1,962 research outputs found
A Novel Distributed Representation of News (DRNews) for Stock Market Predictions
In this study, a novel Distributed Representation of News (DRNews) model is
developed and applied in deep learning-based stock market predictions. With the
merit of integrating contextual information and cross-documental knowledge, the
DRNews model creates news vectors that describe both the semantic information
and potential linkages among news events through an attributed news network.
Two stock market prediction tasks, namely the short-term stock movement
prediction and stock crises early warning, are implemented in the framework of
the attention-based Long Short Term-Memory (LSTM) network. It is suggested that
DRNews substantially enhances the results of both tasks comparing with five
baselines of news embedding models. Further, the attention mechanism suggests
that short-term stock trend and stock market crises both receive influences
from daily news with the former demonstrates more critical responses on the
information related to the stock market {\em per se}, whilst the latter draws
more concerns on the banking sector and economic policies.Comment: 25 page
Exploring Interpretable LSTM Neural Networks over Multi-Variable Data
For recurrent neural networks trained on time series with target and
exogenous variables, in addition to accurate prediction, it is also desired to
provide interpretable insights into the data. In this paper, we explore the
structure of LSTM recurrent neural networks to learn variable-wise hidden
states, with the aim to capture different dynamics in multi-variable time
series and distinguish the contribution of variables to the prediction. With
these variable-wise hidden states, a mixture attention mechanism is proposed to
model the generative process of the target. Then we develop associated training
methods to jointly learn network parameters, variable and temporal importance
w.r.t the prediction of the target variable. Extensive experiments on real
datasets demonstrate enhanced prediction performance by capturing the dynamics
of different variables. Meanwhile, we evaluate the interpretation results both
qualitatively and quantitatively. It exhibits the prospect as an end-to-end
framework for both forecasting and knowledge extraction over multi-variable
data.Comment: Accepted to International Conference on Machine Learning (ICML), 201
Numeral Understanding in Financial Tweets for Fine-grained Crowd-based Forecasting
Numerals that contain much information in financial documents are crucial for
financial decision making. They play different roles in financial analysis
processes. This paper is aimed at understanding the meanings of numerals in
financial tweets for fine-grained crowd-based forecasting. We propose a
taxonomy that classifies the numerals in financial tweets into 7 categories,
and further extend some of these categories into several subcategories. Neural
network-based models with word and character-level encoders are proposed for
7-way classification and 17-way classification. We perform backtest to confirm
the effectiveness of the numeric opinions made by the crowd. This work is the
first attempt to understand numerals in financial social media data, and we
provide the first comparison of fine-grained opinion of individual investors
and analysts based on their forecast price. The numeral corpus used in our
experiments, called FinNum 1.0 , is available for research purposes.Comment: Accepted by the 2018 IEEE/WIC/ACM International Conference on Web
Intelligence (WI 2018), Santiago, Chil
DeepLOB: Deep Convolutional Neural Networks for Limit Order Books
We develop a large-scale deep learning model to predict price movements from
limit order book (LOB) data of cash equities. The architecture utilises
convolutional filters to capture the spatial structure of the limit order books
as well as LSTM modules to capture longer time dependencies. The proposed
network outperforms all existing state-of-the-art algorithms on the benchmark
LOB dataset [1]. In a more realistic setting, we test our model by using one
year market quotes from the London Stock Exchange and the model delivers a
remarkably stable out-of-sample prediction accuracy for a variety of
instruments. Importantly, our model translates well to instruments which were
not part of the training set, indicating the model's ability to extract
universal features. In order to better understand these features and to go
beyond a "black box" model, we perform a sensitivity analysis to understand the
rationale behind the model predictions and reveal the components of LOBs that
are most relevant. The ability to extract robust features which translate well
to other instruments is an important property of our model which has many other
applications.Comment: 12 pages, 9 figure
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