The Value Line Investment Survey is one of the oldest, continuously running investment advisory publications. Since 1955, the Survey has been published in multiple formats including print, loose-leaf, microfilm and microfiche. Data from 1997 to present is now available online. The Survey tracks 1700 stocks across 92 industry groups. It provides reported and projected measures of firm performance, proprietary rankings and analysis for each stock on a quarterly basis. DATA AVAILABLE FOR YEARS: 1980-1989 This dataset, a subset of the Survey covering the years 1980-1989 has been digitized from the microfiche collection available at the Dewey Library (FICHE HG 4501.V26). It is only available to MIT students and faculty for academic research. Published weekly, each edition of the Survey has the following three parts: Summary & Index: includes an alphabetical listing of all industries with their relative ranking and the page number for detailed industry analysis. It also includes an alphabetical listing of all stocks in the publication with references to their location in Part 3, Ratings & Reports. Selection & Opinion: contains the latest economic and stock market commentary and advice along with one or more pages of research on interesting stocks or industries, and a variety of pertinent economic and stock market statistics. It also includes three model stock portfolios. Ratings & Reports: This is the core of the Value Line Investment Survey. Preceded by an industry report, each one-page stock report within that industry includes Timeliness, Safety and Technical rankings, 3-to 5-year analyst forecasts for stock prices, income and balance sheet items, up to 17 years of historical data, and Value Line analysts’ commentaries. The report also contains stock price charts, quarterly sales, earnings, and dividend information. Publication Schedule: Each edition of the Survey covers around 130 stocks in seven to eight industries on a preset sequential schedule so that all 1700 stocks are analyzed once every 13 weeks or each quarter. All editions are numbered 1-13 within each quarter. For example, in 1980, reports for Chrysler appear in edition 1 of each quarter on the following dates: January 4, 1980 – page 132 April 4, 1980 – page 133 July 4, 1980 – page 133 October 1, 1980 – page 133 Reports for Coca-Cola were published in edition 10 of each quarter on: March 7, 1980 – page 1514 June 6, 1980 – page 1518 Sept. 5, 1980 – page 1517 Dec. 5, 1980 – page 1548 Any significant news affecting a stock between quarters is covered in the supplementary reports that appear at the end of part 3, Ratings & Reports. File format: Digitized files within this dataset are in PDF format and are arranged by publication date within each compressed annual folder. How to Consult the Value Line Investment Survey: To find reports on a particular stock, consult the alphabetical listing of stocks in the Summary & Index part of the relevant weekly edition. Look for the page number just to the left of the company name and then use the table below to identify the edition where that page number appears. All editions within a given quarter are numbered 1-13 and follow equally sized page ranges for stock reports. The table provides page ranges for stock reports within editions 1-13 of 1980 Q1. It can be used to identify edition and page numbers for any quarter within a given year. Ratings & Reports Edition Pub. Date Pages 1 04-Jan-80 100-242 2 11-Jan-80 250-392 3 18-Jan-80 400-542 4 25-Jan-80 550-692 5 01-Feb-80 700-842 6 08-Feb-80 850-992 7 15-Feb-80 1000-1142 8 22-Feb-80 1150-1292 9 29-Feb-80 1300-1442 10 07-Mar-80 1450-1592 11 14-Mar-80 1600-1742 12 21-Mar-80 1750-1908 13 28-Mar-80 2000-2142 Another way to navigate to the Ratings & Reports part of an edition would be to look around page 50 within the PDF document. Note that the page numbers of the PDF will not match those within the publication.

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.

The Dow Jones U.S. Completion Total Stock Market Index

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

Indexes included in the Russell U.S. Index Series Russell 3000®: The Russell 3000 Index measures the performance of the largest 3,000 U.S. companies representing approximately 98% of the investable U.S. equity market. Russell 1000®: The Russell 1000 Index measures the performance of the large-cap segment of the U.S. equity universe. It is a subset of the Russell 3000 Index and includes approximately 1,000 of the largest securities based on a combination of their market cap and current index membership. The Russell 1000 represents approximately 91% of the U.S. market. Russell 2000®: The Russell 2000 Index measures the performance of the small-cap segment of the U.S. equity universe. The Russell 2000 Index is a subset of the Russell 3000 Index representing approximately 9% of the total market capitalization of that index. It includes approximately 2,000 of the smallest securities based on a combination of their market cap and current index membership. Index Inception Dates Russell 1000® Index (1/1979) Russell 1000® Growth Index (1/1979) Russell 1000® Value Index (1/1979) Russell 2000® Index (1/1979) Russell 2000® Growth Index (1/1979) Russell 2000® Value Index (1/1979) Russell 2500™ Index (4/2003) Russell 2500™ Growth Index (4/2003) Russell 2500™ Value Index (4/2003) Russell 3000® Index (1/1979) Russell 3000® Growth Index (1/1979) Russell 3000® Value Index (1/1979) Russell Midcap® Index (1/1986) Russell Midcap® Growth Index (1/1987) Russell Midcap® Value Index (1/1987) Russell Small Cap Completeness Index (4/2003) Russell Small Cap Completeness Growth Index (4/2003) Russell Small Cap Completeness Value Index (4/2003) Russell Top 200® Index (7/1996) Russell Top 200® Growth Index (7/2001) Russell Top 200® Value Index (7/2001) Monthly Files included in the Russell U.S. Index Series Monthly Closing Files – RGS These holdings files reflect the official closing positions for all constituents of the 21 U.S. Russell Indexes at month-end back to December 1986 and at quarter-end from September 1986 back to December 1978. Security level information such as returns, market values, sector and industry classifications, and security weights are included in the file. Files are fixed-width text files and have a naming convention of H_yyyymmdd_RGS.txt. Monthly Closing Files – ICB These holdings files reflect the official closing positions for all constituents of the 21 U.S. Russell Indexes at month-end back to January 2010. Security level information such as returns, market values, sector and industry classifications, and security weights are included in the file. Files are comma delimited text files and have a naming convention of H_yyyymmdd.csv. Monthly Contribution to Return by RGS Files These files provide contribution to return using RGS as of the end of the month for each of the 21 U.S. Russell Indexes back to August 2008. Files are tab delimited text files and have a naming convention of CTR_MONTHLY_RGS_yyyymmdd.txt.. Monthly Contribution to Return by ICB Files These files provide contribution to return using ICB as of the end of the month for each of the 21 U.S. Russell Indexes back to August 2020. Files are comma delimited text files and have a naming convention of CTR_MONTHLY_yyyymmdd.csv. Monthly RGS Sector Weights Files These files provide monthly Russell Global Sector (RGS) weights for all 21 US Indexes at month-end back to November 2009. Files are comma delimited text files and have a naming convention of SWH_RGS_ALL_yyyymmdd.txt. Monthly ICB Sector Weights Files These files provide monthly Industrial Classification Benchmark (ICB) weights for all 21 US Indexes at month-end back to March 2020. Files are comma delimited text files and have a naming convention of SWH_ALL_yyyymmdd.csv. Note: In August 2020 FTSE Russell transitioned to ICB classification from the RGS classification. All data from September, 2020 is only available using ICB Classification. Data is current to April 28, 2023.

Daily realised volatilities for the Dow Jones Index and 26 individual stocks.

The Realised Volatility data was used to evaluate different volatility forecasting methods. The Realised Volatility data was calculated using underlying high frequency prices obtained from Thomson Reuters Datascope.

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.

Dow Jones U.S. Select Insurance Index: Poised for a Rebound?

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.

Nifty 50: Climb or Crash?

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...

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

This is not going to be an article or Op-Ed about Michael Jordan. Since 2009 we've been in the longest bull-market in history, that's 11 years and counting. However a few metrics like the stock market P/E, the call to put ratio and of course the Shiller P/E suggest a great crash is coming in-between the levels of 1929 and the dot.com bubble. Mean reversion historically is inevitable and the Fed's printing money experiment could end in disaster for the stock market in late 2021 or 2022. You can read Jeremy Grantham's Last Dance article here. You are likely well aware of Michael Burry's predicament as well. It's easier for you just to skim through two related videos on this topic of a stock market crash. Michael Burry's Warning see this YouTube. Jeremy Grantham's Warning See this YouTube. Typically when there is a major event in the world, there is a crash and then a bear market and a recovery that takes many many months. In March, 2020 that's not what we saw since the Fed did some astonishing things that means a liquidity sloth and the risk of a major inflation event. The pandemic represented the quickest decline of at least 30% in the history of the benchmark S&P 500, but the recovery was not correlated to anything but Fed intervention. Since the pandemic clearly isn't disappearing and many sectors such as travel, business travel, tourism and supply chain disruptions appear significantly disrupted - the so-called economic recovery isn't so great. And there's this little problem at the heart of global capitalism today, the stock market just keeps going up. Crashes and corrections typically occur frequently in a normal market. But the Fed liquidity and irresponsible printing of money is creating a scenario where normal behavior isn't occurring on the markets. According to data provided by market analytics firm Yardeni Research, the benchmark index has undergone 38 declines of at least 10% since the beginning of 1950. Since March, 2020 we've barely seen a down month. September, 2020 was flat-ish. The S&P 500 has more than doubled since those lows. Look at the angle of the curve: The S&P 500 was 735 at the low in 2009, so in this bull market alone it has gone up 6x in valuation. That's not a normal cycle and it could mean we are due for an epic correction. I have to agree with the analysts who claim that the long, long bull market since 2009 has finally matured into a fully-fledged epic bubble. There is a complacency, buy-the dip frenzy and general meme environment to what BigTech can do in such an environment. The weight of Apple, Amazon, Alphabet, Microsoft, Facebook, Nvidia and Tesla together in the S&P and Nasdaq is approach a ridiculous weighting. When these stocks are seen both as growth, value and companies with unbeatable moats the entire dynamics of the stock market begin to break down. Check out FANG during the pandemic. BigTech is Seen as Bullet-Proof me valuations and a hysterical speculative behavior leads to even higher highs, even as 2020 offered many younger people an on-ramp into investing for the first time. Some analysts at JP Morgan are even saying that until retail investors stop charging into stocks, markets probably don’t have too much to worry about. Hedge funds with payment for order flows can predict exactly how these retail investors are behaving and monetize them. PFOF might even have to be banned by the SEC. The risk-on market theoretically just keeps going up until the Fed raises interest rates, which could be in 2023! For some context, we're more than 1.4 years removed from the bear-market bottom of the coronavirus crash and haven't had even a 5% correction in nine months. This is the most over-priced the market has likely ever been. At the night of the dot-com bubble the S&P 500 was only 1,400. Today it is 4,500, not so many years after. Clearly something is not quite right if you look at history and the P/E ratios. A market pumped with liquidity produces higher earnings with historically low interest rates, it's an environment where dangerous things can occur. In late 1997, as the S&P 500 passed its previous 1929 peak of 21x earnings, that seemed like a lot, but nothing compared to today. For some context, the S&P 500 Shiller P/E closed last week at 38.58, which is nearly a two-decade high. It's also well over double the average Shiller P/E of 16.84, dating back 151 years. So the stock market is likely around 2x over-valued. Try to think rationally about what this means for valuations today and your favorite stock prices, what should they be in historical terms? The S&P 500 is up 31% in the past year. It will likely hit 5,000 before a correction given the amount of added liquidity to the system and the QE the Fed is using that's like a huge abuse of MMT, or Modern Monetary Theory. This has also lent to bubbles in the housing market, crypto and even commodities like Gold with long-term global GDP meeting many headwinds in the years ahead due to a demographic shift of an ageing population and significant technological automation. So if you think that stocks or equities or ETFs are the best place to put your money in 2022, you might want to think again. The crash of the OTC and small-cap market since February 2021 has been quite an indication of what a correction looks like. According to the Motley Fool what happens after major downturns in the market historically speaking? In each of the previous four instances that the S&P 500's Shiller P/E shot above and sustained 30, the index lost anywhere from 20% to 89% of its value. So what's what we too are due for, reversion to the mean will be realistically brutal after the Fed's hyper-extreme intervention has run its course. Of course what the Fed stimulus has really done is simply allowed the 1% to get a whole lot richer to the point of wealth inequality spiraling out of control in the decades ahead leading us likely to a dystopia in an unfair and unequal version of BigTech capitalism. This has also led to a trend of short squeeze to these tech stocks, as shown in recent years' data. Of course the Fed has to say that's its done all of these things for the people, employment numbers and the labor market. Women in the workplace have been set behind likely 15 years in social progress due to the pandemic and the Fed's response. While the 89% lost during the Great Depression would be virtually impossible today thanks to ongoing intervention from the Federal Reserve and Capitol Hill, a correction of 20% to 50% would be pretty fair and simply return the curve back to a normal trajectory as interest rates going back up eventually in the 2023 to 2025 period. It's very unlikely the market has taken Fed tapering into account (priced-in), since the euphoria of a can't miss market just keeps pushing the markets higher. But all good things must come to an end. Earlier this month, the U.S. Bureau of Labor Statistics released inflation data from July. This report showed that the Consumer Price Index for All Urban Consumers rose 5.2% over the past 12 months. While the Fed and economists promise us this inflation is temporary, others are not so certain. As you print so much money, the money you have is worth less and certain goods cost more. Wage gains in some industries cannot be taken back, they are permanent - in the service sector like restaurants, hospitality and travel that have been among the hardest hit. The pandemic has led to a paradigm shift in the future of work, and that too is not temporary. The Great Resignation means white collar jobs with be more WFM than ever before, with a new software revolution, different transport and energy behaviors and so forth. Climate change alone could slow down global GDP in the 21st century. How can inflation be temporary when so many trends don't appear to be temporary? Sure the price of lumber or used-cars could be temporary, but a global chip shortage is exasperating the automobile sector. The stock market isn't even behaving like it cares about anything other than the Fed, and its $billions of dollars of buying bonds each month. Some central banks will start to taper about December, 2021 (like the European). However Delta could further mutate into a variant that makes the first generation of vaccines less effective. Such a macro event could be enough to trigger the correction we've been speaking about. So stay safe, and keep your money safe. The Last Dance of the 2009 bull market could feel especially more painful because we've been spoiled for so long in the markets. We can barely remember what March, 2020 felt like. Some people sold their life savings simply due to scare tactics by the likes of Bill Ackman. His scare tactics on CNBC won him likely hundreds of millions as the stock market tanked. Hedge funds further gamed the Reddit and Gamestop movement, orchestrating them and leading the new retail investors into meme speculation and a whole bunch of other unsavory things like options trading at such scale we've never seen before. It's not just inflation and higher interest rates, it's how absurdly high valuations have become. Still correlation does not imply causation. Just because inflation has picked up, it doesn't guarantee that stocks will head lower. Nevertheless, weaker buying power associated with higher inflation can't be overlooked as a potential negative for the U.S. economy and equities. The current S&P500 10-year P/E Ratio is 38.7. This is 97% above the modern-era market average of 19.6, putting the current P/E 2.5 standard deviations above the modern-era average. This is just math, folks. History is saying the stock market is 2x its true value. So why and who would be full on the market or an asset class like crypto that is mostly speculative in nature to begin with? Study the following on a historical basis, and due your own due diligence as to the health of the markets: Debt-to-GDP ratio Call to put ratio

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 SET Index stock 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

United States - Equity Market Volatility Tracker: Housing And Land Management was 0.45227 Index in May of 2025, according to the United States Federal Reserve. Historically, United States - Equity Market Volatility Tracker: Housing And Land Management reached a record high of 1.80535 in September of 2008 and a record low of 0.00000 in April of 1985. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Equity Market Volatility Tracker: Housing And Land Management - last updated from the United States Federal Reserve on July of 2025.

As of June 2024, the Vanguard Mega Cap Growth Index provided the ******* one-year return rate. The Vanguard Russell 1000 Growth Index Fund ranked ****** having a one-year return rate of **** percent. As of June 2024, 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 almost *** trillion U.S. dollars. Likewise, in 2024, 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 2024.

Saudi Arabia Index: Tadawul: Real Estate Management and Development data was reported at 3,354.170 05Jan2017=5000 in Oct 2018. This records a decrease from the previous number of 3,535.170 05Jan2017=5000 for Sep 2018. Saudi Arabia Index: Tadawul: Real Estate Management and Development data is updated monthly, averaging 4,342.335 05Jan2017=5000 from Jan 2016 (Median) to Oct 2018, with 34 observations. The data reached an all-time high of 5,016.110 05Jan2017=5000 in Dec 2016 and a record low of 3,354.170 05Jan2017=5000 in Oct 2018. Saudi Arabia Index: Tadawul: Real Estate Management and Development data remains active status in CEIC and is reported by Tadawul. The data is categorized under Global Database’s Saudi Arabia – Table SA.Z001: Tadawul Stock Exchange: Index.

From November 2024 to May 2025, the Nasdaq Bank Index, which tracks hundreds of banks whose shares are traded on the Nasdaq stock exchange, showed the continued impact of the Trump administration. In April 2025, the announcement of renewed Trump-era tariffs triggered a sharp drop in the index, with markets reacting swiftly to fears of escalating trade tensions. The impact was immediate across several sectors, but the banking industry showed notable resilience. Despite the initial selloff, banks recovered quickly. This resilience helped stabilize the broader index despite ongoing trade-related uncertainties.

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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

Supplementary information files for the article Emerging stock market volatility and economic fundamentals: the importance of US uncertainty spillovers, financial and health crises

Abstract: This paper studies the US and global economic fundamentals that exacerbate emerging stock markets volatility and can be considered as systemic risk factors increasing financial stability vulnerabilities. We apply the bivariate HEAVY system of daily and intra-daily volatility equations enriched with powers, leverage, and macro-effects that improve its forecasting accuracy significantly. Our macro-augmented asymmetric power HEAVY model estimates the inflammatory effect of US uncertainty and infectious disease news impact on equities alongside global credit and commodity factors on emerging stock index realized volatility. Our study further demonstrates the power of the economic uncertainty channel, showing that higher US policy uncertainty levels increase the leverage effects and the impact from the common macro-financial proxies on emerging markets’ financial volatility. Lastly, we provide evidence on the crucial role of both financial and health crisis events (the 2008 global financial turmoil and the recent Covid-19 pandemic) in raising markets’ turbulence and amplifying the volatility macro-drivers impact, as well.

As of January 2025, the Vanguard Group ranked ****** among global fund managers by assets under management (AUM). Rounding out the top *****, Charles Schwab ranked *****, managing fund assets totaling **** trillion U.S. dollars. BlackRock was the ******* fund manager, managing fund assets exceeding ***** trillion U.S. Types of investment funds. Investment funds are an important part of financial planning and investing. There are several different types of investment funds offered by fund managers, each with their own purpose and asset types. Mutual funds pool money from many investors and use that money to purchase a portfolio of stocks, bonds, and other securities. Index funds are a type of mutual fund that tracks a market index, like the S&P 500. Exchange-traded funds (ETFs) are a type of mutual fund, that is continuously traded on a stock exchange. ETFs often track market indexes or sectors. Real estate investment trusts (REITs) provide both retail and institutional investors with exposure to income-generating real estate assets such as office buildings, apartments and hotels, without having to fully invest in an individual property. The benefits of investment funds. The main advantage of investment funds is that they provide instant portfolio diversification. Rather than choosing just a few stocks or bonds, funds allow you to invest in a wide variety of different securities in one purchase. This helps reduce risk, as poor performance of one holding has less impact on the overall fund. Funds also provide access to professional management and research. Managers can take advantage of opportunities and insights that an individual investor may not have the ability to leverage. Finally, funds offer convenience. Investors won't be required to constantly rebalance portfolios. While costs and fees are a consideration, investment funds can be an excellent hands-off way for both retail and institutional investors to benefit from the market while spreading risk over many asset classes and securities.

Explore LSEG S&P Global Purchasing Managers Index (PMI) for monthly surveys that provide up-to-date, accurate, and unique indicators of economic trends.

The Dow Jones U.S. Select Insurance index is poised for potential gains, driven by rising demand for insurance products amid increasing risks and uncertainties. The sector may benefit from favorable economic conditions, as steady growth and low interest rates support business expansion and insurance coverage needs. However, geopolitical tensions, inflation, and the threat of natural disasters pose risks that could lead to market volatility and unexpected insurance claims. Insurers with strong financial fundamentals, effective risk management strategies, and a diversified product mix are likely to navigate these challenges and deliver positive returns.

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.

Nikkei 225 Index Target Price 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