Graph and download economic data for CBOE Volatility Index: VIX (VIXCLS) from 1990-01-02 to 2025-09-04 about VIX, volatility, stock market, 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.

CBOE Volatility Index Options & Futures Prediction

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 dataset captures multi-market financial indicators that can be used to study financial crises, market stress, and economic stability. It integrates simulated data from stock, bond, and foreign exchange (forex) markets, along with volatility metrics and a binary crisis label.

The dataset provides a comprehensive view of cross-market behavior and is suitable for tasks such as crisis detection, financial risk analysis, and market interdependence studies.

Key Features Time Series Coverage:

Daily data over ~1,000 days for multiple countries

Stock Market Indicators:

Stock_Index → Simulated stock market index values

Stock_Return → Daily percentage change in stock index

Stock_Volatility → 5-day rolling standard deviation of stock returns

Bond Market Indicators:

Bond_Yield → Simulated 10-year government bond yield

Bond_Yield_Spread → Difference between long-term and short-term yields

Bond_Volatility → Simulated volatility in bond yields

Forex Market Indicators:

FX_Rate → Simulated currency exchange rate

FX_Return → Daily percentage change in exchange rate

FX_Volatility → 5-day rolling standard deviation of forex returns

Global Market Stress Indicator:

VIX → Simulated volatility index representing market stress

Target Variable:

Crisis_Label → Binary flag indicating market condition (0 = Normal, 1 = Crisis)

File Information Format: CSV

Rows: ~3,000 (1,000 days × 3 countries)

Columns: 13 (including target label)

Use Cases:

Financial crisis detection

Market stress and contagion analysis

Cross-market economic studies

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 the Market on Edge? VIX Reveals

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 - Equity Market Volatility Tracker: Government Spending Deficits And Debt was 1.64486 Index in July of 2025, according to the United States Federal Reserve. Historically, United States - Equity Market Volatility Tracker: Government Spending Deficits And Debt reached a record high of 21.12630 in August of 2011 and a record low of 0.00000 in August of 2016. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Equity Market Volatility Tracker: Government Spending Deficits And Debt - last updated from the United States Federal Reserve on September of 2025.

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.

VIX: Fear or Opportunity?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The data are used to examines the impact of financial uncertainty shocks on the return and volatility dynamics of global bond funds using four key indicators: The VIX (equity market volatility), MOVE (bond market volatility), GPR (geopolitical risk), and CBDCU (central bank digital currency uncertainty).

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.

Buy or Sell: CBOE Volatility Index Stock

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

Dataset Description: 34-year Daily Stock Data (1990-2024)

Context and Inspiration

This dataset captures historical financial market data and macroeconomic indicators spanning over three decades, from 1990 onwards. It is designed for financial analysis, time series forecasting, and exploring relationships between market volatility, stock indices, and macroeconomic factors. This dataset is particularly relevant for researchers, data scientists, and enthusiasts interested in studying: - Volatility forecasting (VIX) - Stock market trends (S&P 500, DJIA, HSI) - Macroeconomic influences on markets (joblessness, interest rates, etc.) - The effect of geopolitical and economic uncertainty (EPU, GPRD)

Sources

The data has been aggregated from a mix of historical financial records and publicly available macroeconomic datasets: - VIX (Volatility Index): Chicago Board Options Exchange (CBOE). - Stock Indices (S&P 500, DJIA, HSI): Yahoo Finance and historical financial databases. - Volume Data: Extracted from official exchange reports. - Macroeconomic Indicators: Bureau of Economic Analysis (BEA), Federal Reserve, and other public records. - Uncertainty Metrics (EPU, GPRD): Economic Policy Uncertainty Index and Global Policy Uncertainty Database.

Columns

1. dt: Date of observation in YYYY-MM-DD format.
2. vix: VIX (Volatility Index), a measure of expected market volatility.
3. sp500: S&P 500 index value, a benchmark of the U.S. stock market.
4. sp500_volume: Daily trading volume for the S&P 500.
5. djia: Dow Jones Industrial Average (DJIA), another key U.S. market index.
6. djia_volume: Daily trading volume for the DJIA.
7. hsi: Hang Seng Index, representing the Hong Kong stock market.
8. ads: Aruoba-Diebold-Scotti (ADS) Business Conditions Index, reflecting U.S. economic activity.
9. us3m: U.S. Treasury 3-month bond yield, a short-term interest rate proxy.
10. joblessness: U.S. unemployment rate, reported as quartiles (1 represents lowest quartile and so on).
11. epu: Economic Policy Uncertainty Index, quantifying policy-related economic uncertainty.
12. GPRD: Geopolitical Risk Index (Daily), measuring geopolitical risk levels.
13. prev_day: Previous day’s S&P 500 closing value, added for lag-based time series analysis.

Key Features

• Cross-Market Analysis: Compare U.S. markets (S&P 500, DJIA) with international benchmarks like HSI.
• Macroeconomic Insights: Assess how external factors like joblessness, interest rates, and economic uncertainty affect markets.
• Temporal Scope: Longitudinal data facilitates trend analysis and machine learning model training.

Potential Use Cases

• Forecasting market indices using machine learning or statistical models.
• Building volatility trading strategies with VIX Futures.
• Economic research on relationships between policy uncertainty and market behavior.
• Educational material for financial data visualization and analysis tutorials.

Feel free to use this dataset for academic, research, or personal projects.

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.

VIX Forecast: Elevated Volatility Expected Amidst Market Uncertainty, S&P 500 VIX index Indicates.

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

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Fixed Income Assets Management Market Size 2025-2029

The fixed income assets management market size is forecast to increase by USD 9.16 tr at a CAGR of 6.3% between 2024 and 2029.

The market is experiencing significant growth, driven by increasing investor interest in fixed income securities as a hedge against market volatility. A key trend in this market is the expansion of bond Exchange-Traded Funds (ETFs), which offer investors liquidity, diversification, and cost savings. However, this market is not without risks. Transactions in fixed income assets involve complexities such as credit risk, interest rate risk, and liquidity risk, which require sophisticated risk management strategies. As global investors seek to capitalize on market opportunities and navigate these challenges effectively, they must stay informed of regulatory changes, market trends, and technological advancements. Companies that can provide innovative solutions for managing fixed income risks and optimizing returns will be well-positioned to succeed in this dynamic market.

What will be the Size of the Fixed Income Assets Management Market during the forecast period?

Request Free SampleThe fixed income assets market in the United States continues to be an essential component of investment portfolios for various official institutions and individual investors. With an expansive market size and growth, fixed income securities encompass various debt instruments, including corporate bonds and government treasuries. Interest rate fluctuations significantly impact this market, influencing investment decisions and affecting the returns from interest payments on these securities. Fixed income Exchange-Traded Funds (ETFs) and index managers have gained popularity due to their cost-effective and diversified investment options. However, the credit market volatility and associated default risk pose challenges for investors. In pursuit of financial goals, investors often choose fixed income funds over equities for their stable dividend income and tax savings benefits. Market risk and investors' risk tolerance are crucial factors in managing fixed income assets. Economic uncertainty and interest rate fluctuations necessitate active management by asset managers, hedge funds, and mutual funds. The fund maturity and investors' financial goals influence the choice between various fixed income securities, such as treasuries and loans. Despite the challenges, the market's direction remains positive, driven by the continuous demand for income-generating investments.

How is this Fixed Income Assets Management Industry segmented?

The fixed income assets management industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD tr' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. TypeCoreAlternativeEnd-userEnterprisesIndividualsGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACChinaIndiaJapanSouth KoreaSouth AmericaMiddle East and Africa

By Type Insights

The core segment is estimated to witness significant growth during the forecast period.The fixed income asset management market encompasses a diverse range of investment vehicles, including index investing, pension funds, official institutions, mutual funds, investment advisory services, and hedge funds. This asset class caters to income holders with varying risk tolerances, offering securities such as municipal bonds, government bonds, and high yield bonds through asset management firms. Institutional investors, insurance companies, and corporations also play significant roles in this sector. Fixed income securities, including Treasuries, municipal bonds, corporate bonds, and debt securities, provide regular interest payments and can offer tax savings, making them attractive for investors with financial goals. However, liquidity issues and credit market volatility can pose challenges. The Federal Reserve's interest rate decisions and economic uncertainty also impact the fixed income market. Asset management firms employ various strategies, such as the core fixed income (CFI) strategy, which invests in a mix of investment-grade fixed-income securities. CFI strategies aim to deliver consistent performance by carefully managing portfolios, considering issuer creditworthiness, maturity, and jurisdiction. Fixed income funds, including government bonds and corporate bonds, offer lower market risk compared to equities. Investors can choose from various investment vehicles, including mutual funds, ETFs, and index funds managed by active managers or index managers. Fixed income ETFs, in particular, provide investors with the benefits of ETFs, such as liquidity and transparency, while offering exposure to the fixed income market. Despite market risks and liquidity issues, the fixed income asset management market continues to be a crucial component of

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 the VIX Index a Reliable Gauge of Market Volatility?

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.

VN30 Index: Navigating Market Volatility, Where Next?

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.

Is the S&P 500 VIX Index Signaling Market Volatility?

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 study initially analyses the connectedness among five indexes representing five asset classes: equities, bonds, commodities, currencies, and housing prices. Subsequently, it analyses the influence of policy uncertainty and equity market risk on the total connectedness of assets and their impact on the significance of the home price index. A time-varying parameter vector autoregressions (TVP-VAR) model has been employed to explore the connectedness of the assets. Secondly, quantile regression is employed to gauge the impact of uncertainty and market risk in this study. The findings show that the S&P500 is the market’s most influential index. The home price index has proven sensitive to shocks from government bonds, commodities, and stock indexes across the period analyzed. Finally, findings have indicated that policy uncertainty positively influences the overall connectedness among the variables at all the quantiles. Moreover, at higher quantiles, an increase in policy uncertainty and equity-market risk decreases the net connectedness of the house price index. These findings hold significant implications for investors and policymakers in terms of diversification of portfolios and mitigating portfolio risk during periods of economic turbulence. This study demonstrates the dynamic interrelationships among five major asset classes and indicates that policy uncertainty and equity market risk significantly influence asset connectivity. Employing TVP-VAR and Bayesian quantile regression, it reveals that the S&P 500 exerts dominant market influence, whereas the house price index exhibits significant sensitivity to shocks. The results highlight the significance of uncertainty for increasing interconnectedness, providing essential insights for investors and policymakers about risk management and portfolio diversification.

Passive ETF Market Outlook

In 2023, the global Passive ETF market size was valued at approximately USD 6.1 trillion and is projected to reach USD 11.4 trillion by 2032, growing at a CAGR of 7.2% over the forecast period. The primary growth factor for this market is the increasing preference for low-cost investment options among retail and institutional investors alike.

One of the significant growth factors driving the Passive ETF market is the rise in awareness and education about financial markets among retail investors. More individuals are becoming informed about the benefits of diversified, low-cost investment portfolios. Passive ETFs, which typically track a specific index, offer a cost-effective way for investors to gain broad market exposure without the need for intensive management. This factor is particularly appealing to new investors who wish to participate in the stock market with minimal fees and reduced risk.

Another critical driver is the surge in technological advancements and digitalization in financial services. Online trading platforms and robo-advisors are making it easier for investors to access a wide array of ETF products. These platforms often provide tools and resources that help investors make informed decisions, thereby encouraging more people to invest in Passive ETFs. The ease of use, coupled with low transaction costs, has further popularized Passive ETFs among various investor segments.

Institutional investors are also increasingly turning to Passive ETFs to optimize their investment strategies. With market volatility and economic uncertainties, institutional investors seek stable and predictable investment solutions. Passive ETFs offer a reliable way to achieve market returns without the need to actively manage individual securities. This stability is particularly important for pension funds, endowments, and insurance companies, which have long-term investment horizons and fiduciary responsibilities to their beneficiaries.

Regionally, North America continues to dominate the Passive ETF market, owing to its mature financial markets and large base of institutional and retail investors. However, other regions like Asia Pacific are catching up rapidly. The growing middle class, rising disposable incomes, and increasing financial literacy are significant factors contributing to the market's growth in this region. Additionally, favorable regulatory changes and the introduction of innovative financial products are expected to drive the market further in Asia Pacific.

Type Analysis

In the Passive ETF market, various types, including Equity ETFs, Bond ETFs, Commodity ETFs, Real Estate ETFs, and others, offer diverse investment opportunities. Equity ETFs hold the largest market share, primarily due to their ability to provide broad exposure to stock markets, mirroring the performance of major indices like the S&P 500 or the NASDAQ. As investors seek to capitalize on market growth while minimizing costs, the demand for Equity ETFs continues to rise. They are particularly popular among retail investors looking to gain diversified exposure to the equity market without picking individual stocks.

Bond ETFs are another critical segment within the Passive ETF market, offering investors a way to gain exposure to the fixed income market. These ETFs are essential for those looking to balance their portfolios with more stable, income-generating investments. Bond ETFs can provide access to government, corporate, and municipal bonds. The predictable income stream and lower risk compared to equities make Bond ETFs a favorite among conservative investors and retirees. Additionally, in a low-interest-rate environment, Bond ETFs become even more attractive as they offer better returns compared to traditional savings accounts.

Commodity ETFs cater to investors looking to diversify their portfolios with tangible assets like gold, silver, oil, and other commodities. These ETFs provide a convenient way to invest in commodities without the complexities involved in holding physical assets. Commodity ETFs are particularly popular during times of economic uncertainty and inflation, as they often serve as a hedge against market volatility and currency devaluation. The demand for these ETFs is expected to grow as investors seek more avenues to protect their wealth.

Real Estate ETFs provide exposure to the real estate market by investing in a diversified portfolio of real estate investment trusts (REITs). These ETFs offer a way to participate in the real estate market without th

We investigate the dynamic volatility connectedness of geopolitical risk, stocks, bonds, bitcoin, gold, and oil from January 2018 to April 2022 in this study. We look at connectivity during the Pre-COVID, COVID, and Russian-Ukraine war subsamples. During the COVID-19 and Russian-Ukraine war periods, we find that conventional, Islamic, and sustainable stock indices are net volatility transmitters, whereas gold, US bonds, GPR, oil, and bitcoin are net volatility receivers. During the Russian-Ukraine war, the commodity index (DJCI) shifted from being a net recipient of volatility to a net transmitter of volatility. Furthermore, we discover that bilateral intercorrelations are strong within stock indices (DJWI, DJIM, and DJSI) but weak across all other financial assets. Our study has important implications for policymakers, regulators, investors, and financial market participants who want to improve their existing strategies for avoiding financial losses.

Broad Based Index Fund Market Outlook

The global broad-based index fund market size was valued at USD 5.3 trillion in 2023 and is projected to reach USD 11.2 trillion by 2032, growing at a compound annual growth rate (CAGR) of 8.5% during the forecast period. This substantial growth is driven by increasing investor interest in passive investment strategies, along with the rising emphasis on cost-effective and diversified portfolio management.

The surge in demand for broad-based index funds can be attributed to several key growth factors. Firstly, the growing awareness and education about the benefits of passive investing over active management have played a significant role. Investors are increasingly leaning towards index funds due to their lower expense ratios, tax efficiency, and the ability to provide broad market exposure with minimal effort. Secondly, technological advancements and the rise of fintech have made these funds more accessible to a wider audience through online platforms and robo-advisors, democratizing investment opportunities for retail investors globally. Lastly, regulatory changes in many regions are encouraging greater transparency and lower fees in the financial services industry, which further bolsters the attractiveness of index funds as a preferred investment vehicle.

The popularity of broad-based index funds is also bolstered by their performance resilience during market volatility. Historical data indicates that while actively managed funds often struggle to outperform the market consistently, index funds tend to provide more stable returns over the long term. This trend has been particularly noticeable during economic downturns and periods of market uncertainty, where investors seek the relative safety and predictability offered by broad-based diversified portfolios. Additionally, the increased focus on retirement planning and the shift from defined benefit to defined contribution retirement plans have spurred the growth of index funds as they are often the preferred choice in retirement accounts due to their long-term growth potential and lower costs.

The regional outlook for the broad-based index fund market highlights significant growth potential across various geographies. North America, particularly the United States, remains the largest market for index funds, driven by the deep-rooted culture of investing and a well-established financial infrastructure. Europe follows closely, with growth fueled by regulatory support and increasing investor awareness. The Asia Pacific region is expected to witness the highest growth rate, propelled by the burgeoning middle class, rising disposable incomes, and increasing penetration of financial services. Latin America and the Middle East & Africa are also anticipated to demonstrate steady growth as financial markets in these regions continue to develop and mature.

Mutual Funds Sales have seen a notable uptick as investors increasingly seek diversified investment options that align with their financial goals. This trend is particularly evident in the context of broad-based index funds, where mutual funds offer a structured approach to investing in a wide array of assets. The appeal of mutual funds lies in their ability to pool resources from multiple investors, enabling access to a diversified portfolio that might otherwise be unattainable for individual investors. This collective investment model not only reduces risk but also provides investors with professional management and oversight. As the financial landscape evolves, mutual funds continue to play a crucial role in facilitating access to index funds, thereby driving sales and expanding their market presence.

Fund Type Analysis

Equity index funds represent a significant portion of the broad-based index fund market. These funds track a variety of stock indices, such as the S&P 500, NASDAQ, and MSCI World Index, providing investors with exposure to a wide array of equity markets. The appeal of equity index funds lies in their ability to offer broad market diversification at a low cost. Investors benefit from the lower fees associated with passive management and the reduced risk of individual stock selection. As a result, equity index funds have become a staple in both retail and institutional portfolios, driving robust demand and growth in this segment.

Bond index funds, though smaller in market share compared to their equity counterparts, are gaining traction as investors seek stable income and risk diversifi

The North American mutual fund industry, exhibiting a Compound Annual Growth Rate (CAGR) exceeding 5%, presents a robust investment landscape. Driven by increasing household savings, favorable regulatory environments, and the growing adoption of digital investment platforms, the market is poised for significant expansion throughout the forecast period (2025-2033). The industry is segmented by fund type (equity, bond, hybrid, money market) and investor type (households, institutional investors), with the United States dominating the market share within North America, followed by Canada and Mexico. Major players like Vanguard, Fidelity Investments, BlackRock, and others compete fiercely, offering diversified product portfolios to cater to various investor risk appetites and financial goals. The increasing demand for passive investment strategies, including index funds and ETFs, alongside the growing adoption of robo-advisors, are shaping the industry's future. While regulatory changes and market volatility pose potential restraints, the overall outlook remains positive, fueled by long-term growth prospects and a rising investor base seeking professional asset management solutions. The substantial market size, estimated at several trillion dollars in 2025, reflects the maturity and significance of this sector. Growth is expected to be particularly strong in the equity and hybrid fund categories, driven by investor confidence and a desire for higher returns. The institutional investor segment is likely to maintain a significant share of the market, with continued institutional allocations to mutual funds for diversification and long-term investment strategies. Geographical diversification within North America will continue, with potential for higher growth rates in Canada and Mexico compared to the already large US market. Competition among leading firms will remain intense, prompting innovation in product offerings, investment strategies, and customer service to maintain market share and attract new investors. The industry's ongoing adaptation to technological advancements and evolving investor preferences will be crucial for sustained success in the coming years. This report provides a detailed analysis of the North America mutual fund industry, covering the period from 2019 to 2033. It offers in-depth insights into market size, growth drivers, challenges, and future trends, incorporating data from the historical period (2019-2024), base year (2025), and forecast period (2025-2033). The report is crucial for investors, fund managers, and industry stakeholders seeking a comprehensive understanding of this dynamic market. Key search terms included: North America mutual funds, mutual fund industry trends, US mutual fund market, Canadian mutual funds, mutual fund investments, equity funds, bond funds, investment management, financial services. Recent developments include: In 2021, Fidelity Investements along with Visa backed Jumo, an emerging fintech startup which offers savings and credit products to entrepreneurs in emerging markets, as well as financial services infrastructure to partners such as eMoney operators, mobile fintech platforms and banks. it raised atotal of USD 120 million., In Dec 2021, T. Rowe Price Group, Inc. announced its acquisition of Oak Hill Advisors, L.P. (OHA), a leading alternative credit manager. The acquisition accelerates T. Rowe Price's expansion into alternative credit markets, complementing its existing global platform and ongoing strategic investments in its core investments and distribution capabilities.. Notable trends are: Market Securities Held By Mutual Funds in United States.

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.

VIX Futures Signal Increased Market Volatility Ahead.

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