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.

The Volcker Shock was a period of historically high interest rates precipitated by Federal Reserve Chairperson Paul Volcker's decision to raise the central bank's key interest rate, the Fed funds effective rate, during the first three years of his term. Volcker was appointed chairperson of the Fed in August 1979 by President Jimmy Carter, as replacement for William Miller, who Carter had made his treasury secretary. Volcker was one of the most hawkish (supportive of tighter monetary policy to stem inflation) members of the Federal Reserve's committee, and quickly set about changing the course of monetary policy in the U.S. in order to quell inflation. The Volcker Shock is remembered for bringing an end to over a decade of high inflation in the United States, prompting a deep recession and high unemployment, and for spurring on debt defaults among developing countries in Latin America who had borrowed in U.S. dollars.

Monetary tightening and the recessions of the early '80s

Beginning in October 1979, Volcker's Fed tightened monetary policy by raising interest rates. This decision had the effect of depressing demand and slowing down the U.S. economy, as credit became more expensive for households and businesses. The Fed funds rate, the key overnight rate at which banks lend their excess reserves to each other, rose as high as 17.6 percent in early 1980. The rate was allowed to fall back below 10 percent following this first peak, however, due to worries that inflation was not falling fast enough, a second cycle of monetary tightening was embarked upon starting in August of 1980. The rate would reach its all-time peak in June of 1981, at 19.1 percent. The second recession sparked by these hikes was far deeper than the 1980 recession, with unemployment peaking at 10.8 percent in December 1980, the highest level since The Great Depression. This recession would drive inflation to a low point during Volcker's terms of 2.5 percent in August 1983.

The legacy of the Volcker Shock

By the end of Volcker's terms as Fed Chair, inflation was at a manageable rate of around four percent, while unemployment had fallen under six percent, as the economy grew and business confidence returned. While supporters of Volcker's actions point to these numbers as proof of the efficacy of his actions, critics have claimed that there were less harmful ways that inflation could have been brought under control. The recessions of the early 1980s are cited as accelerating deindustrialization in the U.S., as manufacturing jobs lost in 'rust belt' states such as Michigan, Ohio, and Pennsylvania never returned during the years of recovery. The Volcker Shock was also a driving factor behind the Latin American debt crises of the 1980s, as governments in the region defaulted on debts which they had incurred in U.S. dollars. Debates about the validity of using interest rate hikes to get inflation under control have recently re-emerged due to the inflationary pressures facing the U.S. following the Coronavirus pandemic and the Federal Reserve's subsequent decision to embark on a course of monetary tightening.

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Modeling and predicting U.S. recessions using machine learning techniques

As variáveis do FRED-MD como preditivas e a USREC como alvo (período de 1979-2019)

Diversos Modelos: probit, logit, LDA, árvores Naive-Bayes Algumas variáveis tiveram que ser transformadas em mensais (interpolação cúbica)

128 varibles. Grupos: Output and Income Labor Market Consumption and Orders Orders and Inventories Money and Credit Interest Rates and Exchange Rates Prices Stock Market

The Global Financial Crisis of 2008-09 was a period of severe macroeconomic instability for the United States and the global economy more generally. The crisis was precipitated by the collapse of a number of financial institutions who were deeply involved in the U.S. mortgage market and associated credit markets. Beginning in the Summer of 2007, a number of banks began to report issues with increasing mortgage delinquencies and the problem of not being able to accurately price derivatives contracts which were based on bundles of these U.S. residential mortgages. By the end of 2008, U.S. financial institutions had begun to fail due to their exposure to the housing market, leading to one of the deepest recessions in the history of the United States and to extensive government bailouts of the financial sector.

Subprime and the collapse of the U.S. mortgage market

The early 2000s had seen explosive growth in the U.S. mortgage market, as credit became cheaper due to the Federal Reserve's decision to lower interest rates in the aftermath of the 2001 'Dot Com' Crash, as well as because of the increasing globalization of financial flows which directed funds into U.S. financial markets. Lower mortgage rates gave incentive to financial institutions to begin lending to riskier borrowers, using so-called 'subprime' loans. These were loans to borrowers with poor credit scores, who would not have met the requirements for a conventional mortgage loan. In order to hedge against the risk of these riskier loans, financial institutions began to use complex financial instruments known as derivatives, which bundled mortgage loans together and allowed the risk of default to be sold on to willing investors. This practice was supposed to remove the risk from these loans, by effectively allowing credit institutions to buy insurance against delinquencies. Due to the fraudulent practices of credit ratings agencies, however, the price of these contacts did not reflect the real risk of the loans involved. As the reality of the inability of the borrowers to repay began to kick in during 2007, the financial markets which traded these derivatives came under increasing stress and eventually led to a 'sudden stop' in trading and credit intermediation during 2008.

Market Panic and The Great Recession

As borrowers failed to make repayments, this had a knock-on effect among financial institutions who were highly leveraged with financial instruments based on the mortgage market. Lehman Brothers, one of the world's largest investment banks, failed on September 15th 2008, causing widespread panic in financial markets. Due to the fear of an unprecedented collapse in the financial sector which would have untold consequences for the wider economy, the U.S. government and central bank, The Fed, intervened the following day to bailout the United States' largest insurance company, AIG, and to backstop financial markets. The crisis prompted a deep recession, known colloquially as The Great Recession, drawing parallels between this period and The Great Depression. The collapse of credit intermediation in the economy lead to further issues in the real economy, as business were increasingly unable to pay back loans and were forced to lay off staff, driving unemployment to a high of almost 10 percent in 2010. While there has been criticism of the U.S. government's actions to bailout the financial institutions involved, the actions of the government and the Fed are seen by many as having prevented the crisis from spiraling into a depression of the magnitude of The Great Depression.

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.

Exploring the Positive Correlation Between Interest Rates and Housing Market Recessions

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

United States banking professionals believed in Q2 2022 that a Fed overcorrection was a probable cause for a recession. ** percent of the respondents believed that the too fast and too highly increasing Fed rates would result in an economic recession. ** percent of the respondents predicted that a recession would occur because of supply chain problems, while **** percent mentioned the conflict in Eastern Europe as the main cause for a possible recession.

Recent sovereign defaults are accompanied by interest rate spikes and deep recessions. This paper develops a small open economy model to study default risk and its interaction with output and foreign debt. Default probabilities and interest rates depend on incentives for repayment. Default is more likely in recessions because this is when it is more costly for a risk averse borrower to repay noncontingent debt. The model closely matches business cycles in Argentina predicting high volatility of interest rates, higher volatility of consumption relative to output, and negative correlations of output with interest rates and the trade balance.

As of April 16, 2025, the yield for a ten-year U.S. government bond was 4.34 percent, while the yield for a two-year bond was 3.86 percent. This represents an inverted yield curve, whereby bonds of longer maturities provide a lower yield, reflecting investors' expectations for a decline in long-term interest rates. Hence, making long-term debt holders open to more risk under the uncertainty around the condition of financial markets in the future. That markets are uncertain can be seen by considering both the short-term fluctuations, and the long-term downward trend, of the yields of U.S. government bonds from 2006 to 2021, before the treasury yield curve increased again significantly in the following years. What are government bonds? Government bonds, otherwise called ‘sovereign’ or ‘treasury’ bonds, are financial instruments used by governments to raise money for government spending. Investors give the government a certain amount of money (the ‘face value’), to be repaid at a specified time in the future (the ‘maturity date’). In addition, the government makes regular periodic interest payments (called ‘coupon payments’). Once initially issued, government bonds are tradable on financial markets, meaning their value can fluctuate over time (even though the underlying face value and coupon payments remain the same). Investors are attracted to government bonds as, provided the country in question has a stable economy and political system, they are a very safe investment. Accordingly, in periods of economic turmoil, investors may be willing to accept a negative overall return in order to have a safe haven for their money. For example, once the market value is compared to the total received from remaining interest payments and the face value, investors have been willing to accept a negative return on two-year German government bonds between 2014 and 2021. Conversely, if the underlying economy and political structures are weak, investors demand a higher return to compensate for the higher risk they take on. Consequently, the return on bonds in emerging markets like Brazil are consistently higher than that of the United States (and other developed economies). Inverted yield curves When investors are worried about the financial future, it can lead to what is called an ‘inverted yield curve’. An inverted yield curve is where investors pay more for short term bonds than long term, indicating they do not have confidence in long-term financial conditions. Historically, the yield curve has historically inverted before each of the last five U.S. recessions. The last U.S. yield curve inversion occurred at several brief points in 2019 – a trend which continued until the Federal Reserve cut interest rates several times over that year. However, the ultimate trigger for the next recession was the unpredicted, exogenous shock of the global coronavirus (COVID-19) pandemic, showing how such informal indicators may be grounded just as much in coincidence as causation.

Graph and download economic data for Bank Prime Loan Rate Changes: Historical Dates of Changes and Rates (PRIME) from 1955-08-04 to 2024-12-20 about prime, loans, interest rate, banks, interest, depository institutions, 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.

TA:TSX Stock: Are We Headed for a Recession?

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

We use the yield curve to predict future GDP growth and recession probabilities. The spread between short- and long-term rates typically correlates with economic growth. Predications are calculated using a model developed by the Federal Reserve Bank of Cleveland. Released monthly.

We investigate the heterogeneous boom and bust patterns across countries that emerge as a result of global shocks. Our analysis sheds light on the emergence of core and periphery countries, and the joint determination of the depth of recessions and tightness of credit across countries. The model implies that interest rates are similar across core and periphery countries in booms, with larger credit and output growth in periphery countries. However, a common global shock that leads to a credit crunch across the globe gives rise to a sharper spike in interest rates and a deeper recession in periphery countries, while a credit flight to the core alleviates the adverse consequences in these countries. We explore the implication of the model about credit spreads, portfolio rebalancing, investment, non-performing debt and concentration of debt ownership during booms and busts, both in the time series and in the cross-section, and connect them to existing stylized facts. We further demonstrate how the anatomy of the global economy evolves as a result of aggregate demand and supply shocks to financing, such as a global saving glut.

This paper proposes a model to predict recessions that accounts for non-linearity and a structural break when the spread between long- and short-term interest rates is the leading indicator. Estimation and model selection procedures allow us to estimate and identify time-varying non-linearity in a VAR. The structural break threshold VAR (SBTVAR) predicts better the timing of recessions than models with constant threshold or with only a break. Using real-time data, the SBTVAR with spread as leading indicator is able to anticipate correctly the timing of the 2001 recession.

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

In June 2024, the European Central Bank (ECB) began reducing its fixed interest rate for the first time since 2016, implementing a series of cuts. The rate decreased from 4.5 percent to 3.15 percent by year-end: a 0.25 percentage point cut in June, followed by additional reductions in September, October, and December. The central bank implemented other cuts in early 2025, setting the rate at 2.4 percent in April 2025. This marked a significant shift from the previous rate hike cycle, which began in July 2022 when the ECB raised rates to 0.5 percent and subsequently increased them almost monthly, reaching 4.5 percent by December 2023 - the highest level since the 2007-2008 global financial crisis. How does this ensure liquidity? Banks typically hold only a fraction of their capital in cash, measured by metrics like the Tier 1 capital ratio. Since this ratio is low, banks prefer to allocate most of their capital to revenue-generating loans. When their cash reserves fall too low, banks borrow from the ECB to cover short-term liquidity needs. On the other hand, commercial banks can also deposit excess funds with the ECB at a lower interest rate. Reasons for fluctuations
The ECB’s primary mandate is to maintain price stability. The Euro area inflation rate is, in theory, the key indicator guiding the ECB's actions. When the fixed interest rate is lower, commercial banks are more likely to borrow from the ECB, increasing the money supply and, in turn, driving inflation higher. When inflation rises, the ECB increases the fixed interest rate, which slows borrowing and helps to reduce inflation.

FocusEconomics' economic data is provided by official state statistical reporting agencies as well as our global network of leading banks, think tanks and consultancies. Our datasets provide not only historical data, but also Consensus Forecasts and individual forecasts from the aformentioned global network of economic analysts. This includes the latest forecasts as well as historical forecasts going back to 2010. Our global network consists of over 1000 world-renowned economic analysts from which we calculate our Consensus Forecasts. In this specific dataset you will find economic data for Nicaragua.

In December 2024, the yield on a 10-year U.S. Treasury note was **** percent, forecasted to decrease to reach **** percent by August 2025. Treasury securities are debt instruments used by the government to finance the national debt. Who owns treasury notes? Because the U.S. treasury notes are generally assumed to be a risk-free investment, they are often used by large financial institutions as collateral. Because of this, billions of dollars in treasury securities are traded daily. Other countries also hold U.S. treasury securities, as do U.S. households. Investors and institutions accept the relatively low interest rate because the U.S. Treasury guarantees the investment. Looking into the future Because these notes are so commonly traded, their interest rate also serves as a signal about the market’s expectations of future growth. When markets expect the economy to grow, forecasts for treasury notes will reflect that in a higher interest rate. In fact, one harbinger of recession is an inverted yield curve, when the return on 3-month treasury bills is higher than the ten-year rate. While this does not always lead to a recession, it certainly signals pessimism from financial markets.

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.

LON:ETX Stock: Are We Headed for a Recession?

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.

Understanding the Dynamics and Implications of a Housing Market Recession

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