest return, the most successful investors have made billions of U.S. dollars through investing.

The Standard & Poor's 500 stock index (S&P 500) is a commonly cited indicator of stock market performance. It is a scaled average of 500 large-capitalization common stocks in the United States. The companies included in the index operate in various sectors across the economy, including energy, finance, telecommunications, retail and manufacturing. The values presented in this report are the December 31 close figures. Data is sourced from the St. Louis Federal Reserve.

Graph and download economic data for Earnings Yield of All Common Stocks on the New York Stock Exchange for United States (A13049USA156NNBR) from 1871 to 1938 about stocks, earnings, NY, yield, interest rate, interest, rate, and USA.

S&P 500 YTD Performance. Interactive chart showing the YTD daily performance of the S&P 500 stock market index. Performance is shown as the percentage gain from the last trading day of the previous year.

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.

[Video] S&P 500: Bull or Bear?

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 500 index dropped significantly between January 3 and September 9, 2022. As of January 3, the index stood at ******** points, and it dropped approximately 15 percent by September 2022. In February 2024, the daily value of the S&P 500 increased over ***** points and reached ******** as of October 16 of the same year.

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.

Does S&P 500 beat inflation?

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.

Is Volatility King of the S&P 500 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

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.

Will the S&P 500 Index Conquer New Heights?

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

Content

Company: Ticker Major index membership: Index Market capitalization: Market Cap Income (ttm): Income Revenue (ttm): Sales Book value per share (mrq): Book/sh Cash per share (mrq): Cash/sh Dividend (annual): Dividend Dividend yield (annual): Dividend % Full time employees: Employees Stock has options trading on a market exchange: Optionable Stock available to sell short: Shortable Analysts' mean recommendation (1=Buy 5=Sell): Recom Price-to-Earnings (ttm): P/E Forward Price-to-Earnings (next fiscal year): Forward P/E Price-to-Earnings-to-Growth: PEG Price-to-Sales (ttm): P/S Price-to-Book (mrq): P/B Price to cash per share (mrq): P/C Price to Free Cash Flow (ttm): P/FCF Quick Ratio (mrq): Quick Ratio Current Ratio (mrq): Current Ratio Total Debt to Equity (mrq): Debt/Eq Long Term Debt to Equity (mrq): LT Debt/Eq Distance from 20-Day Simple Moving Average: SMA20 Diluted EPS (ttm): EPS (ttm) EPS estimate for next year: EPS next Y EPS estimate for next quarter: EPS next Q EPS growth this year: EPS this Y EPS growth next year: EPS next Y Long term annual growth estimate (5 years): EPS next 5Y Annual EPS growth past 5 years: EPS past 5Y Annual sales growth past 5 years: Sales past 5Y Quarterly revenue growth (yoy): Sales Q/Q Quarterly earnings growth (yoy): EPS Q/Q Earnings date

BMO = Before Market Open
AMC = After Market Close: Earnings Distance from 50-Day Simple Moving Average: SMA50 Insider ownership: Insider Own Insider transactions (6-Month change in Insider Ownership): Insider Trans Institutional ownership: Inst Own Institutional transactions (3-Month change in Institutional Ownership): Inst Trans Return on Assets (ttm): ROA Return on Equity (ttm): ROE Return on Investment (ttm): ROI Gross Margin (ttm): Gross Margin Operating Margin (ttm): Oper. Margin Net Profit Margin (ttm): Profit Margin Dividend Payout Ratio (ttm): Payout Distance from 200-Day Simple Moving Average: SMA200 Shares outstanding: Shs Outstand Shares float: Shs Float Short interest share: Short Float Short interest ratio: Short Ratio Analysts' mean target price: Target Price 52-Week trading range: 52W Range Distance from 52-Week High: 52W High Distance from 52-Week Low: 52W Low Relative Strength Index: RSI (14) Relative volume: Rel Volume Average volume (3 month): Avg Volume Volume: Volume Performance (Week): Perf Week Performance (Month): Perf Month Performance (Quarter): Perf Quarter Performance (Half Year): Perf Half Y Performance (Year): Perf Year Performance (Year To Date): Perf YTD Beta: Beta Average True Range (14): ATR Volatility (Week, Month): Volatility Previous close: Prev Close Current stock price: Price Performance (today): Change

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.

Speculative News and the S&P 500: A Dance of Sentiment and Performance?

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

In 2023, the S&P 500 Information Technology Index outperformed other sectors, with annual return of **** percent. On the other hand, the S&P 500 Utilities Index recorded the lowest returns, with a loss of *** percent.

Israel's main stock market index, the TA-125, fell to 3051 points on July 13, 2025, losing 2.22% from the previous session. Over the past month, the index has climbed 12.37% and is up 48.25% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Israel. Israel Stock Market (TA-125) - values, historical data, forecasts and news - updated on July of 2025.