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A Tensor-Based Information Framework for Predicting the Stock Market
2016
ACM Transactions on Information Systems
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A tensor-based information framework for predicting the stock market. ...
For this purpose, we introduce a tensorbased information framework to predict stock movements. Specifically, our framework models the complex investor information environment with tensors. ...
In this study, we introduced a tensor-based information framework for predicting stock movements in response to new information. ...
doi:10.1145/2838731
fatcat:imbf57o24zdovb24sgqjuryd6y
A Tensor-Based Sub-Mode Coordinate Algorithm for Stock Prediction
[article]
2018
arXiv
pre-print
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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. ...
SMC is based on the stock similarity, aiming to reduce the variance of their subspace in each dimension produced by the tensor decomposition. ...
In this paper, we present a novel tensor-based computational framework that can effectively predict stock price movements by fusing various sources of information. ...
arXiv:1805.07979v1
fatcat:543ubzrfczhnjleh7hlqxelkdm
Improving stock market prediction via heterogeneous information fusion
2018
Knowledge-Based Systems
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2018 pre-print arXiv:1801.00588v1 fatcat:krfe5cas25etpk3c7mctsljgra
models for improving the prediction accuracy. ...
Traditional stock market prediction approaches commonly utilize the historical price-related data of the stocks to forecast their future trends. ...
Although a tensor-based computational framework has been developed to model the joint impacts of different information sources for stock prediction [12, 13] , each stock prediction in that framework is ...
doi:10.1016/j.knosys.2017.12.025
fatcat:kehwo6ffsbblxiybgz2hq3n6im
Tensor-Based Learning for Predicting Stock Movements
2015
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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To identify the nonlinear patterns between stock movements and new information, we propose a supervised tensor regression learning approach to investigate the joint impact of different information sources ...
Stock movements are essentially driven by new information. Market data, financial news, and social sentiment are believed to have impacts on stock markets. ...
Acknowledgments This work has been supported by the National Natural Science Foundation of China (NSFC) (60803106, 61170133), and the Program for Sichuan National Science Foundation for Distinguished Young ...
doi:10.1609/aaai.v29i1.9452
fatcat:qq3txrgbdjhrrhyt4bt5ipfuaa
Enhancing Stock Market Prediction with Extended Coupled Hidden Markov Model over Multi-Sourced Data
[article]
2018
arXiv
pre-print
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Traditional stock market prediction methods commonly only utilize the historical trading data, ignoring the fact that stock market fluctuations can be impacted by various other information sources such ...
Evaluations on China A-share market data in 2016 show the superior performance of our model against previous methods. ...
So our goal is to propose a framework to accurately predict the stock market by fusing the quantitative trading information and news events. ...
arXiv:1809.00306v1
fatcat:xr3nwydixncgnfc3bymmbcce2i
Stock Price Prediction using Technical Indicators: A Predictive Model using Optimal Deep Learning
2019
International journal of recent technology and engineering
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The machine learning coupled with fundamental and / or Technical Analysis also yields satisfactory results for stock market prediction. ...
The tensor with adaptive indicators is passed to the model for better and accurate prediction. ...
In section 3, we discuss the concept of correlation tensor, framework with Optimal LSTM for stock trends and closing price prediction (high/low) for next day. ...
doi:10.35940/ijrte.b3048.078219
fatcat:ni7wuaa6mraydhn32es7qz5aiu
Deep Learning for Price Movement Prediction Using Convolutional Neural Network and Long Short-Term Memory
2020
Mathematical Problems in Engineering
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This paper presents a deep learning framework to predict price movement direction based on historical information in financial time series. ...
We specifically use a three-dimensional CNN for data input in the framework, including the information on time series, technical indicators, and the correlation between stock indices. ...
Conflicts of Interest e authors declare that there are no conflicts of interest regarding the publication of this paper. ...
doi:10.1155/2020/2746845
fatcat:ondpaixi5zhcfaapouxzjmzj6y
CNNPred: CNN-based stock market prediction using several data sources
[article]
2018
arXiv
pre-print
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The suggested framework has been applied for predicting the next day's direction of movement for the indices of S&P 500, NASDAQ, DJI, NYSE, and RUSSELL markets based on various sets of initial features ...
for predicting the future of those markets. ...
The main contributions of this work can be summarized as follows: • Aggregating several sources of information in a CNN-based framework for feature extraction and market prediction. ...
arXiv:1810.08923v1
fatcat:kgoctr7p6fhylmlwxd35ipzaxa
Stock Prediction Based on Technical Indicators Using Deep Learning Model
2022
Computers Materials & Continua
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The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange (NSE) -India, a Long Short Term Memory (LSTM) is used. ...
The stock data is usually non-stationary, and attributes are non-correlative to each other. Several traditional Stock Technical Indicators (STIs) may incorrectly predict the stock market trends. ...
the encouragement. ...
doi:10.32604/cmc.2022.014637
fatcat:qvxkfghtqjdgbournwwx4gl4ku
Knowledge-Driven Event Embedding for Stock Prediction
2016
International Conference on Computational Linguistics
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Experiments on event similarity and stock market prediction show that our model is more capable of obtaining better event embeddings and making more accurate prediction on stock market volatilities. ...
as event-driven stock prediction. ...
Acknowledgments We thank the anonymous reviewers for their constructive comments, and gratefully acknowledge the support of the National Key Basic Research Program of China (973 Program) of China via Grant ...
dblp:conf/coling/DingZLD16
fatcat:cm23wzwyanhvnir3lasyym3ehe
3D Tensor-based Deep Learning Models for Predicting Option Price
[article]
2021
arXiv
pre-print
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Option pricing is a significant problem for option risk management and trading. In this article, we utilize a framework to present financial data from different sources. ...
Experiments performed on the Chinese market option dataset prove the practicability of the proposed strategies over commonly used ways, including B-S model and vector-based LSTM. ...
Acknowledgement The authors declare that there is no conflict of interests regarding the publication of this article. This research is financed by NSFC grant 91646106. ...
arXiv:2106.02916v2
fatcat:osprl3jmyfgjxcpatpvouyaijq
Event Representation Learning Enhanced with External Commonsense Knowledge
[article]
2020
arXiv
pre-print
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., event similarity, script event prediction and stock market prediction, show that our model obtains much better event embeddings for the tasks, achieving 78% improvements on hard similarity task, yielding ...
more precise inferences on subsequent events under given contexts, and better accuracies in predicting the volatilities of the stock market. ...
Acknowledgments We thank the anonymous reviewers for their constructive comments, and gratefully acknowl- ...
arXiv:1909.05190v2
fatcat:t4jj5czdqbfydiykoplgou52eq
Event Representation Learning Enhanced with External Commonsense Knowledge
2019
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
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., event similarity, script event prediction and stock market prediction, show that our model obtains much better event embeddings for the tasks, achieving 78% improvements on hard similarity task, yielding ...
more precise inferences on subsequent events under given contexts, and better accuracies in predicting the volatilities of the stock market 1 . ...
Acknowledgments We thank the anonymous reviewers for their constructive comments, and gratefully acknowledge the support of the National Key Research and Development Program of China (SQ2018AAA010010), ...
doi:10.18653/v1/d19-1495
dblp:conf/emnlp/DingLLLD19
fatcat:qxnplf3wunholo7d5t4vwqsgba
Short-Term Stock Price-Trend Prediction Using Meta-Learning
[article]
2021
arXiv
pre-print
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Such methods are beneficial under conditions of limited data availability, which often obtain for trend prediction based on time-series data limited by sparse information. ...
In this study, we consider short-term stock price prediction using a meta-learning framework with several convolutional neural networks, including the temporal convolution network, fully convolutional ...
PROPOSED META-LEARNING FRAMEWORK In this paper, we propose a novel meta-learning framework for price trend forecasting based on financial time-series data. ...
arXiv:2105.13599v1
fatcat:aga7cjeksbfz7mlwvliizrq6xa
Stock Price Forecasting Framework based on the Support Vector Regression and Monte Carlo Method
2019
International journal of recent technology and engineering
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In my research, the framework is created based on the support vector regression (SVR) and Monte Carlo method to predict the stock price. ...
Stock market and its prices prediction are considered as one of the challenging task in financial forecasting. ...
[12] presented a model to predict the stock market variables and information extracted from the micro-blog of twitter. ...
doi:10.35940/ijrte.b1471.0982s1119
fatcat:6gpmwrcievasvkoznvphkr6ewm
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