Chi Chen
ABSTRACT
Stock trend prediction, aiming at predicting future price trend of stocks, plays a key role in seeking maximized profit from the stock investment. Recent years have witnessed increasing efforts in applying machine learning techniques, especially deep learning, to pursue more promising stock prediction. While deep learning has given rise to significant improvement, human investors still retain the leading position due to their understanding on stock intrinsic properties, which can imply invaluable principles for stock prediction. In this paper, we propose to extract and explore stock intrinsic properties to enhance stock trend prediction. Fortunately, we discover that the repositories of investment behaviors within mutual fund portfolio data form up a gold mine to extract latent representations of stock properties, since such collective investment behaviors can reflect the professional fund managers' common beliefs on stock intrinsic properties. Powered by extracted stock properties, we further propose to model the dynamic market state and trend using stock representations so as to generate the dynamic correlation between the stock and the market, and then we aggregate such correlation with dynamic stock indicators to achieve more accurate stock prediction. Extensive experiments on real-world stock market data demonstrate the effectiveness of stock properties extracted from collective investment behaviors in the task of stock prediction.
- Ayodele Ariyo Adebiyi, Aderemi Oluyinka Adewumi, and Charles Korede Ayo.2014. Comparison of ARIMA and artificial neural networks models for stock price prediction. Journal of Applied Mathematics2014 (2014).Google Scholar
- Ryo Akita, Akira Yoshihara, Takashi Matsubara, and Kuniaki Uehara. 2016. Deep learning for stock prediction using numerical and textual information. In Computer and Information Science (ICIS), 2016 IEEE/ACIS 15th International Conference on. IEEE, 1--6.Google ScholarCross Ref
- Ranjeeta Bisoi and Pradipta K Dash. 2014. A hybrid evolutionary dynamic neural network for stock market trend analysis and prediction using unscented Kalmanfilter. Applied Soft Computing19 (2014), 41--56.Google Scholar
- Louis KC Chan, Hsiu-Lang Chen, and Josef Lakonishok. 2002. On mutual fund investment styles. The Review of Financial Studies15, 5 (2002), 1407--1437.Google Scholar
- Prasanna Chandra. 2017. Investment analysis and portfolio management. McGraw-Hill Education.Google Scholar
- Robert D Edwards, John Magee, and WH Charles Bassetti. 2007. Technical analysis of stock trends. CRC press.Google Scholar
- Eric G Falkenstein. 1996. Preferences for stock characteristics as revealed by mutual fund portfolio holdings. The Journal of Finance51, 1 (1996), 111--135.Google ScholarCross Ref
- Thomas Fischer and Christopher Krauss. 2018. Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270, 2 (2018), 654--669.Google ScholarCross Ref
- Brendan J Frey and Delbert Dueck. 2007. Clustering by passing messages between data points. science 315, 5814 (2007), 972--976.Google Scholar
- Qiyuan Gao. 2016. Stock market forecasting using recurrent neural network. Ph.D. Dissertation. University of Missouri--Columbia.Google Scholar
- Felix A Gers, Nicol N Schraudolph, and Jürgen Schmidhuber. 2002. Learning precise timing with LSTM recurrent networks. Journal of machine learningresearch3, Aug (2002), 115--143. Google ScholarDigital Library
- Mustafa Göçken, Mehmet Özçalc", Asl" Boru, and Ay"e Tu"ba Dosdoru. 2016. Integrating metaheuristics and artificial neural networks for improved stock price prediction. Expert Systems with Applications44 (2016), 320--331. Google ScholarDigital Library
- Zura Kakushadze. 2016. 101 formulaic alphas.Wilmott2016, 84 (2016), 72--81.Google Scholar
- Kyoung-Jae Kim and Hyunchul Ahn. 2012. Simultaneous optimization of artificial neural networks for financial forecasting. Applied Intelligence36, 4 (2012), 887--898. Google ScholarDigital Library
- Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 42, 8 (2009). Google ScholarDigital Library
- Leonel A Laboissiere, Ricardo AS Fernandes, and Guilherme G Lage. 2015. Maximum and minimum stock price forecasting of Brazilian power distribution companies based on artificial neural networks. Applied Soft Computing 35 (2015),66--74. Google ScholarDigital Library
- Lili Li, Shan Leng, Jun Yang, and Mei Yu. 2016. Stock Market Autoregressive Dynamics: A Multinational Comparative Study with Quantile Regression. Mathematical Problems in Engineering 2016 (2016).Google Scholar
- Tie-Yan Liu et al. 2009. Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval 3, 3 (2009), 225--331. Google ScholarDigital Library
- LR Medsker and LC Jain. 2001. Recurrent neural networks. Design and Applications 5 (2001).Google Scholar
- John J Murphy. 1999.Technical analysis of the financial markets: A comprehensive guide to trading methods and applications. Penguin.Google Scholar
- Arman Khadjeh Nassirtoussi, Saeed Aghabozorgi, Teh Ying Wah, and David Chek Ling Ngo. 2015. Text mining of news-headlines for FOREX market prediction: A Multi-layer Dimension Reduction Algorithm with semantics and sentiment. Expert Systems with Applications 42, 1 (2015), 306--324. Google ScholarDigital Library
- David MQ Nelson, Adriano CM Pereira, and Renato A de Oliveira. 2017. Stock market's price movement prediction with LSTM neural networks. In Neural Networks (IJCNN), 2017 International Joint Conference on. IEEE, 1419--1426.Google ScholarCross Ref
- Jigar Patel, Sahil Shah, Priyank Thakkar, and K Kotecha. 2015. Predicting stock market index using fusion of machine learning techniques. Expert Systems with Applications 42, 4 (2015), 2162--2172. Google ScholarDigital Library
- V Paul Pauca, Farial Shahnaz, Michael W Berry, and Robert J Plemmons. 2004. Text mining using non-negative matrix factorizations. In Proceedings of the 2004 SIAM International Conference on Data Mining. SIAM, 452--456.Google ScholarCross Ref
- G Preethi and B Santhi. 2012. STOCK MARKET FORECASTING TECHNIQUES: A SURVEY. Journal of Theoretical & Applied Information Technology 46, 1 (2012).Google Scholar
- Akhter Mohiuddin Rather, Arun Agarwal, and VN Sastry. 2015. Recurrent neural network and a hybrid model for prediction of stock returns. Expert Systems with Applications42, 6 (2015), 3234--3241. Google ScholarDigital Library
- Eberhard Schöneburg. 1990. Stock price prediction using neural networks: A project report. Neurocomputing 2, 1 (1990), 17--27.Google ScholarCross Ref
- Amnon Shashua and Tamir Hazan. 2005. Non-negative tensor factorization with applications to statistics and computer vision. In Proceedings of the 22nd international conference on Machine learning. ACM, 792--799. Google ScholarDigital Library
- Jianfeng Si, Arjun Mukherjee, Bing Liu, Qing Li, Huayi Li, and Xiaotie Deng. 2013. Exploiting topic based twitter sentiment for stock prediction. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Vol. 2. 24--29.Google Scholar
- Jonathan L Ticknor. 2013. A Bayesian regularized artificial neural network for stock market forecasting. Expert Systems with Applications40, 14 (2013),5501--5506.Google Scholar
- Liheng Zhang, Charu Aggarwal, and Guo-Jun Qi. 2017. Stock Price Prediction via Discovering Multi-Frequency Trading Patterns. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2141--2149. Google ScholarDigital Library
- Zhenkun Zhou, Jichang Zhao, and Ke Xu. 2016. Can online emotions predict the stock market in china?. In International Conference on Web Information Systems Engineering. Springer, 328--342.Google ScholarCross Ref
Index Terms
- Investment Behaviors Can Tell What Inside: Exploring Stock Intrinsic Properties for Stock Trend Prediction
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