132 research outputs found
A generative model for feedback networks
We investigate a simple generative model for network formation. The model is
designed to describe the growth of networks of kinship, trading, corporate
alliances, or autocatalytic chemical reactions, where feedback is an essential
element of network growth. The underlying graphs in these situations grow via a
competition between cycle formation and node addition. After choosing a given
node, a search is made for another node at a suitable distance. If such a node
is found, a link is added connecting this to the original node, and increasing
the number of cycles in the graph; if such a node cannot be found, a new node
is added, which is linked to the original node. We simulate this algorithm and
find that we cannot reject the hypothesis that the empirical degree
distribution is a q-exponential function, which has been used to model
long-range processes in nonequilibrium statistical mechanics.Comment: 11 pages, 6 figure
Physics of Fashion Fluctuations
We consider a market where many agents trade many different types of products
with each other. We model development of collective modes in this market, and
quantify these by fluctuations that scale with time with a Hurst exponent of
about 0.7. We demonstrate that individual products in the model occationally
become globally accepted means of exchange, and simultaneously become very
actively traded. Thus collective features similar to money spontaneously
emerge, without any a priori reason.Comment: 9 pages RevTeX, 5 Postscript figure
Forecasting the propagation of pandemic shocks with a dynamic input-output model
We introduce a dynamic disequilibrium input-output model that was used to forecast the economics of the COVID-19 pandemic. This model was designed to understand the upstream and downstream propagation of the industry-specific demand and supply shocks caused by COVID-19, which were exceptional in their severity, suddenness and heterogeneity across industries. The model, which was inspired in part by previous work on the response to natural disasters, includes the introduction of a new functional form for production functions, which allowed us to create bespoke production functions for each industry based on a survey of industry analysts. We also introduced new elements for modeling inventories, consumption and labor. The resulting model made accurate real-time forecasts for the decline of sectoral and aggregate economic activity in the United Kingdom in the second quarter of 2020. We examine some of the theoretical implications of our model and find that the choice of production functions and inventory levels plays a key role in the propagation of pandemic shocks. Our work demonstrates that an out of equilibrium model calibrated against national accounting data can serve as a useful real time policy evaluation and forecasting tool
Market impact and trading profile of large trading orders in stock markets
We empirically study the market impact of trading orders. We are specifically
interested in large trading orders that are executed incrementally, which we
call hidden orders. These are reconstructed based on information about market
member codes using data from the Spanish Stock Market and the London Stock
Exchange. We find that market impact is strongly concave, approximately
increasing as the square root of order size. Furthermore, as a given order is
executed, the impact grows in time according to a power-law; after the order is
finished, it reverts to a level of about 0.5-0.7 of its value at its peak. We
observe that hidden orders are executed at a rate that more or less matches
trading in the overall market, except for small deviations at the beginning and
end of the order.Comment: 9 pages, 7 figure
A quantitative model of trading and price formation in financial markets
We use standard physics techniques to model trading and price formation in a
market under the assumption that order arrival and cancellations are Poisson
random processes. This model makes testable predictions for the most basic
properties of a market, such as the diffusion rate of prices, which is the
standard measure of financial risk, and the spread and price impact functions,
which are the main determinants of transaction cost. Guided by dimensional
analysis, simulation, and mean field theory, we find scaling relations in terms
of order flow rates. We show that even under completely random order flow the
need to store supply and demand to facilitate trading induces anomalous
diffusion and temporal structure in prices.Comment: 5 pages, 4 figure
Significant Reduction of Antibiotic Use in the Community after a Nationwide Campaign in France, 2002–2007
Didier Guillemot and colleagues describe the evaluation of a nationwide programme in France aimed at decreasing unnecessary outpatient prescriptions for antibiotics. The campaign was successful, particularly in reducing prescriptions for children
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