The structural opportunity "gray cow" has arrived. Is it too late to get on the bus now?

 This bull market will be very different.

In the early morning of November 18, 2020, Bitcoin broke through 17,500 USDT, setting a new high since December 21, 2017. Interestingly, in the entire history of Bitcoin, the time for the price to exceed 17,500 USDT was only 7 days.

In order to better summarize and summarize the structural transition under Bitcoin's periodicity, Odaily Planet Daily judged this round of rise as the early stage of "gray bull".

What is the difference between "Gray Bull" and the previous bull market? Who is behind the "Gray Cow"? What is it that makes this year's Bitcoin more popular than gold ETFs? Is Bitcoin currently high? Will there be a sharp retracement like in 2017? This article will answer these questions one by one.

A big gray bull is coming towards us

In ancient Western civilization, the bull represented strength, wealth and hope, while the bear represented restraining fanaticism, digesting oneself, and rebirth. As early as the 18th century, West Convenience chose the two species "bull" and "bear" to express the market's ups and downs.

From 2017 to 2020, there have been three "bulls" and two "bears" in the crypto market. Of these three cows, one is very big, the other is very empty, and the other has its horns; the two bears, one is "Big Bear" and the other is "Bear Two".

2017, Bitcoin rose from $ 735.3 198 91 Mei Yuan, cow feed because 1CO grew up, we call it a "ICO cow."

In 2018, Bitcoin almost used up its full-year retracement, which was a year of "bears".

In 2019, the rise of contract trading has accelerated the rise and fall of Bitcoin, which we call the "contract bull".

Looking back at history, the "contract bull" is still empty, and "Xiong Er" is not too strenuous to fight it back.

Beginning in March 2020, Bitcoin has risen from the lowest point of about $4,000, and has risen all the way to over $16,000, and it has become more aggressive. As the horns were first revealed and "Xiong San" hid his figure, we gradually saw the outline of this cow-the gray cow.

Why is this bull market a "gray bull"?

Grayscale currently manages $10 billion worth of cryptocurrency. Among them, Grayscale Bitcoin Trust (GBTC) was founded in 2013 and is the company's largest trust. Grayscale purchased 15,114 Bitcoins (approximately US$241 million) in the last week alone. As of November 15th, Eastern Time, Grayscale Bitcoin Trust's total holdings have reached 509,581 BTC.

The Odaily Planet Daily reported the relationship between the increase in GBTC holdings and the price of BTC from October 19 to November 16.

As can be seen from the above figure, as time goes by, the gray-scale buying volume presents a continuous enlargement trend.

At the same time, from the technical chart, after BTC broke through $12,000, the higher the attack, the greater the resistance, but it still maintained a healthy upward trend.

Combining with the above grayscale accumulation chart, it can be seen that grayscale played a big role in BTC's upswing (huge buying power). The more sell-offs above, the larger the grayscale accumulation.

Extending the timeline a bit, macro investor Dan Tapiero said that the share of bitcoins purchased by Grayscale Trust accounted for the share of bitcoins generated by mining, which increased from 27% in Q1 to 77% in Q3.

On October 14, Grayscale stated in its financial report for the third quarter of 2020 that the inflow of funds for all of the company's products was US$1.05 billion. So far this year, this figure is US$2.4 billion, which is more than twice the total amount raised in 2013-2019. Grayscale Bitcoin Trust's inflow of funds in the third quarter was $719.3 million, and total Bitcoin asset management (AUM) increased by 147% in 2020.

It can be seen that this round of rise compared with the previous several times, the biggest variable is the large-scale buying of gray, which increases the demand for the market, solidly "builds the bottom", and holds the "selling pressure."

In addition, we also discovered another difference between Grayscale and "previous" investors.

Dare to buy if you dare to fall, "Gray Bull" is not "fear of heights"

Grayscale's investment style is a bit special: As long as Bitcoin dared to fall, Grayscale would dare to buy, and there was no trace of "fear of heights." This is clear from the technical drawing.

At the end of 2017, Bitcoin rose sharply, but the higher the price of the currency, the greater the volatility, and the BTC stayed at the high position for a short time, which could not be stabilized at all, which can reflect the FOMO mood.

Compared with the trend of BTC's high in 2020, BTC can be said to be a "small step" all the way up. As long as there is a big selling pressure, the bottom will take over. This makes the overall volatility of the current high of BTC much lower than in 2017, and the trend is relatively More stable.

As for the wave of "contract bulls" in 2019, because the trend was very weak, they didn't stand up to $14,000, so nothing to mention.

These are just "phenomena" summed up from the disk.

The main force of the bull market is different: from retail investors to institutions

The reason why this round of bull market behaves differently is mainly because the "gray bull" has changed from the main driving force of the previous bull market. Grayscale itself is a trust company. It mainly earns management fees for trust products. It does not specialize in "money speculation". The main buyers behind it are not ordinary large investors. It can be seen from the grayscale financial report that the "Grayscale Bull" is the result of real money pushing up, not manipulation. So, who is so rich?

According to the data disclosed by Grayscale in the third quarter of 2020, the purchase users of Grayscale's products are mainly institutional investors (81%), followed by qualified investors and family offices (8% each). 57% of purchase users are from outside the United States.

As of November 9, 2020, according to publicly disclosed information, a total of 23 companies (a total of 29 institutional-level accounts) hold Grayscale Bitcoin trust shares, with a total of 59,553,200 trust shares held, accounting for Grayscale Bitcoin 11.5% of the issued share of the trust (Note: The statistical caliber is the information disclosed by the institution in the US SEC. The share of trust held by the institution may change in different reporting periods. The statistics in this article are still holding trusts as of November 9, 2020 Share of institutions). The 23 companies include crypto asset lending companies, hedge funds, mutual funds, private wealth companies, consulting companies, family offices, etc.

Data source: Chain Hill Capital

It can be seen from these data that the main buyers of Grayscale are institutional investors, and “Grayscale Bull” is essentially an institutional bull. The previous bull market was dominated by retail investors and major currency players in the carnival, and institutional funds were not dominant. Since the beginning of this year, the inflow of institutional funds has been extremely rapid, and the leading role has been obvious. The strategic position of the institution also explains to a certain extent the "panel appearance" mentioned above-this time the rise has become more stable.

In addition, after the main force of the bull market has changed, the rhythm of the bull market has also changed a lot.

There were obvious signs of manipulation and hype by big players before, which caused the market to have a phenomenon of alternate rises. It was roughly that Bitcoin finished the rise of mainstream currencies, and then altcoins made up for the rise, followed by a major correction. But now institutional investors are generally value investors, who mainly invest in Bitcoin and a few other mainstream currencies, so the rotation market is very weak this year. However, we are also concerned about the recent surge in DeFi leaders. Because these DeFi projects have their own value, the recent rise is more of the return of value after the previous oversold.

Pushing hands more macro than institutions: a great transfer of world wealth

We believe that institutional investors will continue to enter the crypto market because the world is undergoing a wealth transfer. Especially this year's new crown epidemic and large amounts of water released by the central banks of major countries have also promoted such a wealth transfer . The following is a brief overview mainly from two dimensions.

• The wealth in the hands of the older generation began to transfer to the hands of the younger generation. Grayscale stated in the report "Wealth Transfer Promotes BTC to Become a Mainstream Investment Target" , although Bitcoin was only a niche asset that attracted a few investors in the early stage, it is now becoming more and more accepted by mainstream investors. . The survey data shows that in 2019, the number of potential investors in the Bitcoin market is about 21 million, but in 2020 it has grown to 32 million. In 2019, 53% of investors said they were “familiar” with Bitcoin, but this has increased to 62% in 2020. More than 50% of respondents predict that digital currency will become mainstream before 2030. Although most Bitcoin investors currently do not have much income, 68 trillion US dollars of wealth will be transferred to the younger generation who tend to invest in digital currencies in the next 25 years.

  Nick Panigirtzoglou, a market quantitative analyst at JPMorgan, believes that "Over time, millennials will become the most important part of the investment world, so Bitcoin and gold will Intense competition is good for Bitcoin's long-term upward trend. From a technical point of view, Bitcoin's market value should increase by at least 10 times in order to match the gold market based on physical gold bars and coins."

• Traditional institutional investors began to transfer traditional assets to digital assets. The fundamental reason for institutional investors' strategic holding of GBTC is that institutional investors recognize that the digital age has arrived and is irreversible, more and more people will turn to digital assets, and human wealth is undergoing a major migration. On November 9, JPMorgan Chase pointed out in a report that Bitcoin is eroding market demand for gold ETFs. Nowadays, institutional investors such as family offices regard Bitcoin as a digital alternative to gold, and their demand for Grayscale's Bitcoin trust exceeds the sum of all gold ETFs.

  

This view is also supported by data: in the process of entering traditional institutions, BTC chips have also been loosened at a high level and have been constantly shifting.

According to BTCparser monitoring, on November 7, 1,000 BTC mined in 2010 were transferred, and on October 3 this year, 50 BTC mined in 2010 were transferred for the first time. The figure below shows the transfer of BTC chips from a larger period.

BTC chip transfer graph over the past ten years

Why is it said that this round of "Gray Bull" will be Long Bull and Slow Bull?

So far, we have basically explained the cause of the original bull market and the differences from the past few times. Of course, for the majority of investors, what is more concerned is whether it is too late to get on the bus now? How long will the gray cow last?

Let me start with the conclusion, we believe that it is still in the early stage of "Gray Bull". There are two reasons:

• The relationship between Grayscale and institutional investors: The compliance of Grayscale Trust products provides more and more restricted institutional investors with channels to buy BTC. In January of this year, Grayscale Bitcoin Trust was approved as the first digital asset tool that meets the standards of the US Securities and Exchange Commission. On October 12, the application for registration of Grayscale Ethereum Trust was officially approved. The compliance of Grayscale Trust products is one of the important incentives to attract institutional investors to enter this year, and this trend will accelerate in the future. As mentioned above, behind the continued purchase of grayscale bitcoin trust products by institutional investors is an irreversible wealth transfer.

  (Odaily Planet Daily Note: In the United States, some institutional investors with investment restrictions cannot enter the cryptocurrency exchange to buy BTC in the name of an institution. They can only purchase bitcoin through trust channels. In addition, institutional investors are more accustomed to trust this A traditional investment method.)

• The principle and mechanism of Grayscale Trust products: The design of Grayscale Bitcoin Trust makes it difficult to sell BTC, and its position planning is more inclined to long-term holding. Institutional investors will get gray-scale GBTC after buying gray-scale Bitcoin. Investors can exchange BTC for GBTC (with half a year to unlock), but cannot exchange GTBC for BTC. Grayscale Bitcoin Trust currently does not have any redemption plan. The trust can seek regulatory approval to implement the redemption plan. This means that neither Grayscale nor institutional investors are likely to directly sell the BTC spot market through the GBTC held in their hands.

Overall, institutional investors are still entering in large numbers, and the possibility of BTC hitting the market by a large margin is relatively low. With huge institutional funds flowing to a limited-scale currency circle, this bull market is bound to emerge from the long and slow bull market.

From the Forrester Wave report, see how self-built AI can help enterprises transform and upgrade

 Self-built AI becomes a To B blue ocean, low threshold, large-scale, and computing power optimization become the three core capabilities

There has never been an era full of optimism about the future of artificial intelligence like today.

In a recent report, Gartner predicts that by 2022, the average number of enterprise applications of AI will increase by 9 times compared to 2019, and by 2022, the commercial value of AI will reach 3.9 trillion US dollars; Forrester is more optimistic, and its comprehensive After a market research analysis, it is believed that by 2025, all companies will use AI.

It can be said that the Al project will flourish in the near future.

However, this is a judgment aimed at the macro trend of the AI ​​industry. For companies that really need AI, what method and what service provider to choose to obtain AI capabilities is still a question that must be considered. Among them, for many large-scale enterprises, self-built AI rather than purchasing off-the-shelf AI services has become the primary choice, opening up a huge business space for AI technology service providers engaged in related fields, and because of the digital transformation and upgrading involved Strategic actions affect the whole body, and these companies will be more cautious in choosing service providers.

At this time, the value of some authoritative industry reports is reflected.

Not long ago, Forrester released "The Forrester Wave™: Predictive Analytics And Machine Learning In China, Q4 2020" report, which conducted an annual evaluation of Chinese PAML (predictive analytics and machine learning) vendors. AI is mainly composed of machine learning (ML) models. This report can be seen as an industry-level inventory, and it is also an authoritative reference for companies that want to choose the correct PAML solution to build AI and improve AI productivity.

From this report, we can see the huge changes in the PAML market industry pattern under the giants' competition, and the unique challenges behind the huge business opportunities of self-built AI.

Self-built AI PAML has become the general trend. Low threshold, scale, and computing power optimization have become the three major capabilities

From the perspective of player distribution, in 2020, PAML serving self-built AI will present a market pattern led by three major technology giants + a unicorn company , with Alibaba Cloud , Huawei Cloud , Fourth Paradigm , and Baidu Smart Cloud in the Leaders quadrant , Showing that they have taken the lead in helping companies build their own AI:

It is worth mentioning that the acquisition of this quadrant chart is based on Forrester's mature indicator system.

For example, the vertical axis represents the advantages of current products, including data, modeling, collaboration, model evaluation, model operations, methods and algorithms, and platform infrastructure; the horizontal axis represents future-oriented strategic advantages, including execution capabilities, solution routes Figure, implementation, partners, price strategy, community, etc.; the size of the circle indicates market position, and the result is derived from three dimensions: customer acceptance, product revenue evaluation, and market awareness.

It is precisely because of the complex and rigorous indicator library that Forrester's various analysis reports are widely recognized by the market.

At the same time, the report pointed out that China's digital economy is booming, and companies choosing the right PAML products can help companies build AI applications quickly and on a large scale, and improve corporate productivity.

Therefore, the Forrester report also emphatically summarizes the three major capabilities that PAML products should possess:

1. Simplify model development for different teams

With the continuous development of enterprise business, AI application scenarios will also expand from a few to thousands. To this end, PAML products should have model development capabilities suitable for different teams and roles. PAML needs a friendly visual interface to develop AI models; code-focused data science teams need a complete and integrated independent development environment that can cover the entire model development life cycle; business users who do not have deep ML knowledge need full-featured automatic machine learning (AutoML) ability to improve ML production efficiency.

2. Machine learning models can be deployed quickly and on a large scale

Building an ML model is just the starting point. In order to achieve business benefits, the company must deploy the model to production applications and supervise and manage it. PAML needs to have the ability to deploy models from development systems to production systems, supervise the performance of ML models in a business-friendly manner, manage ML models and ensure collaboration between departments, and use new data to retrain online ML models to prevent performance degradation.

3. Distributed and hybrid architecture can be used to accelerate training and inference

In the process of model training, a large number of parameter calculations are involved, which increases the burden of computing infrastructure. PAML should help companies effectively allocate training workloads to a distributed architecture to reduce waiting time for developers. In addition, model reasoning directly determines customer experience. In order to meet reasoning requirements and comply with privacy regulations, PAML should provide a hybrid architecture to facilitate the deployment of models across clouds, data centers, and edges.

To seize the blue ocean of enterprise self-built AI, PAML players still face three major challenges

The waves of the blue ocean do not come from competition among peers. The needs of customers determine whether players can go more stable and farther. The current self-built AI market is also the same.

From the perspective of customer needs, combined with some insights from the Forrester report, we can get the three major challenges faced by players on the PAML track today. These challenges are issues that latecomers must think about. On the other hand, it is the effective response to these challenges that Alibaba Cloud, Huawei Cloud, Fourth Paradigm, and Baidu Smart Cloud can be ranked in the Leaders quadrant. Or, from another perspective, the unicorn of Fourth Paradigm can compete with giants. We sit together.

1. Technically, the ability is infinitely high, but the threshold is infinitely low

There is definitely no upper limit for companies to claim the value of AI technology. AI modeling must be of high quality. Therefore, self-built AI is a matter of "technical content". If you put aside the external service platform, they will do it themselves. Generally, it needs to be completed by AI experts. In the context of the shortage of AI talents, this is a very expensive task and has a high threshold for resource input.

Therefore, as an external technical service provider, PAML players need to provide sufficient technical capabilities to enter the field, and they cannot make self-built AI become a high threshold. According to Forrester’s statement in the report, companies with advantages must “simplify model development for different teams” and “empower data engineers, scientists, business professionals, and application developers with more capabilities”-this is also an AI giant. The scope is a necessity and necessity for the popularization of enterprises.

In this regard, players such as Alibaba Cloud in the head quadrant have provided customers with a systematic tool platform. How can business personnel achieve the ability of AI experts while ensuring technical depth while lowering the threshold? Forrester mentioned in the report that automated machine learning (AutoML) should be the way to break the game. In simple terms, AutoML is a process of automating AI modeling, thus greatly reducing the threshold of AI application. Currently, major vendors have added AutoML capabilities to PAML vendors. However, AutoML in the fourth paradigm is favored by Forrester and fourth paradigm customers because of its technical and functional simplifications such as shortening the data preparation cycle, improving model performance through ultra-high-dimensional algorithms, and continuously optimizing the model. In the report, the interviewed customers of the fourth paradigm said that AutoML can be as good as a data scientist in some scenarios, and they are also satisfied with the project management and security features of the fourth paradigm ML.

Using AI to build an AI expert, or one of the ways to build your own AI in the next step, but its own demand for technology is another level higher.

2. At the application level, the landing of the in-depth scenario can make the value of self-built AI established

The ultimate goal of an enterprise's self-built AI is to improve business performance. No matter what advanced technology or refined modeling, it needs to be implemented in the scenario plan.

A high-quality PAML manufacturer that is widely recognized by the market must have a large number of scene practices in hand. These landings are the only final measure of their technical service capabilities.

Alibaba Cloud, Huawei Cloud, and Baidu Cloud rely on the market performance of cloud computing and the trend of AI to go to the cloud. They have natural practical advantages in the field of self-built AI. It is not surprising that they can immediately be recognized by customers once they are launched on PAML. The fourth paradigm, an AI platform and technical service provider that was founded as early as 2014 and is rarely heard by ordinary people, is recognized in the field of PAML, which is related to the promotion of enterprise self-built AI to a wide range of scenarios. Direct relationship.

The intelligent protection of transactions enjoyed by 90% of cardholders in China has the shadow of the fourth paradigm service. The total assets of financial institutions served by it exceed 50 trillion; in addition, the intelligent computing (intelligent calculation) behind each KFC order Recommendations, etc.) are also derived from the services of the fourth paradigm; during the epidemic, the AI ​​anti-epidemic program of the CDC and the Ministry of Industry and Information Technology was also the company's backing support.

It is normal for the mass market to be unfamiliar with To B technology service providers. Alibaba Cloud, Huawei Cloud, Fourth Paradigm, Baidu Smart Cloud, etc. already have a wide-ranging layout in serving enterprise AI capabilities, which is self-built. AI’s competitive barriers. According to public information, even the “less-known” fourth paradigm has successfully implemented tens of thousands of AI applications in the fields of finance, retail, manufacturing, healthcare, energy, and the Internet. Including Industrial and Commercial Bank of China, Bank of Communications, China Merchants Bank, PetroChina, Huayou Energy , Yum China , Yonghui Supermarket, Budweiser, Laiyifen , Mei Su Jiaer, People's Daily, Ruijin Hospital, etc.

3. At the level of integrated promotion, self-built AI is not only technical services but also systematic improvement

In the field of To B services, a trend is becoming more and more obvious, that is, the service provider is no longer limited to providing technical services for a certain module, but is turning to the overall improvement of the business capabilities of the client enterprise. In addition to the consideration of business opportunities, the core service content must rely on the improvement of other supporting capabilities of the enterprise to achieve a better landing.

The same is true for self-built AI services. Even if PAML providers provide solutions with high technical standards, low thresholds, and practical support for a wide range of scenarios, they also need client companies to adapt to business lines, data logic, system integration, and talent training. Only by matching, can self-built AI better generate applications and create scene landing value.

According to the Forrester report, this is a large-scale deployment level "model deployment pipeline from development system to production system, supervising the performance of ML models in a business-friendly way, managing ML models and ensuring inter-departmental AI teams can collaborate."

Finally, looking back, although several players in the head quadrant have done a good job in dealing with these challenges, the challenges are not over. They will be a continuous process. Who can finally gain more recognition from customers and gain the market? The favor, it needs more time to verify. .

Self-built AI is improving, but market disputes are still full of unknowns

In another Forrester report in related fields, it can be found that more and more companies are beginning to attach importance to self-built AI capabilities, and the willingness ratio of interviewed companies has increased from 25% to 42%:

Correspondingly, some core AI technologies that can help companies build their own AI capabilities, such as platform-based AI technologies that have an advantage in versatility, and automated machine learning (AutoML), which can lower the threshold of self-built AI, have become companies' priority investment Technical points:

This trend is also embodied in Chinese companies. The fourth paradigm revealed that its important client, a leading financial institution, plans to increase its AI machine learning projects by 5 times in the next two years.

The larger the market, the fiercer the competition.

It can be found that in the 2020 Forrester quadrant chart, a giant's performance does not seem to be good- Tencent Cloud "ranked" in the second quadrant , and the market performance is relatively average.

Is Tencent Cloud really bad? In fact, Tencent Cloud has just launched its PAML and not long ago. Before the deadline for the report and research, the market has not given this fast-growing cloud computing giant a sufficient opportunity to showcase it.

This reminds us that since the PAML market structure can undergo such a big change from 2018 to 2020, the market structure in 2020 will certainly not be "steady", it is only a section of the rapid development process.

The future, such as Tencent cloud players will go in, who do not know, this is self-built AI field have charged performance points viable market - is an uncertain future faced by each player.

However, in this uncertainty, one thing is certain. Players who can better deal with the challenges of technology and application to the integration and promotion of the three dimensions will have an advantage.

And for those non-head PAML players, how should they gain a foothold in the future competition?

From 2018 to 2020, the rigorous indicator system used in Forrester's report has also undergone some adaptive adjustments. This adjustment to a certain extent represents some refined trend changes in self-built AI.

For example, it emphasizes inference optimization, edge computing support, etc., showing the improvement of IoT edge computing in self-built AI capabilities;

Added the evaluation of creating applications based on the PAML model, using the PAML model to create business workflows, etc., to express the increased demand for the implementation of self-built AI;

Suggest data relationships and automatic data statistical analysis indicators to reflect that "decision-making AI" has become an important trend in self-built AI;

Strengthening model verification and interpretation evaluation shows that many companies are beginning to pay attention to the interpretability of models in the process of self-built AI. This may mainly occur in the financial sector’s demand for compliance...

In the face of more obvious wishes, the demand for self-built AI has begun to go deeper. In addition to driving subversive changes in the market structure, it has also spawned a series of specific refinement trends, and these "small trends" may become customers in the future. An important factor in choosing a certain PAML platform.

The "big money" transactions behind Bitcoin's surge

 Behind Bitcoin's price rise again, this controversial cryptocurrency is attracting new institutional funds.

Statistics show that the open positions of large institutional holders in bitcoin futures contracts have reached the highest level in history.

Bitcoin broke through the $17,500 mark on November 17 (Tuesday), approaching its historical high. (Editor's note: Bitcoin rose above US$18,000 on Wednesday afternoon, the first time since December 2017 to reach this high level. As of press time, the price has fallen below US$18,000.)

As the "big money" begins to flood in, this cryptocurrency is benefiting, and regulators have tacitly (if not very formally) recognized Bitcoin as an acceptable asset class. It is up 147% this year and has shown particularly strong momentum in the past few weeks. Bitcoin's all-time high is $19,783. It reached this level in December 2017 and fell sharply in the following months.

Although Bitcoin is still used for things that once disgusted investors — such as ransomware attacks against institutions — it has also been increasingly accepted by some investors as a hedge against the US dollar. Some people expect that the Fed's decision to keep interest rates low will stimulate inflation, and they believe that Bitcoin can withstand this depreciation. The supply of Bitcoin is capped at 21 million, so supporters believe that it will not depreciate in the same way. Hedge fund investor Stanley Druckenmiller (Stanley Druckenmiller) said earlier this month that he holds a "very small amount" of bitcoin because he expects the dollar to depreciate.

"I hold many, many times more gold than I hold Bitcoin, but frankly speaking, if gold investment works, Bitcoin investment may be better because it has smaller trading volume and less liquidity. There are more beta gains." Druckenmiller said on CNBC.

Concerns about inflation have become a standard explanation for the rise of Bitcoin, although there are some factors that complicate this statement. On the one hand, Ethereum, the second largest cryptocurrency by market capitalization, is also in a bull market, and its supply has no upper limit. And, in the past few years, fear of inflation has not been the first time. Investors often complain that the Fed is "printing money." During this period, Bitcoin sometimes rose and sometimes fell.

Right now, the regulation of cryptocurrencies is fragmented, with some things being handled by state governments, and some by different federal agencies. But in recent months, most of the regulatory decisions have been positive for cryptocurrencies.

Online payment company PayPal Holdings (PYPL) said last month that it had obtained a special license from the New York State Department of Financial Services as part of a plan to allow users to trade bitcoin.

Although Trump stated that he "dislikes" Bitcoin and the US Securities Regulatory Commission has also cracked down on alleged fraud by some cryptocurrency companies, institutions such as the US Commodity Futures Trading Commission have opened the door to various cryptocurrency services such as asset custody. . This kind of basically friendly regulation may continue. Edward Moya, an analyst at currency broker OANDA, pointed out that Gary Gensler, one of President-elect Biden 's senior advisers to financial regulation, "is considered to be friendly to cryptocurrencies."

Bitcoin has also been accepted by more companies-such as payment company Square-and has attracted new institutional funding. Statistics from the Chicago Mercantile Exchange (CME), which provides bitcoin futures, show that trading volumes have soared in recent weeks. The Chicago Mercantile Exchange told Barron’s that the open positions of large institutional holders in bitcoin futures contracts reached an all-time high. Compared with October, this month’s average daily transaction volume has increased by 7% to 7,900 contracts.

Nevertheless, Bitcoin is still being questioned. As prices continue to rise, well-known hedge fund investor Ray Dalio expressed his skepticism on Tuesday.

Dalio wrote on Twitter that considering the volatility of Bitcoin and the fact that you can't buy a lot of things with it, it is neither a good medium of exchange nor a good store of value. Even if governments have not tried to stop it so far, “if it succeeds enough to compete with currencies controlled by governments and pose a sufficient threat to government currencies, then governments will declare it illegal and make it illegal. Too dangerous to use."

"In addition, unlike gold, which is the third largest reserve asset held by central banks, I cannot imagine that central banks, large institutional investors, corporations or multinational corporations will use Bitcoin."

Nevertheless, he made it clear that he is willing to reconsider his position. "If my judgment on these things is wrong, I am happy to be corrected." he wrote.

*For the English version, see the report "Matthew C. Klein Divided Government May Push the Fed to Go Bigger. Here's What That Might Look Like." on November 17, 2020.

*The content of this article is for reference only. Investment advice does not represent the inclination of "Barron Weekly"; the market is risky and investment must be cautious.