6,534 research outputs found
Impact of Data Normalization on Deep Neural Network for Time Series Forecasting
For the last few years it has been observed that the Deep Neural Networks
(DNNs) has achieved an excellent success in image classification, speech
recognition. But DNNs are suffer great deal of challenges for time series
forecasting because most of the time series data are nonlinear in nature and
highly dynamic in behaviour. The time series forecasting has a great impact on
our socio-economic environment. Hence, to deal with these challenges its need
to be redefined the DNN model and keeping this in mind, data pre-processing,
network architecture and network parameters are need to be consider before
feeding the data into DNN models. Data normalization is the basic data
pre-processing technique form which learning is to be done. The effectiveness
of time series forecasting is heavily depend on the data normalization
technique. In this paper, different normalization methods are used on time
series data before feeding the data into the DNN model and we try to find out
the impact of each normalization technique on DNN to forecast the time series.
Here the Deep Recurrent Neural Network (DRNN) is used to predict the closing
index of Bombay Stock Exchange (BSE) and New York Stock Exchange (NYSE) by
using BSE and NYSE time series data
Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction
Stock trend prediction plays a critical role in seeking maximized profit from
stock investment. However, precise trend prediction is very difficult since the
highly volatile and non-stationary nature of stock market. Exploding
information on Internet together with advancing development of natural language
processing and text mining techniques have enable investors to unveil market
trends and volatility from online content. Unfortunately, the quality,
trustworthiness and comprehensiveness of online content related to stock market
varies drastically, and a large portion consists of the low-quality news,
comments, or even rumors. To address this challenge, we imitate the learning
process of human beings facing such chaotic online news, driven by three
principles: sequential content dependency, diverse influence, and effective and
efficient learning. In this paper, to capture the first two principles, we
designed a Hybrid Attention Networks to predict the stock trend based on the
sequence of recent related news. Moreover, we apply the self-paced learning
mechanism to imitate the third principle. Extensive experiments on real-world
stock market data demonstrate the effectiveness of our approach
Improving Factor-Based Quantitative Investing by Forecasting Company Fundamentals
On a periodic basis, publicly traded companies are required to report
fundamentals: financial data such as revenue, operating income, debt, among
others. These data points provide some insight into the financial health of a
company. Academic research has identified some factors, i.e. computed features
of the reported data, that are known through retrospective analysis to
outperform the market average. Two popular factors are the book value
normalized by market capitalization (book-to-market) and the operating income
normalized by the enterprise value (EBIT/EV). In this paper: we first show
through simulation that if we could (clairvoyantly) select stocks using factors
calculated on future fundamentals (via oracle), then our portfolios would far
outperform a standard factor approach. Motivated by this analysis, we train
deep neural networks to forecast future fundamentals based on a trailing
5-years window. Quantitative analysis demonstrates a significant improvement in
MSE over a naive strategy. Moreover, in retrospective analysis using an
industry-grade stock portfolio simulator (backtester), we show an improvement
in compounded annual return to 17.1% (MLP) vs 14.4% for a standard factor
model
Using Deep Learning for price prediction by exploiting stationary limit order book features
The recent surge in Deep Learning (DL) research of the past decade has
successfully provided solutions to many difficult problems. The field of
quantitative analysis has been slowly adapting the new methods to its problems,
but due to problems such as the non-stationary nature of financial data,
significant challenges must be overcome before DL is fully utilized. In this
work a new method to construct stationary features, that allows DL models to be
applied effectively, is proposed. These features are thoroughly tested on the
task of predicting mid price movements of the Limit Order Book. Several DL
models are evaluated, such as recurrent Long Short Term Memory (LSTM) networks
and Convolutional Neural Networks (CNN). Finally a novel model that combines
the ability of CNNs to extract useful features and the ability of LSTMs' to
analyze time series, is proposed and evaluated. The combined model is able to
outperform the individual LSTM and CNN models in the prediction horizons that
are tested
Decision support from financial disclosures with deep neural networks and transfer learning
Company disclosures greatly aid in the process of financial decision-making;
therefore, they are consulted by financial investors and automated traders
before exercising ownership in stocks. While humans are usually able to
correctly interpret the content, the same is rarely true of computerized
decision support systems, which struggle with the complexity and ambiguity of
natural language. A possible remedy is represented by deep learning, which
overcomes several shortcomings of traditional methods of text mining. For
instance, recurrent neural networks, such as long short-term memories, employ
hierarchical structures, together with a large number of hidden layers, to
automatically extract features from ordered sequences of words and capture
highly non-linear relationships such as context-dependent meanings. However,
deep learning has only recently started to receive traction, possibly because
its performance is largely untested. Hence, this paper studies the use of deep
neural networks for financial decision support. We additionally experiment with
transfer learning, in which we pre-train the network on a different corpus with
a length of 139.1 million words. Our results reveal a higher directional
accuracy as compared to traditional machine learning when predicting stock
price movements in response to financial disclosures. Our work thereby helps to
highlight the business value of deep learning and provides recommendations to
practitioners and executives
Deep Learning Stock Volatility with Google Domestic Trends
We have applied a Long Short-Term Memory neural network to model S&P 500
volatility, incorporating Google domestic trends as indicators of the public
mood and macroeconomic factors. In a held-out test set, our Long Short-Term
Memory model gives a mean absolute percentage error of 24.2%, outperforming
linear Ridge/Lasso and autoregressive GARCH benchmarks by at least 31%. This
evaluation is based on an optimal observation and normalization scheme which
maximizes the mutual information between domestic trends and daily volatility
in the training set. Our preliminary investigation shows strong promise for
better predicting stock behavior via deep learning and neural network models
Temporal Relational Ranking for Stock Prediction
Stock prediction aims to predict the future trends of a stock in order to
help investors to make good investment decisions. Traditional solutions for
stock prediction are based on time-series models. With the recent success of
deep neural networks in modeling sequential data, deep learning has become a
promising choice for stock prediction. However, most existing deep learning
solutions are not optimized towards the target of investment, i.e., selecting
the best stock with the highest expected revenue. Specifically, they typically
formulate stock prediction as a classification (to predict stock trend) or a
regression problem (to predict stock price). More importantly, they largely
treat the stocks as independent of each other. The valuable signal in the rich
relations between stocks (or companies), such as two stocks are in the same
sector and two companies have a supplier-customer relation, is not considered.
In this work, we contribute a new deep learning solution, named Relational
Stock Ranking (RSR), for stock prediction. Our RSR method advances existing
solutions in two major aspects: 1) tailoring the deep learning models for stock
ranking, and 2) capturing the stock relations in a time-sensitive manner. The
key novelty of our work is the proposal of a new component in neural network
modeling, named Temporal Graph Convolution, which jointly models the temporal
evolution and relation network of stocks. To validate our method, we perform
back-testing on the historical data of two stock markets, NYSE and NASDAQ.
Extensive experiments demonstrate the superiority of our RSR method. It
outperforms state-of-the-art stock prediction solutions achieving an average
return ratio of 98% and 71% on NYSE and NASDAQ, respectively.Comment: Transactions on Information Systems (TOIS
A Tensor-Based Sub-Mode Coordinate Algorithm for Stock Prediction
The investment on the stock market is prone to be affected by the Internet.
For the purpose of improving the prediction accuracy, we propose a multi-task
stock prediction model that not only considers the stock correlations but also
supports multi-source data fusion. Our proposed model first utilizes tensor to
integrate the multi-sourced data, including financial Web news, investors'
sentiments extracted from the social network and some quantitative data on
stocks. In this way, the intrinsic relationships among different information
sources can be captured, and meanwhile, multi-sourced information can be
complemented to solve the data sparsity problem. Secondly, we propose an
improved sub-mode coordinate algorithm (SMC). SMC is based on the stock
similarity, aiming to reduce the variance of their subspace in each dimension
produced by the tensor decomposition. The algorithm is able to improve the
quality of the input features, and thus improves the prediction accuracy. And
the paper utilizes the Long Short-Term Memory (LSTM) neural network model to
predict the stock fluctuation trends. Finally, the experiments on 78 A-share
stocks in CSI 100 and thirteen popular HK stocks in the year 2015 and 2016 are
conducted. The results demonstrate the improvement on the prediction accuracy
and the effectiveness of the proposed model
Visual Attention Model for Cross-sectional Stock Return Prediction and End-to-End Multimodal Market Representation Learning
Technical and fundamental analysis are traditional tools used to analyze
individual stocks; however, the finance literature has shown that the price
movement of each individual stock correlates heavily with other stocks,
especially those within the same sector. In this paper we propose a general
purpose market representation that incorporates fundamental and technical
indicators and relationships between individual stocks. We treat the daily
stock market as a "market image" where rows (grouped by market sector)
represent individual stocks and columns represent indicators. We apply a
convolutional neural network over this market image to build market features in
a hierarchical way. We use a recurrent neural network, with an attention
mechanism over the market feature maps, to model temporal dynamics in the
market. We show that our proposed model outperforms strong baselines in both
short-term and long-term stock return prediction tasks. We also show another
use for our market image: to construct concise and dense market embeddings
suitable for downstream prediction tasks.Comment: Accepted as full paper in the 32nd International FLAIRS Conferenc
Combining time-series and textual data for taxi demand prediction in event areas: a deep learning approach
Accurate time-series forecasting is vital for numerous areas of application
such as transportation, energy, finance, economics, etc. However, while modern
techniques are able to explore large sets of temporal data to build forecasting
models, they typically neglect valuable information that is often available
under the form of unstructured text. Although this data is in a radically
different format, it often contains contextual explanations for many of the
patterns that are observed in the temporal data. In this paper, we propose two
deep learning architectures that leverage word embeddings, convolutional layers
and attention mechanisms for combining text information with time-series data.
We apply these approaches for the problem of taxi demand forecasting in event
areas. Using publicly available taxi data from New York, we empirically show
that by fusing these two complementary cross-modal sources of information, the
proposed models are able to significantly reduce the error in the forecasts.Comment: 20 pages, 6 figure
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