15 research outputs found
On random number generators and practical market efficiency
Modern mainstream financial theory is underpinned by the efficient market
hypothesis, which posits the rapid incorporation of relevant information into
asset pricing. Limited prior studies in the operational research literature
have investigated the use of tests designed for random number generators to
check for these informational efficiencies. Treating binary daily returns as a
hardware random number generator analogue, tests of overlapping permutations
have indicated that these time series feature idiosyncratic recurrent patterns.
Contrary to prior studies, we split our analysis into two streams at the annual
and company level, and investigate longer-term efficiency over a larger time
frame for Nasdaq-listed public companies to diminish the effects of trading
noise and allow the market to realistically digest new information. Our results
demonstrate that information efficiency varies across different years and
reflects large-scale market impacts such as financial crises. We also show the
proximity to results of a logistic map comparison, discuss the distinction
between theoretical and practical market efficiency, and find that the
statistical qualification of stock-separated returns in support of the
efficient market hypothesis is dependent on the driving factor of small
inefficient subsets that skew market assessments.Comment: Preprint, accepted for publication in Journal of the Operational
Research Societ
Gaussbock:Fast parallel-iterative cosmological parameter estimation with Bayesian nonparametrics
We present and apply Gaussbock, a new embarrassingly parallel iterative
algorithm for cosmological parameter estimation designed for an era of cheap
parallel computing resources. Gaussbock uses Bayesian nonparametrics and
truncated importance sampling to accurately draw samples from posterior
distributions with an orders-of-magnitude speed-up in wall time over
alternative methods. Contemporary problems in this area often suffer from both
increased computational costs due to high-dimensional parameter spaces and
consequent excessive time requirements, as well as the need for fine tuning of
proposal distributions or sampling parameters. Gaussbock is designed
specifically with these issues in mind. We explore and validate the performance
and convergence of the algorithm on a fast approximation to the Dark Energy
Survey Year 1 (DES Y1) posterior, finding reasonable scaling behavior with the
number of parameters. We then test on the full DES Y1 posterior using
large-scale supercomputing facilities, and recover reasonable agreement with
previous chains, although the algorithm can underestimate the tails of
poorly-constrained parameters. Additionally, we discuss and demonstrate how
Gaussbock recovers complex posterior shapes very well at lower dimensions, but
faces challenges to perform well on such distributions in higher dimensions. In
addition, we provide the community with a user-friendly software tool for
accelerated cosmological parameter estimation based on the methodology
described in this paper.Comment: 19 pages, 10 figures, accepted for publication in Ap
Physics-informed neural networks in the recreation of hydrodynamic simulations from dark matter
Physics-informed neural networks have emerged as a coherent framework for
building predictive models that combine statistical patterns with domain
knowledge. The underlying notion is to enrich the optimization loss function
with known relationships to constrain the space of possible solutions.
Hydrodynamic simulations are a core constituent of modern cosmology, while the
required computations are both expensive and time-consuming. At the same time,
the comparatively fast simulation of dark matter requires fewer resources,
which has led to the emergence of machine learning algorithms for baryon
inpainting as an active area of research; here, recreating the scatter found in
hydrodynamic simulations is an ongoing challenge. This paper presents the first
application of physics-informed neural networks to baryon inpainting by
combining advances in neural network architectures with physical constraints,
injecting theory on baryon conversion efficiency into the model loss function.
We also introduce a punitive prediction comparison based on the
Kullback-Leibler divergence, which enforces scatter reproduction. By
simultaneously extracting the complete set of baryonic properties for the Simba
suite of cosmological simulations, our results demonstrate improved accuracy of
baryonic predictions based on dark matter halo properties, successful recovery
of the fundamental metallicity relation, and retrieve scatter that traces the
target simulation's distribution
Predictive intraday correlations in stable and volatile market environments:Evidence from deep learning
Standard methods and theories in finance can be ill-equipped to capture
highly non-linear interactions in financial prediction problems based on
large-scale datasets, with deep learning offering a way to gain insights into
correlations in markets as complex systems. In this paper, we apply deep
learning to econometrically constructed gradients to learn and exploit lagged
correlations among S&P 500 stocks to compare model behaviour in stable and
volatile market environments, and under the exclusion of target stock
information for predictions. In order to measure the effect of time horizons,
we predict intraday and daily stock price movements in varying interval lengths
and gauge the complexity of the problem at hand with a modification of our
model architecture. Our findings show that accuracies, while remaining
significant and demonstrating the exploitability of lagged correlations in
stock markets, decrease with shorter prediction horizons. We discuss
implications for modern finance theory and our work's applicability as an
investigative tool for portfolio managers. Lastly, we show that our model's
performance is consistent in volatile markets by exposing it to the environment
of the recent financial crisis of 2007/2008.Comment: 15 pages, 6 figures, preprint submitted to Physica
Physics-informed neural networks in the recreation of hydrodynamic simulations from dark matter
Physics-informed neural networks have emerged as a coherent framework for building predictive models that combine statistical patterns with domain knowledge. The underlying notion is to enrich the optimization loss function with known relationships to constrain the space of possible solutions. Hydrodynamic simulations are a core constituent of modern cosmology, while the required computations are both expensive and time-consuming. At the same time, the comparatively fast simulation of dark matter requires fewer resources, which has led to the emergence of machine learning algorithms for baryon inpainting as an active area of research; here, recreating the scatter found in hydrodynamic simulations is an ongoing challenge. This paper presents the first application of physics-informed neural networks to baryon inpainting by combining advances in neural network architectures with physical constraints, injecting theory on baryon conversion efficiency into the model loss function. We also introduce a punitive prediction comparison based on the Kullback-Leibler divergence, which enforces scatter reproduction. By simultaneously extracting the complete set of baryonic properties for the simba suite of cosmological simulations, our results demonstrate improved accuracy of baryonic predictions based on dark matter halo properties and successful recovery of the fundamental metallicity relation, and retrieve scatter that traces the target simulation's distribution.</p
Filaments of crime: Informing policing via thresholded ridge estimation
Objectives: We introduce a new method for reducing crime in hot spots and
across cities through ridge estimation. In doing so, our goal is to explore the
application of density ridges to hot spots and patrol optimization, and to
contribute to the policing literature in police patrolling and crime reduction
strategies.
Methods: We make use of the subspace-constrained mean shift algorithm, a
recently introduced approach for ridge estimation further developed in
cosmology, which we modify and extend for geospatial datasets and hot spot
analysis. Our experiments extract density ridges of Part I crime incidents from
the City of Chicago during the year 2018 and early 2019 to demonstrate the
application to current data.
Results: Our results demonstrate nonlinear mode-following ridges in agreement
with broader kernel density estimates. Using early 2019 incidents with
predictive ridges extracted from 2018 data, we create multi-run confidence
intervals and show that our patrol templates cover around 94% of incidents for
0.1-mile envelopes around ridges, quickly rising to near-complete coverage. We
also develop and provide researchers, as well as practitioners, with a
user-friendly and open-source software for fast geospatial density ridge
estimation.
Conclusions: We show that ridges following crime report densities can be used
to enhance patrolling capabilities. Our empirical tests show the stability of
ridges based on past data, offering an accessible way of identifying routes
within hot spots instead of patrolling epicenters. We suggest further research
into the application and efficacy of density ridges for patrolling.Comment: 17 pages, 3 figure
Photometric Redshift Uncertainties in Weak Gravitational Lensing Shear Analysis: Models and Marginalization
Recovering credible cosmological parameter constraints in a weak lensing
shear analysis requires an accurate model that can be used to marginalize over
nuisance parameters describing potential sources of systematic uncertainty,
such as the uncertainties on the sample redshift distribution . Due to
the challenge of running Markov Chain Monte-Carlo (MCMC) in the high
dimensional parameter spaces in which the uncertainties may be
parameterized, it is common practice to simplify the parameterization or
combine MCMC chains that each have a fixed resampled from the
uncertainties. In this work, we propose a statistically-principled Bayesian
resampling approach for marginalizing over the uncertainty using
multiple MCMC chains. We self-consistently compare the new method to existing
ones from the literature in the context of a forecasted cosmic shear analysis
for the HSC three-year shape catalog, and find that these methods recover
similar cosmological parameter constraints, implying that using the most
computationally efficient of the approaches is appropriate. However, we find
that for datasets with the constraining power of the full HSC survey dataset
(and, by implication, those upcoming surveys with even tighter constraints),
the choice of method for marginalizing over uncertainty among the
several methods from the literature may significantly impact the statistical
uncertainties on cosmological parameters, and a careful model selection is
needed to ensure credible parameter intervals.Comment: 15 pages, 8 figures, submitted to mnra
On the road to percent accuracy II:calibration of the non-linear matter power spectrum for arbitrary cosmologies
We introduce an emulator approach to predict the non-linear matter power
spectrum for broad classes of beyond-CDM cosmologies, using only a
suite of CDM -body simulations. By including a range of suitably
modified initial conditions in the simulations, and rescaling the resulting
emulator predictions with analytical `halo model reactions', accurate
non-linear matter power spectra for general extensions to the standard
CDM model can be calculated. We optimise the emulator design by
substituting the simulation suite with non-linear predictions from the standard
{\sc halofit} tool. We review the performance of the emulator for artificially
generated departures from the standard cosmology as well as for theoretically
motivated models, such as gravity and massive neutrinos. For the
majority of cosmologies we have tested, the emulator can reproduce the matter
power spectrum with errors deep into the highly non-linear
regime. This work demonstrates that with a well-designed suite of CDM
simulations, extensions to the standard cosmological model can be tested in the
non-linear regime without any reliance on expensive beyond-CDM
simulations.Comment: 16 pages, 13 figures, accepted for publication in MNRA
Stress testing the dark energy equation of state imprint on supernova data
International audienceThis work determines the degree to which a traditional analysis of the standard model of cosmology (ΛCDM) based on type Ia supernovae can identify deviations from a cosmological constant in the form of a redshift-dependent dark energy equation of state w(z). We introduce and apply a novel random curve generator to simulate instances of w(z) from constraint families with increasing distinction from a cosmological constant. After producing a series of mock catalogs of binned type Ia supernovae corresponding to each w(z) curve, we perform a standard ΛCDM analysis to estimate the corresponding posterior densities of the absolute magnitude of type Ia supernovae, the present-day matter density, and the equation of state parameter. Using the Kullback-Leibler divergence between posterior densities as a difference measure, we demonstrate that a standard type Ia supernova cosmology analysis has limited sensitivity to extensive redshift dependencies of the dark energy equation of state. In addition, we report that larger redshift-dependent departures from a cosmological constant do not necessarily manifest easier-detectable incompatibilities with the ΛCDM model. Our results suggest that physics beyond the standard model may simply be hidden in plain sight