research-article

Deep Learning-based Integrated Framework for stock price movement prediction

Authors:

Guang YangAuthors Info & Claims

Volume 133, Issue C

https://doi.org/10.1016/j.asoc.2022.109921

Published: 01 January 2023 Publication History

Abstract

Stock market prediction is a very important problem in the economics field. With the development of machine learning, more and more algorithms are applied in the stock market to predict the stock price movement. However, stock market prediction is regarded as a challenging task for the noise and volatility of stock market data. Therefore, in this paper, a novel hybrid model SA-DLSTM is proposed to predict stock market and simulation trading by combine a emotion enhanced convolutional neural network (ECNN), the denoising autoencoder (DAE) models, and long short-term memory model (LSTM). Firstly, user-generated comments on Internet were used as a complement to stock market data, and ECNN was applied to extract the sentiment representation. Secondly, we extract the key features of stock market data by DAE, which can improve the prediction accuracy. Thirdly, we take the timeliness of emotion on stock market into consideration and construct more reliable and realistic sentiment indexes. Finally, the key features of stock data and sentiment indexes are fed into LSTM to make stock market prediction. Experiment results show that the prediction accuracy of SA-DLSTM are superior to other compared models. Meanwhile, SA-DLSTM has a good performance both in return and risk. It can help investors make wise decisions.

Highlights

•

The combination of public opinions and sentiments, SA-DLSTM can provide robust and accurate predictions for the stock market trends.

•

SA-DLSTM can calculate the sentiment indexes in which they are with different emotion strength, and use an exponential time function to calculate the timeliness of emotion.

•

SA-DLSTM can extract different features from multivariate financial time series, and integrate features for further classification.

References

[1]

Zhou L., Lu D., Fujita H., The performance of corporate financial distress prediction models with features selection guided by domain knowledge and data mining approaches, Knowl.-Based Syst. 85 (2015) 52–61.

[2]

Zhou L., Si Y.W., Fujita H., Predicting the listing statuses of Chinese-listed companies using decision trees combined with an improved filter feature selection method, Knowl.-Based Syst. 128 (2017) 93–101.

[3]

Geva T., Zahavi J., Empirical evaluation of an automated intraday stock recommendation system incorporating both market data and textual news, Decis. Support Syst. 57 (2014) 212–223.

Digital Library

[4]

Li Q., Wang T., Li P., Liu L., Gong Q., Chen Y., The effect of news and public mood on stock movements, Inform. Sci. 278 (2014) 826–840.

[5]

Chen N.-F., Roll R., Ross S.A., Economic forces and the stock market, J. Bus. (1986) 383–403.

[6]

Rai Anish, Mahata Ajit, Nurujjaman M.d., Majhi Sushovan, Debnath Kanish, A sentiment-based modeling and analysis of stock price during the COVID-19: U- and Swoosh-shaped recovery, Physica A 592 (2022).

[7]

Ruan Y., Durresi A., Alfantoukh L., Using Twitter trust network for stock market analysis, Knowl.-Based Syst. 145 (2018) 207–218.

[8]

Yang Shuigen, A novel study on deep learning framework to predict and analyze the financial time series information, Future Gener. Comput. Syst. 125 (2021) 812–819.

[9]

Shynkevich Y., McGinnity T., Coleman S., Belatreche A., Li Y., Forecasting price movements using technical indicators: Investigating the impact of varying input window length, Neurocomputing 264 (2017) 71–88.

[10]

Zhang Dehua, Lou Sha, The application research of neural network and BP algorithm in stock price pattern classification and prediction, Future Gener. Comput. Syst. 115 (2021) 872–879.

Digital Library

[11]

Wei Chen, Haoyu Zhang, Kumar Mehlawat Mukesh, Lifen Jia, Mean–variance portfolio optimization using machine learning-based stock price prediction, Appl. Soft Comput. 100 (2021).

[12]

Jiawei Long, Zhaopeng Chen, Weibing He, Taiyu Wu, Jiangtao Ren, An integrated framework of deep learning and knowledge graph for prediction of stock price trend: An application in Chinese stock exchange market, Appl. Soft Comput. 91 (2020).

[13]

Bileki Guilherme A., Flávio Barboza, Silva Luiz Henrique C., Vanderlei Bonato, Order book mid-price movement inference by CatBoost classifier from convolutional feature maps, Appl. Soft Comput. 116 (2022).

[14]

Shangkun Deng, Chongyi Xiao, Yingke Zhu, Yu Tian, Zonghua Liu, Tianxiang Yang, Dynamic forecasting of the Shanghai Stock Exchange index movement using multiple types of investor sentiment, Appl. Soft Comput. (2022).

[15]

Shiller R.J., Irrational Exuberance: Revised and Expanded, third ed., Princeton University Press, 2015.

[16]

Perikos I., Hatzilygeroudis I., Recognizing emotions in text using ensemble of classifiers, Eng. Appl. Artif. Intell. 51 (2016) 191–201.

Digital Library

[17]

Yang G., He H., Chen Q., Emotion-semantic-enhanced neural network, IEEE Trans. Audio Speech Lang. Process. 27 (3) (2019) 531–543.

[18]

Bartov E., Faurel L., Mohanram P.S., Can twitter help predict firm-level earnings and stock returns?, Account. Rev. 93 (3) (2018) 25–57.

[19]

Hasan A.A., Fong A.C., Sentiment analysis based fuzzy decision platform for the Saudi Stock Market, in: 2018 IEEE International Conference on Electro/Information Technology, EIT, IEEE, 2018, pp. 0023–0029.

[20]

Tumarkin R., From A., Whitelaw R., Internet message board activity and market efficiency: A case study of the internet service sector using Ragingbull. com, in: Financial Markets Institutions and Instruments, Citeseer, 2002.

[21]

Nuno O., Paulo C., Nelson A., Stock market sentiment lexicon acquisition using microblogging data and statistical measures, Decis. Support Syst. 85 (2016) 62–73.

[22]

Wu D.D., Zheng L., Olson D.L., A decision support approach for online stock forum sentiment analysis, IEEE Trans. Syst. Man Cybern.: Syst. 44 (2014) 1077–1087.

[23]

Ren R., Wu D.D., Liu T., Forecasting stock market movement direction using sentiment analysis and support vector machine, IEEE Syst. J. 13 (1) (2019) 760–770.

[24]

Weng B., Lu L., Wang X., Megahed F.M., Martinez W., Predicting short-term stock prices using ensemble methods and online data sources, Expert Syst. Appl. 112 (2018) 258–273.

[25]

Siganos A., Vagenasnanos E., Verwijmeren P., Facebook’s daily sentiment and international stock markets, J. Econ. Behav. Organ. 107 (2014) 730–743.

[26]

Qadan M., Nama H., Investor sentiment and the price of oil, Energy Econ. 69 (2018) 42–58.

[27]

Yang C., Zhou L., Individual stock crowded trades, individual stock investor sentiment and excess returns, North Am. J. Econ. Finance 38 (2016) 39–53.

[28]

M N., M P., M N., Walking down wall street with a tablet: A survey of stock market predictions using the web, J. Econ. Surv. 30 (2) (2016) 356–369.

[29]

Tetlock P.C., Giving content to investor sentiment: The role of media in the stock market, J. Finance 62 (3) (2007) 1139–1168.

[30]

Gillam R.A., Guerard J.B., Cahan R., News volume information: Beyond earnings forecasting in a global stock selection model, Int. J. Forecast. 31 (2) (2015) 575–581.

[31]

Rui H., Liu Y., Whinston A.B., Whose and what chatter matters? the effect of tweets on movie sales, Decis. Support Syst. 55 (4) (2013) 863–870.

[32]

Derakhshan A., Beigy H., Sentiment analysis on stock social media for stock price movement prediction, Eng. Appl. Artif. Intell. 85 (2019) 569–578.

[33]

Long W., Lu Z., C L., Deep learning-based feature engineering for stock price movement prediction, Knowl.-Based Syst. 164 (2019) 163–173.

[34]

Chakole J.B., Kolhe M.S., Mahapurush G.D., Yadav A., Kurhekar M.P., A q-learning agent for automated trading in equity stock markets, Expert Syst. Appl. (2020).

[35]

P. L, D. M, Stock market prediction with deep learning: A character-based neural language model for event-based trading, in: Proceedings of Australasian Language Technology Association Workshop, 2017, pp. 6–15.

[36]

Yu-Chen Chen, Wen-Chen Huang, Constructing a stock-price forecast CNN model with gold and crude oil indicators, Appl. Soft Comput. 112 (2021).

[37]

Baek Y., Kim H.Y., Modaugnet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module, Expert Syst. Appl. 113 (2018) 457–480.

[38]

Lee S.W., Kim H.Y., Stock market forecasting with super-high dimensional time-series data using convLSTM, trend sampling, and specialized data augmentation, Exp. Syst. Appl. (2020).

[39]

Barberis N., Greenwood R., Jin L.J., Shleifer A., X-CAPM: An extrapolative capital asset pricing model, J. Financ. Econ. 115 (1) (2015) 1–24.

[40]

Vincent P., Larochelle H., Bengio Y., Manzagol P., Extracting and composing robust features with denoising autoencoders, 2008, pp. 1096–1103.

Digital Library

[41]

Hochreiter S., Schmidhuber J., Long short-term memory, Neural Comput. 9 (8) (1997) 1735–1780.

Digital Library

[42]

Mng J.C.P., Mehralizadeh M., Forecasting east asian indices futures via a novel hybrid of wavelet-PCA denoising and artificial neural network models, PLoS One 11 (6) (2016).

[43]

L W., S G., Neural Networks and Wavelet de-Noising for Stock Trading and Prediction, in: Time Series Analysis, Modeling and Applications, Springer, 2013, pp. 229–247.

[44]

Moghar Adil, Hamiche Mhamed, Stock market prediction using LSTM recurrent neural network, Procedia Comput. Sci. 170 (2020) 1168–1173.

[45]

Nonita Sharma, Akanksha Juneja, Combining of random forest estimates using LSboost for stock market index prediction, in: 2017 2nd International Conference for Convergence in Technology, I2CT, 2017, pp. 1199–1202.

[46]

Li Ming-Wei, Xu Dong-Yang, Geng Jing, Hong Wei-Chiang, A hybrid approach for forecasting ship motion using CNN-GRU-AM and GCWOA, Appl. Soft Comput. 114 (2022).

Digital Library

[47]

Ledoit O., Wolf M., Robust performance hypothesis testing with the Sharpe ratio, J. Empir. Financ. 15 (5) (2008) 850–859.

Cited By

Liao SXie LDu YChen SWan HXu H(2024)Stock trend prediction based on dynamic hypergraph spatio-temporal networkApplied Soft Computing10.1016/j.asoc.2024.111329154:COnline publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111329
Wang CYuan JZeng YLin S(2024)A deep learning integrated framework for predicting stock index price and fluctuation via singular spectrum analysis and particle swarm optimizationApplied Intelligence10.1007/s10489-024-05271-x54:2(1770-1797)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s10489-024-05271-x

Index Terms

Deep Learning-based Integrated Framework for stock price movement prediction

Index terms have been assigned to the content through auto-classification.

Recommendations

Deep Learning Using Risk-Reward Function for Stock Market Prediction
CSAI '18: Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence

Many recent studies have attempted to apply a deep learning approach to build a model for stock market prediction. Most of these studies have concentrated on using prediction accuracy as a performance metric. Some of them have also performed trading ...
Multi-source aggregated classification for stock price movement prediction
Abstract
Predicting stock price movements is a challenging task. Previous studies mostly used numerical features and news sentiments of target stocks to predict stock price movements. However, their semantics-based sentiment analysis is sub-...
Highlights
- We incorporate news about related companies for stock price movement forecasting.
A deep learning approach for stock market prediction using deep autoencoder and long short-term memory

The stock market prediction problems have received increased attention from researchers due to the high stakes involved and the need for better prediction accuracy. We have developed an architecture by combining a deep autoencoder and long short-term ...

Comments

Information & Contributors

Information

Published In

cover image Applied Soft Computing

Applied Soft Computing Volume 133, Issue C

Jan 2023

848 pages

ISSN:1568-4946

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 January 2023

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 04 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Liao SXie LDu YChen SWan HXu H(2024)Stock trend prediction based on dynamic hypergraph spatio-temporal networkApplied Soft Computing10.1016/j.asoc.2024.111329154:COnline publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111329
Wang CYuan JZeng YLin S(2024)A deep learning integrated framework for predicting stock index price and fluctuation via singular spectrum analysis and particle swarm optimizationApplied Intelligence10.1007/s10489-024-05271-x54:2(1770-1797)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s10489-024-05271-x

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents