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, Level (BOGZ1FL073164013A) from 1970 to 2024 about mutual funds, equity, liabilities, interest rate, interest, rate, price index, indexes, price, and USA.

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.

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.

Capital market interest rate......................

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).

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.

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

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 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...

The Equity Market Volatility tracker moves with the VIX and with the realized volatility of returns on the S&P 500.

For more information, see Baker, Scott, Nicholas Bloom and Steven Davis (2019), 'Policy News and Stack Market Volatility' (https://www.policyuncertainty.com/media/Policy%20News%20and%20Stock%20Market%20Volatility.pdf)

The Average Interest Rates on U.S. Treasury Securities dataset provides average interest rates on U.S. Treasury securities on a monthly basis. Its primary purpose is to show the average interest rate on a variety of marketable and non-marketable Treasury securities. Marketable securities consist of Treasury Bills, Notes, Bonds, Treasury Inflation-Protected Securities (TIPS), Floating Rate Notes (FRNs), and Federal Financing Bank (FFB) securities. Non-marketable securities consist of Domestic Series, Foreign Series, State and Local Government Series (SLGS), U.S. Savings Securities, and Government Account Series (GAS) securities. Marketable securities are negotiable and transferable and may be sold on the secondary market. Non-marketable securities are not negotiable or transferrable and are not sold on the secondary market. This is a useful dataset for investors and bond holders to compare how interest rates on Treasury securities have changed over time.

The three month interbank lending rate in the United States has declined sharply since 2007, when it stood at over five percent. The global financial crisis of 2008-2009 saw the rate collapse to around 0.2 percent by early 2010, with the figure generally remaining below 0.5 percent until late 2015. From late 2015 the rate somewhat recovered, reaching a peak of 2.69 percent in December 2018. However, the economic impact of the global coronavirus (COVID-19) pandemic saw the three month interbank rate collapse again. Since the beginning of 2022, however, the rate increased again and peaked 5.49 percent in September 2023. As of June 2025 the rate stood at 4.31 percent.

The Apple share market data of 10 years can be used for educational purposes in a variety of ways, such as:

To learn about the stock market and how it works. By studying the historical price movements of Apple stock, you can learn about the different factors that can affect the stock market, such as economic conditions, interest rates, and company earnings. To develop investment strategies. By analyzing the Apple share market data, you can identify patterns and trends that can help you make better investment decisions. For example, you might notice that Apple stock tends to perform well in certain economic conditions or when the company releases new products. To learn about Apple's business. By tracking the company's stock price, you can get a sense of how investors are viewing Apple's financial performance and future prospects. This information can be helpful for making decisions about whether or not to invest in Apple stock. To conduct research on financial topics. The Apple share market data can be used to support research on a variety of financial topics, such as the impact of inflation on stock prices, the relationship between stock prices and interest rates, and the performance of different investment strategies. In addition to these educational purposes, the Apple share market data can also be used for other purposes, such as:

To create trading algorithms. Trading algorithms are computer programs that automatically buy and sell stocks based on certain criteria. The Apple share market data can be used to train trading algorithms to identify profitable trading opportunities. To develop risk management strategies. Risk management strategies are used to protect investors from losses. The Apple share market data can be used to identify risks associated with investing in Apple stock and to develop strategies to mitigate those risks. To make corporate decisions. The Apple share market data can be used by companies to make decisions about their business, such as how much to invest in research and development, how to allocate capital, and when to issue new shares. Overall, the Apple share market data is a valuable resource that can be used for a variety of educational and practical purposes. If you are interested in learning more about the stock market or investing, I encourage you to explore the Apple share market data.

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.

Graph and download economic data for Interest Rates and Price Indexes; Dow Jones U.S. Total Market Index, Percent Change in Index (BOGZ1PC073164013Q) from Q1 1971 to Q1 2025 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.

Interest Rates, High Prices, and Inventory Shortage to Slow Down Housing Market

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.

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

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

Interest rates on the money market and capital market. The figures are available on a monthly basis from January 1999.