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

Kenya's main stock market index, the Nairobi 20, fell to 2514 points on July 11, 2025, losing 0.10% from the previous session. Over the past month, the index has climbed 11.00% and is up 48.23% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Kenya. Kenya Stock Market (NSE20) - values, historical data, forecasts and news - updated on July of 2025.

Financing conditions in the apartment market in the United States improved in *********, according to the National Multifamily Housing Council's (NMHC) finance index. The index is a standard diffusion index and is based on a quarterly survey among NMHC members. A value over ** indicates improving finance availability, while under **, it shows that financing is becoming harder to obtain. In **********, the debt financing index reached its peak at ** index points, meaning that debt financing conditions improved the most. In *********, the debt index stood at ** index points, which was an improvement from the same quarter in 2023.

The S&P 500, an index of 500 publicly traded companies in the United States, closed at 5,881.63 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.

Denmark Index: Copenhagen Stock Exchange: OMX Copenhagen Ex OMXC 20 data was reported at 4,968.520 31Dec1995=100 in Nov 2018. This records a decrease from the previous number of 5,078.930 31Dec1995=100 for Oct 2018. Denmark Index: Copenhagen Stock Exchange: OMX Copenhagen Ex OMXC 20 data is updated monthly, averaging 2,242.556 31Dec1995=100 from Dec 1999 (Median) to Nov 2018, with 228 observations. The data reached an all-time high of 5,648.470 31Dec1995=100 in Aug 2018 and a record low of 893.460 31Dec1995=100 in Sep 2002. Denmark Index: Copenhagen Stock Exchange: OMX Copenhagen Ex OMXC 20 data remains active status in CEIC and is reported by Copenhagen Stock Exchange. The data is categorized under Global Database’s Denmark – Table DK.Z001: Copenhagen Stock Exchange: Index. On May 13, 2013 NASDAQ OMX performed changes to the KFMX indexes. The name was changeed from KFMX to OMX Copenhagen ex OMX Copenhagen 20, and the price algorithm was changed from NEWNX to Last Paid, meaning that the official closing price becomes the latest price regardless of closing best bid and ask prices.

In the dynamic landscape of financial markets, accurate forecasting of stock indices remains a pivotal yet challenging task, essential for investors and policymakers alike. This study is motivated by the need to enhance the precision of predicting the Shanghai Composite Index’s opening price spread, a critical measure reflecting market volatility and investor sentiment. Traditional time series models like ARIMA have shown limitations in capturing the complex, nonlinear patterns inherent in stock price movements, prompting the exploration of advanced methodologies. The aim of this research is to bridge the gap in forecasting accuracy by developing a hybrid model that integrates the strengths of ARIMA with deep learning techniques, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. This novel approach leverages the ARIMA model’s proficiency in linear trend analysis and the deep learning models’ capability in modeling nonlinear dependencies, aiming to provide a comprehensive tool for market prediction. Utilizing a comprehensive dataset covering the period from December 20, 1990, to June 2, 2023, the study develops and assesses the efficacy of ARIMA, LSTM, GRU, ARIMA-LSTM, and ARIMA-GRU models in forecasting the Shanghai Composite Index’s opening price spread. The evaluation of these models is based on key statistical metrics, including Mean Squared Error (MSE) and Mean Absolute Error (MAE), to gauge their predictive accuracy. The findings indicate that the hybrid models, ARIMA-LSTM and ARIMA-GRU, perform better in forecasting the opening price spread of the Shanghai Composite Index than their standalone counterparts. This outcome suggests that combining traditional statistical methods with advanced deep learning algorithms can enhance stock market prediction. The research contributes to the field by providing evidence of the potential benefits of integrating different modeling approaches for financial forecasting, offering insights that could inform investment strategies and financial decision-making.

This paper investigates whether gold and silver can be considered safe havens by examining their long-run linkages with 13 stock price indices. More specifically, the stochastic properties of the differential between gold/silver prices and 13 stock indices are analysed applying fractional integration/cointegration methods to daily data, first for a sample from January 2010 until December 2019, then for one from January 2020 until June 2022 which includes the Covid-19 pandemic. The results can be summarised as follows. In the case of the pre-Covid-19 sample ending in December 2019, mean reversion is found for the gold price differential only vis-à-vis a single stock index (SP500). whilst in seven other cases, although the estimated value of d is below 1, the value 1 is inside the confidence interval and thus the unit root null hypothesis cannot be rejected. In the remaining cases the estimated values of d are significantly higher than 1. As for the silver differential, the upper bound is 1 only in two cases, whilst in the others mean reversion does not occur. Thus, the evidence is mixed on whether these precious metals can be seen as safe havens, though it appears that this property characterises gold in a slightly higher number of cases. By contrast, when using the sample starting in January 2020, the evidence in favour of gold and silver as possible safe havens is pretty conclusive since mean reversion is only found in a single case, namely that of the gold differential vis-à-vis the New Zealand stock index.

The heteroscedastic and volatile characteristics of stock price data have attracted the interest of researchers from various disciplines, particularly in the realm of price forecasting. The stock market’s non-stationary and volatile nature, driven by complex interrelationships among financial assets, economic developments, and market participants, poses significant challenges for accurate forecasting. This research aims to develop a robust forecasting model to improve the accuracy and reliability of stock price predictions using machine learning. A two-stage forecasting model is introduced. First, a random forest subset-based (RFS) feature selection with repeated -fold cross-validation selects the best subset of features from eight predictors: highest price, lowest price, closing price, volume, change, price change ratio, and amplitude. These features are then used as input in a bidirectional gated recurrent unit with an attention mechanism (BiGRU-AM) model to forecast daily opening prices of ten stock indices. The proposed model exhibits superior forecasting performance across ten stock indices when compared to twelve benchmarks, evaluated using root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination, . The improved prediction accuracy enables financial professionals to make more reliable investment decisions, reducing risks and increasing profits.

United States CSI: Savings: Stock Market Increase Probability: Next Yr: Mean data was reported at 59.400 % in May 2018. This records a decrease from the previous number of 60.800 % for Apr 2018. United States CSI: Savings: Stock Market Increase Probability: Next Yr: Mean data is updated monthly, averaging 54.500 % from Jun 2002 (Median) to May 2018, with 191 observations. The data reached an all-time high of 66.700 % in Jan 2018 and a record low of 34.000 % in Mar 2009. United States CSI: Savings: Stock Market Increase Probability: Next Yr: Mean data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H026: Consumer Sentiment Index: Savings & Retirement. The question was: What do you think the percent change that this one thousand dollar investment will increase in value in the year ahead, so that it is worth more than one thousand dollars one year from now?

The Dow Jones Composite Index finished the year 2024 at 13,391.71 points, an increase compared to the previous year. Even with the economic effects of the global coronavirus (COVID-19) pandemic, 2021 had the highest point of the index in the past two decades. What is Dow Jones Composite Index? The Dow Jones Composite Index is one of the indices from the Dow Jones index family. It is composed of 65 leading U.S. companies: 30 stocks forming the Dow Jones Industrial Average index, 20 stocks from the Dow Jones Transportation index and 15 stocks from the Dow Jones Utility Average index. Importance of stock indices A stock market index shows an average performance of companies from a given section of the market. It is usually a weighted average, meaning that such factors as price of companies or their market capitalization are taken into consideration when calculating the index value. Stock indices are very useful for the financial market participants, as they instantly show the sentiments prevailing on a given market. They are also commonly used as a benchmark against portfolio performance, showing if a given portfolio has outperformed, or underperformed the market.

Denmark Index: Copenhagen Stock Exchange: Gross: OMX Copenhagen Ex OMXC 20 data was reported at 8,112.710 31Dec1995=100 in Oct 2018. This records a decrease from the previous number of 8,752.230 31Dec1995=100 for Sep 2018. Denmark Index: Copenhagen Stock Exchange: Gross: OMX Copenhagen Ex OMXC 20 data is updated monthly, averaging 4,294.127 31Dec1995=100 from Nov 2005 (Median) to Oct 2018, with 156 observations. The data reached an all-time high of 8,991.170 31Dec1995=100 in Aug 2018 and a record low of 1,886.816 31Dec1995=100 in Mar 2009. Denmark Index: Copenhagen Stock Exchange: Gross: OMX Copenhagen Ex OMXC 20 data remains active status in CEIC and is reported by Copenhagen Stock Exchange. The data is categorized under Global Database’s Denmark – Table DK.Z001: Copenhagen Stock Exchange: Index. On May 13, 2013 NASDAQ OMX performed changes to the KFMX indexes. The name was changeed from KFMX to OMX Copenhagen ex OMX Copenhagen 20, and the price algorithm was changed from NEWNX to Last Paid, meaning that the official closing price becomes the latest price regardless of closing best bid and ask prices.

This paper investigates whether gold and silver can be considered safe havens by examining their long-run linkages with 13 stock price indices. More specifically, the stochastic properties of the differential between gold/silver prices and 13 stock indices are analysed applying fractional integration/cointegration methods to daily data, first for a sample from January 2010 until December 2019, then for one from January 2020 until June 2022 which includes the Covid-19 pandemic. The results can be summarised as follows. In the case of the pre-Covid-19 sample ending in December 2019, mean reversion is found for the gold price differential only vis-à-vis a single stock index (SP500). whilst in seven other cases, although the estimated value of d is below 1, the value 1 is inside the confidence interval and thus the unit root null hypothesis cannot be rejected. In the remaining cases the estimated values of d are significantly higher than 1. As for the silver differential, the upper bound is 1 only in two cases, whilst in the others mean reversion does not occur. Thus, the evidence is mixed on whether these precious metals can be seen as safe havens, though it appears that this property characterises gold in a slightly higher number of cases. By contrast, when using the sample starting in January 2020, the evidence in favour of gold and silver as possible safe havens is pretty conclusive since mean reversion is only found in a single case, namely that of the gold differential vis-à-vis the New Zealand stock index.

The heteroscedastic and volatile characteristics of stock price data have attracted the interest of researchers from various disciplines, particularly in the realm of price forecasting. The stock market’s non-stationary and volatile nature, driven by complex interrelationships among financial assets, economic developments, and market participants, poses significant challenges for accurate forecasting. This research aims to develop a robust forecasting model to improve the accuracy and reliability of stock price predictions using machine learning. A two-stage forecasting model is introduced. First, a random forest subset-based (RFS) feature selection with repeated -fold cross-validation selects the best subset of features from eight predictors: highest price, lowest price, closing price, volume, change, price change ratio, and amplitude. These features are then used as input in a bidirectional gated recurrent unit with an attention mechanism (BiGRU-AM) model to forecast daily opening prices of ten stock indices. The proposed model exhibits superior forecasting performance across ten stock indices when compared to twelve benchmarks, evaluated using root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination, . The improved prediction accuracy enables financial professionals to make more reliable investment decisions, reducing risks and increasing profits.

In 2021, the Nasdaq 100 closed at 16,320.08 points, which was the second highest value on record despite the economic effects of the global coronavirus (COVID-19) pandemic. The index value closed at 21,012.17 points in 2024, an increase of more than 4,000 points compared to its closing value for the previous year. What does the NASDAQ tell us? The Nasdaq 100 index is comprised of 100 largest and most actively traded non-financial companies listed on the Nasdaq stock exchange. Financial firms are represented by the NASDAQ Bank Index. A stock market index is a measurement of average performance of companies forming the index. It gives a snapshot of what investors are thinking at that particular moment. Other indices The Dow Jones Industrial Average gets more attention than the NASDAQ 100, though it only represents 30 companies. It’s best and worst days mark some of the major financial events of the past century. This helps to put more meaning behind events like Black Monday, the Wall Street crash of 1929, or the 2008 Financial Crisis, as well as the speed of their recoveries in financial markets.

For the WIG, WIG20 and mWIG40 indices, no day, week or month statistically differs from the average level of the index, which indicates no anomalies. The situation is different only for the index of small companies. In the case of sWIG80, the mean values on Friday, week 5 and 6, and during January, February and June were statistically different at the level of 1%. The second week of the month also turns out to be statistically different at the significance level of 5%. The study of the mean in the case of sWIG80 indicates the same periods as previously indicated during the preliminary comparison of the means in the first part of the study.

In the dynamic landscape of financial markets, accurate forecasting of stock indices remains a pivotal yet challenging task, essential for investors and policymakers alike. This study is motivated by the need to enhance the precision of predicting the Shanghai Composite Index’s opening price spread, a critical measure reflecting market volatility and investor sentiment. Traditional time series models like ARIMA have shown limitations in capturing the complex, nonlinear patterns inherent in stock price movements, prompting the exploration of advanced methodologies. The aim of this research is to bridge the gap in forecasting accuracy by developing a hybrid model that integrates the strengths of ARIMA with deep learning techniques, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. This novel approach leverages the ARIMA model’s proficiency in linear trend analysis and the deep learning models’ capability in modeling nonlinear dependencies, aiming to provide a comprehensive tool for market prediction. Utilizing a comprehensive dataset covering the period from December 20, 1990, to June 2, 2023, the study develops and assesses the efficacy of ARIMA, LSTM, GRU, ARIMA-LSTM, and ARIMA-GRU models in forecasting the Shanghai Composite Index’s opening price spread. The evaluation of these models is based on key statistical metrics, including Mean Squared Error (MSE) and Mean Absolute Error (MAE), to gauge their predictive accuracy. The findings indicate that the hybrid models, ARIMA-LSTM and ARIMA-GRU, perform better in forecasting the opening price spread of the Shanghai Composite Index than their standalone counterparts. This outcome suggests that combining traditional statistical methods with advanced deep learning algorithms can enhance stock market prediction. The research contributes to the field by providing evidence of the potential benefits of integrating different modeling approaches for financial forecasting, offering insights that could inform investment strategies and financial decision-making.

Content

Company: Ticker Major index membership: Index Market capitalization: Market Cap Income (ttm): Income Revenue (ttm): Sales Book value per share (mrq): Book/sh Cash per share (mrq): Cash/sh Dividend (annual): Dividend Dividend yield (annual): Dividend % Full time employees: Employees Stock has options trading on a market exchange: Optionable Stock available to sell short: Shortable Analysts' mean recommendation (1=Buy 5=Sell): Recom Price-to-Earnings (ttm): P/E Forward Price-to-Earnings (next fiscal year): Forward P/E Price-to-Earnings-to-Growth: PEG Price-to-Sales (ttm): P/S Price-to-Book (mrq): P/B Price to cash per share (mrq): P/C Price to Free Cash Flow (ttm): P/FCF Quick Ratio (mrq): Quick Ratio Current Ratio (mrq): Current Ratio Total Debt to Equity (mrq): Debt/Eq Long Term Debt to Equity (mrq): LT Debt/Eq Distance from 20-Day Simple Moving Average: SMA20 Diluted EPS (ttm): EPS (ttm) EPS estimate for next year: EPS next Y EPS estimate for next quarter: EPS next Q EPS growth this year: EPS this Y EPS growth next year: EPS next Y Long term annual growth estimate (5 years): EPS next 5Y Annual EPS growth past 5 years: EPS past 5Y Annual sales growth past 5 years: Sales past 5Y Quarterly revenue growth (yoy): Sales Q/Q Quarterly earnings growth (yoy): EPS Q/Q Earnings date

BMO = Before Market Open
AMC = After Market Close: Earnings Distance from 50-Day Simple Moving Average: SMA50 Insider ownership: Insider Own Insider transactions (6-Month change in Insider Ownership): Insider Trans Institutional ownership: Inst Own Institutional transactions (3-Month change in Institutional Ownership): Inst Trans Return on Assets (ttm): ROA Return on Equity (ttm): ROE Return on Investment (ttm): ROI Gross Margin (ttm): Gross Margin Operating Margin (ttm): Oper. Margin Net Profit Margin (ttm): Profit Margin Dividend Payout Ratio (ttm): Payout Distance from 200-Day Simple Moving Average: SMA200 Shares outstanding: Shs Outstand Shares float: Shs Float Short interest share: Short Float Short interest ratio: Short Ratio Analysts' mean target price: Target Price 52-Week trading range: 52W Range Distance from 52-Week High: 52W High Distance from 52-Week Low: 52W Low Relative Strength Index: RSI (14) Relative volume: Rel Volume Average volume (3 month): Avg Volume Volume: Volume Performance (Week): Perf Week Performance (Month): Perf Month Performance (Quarter): Perf Quarter Performance (Half Year): Perf Half Y Performance (Year): Perf Year Performance (Year To Date): Perf YTD Beta: Beta Average True Range (14): ATR Volatility (Week, Month): Volatility Previous close: Prev Close Current stock price: Price Performance (today): Change

Browse LSEG's I/B/E/S Estimates, discover our range of data, indices & benchmarks. Our Data Catalogue offers unrivalled data and delivery mechanisms.

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 on the Rise: What Does This Mean for You?

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

Abstract The issue of social inequalities is a subject of recurrent studies and remains relevant due to the growing trend of these inequalities over the years. This study proposes the creation of the Health Inequality Index (HII) composed of health indicators – Mean life span and Mean Potential Years of Life Lost (PYLL) – and socioeconomic indicators of income, schooling, and population living in poverty in the city of Natal – the State Capital of Rio Grande do Norte, Brazil. Therefore, a probabilistic linkage was made between mortality and socioeconomic databases in order to capture the census tracts of households with death records from 2007 to 2013. The authors used the Principal Component Factor Analysis to calculate the index. The Health Inequality Index showed areas with worse socioeconomic and health conditions located in the suburban areas of the city, with differences between and within the districts. The difference in the mean life span between the districts of Natal arrives at 25 years, and the worst district has mortality rates comparable to poor African countries. Public policymakers can use the index to prioritize actions aimed at reducing or eliminating health inequalities.