22 research outputs found
Reducing Cascading Failure Risk by Increasing Infrastructure Network Interdependency
Increased coupling between critical infrastructure networks, such as power
and communication systems, will have important implications for the reliability
and security of these systems. To understand the effects of power-communication
coupling, several have studied interdependent network models and reported that
increased coupling can increase system vulnerability. However, these results
come from models that have substantially different mechanisms of cascading,
relative to those found in actual power and communication networks. This paper
reports on two sets of experiments that compare the network vulnerability
implications resulting from simple topological models and models that more
accurately capture the dynamics of cascading in power systems. First, we
compare a simple model of topological contagion to a model of cascading in
power systems and find that the power grid shows a much higher level of
vulnerability, relative to the contagion model. Second, we compare a model of
topological cascades in coupled networks to three different physics-based
models of power grids coupled to communication networks. Again, the more
accurate models suggest very different conclusions. In all but the most extreme
case, the physics-based power grid models indicate that increased
power-communication coupling decreases vulnerability. This is opposite from
what one would conclude from the coupled topological model, in which zero
coupling is optimal. Finally, an extreme case in which communication failures
immediately cause grid failures, suggests that if systems are poorly designed,
increased coupling can be harmful. Together these results suggest design
strategies for reducing the risk of cascades in interdependent infrastructure
systems
Price Discovery and the Accuracy of Consolidated Data Feeds in the U.S. Equity Markets
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
accuracy. Relying on Trade and Quote data, we provide various measures of SIP
latency relative to high-speed data feeds between exchanges, known as direct
feeds. We use first differences to highlight not only the divergence between
the direct feeds and the SIP, but also the fundamental inaccuracy of the SIP.
We find that as many as 60 percent or more of trades are reported out of
sequence for stocks with high trade volume, therefore skewing simple measures
such as returns. While not yet definitive, this analysis supports our
preliminary conclusion that the underlying infrastructure of the SIP is
currently unable to keep pace with the trading activity in today's stock
market.Comment: 18 pages, 20 figures, 2 table
Adaptive Agents and Data Quality in Agent-Based Financial Markets
We present our Agent-Based Market Microstructure Simulation (ABMMS), an
Agent-Based Financial Market (ABFM) that captures much of the complexity
present in the US National Market System for equities (NMS). Agent-Based models
are a natural choice for understanding financial markets. Financial markets
feature a constrained action space that should simplify model creation, produce
a wealth of data that should aid model validation, and a successful ABFM could
strongly impact system design and policy development processes. Despite these
advantages, ABFMs have largely remained an academic novelty. We hypothesize
that two factors limit the usefulness of ABFMs. First, many ABFMs fail to
capture relevant microstructure mechanisms, leading to differences in the
mechanics of trading. Second, the simple agents that commonly populate ABFMs do
not display the breadth of behaviors observed in human traders or the trading
systems that they create. We investigate these issues through the development
of ABMMS, which features a fragmented market structure, communication
infrastructure with propagation delays, realistic auction mechanisms, and more.
As a baseline, we populate ABMMS with simple trading agents and investigate
properties of the generated data. We then compare the baseline with
experimental conditions that explore the impacts of market topology or
meta-reinforcement learning agents. The combination of detailed market
mechanisms and adaptive agents leads to models whose generated data more
accurately reproduce stylized facts observed in actual markets. These
improvements increase the utility of ABFMs as tools to inform design and policy
decisions.Comment: 11 pages, 6 figures, and 1 table. Contains 12 pages of supplemental
information with 1 figure and 22 table
Erratum: Reducing Cascading Failure Risk by Increasing Infrastructure Network Interdependence
This corrects the article DOI: 10.1038/srep44499
Revisiting Stylized Facts for Modern Stock Markets
In 2001, Rama Cont introduced a now-widely used set of 'stylized facts' to
synthesize empirical studies of financial time series, resulting in 11
qualitative properties presumed to be universal to all financial markets. Here,
we replicate Cont's analyses for a convenience sample of stocks drawn from the
U.S. stock market following a fundamental shift in market regulation. Our study
relies on the same authoritative data as that used by the U.S. regulator. We
find conclusive evidence in the modern market for eight of Cont's original
facts, while we find weak support for one additional fact and no support for
the remaining two. Our study represents the first test of the original set of
11 stylized facts against the same stocks, therefore providing insight into how
Cont's stylized facts should be viewed in the context of modern stock markets.Comment: 19 pages, 11 figure
Fragmentation and inefficiencies in US equity markets: Evidence from the Dow 30
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 calendar year 2016 from the vantage point of a single and fixed frame of reference. We find that inefficiencies created in part by the fragmentation of the equity marketplace are relatively common and persist for longer than what physical constraints may suggest. Information feeds reported different prices for the same equity more than 120 million times, with almost 64 million dislocation segments featuring meaningfully longer duration and higher magnitude. 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, a conservative estimate that does not take into account intra-day offsetting events
Reply to Garcia et al.: Common mistakes in measuring frequency-dependent word characteristics
We demonstrate that the concerns expressed by Garcia et al. are misplaced,
due to (1) a misreading of our findings in [1]; (2) a widespread failure to
examine and present words in support of asserted summary quantities based on
word usage frequencies; and (3) a range of misconceptions about word usage
frequency, word rank, and expert-constructed word lists. In particular, we show
that the English component of our study compares well statistically with two
related surveys, that no survey design influence is apparent, and that
estimates of measurement error do not explain the positivity biases reported in
our work and that of others. We further demonstrate that for the frequency
dependence of positivity---of which we explored the nuances in great detail in
[1]---Garcia et al. did not perform a reanalysis of our data---they instead
carried out an analysis of a different, statistically improper data set and
introduced a nonlinearity before performing linear regression.Comment: 5 pages, 2 figures, 1 table. Expanded version of reply appearing in
PNAS 201
Human language reveals a universal positivity bias
Using human evaluation of 100,000 words spread across 24 corpora in 10 languages diverse in origin and culture, we present evidence of a deep imprint of human sociality in language, observing that (i ) the words of natural human language possess a universal positivity bias, (ii ) the estimated emotional content of words is consistent between languages under translation, and (iii ) this positivity bias is strongly independent of frequency of word use. Alongside these general regularities, we describe interlanguage variations in the emotional spectrum of languages that allow us to rank corpora. We also show how our word evaluations can be used to construct physical-like instruments for both real-time and offline measurement of the emotional content of large-scale texts