The value of the DJIA index amounted to ********* at the end of February 2026, up from ********* 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 29, 2008, for instance, the Dow had a loss of ****** 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 ***** percent in one year, and 1933, year when the index registered a growth of ***** percent.

The main stock market index of United States, the US500, fell to 6369 points on March 27, 2026, losing 1.67% from the previous session. Over the past month, the index has declined 7.45%, though it remains 14.12% higher than a year ago, 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 March of 2026.

The Dow Jones Industrial Average is (DJIA) is possibly the most well-known and commonly used stock index in the United States. It is a price-weighted index that assesses the stock prices of 30 prominent companies, whose combined prices are then divided by a regularly-updated divisor (0.15199 in February 2021), which gives the index value. The companies included are rotated in and out on a regular basis; as of mid-2022, the longest mainstay on the list is Procter & Gamble, which was added in 1932; whereas Amgen, Salesforce, and Honeywell were all added in 2020. As one of the oldest indices for stock market analysis, the impact of major events, recessions, and economic shocks or booms can be tracked and contextualized over longer periods of time.

Due to inflation, unadjusted figures appear to be more sporadic in recent years, however the greatest fluctuations came in the earliest years of the index. In the given period, the greatest decline came in the wake of the Wall Street Crash in 1929; by 1932 average values had fallen to just one fifth of their 1929 average, from roughly 314 to 65.

Graph and download economic data for Dow Jones Industrial Average (DJIA) from 2016-03-28 to 2026-03-27 about stock market, average, industry, and USA.

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 *********. 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 ****** points, the third largest single-day loss of all times. Since 2008 the index has generally been increasing, registering a high of ********* 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.

Datasets contain a sample of the Dow Jones Industrial Average (DJIA) index values, as well as stock prices of the companies that form the DJIA today. A little about this stock market index: https://en.wikipedia.org/wiki/Dow_Jones_Industrial_Average Daily DJIA index values and stock prices are taken from March 01, 2017 to March 19, 2020. All prices are the closing prices (the last price at which the stock traded during the regular trading day). It should be noted that stock prices Dow Inc. available only from March 20, 2019, the remaining price values are NaN (Dow Inc. was added to the DJIA since April 02, 2019).

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.

Context The Dow Jones Industrial Average is one of the most followed stock market indexes by investors, financial professionals and the media. Content It measures the daily price movements of 30 large American companies on the Nasdaq and the New York Stock Exchange. The Dow Jones Industrial Average is widely viewed as a proxy for general market conditions and even the economy of the United States. This Dow Jones Industrial Average dataset is downloaded from https://www.investing.com/indices/us-30-historical-data, including 2767 closing records from January 4th 2009 to December 31st 2019.

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.

Context

Historical Data from Yahoo Financial for NASDAQ, S&P 500, and Dow Jones Indices. From April 20, 2019 - April 20, 2020

Content

High, Low, Open, Close, Volume daily for the period

Acknowledgements

Scott and Dan for their support

Inspiration

Use for algorithmic modelling and back testing

Context

The algorithmic trading space is buzzing with new strategies. Companies have spent billions in infrastructures and R&D to be able to jump ahead of the competition and beat the market. Still, it is well acknowledged that the buy & hold strategy is able to outperform many of the algorithmic strategies, especially in the long-run. However, finding value in stocks is an art that very few mastered, can a computer do that?

Content

This Data repo contains two datasets:

1. Example_2019_price_var.csv. I built this dataset thanks to Financial Modeling Prep API and to pandas_datareader. Each row is a stock from the technology sector of the US stock market (that is available from the aforementioned API, which is free and highly recommended). The column contains the percent price variation of each stock for the year 2019. In other words, it collects the percent price variation of each stock from the first trading day on Jan 2019 to the last trading day of Dec 2019. To compute this price variation I decided to consider the Adjusted Close Price.

2. Example_DATASET.csv. I built this dataset thanks to Financial Modeling Prep API. Each row is a stock from the technology sector of the US stock market (that is available from the aforementioned API). Each column is a financial indicator that can be found in the 2018 10-K filings of each company. There are no Nans or empty cells. Furthermore, the last column is the CLASS of each stock, where:

   1. class = 1 if the price of the stock increases during 2019
   2. class = 0 if the price of the stock decreases during 2019

In other words, the last column is used to classify each stock in buy-worthy or not, and this relationship is what should allow a machine learning model to learn to recognize stocks that will increase their value from those that won't.

NOTE: the number of stocks does not match between the two datasets because the API did not have all the required financial indicators for some stocks. It is possible to remove from Example_2019_price_var.csv those rows that do not appear in Example_DATASET.csv.

Inspiration

I built this dataset during the 2019 winter holidays period, because I wanted to answer a simple question: is it possible to have a machine learning model learn the differences between stocks that perform well and those that don't, and then leverage this knowledge in order to predict which stock will be worth buying? Moreover, is it possible to achieve this simply by looking at financial indicators found in the 10-K filings?

In 2025, most of the Polish population stated they know very little about the stock market. Only *** percent had a lot of knowledge, while *** percent had rather little knowledge.

United Kingdom's main stock market index, the GB100, fell to 9972 points on March 26, 2026, losing 1.33% from the previous session. Over the past month, the index has declined 8.60%, though it remains 15.07% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United Kingdom. United Kingdom Stock Market Index (GB100) - values, historical data, forecasts and news - updated on March of 2026.

This statistic shows the main reasons not to invest in the stock market in France in *************. At that time, the main reason for not investing in the stock market was that it was too risky: ** percent of French people didn't want to invest because they were afraid of losing their savings. The second main reason was lack of funds as ** percent of people didn't want to invest in the stock market because they didn't have money to invest.

Ukraine: Stock market value traded, percent of GDP: The latest value from 2019 is 0 percent, a decline from 0.01 percent in 2018. In comparison, the world average is 26.08 percent, based on data from 76 countries. Historically, the average for Ukraine from 2010 to 2019 is 0.34 percent. The minimum value, 0 percent, was reached in 2019 while the maximum of 2.01 percent was recorded in 2010.

Graph and download economic data for Stock Market Total Value Traded to GDP for United States (DDDM02USA156NWDB) from 1975 to 2019 about market cap, stock market, trade, GDP, and USA.

This statistic shows the main types of shares purchased in the stock market in France in February 2019. At that time, ** percent of French people who were investing in the stock market had purchased shares of large companies listed on the CAC 40 stock market. ** percent of them had invested in shares of other big companies listed on the stock market, but not on CAC 40.

This data set contains the value of the Dow Jones Industrial Average on daily close for all available dates (to the best of my knowledge) from 1885 to the most recently concluded calendar year. Extensions shouldn't be too difficult with existing packages.

Observations before October 7, 1896 are from the single Dow Jones Average. Observations from October 7, 1896 to July 30, 1914 are from the first DJIA. Observations before the 1914 closure of the first DJIA in July 1914 come from MeasuringWorth. Observations from its reopening in Dec. 12, 1914 to January 28, 1985 come from Pinnacle Systems. Observations from January 29, 1985 to the most recent observation come from a quantmod call.

References

Samuel H. Williamson, 'Daily Closing Value of the Dow Jones Average, 1885 to Present,' MeasuringWorth, 2019.

Jeffrey A. Ryan and Joshua M. Ulrich, 'quantmod: Quantitative Financial Modelling Framework,' 2018.

Acknowledgement

Original Data

Foto von Aditya Vyas auf Unsplash

Stock Market Total Value Traded to GDP for United States was 108.51% in January of 2019, according to the United States Federal Reserve. Historically, Stock Market Total Value Traded to GDP for United States reached a record high of 319.88 in January of 2008 and a record low of 7.97 in January of 1977. Trading Economics provides the current actual value, an historical data chart and related indicators for Stock Market Total Value Traded to GDP for United States - last updated from the United States Federal Reserve on March of 2026.

Norway: Stock market value traded, percent of GDP: The latest value from 2019 is 24.88 percent, a decline from 27.1 percent in 2018. In comparison, the world average is 26.08 percent, based on data from 76 countries. Historically, the average for Norway from 1980 to 2019 is 24.55 percent. The minimum value, 0.13 percent, was reached in 1980 while the maximum of 101.29 percent was recorded in 2007.