Until the fourth quarter of 2023, the S&P 500 and the S&P 500 ESG index exhibited similar performance, both indexes were weighted to similar industries as the S&P 500 followed the leading 500 companies in the United States. Throughout 2024, the S&P 500 ESG index steadily outperformed the S&P 500 by ***** points on average. During the coronavirus pandemic, the technology sector was one of the best-performing sectors in the market. The major differences between the two indexes were the S&P 500 ESG index was skewed towards firms with higher environmental, social, and governance (ESG) scores and had a higher concentration of technology securities than the S&P 500 index. What is a market capitalization index? Both the S&P 500 and the S&P 500 ESG are market capitalization indexes, meaning the individual components (such as stocks and other securities) weighted to the indexes influence the overall value. Market trends such as inflation, interest rates, and international issues like the coronavirus pandemic and the popularity of ESG among professional investors affect the performance of stocks. When weighted components rise in value, this causes an increase in the overall value of the index they are weighted too. What trends are driving index performance? Recent economic and social trends have led to higher levels of ESG integration and maintenance among firms worldwide and higher prioritization from investors to include ESG-focused firms in their investment choices. From a global survey group over ********* of the respondents were willing to prioritize ESG benefits over a higher return on their investment. These trends influenced the performance of securities on the market, leading to an increased value of individual weighted stocks, resulting in an overall increase in the index value.

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 150 companies listed on the Zagreb Stock Exchange (XZAG) in Croatia. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

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Top 5 used data fields in the End-of-Day Pricing Dataset for Croatia:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

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Top 5 financial instruments with End-of-Day Pricing Data in Croatia:

Zagreb Stock Exchange (ZSE) CROBEX Index: The main index that tracks the performance of domestic companies listed on the Zagreb Stock Exchange. Monitoring this index provides insights into the overall trends and performance of the Croatian stock market.

Zagreb Stock Exchange (ZSE) CROBIS Index: The index that tracks the performance of foreign companies listed on the Zagreb Stock Exchange. This index reflects the influence of international companies operating within the Croatian market.

Agrokor Group: A major Croatian conglomerate with interests in various sectors including retail, food production, and agriculture. This company's stock is a key indicator of economic activity within Croatia.

Financial Services Company D: A significant financial services company based in Croatia, offering services such as banking, insurance, or asset management. Monitoring the stock of this company provides insights into the financial sector's performance.

Tourism Company E: A leading company in the Croatian tourism industry, contributing to the country's vital tourism sector. Monitoring the stock of this company reflects trends in the tourism industry's performance.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Croatia, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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.

Elon Musk's Speculation Strategy: Unraveling the Influence of a Maverick on the Stock Market

Financial data:

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

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

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

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

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

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

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

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

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

• Data cleaning and preprocessing are essential before model training

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

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...

Carbon price prediction is of great importance to regulators and participants in the carbon trading market. It is the basis for developing policies related to the carbon trading market and stabilizing that market. Considering the numerous factors that influence carbon prices in China, dimensionality reduction is needed to improve the prediction accuracy and efficiency. However, the traditional dimensionality reduction methods fail to fully consider the role of influencing factors, which has certain limitations. In this paper, a new dimensionality reduction method, namely scaled principal component analysis (s-PCA), is employed to improve the prediction accuracy of carbon prices. Firstly, a factor library that influence carbon prices is constructed from three perspectives: technical indicators, financial indicators and commodities indicators. Then, the s-PCA method is used to reduce the dimensionality of factors influencing carbon price. Next, two different methods are used to predict carbon prices, including traditional regression method and Long Short-Term Memory (LSTM) method. Finally, the economic value of the s-PCA method is examined by constructing investment portfolios. The empirical results of the Hubei Emissions Exchange show that the s-PCA model outperforms other competing models both in- and out-of-sample. In addition, the LSTM model could improve the performance of the s-PCA model in carbon price prediction. From a market timing perspective, investors can achieve a greater return and a larger Sharpe ratio using the s-PCA method than using other comparative methods and buy-and-hold strategy. Therefore, the s-PCA method is effective and robust in predicting carbon price.

While the size of the market determines the amount of service activity within a city, it may have more service activity than indicated by the size of the market, and this surplus of facilities is called "centrality". A high index value of centrality implies that the city is serving an extensive region outside the city, as well as the urban market itself. Conversely, a deficiency of service facilities suggests that the city's external role is quite limited, or that it may even import goods and service from nearby centres. In general the agricultural centres of western Canada have the highest values of centrality, while the lower values are found in industrial cities of central Canada (Ontario and Quebec) or isolated resource towns. Centrality implies an extensive and well-populated service area.

Baidu Search Index is a big data analytics tool developed by Baidu to track changes in keyword search popularity within its search engine. By analyzing trends in the Baidu Search Index for specific keywords, users can effectively monitor public interest in topics, companies, or brands.

As an ecosystem partner of Baidu Index, Datago has direct access to keyword search index data from Baidu's database, leveraging this information to build the BSIA-Consumer. This database encompasses popular brands that are actively searched by Chinese consumers, along with their commonly used names. By tracking Baidu Index search trends for these keywords, Datago precisely maps them to their corresponding publicly listed stocks.

The database covers over 1,100 consumer stocks and 3,000+ brand keywords across China, the United States, Europe, and Japan, with a particular focus on popular sectors like luxury goods and vehicles. Through its analysis of Chinese consumer search interest, this database offers investors a unique perspective on market sentiment, consumer preferences, and brand influence, including:

• Brand Influence Tracking – By leveraging Baidu Search Index data, investors can assess the level of consumer interest in various brands, helping to evaluate their influence and trends within the Chinese market.

• Consumer Stock Mapping – BSIA-consumer provides an accurate linkage between brand keywords and their associated consumer stocks, enabling investor analysis driven by consumer interest.

• Coverage of Popular Consumer Goods – BSIA-consumer focuses specifically on trending sectors like luxury goods and vehicles, offering valuable insights into these industries.

• Coverage: 1000+ consumer stocks

• History: 2016-01-01

• Update Frequency: Daily

About Transportation Services Index

The Transportation Services Index (TSI), created by the U.S. Department of Transportation (DOT), Bureau of Transportation Statistics (BTS), measures the movement of freight and passengers. The index, which is seasonally adjusted, combines available data on freight traffic, as well as passenger travel, that have been weighted to yield a monthly measure of transportation services output.

For charts and discussion on the relationship of the TSI to the economy, see our Transportation as an Economic Indicator: Transportation Services Index page (https://data.bts.gov/stories/s/TET-indicator-1/9czv-tjte)

For release schedule see: https://www.bts.gov/newsroom/transportation-services-index-release-schedule

About seasonally-adjusted data

Statisticians use the process of seasonal-adjustment to uncover trends in data. Monthly data, for instance, are influenced by the number of days and the number of weekends in a month as well as by the timing of holidays and seasonal activity. These influences make it difficult to see underlying changes in the data. Statisticians use seasonal adjustment to control for these influences.

Controlling of seasonal influences allows measurement of real monthly changes; short and long term patterns of growth or decline; and turning points. Data for one month can be compared to data for any other month in the series and the data series can be ranked to find high and low points. Any observed differences are “real” differences; that is, they are differences brought about by changes in the data and not brought about by a change in the number of days or weekends in the month, the occurrence or non-occurrence of a holiday, or seasonal activity.

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Mutual Funds Market Size 2025-2029

The mutual funds market size is forecast to increase by USD 85.5 trillion, at a CAGR of 9.9% between 2024 and 2029.

The market is characterized by the significant growth of mutual fund assets in developing nations, driven by increasing financial literacy and expanding middle classes. This trend is fueled by the desire for diversified investment opportunities and the convenience of mutual funds as an investment vehicle. Asset managers must mitigate these risks through effective risk management software and practices and transparent communication with investors. However, these regions also pose risks such as political instability, regulatory uncertainties, and currency fluctuations. Banks, FIIs, insurance companies, and other financial institutions offer mutual funds, providing access to a diverse range of securities. Companies seeking to capitalize on market opportunities must navigate these challenges effectively by implementing robust risk management strategies and maintaining transparency with investors.
Additionally, they can explore partnerships with local financial institutions and offer tailored investment solutions to cater to the unique needs of developing markets. By focusing on risk mitigation and local market expertise, mutual fund providers can effectively tap into the vast potential of emerging markets and drive sustainable growth.

What will be the Size of the Mutual Funds Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the ever-evolving mutual fund market, dynamics continue to unfold, shaping the landscape across various sectors. Index funds, with their passive investment strategy, have gained significant traction, challenging active management's traditional dominance. Performance measurement remains a critical focus, with benchmarks providing a yardstick for evaluation. Fund compliance adheres to regulations, ensuring transparency and fairness. Active management persists, with fund managers employing diverse investment strategies, from value investing to ESG and quantitative approaches. Fund holdings and returns are closely monitored, with tax implications and volatility influencing investor decisions. Fund advisory services offer guidance, while private equity and alternative investments broaden the investment universe.

Expense ratios and fund administration costs are under constant scrutiny, with risk management and fund distribution channels optimizing accessibility. The investment horizon, asset allocation, and fund ratings influence investor behavior. Fund sales, rebalancing, and redemption processes continue to evolve, ensuring flexibility for investors. Fund transparency and disclosure are paramount, with share classes catering to different investor needs. Hedge funds and mutual funds coexist, offering distinct investment opportunities. Fund prospectuses provide essential information, while marketing and comparison tools facilitate informed decisions. Investment objectives and reviews enable continuous improvement. The mutual fund market's continuous dynamism underscores the importance of adaptability and knowledge.

How is this Mutual Funds Industry segmented?

The mutual funds industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD trillion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Stock funds
  Bond funds
  Money market funds
  Hybrid funds


Distribution Channel

  Advice channel
  Retirement plan channel
  Institutional channel
  Direct channel
  Supermarket channel


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    Spain
    UK


  APAC

    Australia
    China
    India


  Rest of World (ROW)

By Type Insights

The stock funds segment is estimated to witness significant growth during the forecast period.

Mutual funds, specifically stock mutual funds, offer investors a diverse range of investment opportunities in corporate equities. These funds differ significantly, with various types catering to distinct investment objectives. For instance, growth funds focus on stocks with high growth potential, while income funds prioritize stocks yielding regular dividends. Index funds mirror a specific market index, such as the S&P 500, and sector funds invest in a particular industry sector. The mutual fund market is regulated, ensuring transparency and compliance with securities laws. Portfolio management plays a crucial role in selecting and managing the fund's holdings to achieve the investment strategy's objectives.

The fund's liquidity, represented by its ability to buy and sell shares, is essential for investors. Exchange-traded fu

As of September 2024, New York ranked as the world's most attractive financial center, earning a score of *** on a comprehensive financial center rating index that considers multiple factors. London followed closely in second place with a rating of ***. What are financial centers? A financial center is a city or region that serves as a strategic hub for the financial industry, bringing together banks, trading firms, stock exchanges, and other financial institutions. These hubs are typically distinguished by strong infrastructure, a stable regulatory and political environment, favorable taxation policies, and ample opportunities for business and trade growth. According to a 2024 survey of financial services professionals, the key factors influencing a financial center's competitiveness were the business environment, human capital, and infrastructure. Financial centers by region According to the Global Financial Centers Index, the most attractive financial hubs in North America are New York, San Francisco, and Chicago. In Latin America and the Caribbean, Bermuda, the Cayman Islands, and Sao Paulo received the highest scores. When financial sector professionals were asked which financial centers were likely to become more significant in the next years, they pointed to Seoul, Singapore, Dubai.

Carbon price prediction is of great importance to regulators and participants in the carbon trading market. It is the basis for developing policies related to the carbon trading market and stabilizing that market. Considering the numerous factors that influence carbon prices in China, dimensionality reduction is needed to improve the prediction accuracy and efficiency. However, the traditional dimensionality reduction methods fail to fully consider the role of influencing factors, which has certain limitations. In this paper, a new dimensionality reduction method, namely scaled principal component analysis (s-PCA), is employed to improve the prediction accuracy of carbon prices. Firstly, a factor library that influence carbon prices is constructed from three perspectives: technical indicators, financial indicators and commodities indicators. Then, the s-PCA method is used to reduce the dimensionality of factors influencing carbon price. Next, two different methods are used to predict carbon prices, including traditional regression method and Long Short-Term Memory (LSTM) method. Finally, the economic value of the s-PCA method is examined by constructing investment portfolios. The empirical results of the Hubei Emissions Exchange show that the s-PCA model outperforms other competing models both in- and out-of-sample. In addition, the LSTM model could improve the performance of the s-PCA model in carbon price prediction. From a market timing perspective, investors can achieve a greater return and a larger Sharpe ratio using the s-PCA method than using other comparative methods and buy-and-hold strategy. Therefore, the s-PCA method is effective and robust in predicting carbon price.