A Universal and Interpretable Method for Enhancing Stock Price Prediction
Authors: 
Yuchen Liu, Shimin Di, Lei Chen, Xiaofang Zhou, Fei LinAuthors Info & Claims
Yuchen Liu, Shimin Di, Lei Chen, Xiaofang Zhou, Fei LinAuthors Info & ClaimsPages 1533 - 1543
Abstract
The prediction of stock prices is a highly sought-after topic in the data mining field. In recent decades, many promising methods have been proposed and widely adopted for stock price prediction. However, these methods have inherent limitations, such as low accuracy, lack of transparency, and failure to consider the interactions among stock factors. To address these issues, we propose a UNIversal and interpretable framework for enhancing Stock Price Prediction (abbreviated to UniSPP), which is capable of modeling the interactions among stock factors. UniSPP first builds a fully connected graph, where the nodes and edges are the stock factors and interactions between them, respectively. However, it is a non-trivial task to discover a proper feature interaction subgraph from a large space, especially in discrete graph modeling. Therefore, UniSPP proposes a novel idea to mine the real factor interactions by iteratively sampling subgraphs and optimizing the sampling controller. Empirical studies show that our framework can be incorporated with many popular forecasting models and can effectively discover the suitable factor interaction, which can significantly improve the prediction results of existing models.
References
[1]
Samarth Agrawal, Manoj Jindal, and GN Pillai. 2010. Momentum analysis based stock market prediction using adaptive neuro-fuzzy inference system (anfis). In IMECS, Vol. 1.
[2]
Youhei Akimoto, Shinichi Shirakawa, Nozomu Yoshinari, Kento Uchida, Shota Saito, and Kouhei Nishida. 2019. Adaptive stochastic natural gradient method for one-shot neural architecture search. In ICML. PMLR, 171--180.
[3]
Youhei Akimoto, Shinichi Shirakawa, Nozomu Yoshinari, Kento Uchida, Shota Saito, and Kouhei Nishida. 2019. Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search. In ICML. 171--180.
[4]
Naomi S Altman. 1992. An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician, Vol. 46, 3 (1992), 175--185.
[5]
Adebiyi A Ariyo, Adewumi O Adewumi, and Charles K Ayo. 2014. Stock price prediction using the ARIMA model. In 2014 UKSim-AMSS 16th international conference on computer modelling and simulation. IEEE, 106--112.
[6]
Michel Ballings, Dirk Van den Poel, Nathalie Hespeels, and Ruben Gryp. 2015. Evaluating multiple classifiers for stock price direction prediction. Expert systems with Applications, Vol. 42, 20 (2015), 7046--7056.
[7]
Armando Bernal, Sam Fok, and Rohit Pidaparthi. 2012. Financial market time series prediction with recurrent neural networks. State College: Citeseer (2012).
[8]
Indronil Bhattacharjee and Pryonti Bhattacharja. 2019. Stock price prediction: a comparative study between traditional statistical approach and machine learning approach. In EICT. IEEE, 1--6.
[9]
Baki Billah, Maxwell L King, Ralph D Snyder, and Anne B Koehler. 2006. Exponential smoothing model selection for forecasting. International journal of forecasting, Vol. 22, 2 (2006), 239--247.
[10]
Milon Biswas, Arafat Jahan Nova, Md Kawsher Mahbub, Sudipto Chaki, Shamim Ahmed, and Md Ashraful Islam. 2021. Stock Market Prediction: A Survey and Evaluation. In ICSCT 2021. IEEE, 1--6.
[11]
Leo Breiman. 2001. Random forests. Machine learning, Vol. 45, 1 (2001), 5--32.
[12]
Chris Chatfield. 1978. The Holt-winters forecasting procedure. Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol. 27, 3 (1978), 264--279.
[13]
Tianqi Chen and Carlos Guestrin. 2016. Xgboost: A scalable tree boosting system. In SIGKDD. 785--794.
[14]
Kyunghyun Cho, Bart Van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014. On the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:1409.1259 (2014).
[15]
B Uma Devi, D Sundar, and P Alli. 2013. An effective time series analysis for stock trend prediction using ARIMA model for nifty midcap-50. IJDKP, Vol. 3, 1 (2013), 65.
[16]
Shubharthi Dey, Yash Kumar, Snehanshu Saha, and Suryoday Basak. 2016. Forecasting to Classification: Predicting the direction of stock market price using Xtreme Gradient Boosting. PESIT South Campus (2016).
[17]
Shimin Di and Lei Chen. 2023. Message Function Search for Knowledge Graph Embedding. In Proceedings of the ACM Web Conference 2023, WWW 2023, Austin, TX, USA, 30 April 2023 - 4 May 2023. ACM, 2633--2644. https://doi.org/10.1145/3543507.3583546
[18]
Shimin Di, Quanming Yao, and Lei Chen. 2021. Searching to Sparsify Tensor Decomposition for N-ary Relational Data. In WWW '21: The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19--23, 2021. ACM / IW3C2, 4043--4054. https://doi.org/10.1145/3442381.3449853
[19]
Shimin Di, Quanming Yao, Yongqi Zhang, and Lei Chen. 2021. Efficient Relation-aware Scoring Function Search for Knowledge Graph Embedding. In 37th IEEE International Conference on Data Engineering, ICDE 2021, Chania, Greece, April 19--22, 2021. IEEE, 1104--1115. https://doi.org/10.1109/ICDE51399.2021.00100
[20]
Luca Di Persio and Oleksandr Honchar. 2017. Recurrent neural networks approach to the financial forecast of Google assets. International journal of Mathematics and Computers in simulation, Vol. 11 (2017), 7--13.
[21]
Avijan Dutta, Gautam Bandopadhyay, and Suchismita Sengupta. 2012. Prediction of stock performance in the Indian stock market using logistic regression. International Journal of Business and Information, Vol. 7, 1 (2012), 105.
[22]
Eugene F Fama and Kenneth R French. 1992. The cross-section of expected stock returns. the Journal of Finance, Vol. 47, 2 (1992), 427--465.
[23]
Eugene F Fama and Kenneth R French. 1993. Common risk factors in the returns on stocks and bonds. Journal of financial economics, Vol. 33, 1 (1993), 3--56.
[24]
Yoav Freund and Robert E Schapire. 1997. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of computer and system sciences, Vol. 55, 1 (1997), 119--139.
[25]
Everette S Gardner Jr. 1985. Exponential smoothing: The state of the art. Journal of forecasting, Vol. 4, 1 (1985), 1--28.
[26]
Achyut Ghosh, Soumik Bose, Giridhar Maji, Narayan Debnath, and Soumya Sen. 2019. Stock price prediction using LSTM on Indian share market. In Proceedings of 32nd international conference on, Vol. 63. 101--110.
[27]
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247 (2017).
[28]
Mark Haug and Mark Hirschey. 2006. The january effect. Financial Analysts Journal, Vol. 62, 5 (2006), 78--88.
[29]
Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yanxin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers, et al. 2014. Practical lessons from predicting clicks on ads at facebook. In Proceedings of the eighth international workshop on data mining for online advertising. 1--9.
[30]
Marti A. Hearst, Susan T Dumais, Edgar Osuna, John Platt, and Bernhard Scholkopf. 1998. Support vector machines. IEEE Intelligent Systems and their applications, Vol. 13, 4 (1998), 18--28.
[31]
Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation, Vol. 9, 8 (1997), 1735--1780.
[32]
Htet Htet Htun, Michael Biehl, and Nicolai Petkov. 2023. Survey of feature selection and extraction techniques for stock market prediction. Financial Innovation, Vol. 9, 1 (2023), 26.
[33]
Zexin Hu, Yiqi Zhao, and Matloob Khushi. 2021. A survey of forex and stock price prediction using deep learning. Applied System Innovation, Vol. 4, 1 (2021), 9.
[34]
Narasimhan Jegadeesh and Sheridan Titman. 1993. Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of finance, Vol. 48, 1 (1993), 65--91.
[35]
Narasimhan Jegadeesh and Sheridan Titman. 2001. Profitability of momentum strategies: An evaluation of alternative explanations. The Journal of finance, Vol. 56, 2 (2001), 699--720.
[36]
Yehuda Koren. 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In SIGKDD. 426--434.
[37]
Guokun Lai, Wei-Cheng Chang, Yiming Yang, and Hanxiao Liu. 2018. Modeling long-and short-term temporal patterns with deep neural networks. In SIGIR. 95--104.
[38]
Yann LeCun, Patrick Haffner, Léon Bottou, and Yoshua Bengio. 1999. Object recognition with gradient-based learning. In Shape, contour and grouping in computer vision. Springer, 319--345.
[39]
Jiawei Li, Jingshu Peng, Haoyang Li, and Lei Chen. 2024. UniCL: A Universal Contrastive Learning Framework for Large Time Series Models. arXiv preprint arXiv:2405.10597 (2024).
[40]
Yang Li and Yi Pan. 2022. A novel ensemble deep learning model for stock prediction based on stock prices and news. International Journal of Data Science and Analytics, Vol. 13, 2 (2022), 139--149.
[41]
Bin Liu, Ruiming Tang, Yingzhi Chen, Jinkai Yu, Huifeng Guo, and Yuzhou Zhang. 2019. Feature generation by convolutional neural network for click-through rate prediction. In WWW. 1119--1129.
[42]
Bin Liu, Chenxu Zhu, Guilin Li, Weinan Zhang, Jincai Lai, Ruiming Tang, Xiuqiang He, Zhenguo Li, and Yong Yu. 2020. Autofis: Automatic feature interaction selection in factorization models for click-through rate prediction. In SIGKDD. 2636--2645.
[43]
Wen Long, Zhichen Lu, and Lingxiao Cui. 2019. Deep learning-based feature engineering for stock price movement prediction. Knowledge-Based Systems, Vol. 164 (2019), 163--173.
[44]
Wolfgang Maass, Thomas Natschläger, and Henry Markram. 2002. Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural computation, Vol. 14, 11 (2002), 2531--2560.
[45]
Aidan Meyler, Geoff Kenny, and Terry Quinn. 1998. Forecasting Irish inflation using ARIMA models. (1998).
[46]
Nikola Milosevic. 2016. Equity forecast: Predicting long term stock price movement using machine learning. arXiv preprint arXiv:1603.00751 (2016).
[47]
Dang Lien Minh, Abolghasem Sadeghi-Niaraki, Huynh Duc Huy, Kyungbok Min, and Hyeonjoon Moon. 2018. Deep learning approach for short-term stock trends prediction based on two-stream gated recurrent unit network. Ieee Access, Vol. 6 (2018), 55392--55404.
[48]
Ali Bou Nassif, Maha AlaaEddin, and Amna Akram Sahib. 2020. Machine learning models for stock price prediction. In ITT. IEEE, 67--71.
[49]
Mehtabhorn Obthong, Nongnuch Tantisantiwong, Watthanasak Jeamwatthanachai, and Gary Wills. 2020. A survey on machine learning for stock price prediction: algorithms and techniques. (2020).
[50]
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. NeurIPS, Vol. 32 (2019).
[51]
Yaohao Peng, Pedro Henrique Melo Albuquerque, Herbert Kimura, and Cayan Atreio Portela Bárcena Saavedra. 2021. Feature selection and deep neural networks for stock price direction forecasting using technical analysis indicators. Machine Learning with Applications, Vol. 5 (2021), 100060.
[52]
Yanru Qu, Bohui Fang, Weinan Zhang, Ruiming Tang, Minzhe Niu, Huifeng Guo, Yong Yu, and Xiuqiang He. 2018. Product-based neural networks for user response prediction over multi-field categorical data. TOIS, Vol. 37, 1 (2018), 1--35.
[53]
J. Ross Quinlan. 1986. Induction of decision trees. Machine learning, Vol. 1, 1 (1986), 81--106.
[54]
Frank K Reilly. 1997. The impact of inflation on ROE, growth and stock prices. Financial Services Review, Vol. 6, 1 (1997), 1--17.
[55]
Steffen Rendle. 2010. Factorization machines. In 2010 IEEE International conference on data mining. IEEE, 995--1000.
[56]
Murtaza Roondiwala, Harshal Patel, and Shraddha Varma. 2017. Predicting stock prices using LSTM. IJSR, Vol. 6, 4 (2017), 1754--1756.
[57]
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams. 1985. Learning internal representations by error propagation. Technical Report. California Univ San Diego La Jolla Inst for Cognitive Science.
[58]
S Saravanan and S Mala. 2018. Stock market prediction system: A wavelet based approach. Applied Mathematics and Information Sciences, Vol. 12, 3 (2018), 579--585.
[59]
Akhil Sethia and Purva Raut. 2019. Application of LSTM, GRU and ICA for stock price prediction. In ICTIS, Vol. 2. Springer, 479--487.
[60]
Jingyi Shen and M Omair Shafiq. 2020. Short-term stock market price trend prediction using a comprehensive deep learning system. Journal of big Data, Vol. 7, 1 (2020), 1--33.
[61]
Fatmawati Sholichah, Nurul Asfiah, Titiek Ambarwati, Bambang Widagdo, Mutia Ulfa, and Muhammad Jihadi. 2021. The Effects of Profitability and Solvability on Stock Prices: Empirical Evidence from Indonesia. The Journal of Asian Finance, Economics and Business, Vol. 8, 3 (2021), 885--894.
[62]
Payal Soni, Yogya Tewari, and Deepa Krishnan. 2022. Machine Learning approaches in stock price prediction: A systematic review. In Journal of Physics: Conference Series, Vol. 2161. IOP Publishing, 012065.
[63]
Md Arif Istiake Sunny, Mirza Mohd Shahriar Maswood, and Abdullah G Alharbi. 2020. Deep learning-based stock price prediction using LSTM and bi-directional LSTM model. In NILES. IEEE, 87--92.
[64]
Nancy Ursel and Marjorie Armstrong-Stassen. 1995. The impact of layoff announcements on shareholders. Relations industrielles, Vol. 50, 3 (1995), 636--649.
[65]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. NeurIPS, Vol. 30 (2017).
[66]
Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang. 2017. Deep & cross network for ad click predictions. In ADKDD. 1--7.
[67]
Zhili Wang, Shimin Di, and Lei Chen. 2021. AutoGEL: An Automated Graph Neural Network with Explicit Link Information. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6--14, 2021, virtual. 24509--24522.
[68]
Bernard L Welch. 1947. The generalization of 'STUDENT'S'problem when several different population varlances are involved. Biometrika, Vol. 34, 1--2 (1947), 28--35.
[69]
Billy M Williams and Lester A Hoel. 2003. Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results. Journal of transportation engineering, Vol. 129, 6 (2003), 664--672.
[70]
Ronald J Williams. 1992. Simple statistical gradient-following algorithms for connectionist reinforcement learning. ML, Vol. 8, 3--4 (1992), 229--256.
[71]
Sirui Xie, Hehui Zheng, Chunxiao Liu, and Liang Lin. 2018. SNAS: stochastic neural architecture search. arXiv preprint arXiv:1812.09926 (2018).
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- A Universal and Interpretable Method for Enhancing Stock Price Prediction
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