# Applying Artificial Intelligence in Cryptocurrency Markets: A Survey
## 2 Cryptocurrency Markets
### Price Determinants of Cryptocurrencies
Despite the general supply-demand equilibrium concept that stabilises the price of commodities, the price of Bitcoin seems to follow different paradigms. [PERSON] et al. [1] expand a gold standard model to investigate factors in the formation of Bitcoin price variation. This study considers three main factors including Bitcoin supply and demand market forces, its attractiveness for investors, and global macro-financial development. The results demonstrate that the impact of attractiveness has changed over time, which means there are periods when investors are interested in investing in Bitcoin regardless of the supply-demand equilibrium, and the macro-financial development factor has a minor influence on Bitcoin price in the short run. The key finding is that the demand-side pressure substantially affects the Bitcoin price in comparison to supply-side factors. Another fundamental question in the price prediction of a cryptocurrency is whether other financial assets impact the change in price value or price fluctuations. Therefore, the degree to which cryptocurrency markets integrate with other financial assets and their own interconnections is important to be investigated by investors for building better price-prediction strategies. [PERSON] et al. [2] use a directed acyclic graph (DAG) and Granger causality test to uncover the causal structure between three categories of assets, including international equities and currency assets. An energy index is also incorporated as a commodity component due to the fact that electricity price is important in Bitcoin mining. The results demonstrate that Bitcoin is an isolated market in the contemporary causal structure. However, there are time-variant causal relationships, especially in bearish market trends, according to the time-lagged causality structure. In another study, [PERSON] et al. [37] use a vector autoregression (VAR) model to examine the connectedness and volatility spillover effects  
Figure 3: Six phases of CRISP-DM for data analysis projects. relationship between Bitcoin and conventional financial assets. There is a weak relationship between Bitcoin and financial assets; however, the correlation among cryptocurrencies is comparably stronger. Moreover, there is a stronger connectedness via negative returns than positive returns. The study also demonstrates that the connectedness between Bitcoin and other financial assets varies over time. [PERSON] et al. [38] also use a spillover approach to explore relationships between Bitcoin, Ripple, and Litecoin, as well as relationships with a combination of financial assets. Volatility spillover results are categorised based on low and high frequencies. There is little evidence of volatility spillovers between cryptocurrencies and financial assets at short frequencies. However, there are obvious connections between cryptocurrencies in high and low frequencies. Furthermore, other studies investigate the correlation between the prices of cryptocurrencies to build better price prediction models. The study by [PERSON] et al. [39] does a pairwise comparison of ten different cryptocurrencies considering ninety combinations of them. They apply multivariate extreme value theory and estimate a bias-corrected extreme correlation coefficient. They find that the extreme correlation coefficient is more observable in bear markets than bull markets among different pairs. In a negative return state, the extreme correlation coefficient generally has the highest value where one side of the cryptocurrency pair is either Bitcoin or Litecoin. On the other hand, for positive return exceeding, either Bitcoin or Ethereum is one side of the cryptocurrency pair. The results also demonstrates pairs including Dash on one side, which has a weak dependency level with other cryptocurrencies. [PERSON] et al. [40] also analyse cross-correlations between 119 cryptocurrencies. This study utilised Random matrix theory and minimum spanning trees (MST) to develop their methods.