Dataset for most widely held stocks across top investor portfolios

Dataset

This dataset was created by Mehmet Sungur

Released under CC BY-SA 4.0

Contents

As of June 17, 2024, the most shorted stock was for, the American holographic technology services provider, MicroCloud Hologram Inc., with 66.64 percent of their total float having been shorted. This is a change from mid-January 2021, when video game retailed GameStop had an incredible 121.07 percent of their available shares in a short position. In effect this means that investors had 'borrowed' more shares (with a future promise to return them) than the total number of shares available for public trading. Owing to this behavior of professional investors, retail investors enacted a campaign to drive up the stock price of Gamestop, leading to losses of billions when investors had to repurchase the stock they had borrowed. At this time, a similar – but less effective – social media campaign was also carried out for the stock price of cinema operator AMC, and the price of silver. What is short selling? Short selling is essentially where an investor bets on a share price falling by: borrowing a number of shares selling these shares while the price is still high; purchasing the same number again once the price falls; then returning the borrowed shares at a profit. Of course, a profit will only be made if the share price does fall; should the share price rise the investor will then need to purchase the shares back at a higher price, and thus incur a loss. Short selling can lead to some very large profits in a short amount of time, with Tesla stock generating over one billion dollars in short sell profits during the first week of March 2020 alone, owing to the financial crash caused by the coronavirus (COVID-19) pandemic. However, owing to the short-term, opportunistic nature of short selling, these returns look less impressive when considered as net profits from short sell positions over the full year. The risks of short selling Short selling carries greater risks than traditional investments, and for this reason financial advisors often recommend against this strategy for ‘retail’ (i.e. non-professional) investors. The reason for this is that losses from short selling are potentially uncapped, whereas losses from traditional investments are limited to the initial cost. For example, if someone purchases 100 dollars of shares, the maximum they can lose is the 100 dollars the spent on those shares. However, say someone borrows 100 dollars of shares instead, betting on the price falling. If these shares are then sold for 100 dollars but the price subsequently rises, the losses could greatly exceed the initial investment should the price rise to, say, 500 dollars. The risks of short selling can be seen by looking again at Tesla, with the company causing the greatest losses over 2020 from short selling at over 40 billion U.S. dollars.

In a survey conducted in 2021, around **** percent of people in Japan reported owning stocks. Stocks and investment trusts were the most common types of securities held by individuals in Japan.

FAANG

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• FAANG is an acronym referring to the stocks of the five most popular and best-performing American technology companies: Meta (formerly known as Facebook), Amazon, Apple, Netflix, and Alphabet (formerly known as Google).

• In addition to being widely known among consumers, the five FAANG stocks are among the largest companies in the world, with a combined market capitalization of nearly $7.1 trillion as of Aug. 19, 2021.

• Some have raised concerns that the FAANG stocks may be in the midst of a bubble, whereas others argue that their growth is justified by the stellar financial and operational performance they have shown in recent years.

Each of the FAANG stocks trades on the Nasdaq exchange and is included in the S&P 500 Index. Since the S&P 500 is a broad representation of the market, the movement of the market mirrors the index's movement. As of August 2021, the FAANGs make up about 19% of the S&P 500—a staggering figure considering the S&P 500 is generally viewed as a proxy for the United States economy as a whole.

This large influence over the index means that volatility in the stock price of the FAANG stocks can have a substantial effect on the performance of the S&P 500 in general. In August 2018, for example, FAANG stocks were responsible for nearly 40% of the index’s gain from the lows reached in February 2018.

What Makes FAANG Stocks So Popular?

• The five stocks that make up the “FAANG” acronym - Meta (FB), Amazon (AMZN), Apple (AAPL), Netflix (NFLX), and Alphabet (GOOG) are all well-known brands among consumers. But they are also famous for their remarkable growth in recent years, with market capitalizations ranging from $240 billion (in the case of Netflix)3 to $2.4 trillion (in the case of Apple), as of August 2021.

• From an investment perspective, these five stocks are generally praised for their stellar historical track records and clear leadership positions within their industries.

Datset Information

The dataset consists of the historical stock prices of the FAANG companies. The dataset has been cleaned and uploaded for easy use and analysis.

• Google Class A(GOOGL) consists of 4340 data records of stock prices starting from 20/08/2004 to date(11/11/2021)
• Google Class C(GOOG) consists of 1924 records of GOOG stocks starting from 28/03/2004 to date.
• Apple (AAPL) consists of 10319 records starting from 12/12/1980 to date
• Amazon(AMZN) consists of 6167 records starting from 15/5/1997 to date
• Meta(FB) consists of 2389 records from 18/05/2012 records to date
• Netflix(NFLX) consists of 4904 records from 24/05/2002 to date

Note:

You might find two different types of google stocks GOOGL and GOOG in the dataset.

• GOOGL

  • GOOGL shares are categorized as Class A shares. Class A shares are known as common shares. They give investors an ownership stake and, typically, voting rights. They are the most common type of shares.

• GOOG

  • GOOG shares are the company's Class C shares. Class C shares give stockholders an ownership stake in the company, just like Class A shares, but unlike common shares, they do not confer voting rights to shareholders.

Class A: Held by a regular investor with regular voting rights (GOOGL) Class B: Held by the founders with 10 times the voting power compared to Class A Class C: No voting rights, normally held by employees and some Class A stockholders (GOOG)

Inspiration

• Annual growth rate of FAANG companies
• Forecast the growth rate of FAANG companies in the next ten years
• Exploratory data analysis

In 2024, 54 percent of the households in the United States owned shares in a mutual fund. This is a significant increase on the 5.7 percent recorded in 1980, but close to 52 percent found in 2022.Mutual fundsA mutual fund is a variety of collective investment vehicle managed professionally that pools money from many investors to purchase securities. They play an important role in household finances in the United States of today, most notably in retirement planning. It is commonly applied only to the forms of collective investment that are regulated and are sold to the public at large. The majority of mutual funds are what is known as ‘open-ended’, meaning that shares can be bought or sold at anytime. There are a number of advantages associated with mutual funds as opposed to direct investment in individual securities. The nature of the fund as a collective investment vehicle provides increased diversification and ease of comparison to investors. The fact that they are managed professionally, and that the investment is pooled, enables participation in investments that would normally only be available to larger investors. Mutual funds are also stable in price as daily liquidity ensures minimum loss of value. Despite several advantages, as with every aspect of investment, some disadvantages are to be considered. Fees are an inevitable part of a professionally managed fund, as is the inability to customize the investment. A common complaint is also that the investor has less control over the timing of the recognition of their gains.

Based on Bloomberg's Billionaires index...

The Bloomberg Billionaires Index is a daily ranking of the world's richest people. In calculating net worth, Bloomberg News strives to provide the most transparent calculations available, and each individual billionaire profile contains a detailed analysis of how that person's fortune is tallied.

The index is a dynamic measure of personal wealth based on changes in markets, the economy and Bloomberg reporting. Each net worth figure is updated every business day after the close of trading in New York. Stakes in publicly traded companies are valued using the share's most recent closing price. Valuations are converted to U.S. dollars at current exchange rates.

Closely held companies are valued in several ways, such as by comparing the enterprise value-to-Ebitda or price-to-earnings ratios of similar public companies or by using comparable transactions. Calculations of closely held company debt -- if net debt cannot be determined -- are based on the net debt-to-Ebitda ratios of comparable peers. The value of closely held companies adjusts daily based on market moves for peer companies or by applying the market movement of a relevant industry index. The criteria used to choose peer companies is based on the closely held asset's industry and size.

When ownership of closely held assets cannot be verified, they aren't included in the calculations. The specific valuation methodology for each closely held company is included in the net worth analysis section of a billionaire's profile. Additional details included in the valuation notes for each asset are available to subscribers of the Bloomberg Professional Service.

A standard liquidity discount of 5 percent is applied to most closely held companies where assets may be hard to sell. When a different percentage is used an explanation is given. No liquidity discounts are applied to the values of public stakes. In some instances, a country risk discount is also applied based on a person's concentration of assets and ease of selling them in a given geography. A country's risk is assessed based on Standard & Poor's sovereign debt ratings.

If a billionaire has pledged as collateral shares he or she holds in a public company, the value of those shares or the value of a loan taken against them is removed from the net worth calculation. If reliable information can be obtained about the ultimate use of those borrowed funds, that value is added back into the calculation.

Hedge fund businesses are valued using the average market capitalization-to-assets under management ratios of the most comparable publicly traded funds. Fee income is not considered because it cannot be uniformly verified. Personal funds invested along with outside capital are not included in the calculation. A "key man" risk discount of 25 percent is applied to funds whose performance is tied to a single individual. Assets under management are updated using ADV forms filed with the federal government and news reports, and returns are factored when sourced to reports from credible news outfits, the HFRI Index and industry analysts.

Net worth calculations include dividend income paid and proceeds from the sale of public and closely held shares. Taxes are deducted based on prevailing income, dividend and capital gains tax rates in a billionaire's country of residence. Taxes are applied at the highest rate unless there is evidence to support a lower percentage, in which case an explanation is given in the net worth summary. For calculations of cash and other investable assets, a hybrid return based on holdings in cash, government bonds, equities and commodities is applied.

No assumptions are made about personal debt. Family members often hold a portion of a billionaire's assets. Such transfers don't change the nature of who ultimately controls the fortune. As a result, Bloomberg News operates under the rule that all billionaire fortunes are inherently family fortunes and credit family fortunes to the founders or ranking family members who are determined to have direct control over the assets. When individual stakes can be verified and adult family members have an active role in a business, the value is credited to each individual.

Each billionaire -- or a representative -- is given an opportunity to respond to questions regarding the net worth calculation, including assets and liabilities.

Bloomberg News editorial policy is to not cover Bloomberg L.P. As a result, Michael Bloomberg, the founder and majority owner of Bloomberg L.P., isn't considered for this ranking.

Because calculating net worth requires a degree of estimation, bull and bear case scenarios that would make a person's fortune higher or lower than the Bloomberg Billionaires Index valuation are included on the Bloomberg Professional Service. A confidence rating also is included on each profile:

Context

A stock market is where buyers and sellers trade shares of a company, and is one of the most popular ways for individuals and companies to invest money. The size of the world stock market is now estimated to be in the trillions. The largest stock market in the world is the New York Stock Exchange (NYSE), located in New York City. About 2,800 companies are listed on the NYSE. In this problem, we'll look at the monthly stock prices of five of these companies: IBM, General Electric (GE), Procter and Gamble, Coca Cola, and Boeing. The data used in this problem comes from Infochimps.

Content

Each data frame has two variables, described as follows:

Date: the date of the stock price, always given as the first of the month. StockPrice: the average stock price of the company in the given month. In this problem, we'll take a look at how the stock dynamics of these companies have changed over time.

Acknowledgements

MITx ANALYTIX

China Retail Investor Sentiment Analytics provides sentiment analytics of Chinese retail investors based on 2 stock forums, Guba (GACRIS dataset) and Xueqiu (XACRIS dataset), the most popular stock forums in China from 2007.

By utilizing in-house NLP models which are dedicatedly optimized for Chinese stock forum posts and trained on a proprietary manually labeled and cross-checked training data, the dataset provides accurate text analytics of post content, including but not limited to quality, sentiment, and relevant stocks with relevance score. In addition to the aggregated statistics of stock sentiment and popularity, the dataset also provides rich and fine-grained information for each user/post in record level. For example, it reports the registration time, number of followers for each user, and also the replies/readings and province being published for each post. Moreover, these meta data are processed in point-in-Time (PIT) manner since 2019.

The dataset could help clients easily capture the sentiment and popularity among millions of Chinese retail investors. On the other hand, it also offers flexibility for clients to customize novel analytics, such as studying the sentiment (conformity/divergence) of users of different level of influence or posts of different hotness, or simply filtering the posts published by users which are too active/positive/negative in a time window when aggregating the statistics.

Coverage: All A-share and Hong Kong stocks, 300+ popular US stocks Update Frequency: Daily or intra-day

In a survey conducted in 2021, around **** percent of people in Japan reported owning securities. Stocks and investment trusts were the most common types of securities held by individuals in Japan.

Key Stock Trading StatisticsTop Stock Trading AppsFinance App Market LandscapeStock Trading App RevenueStock Trading Revenue by AppStock Trading App UsersStock Trading Users by AppStock Trading App...

Because we have the largest, most complete data set from the notorious r/wallstreetbets (and 4Chan's /Biz) back to 2019, our data has been regularly featured in the WSJ in stories in the WSJ, Forbes, and other major publications.

This product includes:

-API for institutional clients -Comment counts by ticker -Cryptocurrencies -Comment sentiment analysis: deep-learning-trained using advanced ensemble modelsThe latest iteration of our product includes:

Full text and metadata of every post and comment, structured and searchable:

-User: u/user on Reddit (with upvotes) -Time: date and time to the second -Ticker: stock ticker -Mentions: each time ticker is referenced -Comments: each comment in which ticker is referenced -Posts: posts referencing ticker (highest level)

DATA: 10 GB of comment data STORAGE: Housed in a PostgreSQL database REDDIT POSTS: User, Time/Date, Number of comments, type of post, text, score REDDIT COMMENTS: Reddit u/ of the comment/post, date/time, text/body, the comment score, the post that the comment was linked to, and all the associated relational data 4CHAN POSTS: Time/Date, comment, number of child comments (if applicable)

A list of the top 50 Vanguard Group Inc holdings showing which stocks are owned by Vanguard Group Inc's hedge fund.

Quickstart

https://colab.research.google.com/drive/1W6TprjcxOdXsNwswkpm_XX2U_xld9_zZ#offline=true&sandboxMode=true

Context

Predicting the stock market is a game as old as the stock market itself. On popular ML platforms like Kaggle, users often compete to come up with highly nuanced, optimized models to solve the stock market starting just from price data. LSTMs may end up being the most effective model, but the real problem isn't the model - it's the data.

Human and algorithmic traders in the financial industry know this, and augment their datasets with lots of useful information about stocks called "technical indicators". These indicators have fancy sounding names - e.g. the "Aroon Oscillator" and the "Chaikin Money Flow Index", but most boil down to simple calculations involving moving averages and volatility. Access to these indicators is unrestricted for humans (you can view them on most trading platforms), but access to well formatted indicators (csvs instead of visual lines) for large datasets reaching back significantly in time is nearly impossible to find. Even if you pay for a service, API usage limits make putting together such a dataset prohibitively expensive.

The fact that this information is largely kept behind paywalls for large firms with proprietary resources makes me question the fairness of this market. With a data imbalance like this, how can a single trader - a daytrader - expect to make money? I wanted to make this data available to the ML community because it is my hope that bringing this data to the community will help to even the scales. Whether you're just looking to toy around and make a few bucks, or interested in contributing to something larger - a group of people working to develop algorithms to help the "little guy" trade - I hope this dataset will be helpful. To the best of my knowledge, this is the first dataset of its kind, but I hope it is not the last.

Data

• I'd recommend starting here: https://colab.research.google.com/drive/1W6TprjcxOdXsNwswkpm_XX2U_xld9_zZ#offline=true&sandboxMode=true In this notebook, I've uploaded the indicators data and made it available for read access. The notebook will walk you through processing the data and putting it to work building advanced ML models.

• If you'd like to download it directly, you can do so here (31GB): https://drive.google.com/file/d/1HVKWtLWlIZj5yY4T3R1CDSn3c_Nh1lB5/view?usp=sharing

• For legal reasons, I use borismarjanovic's dataset as a baseline, which ends in 2017. If you'd like to update your prices and indicators so your model can trade day to day, I've included instructions and code in the quickstart that should be helpful.

Acknowledgements

• The many online tutorials and specifications which helped me write and test the indicator functions
• borismarjanovic for making public an amazing dataset that I use as a baseline for the colab notebook and the direct download file above
• The many online services that have allowed me to download all the recent price information to augment Boris' dataset (which legally I cannot share, but which helped me develop the infrastructure to update the indicators given new prices data that I share in the quickstart and repo).

Next Steps / Future Directions

• Building inventive models using this dataset to more and more accurately predict stock price movements
• Incorporating arbitrage analysis across stocks
• Hedging
• Options and selling short
• Commodities, currencies, ETFs

Collaboration

If this interests you, reach out! My email is abwilf [at] umich [dot] edu. The repository I used to generate the dataset is here: https://github.com/abwilf/daytrader. I love forks. If you want to work on the project, send me a pull request!

The total global net assets of mutual funds registered in the United States amounted to approximately 28.54 trillion U.S. dollars in 2024, compared to around 5.53 trillion U.S. dollars in 1998. Mutual funds: additional information Mutual funds are investment funds in which the capital is pooled from several investors and then used to buy securities such as stocks, bonds, or money market instruments. Although investing in mutual funds rather than direct investment in individual securities still presents a certain degree of risk, it has become more and more common practice around the world. One of the biggest advantages of this type of investment is the fact that the fund assets are managed by professionals. They aim to eliminate some risk involved in investing in individual stocks and bonds through diversification of assets. As of 2024, there were almost 7,038 mutual funds domiciled in the United States. There are four main types of mutual funds, categorized by the nature of their principal investments, namely: stock or equity funds (whether domestic or international), bond or fixed income funds, money market funds, and hybrid funds. In 2023, domestic equity funds were the most popular category in the United States, representing 46 percent of all mutual fund and ETF assets.

Background

Intentional self-harm is a common cause of hospital presentations in New Zealand and across the world, and self-poisoning is the most common method of self-harm. Paracetamol (acetaminophen) is frequently used in impulsive intentional overdoses, where ease of access may determine the choice of substance.

Objective

This cross-sectional study aimed to determine how much paracetamol is present and therefore accessible in urban New Zealand households, and sources from where it has been obtained. This information is not currently available through any other means, but could inform New Zealand drug policy on access to paracetamol.

Methods

Random cluster-sampling of households was performed in major urban areas of two cities in New Zealand, and the paracetamol-containing products, quantities, and sources were recorded. Population estimates of proportions of various types of paracetamol products were calculated.

Results

A total of 174 of the 201 study households (86.6%) had at least one paracetamol product. Study households had mostly prescription products (78.2% of total mass), and a median of 24.0 g paracetamol present per household (inter-quartile range 6.0-54.0 g). Prescribed paracetamol was the main source of large stock. Based on the study findings, 53% of New Zealand households had 30 g or more paracetamol present, and 36% had 30 g or more of prescribed paracetamol, specifically.

Conclusions

This study highlights the importance of assessing whether and how much paracetamol is truly needed when prescribing and dispensing it. Convenience of appropriate access to therapeutic paracetamol needs to be balanced with preventing unnecessary accumulation of paracetamol stocks in households and inappropriate access to it. Prescribers and pharmacists need to be aware of the risks of such accumulation and assess the therapeutic needs of their patients. Public initiatives should be rolled out at regular intervals to encourage people to return unused or expired medicines to pharmacies for safe disposal.

Methods The stocks of paracetamol-containing medicines (acetaminophen) held at a single time point in New Zealand households are described in this dataset. These data were collected via a cluster-sampling survey of two cities in New Zealand.

A door-to-door survey study with random, clustered sampling of consenting household members in two cities in New Zealand was designed. A total of 201 households in 40 meshblocks in two Major Urban Areas (MUAs; areas of 100,000 or more residents) of Dunedin and Auckland were sampled. Meshblocks are Statistics NZ’s smallest geographic unit, and roughly correspond to a city block or part of it. Random cluster-sampling of 20 meshblocks in each city was performed by deprivation level, where all eligible MUA meshblocks were stratified by their New Zealand Deprivation Index 2013 (NZDep2013) index scores, which describe the level of area deprivation by taking into account multiple relevant area and household variables. Six meshblocks were randomly selected from each city from NZDep2013 8-10 meshblocks (most deprived), eight from NZDep 4-7, and 6 from NZDep2013 1-2 (least deprived), for a total of 40 meshblocks. This was done to obtain a sample that would be representative of the general New Zealand population by levels of deprivation. Each meshblock was sampled by starting from a random end of the street and then tossing a dice to choose a house to approach, and repeating this until either five households were recruited or there were no more households to sample.

Trained Research Assistants (RAs) knocked on the doors of domiciles in each meshblock to be sampled, chosen by tossing a dice as described. Inclusion criteria: person present and usually residing in a domicile in a meshblock which was sampled, and aged 16 or over. Exclusion criteria: not able to give informed consent (intoxicated, aggressive, otherwise not safe to approach – nobody was excluded for this reason).

Household members aged 16 years and over were eligible to participate, and if consent was obtained, basic demographics were collected about the household (number of people usually residing in the household, their age, sex, ethnicity). Participants were then shown images of paracetamol-containing products (sole and combination), and requested to bring out all paracetamol products of their own, and any that were shared by the household in communal areas of the domicile. Private stock of any other residents of the household who were not present and were therefore unable to consent was not recorded for ethical reasons. If there were no paracetamol products present, that was recorded. If there were paracetamol products present, product type, strength, expiry date, purchase date and means of obtaining (by prescription, pharmacy over-the-counter [OTC], other retailer [i.e. not a pharmacy; e.g. supermarket, petrol station], other, unknown) were recorded.

The data were entered into a main database which is fully de-identified. Meshblock numbers are included in the dataset, but households are only given an identifier derived from the meshblock code. It would not be possible to identify a specific household from the data. Paracetamol product names were cleaned in the dataset (if there were any misspellings), and new variables were calculated to summarise the data (e.g. total household stock of prescribed paracetamol products, etc.).

Alibaba has had a bad week when it was revealed that it will donate $15 Billion to ‘common prosperity’, really this just means that it will contribute more to development projects, which is already does as evidenced by its massive financing of startups already. Secondly, the breakup and re-organization of Ant Group, where it will still have a sizeable share. In both cases it’s likely to profit from the moves. Thirdly, $15 billion isn’t that much for Alibaba’s core revenue and growth in the Cloud and in Ads. So let’s get down to it with some of the facts. Ant Group is massive: According to the most recent numbers, Alipay has over 1.2 billion users overall, while its credit card platform Huabei had 190 million users, and its installment loan product Jiebei had 500 million users. Reported in June, the new lending company will be called Chongqing Ant Consumer Finance Co. It will be 50% owned by Alipay, with the other 50% coming from other companies, including some state-owned banks. The new company will also be liable for up to 30% of the loans it issues, which means the new company will need to hold more capital on its balance sheet, and will likely get a much lower valuation in the marketplace. This is all quite far and reasonable although Ant Group will have to hand over the precious data to the State. Not a big deal. That was bound to occur. Alibaba’s current market cap is just over $422 Billion, which makes no sense, that is, it’s currently undervalued. The P/E is now 18.77 that is very reasonable. Remember this company has income of nearly $23 Billion. At the end of August, the company pledged to donate $15.5 billion to China’s ‘Common Prosperity’ initiative . The money will be paid out over five years to support various technology and small business initiatives. It’s unclear at this stage whether Alibaba will receive any equity in return for the donations. It’s highly likely the donations won’t be fully without Alibaba profiting. China isn’t crazy, it just wants to spread the wealth around a bit better. So which other Chinese stocks appear very undervalued? $VIPS $BEKE $MOMO $YINN (as a long-term play) Do your own due diligence if you don’t believe me. If there is a correction of Western equities in October, 2021 or later before 2022, those are stock names I’d take a closer look at. While Alibaba is a huge company its growth in the Cloud and Ads should be able to absorb its serious setback. $15.5 billion is a lot of money, even for a company of Alibaba’s size. This sum is also in addition to a $2.75b fine imposed by China’s anti-monopoly regulator, which has already been paid. However it doesn’t justify the stock going much below $150, unless there is a strong push from short squeeze effort from other big investors. Chinese stocks will continue to go down as the sentiment and regulation puts a lot of uncertainty for their future in the West. However those companies are not drastically impacted from a business perspective. Alipay will likely also have to spin off its credit-scoring wing into a new joint venture that will also share with state-owned entities. Reuters has reported that Alipay will only retain a 35% stake in the new joint venture. So even in the shut-down of Ant Group as we knew it, Alibaba retains quite a sizeable portion of the businesses. Additionally BAT companies keep investing in very legit startups that will do incredibly well in the years ahead as China’s economy keeps maturing even with various bumps and dips on the macro landscape. While Western stocks are in a massive equity bubble, since a bull-market since 2009, Chinese stocks are nearing fair value. Alibaba has led investments worth more than $300 million into Chinese autonomous driving start-up DeepRoute.ai recently, for the most part its business as usual. Chinese regulation is actually good for its own particular version of state augmented capitalism. It can no longer tolerate monopolies abusing their position. On the operating side, things are looking good for BABA, as it continues to deliver sizeable business growth in its core business as well as in other areas, such as cloud computing. It’s cloud computing segment itself as a huge runway for growth with limited competition from Baidu, Huawei, Tencent and so forth. It’s the AWS of China for sure. Alibaba only owns 33% of Alipay, so the growth headwinds at Alipay aren’t likely to warrant Alibaba’s 50% haircut. Alibaba’s own investments are maturing, and ChinaTech is just beginning their global play with ByteDance, Xioami, JD.com and others. Alibaba’s moat is stronger in China than Amazon’s is in the U.S., which is saying a lot. Legitimate growth from JD.com and Pinduoduo keep Alibaba innovative. When you look at the E-commerce growth of $VIPS you begin to understand just how many winners can fit in China’s massive ecosystem of consumers. The exodus from Chinese stocks won’t last forever as as a whole those companies will grow faster than their American peers, who are concentrated in too few names. The U.S. will likely be 10-15 years late in its own common prosperity and antitrust regulation fixes to a broken Pyramid of U.S. capitalism. Few actually understand this and how the move is inevitable. So China is regulating technology is a superior way, not just building more innovative companies better, faster and with more of them. The EV sector in China is the perfect example. While the U.S. has about a dozen okay EV efforts, with Rivian and Lucid perhaps the most shiny among them, China has around 30x to 50x as many. China’s electric car sector is seeing rapid growth, with tens of thousands of companies jumping on the bandwagon and shares of Chinese electric car makers such as Nio and Xpeng surge, according to business database Qichacha. Alibaba is the most diversified Chinese company, and with State intervention it can only get stronger in the end, not weaker. When you do the math it should be a $1 trillion dollar company again by 2023 in terms of market cap. Right now it’s likely around at least 20% undervalued. Regulation in China is good, not bad. Antitrust, consumer protections and investor confidence will gain higher as more Billionaires understand that the common good is what’s important in China, not their personal wallets. The real-estate, technology, education and many other spaces will slowly be cleaned up. China’s long-term vision of innovation and economic superiority is rooted in master plans with layers and 5-year plans the likes of which make the U.S. corporate monopolies that aren’t regulated look like tyrants of an old outdated version of capitalism. Alibaba is not there for Jack Ma to be a celebrity but for China to improve itself economically for the benefit of all of its consumers. With Beijing as the hub and on the board rooms of these companies, China’s astounding growth can work in a cohesive harmony that won’t be possible in any other country. ByteDance, Alibaba, JD.com and others will be huge winners in the New China capitalism with state intervention.

Global Stock Music Market size was valued at USD 1.2 Billion in 2024 and is projected to reach USD 2.82 Billion by 2032, growing at a CAGR 4.5% during the forecast period 2026-2032.

Global Stock Music Market Drivers

The market drivers for the Global Stock Music Market can be influenced by various factors. These may include:

Growing Need for Video Content: Stock music is in more demand as a result of the explosion in the production of online video content, which is being fuelled by sites like YouTube, TikTok, and other streaming services. Stock music is used by creators to improve their videos and give them a more polished, engaging feel. Growth of Digital Advertising: As digital advertising, such as social media advertisements and online marketing campaigns, has grown, so too has the need for stock music to go with these commercials. Podcasting and Broadcasting: The growing demand for background music and intro/outro tracks has been fueled by the popularity of podcasts and online broadcasting. Easy Accessibility and Affordability: Stock music libraries are accessible and reasonable for a broad spectrum of customers, from inexperienced producers to seasoned production organisations, thanks to their extensive selection of selections at different price ranges. Technological Advancements: With the ability to search for, preview, and download stock music more easily, users may now have a better and more widely used experience. Expansion of Independent and Freelance Projects: With the rise of the independent film industry, video producers, and multimedia artists, as well as the gig economy, more independent filmmakers and producers are turning to stock music for their work. Broad Variety of Genres and Styles: To meet the varying needs of content creators on various platforms and media, stock music libraries today provide a broad range of genres and styles. Use in Business and Education: Stock music is becoming more and more popular in businesses and educational institutions for use in multimedia projects such as corporate videos, training manuals, and presentations. Clarity Regarding Law and Copyright: Using stock music is a straightforward and lawful substitute for popular music, which can be complicated by copyright difficulties and costly licencing costs. Needs for Localization and Globalisation: Stock music libraries frequently offer the kind of music that appeals to a wide range of cultural backgrounds, which is necessary for companies and content producers looking to reach a worldwide audience. Integration with Editing tools and Platforms: To make it simpler for creators to smoothly include music into their projects, a number of stock music providers are linking their collections with video editing tools and platforms. Growing Awareness and Acceptance: Professionals in the business are becoming more accepting of stock music as a reliable and high-quality source of audio files.

The most popular investment security among investors in the United Kingdom was stocks and or shares, with almost ********* of investors holding this financial security in their portfolio. Bonds ranked second in popularity among UK investors.

As of 2024, the top-ranking product among Millennials and Gen Z was stocks, with roughly ** percent of Millennials and ** percent of Gen Z survey respondents stating they held positions. The next most popular financial security was retirement accounts, with ** percent of Millennials and ** percent of Gen Z currently holding retirement accounts in their portfolio.