Ten years of daily data for the Dow Jones Industrial Average (DJIA) market index. Each point of the dataset is represented by the daily closing price for the DJIA. Historical data can be downloaded via the red button on the upper right corner of the chart.

Historical dataset of the Dow Jones Industrial Average (DJIA) stock market index for the last 100 years. 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.

Graph and download economic data for Dow Jones Industrial Average (DJIA) from 2015-06-08 to 2025-06-06 about stock market, average, industry, and USA.

The main stock market index of United States, the US500, rose to 6008 points on June 9, 2025, gaining 0.13% from the previous session. Over the past month, the index has climbed 2.80% and is up 12.07% 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 June of 2025.

The value of the DJIA index amounted to 43,191.24 at the end of March 2025, up from 21,917.16 at the end of March 2020. Global panic about the coronavirus epidemic caused the drop in March 2020, which was the worst drop since the collapse of Lehman Brothers in 2008. Dow Jones Industrial Average index – additional information The Dow Jones Industrial Average index is a price-weighted average of 30 of the largest American publicly traded companies on New York Stock Exchange and NASDAQ, and includes companies like Goldman Sachs, IBM and Walt Disney. This index is considered to be a barometer of the state of the American economy. DJIA index was created in 1986 by Charles Dow. Along with the NASDAQ 100 and S&P 500 indices, it is amongst the most well-known and used stock indexes in the world. The year that the 2018 financial crisis unfolded was one of the worst years of the Dow. It was also in 2008 that some of the largest ever recorded losses of the Dow Jones Index based on single-day points were registered. On September 29th of 2008, for instance, the Dow had a loss of 106.85 points, one of the largest single-day losses of all times. The best years in the history of the index still are 1915, when the index value increased by 81.66 percent in one year, and 1933, year when the index registered a growth of 63.74 percent.

The Dow Jones Industrial Average (DJIA) index dropped around 8,000 points in the four weeks from February 12 to March 11, 2020, but has since recovered and peaked at 44,910.65 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 29,000 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 3,500 points in the week from February 21 to February 28, which was a fall of 12.4 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.

Interactive chart showing the annual percentage change of the Dow Jones Industrial Average back to 1916. Performance is calculated as the % change from the last trading day of each year from the last trading day of the previous year.

Throughout the 1920s, prices on the U.S. stock exchange rose exponentially, however, by the end of the decade, uncontrolled growth and a stock market propped up by speculation and borrowed money proved unsustainable, resulting in the Wall Street Crash of October 1929. This set a chain of events in motion that led to economic collapse - banks demanded repayment of debts, the property market crashed, and people stopped spending as unemployment rose. Within a year the country was in the midst of an economic depression, and the economy continued on a downward trend until late-1932.

It was during this time where Franklin D. Roosevelt (FDR) was elected president, and he assumed office in March 1933 - through a series of economic reforms and New Deal policies, the economy began to recover. Stock prices fluctuated at more sustainable levels over the next decades, and developments were in line with overall economic development, rather than the uncontrolled growth seen in the 1920s. Overall, it took over 25 years for the Dow Jones value to reach its pre-Crash peak.

Prices for United States Stock Market Index (US30) including live quotes, historical charts and news. United States Stock Market Index (US30) was last updated by Trading Economics this June 9 of 2025.

Historical dataset for 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.

Graph and download economic data for Dow-Jones Industrial Stock Price Index for United States (M1109BUSM293NNBR) from Dec 1914 to Dec 1968 about stock market, industry, price index, indexes, price, and USA.

Dow stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Following the announcement of sweeping tariffs on all countries by Donald Trump, April 4, 2025 became the day with the third-highest point losses for the Dow Jones Industrial Average in history. Worse than the loss experienced on that day were only the losses that occurred following the beginning of the COVID-19 pandemic. The Dow Jones Industrial Average posted significant points losses due to the global impact of the coronavirus pandemic in 2020. With stocks falling sharply, the Dow recorded its worst single-day points drop ever, plunging 2,997 points – nearly 13 percent – on March 16, 2020.

Graph and download economic data for CBOE DJIA Volatility Index (VXDCLS) from 1997-10-07 to 2025-06-05 about VIX, volatility, stock market, and USA.

The statistic shows the worst days of the Dow Jones Industrial Average index from 1897 to 2024. The worst day in the history of the index was October 19 1987, when the index value decreased by 22.61 percent. The largest single day loss in points was on May 2, 2018.

Graph and download economic data for 10-Year 0.5% Treasury Inflation-Indexed Note, Due 1/15/2028 (DTP10J28) from 2018-01-19 to 2025-06-05 about TIPS, 10-year, bonds, Treasury, interest rate, interest, real, rate, and USA.

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.

Dow Jones Industrial Average Index Target Price Prediction

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.

Dow Jones Industrial Average Index assigned short-term B1 & long-term Ba1 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

Dow current price/book ratio as of May 10, 2025 is 1.2. Dow average price/book ratio for 2024 was 1.86, a 8.77% decline from 2023. Dow average price/book ratio for 2023 was 1.71, a 3.64% increase from 2022. Dow average price/book ratio for 2022 was 1.65, a 26.67% increase from 2021. Price/book ratio can be defined as