313 research outputs found
Quantitative Stock Investment by Routing Uncertainty-Aware Trading Experts: A Multi-Task Learning Approach
Quantitative investment is a fundamental financial task that highly relies on
accurate stock prediction and profitable investment decision making. Despite
recent advances in deep learning (DL) have shown stellar performance on
capturing trading opportunities in the stochastic stock market, we observe that
the performance of existing DL methods is sensitive to random seeds and network
initialization. To design more profitable DL methods, we analyze this
phenomenon and find two major limitations of existing works. First, there is a
noticeable gap between accurate financial predictions and profitable investment
strategies. Second, investment decisions are made based on only one individual
predictor without consideration of model uncertainty, which is inconsistent
with the workflow in real-world trading firms. To tackle these two limitations,
we first reformulate quantitative investment as a multi-task learning problem.
Later on, we propose AlphaMix, a novel two-stage mixture-of-experts (MoE)
framework for quantitative investment to mimic the efficient bottom-up trading
strategy design workflow of successful trading firms. In Stage one, multiple
independent trading experts are jointly optimized with an individual
uncertainty-aware loss function. In Stage two, we train neural routers
(corresponding to the role of a portfolio manager) to dynamically deploy these
experts on an as-needed basis. AlphaMix is also a universal framework that is
applicable to various backbone network architectures with consistent
performance gains. Through extensive experiments on long-term real-world data
spanning over five years on two of the most influential financial markets (US
and China), we demonstrate that AlphaMix significantly outperforms many
state-of-the-art baselines in terms of four financial criteria
Deforming black holes with even multipolar differential rotation boundary
Motivated by the novel asymptotically global AdS solutions with deforming
horizon in [JHEP {\bf 1802}, 060 (2018)], we analyze the boundary metric with
even multipolar differential rotation and numerically construct a family of
deforming solutions with quadrupolar differential rotation boundary, including
two classes of solutions: solitons and black holes. In contrast to solutions
with dipolar differential rotation boundary, we find that even though the norm
of Killing vector becomes spacelike for certain regions of polar
angle when , solitons and black holes with quadrupolar
differential rotation still exist and do not develop hair due to superradiance.
Moreover, at the same temperature, the horizonal deformation of quadrupolar
rotation is smaller than that of dipolar rotation. Furthermore, we also study
the entropy and quasinormal modes of the solutions, which have the analogous
properties to that of dipolar rotation.Comment: 18 pages, 21 figure
Market-GAN: Adding Control to Financial Market Data Generation with Semantic Context
Financial simulators play an important role in enhancing forecasting
accuracy, managing risks, and fostering strategic financial decision-making.
Despite the development of financial market simulation methodologies, existing
frameworks often struggle with adapting to specialized simulation context. We
pinpoint the challenges as i) current financial datasets do not contain context
labels; ii) current techniques are not designed to generate financial data with
context as control, which demands greater precision compared to other
modalities; iii) the inherent difficulties in generating context-aligned,
high-fidelity data given the non-stationary, noisy nature of financial data. To
address these challenges, our contributions are: i) we proposed the Contextual
Market Dataset with market dynamics, stock ticker, and history state as
context, leveraging a market dynamics modeling method that combines linear
regression and Dynamic Time Warping clustering to extract market dynamics; ii)
we present Market-GAN, a novel architecture incorporating a Generative
Adversarial Networks (GAN) for the controllable generation with context, an
autoencoder for learning low-dimension features, and supervisors for knowledge
transfer; iii) we introduce a two-stage training scheme to ensure that
Market-GAN captures the intrinsic market distribution with multiple objectives.
In the pertaining stage, with the use of the autoencoder and supervisors, we
prepare the generator with a better initialization for the adversarial training
stage. We propose a set of holistic evaluation metrics that consider alignment,
fidelity, data usability on downstream tasks, and market facts. We evaluate
Market-GAN with the Dow Jones Industrial Average data from 2000 to 2023 and
showcase superior performance in comparison to 4 state-of-the-art time-series
generative models
CAFE Learning to Condense Dataset by Aligning Features
Dataset condensation aims at reducing the network training effort through
condensing a cumbersome training set into a compact synthetic one.
State-of-the-art approaches largely rely on learning the synthetic data by
matching the gradients between the real and synthetic data batches. Despite the
intuitive motivation and promising results, such gradient-based methods, by
nature, easily overfit to a biased set of samples that produce dominant
gradients, and thus lack global supervision of data distribution. In this
paper, we propose a novel scheme to Condense dataset by Aligning FEatures
(CAFE), which explicitly attempts to preserve the real-feature distribution as
well as the discriminant power of the resulting synthetic set, lending itself
to strong generalization capability to various architectures. At the heart of
our approach is an effective strategy to align features from the real and
synthetic data across various scales, while accounting for the classification
of real samples. Our scheme is further backed up by a novel dynamic bi-level
optimization, which adaptively adjusts parameter updates to prevent
over-/under-fitting. We validate the proposed CAFE across various datasets, and
demonstrate that it generally outperforms the state of the art: on the SVHN
dataset, for example, the performance gain is up to 11%. Extensive experiments
and analyses verify the effectiveness and necessity of proposed designs.Comment: The manuscript has been accepted by CVPR-2022
Deforming charged black holes with dipolar differential rotation boundary
Motivated by the recent studies of the novel asymptotically global AdS
black hole with deforming horizon, we consider the action of Einstein-Maxwell
gravity in AdS spacetime and construct the charged deforming AdS black holes
with differential boundary. In contrast to deforming black hole without charge,
there exists at least one value of horizon for an arbitrary temperature. The
extremum of temperature is determined by charge and divides the range of
temperature into several parts. Moreover, we use an isometric embedding in the
three-dimensional space to investigate the horizon geometry. We also study the
entropy and quasinormal modes of deforming charged AdS black hole. It is
interesting to find there exist two families of black hole solutions with
different horizon radius for a fixed temperature, but these two black holes
have same horizon geometry and entropy. Due to the existence of charge , the
phase diagram of entropy is more complicated.Comment: 19 pages, 9 figure
ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning
Real-Time Bidding (RTB) is an important mechanism in modern online
advertising systems. Advertisers employ bidding strategies in RTB to optimize
their advertising effects subject to various financial requirements, especially
the return-on-investment (ROI) constraint. ROIs change non-monotonically during
the sequential bidding process, and often induce a see-saw effect between
constraint satisfaction and objective optimization. While some existing
approaches show promising results in static or mildly changing ad markets, they
fail to generalize to highly dynamic ad markets with ROI constraints, due to
their inability to adaptively balance constraints and objectives amidst
non-stationarity and partial observability. In this work, we specialize in
ROI-Constrained Bidding in non-stationary markets. Based on a Partially
Observable Constrained Markov Decision Process, our method exploits an
indicator-augmented reward function free of extra trade-off parameters and
develops a Curriculum-Guided Bayesian Reinforcement Learning (CBRL) framework
to adaptively control the constraint-objective trade-off in non-stationary ad
markets. Extensive experiments on a large-scale industrial dataset with two
problem settings reveal that CBRL generalizes well in both in-distribution and
out-of-distribution data regimes, and enjoys superior learning efficiency and
stability.Comment: Accepted by SIGKDD 202
EarnHFT: Efficient Hierarchical Reinforcement Learning for High Frequency Trading
High-frequency trading (HFT) uses computer algorithms to make trading
decisions in short time scales (e.g., second-level), which is widely used in
the Cryptocurrency (Crypto) market (e.g., Bitcoin). Reinforcement learning (RL)
in financial research has shown stellar performance on many quantitative
trading tasks. However, most methods focus on low-frequency trading, e.g.,
day-level, which cannot be directly applied to HFT because of two challenges.
First, RL for HFT involves dealing with extremely long trajectories (e.g., 2.4
million steps per month), which is hard to optimize and evaluate. Second, the
dramatic price fluctuations and market trend changes of Crypto make existing
algorithms fail to maintain satisfactory performance. To tackle these
challenges, we propose an Efficient hieArchical Reinforcement learNing method
for High Frequency Trading (EarnHFT), a novel three-stage hierarchical RL
framework for HFT. In stage I, we compute a Q-teacher, i.e., the optimal action
value based on dynamic programming, for enhancing the performance and training
efficiency of second-level RL agents. In stage II, we construct a pool of
diverse RL agents for different market trends, distinguished by return rates,
where hundreds of RL agents are trained with different preferences of return
rates and only a tiny fraction of them will be selected into the pool based on
their profitability. In stage III, we train a minute-level router which
dynamically picks a second-level agent from the pool to achieve stable
performance across different markets. Through extensive experiments in various
market trends on Crypto markets in a high-fidelity simulation trading
environment, we demonstrate that EarnHFT significantly outperforms 6
state-of-art baselines in 6 popular financial criteria, exceeding the runner-up
by 30% in profitability
Metachronous pulmonary and adrenal metastases after liver transplantation for hepatocarcinoma
<p>Abstract</p> <p>Background</p> <p>The worldwide experience of surgical resection for isolated metastasis following liver transplantation (LT) for hepatocellular carcinoma (HCC) is limited.</p> <p>Methods</p> <p>The case of a 60-year-old patient performed successful surgical management for metachronous pulmonary and adrenal metastases from HCC after LT.</p> <p>Results</p> <p>Eighty months after LT, he was presently alive and disease-free with a normal AFP value.</p> <p>Conclusion</p> <p>The case is an interesting report on a somehow indolent metastatic spread of HCC after LT. It should be considered that metachronous metastatic resectable disease, with no data of recurrence at the primary site in an operable patient, is an indication to perform a surgical resection.</p
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