8,692 research outputs found
Sustainable Investing and the Cross-Section of Maximum Drawdown
We use supervised learning to identify factors that predict the cross-section
of maximum drawdown for stocks in the US equity market. Our data run from
January 1980 to June 2018 and our analysis includes ordinary least squares,
penalized linear regressions, tree-based models, and neural networks. We find
that the most important predictors tended to be consistent across models, and
that non-linear models had better predictive power than linear models.
Predictive power was higher in calm periods than stressed periods, and
environmental, social, and governance indicators augmented predictive power for
non-linear models
Similarity-Aware Spectral Sparsification by Edge Filtering
In recent years, spectral graph sparsification techniques that can compute
ultra-sparse graph proxies have been extensively studied for accelerating
various numerical and graph-related applications. Prior nearly-linear-time
spectral sparsification methods first extract low-stretch spanning tree from
the original graph to form the backbone of the sparsifier, and then recover
small portions of spectrally-critical off-tree edges to the spanning tree to
significantly improve the approximation quality. However, it is not clear how
many off-tree edges should be recovered for achieving a desired spectral
similarity level within the sparsifier. Motivated by recent graph signal
processing techniques, this paper proposes a similarity-aware spectral graph
sparsification framework that leverages efficient spectral off-tree edge
embedding and filtering schemes to construct spectral sparsifiers with
guaranteed spectral similarity (relative condition number) level. An iterative
graph densification scheme is introduced to facilitate efficient and effective
filtering of off-tree edges for highly ill-conditioned problems. The proposed
method has been validated using various kinds of graphs obtained from public
domain sparse matrix collections relevant to VLSI CAD, finite element analysis,
as well as social and data networks frequently studied in many machine learning
and data mining applications
Do Campaign Contribution Limits Curb the Influence of Money in Politics?
Over 40% of countries around the world have adopted limits on campaign contributions to curb the influence of money in politics. Yet, we have limited knowledge on whether and how these limits achieve this goal. With a regression discontinuity design that uses institutional rules on contribution limits in Colombian municipalities, we show that looser limits increase the number and value of public contracts assigned to the winning candidate’s donors. The evidence suggests that this is explained by looser limits concentrating influence over the elected candidate among top donors and not by a reduction in electoral competition or changes in who runs for office. We further show that looser limits worsen the performance of donor-managed contracts: they are more likely to run over costs and require time extensions. Overall, this paper demonstrates a direct link between campaign contribution limits, donor kickbacks, and worse government contract performance
CryoTran user's manual, version 1.0
The development of cryogenic fluid management systems for space operation is a major portion of the efforts of the Cryogenic Fluids Technology Office (CFTO) at the NASA Lewis Research Center. Analytical models are a necessary part of experimental programs which are used to verify the results of experiments and are also used as a predictor for parametric studies. The CryoTran computer program is a bridge to obtain analytical results. The object of CryoTran is to coordinate these separate analyses into an integrated framework with a user-friendly interface and a common cryogenic property database. CryoTran is an integrated software system designed to help solve a diverse set of problems involving cryogenic fluid storage and transfer in both ground and low-g environments
On the Shapley-like Payoff Mechanisms in Peer-Assisted Services with Multiple Content Providers
This paper studies an incentive structure for cooperation and its stability
in peer-assisted services when there exist multiple content providers, using a
coalition game theoretic approach. We first consider a generalized coalition
structure consisting of multiple providers with many assisting peers, where
peers assist providers to reduce the operational cost in content distribution.
To distribute the profit from cost reduction to players (i.e., providers and
peers), we then establish a generalized formula for individual payoffs when a
"Shapley-like" payoff mechanism is adopted. We show that the grand coalition is
unstable, even when the operational cost functions are concave, which is in
sharp contrast to the recently studied case of a single provider where the
grand coalition is stable. We also show that irrespective of stability of the
grand coalition, there always exist coalition structures which are not
convergent to the grand coalition. Our results give us an important insight
that a provider does not tend to cooperate with other providers in
peer-assisted services, and be separated from them. To further study the case
of the separated providers, three examples are presented; (i) underpaid peers,
(ii) service monopoly, and (iii) oscillatory coalition structure. Our study
opens many new questions such as realistic and efficient incentive structures
and the tradeoffs between fairness and individual providers' competition in
peer-assisted services.Comment: 13 pages, 4 figures, an extended version of the paper to be presented
in ICST GameNets 2011, Shanghai, China, April 201
Linear response strength functions with iterative Arnoldi diagonalization
We report on an implementation of a new method to calculate RPA strength
functions with iterative non-hermitian Arnoldi diagonalization method, which
does not explicitly calculate and store the RPA matrix. We discuss the
treatment of spurious modes, numerical stability, and how the method scales as
the used model space is enlarged. We perform the particle-hole RPA benchmark
calculations for double magic nucleus 132Sn and compare the resulting
electromagnetic strength functions against those obtained within the standard
RPA.Comment: 9 RevTeX pages, 11 figures, submitted to Physical Review
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