Mining cost is no longer a predictor of coin price! Yale University research points out new factors affecting Bitcoin price

What exactly is cryptocurrency? Is it a currency, a commodity, a stock, or a new type of asset? This has always been a hotly debated topic in the cryptocurrency industry and even the entire financial circle. While investors are seeking to characterize cryptocurrencies, they are also struggling to find factors that affect the performance of the cryptocurrency market.

Recently, a new research report from Yale University showed that the market performance of cryptocurrencies is closely related to investor attention and market momentum, which can be used to predict the market performance of cryptocurrencies. The report also proves that the market performance of cryptocurrencies is weakly correlated with stocks, currencies, and precious metals.

Unique price factors in the cryptocurrency market: market momentum and investor attention

What factors can be used as a reference for cryptocurrency price prediction? A Yale University research paper pointed out the unique factors that affect the performance of the cryptocurrency market - market momentum and investor attention. Among them, market momentum refers to the market continuing the current trend, rising and falling. The so-called momentum investment is to chase the rise and sell the fall. Investor sentiment is a popular concept in behavioral finance in recent years, and it is now also used in market predictions in the field of cryptocurrency.

The Yale study found that for the three selected cryptocurrencies, current gains are very likely to predict continued gains in the future . Taking Bitcoin as an example, a one-day increase in Bitcoin price statistically significantly predicted price increases on the first, third, fifth, and sixth days. Similarly, a one-week increase in Bitcoin price also significantly predicted price increases on the first, second, third, and fourth weeks.

In addition, the correlation between "Google search trends", as an indicator of investor attention, and the price of cryptocurrencies is confirmed by this research paper. Taking Bitcoin as an example, the increase in search popularity for the word "Bitcoin" in a certain week significantly predicts the increase in Bitcoin price in the first and second weeks . The search popularity of the word "Ethereum" predicts the price increase of Ethereum in the first, third, and sixth weeks.

The research paper also found that negative attention is also crucial to price impact . For example, an increase in the proportion of the term "Bitcoin Hack" to the term "Bitcoin" significantly predicted a decline in Bitcoin prices for the next five consecutive weeks. A 1% standard deviation increase in the share of the term "Bitcoin Hack" predicted a 2.75% drop in Bitcoin prices in the next week.

Previously, Fundstrat, a Wall Street market analysis company, proposed a model that uses the cost of Bitcoin mining to predict prices. The model predicts that Bitcoin will reach between $20,000 and $64,000 by the end of 2019. This Yale University paper refutes Fundstrat's model, arguing that Bitcoin prices have nothing to do with mining costs.

As proof, the Yale University paper uses the stock returns of the US and Chinese power industries and Sinopec as proxy variables for electricity costs, and uses the stock returns of manufacturers of GPU mining chips and ASIC mining chips (Nvidia, AMD, etc.) as proxy variables for computer costs. The results show that the price of Bitcoin and Ethereum is not significantly exposed to these factors, and Ripple is only significantly correlated with the stock returns of AMD.

Cryptocurrency prices have weak correlations with stocks, currencies, and precious metals

Xiaocong has explained in previous articles that from the perspective of correlation analysis, cryptocurrency as a whole is almost completely an event-driven asset, with little correlation with all other major asset classes, and interest rates and inflation have no regular impact on it in the short and medium term. (For details, see "Bitcoin's Last "White Knight" - Hedge Funds")

In the Yale University paper, six commonly used stock market pricing models were selected to detect the degree of stock factor loading: CAPM model, FF three-factor model, Carhart four-factor model, FF five-factor model, and FF six-factor model. The results show that the constant ALPHA after regression of the three mainstream cryptocurrencies, Bitcoin, Ethereum, and Ripple, is very high. ALPHA is the part of the return that is not related to the factors in the model. A high value of ALPHA indicates that the correlation between the currency price and the factors in the model is small.

Only in the FF five-factor model and the FF six-factor model does the BETA coefficient of Bitcoin reach a significant level of 10% with the market risk-free rate, and the rest are not significant. The correlation between Bitcoin and some factors varies. Most of the various coefficients of Ethereum and Ripple are also not significant. Such results show that the correlation between cryptocurrency prices and stocks is low.

In measuring the exposure of Bitcoin, Ethereum and Ripple to foreign exchange factors, the study selected five currencies: Australian dollar, Canadian dollar, euro, Singapore dollar and British pound. In measuring the exposure of precious metal factors, three precious metals: gold, platinum and silver were selected. The study concluded that there is no evidence that the returns of cryptocurrencies are affected by the prices of these currencies or precious metals.

Cryptocurrency risk-adjusted returns may be higher than stock returns

The high growth of cryptocurrencies in recent years is much higher than that of traditional stocks, but its high volatility and high risk characteristics often discourage investors. The Yale University research report uses the Sharpe ratio to prove that after adjusting for risk, investing in Bitcoin can still achieve higher returns than investing in stocks .

The Sharpe ratio is the ratio of the difference between the expected return of a portfolio and the risk-free rate to the standard deviation of the portfolio. The Sharpe ratio calculates how much excess return is generated for each unit of risk in a portfolio and is a common method for comparing the risk-return of different assets. The higher the Sharpe ratio, the higher the return investors receive for taking the same degree of risk.

The results show that, in terms of monthly cycles, the Sharpe ratio of Bitcoin is very close to that of stocks in the same period, which are 0.31 and 0.33 respectively, and are significantly higher than the historical stock Sharpe ratio starting in 1953. In terms of weekly or daily cycles, the Sharpe ratio of Bitcoin is significantly higher than that of stocks. The Sharpe ratios of Ethereum and Ripple are both close to that of Bitcoin in the same period.