Many crypto enthusiasts try to predict the price of Bitcoin based on their observations of crypto markets. Some predictions seem to be really incredible, while others are quite logical. Among the latest Bitcoin price predictions are those made by Tom Lee, the co-founder and head strategist of Fundstrat Global Advisors, and billionaire investor Marc Lasry. As was previously reported by CoinSpeaker, Tom Lee For supposed that the price of Bitcoin should be a result of doubling of its mining costs and concluded that it will reach $22,000 in 2018. As for Marc Lasry, he supported crypto optimists, saying that the price of bitcoin could reach up to $40,000 as it becomes more accepted and easier to trade.
A new model for predicting bitcoin’s future valuation has been recently proposed by Yukun Liu and Aleh Tsyvinski, economists from Yale University. They believe that the Bitcoin price is based on historical price data and Google search queries. Cryptocurrency markets doesn’t behave like the traditional financial markets, and that’s why momentum and investor attention are the two key factors that can influence prices of digital currencies.
The researches drafted a paper in which they have formulated certain parameters which can be used to predict the returns on coins like Bitcoin, XRP and Ethereum. The paper is circulated by US-based think tank National Bureau of Economic Research. According to the paper, cryptocurrencies have no exposure to most stock markets and macroeconomic factors, that’s why they have no exposure to returns of currencies or commodities, a completely different set of factors affect their price movements and thus returns.
The study is based on a financial model called capital asset pricing model (CAPM). It suggests that a one-standard-deviation increase in the Google search for Bitcoin yields a 2.3 percent increase in the next two weeks. Standard deviation is a statistical measure of the spread of data points. The greater the deviation, more spread out the data points. The paper reads:
“High investor attention predicts high future returns over a 1-2 week horizon for Bitcoin, a one-week horizon for Ripple (XRP), and 1-6 week horizon for Ethereum.”
The researches believe that “the markets do not view cryptocurrencies similarly to standard asset classes.” Liu and Tsyvinski compared cryptocurrency returns to that of traditional currencies such as the euro, metals like gold and macroeconomic factors such as consumption growth. Moreover, they studied the price data for Bitcoin from 2011 to 2018, along with that of Ripple’s XRP and Ethereum’s ether from the newer currencies’ inceptions in 2012 and 2015 respectively, and calculated the probability of a drop in price to zero in a day. The paper is provided with numerous tables confirming the researches’ suggestions.
Saying that momentum and investor attention are the two key factors that can enable market participants to accurately predict future returns, Aleh Tsyvinski and Yukun Liu claim that that negative investor attention also affects the crypto returns. It is interesting that constructing a ratio between Google searches for the phrase “Bitcoin hack” and searches for the word “Bitcoin” to proxy for negative investor attention, the economists found out that the ratio negatively and significantly predicts 1-5 week Bitcoin retruns. For example, a one-standard-devation increase of the ratio leads to a 2.75 percent decrease of Bitcoin returns in next week.
The researches summed up:
“We conclude that cryptocurrency returns have low exposures to traditional asset classes – stocks, currencies, and commodities. Our findings cast doubt on popular explanations that the behavior of cryptocurrencies is driven by its functions as a stake in the future of blockhain technology similar to stocks, as a unit of account similar to currencies, or as a store of value similar to precious metal commodities. At the same time, the returns of cryptocurrency can be predicted by two factors specific to its markets – momentum and investors attention. Our findings call into question popular explanations that supply factors such as mining costs, price-to-”dividend” ratio, or realized volatitility are useful for predicting the behavior of cryptocurrency returns. Finally, we document that the blockchain technology embodied in cryptocurrencies has a potential to affect a number of important industries.”
The researchers believe that the best strategy for making money in crypto is to buy an asset after its price has already spiked and sell it just seven days after purchase. Using this strategy, a trader can still make an average of 11% on bitcoin even if they bought it following a 20% increase.