Graph and download economic data for Equity Market Volatility Tracker: Macroeconomic News and Outlook: Interest Rates (EMVMACROINTEREST) from Jan 1985 to Jun 2025 about volatility, uncertainty, equity, interest rate, interest, rate, and USA.

Graph and download economic data for Interest Rates and Price Indexes; Dow Jones U.S. Total Market Index, Percent Change in Index (BOGZ1PC073164013A) from 1971 to 2024 about mutual funds, equity, liabilities, interest rate, interest, rate, 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.

[Video] S&P 500: Bull or Bear?

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

Over 2021 the most commonly traded interest rate derivatives on the London Stock Exchange were three month futures for British pounds, of varying expiration dates. This was followed by futures on the euro interbank offered rate (Euribor), and then futures on the Sterling Overnight Interbank Average Rate (SONIA).

Interest rate futures are essentially a contact that fixes the interest rate on a loan or deposit for a period of time in the future, which (in the case of this statistic) is then tradable on a stock exchange. The type of future relates the underlying reference interest rate (LIBOR in the case of Sterling futures, or Eurobor, or SONIA).

Graph and download economic data for Interest Rates and Price Indexes; Dow Jones U.S. Total Market Index, Level (BOGZ1FL073164013Q) from Q4 1970 to Q1 2025 about mutual funds, equity, liabilities, interest rate, interest, rate, price index, indexes, price, and USA.

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.

Predictions and Risks for Stifel Financial Corporation 5.20% Senior Notes due 2047: Fixed income markets remain volatile amidst rising interest rates, affecting bond prices. Stifel Financial Corporation's strong financial position and consistent dividend payments indicate resilience but fluctuations in interest rates pose risks to bond value. The company's exposure to economic downturns and regulatory changes can impact cash flows and the ability to meet debt obligations. Investors should consider the potential for interest rate fluctuations, economic headwinds, and regulatory challenges when assessing the risk and potential returns of the bonds.

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.

What is a regression analysis for stock prices?

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 Federal National Mortgage Association, commonly known as Fannie Mae, was created by the U.S. congress in 1938, in order to maintain liquidity and stability in the domestic mortgage market. The company is a government-sponsored enterprise (GSE), meaning that while it was a publicly traded company for most of its history, it was still supported by the federal government. While there is no legally binding guarantee of shares in GSEs or their securities, it is generally acknowledged that the U.S. government is highly unlikely to let these enterprises fail. Due to these implicit guarantees, GSEs are able to access financing at a reduced cost of interest. Fannie Mae's main activity is the purchasing of mortgage loans from their originators (banks, mortgage brokers etc.) and packaging them into mortgage-backed securities (MBS) in order to ease the access of U.S. homebuyers to housing credit. The early 2000s U.S. mortgage finance boom During the early 2000s, Fannie Mae was swept up in the U.S. housing boom which eventually led to the financial crisis of 2007-2008. The association's stated goal of increasing access of lower income families to housing finance coalesced with the interests of private mortgage lenders and Wall Street investment banks, who had become heavily reliant on the housing market to drive profits. Private lenders had begun to offer riskier mortgage loans in the early 2000s due to low interest rates in the wake of the "Dot Com" crash and their need to maintain profits through increasing the volume of loans on their books. The securitized products created by these private lenders did not maintain the standards which had traditionally been upheld by GSEs. Due to their market share being eaten into by private firms, however, the GSEs involved in the mortgage markets began to also lower their standards, resulting in a 'race to the bottom'. The fall of Fannie Mae The lowering of lending standards was a key factor in creating the housing bubble, as mortgages were now being offered to borrowers with little or no ability to repay the loans. Combined with fraudulent practices from credit ratings agencies, who rated the junk securities created from these mortgage loans as being of the highest standard, this led directly to the financial panic that erupted on Wall Street beginning in 2007. As the U.S. economy slowed down in 2006, mortgage delinquency rates began to spike. Fannie Mae's losses in the mortgage security market in 2006 and 2007, along with the losses of the related GSE 'Freddie Mac', had caused its share value to plummet, stoking fears that it may collapse. On September 7th 2008, Fannie Mae was taken into government conservatorship along with Freddie Mac, with their stocks being delisted from stock exchanges in 2010. This act was seen as an unprecedented direct intervention into the economy by the U.S. government, and a symbol of how far the U.S. housing market had fallen.

Celsius Holdings' stock increased by 5.7% as the Fed maintained interest rates, signaling potential rate cuts amidst economic uncertainty. The company recently expanded by acquiring Alani Nu.

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.

Financial Future with Stifel: Is SFB's Senior Note a Wise Investment?

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 U.S. federal funds rate peaked in 2023 at its highest level since the 2007-08 financial crisis, reaching 5.33 percent by December 2023. A significant shift in monetary policy occurred in the second half of 2024, with the Federal Reserve implementing regular rate cuts. By December 2024, the rate had declined to 4.48 percent. What is a central bank rate? The federal funds rate determines the cost of overnight borrowing between banks, allowing them to maintain necessary cash reserves and ensure financial system liquidity. When this rate rises, banks become more inclined to hold rather than lend money, reducing the money supply. While this decreased lending slows economic activity, it helps control inflation by limiting the circulation of money in the economy. Historic perspective The federal funds rate historically follows cyclical patterns, falling during recessions and gradually rising during economic recoveries. Some central banks, notably the European Central Bank, went beyond traditional monetary policy by implementing both aggressive asset purchases and negative interest rates.

Investment Trust Market Outlook

The global investment trust market size was valued at approximately USD 2.5 trillion in 2023 and is projected to reach around USD 4.1 trillion by 2032, growing at a compound annual growth rate (CAGR) of 5.5% during the forecast period. The growth of this market is driven by several factors including increasing investor preference for diversified portfolios and the growing availability of various types of investment trusts to meet different investment goals. These factors are expected to propel the market significantly over the coming years.

Expanding middle-class populations and increasing disposable incomes in emerging economies are also contributing significantly to the growth of the investment trust market. With more individuals seeking avenues for better returns on their investments, investment trusts offer an attractive proposition due to their diversified nature and professional management. Additionally, the growing awareness about the benefits of investing in such diversified instruments, as opposed to individual stocks or bonds, is a crucial growth factor.

Technological advancements and digitalization have made it easier for investors to access investment trusts. Online platforms have simplified the process of investing, enabling real-time tracking and management of investment portfolios. This ease of access has broadened the market's appeal, attracting a younger, tech-savvy investor base. The integration of artificial intelligence and machine learning in these platforms further enhances their capabilities, making investment decisions more data-driven and informed.

The rising trend of sustainable and responsible investing is another significant driver for the investment trust market. Many investors are now seeking to align their portfolios with their personal values, focusing on environmental, social, and governance (ESG) criteria. Investment trusts that prioritize ESG factors are seeing increased demand, as investors look to not only generate financial returns but also contribute positively to society and the environment.

Regionally, North America and Europe dominate the investment trust market, primarily due to their well-established financial sectors and higher levels of investor sophistication. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The increasing economic development and growing middle-class population in countries like China and India are major contributors to this growth. As more individuals in these regions become financially literate, the demand for diverse investment options like investment trusts is expected to rise steadily.

Type Analysis

Equity investment trusts, fixed-income investment trusts, hybrid investment trusts, and other specialized types form the various segments of the investment trust market. Equity investment trusts, which primarily invest in stocks, remain the most popular due to their potential for high returns. These trusts appeal to investors looking for growth opportunities, particularly in sectors showing robust performance. The volatility of stock markets, however, poses a risk, making it essential for these trusts to maintain a well-diversified portfolio to mitigate potential losses.

Fixed-income investment trusts focus on bonds and other debt instruments, offering a more stable and predictable income stream, which is particularly attractive to conservative investors or those nearing retirement. These trusts typically have lower risk compared to equity trusts, but also potentially lower returns. With interest rates playing a critical role in their performance, the recent trends of fluctuating interest rates have made these trusts more appealing as they adapt to the changing economic landscape.

Hybrid investment trusts combine both equity and fixed-income investments, providing a balanced approach that appeals to a broader range of investors. These trusts aim to achieve a mix of income generation and capital appreciation, making them suitable for investors with moderate risk tolerance. The flexibility offered by hybrid trusts allows them to adjust their asset allocation based on market conditions, enhancing their appeal in uncertain economic climates.

Other types of investment trusts include those specializing in real estate, commodities, and niche sectors like technology or healthcare. These specialized trusts cater to investors looking to focus on specific sectors that they believe will outperform the broader market. While they offer t

Descriptive statistics of SHIBOR and SHSML.

According to Cognitive Market Research, the global stock market size will be USD 3645.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 13% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 1458.1 million in 2024 and will grow at a compound annual growth rate (CAGR) of 11.2% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 1093.6 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 838.4 million in 2024 and will grow at a compound annual growth rate (CAGR) of 15% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 182.3 million in 2024 and will grow at a compound annual growth rate (CAGR) of 12.4% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 72.9 million in 2024 and will grow at a compound annual growth rate (CAGR) of 12.7% from 2024 to 2031.
The broker end users held the highest stock market revenue share in 2024.

Market Dynamics of Stock Market

Key Drivers for the Stock Market

Rising Demand for Real-Time Data and Analytics to be an Emerging Market Trend

The increasing need for real-time data and advanced analytics is a significant driver in the stock trading and investing market growth. Investors and traders require up-to-the-minute information on stock prices, market trends, and financial news to make informed decisions quickly. As financial markets become more dynamic and competitive, the ability to access and analyze real-time data becomes crucial for success. Trading applications that offer real-time updates, advanced charting tools, and detailed analytics provide users with a competitive edge by enabling them to react swiftly to market movements. This heightened demand for real-time insights fuels the development and adoption of sophisticated trading platforms that cater to both professional traders and retail investors seeking to maximize their investment opportunities.

Increasing Adoption of Mobile Trading Platforms to Boost Market Growth

The rapid adoption of mobile trading platforms is another key driver for the stock market expansion. With the proliferation of smartphones and mobile internet access, investors are increasingly favoring mobile platforms for their trading activities due to their convenience and accessibility. Mobile trading apps offer users the ability to trade, monitor portfolios, and access financial information on the go, which appeals to both active traders and casual investors. This shift towards mobile platforms is supported by innovations in-app functionality, user experience, and security features. As more investors seek flexibility and real-time engagement with their investments, the demand for sophisticated and user-friendly mobile trading applications continues to rise, propelling market growth.

Restraint Factor for the Stock Market

Stringent Rules and Regulations to Impede the Adoption of Online Trading Platforms

Regulatory compliance and legal challenges are major restraints for the stock trading and investing market share. The financial industry is heavily regulated, with strict rules governing trading practices, data protection, and financial disclosures. Compliance with these regulations requires substantial investment in legal expertise, technology, and administrative processes. Changes in regulations can also introduce uncertainty and additional compliance costs for application providers. For example, regulations such as the Markets in Financial Instruments Directive II (MiFID II) in Europe and the Dodd-Frank Act in the U.S. impose stringent requirements on trading practices and transparency. Failure to adhere to these regulations can result in legal penalties and damage to a company’s reputation, which can inhibit market growth and innovation in trading applications.

Market Volatility and Investor Uncertainty

The stock market is highly sensitive to global economic conditions, geopolitical tensions, interest rate fluctuations, and unexpected events (such as pandemics or wars). This inherent volatility can lead to sharp declines in investor confidence and capital outflows, especially among retai...

Graph and download economic data for Earnings Yield of All Common Stocks on the New York Stock Exchange for United States (A13049USA156NNBR) from 1871 to 1938 about stocks, earnings, NY, yield, interest rate, interest, rate, and USA.

The value of ρDCCA for a given window size s.

S&P 500 index is predicted to continue its upward trajectory, driven by strong earnings and economic growth. However, risks to this prediction include geopolitical tensions, rising interest rates, and inflation.

IP Derivatives Market Outlook

The global IP derivatives market size was valued at approximately USD 1.5 trillion in 2023 and is projected to reach USD 2.7 trillion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 6.8%. This growth is driven primarily by increasing demand for risk management instruments, innovation in financial products, and the expanding reach of financial markets globally.

One of the primary growth factors for the IP derivatives market is the rising need for sophisticated risk management tools among investors. Equity derivatives, interest rate derivatives, and currency derivatives provide mechanisms to hedge against market volatility and economic uncertainties. This has garnered interest from institutional investors and corporates seeking to stabilize their financial performance amidst fluctuating market conditions. Innovations in derivative products, such as the introduction of new commodity derivatives and customized financial instruments, are further fueling the expansion of this market.

Technological advancements in trading platforms also play a significant role in the market's growth. The incorporation of artificial intelligence, machine learning, and blockchain technologies into trading systems has revolutionized trading efficiency, transparency, and security. These advancements are making derivatives trading more accessible and appealing to a broader range of investors, including retail investors who previously may have found such instruments too complex or risky. Additionally, the ongoing digital transformation within financial institutions is fostering the development and deployment of advanced trading platforms, further propelling the market forward.

Regulatory changes and enhancements in financial markets infrastructure are also contributing to the market's upward trajectory. Governments and financial regulatory bodies are increasingly recognizing the importance of derivatives in financial markets, leading to more supportive regulatory frameworks. Enhanced transparency and standardized procedures in over-the-counter (OTC) trading, as well as the growth of exchange-traded derivatives, are encouraging greater participation from various market players. The robust development of financial markets in emerging economies is also expanding the global footprint of IP derivatives.

The regional outlook reveals significant growth potential in the Asia Pacific region, driven by the rapid economic development in countries like China and India, along with the increasing sophistication of financial markets in these regions. North America and Europe continue to be significant players due to their established financial markets and advanced regulatory frameworks. However, Latin America and the Middle East & Africa are also emerging as key regions, thanks to improvements in financial infrastructure and growing investor awareness and participation in these markets.

Product Type Analysis

Equity derivatives, one of the primary segments of IP derivatives, include options, futures, and swaps based on underlying equity securities. The growth in equity derivatives is driven by the increasing volatility in stock markets, prompting investors to seek mechanisms to hedge against potential losses. The ability of equity derivatives to offer high leverage and flexibility makes them particularly attractive to both institutional and retail investors. Innovations such as exotic options and structured products have added further diversity and depth to this segment, enhancing its appeal.

Interest rate derivatives, which include products like interest rate swaps, futures, and options, are another significant segment. These derivatives are crucial for managing the risk associated with fluctuations in interest rates, which can impact borrowing costs, investment returns, and overall economic stability. The recent economic uncertainties and fluctuating interest rate environment have led to increased demand for such instruments. Financial institutions, in particular, leverage interest rate derivatives to manage their exposure to interest rate risk more effectively.

Currency derivatives, encompassing futures, options, and swaps based on currency exchange rates, are essential for managing foreign exchange risk. The global nature of trade and investment necessitates effective management of currency exposure, and currency derivatives provide a vital tool for achieving this. The volatility in foreign exchange markets, driven by geopolitical tensions, economic policies, and other macroeconomic factors, has heightened th

Historical dataset of the 12 month LIBOR rate back to 1986. The London Interbank Offered Rate is the average interest rate at which leading banks borrow funds from other banks in the London market. LIBOR is the most widely used global "benchmark" or reference rate for short term interest rates.