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Cryptocurrencies including Bitcoin are known to be vulnerable to so-called 'double-spending' attacks, where the same digital currency is used to execute multiple different transactions simultaneously. Little is known, however, about the underlying reasons for this vulnerability. Here we develop an agent-based model to study how features of cryptocurrency networks contribute to their vulnerability to double-spending attacks. Perhaps surprisingly, we find neither the number of network nodes nor its path length seem to influence the probability of successful attacks. We find robust evidence that the network's clustering coefficient has substantial influence. In particular, scale-free networks, with their small clustering coefficients, are more than twice as likely to succumb to double-spending attacks than are networks with larger coefficients, such as regular networks. The implication is that cryptocurrency networks, which are scale-free, may be uniquely susceptible to double-spending attacks. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of individual rationality of buyers and sellers on continuous double auction market outcomes in terms of the proportion of boundedly-rational buyers and sellers. The individual rationality is discussed in a social network artificial stock market model by embedding network formation and information set. Traders automatically select the most profitable trading strategy based on individual and social learning of the profits and trading strategies of themselves and their neighbors, and submit orders to markets. The results show that (i) a higher proportion of boundedly-rational sellers induces a higher market price, higher sellers’ profits and a higher market efficiency; (ii) a higher proportion of boundedly-rational sellers induces a lower number of trades and lower buyers’ profits; (iii) a higher proportion of boundedly-rational buyers induces a lower market price, a lower number of trades, and lower sellers’ profits; (iv) a higher proportion of boundedly-rational buyers induces higher buyers’ profits and a higher market efficiency. [ABSTRACT FROM AUTHOR]

This paper considers the convergence of trading strategies among artificial traders connected to one another in a social network and trading in a continuous double auction financial marketplace. Convergence is studied by means of an agent-based simulation model called the Social Network Artificial stoCk marKet model. Six different canonical network topologies (including no-network) are used to represent the possible connections between artificial traders. Traders learn from the trading experiences of their connected neighbours by means of reinforcement learning. The results show that the proportions of traders using particular trading strategies are eventually stable. Which strategies dominate in these stable states depends to some extent on the particular network topology of trader connections and the types of traders. [ABSTRACT FROM AUTHOR]

This paper studies the multi-asset pricing problem in the stock market with holding-based networks. We derive a linear noisy rational expectation equilibrium (LNREE) in the network, construct holding-based networks and test the LNREE's efficiency in generating predictive output validation. The results show that the LNREE fits the observed prices and returns of stocks quite well, especially for U.S. energy stocks. By changing the average risk aversion, the predictive performance of the model can be optimized. It provides a novel perspective for the empirical study on multi-asset pricing modeling. • We study multi-asset pricing model in the stock market with holding-based networks. • A linear noisy rational expectations equilibrium (LNREE) in the network is derived. • We analyze the co-holding behavior of shareholders from four energy indices. • LNREE performs well in the predictive output validation, especially for U.S. stocks. • Our model is more efficient in the forecasting process relative to the original one. [ABSTRACT FROM AUTHOR]