37 research outputs found
Cross-correlation asymmetries and causal relationships between stock and market risk
We study historical correlations and lead-lag relationships between
individual stock risk (volatility of daily stock returns) and market risk
(volatility of daily returns of a market-representative portfolio) in the US
stock market. We consider the cross-correlation functions averaged over all
stocks, using 71 stock prices from the Standard \& Poor's 500 index for
1994--2013. We focus on the behavior of the cross-correlations at the times of
financial crises with significant jumps of market volatility. The observed
historical dynamics showed that the dependence between the risks was almost
linear during the US stock market downturn of 2002 and after the US housing
bubble in 2007, remaining on that level until 2013. Moreover, the averaged
cross-correlation function often had an asymmetric shape with respect to zero
lag in the periods of high correlation. We develop the analysis by the
application of the linear response formalism to study underlying causal
relations. The calculated response functions suggest the presence of
characteristic regimes near financial crashes, when the volatility of an
individual stock follows the market volatility and vice versa.Comment: 17 pages, 8 figures, 1 tabl
Scalable Population Synthesis with Deep Generative Modeling
Population synthesis is concerned with the generation of synthetic yet
realistic representations of populations. It is a fundamental problem in the
modeling of transport where the synthetic populations of micro-agents represent
a key input to most agent-based models. In this paper, a new methodological
framework for how to 'grow' pools of micro-agents is presented. The model
framework adopts a deep generative modeling approach from machine learning
based on a Variational Autoencoder (VAE). Compared to the previous population
synthesis approaches, including Iterative Proportional Fitting (IPF), Gibbs
sampling and traditional generative models such as Bayesian Networks or Hidden
Markov Models, the proposed method allows fitting the full joint distribution
for high dimensions. The proposed methodology is compared with a conventional
Gibbs sampler and a Bayesian Network by using a large-scale Danish trip diary.
It is shown that, while these two methods outperform the VAE in the
low-dimensional case, they both suffer from scalability issues when the number
of modeled attributes increases. It is also shown that the Gibbs sampler
essentially replicates the agents from the original sample when the required
conditional distributions are estimated as frequency tables. In contrast, the
VAE allows addressing the problem of sampling zeros by generating agents that
are virtually different from those in the original data but have similar
statistical properties. The presented approach can support agent-based modeling
at all levels by enabling richer synthetic populations with smaller zones and
more detailed individual characteristics.Comment: 27 pages, 15 figures, 4 table
Dynamic Calibration of Higher Eigenmode Parameters of a Cantilever in Atomic Force Microscopy Using Tip-Surface Interactions
We present a theoretical framework for the dynamic calibration of the higher
eigenmode parameters (stiffness and optical lever responsivity) of a
cantilever. The method is based on the tip-surface force reconstruction
technique and does not require any prior knowledge of the eigenmode shape or
the particular form of the tip-surface interaction. The calibration method
proposed requires a single-point force measurement using a multimodal drive and
its accuracy is independent of the unknown physical amplitude of a higher
eigenmode.Comment: 4 pages, 4 figure
Band gap prediction for large organic crystal structures with machine learning
Machine-learning models are capable of capturing the structure-property
relationship from a dataset of computationally demanding ab initio
calculations. Over the past two years, the Organic Materials Database (OMDB)
has hosted a growing number of calculated electronic properties of previously
synthesized organic crystal structures. The complexity of the organic crystals
contained within the OMDB, which have on average 82 atoms per unit cell, makes
this database a challenging platform for machine learning applications. In this
paper, the focus is on predicting the band gap which represents one of the
basic properties of a crystalline materials. With this aim, a consistent
dataset of 12 500 crystal structures and their corresponding DFT band gap are
released, freely available for download at https://omdb.mathub.io/dataset. An
ensemble of two state-of-the-art models reach a mean absolute error (MAE) of
0.388 eV, which corresponds to a percentage error of 13% for an average band
gap of 3.05 eV. Finally, the trained models are employed to predict the band
gap for 260 092 materials contained within the Crystallography Open Database
(COD) and made available online so that the predictions can be obtained for any
arbitrary crystal structure uploaded by a user.Comment: 10 pages, 6 figure
Online Search Tool for Graphical Patterns in Electronic Band Structures
We present an online graphical pattern search tool for electronic band
structure data contained within the Organic Materials Database (OMDB) available
at https://omdb.diracmaterials.org/search/pattern. The tool is capable of
finding user-specified graphical patterns in the collection of thousands of
band structures from high-throughput ab initio calculations in the online
regime. Using this tool, it only takes a few seconds to find an arbitrary
graphical pattern within the ten electronic bands near the Fermi level for
26,739 organic crystals. The tool can be used to find realizations of
functional materials characterized by a specific pattern in their electronic
structure, for example, Dirac materials, characterized by a linear crossing of
bands; topological insulators, characterized by a "Mexican hat" pattern or an
effectively free electron gas, characterized by a parabolic dispersion. The
source code of the developed tool is freely available at
https://github.com/OrganicMaterialsDatabase/EBS-search and can be transferred
to any other electronic band structure database. The approach allows for an
automatic online analysis of a large collection of band structures where the
amount of data makes its manual inspection impracticable.Comment: 8 pages, 8 figure
Prediction of rare feature combinations in population synthesis: Application of deep generative modelling
In population synthesis applications, when considering populations with many
attributes, a fundamental problem is the estimation of rare combinations of
feature attributes. Unsurprisingly, it is notably more difficult to reliably
representthe sparser regions of such multivariate distributions and in
particular combinations of attributes which are absent from the original
sample. In the literature this is commonly known as sampling zeros for which no
systematic solution has been proposed so far. In this paper, two machine
learning algorithms, from the family of deep generative models,are proposed for
the problem of population synthesis and with particular attention to the
problem of sampling zeros. Specifically, we introduce the Wasserstein
Generative Adversarial Network (WGAN) and the Variational Autoencoder(VAE), and
adapt these algorithms for a large-scale population synthesis application. The
models are implemented on a Danish travel survey with a feature-space of more
than 60 variables. The models are validated in a cross-validation scheme and a
set of new metrics for the evaluation of the sampling-zero problem is proposed.
Results show how these models are able to recover sampling zeros while keeping
the estimation of truly impossible combinations, the structural zeros, at a
comparatively low level. Particularly, for a low dimensional experiment, the
VAE, the marginal sampler and the fully random sampler generate 5%, 21% and
26%, respectively, more structural zeros per sampling zero generated by the
WGAN, while for a high dimensional case, these figures escalate to 44%, 2217%
and 170440%, respectively. This research directly supports the development of
agent-based systems and in particular cases where detailed socio-economic or
geographical representations are required
A Bayesian Additive Model for Understanding Public Transport Usage in Special Events
Public special events, like sports games, concerts and festivals are well
known to create disruptions in transportation systems, often catching the
operators by surprise. Although these are usually planned well in advance,
their impact is difficult to predict, even when organisers and transportation
operators coordinate. The problem highly increases when several events happen
concurrently. To solve these problems, costly processes, heavily reliant on
manual search and personal experience, are usual practice in large cities like
Singapore, London or Tokyo. This paper presents a Bayesian additive model with
Gaussian process components that combines smart card records from public
transport with context information about events that is continuously mined from
the Web. We develop an efficient approximate inference algorithm using
expectation propagation, which allows us to predict the total number of public
transportation trips to the special event areas, thereby contributing to a more
adaptive transportation system. Furthermore, for multiple concurrent event
scenarios, the proposed algorithm is able to disaggregate gross trip counts
into their most likely components related to specific events and routine
behavior. Using real data from Singapore, we show that the presented model
outperforms the best baseline model by up to 26% in R2 and also has explanatory
power for its individual components.Comment: 14 pages, IEEE Transactions on Pattern Analysis and Machine
Intelligence (Volume: 39 , Issue: 11 , Nov. 1 2017