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.

In 2021, the Nasdaq 100 closed at 16,320.08 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 21,012.17 points in 2024, an increase of more than 4,000 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 30 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.

The Dow Jones Composite Index finished the year 2024 at 13,391.71 points, an increase compared to the previous year. Even with the economic effects of the global coronavirus (COVID-19) pandemic, 2021 had the highest point of the index in the past two decades. What is Dow Jones Composite Index? The Dow Jones Composite Index is one of the indices from the Dow Jones index family. It is composed of 65 leading U.S. companies: 30 stocks forming the Dow Jones Industrial Average index, 20 stocks from the Dow Jones Transportation index and 15 stocks from the Dow Jones Utility Average index. Importance of stock indices A stock market index shows an average performance of companies from a given section of the market. It is usually a weighted average, meaning that such factors as price of companies or their market capitalization are taken into consideration when calculating the index value. Stock indices are very useful for the financial market participants, as they instantly show the sentiments prevailing on a given market. They are also commonly used as a benchmark against portfolio performance, showing if a given portfolio has outperformed, or underperformed the market.

The main stock market index in Philippines (PSEi) decreased 363 points or 5.56% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks this benchmark index from Philippines. Philippines Stock Market (PSEi) - values, historical data, forecasts and news - updated on March of 2025.

In the dynamic landscape of financial markets, accurate forecasting of stock indices remains a pivotal yet challenging task, essential for investors and policymakers alike. This study is motivated by the need to enhance the precision of predicting the Shanghai Composite Index’s opening price spread, a critical measure reflecting market volatility and investor sentiment. Traditional time series models like ARIMA have shown limitations in capturing the complex, nonlinear patterns inherent in stock price movements, prompting the exploration of advanced methodologies. The aim of this research is to bridge the gap in forecasting accuracy by developing a hybrid model that integrates the strengths of ARIMA with deep learning techniques, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. This novel approach leverages the ARIMA model’s proficiency in linear trend analysis and the deep learning models’ capability in modeling nonlinear dependencies, aiming to provide a comprehensive tool for market prediction. Utilizing a comprehensive dataset covering the period from December 20, 1990, to June 2, 2023, the study develops and assesses the efficacy of ARIMA, LSTM, GRU, ARIMA-LSTM, and ARIMA-GRU models in forecasting the Shanghai Composite Index’s opening price spread. The evaluation of these models is based on key statistical metrics, including Mean Squared Error (MSE) and Mean Absolute Error (MAE), to gauge their predictive accuracy. The findings indicate that the hybrid models, ARIMA-LSTM and ARIMA-GRU, perform better in forecasting the opening price spread of the Shanghai Composite Index than their standalone counterparts. This outcome suggests that combining traditional statistical methods with advanced deep learning algorithms can enhance stock market prediction. The research contributes to the field by providing evidence of the potential benefits of integrating different modeling approaches for financial forecasting, offering insights that could inform investment strategies and financial decision-making.

Denmark Index: Copenhagen Stock Exchange: Gross: OMX Copenhagen Ex OMXC 20 data was reported at 8,112.710 31Dec1995=100 in Oct 2018. This records a decrease from the previous number of 8,752.230 31Dec1995=100 for Sep 2018. Denmark Index: Copenhagen Stock Exchange: Gross: OMX Copenhagen Ex OMXC 20 data is updated monthly, averaging 4,294.127 31Dec1995=100 from Nov 2005 (Median) to Oct 2018, with 156 observations. The data reached an all-time high of 8,991.170 31Dec1995=100 in Aug 2018 and a record low of 1,886.816 31Dec1995=100 in Mar 2009. Denmark Index: Copenhagen Stock Exchange: Gross: OMX Copenhagen Ex OMXC 20 data remains active status in CEIC and is reported by Copenhagen Stock Exchange. The data is categorized under Global Database’s Denmark – Table DK.Z001: Copenhagen Stock Exchange: Index. On May 13, 2013 NASDAQ OMX performed changes to the KFMX indexes. The name was changeed from KFMX to OMX Copenhagen ex OMX Copenhagen 20, and the price algorithm was changed from NEWNX to Last Paid, meaning that the official closing price becomes the latest price regardless of closing best bid and ask prices.

Composition of uninterrupted trends observed in the Nasdaq data sample.

This paper examines the intertemporal relation between risk and return for the aggregate stock market using high-frequency data. We use daily realized, GARCH, implied, and range-based volatility estimators to determine the existence and significance of a risk-return trade-off for several stock market indices. We find a positive and statistically significant relation between the conditional mean and conditional volatility of market returns at the daily level. This result is robust to alternative specifications of the volatility process, across different measures of market return and sample periods, and after controlling for macro-economic variables associated with business cycle fluctuations. We also analyze the risk-return relationship over time using rolling regressions, and find that the strong positive relation persists throughout our sample period. The market risk measures adopted in the paper add power to the analysis by incorporating valuable information, either by taking advantage of high-frequency intraday data (in the case of realized, GARCH, and range volatility) or by utilizing the market's expectation of future volatility (in the case of implied volatility index).

Baltic Dry increased 637 points or 63.89% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Baltic Exchange Dry Index - values, historical data, forecasts and news - updated on March of 2025.

Descriptive statistics of data presented in Tables 2–5.

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.

Inflation on the Rise: What Does This Mean for You?

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 paper studies subsampling hypothesis tests for panel data that may be nonstationary, cross-sectionally correlated, and cross-sectionally cointegrated. The subsampling approach provides approximations to the finite sample distributions of the tests without estimating nuisance parameters. The tests include panel unit root and cointegration tests as special cases. The number of cross-sectional units is assumed to be finite and that of time-series observations infinite. It is shown that subsampling provides asymptotic distributions that are equivalent to the asymptotic distributions of the panel tests. In addition, the tests using critical values from subsampling are shown to be consistent. The subsampling methods are applied to panel unit root tests. The panel unit root tests considered are Levin, Lin, and Chu's (2002) t-test; Im, Pesaran, and Shin's (2003) averaged t-test; and Choi's (2001) inverse normal test. Simulation results regarding the subsampling panel unit root tests and some existing unit root tests for cross-sectionally correlated panels are reported. In using the subsampling approach to examine the real exchange rates of the G7 countries and a group of 26 OECD countries, we find only mixed support for the purchasing power parity (PPP) hypothesis. We then examine a panel of 17 developed stock market indexes, and also find only mixed empirical support for them exhibiting relative mean reversion with respect to the US stock market index.

Redbook Index in the United States increased by 5.60 percent in the week ending March 22 of 2025 over the same week in the previous year. This dataset provides the latest reported value for - United States Redbook Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

As of 2025, the Frankfurt Stock Exchange was the oldest existing stock exchange, having been in operation for 440 years. The youngest major exchange at this time was the Korea Exchange, which has been in operation for 20 years. Note these values refer to stock market operators, meaning historical exchanges in places like as the Amsterdam or Paris are counted from the founding of the Euronext, not from when the original stock exchange was founded in that city.

Until the third quarter of 2021, the S&P 500 and the S&P 500 ESG index exhibited similar performance, both indexes were weighted to similar industries as the S&P 500 followed the leading 500 companies in the United States. By the fourth quarter of 2021, the S&P 500 ESG index began to steadily outperform the S&P 500 by four points on average. During the coronavirus pandemic, the technology sector was one of the best-performing sectors in the market. The major differences between the two indexes were the S&P 500 ESG index was skewed towards firms with higher environmental, social, and governance (ESG) scores and had a higher concentration of technology securities than the S&P 500 index. What is a market capitalization index? Both the S&P 500 and the S&P 500 ESG are market capitalization indexes, meaning the individual components (such as stocks and other securities) weighted to the indexes influence the overall value. Market trends such as inflation, interest rates, and international issues like the coronavirus pandemic and the popularity of ESG among professional investors affect the performance of stocks. When weighted components rise in value this causes an increase in the overall value of the index they are weighted too. What trends are driving index performance? Recent economic and social trends have led to higher levels of ESG integration and maintenance among firms worldwide and higher prioritization from investors to include ESG-focused firms in their investment choices. From a global survey group over one-third of the respondents were willing to prioritize ESG benefits over a higher return on their investment. These trends influenced the performance of securities on the market, leading to an increased value of individual weighted stocks, resulting in an overall increase in the index value.

Financing conditions in the apartment market in the United States improved in July 2024, according to the National Multifamily Housing Council's (NMHC) finance index. The index is a standard diffusion index and is based on a quarterly survey among NMHC members. A value over 50 indicates improving finance availability, while under 50, it shows that financing is becoming harder to obtain. In April 2019, the debt financing index reached its peak at 81 index points, meaning that debt financing conditions improved the most. In July 2024, the debt index stood at 63 index points, which was an improvement from the same quarter in 2023.

As of June 19, 2024, the largest mutual fund in the world was the Vanguard Total Intl Stock Index Admiral, listed under the ticker VTSAX, which had an astonishing 1.55 trillion U.S. dollars of net assets under management (AUM). However, it should be noted that this investment fund has been divided into multiple distinct products - not all of which are sold as mutual funds. Some shares in the fund are sold as an exchange traded, meaning it could be argued that, strictly speaking, the Vanguard Total Stock Market Index Fund in its totality cannot be classed as a mutual fund. A similar situation holds for several other investment funds included in this statistic. An ETF is a basket of shares (or other financial assets) which generally tracks an underlying index. They are similar to mutual funds, with the fundamental difference that ETFs are listed on stock exchanges, with ETF shares being traded just like regular stock.

The Emerging Markets Bond Index (EMBI), commonly known as "riesgo país" in Spanish speaking countries, is a weighted financial benchmark that measures the interest rates paid each day by a selected portfolio of government bonds from emerging countries. It is measured in base points, which reflect the difference between the return rates paid by emerging countries' government bonds and those offered by U.S. Treasury bills. This difference is defined as "spread". Which Latin American country has the highest risk bonds? As of September 19, 2024, Venezuela was the Latin American country with the greatest financial risk and highest expected returns of government bonds, with an EMBI spread of around 254 percent. This means that the annual interest rates paid by Venezuela's sovereign debt titles were estimated to be exponentially higher than those offered by the U.S. Treasury. On the other hand, Brazil's EMBI reached 207 index points at the end of August 2023. In 2023, Venezuela also had the highest average EMBI in Latin America, exceeding 40,000 base points. The impact of COVID-19 on emerging market bonds The economic crisis spawned by the coronavirus pandemic heavily affected the financial market's estimated risks of emerging governmental bonds. For instance, as of June 30, 2020, Argentina's EMBI spread had increased more than four percentage points in comparison to January 30, 2020. All the Latin American economies measured saw a significant increase of the EMBI spread in the first half of the year.