85,430 research outputs found
Portfolio selection using neural networks
In this paper we apply a heuristic method based on artificial neural networks
in order to trace out the efficient frontier associated to the portfolio
selection problem. We consider a generalization of the standard Markowitz
mean-variance model which includes cardinality and bounding constraints. These
constraints ensure the investment in a given number of different assets and
limit the amount of capital to be invested in each asset. We present some
experimental results obtained with the neural network heuristic and we compare
them to those obtained with three previous heuristic methods.Comment: 12 pages; submitted to "Computers & Operations Research
Simulations on High-z Long Gamma-Ray Burst Rate
Since the launch of Swift satellite, the detections of high-z (z>4) long
gamma-ray bursts (LGRBs) have been rapidly growing, even approaching the very
early Universe (the record holder currently is z=8.3). The observed high-z LGRB
rate shows significant excess over that estimated from the star formation
history. We investigate what may be responsible for this high productivity of
GRBs at high-z through Monte Carlo simulations, with effective Swif/BAT trigger
and redshift detection probabilities based on current Swift/BAT sample and
CGRO/BATSE LGRB sample. We compare our simulations to the Swift observations
via log N-log P, peak luminosity (L) and redshift distributions. In the case
that LGRB rate is purely proportional to the star formation rate (SFR), our
simulations poorly reproduce the LGRB rate at z>4, although the simulated log
N-log P distribution is in good agreement with the observed one. Assuming that
the excess of high-z GRB rate is due to the cosmic metallicity evolution or
unknown LGRB rate increase parameterized as (1+z)^delta, we find that although
the two scenarios alone can improve the consistency between our simulations and
observations, incorporation of them gives much better consistency. We get
0.2<epsilon<0.6 and delta<0.6, where epsilon is the metallicity threshold for
the production of LGRBs. The best consistency is obtained with a parameter set
(epsilon, delta)=(~0.4, ~0.4), and BAT might trigger a few LGRBs at z~14. With
increasing detections of GRBs at z>4 (~15% of GRBs in current Swift LGRB sample
based on our simulations), a window for very early Universe is opening by Swift
and up-coming SVOM missions.Comment: 9 pages, including 8 figures and 1 table, one more figure added.
Accepted for publication in MNRA
Recent progress on intrinsic charm
Over the past years, the topic of the nucleon's nonperturbative
or charm (IC) content has enjoyed something of a
renaissance, largely motivated by theoretical developments involving quark
modelers and PDF fitters. In this talk I will briefly describe the importance
of intrinsic charm to various issues in high-energy phenomenology, and survey
recent progress in constraining its overall normalization and contribution to
the momentum sum rule of the nucleon. I end with the conclusion that progress
on the side of calculation has now placed the onus on experiment to
unambiguously resolve the proton's intrinsic charm component.Comment: Invited talk at the Conference "XIIth Quark Confinement and the
Hadron Spectrum" (Thessaloniki, Greece; 29th August - 3rd September 2016). 9
pages, 4 figures; reference added in version
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Network-constrained models of liberalized electricity markets: the devil is in the details
Numerical models for electricity markets are frequently used to inform and support decisions. How robust are the results? Three research groups used the same, realistic data set for generators, demand and transmission network as input for their numerical models. The results coincide when predicting competitive market results. In the strategic case in which large generators can exercise market power, the predicted prices differed significantly. The results are highly sensitive to assumptions about market design, timing of the market and assumptions about constraints on the rationality of generators. Given the same assumptions the results coincide. We provide a checklist for users to understand the implications of different modelling assumptions
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