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 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.

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.

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.

This statistic presents the development of the Dow Jones Industrial Average index from 1986 to 2023. The 2023 year-end value of Dow Jones Industrial Average index amounted to 37,689.54. What is the Dow Jones Industrial Average index? Along with the NASDAQ 100 index, the Dow Jones Industrial Average (DJIA) is amongst the most well-known and used stock indexes in the world. DJIA index was created in 1985 by Charles Dow. It is second oldest U.S. index and one of the most important U.S. stock market indices. It reflects the performance of 30 of the most influential U.S. based companies from various industries, such as JPMorgan Chase, IBM and Walt Disney traded on the New York Stock Exchange and the NASDAQ. Performance of the Dow Jones Industrial Average The year that the financial crisis unfolded, 2008, was one of the worst years of the Dow. It was also in 2008 that some of the largest ever recorded losses of the DJIA based on single-day points were registered. On September 29th of 2008, for instance, the Dow had a loss of 106.85 points, the third largest single-day loss of all times. Since 2008 the index has generally been increasing, registering a high of 28,538.44 in 2019 before the economic effects of the global coronavirus (COVID-19) pandemic caused both the largest single-day losses, and largest single-day gains of the DJIA.

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.

April 9, 2025, saw the largest one-day gain in the history of the Dow Jones Industrial Average (DJIA), follwing Trump's announcement of 90-day delay in the introduction of tariffs imposed on imports from all countries. The second-largest one-day gain occurred on March 24, 2020, with the index increasing 2,112.98 points. This occurred approximately two weeks after the largest one-day point loss occurred on March 9, 2020, which was triggered by the growing panic about the coronavirus outbreak worldwide. Index fluctuations The DJIA is an index of 30 large companies traded on the New York Stock Exchange. It is one of the numbers that financial analysts watch closely, using it as a bellwether for the United States economy. Seeing when these large gains occur, as well as the largest one-day point losses, gives insight to why these fluctuations may occur. The gains in 2009 are likely adjustments after major losses during the Financial Crisis, but those in 2018 are probably signs of high market volatility. Other leading financial indicators While the DJIA is closely watched, it only gives insight on the performance of thirty leading U.S. companies. An index like the S&P 500, tracking 500 companies, can give a more comprehensive overview of the United States economy. Even so, this only reflects investment. Other parts of the economy, such as consumer spending or unemployment rate are not well reflected in stock market indices.

United States - Dow Jones Industrial Average was 40527.62000 Index in April of 2025, according to the United States Federal Reserve. Historically, United States - Dow Jones Industrial Average reached a record high of 45014.04000 in December of 2024 and a record low of 6547.05000 in March of 2009. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Dow Jones Industrial Average - last updated from the United States Federal Reserve on April of 2025.

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.

United States Index: Dow Jones: Industrial Average data was reported at 25,538.460 26May1896=40.94 in Nov 2018. This records an increase from the previous number of 25,115.760 26May1896=40.94 for Oct 2018. United States Index: Dow Jones: Industrial Average data is updated monthly, averaging 1,546.670 26May1896=40.94 from Jan 1953 (Median) to Nov 2018, with 791 observations. The data reached an all-time high of 26,458.310 26May1896=40.94 in Sep 2018 and a record low of 261.220 26May1896=40.94 in Aug 1953. United States Index: Dow Jones: Industrial Average data remains active status in CEIC and is reported by Dow Jones. The data is categorized under Global Database’s United States – Table US.Z015: Dow Jones: Indexes.

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.

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.

The statistic shows the best years of the Dow Jones Industrial Average index from 1897 to 2024. The best year in the history of the index was 1915, when the index value increased by 81.66 percent to close at 99.15 points compared to 54.58 in the previous year.

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.

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 Dow Jones Industrial Average

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 New Zealand 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

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.

There have been ups and downs in the history of the Dow Jones Industrial Average index (DJIA). Some years brought as much as 30 percent of decrease in its value. Great Recession, however, took the largest toll on the Dow. In 1931, the index lost 52.67 percent of its value.

Index history

Dow Jones Industrial Average index (DJIA) is one of the most important stock market indices worldwide. It was created in 1896 by Charles Dow and Edward Jones. DJIA is the second oldest U.S. stock index after the Dow Jones Transportation Average, which was established in 1984.

Index components

DJIA reflects the performance of thirty large U.S. publicly traded companies. When the index was created, it was primarily composed of industrial companies, hence the index name. With time, the economic situation in the U.S. has changed and apart from industrial companies, which played a huge role in the market in the 19th and the beginning of the 20th century, also companies from other leading industries were incorporated into the index. At present, the DJIA index is composed of most renowned U.S. corporations, such as Coca Cola, Microsoft or Walt Disney.

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

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.