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A Tensor-based eLSTM Model to Predict Stock Price Using Financial News
2019
Proceedings of the 52nd Hawaii International Conference on System Sciences
unpublished
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Such heterogeneity leads to miss valuable information partially or twist the feature spaces. In this article, we propose a tensor-based event-LSTM (eLSTM) to solve these two challenges. ...
Stock market prediction has attracted much attention from both academia and business. Both traditional finance and behavioral finance believe that market information affects stock movements. ...
model to do trends prediction for the stock market. • Tensor-based LSTM model: We use the tensor to represent the multiple market information and then feed the tensor into a LSTM model. • Vector-based ...
doi:10.24251/hicss.2019.201
fatcat:qafd7yudtrbh5ejyzzqsvax7q4
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 tensor with adaptive indicators is passed to the model for better and accurate prediction. ...
The results are analyzed using popular metrics and compared with two benchmark ML classifiers and a recent classifier based on deep learning. ...
INTRODUCTION In an attempt to predict stock market trends and future stock prices, market researchers, investors and scholars regularly propose a range of models. ...
doi:10.35940/ijrte.b3048.078219
fatcat:ni7wuaa6mraydhn32es7qz5aiu
Stock Prediction Based on Technical Indicators Using Deep Learning Model
2022
Computers Materials & Continua
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This paper aims to build up an Evolutionary Deep Learning Model (EDLM) to identify stock trends' prices by using STIs. ...
To study the stock market characteristics using STIs and make efficient trading decisions, a robust model is built. ...
Acknowledgement: We are very thankful to RGPV, Jagran Lakecity University, Taif University Researchers Supporting Project Number (TURSP-2020/10) and Duy Tan University for their continuous support and ...
doi:10.32604/cmc.2022.014637
fatcat:qvxkfghtqjdgbournwwx4gl4ku
AI in Asset Management and Rebellion Research
[article]
2022
arXiv
pre-print
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How could the Rebellion strategically move towards a more broad area? What were Rebellion's new or alternative business models? ...
"It's no surprise", Alex told us, "Our Machine Learning global strategy has a history of outperforming the S&P 500 for 14 years". ...
The authors also want to thank Alexander Fleiss, CEO of Rebellion Research, for providing useful information. ...
arXiv:2206.14876v1
fatcat:supe7yhahneujnrpfgzf2vmszi
AlphaMLDigger: A Novel Machine Learning Solution to Explore Excess Return on Investment
[article]
2022
arXiv
pre-print
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In phase 2, the predicted market sentiment is combined with social network indicator features and stock market history features to predict the stock movements with different Machine Learning models and ...
In phase 1, a deep sequential NLP model is proposed to transfer blogs on Sina Microblog to market sentiment. ...
Acknowledgments The authors would like to thank Professor Rajesh Ranganath and Mark Goldstein for their insightful comments and suggestions which motivates us to broaden our research in various aspects ...
arXiv:2206.11072v1
fatcat:w3dy4ocv3vhbzbbm76emc6qpy4
A New Stock Price Forecasting Method Using Active Deep Learning Approach
2022
Journal of Open Innovation: Technology, Market and Complexity
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Stock price prediction is a significant research field due to its importance in terms of benefits for individuals, corporations, and governments. ...
This research explores the application of the new approach to predict the adjusted closing price of a specific corporation. ...
A novel model is proposed in [62] to predict Bitcoin prices, similar to stock price prediction. Three are deep learning models, vanilla RNN, LSTM, and ARIMA. ...
doi:10.3390/joitmc8020096
fatcat:l7qgxteodrccnfks6nyxeel36m
Applications of deep learning in stock market prediction: recent progress
[article]
2020
arXiv
pre-print
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Hence, our motivation for this survey is to give a latest review of recent works on deep learning models for stock market prediction. ...
Stock market prediction has been a classical yet challenging problem, with the attention from both economists and computer scientists. ...
Ding et al. (2015) uses a neural tensor network to learn event embeddings for representing news documents. Hu et al. (2018b) uses a news embedding layer to encode each news into a news vector. ...
arXiv:2003.01859v1
fatcat:rdwsi5xpozfyjeg7oatfzwbg7a
Stock Market Forecasting Using Deep Learning and Technical Analysis: A Systematic Review
2020
IEEE Access
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Table 6 shows a variety of datasets used to collect historical stock prices. ...
Which markets and timeframes are most used for price prediction? RQ3 What are the metrics used to validate the performance of the proposed model? ...
doi:10.1109/access.2020.3030226
fatcat:ti4gmwnxbbe75l5hagq3ggakce
Introduction to the Minitrack on Machine Learning and Network Analytics in Finance
2018
Proceedings of the 51st Hawaii International Conference on System Sciences
unpublished
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The second article "A Tensor-based eLSTM model to predict stock price using financial news" by Jinghua Tan, Jun Wang, Denisa Rinprasertmeechai, Rong Xing and Qing Li (Southwestern University of Finance ...
and Economics, Chengdu) proposes a new Long Short-term Memory (LSTM) network model to predict stock prices. ...
The second article "A Tensor-based eLSTM model to predict stock price using financial news" by Jinghua Tan, Jun Wang, Denisa Rinprasertmeechai, Rong Xing and Qing Li (Southwestern University of Finance ...
doi:10.24251/hicss.2018.167
fatcat:24cmj4sth5a6jl7xtcd4ue53by
Introduction to the Minitrack on Machine Learning and Network Analytics in Finance
2019
Proceedings of the 52nd Hawaii International Conference on System Sciences
unpublished
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
The second article "A Tensor-based eLSTM model to predict stock price using financial news" by Jinghua Tan, Jun Wang, Denisa Rinprasertmeechai, Rong Xing and Qing Li (Southwestern University of Finance ...
and Economics, Chengdu) proposes a new Long Short-term Memory (LSTM) network model to predict stock prices. ...
The second article "A Tensor-based eLSTM model to predict stock price using financial news" by Jinghua Tan, Jun Wang, Denisa Rinprasertmeechai, Rong Xing and Qing Li (Southwestern University of Finance ...
doi:10.24251/hicss.2019.200
fatcat:ikasermqfbfglgyarhqyfdjcu4
Machine learning in stock indices trading and pairs trading
2021
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In general, this study proves that a profitable trading strategy based on BGSA-SVM prediction can be realized in a real stock market. ...
The first field uses machine learning to forecast financial time series (Chapters 2 and 3), and then builds a simple trading strategy based on the forecast results. ...
The Ornstein-Uhlenbeck model is used to simulate the movement of a pair of stock prices as a Gaussian Markov chain model. ...
doi:10.5525/gla.thesis.82188
fatcat:ykinbzqadrdz7oyqhhhxeerr2u