Abstract: Using the most comprehensive source of commercially available data on the US National Market System, we analyze all quotes and trades associated with Dow 30 stocks in 2016 from the vantage point of a single and fixed frame of reference. Contrary to prevailing academic and popular opinion, we find that inefficiencies created in part by the fragmentation of the equity marketplace are widespread and potentially generate substantial profit for agents with superior market access. Information feeds reported different prices for the same equity---violating the commonly-supposed economic behavior of a unified price for an indistinguishable product---more than 120 million times, with "actionable" latency arbitrage opportunities totaling almost 64 million. During this period, roughly 22% of all trades occurred while the SIP and aggregated direct feeds were dislocated. The current market configuration resulted in a realized opportunity cost totaling over $160 million when compared with a single feed, single exchange alternative---a conservative estimate that does not take into account intra-day offsetting events.
Abstract: Both the scientific community and the popular press have paid much attention to the speed of the Securities Information Processor - the data feed consolidating all trades and quotes across the US stock market. Rather than the speed of the Securities Information Processor, or SIP, we focus here on its importance to efficient, price discovery. Via extensions to a previous market model, we experiment with four different coupling mechanisms which operate across the US stock market. Of the four, we find that the SIP contributes most to efficient price discovery.
Abstract: Increased interconnection between critical infrastructure networks, such as electric power and communications systems, has important implications for infrastructure reliability and security. Others have shown that increased coupling between networks that are vulnerable to internetwork cascading failures can increase vulnerability. However, the mechanisms of cascading in these models differ from those in real systems and such models disregard new functions enabled by coupling, such as intelligent control during a cascade. This paper compares the robustness of simple topological network models to models that more accurately reflect the dynamics of cascading in a particular case of coupled infrastructures. First, we compare a topological contagion model to a power grid model. Second, we compare a percolation model of internetwork cascading to three models of interdependent power-communication systems. In both comparisons, the more detailed models suggest substantially different conclusions, relative to the simpler topological models. In all but the most extreme case, our model of a “smart” power network coupled to a communication system suggests that increased power-communication coupling decreases vulnerability, in contrast to the percolation model. Together, these results suggest that robustness can be enhanced by interconnecting networks with complementary capabilities if modes of internetwork failure propagation are constrained.
Abstract: Smart grid technology has the potential to substantially improve electricity
service by increasing reliability, reducing environmental impacts, and decreasing
costs. However, smart grid deployment involves, by definition, an increased
coupling between communication networks and electric power networks. Research
on abstract networks indicates that increased coupling between networks
can increase the risk of large failures. However, the existing research provides
little understanding of how these general findings apply to the specific problems
posed by the coupling of power grids to communication networks. Effectively
understanding and mitigating new risks will require substantial improvements
in our understanding of coupled networks. A first step in that direction is to
clarify how risk is described in both systems, since similar terms are used to
describe different concepts in these two increasingly coupled industries.