The Implications of Alternative Data for Digital Assets
Can we fully understand, or even predict, the price movements of crypto assets?
Today we’ll be expanding our field of vision using alternative data and providing examples that can be implemented in the crypto space.
The financial industry has a voracious appetite for data. It is estimated that the data industry will grow to over $200bn by 2020, with the financial sector spearheading the growth with roughly 15% of the total spend.1
With astonishing sums of money being aimed towards correlating external information with the financial industry, the diversity of the data being employed might surprise you.
As seen in the chart below, the list of alternative data utilized by the traditional financial industry has grown to include many diverse subsets of information, each providing its own unique view of the world.
For instance, J.P. Morgan analyzed consumer transaction data for S&P 500 companies, and was able to achieve positive sharpe ratio with a basic trading strategy by leveraging the dollar spend data of the consumers.2
Even more interestingly, companies have been able to leverage geolocation on mobile devices to forecast revenues of retail, big box stores, supermarkets, and more.
As we’ve seen with the example above, investors are able to map new models of financial markets and paint a clearer picture of the driving forces for its present and future state.
It should be noted that the one true motivator for the data rush comes from an information advantage over the market regarding investment management decisions.
It may not be the best approach to use satellite imaging of parking lots or to map the transactions of crypto enthusiasts to discover insights about the retail performance of a token.
The decentralized and almost purely-digital nature of the crypto space make some of these alternative data routes a bit more challenging to execute.
Despite these challenges, there are numerous alternatives that have a proven track record of providing investors an edge that could translate well for the crypto world. Here are a few:
1. Search Trend Indices
By analyzing the popularity of keywords on search engines, investors are able to predict volatility & price changes in the near future.
For example , in their study, Yelowitz & Wilson found a negative correlation between the USA Google Search for Bitcoin and it’s prices for the period 2011–2016.3
Google Trend serves as an good proxy to capture the attention of retail investors. The graph above shows the Worldwide Google Trends data of Bitcoin for the period 2017–2018.
2. Sentiment Analysis for News, Blogs, Social Media, etc.
Sentiment analysis enables investors to process the massive amount of text data generated by the public to predict whether the market is bullish or bearish on a certain asset or industry.
In his study, Ciaran McAteer was able to find a positive correlation between the Twitter sentiment and the price of bitcoin, and found that sentiment is reflected in price after a time delay of 24 hours.4
In the picture above, Blogger and Crypto-enthousiast, Sam Couch, analyzed sentiment of Twitter posts. The graph above demonstrates the correlation between sentiment for Bitcoin and its price over the period 2017–09–02 to 2017–09–07.5
3. White paper & Core team information
Slightly more niche, the analysis of a white paper and the core team would serve as a good indicator to predict an ICO’s ability to raise capital, meet milestones, and ultimately provide ROI to its backers.
While not a perfect model, ICObench leverages the “Wisdom of the Crowd” to assess the ICOs’ quality, which is a great starting point.⁶ The scoring table above shows different scores based on “experts” analysis of the product.
Everything comes with its share of risk, alternative data is no exception.
Quality of data
Data that is unreliable will produce inaccurate trading signals (garbage in, garbage out), which may nullify the value of some alternative datasets
Make sure that the data collection process is well documented, and the data is properly cleaned
Data that does not have the right output will be useless for certain demographics
Raw data might be useful for researchers and data scientist but useless for traders/investors who are looking for instant insights
Some datasets may not generate significant alpha for investors
Have a sound theory for the type of datasets that you want to use and why it would work with the crypto market
Raw data could be a waste of time for traders or investors that do not have the expertise to process it.
Can we fully understand and even predict the movements of cryptocurrencies?
No, not entirely.
Can we paint a clearer picture of the crypto markets to create profitable trading strategies?
It is not a trivial task, but through the use of alternative data, it’s possible to obtain a better understanding of past, present, and future movements of the crypto markets.
If you would like to learn more about our methodology, or if you would like to receive data-driven insights more often you can sign up here: http://bit.ly/consilium-crypto
Consilium Crypto is a big data company that provides quantitative and qualitative insights to market participants in the digital asset space, including funds, family offices and exchanges.
Consilium analyzes 17,000 trading pairs, over 1000 assets, across 50+ exchanges, and tracks trading activity to the millisecond. Our system monitors raw transaction data, as well as complete price and liquidity information from order books around the globe. These data pipelines power our core products, designed to help funds find alpha and place large orders efficiently in times of thin liquidity.
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