530 research outputs found
Finite size scaling of conformal theories in the presence of a near-marginal operator
The slowly evolving gauge coupling of gauge-fermion systems near the
conformal window makes numerical investigations of these models challenging. We
consider finite size scaling and show that this often used technique leads to
inconsistent results if the leading order scaling corrections are neglected.
When the corrections are included the results become consistent not only
between different operators but even when data obtained at different gauge
couplings or with different lattice actions are combined. Our results indicate
that the SU(3) 12-fermion system is conformal with mass anomalous dimension
Improving the continuum limit of gradient flow step scaling
We introduce a non-perturbative improvement for the renormalization group
step scaling function based on the gradient flow running coupling, which may be
applied to any lattice gauge theory of interest. Considering first SU(3) gauge
theory with massless staggered fermions, we demonstrate that this
improvement can remove lattice artifacts, and thereby increases our
control over the continuum extrapolation. Turning to the 12-flavor system, we
observe an infrared fixed point in the infinite-volume continuum limit.
Applying our proposed improvement reinforces this conclusion by removing all
observable effects. For the finite-volume gradient flow
renormalization scheme defined by , we find the
continuum conformal fixed point to be located at Comment: 12 pages, 4 figures; Minor changes, published versio
Stock portfolio selection using learning-to-rank algorithms with news sentiment
In this study, we apply learning-to-rank algorithms to design trading strategies
using relative performance of a group of stocks based on investors' sentiment
toward these stocks. We show that learning-to-rank algorithms are effective in
producing reliable rankings of the best and the worst performing stocks based
on investors' sentiment. More specifically, we use the sentiment shock and trend
indicators introduced in the previous studies, and we design stock selection rules
of holding long positions of the top 25% stocks and short positions of the bottom
25% stocks according to rankings produced by learning-to-rank algorithms.
We then apply two learning-to-rank algorithms, ListNet and RankNet, in stock
selection processes and test long-only and long-short portfolio selection strategies
using 10 years of market and news sentiment data. Through backtesting of
these strategies from 2006 to 2014, we demonstrate that our portfolio strategies
produce risk-adjusted returns superior to the S&P500 index return, the hedge
fund industry average performance - HFRIEMN, and some sentiment-based approaches
without learning-to-rank algorithm during the same period
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