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

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

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.

Graph and download economic data for Interest Rates and Price Indexes; Dow Jones U.S. Total Market Index, Level (BOGZ1FL073164013A) from 1970 to 2024 about mutual funds, equity, liabilities, interest rate, interest, rate, price index, indexes, price, and USA.

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.

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

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.

Graph and download economic data for Interest Rates and Price Indexes; Dow Jones U.S. Total Market Index, Level (BOGZ1FL073164013Q) from Q4 1970 to Q4 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.

if the stock market goes down during a recession, you should sell all of your investments to minimize your losses.

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

Finland BOF Forecast: Interest Rate: Average: Stock of Loans data was reported at 1.800 % pa in 2020. This records an increase from the previous number of 1.500 % pa for 2019. Finland BOF Forecast: Interest Rate: Average: Stock of Loans data is updated yearly, averaging 1.500 % pa from Dec 2015 (Median) to 2020, with 6 observations. The data reached an all-time high of 1.800 % pa in 2020 and a record low of 1.400 % pa in 2018. Finland BOF Forecast: Interest Rate: Average: Stock of Loans data remains active status in CEIC and is reported by Bank of Finland. The data is categorized under Global Database’s Finland – Table FI.M007: Lending Rates: Forecast: Bank of Finland.

The aim of this study is to investigate the effects of monetary policy on financial asset prices in Poland. Following Gürkaynak et al. (2005) I test how many factors adequately explain the variability of short-term interest rates around MPC meetings, finding that there are two such factors. The first one has a structural interpretation as a “current interest rate change” factor, and the second one as a “future interest rate changes” factor, with the latter related to MPC communication. Regression analysis shows that, controlling for foreign interest rates and global risk aversion, both MPC actions and communication matter for government bond yields, and that communication is more important for stock prices. Furthermore, the foreign exchange rate used to depreciate (appreciate) after MPC statements signalling tighter (easier) future monetary policy. However, the effect disappeared at the end of the sample. For most of the sample the exchange rate would appreciate (depreciate) or would not change in a statistically significant manner after an increase (a decrease) of the current interest rate. The results indicate that not only changes of the current interest rate but also MPC communication matters for financial asset prices in Poland. It has important implications for the conduct of monetary policy, especially in a low inflation and low interest rate environment.

NASDAQ reported $11M in Interest Income for its fiscal quarter ending in March of 2025. Data for NASDAQ | NDAQ - Interest Income including historical, tables and charts were last updated by Trading Economics this last June in 2025.

Argentina AR: External Debt: DOD: Stocks: Variable Rate data was reported at 60.545 USD bn in 2016. This records a decrease from the previous number of 63.756 USD bn for 2015. Argentina AR: External Debt: DOD: Stocks: Variable Rate data is updated yearly, averaging 37.345 USD bn from Dec 1970 (Median) to 2016, with 47 observations. The data reached an all-time high of 63.756 USD bn in 2015 and a record low of 3.291 USD bn in 1970. Argentina AR: External Debt: DOD: Stocks: Variable Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Argentina – Table AR.World Bank.WDI: External Debt: Debt Outstanding, Debt Ratio and Debt Service. Variable interest rate is long-term external debt with interest rates that float with movements in a key market rate; for example, the London interbank offered rate (LIBOR) or the U.S. prime rate. This item conveys information about the borrower's exposure to changes in international interest rates. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars.; ; World Bank, International Debt Statistics.; Sum;

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.

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.

How accurate is machine learning in stock market? (TD 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

This is not going to be an article or Op-Ed about Michael Jordan. Since 2009 we've been in the longest bull-market in history, that's 11 years and counting. However a few metrics like the stock market P/E, the call to put ratio and of course the Shiller P/E suggest a great crash is coming in-between the levels of 1929 and the dot.com bubble. Mean reversion historically is inevitable and the Fed's printing money experiment could end in disaster for the stock market in late 2021 or 2022. You can read Jeremy Grantham's Last Dance article here. You are likely well aware of Michael Burry's predicament as well. It's easier for you just to skim through two related videos on this topic of a stock market crash. Michael Burry's Warning see this YouTube. Jeremy Grantham's Warning See this YouTube. Typically when there is a major event in the world, there is a crash and then a bear market and a recovery that takes many many months. In March, 2020 that's not what we saw since the Fed did some astonishing things that means a liquidity sloth and the risk of a major inflation event. The pandemic represented the quickest decline of at least 30% in the history of the benchmark S&P 500, but the recovery was not correlated to anything but Fed intervention. Since the pandemic clearly isn't disappearing and many sectors such as travel, business travel, tourism and supply chain disruptions appear significantly disrupted - the so-called economic recovery isn't so great. And there's this little problem at the heart of global capitalism today, the stock market just keeps going up. Crashes and corrections typically occur frequently in a normal market. But the Fed liquidity and irresponsible printing of money is creating a scenario where normal behavior isn't occurring on the markets. According to data provided by market analytics firm Yardeni Research, the benchmark index has undergone 38 declines of at least 10% since the beginning of 1950. Since March, 2020 we've barely seen a down month. September, 2020 was flat-ish. The S&P 500 has more than doubled since those lows. Look at the angle of the curve: The S&P 500 was 735 at the low in 2009, so in this bull market alone it has gone up 6x in valuation. That's not a normal cycle and it could mean we are due for an epic correction. I have to agree with the analysts who claim that the long, long bull market since 2009 has finally matured into a fully-fledged epic bubble. There is a complacency, buy-the dip frenzy and general meme environment to what BigTech can do in such an environment. The weight of Apple, Amazon, Alphabet, Microsoft, Facebook, Nvidia and Tesla together in the S&P and Nasdaq is approach a ridiculous weighting. When these stocks are seen both as growth, value and companies with unbeatable moats the entire dynamics of the stock market begin to break down. Check out FANG during the pandemic. BigTech is Seen as Bullet-Proof me valuations and a hysterical speculative behavior leads to even higher highs, even as 2020 offered many younger people an on-ramp into investing for the first time. Some analysts at JP Morgan are even saying that until retail investors stop charging into stocks, markets probably don’t have too much to worry about. Hedge funds with payment for order flows can predict exactly how these retail investors are behaving and monetize them. PFOF might even have to be banned by the SEC. The risk-on market theoretically just keeps going up until the Fed raises interest rates, which could be in 2023! For some context, we're more than 1.4 years removed from the bear-market bottom of the coronavirus crash and haven't had even a 5% correction in nine months. This is the most over-priced the market has likely ever been. At the night of the dot-com bubble the S&P 500 was only 1,400. Today it is 4,500, not so many years after. Clearly something is not quite right if you look at history and the P/E ratios. A market pumped with liquidity produces higher earnings with historically low interest rates, it's an environment where dangerous things can occur. In late 1997, as the S&P 500 passed its previous 1929 peak of 21x earnings, that seemed like a lot, but nothing compared to today. For some context, the S&P 500 Shiller P/E closed last week at 38.58, which is nearly a two-decade high. It's also well over double the average Shiller P/E of 16.84, dating back 151 years. So the stock market is likely around 2x over-valued. Try to think rationally about what this means for valuations today and your favorite stock prices, what should they be in historical terms? The S&P 500 is up 31% in the past year. It will likely hit 5,000 before a correction given the amount of added liquidity to the system and the QE the Fed is using that's like a huge abuse of MMT, or Modern Monetary Theory. This has also lent to bubbles in the housing market, crypto and even commodities like Gold with long-term global GDP meeting many headwinds in the years ahead due to a...

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.

S&P 500: A Bull or a 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

Turkey External Debt Stock: Treasury Guaranteed: Interest Rate: Combined data was reported at 110.000 USD mn in 2017. This records an increase from the previous number of 64.000 USD mn for 2016. Turkey External Debt Stock: Treasury Guaranteed: Interest Rate: Combined data is updated yearly, averaging 139.000 USD mn from Dec 2002 (Median) to 2017, with 16 observations. The data reached an all-time high of 271.000 USD mn in 2008 and a record low of 64.000 USD mn in 2016. Turkey External Debt Stock: Treasury Guaranteed: Interest Rate: Combined data remains active status in CEIC and is reported by Turkish Treasury. The data is categorized under Global Database’s Turkey – Table TR.JB014: Treasury Guaranteed External Debt Stock.