Forecast: Bank Lending Interest Rate in Australia 2024 - 2028 Discover more data with ReportLinker!

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Graph and download economic data for FOMC Summary of Economic Projections for the Fed Funds Rate, Median (FEDTARMD) from 2025 to 2027 about projection, federal, median, rate, and USA.

Forecast: Bank Lending Interest Rate in Egypt 2024 - 2028 Discover more data with ReportLinker!

The inflation rate in the United States is expected to decrease to 2.1 percent by 2029. 2022 saw a year of exceptionally high inflation, reaching eight percent for the year. The data represents U.S. city averages. The base period was 1982-84. In economics, the inflation rate is a measurement of inflation, the rate of increase of a price index (in this case: consumer price index). It is the percentage rate of change in prices level over time. The rate of decrease in the purchasing power of money is approximately equal. According to the forecast, prices will increase by 2.9 percent in 2024. The annual inflation rate for previous years can be found here and the consumer price index for all urban consumers here. The monthly inflation rate for the United States can also be accessed here. Inflation in the U.S.Inflation is a term used to describe a general rise in the price of goods and services in an economy over a given period of time. Inflation in the United States is calculated using the consumer price index (CPI). The consumer price index is a measure of change in the price level of a preselected market basket of consumer goods and services purchased by households. This forecast of U.S. inflation was prepared by the International Monetary Fund. They project that inflation will stay higher than average throughout 2023, followed by a decrease to around roughly two percent annual rise in the general level of prices until 2028. Considering the annual inflation rate in the United States in 2021, a two percent inflation rate is a very moderate projection. The 2022 spike in inflation in the United States and worldwide is due to a variety of factors that have put constraints on various aspects of the economy. These factors include COVID-19 pandemic spending and supply-chain constraints, disruptions due to the war in Ukraine, and pandemic related changes in the labor force. Although the moderate inflation of prices between two and three percent is considered normal in a modern economy, countries’ central banks try to prevent severe inflation and deflation to keep the growth of prices to a minimum. Severe inflation is considered dangerous to a country’s economy because it can rapidly diminish the population’s purchasing power and thus damage the GDP .

United States CBO Projection: Federal Debt Held by Public: Average Interest Rate data was reported at 3.448 % in 2028. This records an increase from the previous number of 3.442 % for 2027. United States CBO Projection: Federal Debt Held by Public: Average Interest Rate data is updated yearly, averaging 3.289 % from Sep 2014 (Median) to 2028, with 15 observations. The data reached an all-time high of 3.531 % in 2023 and a record low of 1.654 % in 2015. United States CBO Projection: Federal Debt Held by Public: Average Interest Rate data remains active status in CEIC and is reported by Congressional Budget Office. The data is categorized under Global Database’s USA – Table US.F006: Federal Debt: Projection: Congressional Budget Office.

Forecast: Bank Lending Interest Rate in Italy 2024 - 2028 Discover more data with ReportLinker!

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The average inflation rate in the Nigeria was forecast to continuously decrease between 2023 and 2028 by in total 6.1 percentage points. The average inflation rate is estimated to amount to 14 percent in 2028.Following the definitions provided by the International Monetary Fund, this indicator measures inflation based upon the year on year change in the average consumer price index. The latter expresses a country's average level of prices based on a typical basket of consumer goods and services. Depicted here is the year-on-year change in said index measure, expressed in percent.Find more key insights for the average inflation rate in countries like Senegal, Mali and Cote D'Ivoire.

Urban versus rural inflation disparity Comparing rural to urban areas in Nigeria showed that inflation was slightly worse in urban areas, with a difference of close to one percent in 2022. Other economic indicators reveal that inflation had a severe impact on the prices of consumer goods. Moreover, the Consumer Index Price of food in Nigeria in 2022 was 590.2. The food products with the highest percentage change in price was beans with 40 percent and over, depending on the color. That was followed by beef articles with 34 to close to 37 percent, depending on the part.

Fuel price surges: a closer look at diesel price fluctuations in Nigeria Another area that saw a dramatic spike in prices was fuel prices. In February 2023, there was a 0.98 percent rise in the cost of diesel in Nigeria when compared to January 2023. The most substantial surge occurred in March 2022. During that month, the average price of diesel surged by nearly 73 percent in contrast to the preceding month. This sharp escalation was attributed to a worldwide deficit in fuel supply and difficulties in the supply chain, which was prompted by the conflict in Ukraine and regulations implemented to control the transmission of COVID-19. Furthermore, consumers in Nigeria faced an average diesel price of 836.91 Nigerian naira (NGN), approximately 1.82 U.S. dollars, per liter. The North-Central States of Nigeria displayed the most elevated prices, with consumers in this region paying an average of 850.65 NGN per liter, roughly 1.85 U.S. dollars. During this specific timeframe, Osun emerged as the State with the highest price across Nigeria, as diesel prices reached a pinnacle of 707 NGN (equivalent to 1.7 U.S. dollars).

The benchmark interest rate in Ireland was last recorded at 4.50 percent. This dataset provides - Ireland Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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Forecast: Bank Lending Interest Rate in Brazil 2024 - 2028 Discover more data with ReportLinker!

The benchmark interest rate in Philippines was last recorded at 5.25 percent. This dataset provides the latest reported value for - Philippines Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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Foreign Exchange Market Size 2025-2029

The foreign exchange market size is forecast to increase by USD 582 billion, at a CAGR of 10.6% between 2024 and 2029.

The Foreign Exchange Market is segmented by type (reporting dealers, financial institutions, non-financial customers), trade finance instruments (currency swaps, outright forward and FX swaps, FX options), trading platforms (electronic trading, over-the-counter (OTC), mobile trading), and geography (North America: US, Canada; Europe: Germany, Switzerland, UK; Middle East and Africa: UAE; APAC: China, India, Japan; South America: Brazil; Rest of World). This segmentation reflects the market's global dynamics, driven by institutional trading, increasing digital adoption through electronic trading and mobile trading, and regional economic activities, with APAC markets like India and China showing significant growth alongside traditional hubs like the US and UK.
The market is experiencing significant shifts driven by the escalating trends of urbanization and digitalization. These forces are creating 24x7 trading opportunities, enabling greater accessibility and convenience for market participants. However, the market's dynamics are not without challenges. The uncertainty of future exchange rates poses a formidable obstacle for businesses and investors alike, necessitating robust risk management strategies. As urbanization continues to expand and digital technologies reshape the trading landscape, market players must adapt to remain competitive. One significant trend is the increasing use of money transfer agencies, venture capital investments, and mutual funds in foreign exchange transactions. Companies seeking to capitalize on these opportunities must navigate the challenges effectively, ensuring they stay abreast of exchange rate fluctuations and implement agile strategies to mitigate risk.
The ability to adapt and respond to these market shifts will be crucial for success in the evolving market.

What will be the Size of the Foreign Exchange Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the dynamic and intricate realm of the market, entities such as algorithmic trading, order book, order management systems, and liquidity risk intertwine, shaping the ever-evolving market landscape. The market's continuous unfolding is characterized by the integration of various components, including sentiment analysis, Fibonacci retracement, mobile trading, and good-for-the-day orders. Market activities are influenced by factors like political stability, monetary policy, and market liquidity, which in turn impact economic growth and trade settlement. Technical analysis, with its focus on chart patterns and moving averages, plays a crucial role in informing trading decisions. The market's complexity is further amplified by the presence of entities like credit risk, counterparty risk, and operational risk.

Central bank intervention, order execution, clearing and settlement, and trade confirmation are essential components of the market's infrastructure, ensuring a seamless exchange of currencies. Geopolitical risk, currency correlation, and inflation rates contribute to currency volatility, necessitating hedging strategies and risk management. Market risk, interest rate differentials, and commodity currencies influence trading strategies, while cross-border payments and brokerage services facilitate international trade. The ongoing evolution of the market is marked by the emergence of advanced trading platforms, automated trading, and real-time data feeds, enabling traders to make informed decisions in an increasingly interconnected and complex global economy.

How is this Foreign Exchange Industry segmented?

The foreign exchange 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.

Type

  Reporting dealers
  Financial institutions
  Non-financial customers


Trade Finance Instruments

  Currency swaps
  Outright forward and FX swaps
  FX options


Trading Platforms

  Electronic Trading
  Over-the-Counter (OTC)
  Mobile Trading


Geography

  North America

    US
    Canada


  Europe

    Germany
    Switzerland
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The reporting dealers segment is estimated to witness significant growth during the forecast period.

The market is a dynamic and complex ecosystem where various entities interplay to manage currency risks and facilitate international trade. Reporting dealers, as key participants,

After a period of rapid increase, house price growth in the UK has moderated. In 2025, house prices are forecast to increase by ****percent. Between 2025 and 2029, the average house price growth is projected at *** percent. According to the source, home building is expected to increase slightly in this period, fueling home buying. On the other hand, higher borrowing costs despite recent easing of mortgage rates and affordability challenges may continue to suppress transaction activity. Historical house price growth in the UK House prices rose steadily between 2015 and 2020, despite minor fluctuations. In the following two years, prices soared, leading to the house price index jumping by about 20 percent. As the market stood in April 2025, the average price for a home stood at approximately ******* British pounds. Rents are expected to continue to grow According to another forecast, the prime residential market is also expected to see rental prices grow in the next five years. Growth is forecast to be stronger in 2025 and slow slightly until 2029. The rental market in London is expected to follow a similar trend, with Outer London slightly outperforming Central London.

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.

Dassault Systemes Aims for Double Digit EPS Growth by 2028

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

Forecast: Bank Lending Interest Rate in Switzerland 2024 - 2028 Discover more data with ReportLinker!

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.

Is iShares iBonds Dec 2028 ETF the Gateway to Treasury Term Securities?

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.

OXSQG Oxford Square Capital Corp. 5.50% Notes due 2028

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