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.

Inflation: Friend or Foe to the Stock 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

Graph and download economic data for Equity Market Volatility Tracker: Macroeconomic News and Outlook: Inflation (EMVMACROINFLATION) from Jan 1985 to Nov 2025 about volatility, uncertainty, equity, inflation, and USA.

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...

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

Graph and download economic data for Producer Price Index by Industry: Investment Banking and Securities Intermediation: Brokerage Services, Equities and ETFs (PCU523120523120101) from Dec 1999 to Aug 2025 about ETF, brokers, stocks, equity, stock market, securities, services, PPI, industry, inflation, price index, indexes, price, and USA.

Analysis of the US stock market retreat from record highs driven by persistent inflation data and losses in big tech stocks, despite indexes posting strong monthly gains.

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

This stock market dataset is designed for financial analysis and predictive modeling. It includes historical stock prices, technical indicators, macroeconomic factors, and sentiment scores to help in developing and testing machine learning models for stock trend prediction.

Dataset Features: Column Description Stock Random stock ticker (AAPL, GOOG, etc.) Date Random business date Open Open price High High price Low Low price Close Close price Volume Trading volume SMA_10 10-day Simple Moving Average RSI Relative Strength Index (10-90 range) MACD MACD indicator (-5 to 5) Bollinger_Upper Upper Bollinger Band Bollinger_Lower Lower Bollinger Band GDP_Growth Random GDP growth rate (2.5% to 3.5%) Inflation_Rate Inflation rate (1.5% to 3.0%) Interest_Rate Interest rate (0.5% to 5.0%) Sentiment_Score Random sentiment score (-1 to 1) Next_Close Next day's closing price (for regression) Target Binary classification (1: Price Increase, 0: Price Decrease)

Key Features: Stock Prices: Open, High, Low, Close, and Volume data. Technical Indicators: Simple Moving Average (SMA), Relative Strength Index (RSI), MACD, and Bollinger Bands. Macroeconomic Factors: Simulated GDP growth, inflation rate, and interest rates. Sentiment Scores: Randomized sentiment values between -1 and 1 to simulate market sentiment. Target Variables: Next-day close price (for regression) and price movement direction (for classification).

Inflation Rate in India decreased to 0.25 percent in October from 1.44 percent in September of 2025. This dataset provides - India Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Israel Expected Inflation Rate: Capital Market: 5 Years Forward data was reported at 2.200 % in Mar 2025. This stayed constant from the previous number of 2.200 % for Feb 2025. Israel Expected Inflation Rate: Capital Market: 5 Years Forward data is updated monthly, averaging 2.200 % from Jan 2008 (Median) to Mar 2025, with 207 observations. The data reached an all-time high of 3.200 % in Mar 2022 and a record low of 0.600 % in Mar 2020. Israel Expected Inflation Rate: Capital Market: 5 Years Forward data remains active status in CEIC and is reported by Bank of Israel. The data is categorized under Global Database’s Israel – Table IL.I067: Inflation Expectations. [COVID-19-IMPACT]

U.S. Economic and Financial Dataset

Dataset Description

This dataset combines historical U.S. economic and financial indicators, spanning the last 50 years, to facilitate time series analysis and uncover patterns in macroeconomic trends. It is designed for exploring relationships between interest rates, inflation, economic growth, stock market performance, and industrial production.

Key Features

• Frequency: Monthly
• Time Period: Last 50 years from Nov-24
• Sources:
  • Federal Reserve Economic Data (FRED)
  • Yahoo Finance

Dataset Feature Description

1. Interest Rate (Interest_Rate):

   • The effective federal funds rate, representing the interest rate at which depository institutions trade federal funds overnight.

2. Inflation (Inflation):

   • The Consumer Price Index for All Urban Consumers, an indicator of inflation trends.

3. GDP (GDP):

   • Real GDP measures the inflation-adjusted value of goods and services produced in the U.S.

4. Unemployment Rate (Unemployment):

   • The percentage of the labor force that is unemployed and actively seeking work.

5. Stock Market Performance (S&P500):

   • Monthly average of the adjusted close price, representing stock market trends.

6. Industrial Production (Ind_Prod):

   • A measure of real output in the industrial sector, including manufacturing, mining, and utilities.

Dataset Statistics

1. Total Entries: 599
2. Columns: 6
3. Memory usage: 37.54 kB
4. Data types: float64

Feature Overview

• Columns:
  • Interest_Rate: Monthly Federal Funds Rate (%)
  • Inflation: CPI (All Urban Consumers, Index)
  • GDP: Real GDP (Billions of Chained 2012 Dollars)
  • Unemployment: Unemployment Rate (%)
  • Ind_Prod: Industrial Production Index (2017=100)
  • S&P500: Monthly Average of S&P 500 Adjusted Close Prices

Executive Summary

This project explores the interconnected dynamics of key macroeconomic indicators and financial market trends over the past 50 years, leveraging data from the Federal Reserve Economic Data (FRED) and Yahoo Finance. The dataset integrates critical variables such as the Federal Funds Rate, Inflation (CPI), Real GDP, Unemployment Rate, Industrial Production, and the S&P 500 Index, providing a holistic view of the U.S. economy and financial markets.

The analysis focuses on uncovering relationships between these variables through time-series visualization, correlation analysis, and trend decomposition. Key findings are included in the Insights section. This project serves as a robust resource for understanding long-term economic trends, policy impacts, and market behavior. It is particularly valuable for students, researchers, policymakers, and financial analysts seeking to connect macroeconomic theory with real-world data.

Potential Use Cases

• Economic Analysis: Examine relationships between interest rates, inflation, GDP, and unemployment.
• Stock Market Prediction: Study how macroeconomic indicators influence stock market trends.
• Time Series Modeling: Perform ARIMA, VAR, or other models to forecast economic trends.
• Cyclic Pattern Analysis: Identify how economic shocks and recoveries impact key indicators.

Snap of Power Analysis

https://github.com/user-attachments/assets/1b40e0ca-7d2e-4fbc-8cfd-df3f09e4fdb8">

To ensure sufficient power, the dataset covers last 50 years of monthly data i.e., around 600 entries.

Key Insights derived through EDA, time-series visualization, correlation analysis, and trend decomposition

• Interest Rate and Inflation Dynamics: The interest Rate and inflation exhibit an inverse relationship, especially during periods of aggressive monetary tightening by the Federal Reserve.
• Economic Growth and Market Performance: GDP growth and the S&P 500 Index show a positive correlation, reflecting how market performance often aligns with overall economic health.
• Labor Market and Industrial Output: Unemployment and industrial production demonstrate a strong inverse relationship. Higher industrial output is typically associated with lower unemployment
• Market Behavior During Economic Shocks: The S&P 500 experienced sharp declines during significant crises, such as the 2008 financial crash and the COVID-19 pandemic in 2020. These events also triggered increased unemployment and contractions in GDP, highlighting the interplay between markets and the broader economy.
• Correlation Highlights: S&P 500 and GDP have a strong positive correlation. Interest rates negatively correlate with GDP and inflation, reflecting monetary policy impacts. Unemployment is negatively correlated with industrial production but positively correlated with interest rates.

Link to GitHub Repo

https:/...

This project studies equity returns in the Madrid Stock Exchange and their connections with the macroeconomy from the emergence of a stock market around 1900 to its “big bang” at the turn of the 21st century. Using high-quality data from primary sources and the methodology of the modern IBEX35 (published since 1987), we constructed an original index, the H-IBEX, for the period 1900-1987. With 120 years of monthly data, we empirically test the ability of stock prices to predict real economic activity, provide a detailed chronology of market cycles and analyze their time-varying characteristics across stages of market development and macroeconomic regimes. We also assess the role of Spanish equities as an inflation hedge and compare their long-run investment performance in an international perspective. Our data confirm that the Civil War (1936-39) had only a moderately negative impact on equity wealth compared to other economic disasters of the 20th century. In the long run Spanish equities underperformed most European markets due to a massive destruction of financial wealth in the stagflation of the 1970s-80 and the transition to an open economy after decades of protectionism. This was the true “rare disaster” suffered by Spanish investors in the 20th century.

The Inflation Devices Market Report is Segmented by Product Type (Analog Inflation Devices, Digital Inflation Devices), Pressure Range (less Than 15 Atm, 15 – 30 Atm, More Than 30 Atm), Application (Coronary Angioplasty, Peripheral Angioplasty, and More), End User (Hospitals, Ambulatory Surgical Centers, and More, and Geography (North America, Europe, and More). The Market Forecasts are Provided in Terms of Value (USD).

Description:

This dataset provides key economic indicators from various countries between 2010 and 2023. The dataset includes monthly data on inflation rates, GDP growth rates, unemployment rates, interest rates, and stock market index values. The data has been sourced from reputable global financial institutions and is suitable for economic analysis, machine learning models, and forecasting economic trends.

Data Sources:

The data has been generated to simulate real-world economic conditions, mimicking information from trusted sources like: - World Bank for GDP growth and inflation data - International Monetary Fund (IMF) for macroeconomic data - OECD for labor market statistics - National Stock Exchanges for stock market index values

Columns:

1. Date: The specific date (in Year/Month/Day format) representing when the data was collected.
2. Country: The country the data pertains to (e.g., USA, Germany, Japan).
3. Inflation Rate (%): The rate of inflation for that country, showing how fast prices for goods and services are increasing.
4. GDP Growth Rate (%): The percentage growth of the country’s Gross Domestic Product (GDP), indicating economic expansion or contraction.
5. Unemployment Rate (%): The percentage of the working-age population that is unemployed.
6. Interest Rate (%): The central bank's interest rate, used to control inflation and influence the economy.
7. Stock Index Value: The value of the country’s main stock market index, reflecting the performance of the stock market.

Potential Uses: - Economic Analysis: Researchers and analysts can use this dataset to study trends in inflation, GDP growth, unemployment, and other economic factors. - Machine Learning: This dataset can be used to train models for predicting economic trends or market performance. Financial Forecasting: Investors and economists can leverage this data for forecasting market movements based on economic conditions. - Comparative Studies: The dataset allows comparisons across countries and regions, offering insights into global economic performance.

United States - Equity Market Volatility Tracker: Macroeconomic News and Outlook: Inflation was 8.28669 Index in September of 2025, according to the United States Federal Reserve. Historically, United States - Equity Market Volatility Tracker: Macroeconomic News and Outlook: Inflation reached a record high of 28.66177 in April of 2025 and a record low of 1.96528 in November of 2003. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Equity Market Volatility Tracker: Macroeconomic News and Outlook: Inflation - last updated from the United States Federal Reserve on November of 2025.

• The global industry was valued at US$ 537.7 Mn in 2022
• It is projected to grow at a CAGR of 5.2% from 2023 to 2031 and reach more than US$ 851.8 Mn by the end of 2031

Global Inflation Devices Market Snapshot

Attribute	Detail
Market Value in 2022	US$ 537.7 Mn
Forecast (Value) in 2031	US$ 851.8 Mn
Growth Rate (CAGR)	5.2%
Forecast Period	2023-2031
Historical Data Available for	2017-2021
Quantitative Units	US$ Mn for Value
Market Analysis	It provides segment analysis as well as regional level analysis. Furthermore, qualitative analysis includes drivers, restraints, opportunities, key trends, Porter’s Five Forces analysis, value chain analysis, and key trend analysis.
Competition Landscape	Market share analysis by company (2022) Company profiles section includes overview, product portfolio, sales footprint, key subsidiaries or distributors, strategy & recent developments, and key financials
Format	Electronic (PDF) + Excel
Market Segmentation	Display Type Analog Inflation Devices Digital Inflation Devices Pressure Capacity 20 ml Inflation Devices 30 ml Inflation Devices 60 ml Inflation Devices Function Balloon Angioplasty Arterial Stent Placement Endoscopy Others Application Coronary Interventions Urology Gastroenterology Peripheral Interventions Others End-user Hospitals Specialty Clinics Ambulatory Surgery Centers
Regions Covered	North America Latin America Europe Asia Pacific Middle East & Africa
Countries Covered	U.S. Canada Germany U.K. France Italy Spain China India Japan Australia & New Zealand Brazil Mexico South Africa GCC Countries
Companies Profiled	Atrion Medical BD Lepu Medical Technology (Beijing) Co., Ltd. Merit Medical Systems Translumina Advin Health Care Dolphin Life Science India LLP Boston Scientific Corporation Acclarent, Inc. (Johnson & Johnson MedTech) B. Braun Interventional Systems Inc. CONMED Corporation Cook Medical
Customization Scope	Available upon request
Pricing	Available upon request

This Economic Commentary examines the recent behavior and the longer-term properties of market-based and non-market-based inflation series, including their cyclical properties, historical revisions, and predictive power in explaining future PCE inflation. The examination reveals a statistically significant association between market-based PCE inflation and estimates of labor market slack, and a strong positive association between movements in the stock market and in some of the financial services components of non-market-based PCE inflation. Disinflation in overall PCE inflation over the course of 2023 and 2024 was largely driven by disinflation in the market-based components, coinciding with a gradual loosening in labor market conditions.

During the period beginning roughly in the mid-1980s until the Global Financial Crisis (2007-2008), the U.S. economy experienced a time of relative economic calm, with low inflation and consistent GDP growth. Compared with the turbulent economic era which had preceded it in the 1970s and the early 1980s, the lack of extreme fluctuations in the business cycle led some commentators to suggest that macroeconomic issues such as high inflation, long-term unemployment and financial crises were a thing of the past. Indeed, the President of the American Economic Association, Professor Robert Lucas, famously proclaimed in 2003 that "central problem of depression prevention has been solved, for all practical purposes". Ben Bernanke, the future chairman of the Federal Reserve during the Global Financial Crisis (GFC) and 2022 Nobel Prize in Economics recipient, coined the term 'the Great Moderation' to describe this era of newfound economic confidence. The era came to an abrupt end with the outbreak of the GFC in the Summer of 2007, as the U.S. financial system began to crash due to a downturn in the real estate market.

Causes of the Great Moderation, and its downfall

A number of factors have been cited as contributing to the Great Moderation including central bank monetary policies, the shift from manufacturing to services in the economy, improvements in information technology and management practices, as well as reduced energy prices. The period coincided with the term of Fed chairman Alan Greenspan (1987-2006), famous for the 'Greenspan put', a policy which meant that the Fed would proactively address downturns in the stock market using its monetary policy tools. These economic factors came to prominence at the same time as the end of the Cold War (1947-1991), with the U.S. attaining a new level of hegemony in global politics, as its main geopolitical rival, the Soviet Union, no longer existed. During the Great Moderation, the U.S. experienced a recession twice, between July 1990 and March 1991, and again from March 2001 tom November 2001, however, these relatively short recessions did not knock the U.S. off its growth path. The build up of household and corporate debt over the early 2000s eventually led to the Global Financial Crisis, as the bursting of the U.S. housing bubble in 2007 reverberated across the financial system, with a subsequent credit freeze and mass defaults.

Inflation-Linked Structured Notes Market Outlook

According to our latest research, the global Inflation-Linked Structured Notes market size reached USD 78.4 billion in 2024, reflecting robust investor demand and heightened awareness of inflationary risks. The market is currently experiencing a strong compound annual growth rate (CAGR) of 7.1% and is projected to expand to USD 145.7 billion by 2033. This significant growth trajectory is primarily driven by increased volatility in global inflation rates, a shift toward inflation-hedged investment products, and evolving regulatory frameworks that favor structured financial solutions.

The growth of the Inflation-Linked Structured Notes market is being propelled by several key factors. One of the most prominent drivers is the resurgence of inflationary pressures across major economies, which has prompted both institutional and retail investors to seek effective hedging mechanisms. As central banks grapple with persistent inflation, traditional fixed-income products have lost their appeal due to eroding real returns. Inflation-linked structured notes, with their embedded inflation protection features, provide a compelling alternative by offering returns that are directly tied to inflation indices, thus preserving purchasing power. Moreover, the increasing sophistication of investors, coupled with greater access to financial education, has led to a surge in demand for customized structured products that align with specific risk-return profiles.

Another significant growth factor is the rapid innovation in product design and the broadening of underlying asset classes available for inflation-linked structured notes. Financial institutions are leveraging advanced analytics and financial engineering to craft notes that cater to diverse investment objectives, ranging from capital preservation to enhanced yield generation. The integration of government bonds, corporate bonds, equities, and commodities as underlying assets has expanded the appeal of these notes, attracting a wider spectrum of investors. Additionally, the proliferation of digital distribution channels and fintech platforms has democratized access to structured notes, enabling retail investors to participate alongside their institutional counterparts. This technological advancement has also streamlined the issuance and management process, reducing operational costs and enhancing transparency.

Regulatory developments are further shaping the trajectory of the Inflation-Linked Structured Notes market. In response to the 2008 financial crisis and subsequent market disruptions, regulators have implemented stricter transparency and disclosure requirements for structured products. These measures have bolstered investor confidence and encouraged greater participation, particularly among risk-averse segments. Furthermore, regulatory frameworks in regions such as North America and Europe are increasingly supportive of innovative financial instruments that offer inflation protection, thereby fostering a conducive environment for market expansion. As a result, market participants are witnessing a steady influx of new product issuances and a growing appetite among both institutional and retail investors.

Equity-Linked Notes have emerged as a notable addition to the structured finance landscape, offering investors a unique blend of equity market exposure and structured note benefits. These instruments are designed to provide returns linked to the performance of specific equities or equity indices, allowing investors to participate in potential market upside while often incorporating protective features to mitigate downside risk. The appeal of Equity-Linked Notes lies in their ability to customize risk-return profiles, making them attractive to both conservative and aggressive investors. As financial markets continue to evolve, the demand for such tailored investment solutions is expected to grow, driven by investors' desire for diversification and enhanced yield potential.

From a regional perspective, North America and Europe continue to dominate the Inflation-Linked Structured Notes market, accounting for a significant share of global issuance and trading volumes. The United States, in particular, benefits from a mature financial ecosystem and a high concentration of institutional investors seeking inflation-hedg

Israel Expected Inflation Rate: Capital Market: 3-5 Years Forward data was reported at 2.300 % in Mar 2025. This records a decrease from the previous number of 2.400 % for Feb 2025. Israel Expected Inflation Rate: Capital Market: 3-5 Years Forward data is updated monthly, averaging 2.400 % from Jan 2008 (Median) to Mar 2025, with 207 observations. The data reached an all-time high of 3.600 % in Apr 2009 and a record low of 1.200 % in Mar 2020. Israel Expected Inflation Rate: Capital Market: 3-5 Years Forward data remains active status in CEIC and is reported by Bank of Israel. The data is categorized under Global Database’s Israel – Table IL.I067: Inflation Expectations. [COVID-19-IMPACT]