In 2024, 62 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 65 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.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

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.

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.

Learn about the interlink between crude oil price and stock market movement, and how fluctuations in oil prices can impact the energy sector, other industries, and the overall economy. Discover the factors influencing oil prices and their cascading effects on stock prices, and understand the broader implications for industries like transportation and manufacturing. Understand the correlation between oil prices and stock market movement, and the role of other factors like interest rates and investor sentimen

The annual returns of the Nasdaq 100 Index from 1986 to 2024. fluctuated significantly throughout the period considered. The Nasdaq 100 index saw its lowest performance in 2008, with a return rate of -41.89 percent, while the largest returns were registered in 1999, at 101.95 percent. As of June 11, 2024, the rate of return of Nasdaq 100 Index stood at 14 percent. The Nasdaq 100 is a stock market index comprised of the 100 largest and most actively traded non-financial companies listed on the Nasdaq stock exchange. How has the Nasdaq 100 evolved over years? The Nasdaq 100, which was previously heavily influenced by tech companies during the dot-com boom, has undergone significant diversification. Today, it represents a broader range of high-growth, non-financial companies across sectors like consumer services and healthcare, reflecting the evolving landscape of the global economy. The annual development of the Nasdaq 100 recently has generally been positive, except for 2022, when the NASDAQ experienced a decline due to worries about escalating inflation, interest rates, and regulatory challenges. What are the leading companies on Nasdaq 100? In August 2023, Apple was the largest company on the Nasdaq 100, with a market capitalization of 2.73 trillion euros. Also, Alphabet C, Alphabet, Amazon, and Broadcom were among the five leading companies included in the index. Market capitalization is one of the most common ways of measuring how big a company is in the financial markets. It is calculated by multiplying the total number of outstanding shares by the current market price.

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.

Can stock prices be predicted? (Jakarta Stock Exchange Composite Index Stock Forecast)

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).

The benchmark interest rate in India was last recorded at 6.25 percent. This dataset provides - India Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

As of June 17, 2024, the most shorted stock was for, the American holographic technology services provider, MicroCloud Hologram Inc., with 66.64 percent of their total float having been shorted. This is a change from mid-January 2021, when video game retailed GameStop had an incredible 121.07 percent of their available shares in a short position. In effect this means that investors had 'borrowed' more shares (with a future promise to return them) than the total number of shares available for public trading. Owing to this behavior of professional investors, retail investors enacted a campaign to drive up the stock price of Gamestop, leading to losses of billions when investors had to repurchase the stock they had borrowed. At this time, a similar – but less effective – social media campaign was also carried out for the stock price of cinema operator AMC, and the price of silver. What is short selling? Short selling is essentially where an investor bets on a share price falling by: borrowing a number of shares selling these shares while the price is still high; purchasing the same number again once the price falls; then returning the borrowed shares at a profit. Of course, a profit will only be made if the share price does fall; should the share price rise the investor will then need to purchase the shares back at a higher price, and thus incur a loss. Short selling can lead to some very large profits in a short amount of time, with Tesla stock generating over one billion dollars in short sell profits during the first week of March 2020 alone, owing to the financial crash caused by the coronavirus (COVID-19) pandemic. However, owing to the short-term, opportunistic nature of short selling, these returns look less impressive when considered as net profits from short sell positions over the full year. The risks of short selling Short selling carries greater risks than traditional investments, and for this reason financial advisors often recommend against this strategy for ‘retail’ (i.e. non-professional) investors. The reason for this is that losses from short selling are potentially uncapped, whereas losses from traditional investments are limited to the initial cost. For example, if someone purchases 100 dollars of shares, the maximum they can lose is the 100 dollars the spent on those shares. However, say someone borrows 100 dollars of shares instead, betting on the price falling. If these shares are then sold for 100 dollars but the price subsequently rises, the losses could greatly exceed the initial investment should the price rise to, say, 500 dollars. The risks of short selling can be seen by looking again at Tesla, with the company causing the greatest losses over 2020 from short selling at over 40 billion U.S. dollars.

The Report Covers US Private Equity Market Size and the Market is Segmented by Investment Type (Large Cap, Mid Cap and Small Cap) and by Application (Add-On, Growth Equity and Leveraged Buyouts).

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.

FNB^E F.N.B. Corporation Depositary Shares each representing a 1/40th interest in a share of Fixed-to-Floating Rate Non-Cumulative Perpetual Preferred Stock Series E

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

Explore the stability of gold prices amidst economic uncertainty in the U.S., despite slight dips and fluctuating market conditions.

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.

Can stock prices be predicted? (LON:MAI Stock Forecast)

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

Based on a survey conducted among American investors in May 2022 in the United States (U.S.), 62 percent of the respondents stated that inflation was the biggest risk facing the U.S. stock market in the next six months. This was followed by concerns for geopolitics (Ukraine) and interest rates (Fed policy), each indicated by 35 percent of the respondents.

Key information about Uruguay Long Term Interest Rate

• Uruguay Long Term Interest Rate: Month End: Uruguay: Sovereign Debt Bond CUD Yield: 10 Years was reported at 2.74 % pa in Jul 2021, compared with 2.76 % pa in the previous month.
• Uruguay Long Term Interest Rate data is updated monthly, available from Oct 2003 to Jul 2021.
• The data reached an all-time high of 13.03 % pa in Oct 2008 and a record low of 2.20 % pa in Dec 2020.
• Long Term Interest Rate is reported by reported by Electronic Stock Exchange of Uruguay.

• In the latest reports, Uruguay Short Term Interest Rates: Month End: Central Bank Bills: Peso: 3 Month was reported at 11.04 % pa in Jun 2023.
• The cash rate (Policy Rate: Month End: Overnight Monetary Policy Rate) was set at 11.25 % pa in Jun 2023.
• Uruguay Exchange Rate against USD averaged 38.20 (USD/UYU) in Jun 2023.

Snapshot img

Home Equity Lending Market Size 2025-2029

The home equity lending market size is forecast to increase by USD 48.16 billion at a CAGR of 4.7% between 2024 and 2029.

The market is experiencing significant growth due to several key trends. One major factor driving market expansion is the massive increase in home prices, which has resulted in homeowners having more equity in their properties. Another trend is the rise in residential property values, leading to an increase in the number of homeowners with sufficient equity to access loans or lines of credit, with property management and digital lending playing a significant role in facilitating these transactions.
However, the lengthy procedures involved in securing these loans can present challenges for both lenders and borrowers. Despite this, the benefits of lending, such as lower interest rates compared to other types of debt, make it an attractive option for many consumers looking to finance home improvements, debt consolidation, or other major expenses. Overall, the market is poised for continued growth in the coming years.

What will be the Size of the Home Equity Lending Market During the Forecast Period?

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The market in the United States has experienced significant growth, driven by the increasing collateral value of residential real estate and the resulting equity available to borrowers. Monetary authorities' efforts to keep inflation in check and stable housing prices have contributed to this trend. Homeowners have utilized loans and lines of credit to fund various expenses, including home improvements, tax deductions, and debt consolidation.



The interest rate on these loans often remains competitive with other forms of borrowing, making them an attractive option for many. Banks and credit unions are the primary providers of these loans, offering borrowers the ability to access a lump sum amount or a revolving line of credit secured against their residence and property. Regulatory restrictions on high-interest debt and outstanding mortgages may impact the market's growth, but the demand for loans is expected to remain strong as homeowners continue to seek ways to access the value of their homes.

How is this Home Equity Lending Industry segmented and which is the largest segment?

The home equity lending industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Source

  Mortgage and credit union
  Commercial banks
  Others


Distribution Channel

  Offline
  Online


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France


  APAC

    China
    Japan
    South Korea


  South America



  Middle East and Africa

By Source Insights

The mortgage and credit union segment is estimated to witness significant growth during the forecast period.

Home equity lending is a financing solution for homeowners looking to access the value of their property. Mortgage and credit unions serve as trusted providers in this market, offering various financial services including loans and lines of credit. These institutions not only offer consumer loans but also manage deposits, handle checking and savings accounts, disburse credit and debit cards, and grant house loans. Credit unions, in particular, provide personalized services with live representatives, ensuring a human touch in understanding complex financial matters.

Homeowners can secure competitive rates on loans through credit unions, making them a preferred choice over other lenders. With a strong focus on consumer protection and affordability, mortgage and credit unions are an excellent option for homeowners seeking to tap into their for renovation projects or other financial needs.

Get a glance at the Home Equity Lending Industry report of share of various segments. Request Free Sample

The mortgage and credit union segment was valued at USD 82.39 billion in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 47% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Request Free Sample

The market in North America experienced notable growth in 2024, driven by the increase in home values and fewer regulations. Homeowners in Canada have been utilizing their properties as collateral for loans, with residential mortgages accounting for 74% of household debt and lines of credit for 16%. The balance of Lines of Credit (HELOC) rose by 1% to USD 128 billion in February 2022.

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.

Can stock prices be predicted? (S&P/BMV IPC Index Stock Forecast)

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

Stock Video Market size was valued at USD 5.99 Billion in 2023 and is projected to reach USD 9.98 Billion by 2031, growing at a CAGR of 8.75% during the forecast period 2024-2031.

Stock Video Market: Definition/ Overview

Stock video is pre-recorded material available for license to filmmakers, video producers and content developers. These movies include a wide range of subjects and scenarios, from natural scenes to urban landscapes and are utilized to supplement video projects without requiring original filming. Stock videos save time and resources by providing high-quality visuals quickly.

Stock video assets are adaptable and can be utilized in a variety of media projects. They improve marketing campaigns, social media postings and advertising by providing professional quality without the cost of specialized shoots. Filmmakers and video developers use them for B-roll, background scenes and visual storytelling. They can also be used in educational videos, presentations and website designs to interest and inform viewers.

Stock video offers the potential to transform content development by allowing for quick, cost-effective production in marketing, education and entertainment. It benefits a wide range of industries, including advertising and movies by strengthening storytelling with high-quality images. As AI progresses, personalized and dynamic stock footage will enhance user experiences making it a useful tool for both creators and corporations.

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.

Can stock prices be predicted? (LON:TRST Stock Forecast)

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