In 2024, ** percent of adults in the United States invested in the stock market. This figure has remained steady over the last few years, and is still below the levels before the Great Recession, when it peaked in 2007 at ** percent. What is the stock market? The stock market can be defined as a group of stock exchanges, where investors can buy shares in a publicly traded company. In more recent years, it is estimated an increasing number of Americans are using neobrokers, making stock trading more accessible to investors. Other investments A significant number of people think stocks and bonds are the safest investments, while others point to real estate, gold, bonds, or a savings account. Since witnessing the significant one-day losses in the stock market during the Financial Crisis, many investors were turning towards these alternatives in hopes for more stability, particularly for investments with longer maturities. This could explain the decrease in this statistic since 2007. Nevertheless, some speculators enjoy chasing the short-run fluctuations, and others see value in choosing particular stocks.

Graph and download economic data for Households and Nonprofit Organizations; Directly and Indirectly Held Corporate Equities as a Percentage of Financial Assets; Assets, Level (BOGZ1FL153064486Q) from Q4 1945 to Q1 2025 about nonprofit organizations, equity, percent, assets, households, and USA.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

Nasdaq, Inc. operates as a technology company that serves capital markets and other industries worldwide. The Market Technology segment includes anti financial crime technology business, which offers Nasdaq Trade Surveillance, a SaaS solution for brokers and other market participants to assist them in complying with market rules, regulations, and internal market surveillance policies; Nasdaq Automated Investigator, a cloud-deployed anti-money laundering tool; and Verafin, a SaaS technology provider of anti-financial crime management solutions. This segment also handles assets, such as cash equities, equity derivatives, currencies, interest-bearing securities, commodities, energy products, and digital currencies. The Investment Intelligence segment sells and distributes historical and real-time market data; develops and licenses Nasdaq-branded indexes and financial products; and provides investment insights and workflow solutions. The Corporate Platforms segment operates listing platforms; and offers investor relations intelligence and governance solutions. As of December 31, 2021, it had 4,178 companies listed securities on The Nasdaq Stock Market, including 1,632 listings on The Nasdaq Global Select Market; 1,169 on The Nasdaq Global Market; and 1,377 on The Nasdaq Capital Market. The Market Services segment includes equity derivative trading and clearing, cash equity trading, fixed income and commodities trading and clearing, and trade management service businesses. This segment operates various exchanges and other marketplace facilities across various asset classes, which include derivatives, commodities, cash equity, debt, structured products, and exchange traded products; and provides broker, clearing, settlement, and central depository services. The company was formerly known as The NASDAQ OMX Group, Inc. and changed its name to Nasdaq, Inc. in September 2015. Nasdaq, Inc. was founded in 1971 and is headquartered in New York, New York.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

Almost one-third of the total assets managed by Vanguard's total stock market index fund traded under the ticket symbol VTI was allocated to technology stocks. Consumer discretionary stocks accounted for the second largest portion of assets. The asset allocation of the total stock market index fund was comparable to that of the asset allocation of the S&P 500 index. The S&P 500 is often quoted as a barometer of U.S. market performance. However, as the S&P 500 tracks 500 of the largest U.S. companies, it is not inclusive of the performance of small and mid-cap companies. Investors can buy into the total stock market index fund (VTI), for wider market exposure.

Graph and download economic data for Rest of the World; Foreign Direct Investment in U.S.: Equity; Asset (Market Value), Level (BOGZ1FL263092141A) from 1945 to 2024 about FDI, market value, equity, assets, 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.

ATIF: Are Timber Assets Fueling Growth?

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

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

The allocation of assets across different markets is a crucial element of investment strategy. In this regard, stocks and bonds are two significant assets that form the backbone of multi-asset allocation. Among publicly offered funds (The publicly offered funds in China correspond to the mutual funds in the United States, with different names and details in terms of legal form and sales channels), the stock-bond hybrid fund gives investors a return while minimizing the risk through capital flow between the stock and bond markets. Our research on China’s financial market data from 2006 to 2022 reveals a cross-asset momentum between the stock and bond markets. We find that the momentum in the stock market negatively influences the bond market’s return, while the momentum in the bond market positively influences the stock market’s return. Portfolios that exploit cross-asset momentum have excess returns that other asset pricing factors cannot explain. Our analysis reveals that hybrid funds play an intermediary role in the transmission mechanism of cross-asset momentum. We observe that the more flexible the asset allocation ratio of the fund, the more crucial the intermediary role played by the fund. Hence, encouraging the development of hybrid funds and relaxing restrictions on asset allocation ratios could improve liquidity and pricing efficiency. These findings have significant implications for investors seeking to optimize their asset allocation across different markets and for policymakers seeking to enhance the efficiency of China’s financial market.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

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.

WMA WAM ALTERNATIVE ASSETS LIMITED

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

Nasdaq, Inc. operates as a technology company that serves capital markets and other industries worldwide. The Market Technology segment includes anti financial crime technology business, which offers Nasdaq Trade Surveillance, a SaaS solution for brokers and other market participants to assist them in complying with market rules, regulations, and internal market surveillance policies; Nasdaq Automated Investigator, a cloud-deployed anti-money laundering tool; and Verafin, a SaaS technology provider of anti-financial crime management solutions. This segment also handles assets, such as cash equities, equity derivatives, currencies, interest-bearing securities, commodities, energy products, and digital currencies. The Investment Intelligence segment sells and distributes historical and real-time market data; develops and licenses Nasdaq-branded indexes and financial products; and provides investment insights and workflow solutions. The Corporate Platforms segment operates listing platforms; and offers investor relations intelligence and governance solutions. As of December 31, 2021, it had 4,178 companies listed securities on The Nasdaq Stock Market, including 1,632 listings on The Nasdaq Global Select Market; 1,169 on The Nasdaq Global Market; and 1,377 on The Nasdaq Capital Market. The Market Services segment includes equity derivative trading and clearing, cash equity trading, fixed income and commodities trading and clearing, and trade management service businesses. This segment operates various exchanges and other marketplace facilities across various asset classes, which include derivatives, commodities, cash equity, debt, structured products, and exchange traded products; and provides broker, clearing, settlement, and central depository services. The company was formerly known as The NASDAQ OMX Group, Inc. and changed its name to Nasdaq, Inc. in September 2015. Nasdaq, Inc. was founded in 1971 and is headquartered in New York, New York.

The growing trend of interdependence between the international stock markets indicated the amalgamation of risk across borders that plays a significant role in portfolio diversification by selecting different assets from the financial markets and is also helpful for making extensive economic policy for the economies. By applying different methodologies, this study undertakes the volatility analysis of the emerging and OECD economies and analyzes the co-movement pattern between them. Moreover, with that motive, using the wavelet approach, we provide strong evidence of the short and long-run risk transfer over different time domains from Malaysia to its trading partners. Our findings show that during the Asian financial crisis (1997–98), Malaysia had short- and long-term relationships with China, Germany, Japan, Singapore, the UK, and Indonesia due to both high and low-frequency domains. Meanwhile, after the Global financial crisis (2008–09), it is being observed that Malaysia has long-term and short-term synchronization with emerging (China, India, Indonesia), OECD (Germany, France, USA, UK, Japan, Singapore) stock markets but Pakistan has the low level of co-movement with Malaysian stock market during the global financial crisis (2008–09). Moreover, it is being seen that Malaysia has short-term at both high and low-frequency co-movement with all the emerging and OECD economies except Japan, Singapore, and Indonesia during the COVID-19 period (2020–21). Japan, Singapore, and Indonesia have long-term synchronization relationships with the Malaysian stock market at high and low frequencies during COVID-19. While in a leading-lagging relationship, Malaysia’s stock market risk has both leading and lagging behavior with its trading partners’ stock market risk in the selected period; this behavior changes based on the different trade and investment flow factors. Moreover, DCC-GARCH findings shows that Malaysian market has both short term and long-term synchronization with trading partners except USA. Conspicuously, the integration pattern seems that the cooperation development between stock markets matters rather than the regional proximity in driving the cointegration. The study findings have significant implications for investors, governments, and policymakers around the globe.

The United States clearing houses and settlements market is experiencing robust growth, fueled by increasing trading volumes, regulatory changes demanding enhanced transparency and risk mitigation, and the expanding adoption of technology within financial markets. The market's Compound Annual Growth Rate (CAGR) exceeding 5% from 2019 to 2024 suggests a significant expansion, projected to continue through 2033. The primary market segment, encompassing direct clearing and settlement activities, likely constitutes the largest share, given the foundational nature of its services. Within financial instruments, the debt market likely dominates due to the higher volume of transactions compared to equity. Major exchanges like the New York Stock Exchange (NYSE), NASDAQ, and CBOE play critical roles, driving market activity and influencing overall growth. Growth is also propelled by the increasing complexity of financial instruments and the need for efficient and reliable clearing and settlement mechanisms. While the precise market size for 2025 is unavailable, considering the provided CAGR and the substantial trading volume in the US financial markets, a reasonable estimate would place it in the tens of billions of dollars, with steady growth anticipated throughout the forecast period. Growth is further reinforced by technological advancements such as blockchain and distributed ledger technology, which promise to enhance efficiency and reduce costs associated with clearing and settlement processes. However, the market faces potential restraints including evolving regulatory landscapes, cybersecurity risks, and the increasing complexity of managing systemic risk. Despite these challenges, the fundamental need for secure and efficient clearing and settlement in the US financial system ensures continued growth. The market is segmented by type of market (primary and secondary), and financial instruments (debt and equity), with the largest players being major exchanges. Regional data focuses predominantly on the United States, reflecting its position as a global financial center. The historical period (2019-2024) and the forecast period (2025-2033) combined provide a comprehensive view of the market's trajectory. Recent developments include: In December 2023, Miami International Holdings, Inc. has introduced new MIAX Sapphire, physical trading floor located in Miami's Wynwood district. The new MIAX Sapphire exchange, which will run both an electronic exchange and a physical trading floor, will be MIAX's fourth national securities exchange for U.S. multi-listed options., In December 2023, Wall Street's top regulators enacted new regulations that force more trades via clearing houses, thus reducing systemic risk in the $26 trillion U.S. Treasury market.. Notable trends are: Digital Assets and Digitalization is Expected to Boost the Growth of the Market.

The percentage of market value attributable to intangible assets has increased exponentially from 32% in 1985 to 87% in 2015. This trend is expected to continue, making valuation of intangible assets vital for investors. (https://www.oceantomo.com/insights/ocean-tomo-releases-2015-annual-study-of-intangible-asset-market-value/) The Patent Valuation System estimates the profits generated by patents based on existing industry financial data and patent data, in order to minimize the subjective analysis involved. This method can be combined with any valuation method that requires an estimate of the expected returns generated by patents. Comparisons between the values generated by the patent valuation system and real-life values of actual patent transactions are carried out in order to gauge the system’s accuracy. Using PTR (price to technology ratio) ratio : The price-to-technology ratio (PTR) is the ratio for valuing a company that measures its current share price relative to its per-share technology value, where technology value of corporations is defined as the sum of value of patents they hold. PTR allows investors to make an investment decision in terms of technology value of corporations. PTR helps investors identify stocks that are overvalued or undervalued by comparing technology value to its stock price.

Passive ETF Market Outlook

In 2023, the global Passive ETF market size was valued at approximately USD 6.1 trillion and is projected to reach USD 11.4 trillion by 2032, growing at a CAGR of 7.2% over the forecast period. The primary growth factor for this market is the increasing preference for low-cost investment options among retail and institutional investors alike.

One of the significant growth factors driving the Passive ETF market is the rise in awareness and education about financial markets among retail investors. More individuals are becoming informed about the benefits of diversified, low-cost investment portfolios. Passive ETFs, which typically track a specific index, offer a cost-effective way for investors to gain broad market exposure without the need for intensive management. This factor is particularly appealing to new investors who wish to participate in the stock market with minimal fees and reduced risk.

Another critical driver is the surge in technological advancements and digitalization in financial services. Online trading platforms and robo-advisors are making it easier for investors to access a wide array of ETF products. These platforms often provide tools and resources that help investors make informed decisions, thereby encouraging more people to invest in Passive ETFs. The ease of use, coupled with low transaction costs, has further popularized Passive ETFs among various investor segments.

Institutional investors are also increasingly turning to Passive ETFs to optimize their investment strategies. With market volatility and economic uncertainties, institutional investors seek stable and predictable investment solutions. Passive ETFs offer a reliable way to achieve market returns without the need to actively manage individual securities. This stability is particularly important for pension funds, endowments, and insurance companies, which have long-term investment horizons and fiduciary responsibilities to their beneficiaries.

Regionally, North America continues to dominate the Passive ETF market, owing to its mature financial markets and large base of institutional and retail investors. However, other regions like Asia Pacific are catching up rapidly. The growing middle class, rising disposable incomes, and increasing financial literacy are significant factors contributing to the market's growth in this region. Additionally, favorable regulatory changes and the introduction of innovative financial products are expected to drive the market further in Asia Pacific.

Type Analysis

In the Passive ETF market, various types, including Equity ETFs, Bond ETFs, Commodity ETFs, Real Estate ETFs, and others, offer diverse investment opportunities. Equity ETFs hold the largest market share, primarily due to their ability to provide broad exposure to stock markets, mirroring the performance of major indices like the S&P 500 or the NASDAQ. As investors seek to capitalize on market growth while minimizing costs, the demand for Equity ETFs continues to rise. They are particularly popular among retail investors looking to gain diversified exposure to the equity market without picking individual stocks.

Bond ETFs are another critical segment within the Passive ETF market, offering investors a way to gain exposure to the fixed income market. These ETFs are essential for those looking to balance their portfolios with more stable, income-generating investments. Bond ETFs can provide access to government, corporate, and municipal bonds. The predictable income stream and lower risk compared to equities make Bond ETFs a favorite among conservative investors and retirees. Additionally, in a low-interest-rate environment, Bond ETFs become even more attractive as they offer better returns compared to traditional savings accounts.

Commodity ETFs cater to investors looking to diversify their portfolios with tangible assets like gold, silver, oil, and other commodities. These ETFs provide a convenient way to invest in commodities without the complexities involved in holding physical assets. Commodity ETFs are particularly popular during times of economic uncertainty and inflation, as they often serve as a hedge against market volatility and currency devaluation. The demand for these ETFs is expected to grow as investors seek more avenues to protect their wealth.

Real Estate ETFs provide exposure to the real estate market by investing in a diversified portfolio of real estate investment trusts (REITs). These ETFs offer a way to participate in the real estate market without th

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.

JPMorgan Global Core: Real Assets, Real Value? (JARA)

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.

(JLEN) Environmental Assets: Green Growth or Greenwash?

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