From an updated take on Bitcoin’s next potential bull run to new support for BTC, ETH and XRP, here’s a look at some of the stories breaking in the world of crypto.
Crypto analyst Tone Vays is updating his timeline on when he thinks Bitcoin may hit $100,000.
In a new interview with BlockTV, Vays pours cold water on predictions that BTC will break through its all-time high next year.
“Once we break $20K, we can go to $50K or $100K pretty quickly, but I’m not expecting us to break $20K in 2020. I’m really looking for the bull market to really take off around 2022, 2023.”
Vays says he’s still bearish on Bitcoin and thinks the leading cryptocurrency will break below $7,000 before May of next year.
“To me, the big run up was 80% exit out of altcoins, 20% institutional investing. Now that the price is starting to go down, they’re probably not as eager. They’ll wait for a bottom because that’s why they’re a little bit smarter.
At the moment, I still see lower prices. I still think we’re going to go lower than $7,000 before the halving. But after that, that would be the final secondary low. Hopefully it’ll be higher than the $3,000 low back in December. And then we can
Ethereum and XRP
The crypto exchange Binance is rolling out support for the Turkish lira.
An updated list of coins available for purchase on Binance.com reveals that the exchange is working with the digital wallet company Papara to offer Bitcoin (BTC), Ethereum (ETH) and XRP in exchange for the lira.
So far, Binance does not allow customers to sell crypto assets for the lira.
Ripple says a new survey shows many financial service providers still have major concerns about the legality of blockchain technology.
The survey questioned 1,053 professionals across 21 countries who are working directly with payment services at their organizations.
According to the San Francisco startup, 35% of respondents say regulations are too uncertain, and 32% believe current regulations are too restrictive.
“These regulatory concerns, however, vary notably by a payment service providers’ adoption stage, level of digital channel usage and location. Not surprisingly, the next wave of adopters (those nearing implementation) are less concerned about regulation than the late adopters (those in conversation).
Similarly, digital banks that have already transformed the bank model are less concerned than traditional retail banks. Respondents in emerging markets have even stronger concerns about regulations being prohibitive, and advanced markets are the most concerned about uncertain regulations.”
You can check out the full survey here.
Myths and Realities: Sentiment Analysis for Crypto Assets
Jesus Rodriguez is the CTO and co-founder of IntoTheBlock, a platform focused on enabling an intelligent infrastructure for the crypto markets, as well as chief scientist of AI firm Invector Labs and an active investor, speaker and author in crypto and artificial intelligence. This article originally appeared in CoinDesk’s Institutional Crypto newsletter.
One of the established beliefs in the cryptocurrency market is its susceptibility to news and social media. Like any other nascent and still irrational financial market, unexpected developments captured in news or social media tend to impact price. As a result, there is increasing interest in leveraging machine learning techniques such as sentiment analysis to detect possible correlations with the price of cryptocurrencies and digital tokens. Despite its importance, most attempts to leverage sentiment analysis are too basic to output any tangible intelligence and quite often produce misleading results.
The challenges of efficiently leveraging sentiment analysis to evaluate the behavior of an asset are not unique to the crypto space. Producing true insights based on textual sentiment is a very difficult task that, most of the time, requires natural language processing (NLP) models optimized for a specific financial domain. Large quantitative hedge funds use armies of machine learning experts to train NLP models in a very specific task like analyzing earning reports in order to get an edge in a medium frequency trade. Efficiently leveraging sentiment analysis for crypto assets requires machine learning depth and rigor.
To understand that statement, let’s start by diving a bit deeper into the characteristics of sentiment analysis methods.
A gentle introduction to sentiment analysis
In Act II, Scene II of the famous play Richelieu; Or the Conspiracy, British playwright Edward Bulwer-Lytton coined a phrase that has transcended generations: “The pen is mightier than the sword.” Centuries after, that famous quote brilliantly encapsulates the importance of sentiment analysis. Emotions in textual communication are sometimes more conducive to actions than physical actions themselves.
Conceptually, sentiment analysis is a subdiscipline of NLP that focuses on identifying the affective states of textual communications. Contrary to popular beliefs, sentiment analysis is not a single technique but rather a subdiscipline of the deep learning space that covers different types of affection detection in textual data. From that perspective, there are several types of sentiment analysis that could be relevant in the context of crypto-asset intelligence:
- Polarity Analysis: This type of sentiment analysis ranks textual sentiment in positive, negative and neutral. For instance, the sentence “the bitcoin price rally has reenergized the market” would likely be classified as positive by most models.
- Emotion/Tone Analysis: Instead of an overall qualifier for the text, this type of analysis centers on scoring the different types of emotions present in a particular text. Emotions such as sadness, happiness or anger are a common focus of emotion analysis algorithms. For instance, the sentence “this bitcoin rally is crazy,” will show high levels of excitement and joy.
- Aspect Sentiment Analysis: This type of sentiment analysis focuses on interpreting the sentiment about specific subjects within a sentence rather than a sentence as a whole. For instance, in the sentence “Bakkt futures are a major milestone for the bitcoin market,” aspect analysis will determine the sentiment related to “Bakkt futures” instead of the complete sentence.
Looking at the previous list, we can clearly see the benefits of sentiment analysis for crypto assets. However, there are also plenty of challenges that should be considered before venturing into using these types of techniques. Contextualization, subjectivity, irony or even bad grammar are among the factors that can easily trick the best NLP algorithms.
Sentiment analysis for crypto assets
Crypto is a nascent asset class that is still vulnerable to the irrationality of financial markets and the lack of proper disclosure channels. From that perspective, it is only logical to assume NLP techniques such as sentiment analysis can identify alpha or smart beta generator factors to predict the behavior of crypto assets. Reality is a bit different.
When applying sentiment analysis to crypto assets, we are likely to encounter two main types of challenges:
- Limitations of mainstream NLP technologies when applied to a domain-specific problem such as crypto asset analysis.
- Incorrect assumptions about how sentiment is reflected in news and social media.
The first challenge can almost be seen as an unexpected side effect of the rapid growth of NLP technologies. Today, it is relatively easy for a developer to incorporate sentiment analysis into applications using simple APIs that don’t require any deep learning expertise.
While NLP APIs can be effective analyzing the sentiment of a generic sentence, they perform extremely poorly
The second challenge is related to misconceptions about how sentiment is reflected in news and social media commentary. As a source of intelligence, news can be highly informative but quite useless when comes to sentiment analysis. The reason is obvious: the sentiment in well-written news should trend around neutral. Social media behaves in the exact opposite way. Conversations about cryptocurrencies in Twitter or Telegram tend to contain relevant sentiment but, for the most part, are based on a reaction to public material information, which means that they are unlikely to generate any informational edge. Additionally, social media threads tend to be noisy and relatively subjective, which can produce misleading sentiment analysis results.
From a purely technological standpoint, building effective sentiment analysis models for crypto assets requires models trained in the terminology of crypto markets, but that also analyze news as sources of information and social media feeds as amplifiers of sentiment. However, if we get past this technological challenge, we are now faced with one of the biggest psychological misconceptions when comes to sentiment analysis models in the crypto space.
The sentiment-market impact fallacy
The sentiment-market impact fallacy describes a phenomenon that is notorious or irrational, such as nascent financial markets in which investors assume a direct correlation between a sentiment score and a price movement. To explain this behavioral economics dynamic, let’s imagine that you are using an analytics tool that analyze the sentiment of recent bitcoin tweets. Psychologically, most investors are inclined to interpret the sentiment as a leading indicator based on the following rules:
- If the sentiment is positive that’s a bullish indicator for the price of bitcoin.
- If the sentiment is negative that’s a bearish indicator for the price of bitcoin.
However, if your model is analyzing public, material information, the sentiment should be interpreted as a lagging indicator following some non-intuitive rules:
- If the sentiment is positive and the price of bitcoin does not go up, that is a bearish signal.
- If the sentiment is negative and the price of bitcoin does not go down, that is a bullish signal.
Being aware of sentiment-price bias positions sentiment analysis not as a leading indicator but as an often relevant factor in a trading strategy.
From sentiment analysis to market impact analysis
From an informational standpoint, the crypto market is noisy and full of unexpected events. In terms of sentiment analysis, that combination of factors is a nightmare. Instead of narrowly focusing on sentiment analysis, we should probably develop a more holistic approach. A sentiment-market impact indicator would be a combination of polarity (negative, positive, neutral), emotion (anxious, excited, sad…) and aspect-based (topics, entities…) analysis over long periods of time. This approach would require the training of models specialized in the dynamics of crypto assets to evaluate the sentiment in the context of specific market conditions.
The idea of sentiment-market impact models is conceptually trivial: quantify the impact that combinations of sentiment, emotions and topics can have on a crypto asset during specific market conditions. Part of the beauty of this approach is that it doesn’t have to be completely unsupervised like most sentiment models today; it can be trained on domain-specific knowledge of crypto markets. For instance, we could train a model to learn that positive articles about Chinese investment in crypto can have a positive impact in a market that had been relatively bearish for the last week. The core principle of sentiment-market impact analysis models would be to contextualize the knowledge of sentiment models to the specifics of the crypto market.
Sentiment analysis is likely to keep sparking flashy headlines in the crypto market. However, in order to be effective, the models require deeper machine learning rigor and the building of knowledge based on the specific dynamics of crypto markets. As the markets evolve, we are likely to see a transition from plain sentiment analysis techniques to more holistic market impact models that quantify the relevance of specific topics in the behavior of the crypto markets
SWEDEN JOINS CHINA AND OTHERS IN CENTRAL BANK CRYPTO RACE
It seems that not a day goes by without another central bank jumping on the crypto train. Yesterday it was South Korea, today it is Sweden as plans to roll out the crypto krona gather pace.
E-KRONA PILOT PARTNERSHIP
Sweden’s central bank is poised to sign a deal with multinational professional services company Accenture to begin pilot testing its CBDC, the e-krona. According to reports the partnership is for an initial year-long run to the end of 2020.
A Riksbank statement added that;
“The primary objective of the e-krona pilot project is to broaden the bank’s understanding of the technological possibilities for the e-krona,”
Cash usage has already started to decline in the Scandinavian country which spurred the central bank to begin researching a digital option in 2017.
The Accenture partnership will involve the development of a payment platform with a user interface that enables e-krona transactions from cards and smartphones. There has been no confirmation from the bank that a crypto krona will see the light of day, but it is highly
Industry observer ‘Rhythm Trader’ likened cryptocurrencies to the ‘space race of our generation’, but still maintained that bitcoin was the king of them all.
CRYPTO ETP APPROVED
Sweden has been more proactive towards crypto assets than other regional countries. Earlier this week the Swedish Financial Supervisory Authority (SFSA) approved Swiss crypto ETP provider Amun.
According to the announcement, Amun is the first issuer to deliver fully collateralized, passive investment products with cryptocurrencies as the underlying asset.
Amun’s President Ms. Ophelia Snyder added;
“Our mission is clear and that is to help investors more safely, cost effectively and easily invest in crypto asset classes through our crypto ETPs. We recognise that the regulatory framework in Sweden has been supportive of such initiatives and we welcome its deliberation.”
From Traditional Finances To Crypto, MakerDAO COO Reflects On His Entrance Into The Space
- The President and COO of MakerDAO spoke in a recent interview where he discussed how he got started in the crypto space.
- For those who don’t know, MakerDAO is a one of a kind dApp that generates the Dai coin.
- Becker spoke on his professional background as and how he got into the crypto space as well as when he started getting involved with the industry.
The President and COO of MakerDAO, Steven Becker spoke in a recent interview where he discussed how he got started in the crypto space as well as talking on the benefits of decentralised finance, the successful launch of multi-collateral Dai, advantages of using the Dai savings rate, his crypto predictions for 2020 and more.
For those who don’t know, MakerDAO is a one of a kind dApp that generates the Dai coin. This is a world-first for decentralised stablecoins that facilitates economic growth on the blockchain as well as empowerment with the blockchain.
Becker spoke on his professional background as and how he got into the crypto space as well as when he started getting involved with the industry.
Before entering crypto I was involved in the traditional finance space. I have been involved with a wide spectrum of businesses, ranging from interest rate derivatives trading, running an equity fund all the way through to corporate financeand private equity. At the center of all of this has been risk management. So you can say that my profession is risk management.
I was aware of Bitcoin and Ether since 2016 but never really thought much of it. I was more interested in the underlying blockchain technology then the tokens. But it was MakerDAO that really got me into the crypto space in April of 2018.”
As previously reported by CryptoDaily, talking in a recent podcast, Rune Christensen, the founder of MakerDAO said that the leading cryptocurrencies, Bitcoin and Ethereum both have the potential to become digital gold. Despite this, Christensen recalls the original vision that BTC envisioned in the beginning and he went onto indicate something completely different is happening to bitcoin now. He believes that this is what drove a lot of people to the other cryptocurrency in the space, Ethereum.
“So I mean, it’s just like there’s different types of technology that have different use cases, right. And Bitcoin’s use case turns out to be digital gold, which is great, but it’s not the original vision of digital cash.”
We will go back to the rest of this interview tomorrow!
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