The value of the DJIA index amounted to ****** at the end of June 2025, 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, rose to 6397 points on August 11, 2025, gaining 0.12% from the previous session. Over the past month, the index has climbed 2.04% and is up 19.69% 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 August of 2025.

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 August 12 of 2025.

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.

In 2021, the Nasdaq 100 closed at ********* points, which was the second highest value on record despite the economic effects of the global coronavirus (COVID-19) pandemic. The index value closed at ********* points in 2024, an increase of more than ***** points compared to its closing value for the previous year. What does the NASDAQ tell us? The Nasdaq 100 index is comprised of 100 largest and most actively traded non-financial companies listed on the Nasdaq stock exchange. Financial firms are represented by the NASDAQ Bank Index. A stock market index is a measurement of average performance of companies forming the index. It gives a snapshot of what investors are thinking at that particular moment. Other indices The Dow Jones Industrial Average gets more attention than the NASDAQ 100, though it only represents ** companies. It’s best and worst days mark some of the major financial events of the past century. This helps to put more meaning behind events like Black Monday, the Wall Street crash of 1929, or the 2008 Financial Crisis, as well as the speed of their recoveries in financial markets.

Japan's main stock market index, the JP225, rose to 42762 points on August 12, 2025, gaining 2.25% from the previous session. Over the past month, the index has climbed 8.37% and is up 18.02% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Japan. Japan Stock Market Index (JP225) - values, historical data, forecasts and news - updated on August of 2025.

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.

Over the course of their first terms in office, no U.S. president in the past 100 years saw as much of a decline in stock prices as Herbert Hoover, and none saw as much of an increase as Franklin D. Roosevelt (FDR) - these were the two presidents in office during the Great Depression. While Hoover is not generally considered to have caused the Wall Street Crash in 1929, less than a year into his term in office, he is viewed as having contributed to its fall, and exacerbating the economic collapse that followed. In contrast, Roosevelt is viewed as overseeing the economic recovery and restoring faith in the stock market played an important role in this.

By the end of Hoover's time in office, stock prices were 82 percent lower than when he entered the White House, whereas prices had risen by 237 percent by the end of Roosevelt's first term. While this is the largest price gain of any president within just one term, it is important to note that stock prices were valued at 317 on the Dow Jones index when Hoover took office, but just 51 when FDR took office four years later - stock prices had peaked in August 1929 at 380 on the Dow Jones index, but the highest they ever reached under FDR was 187, and it was not until late 1954 that they reached pre-Crash levels once more.

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.

Bulls Are Back on Wall Street

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.

Flowering on Wall Street? (FLWS)

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

Kitchener street index wall size map.PDF document updates daily. The date above is the date the document link was created or last updated. Check the date in the pdf for the date it was created.

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 ****************, when the index value decreased by ***** percent. The largest single day loss in points was on ***********.

Kitchener street index with wards wall size map.PDF document updates daily. The date above is the date the document link was created or last updated. Check the date in the pdf for the date it was created.

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.

Bank of New York Mellon (BK) - A Wall Street Titan: Is the Future Bright or Fading?

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

Uncover historical ownership history and changes over time by performing a reverse Whois lookup for the company Wall-Street-Directory.

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.

Piper Sandler (PIPR) - A Bullish Bet on Wall Street's Next Generation

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 New York Stock Exchange (NYSE) is the largest stock exchange in the world, with an equity market capitalization of almost ** trillion U.S. dollars as of June 2025. The following three exchanges were the NASDAQ, PINK Exchange, and the Frankfurt Exchange. What is a stock exchange? A stock exchange is a marketplace where stockbrokers, traders, buyers, and sellers can trade in equities products. The largest exchanges have thousands of listed companies. These companies sell shares of their business, giving the general public the opportunity to invest in them. The oldest stock exchange worldwide is the Frankfurt Stock Exchange, founded in the late sixteenth century. Other functions of a stock exchange Since these are publicly traded companies, every firm listed on a stock exchange has had an initial public offering (IPO). The largest IPOs can raise billions of dollars in equity for the firm involved. Related to stock exchanges are derivatives exchanges, where stock options, futures contracts, and other derivatives can be traded.

This repository contains Matlab code and, partly artificial (as described below), data for the paper "Warp speed price moves: Jumps after earnings announcements" to appear in Journal of Financial Economics.

The research findings in the paper are based on the following data sources:

• Bloomberg Supplied the weights of the S&P 500 index members
• CRSP Employed for close-to-open return-based jump test in the supplemental Appendix D
• Factiva Provided timing of earnings announcement (to nearest minute)
• Refinitiv Provided I/B/E/S analyst earnings information
• TAQ Source of high-frequency transaction and quotation data
• Wall Street Horizon Provided timing of earnings announcement (to nearest minute)
• Zacks Investment Research Supplied the placement of earnings announcement (“Before Market Open”, “During Regular Trading”, or “After Market Close”).

The data are protected by copyright and cannot be shared in original form. Thus, to be able to run the supplied code, we generated some synthetic data for a small subset of the stock universe analyzed in the paper, i.e. Facebook (FB) and Apple (AAPL) for the period 2008 - 2012.

The following modifications of the original data were made:

The S&P 500 index weights were randomly reshuffled.

In the original tick-by-tick transaction data files, the traded prices were replaced by a Geometric Brownian motion (GBM) with an annuliazed volatility of 20%. Each day, we start the GBM in the first available transaction price, as extracted from the TAQ database, and round the GBM to two decimals. Trade sizes are drawn randomly from the set [1,2,10]. Transaction times are randomly shifted by a number of seconds drawn uniformly from the interval [-5,5] and resorted.

In the original quotation data files, the bid is simulated by a GBM, as above, and the ask is constructed from the simulated bid after a random permutation of the original quoted spread. Bid and ask sizes are drawn randomly from the set [1,2,10].

We should note that realized volatility measures are provided for ALL stocks over the WHOLE sample, which allows for replication of several results presented in the paper. However, computation of these volatility estimators requires access to NYSE TAQ data.

In the file "crsp_ea_info.mat", the "placement" indicator provided by Zack Investment Research has been randomly reshuffled.

In the file "crsp_price_info.mat", the variables "cfacpr" (correction factor), "volume", and "ntrade" were permuted, while the remaining variables can be obtained from public sources and are unaltered.

Australia's main stock market index, the ASX200, rose to 8855 points on August 12, 2025, gaining 0.12% from the previous session. Over the past month, the index has climbed 3.32% and is up 13.14% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Australia. Australia Stock Market Index - values, historical data, forecasts and news - updated on August of 2025.

South Korea's main stock market index, the KOSPI, fell to 3190 points on August 12, 2025, losing 0.53% from the previous session. Over the past month, the index has declined 0.38%, though it remains 21.68% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from South Korea. South Korea Stock Market - values, historical data, forecasts and news - updated on August of 2025.