Journal of Stock & Forex Trading
Open Access

Note on Common Risk Factors of Cryptocurrencies

Atif Sattar^{* *}Correspondence: Atif Sattar, Department of Finance, Aristotle University of Thessaloniki, Thessaloniki, Greece, Email:

Author info »

Description

Increasingly significant financial software systems now use crypto currencies. These systems depend on a safe distributed ledger data structure, and mining is a crucial component of them. The distributed ledger known as Block chain gains records of previous transactions through mining, enabling users to form a secure, reliable consensus for each transaction. Additionally, mining creates wealth in the form of new currency units. Because they were created as peer-to-peer systems, cryptocurrencies do not have a central authority to mediate transactions. In order to validate transactions, they rely on miners. Mining algorithms need to be robust and secure for cryptocurrencies. We assess each mining strategy's advantages, disadvantages, and potential risks. The latest phenomenon of cryptocurrency is getting a lot of attention. On the one hand, it was founded on a wholly novel technology, the potential of which is still not fully appreciated.

On the other hand, it serves the same purposes as other, more conventional assets, at least in its current form. The creation of theoretical models for cryptocurrencies has received a great deal of academic attention. A variety of elements that may be significant in determining a cryptocurrency's value have been mentioned by the theoretical literature on cryptocurrencies. Creating an index of cryptocurrency (or coin) market returns is the first step. This index represents the value-weighted returns for all currencies having a market capitalization more than $1 million USD (1,707 coins total) over a specific time period. Research the effects of cryptocurrency models based on the production issue for miners. To simulate the cost of mining, we create production factors for cryptocurrencies and investigate how these production variables affect price movements. A cryptocurrency can be mined using, two inputs: computer power and electricity. Eight proxies for power costs and six proxies for computation expenses are separately constructed. We analyse the price, usage, and generation of electricity in China and the United States using time-varying and location-specific indicators (including Sichuan province, which hosts the largest mining farm in the world). We base our major estimate of computational expenses on the costs of Bitmain Antminer, one of the popular pieces of Bitcoin mining hardware. The stockreturns of the companies that are big producers of mining chips are another indirect measure that we take into account. Overall, we discover that the factors affecting the manufacturing of cryptocurrencies have little impact on the coin market returns. The predictability of cryptocurrency returns is affected in a number of ways by the available theoretical models of cryptocurrencies. Some publications contend that bit coin gains are unpredictable since the evolution of cryptocurrency prices should follow a martingale. Other studies contend that momentum, investor interest, and cryptocurrency valuation ratios may be used to predict cryptocurrency returns in dynamic cryptocurrency valuation models. We demonstrate that while cryptocurrency valuation ratios may not accurately forecast future cumulative returns, momentum and investor interest do. First, we demonstrate that the cryptocurrency market exhibits a substantial time-series momentum phenomena. We discover that the cumulative future coin market returns from one week to eight weeks out are predicted by the current coin market returns. For instance, an increase of one standard deviation in the current coin market returns forecasts an increase of 3.30 percent in the weekly returns the next week. When we divide weekly returns into terciles, we discover that over one to four weeks, the top terciles outperform the lowest terciles. The average return of the top tercile, for instance, is 8.01 percent per week with a tstatistic of 4.30 at the one-week horizon, but the average return of the bottom tercile is just 1.10 percent per week with a tstatistic of 0.92. The results of the time-series momentum analysis hold true both inside and outside of samples.

Second, using Google searches as proxy for investor attention, we demonstrate that high investor attention accurately predicts high future returns over time horizons of one to six weeks. One-week ahead future coin market returns, for instance, increase by 3.0 percent for every one standard deviation increase in the investor attention measure.

Conclusion

The bottom tercile's average return is merely 0.43 percent per week with a t-statistic of 0.42, while the investor attention tercile's average return at the one-week horizon is 6.53 percent per week with a t-statistic of 3.82. Twitter post counts are adifferent proxy for investor interest that we create, and we get comparable results with the Twitter measure. Furthermore, we create a proxy for negative investor attention and demonstratethat a reasonably high level of negative investor attention negatively predicts upcoming cumulative coin market returns.