3,509 research outputs found
News-Driven Stock Prediction With Attention-Based Noisy Recurrent State Transition
We consider direct modeling of underlying stock value movement sequences over
time in the news-driven stock movement prediction. A recurrent state transition
model is constructed, which better captures a gradual process of stock movement
continuously by modeling the correlation between past and future price
movements. By separating the effects of news and noise, a noisy random factor
is also explicitly fitted based on the recurrent states. Results show that the
proposed model outperforms strong baselines. Thanks to the use of attention
over news events, our model is also more explainable. To our knowledge, we are
the first to explicitly model both events and noise over a fundamental stock
value state for news-driven stock movement prediction.Comment: 12 page
Stock Market Prediction via Deep Learning Techniques: A Survey
The stock market prediction has been a traditional yet complex problem
researched within diverse research areas and application domains due to its
non-linear, highly volatile and complex nature. Existing surveys on stock
market prediction often focus on traditional machine learning methods instead
of deep learning methods. Deep learning has dominated many domains, gained much
success and popularity in recent years in stock market prediction. This
motivates us to provide a structured and comprehensive overview of the research
on stock market prediction focusing on deep learning techniques. We present
four elaborated subtasks of stock market prediction and propose a novel
taxonomy to summarize the state-of-the-art models based on deep neural networks
from 2011 to 2022. In addition, we also provide detailed statistics on the
datasets and evaluation metrics commonly used in the stock market. Finally, we
highlight some open issues and point out several future directions by sharing
some new perspectives on stock market prediction
Techniques for Stock Market Prediction: A Review
Stock market forecasting has long been viewed as a vital real-life topic in economics world. There are many challenges in stock market prediction systems such as the Efficient Market Hypothesis (EMH), Nonlinearity, complex, diverse datasets, and parameter optimization. A stock's value on the stock market fluctuates due to many factors like previous trends of the stock, the current news, twitter feeds, any online customer feedbacks etc. In this paper, the literature is critically analysed on approaches used for stock market prediction in terms of stock datasets, features used, evaluation metrics used, statistical, machine learning and deep learning techniques along with the directions for the future. The focus of this review is on trend and value prediction for stocks. Overall, 68 research papers have been considered for review from years 1998-2023. From the review, Indian stock market datasets are found to be most frequently used datasets. Evaluation metrics used commonly are accuracy and Mean Absolute Percentage Error. ARIMA is reported as the most used frequently statistical technique for stick market prediction. Long-Short Term Memory and Support Vector Machine are the commonly used algorithms in stock market prediction. The advantages and disadvantages of frequently used evaluation metrics, machine learning, deep learning and statistical approaches are also included in this survey
A Comprehensive Survey on Generative Diffusion Models for Structured Data
In recent years, generative diffusion models have achieved a rapid paradigm
shift in deep generative models by showing groundbreaking performance across
various applications. Meanwhile, structured data, encompassing tabular and time
series data, has been received comparatively limited attention from the deep
learning research community, despite its omnipresence and extensive
applications. Thus, there is still a lack of literature and its reviews on
structured data modelling via diffusion models, compared to other data
modalities such as visual and textual data. To address this gap, we present a
comprehensive review of recently proposed diffusion models in the field of
structured data. First, this survey provides a concise overview of the
score-based diffusion model theory, subsequently proceeding to the technical
descriptions of the majority of pioneering works that used structured data in
both data-driven general tasks and domain-specific applications. Thereafter, we
analyse and discuss the limitations and challenges shown in existing works and
suggest potential research directions. We hope this review serves as a catalyst
for the research community, promoting developments in generative diffusion
models for structured data.Comment: 20 pages, 1 figure, 2 table
Crises and collective socio-economic phenomena: simple models and challenges
Financial and economic history is strewn with bubbles and crashes, booms and
busts, crises and upheavals of all sorts. Understanding the origin of these
events is arguably one of the most important problems in economic theory. In
this paper, we review recent efforts to include heterogeneities and
interactions in models of decision. We argue that the Random Field Ising model
(RFIM) indeed provides a unifying framework to account for many collective
socio-economic phenomena that lead to sudden ruptures and crises. We discuss
different models that can capture potentially destabilising self-referential
feedback loops, induced either by herding, i.e. reference to peers, or
trending, i.e. reference to the past, and account for some of the phenomenology
missing in the standard models. We discuss some empirically testable
predictions of these models, for example robust signatures of RFIM-like herding
effects, or the logarithmic decay of spatial correlations of voting patterns.
One of the most striking result, inspired by statistical physics methods, is
that Adam Smith's invisible hand can badly fail at solving simple coordination
problems. We also insist on the issue of time-scales, that can be extremely
long in some cases, and prevent socially optimal equilibria to be reached. As a
theoretical challenge, the study of so-called "detailed-balance" violating
decision rules is needed to decide whether conclusions based on current models
(that all assume detailed-balance) are indeed robust and generic.Comment: Review paper accepted for a special issue of J Stat Phys; several
minor improvements along reviewers' comment
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