As of August 2025, the Vanguard Information Technology Index Fund provided the ******* one-year return rate. The Vanguard S&P 500 Growth Index Fund ranked ****** having a one-year return rate of *****percent. As of August 2025, the Vanguard Total Stock Market Index Fund was the largest fund owned by Vanguard, with net assets under management worth approximately **** trillion U.S. dollars. What is the difference between mutual funds and exchange traded funds? Both mutual funds and exchange traded funds (ETFs) originate from the concept of pooled fund investing, which bundles securities together to offer investors a more diversified portfolio. However, mutual funds and ETFs have some key differences. For instance, ETFs offer more flexible trading as they trade during the day like stocks, while mutual funds only allow transactions at the end of the day. Moreover, ETFs are mostly passively-managed and mirror a designated index. On the other hand, mutual funds are typically actively-managed, as it can be seen by comparing the number of actively and passively-managed mutual funds in the United States. Vanguard Founded by John C. Bogle in 1975, Vanguard is a U.S. asset management company that offers both mutual funds and ETFs. Headquartered in Malvern, Pennsylvania, Vanguard was the ****** largest provider of ETFs in the United States after BlackRock Financial Management, with assets under management worth *** trillion U.S. dollars. Likewise, in 2025, Vanguard ranked among the largest providers of mutual funds worldwide. The total assets under management of Vanguard increased considerably since its foundation in 1975, and peaked at *****trillion U.S. dollars in April 2025.

According to Cognitive Market Research, the global index fund market size will be USD XX million in 2024. It will expand at a compound annual growth rate (CAGR) of 6.00% from 2024 to 2031. North America held the major market share for more than 40% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 4.2% from 2024 to 2031. Europe accounted for a market share of over 30% of the global revenue with a market size of USD XX million. Asia Pacific held a market share of around 23% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.0% from 2024 to 2031. Latin America had a market share of more than 5% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 5.4% from 2024 to 2031. Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 5.7% from 2024 to 2031. The insurance fund held the highest index fund market revenue share in 2024. Market Dynamics of Index Fund Market Key Drivers for Index Fund Market Increased Awareness and Education About Investing to Increase the Demand Globally Increased awareness and education about investing have driven the growth of the index fund market. As people become more informed about financial principles, they realize the advantages of index funds, including low expenses, diversification, and transparency. Understanding the advantages of passive investing over operational management fosters confidence in index funds as dedicated vehicles for long-term wealth accumulation. This heightened attention drives greater participation in the market, shaping it into a key element of many investors' portfolios and contributing to its ongoing expansion. Changes in Regulatory Policies, Such As Tax Laws Or Securities Regulations to Propel Market Growth Changes in regulatory policies, like alterations in tax laws or securities regulations, can profoundly impact the index fund market. Shifts in tax codes may affect investors' after-tax returns, influencing their investment decisions. Similarly, changes in securities regulations can influence the structure and function of index funds, potentially limiting their attractiveness or compliance needs. Such changes can lead to changes in investor behavior, fund implementation, and market dynamics, highlighting the interconnectedness between regulatory conditions and the index fund market's strength and development trajectory?. Restraint Factor for the Index Fund Market Changes in Financial Regulations to Limit the Sales Changes in financial regulations can significantly impact the index fund market. Stricter regulatory requirements may improve compliance expenses for fund managers, potentially directing investors to higher fees. Additionally, regulations that restrict certain types of investments or mandate more comprehensive reporting can decrease the flexibility and attractiveness of index funds. Conversely, regulations encouraging transparency and investor protection can increase confidence and participation in the market. Impact of Covid-19 on the Index Fund Market The COVID-19 pandemic significantly impacted the index fund market, initially causing volatility and sharp drops. However, it also revved a shift towards passive investing due to market anticipation and the search for stability. Investors flocked to index funds for their low expenses, diversification, and constant performance. The subsequent market recovery, fueled by monetary and fiscal stimulation, further expanded index fund assets. Overall, the pandemic highlighted the resilience of index funds and solidified their attraction as a core investment strategy during times of economic uncertainty. Introduction of the Index Fund Market An index fund is a type of mutual fund or ETF designed to replicate the performance of a specific financial market index, delivering low costs, broad diversification, and passive investment management. Growing disposable incomes in developing regions significantly boost the index fund market. As individuals in these areas gain more financial stability, they seek investment opportunities to increase their wealth. Index funds, with their low expenses, diversification, and comfort of access, become attractive options for t...

The annual returns of the Nasdaq 100 Index from 1986 to 2024. fluctuated significantly throughout the period considered. The Nasdaq 100 index saw its lowest performance in 2008, with a return rate of ****** percent, while the largest returns were registered in 1999, at ****** percent. As of June 11, 2024, the rate of return of Nasdaq 100 Index stood at ** percent. The Nasdaq 100 is a stock market index comprised of the 100 largest and most actively traded non-financial companies listed on the Nasdaq stock exchange. How has the Nasdaq 100 evolved over years? The Nasdaq 100, which was previously heavily influenced by tech companies during the dot-com boom, has undergone significant diversification. Today, it represents a broader range of high-growth, non-financial companies across sectors like consumer services and healthcare, reflecting the evolving landscape of the global economy. The annual development of the Nasdaq 100 recently has generally been positive, except for 2022, when the NASDAQ experienced a decline due to worries about escalating inflation, interest rates, and regulatory challenges. What are the leading companies on Nasdaq 100? In August 2023, ***** was the largest company on the Nasdaq 100, with a market capitalization of **** trillion euros. Also, ****************************************** were among the five leading companies included in the index. Market capitalization is one of the most common ways of measuring how big a company is in the financial markets. It is calculated by multiplying the total number of outstanding shares by the current market price.

As of August 7, 2025, the fund with the third-highest return based on net asset value (NAV) was Back Rock's iShares MSCI Poland ETF. The iShares MSCI China Small-Cap ETF ranked second with a one-year NAV return of over 61 percent. The top ranking spot went to iShares Bitcoin Trust ETF with a NAV return of nearly 77.5 percent.

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.

The S&P 500, an index of 500 publicly traded companies in the United States, closed at ******** points on the last trading day of December 2024. What is the S&P 500? The S&P 500 is a stock market index that tracks the evolution of 500 companies. In contrast to the Dow Jones Industrial Index, which measures the performance of thirty large U.S. companies, the S&P 500 shows the sentiments in the broader market. Publicly traded companies Companies on the S&P 500 are publicly traded, meaning that anyone can invest in them. A large share of adults in the United States invest in the stock market, though many of these are through a retirement account or mutual fund. While most people make a modest return, the most successful investors have made billions of U.S. dollars through investing.

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.

Are Index Funds Good Investments?

Financial data:

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

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

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

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

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

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

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

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

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

• Data cleaning and preprocessing are essential before model training

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

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

What is a index fund and how does it work?

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

Market timing is an investment technique that tries to continuously switch investment into assets forecast to have better returns. What is the likelihood of having a successful market timing strategy? With an emphasis on modeling simplicity, I calculate the feasible set of market timing portfolios using index mutual fund data for perfectly timed (by hindsight) all or nothing quarterly switching between two asset classes, US stocks and bonds over the time period 1993–2017. The historical optimal timing path of switches is shown to be indistinguishable from a random sequence. The key result is that the probability distribution function of market timing returns is asymmetric, that the highest probability outcome for market timing is a below median return. Put another way, simple math says market timing is more likely to lose than to win—even before accounting for costs. The median of the market timing return probability distribution can be directly calculated as a weighted average of the returns of the model assets with the weights given by the fraction of time each asset has a higher return than the other. For the time period of the data the median return was close to, but not identical with, the return of a static 60:40 stock:bond portfolio. These results are illustrated through Monte Carlo sampling of timing paths within the feasible set and by the observed return paths of several market timing mutual funds.

Index Time Series for Vanguard Whitehall Funds - Vanguard High Dividend Yield ETF. The frequency of the observation is daily. Moving average series are also typically included. The manager employs an indexing investment approach designed to track the performance of the index, which consists of common stocks of companies that pay dividends that generally are higher than average. The adviser attempts to replicate the target index by investing all, or substantially all, of the fund's assets in the stocks that make up the index, holding each stock in approximately the same proportion as its weighting in the index.

China Index: Shanghai Stock Exchange: Fund data was reported at 6,772.720 25Apr2000=1000 in Apr 2025. This records a decrease from the previous number of 6,942.700 25Apr2000=1000 for Mar 2025. China Index: Shanghai Stock Exchange: Fund data is updated monthly, averaging 4,438.820 25Apr2000=1000 from Jan 2001 (Median) to Apr 2025, with 292 observations. The data reached an all-time high of 7,614.930 25Apr2000=1000 in Jun 2021 and a record low of 740.850 25Apr2000=1000 in May 2005. China Index: Shanghai Stock Exchange: Fund data remains active status in CEIC and is reported by Shanghai Stock Exchange. The data is categorized under Global Database’s China – Table CN.ZA: Shanghai Stock Exchange: Indices.

Index Time Series for Invesco Exchange-Traded Fund Trust II - Invesco S&P 500 QVM Multi-factor ETF. The frequency of the observation is daily. Moving average series are also typically included. The fund generally will invest at least 90% of its total assets in the securities that comprise the underlying index. S&P DJI compiles, maintains and calculates the index, which is designed to measure the performance of 90% of the stocks within the S&P 500® Index after excluding those with the lowest quality, value and momentum multi-factor score. The index is composed of securities with multi-factor scores representing the top 90% of the parent index, as determined by the index provider.

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.

Schwab Total Stock Market Index Fund

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

Norway Oslo Bors: Index: OSEFX Mutual Fund Gross Return data was reported at 1,448.510 NA in Apr 2025. This records a decrease from the previous number of 1,463.590 NA for Mar 2025. Norway Oslo Bors: Index: OSEFX Mutual Fund Gross Return data is updated monthly, averaging 842.000 NA from Jun 2013 (Median) to Apr 2025, with 143 observations. The data reached an all-time high of 1,473.730 NA in Jan 2025 and a record low of 461.090 NA in Jun 2013. Norway Oslo Bors: Index: OSEFX Mutual Fund Gross Return data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s Norway – Table NO.EDI.SE: Oslo Bors: Monthly.

Sources:

German Central Bank (ed.), 1975: Deutsches Geld- und Bankwesen in Zahlen 1876 – 1975. (German monetary system and banking system in numbers 1876 – 1975) German Central Bank (ed.), different years: monthly reports of the German Central Bank, statistical part, interest rates German Central Bank (ed.), different years: Supplementary statistical booklets for the monthly reports of the German Central Bank 1959 – 1992, security statistics Reich Statistical Office (ed.), different years: Statistical yearbook of the German empire Statistical Office (ed.), 1985: Geld und Kredit. Index der Aktienkurse (Money and Credit. Index of share prices) – Lange Reihe; Fachserie 9, Reihe 2. Statistical Office (ed.), 1987: Entwicklung der Nahrungsmittelpreise von 1800 – 1880 in Deutschland. (Development of food prices in Germany 1800 – 1880) Statistical Office (ed.), 1987: Entwicklung der Verbraucherpreise (Development of consumer prices) seit 1881 in Deutschland. (Development of consumer prices since 1881 in Germany) Statistical Office (ed.), different years: Fachserie 17, Reihe 7, Preisindex für die Lebenshaltung (price index for costs of living) Donner, 1934: Kursbildung am Aktienmarkt; Grundlagen zur Konjunkturbeobachtung an den Effektenmärkten. (Prices on the stock market; groundwork for observation of economic cycles on the stock market) Homburger, 1905: Die Entwicklung des Zinsfusses in Deutschland von 1870 – 1903. (Development of the interest flow in Germany, 1870 – 1903) Voye, 1902: Über die Höhe der verschiedenen Zinsarten und ihre wechselseitige Abhängigkeit.(On the values of different types of interests and their interdependence).

China's main stock market index, the SHANGHAI, rose to 3766 points on August 20, 2025, gaining 1.04% from the previous session. Over the past month, the index has climbed 5.80% and is up 31.84% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from China. China Shanghai Composite Stock Market Index - values, historical data, forecasts and news - updated on August of 2025.

Russia's main stock market index, the MOEX, fell to 2941 points on August 20, 2025, losing 0.84% from the previous session. Over the past month, the index has climbed 4.21% and is up 6.07% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Russia. Russia Stock Market Index MOEX CFD - values, historical data, forecasts and news - updated on August of 2025.

Norway Index: Oslo Bors Stock Exchange: Mutual Fund data was reported at 848.040 29Dec1995=100 in Jun 2018. This records an increase from the previous number of 846.830 29Dec1995=100 for May 2018. Norway Index: Oslo Bors Stock Exchange: Mutual Fund data is updated monthly, averaging 410.070 29Dec1995=100 from Jun 2001 (Median) to Jun 2018, with 205 observations. The data reached an all-time high of 848.040 29Dec1995=100 in Jun 2018 and a record low of 100.810 29Dec1995=100 in Feb 2003. Norway Index: Oslo Bors Stock Exchange: Mutual Fund data remains active status in CEIC and is reported by Oslo Stock Exchange. The data is categorized under Global Database’s Norway – Table NO.Z001: Oslo Stock Exchange: Index.

Share index and total return index Investment - and property investmentfunds (average of the month) December 1993 - November 2003 Changed on December 19 2003. Frequency: Discontinued.

Nepal Stock Exchange: Index: Mutual Fund data was reported at 19.590 NA in Apr 2025. This records an increase from the previous number of 19.120 NA for Mar 2025. Nepal Stock Exchange: Index: Mutual Fund data is updated monthly, averaging 14.750 NA from May 2019 (Median) to Apr 2025, with 71 observations. The data reached an all-time high of 21.970 NA in Aug 2024 and a record low of 9.100 NA in Oct 2019. Nepal Stock Exchange: Index: Mutual Fund data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s Nepal – Table NP.EDI.SE: Nepal Stock Exchange: Monthly.