Abstract
Forecasting stock returns is a challenging problem due to the highly stochastic nature of the market and the vast array of factors and events that can influence trading volume and prices. Nevertheless it has proven to be an attractive target for machine learning research because of the potential for even modest levels of prediction accuracy to deliver significant benefits. In this paper, we describe a case-based reasoning approach to predicting stock market returns using only historical pricing data. We argue that one of the impediments for case-based stock prediction has been the lack of a suitable similarity metric when it comes to identifying similar pricing histories as the basis for a future prediction—traditional Euclidean and correlation based approaches are not effective for a variety of reasons—and in this regard, a key contribution of this work is the development of a novel similarity metric for comparing historical pricing data. We demonstrate the benefits of this metric and the case-based approach in a real-world application in comparison to a variety of conventional benchmarks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
The relevant code can be found at https://github.com/rian-dolphin/ICCBR2021-Financial-TS-Similarity.
References
Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun. 7(1), 39–59 (1994)
Ahn, H., Kim, K.J.: Bankruptcy prediction modeling with hybrid case-based reasoning and genetic algorithms approach. Appl. Soft Comput. 9(2), 599–607 (2009)
Alaka, H.A., et al.: Systematic review of bankruptcy prediction models: Towards a framework for tool selection. Expert Syst. Appl. 94, 164–184 (2018)
Alostad, H., Davulcu, H.: Directional prediction of stock prices using breaking news on twitter. In: 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 1, pp. 523–530. IEEE (2015)
Ariyo, A.A., Adewumi, A.O., Ayo, C.K.: Stock price prediction using the ARIMA model. In: 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, pp. 106–112. IEEE (2014)
Bachelier, L.: Théorie de la spéculation. In: Annales scientifiques de l’École normale supérieure, vol. 17, pp. 21–86 (1900)
Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLoS One 12(7), e0180944 (2017)
Bedo, M.V.N., dos Santos, D.P., Kaster, D.S., Traina, C.: A similarity-based approach for financial time series analysis and forecasting. In: Decker, H., Lhotská, L., Link, S., Basl, J., Tjoa, A.M. (eds.) DEXA 2013. LNCS, vol. 8056, pp. 94–108. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40173-2_11
Chan Phooi M’ng, J., Mehralizadeh, M.: Forecasting east Asian indices futures via a novel hybrid of wavelet-PCA denoising and artificial neural network models. PloS One 11(6), e0156338 (2016)
Chang, P.C., Fan, C.Y., Lin, J.L.: Trend discovery in financial time series data using a case based fuzzy decision tree. Expert Syst. Appl. 38, 6070–6080 (2011). https://doi.org/10.1016/j.eswa.2010.11.006
Chang, P.C., Liu, C.H., Lin, J.L., Fan, C.Y., Ng, C.S.: A neural network with a case based dynamic window for stock trading prediction. Expert Syst. Appl. 36, 6889–6898 (2009). https://doi.org/10.1016/j.eswa.2008.08.077
Chun, S.H., Kim, S.H.: Data mining for financial prediction and trading: application to single and multiple markets. Expert Syst. Appl. 26(2), 131–139 (2004)
Chun, S.H., Ko, Y.W.: Geometric case based reasoning for stock market prediction. Sustainability (Switzerland) 12 (2020). https://doi.org/10.3390/su12177124
Chun, S.H., Park, Y.J.: Dynamic adaptive ensemble case-based reasoning: application to stock market prediction. Expert Syst. Appl. 28(3), 435–443 (2005)
Chun, S.H., Park, Y.J.: A new hybrid data mining technique using a regression case based reasoning: application to financial forecasting. Expert Syst. Appl. 31(2), 329–336 (2006)
Fama, E.F.: The behavior of stock-market prices. J. Bus. 38(1), 34–105 (1965)
Goswami, M.M., Bhensdadia, C.K., Ganatra, A.: Candlestick analysis based short term prediction of stock price fluctuation using SOM-CBR. In: 2009 IEEE International Advance Computing Conference, pp. 1448–1452. IEEE (2009)
Haque, S., Faruquee, M.: Impact of fundamental factors on stock price: a case based approach on pharmaceutical companies listed with Dhaka stock exchange (2013)
Hu, Z., Zhao, Y., Khushi, M.: A survey of forex and stock price prediction using deep learning. Appl. Syst. Innov. 4(1), 9 (2021)
Ince, H.: Short term stock selection with case-based reasoning technique. Appl. Soft Comput. J. 22, 205–212 (2014). https://doi.org/10.1016/j.asoc.2014.05.017
Jo, H., Han, I., Lee, H.: Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis. Expert Syst. Appl. 13(2), 97–108 (1997)
Kapdan, F., Aktaş, M.G., Aktaş, M.S.: Financial risk prediction based on case based reasoning methodology. In: 2019 Innovations in Intelligent Systems and Applications Conference (ASYU), pp. 1–6. IEEE (2019)
Kenton, W.: Noise and time frames (2020). https://www.investopedia.com/terms/n/noise.asp. Accessed 21 Mar 2021
Kim, K.J.: Toward global optimization of case-based reasoning systems for financial forecasting. Appl. Intell. 21(3), 239–249 (2004)
Kumar, G., Jain, S., Singh, U.P.: Stock market forecasting using computational intelligence: a survey. Arch. Comput. Methods Eng. 28(3), 1069–1101 (2020). https://doi.org/10.1007/s11831-020-09413-5
Lhabitant, F.S.: Correlation vs. trends: a common misinterpretation (2020)
Li, S.T., Ho, H.F.: Predicting financial activity with evolutionary fuzzy case-based reasoning. Expert Syst. Appl. 36(1), 411–422 (2009)
Libesa: Correlation with prices or returns: that is the question. https://quantdare.com/correlation-prices-returns/. Accessed 03 Apr 2020
Long, J., Chen, Z., He, W., Wu, T., Ren, J.: 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, 106205 (2020)
Malkiel, B.G., Fama, E.F.: Efficient capital markets: a review of theory and empirical work. J. Finance 25(2), 383–417 (1970)
Oh, K.J., Kim, T.Y.: Financial market monitoring by case-based reasoning. Expert Syst. Appl. 32(3), 789–800 (2007)
Ozbayoglu, A.M., Gudelek, M.U., Sezer, O.B.: Deep learning for financial applications: a survey. Appl. Soft Comput. 93, 106384 (2020)
Selvin, S., Vinayakumar, R., Gopalakrishnan, E., Menon, V.K., Soman, K.: Stock price prediction using LSTM, RNN and CNN-sliding window model. In: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1643–1647. IEEE (2017)
Shin, K.S., Han, I.: Case-based reasoning supported by genetic algorithms for corporate bond rating. Expert Syst. Appl. 16(2), 85–95 (1999)
Shin, K.S., Han, I.: A case-based approach using inductive indexing for corporate bond rating. Decis. Support Syst. 32(1), 41–52 (2001)
Slade, S.: Case-based reasoning for financial decision making. In: Proceedings of the First International Conference on Artificial Intelligence Applications on Wall Street, New York, NY. IEEE Computer Society (1991)
Wang, Y., Wang, Y.: A case-based reasoning-decision tree hybrid system for stock selection. Int. J. Comput. Inf. Eng. 10(6), 1223–1229 (2016)
Yang, L., et al.: Explainable text-driven neural network for stock prediction. In: 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS), pp. 441–445. IEEE (2018)
Yeh, I.C., Hsu, T.K.: Building real estate valuation models with comparative approach through case-based reasoning. Appl. Soft Comput. 65, 260–271 (2018)
Acknowledgments
This publication has emanated from research conducted with the financial support of Science Foundation Ireland under Grant number 18/CRT/6183.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Dolphin, R., Smyth, B., Xu, Y., Dong, R. (2021). Measuring Financial Time Series Similarity with a View to Identifying Profitable Stock Market Opportunities. In: Sánchez-Ruiz, A.A., Floyd, M.W. (eds) Case-Based Reasoning Research and Development. ICCBR 2021. Lecture Notes in Computer Science(), vol 12877. Springer, Cham. https://doi.org/10.1007/978-3-030-86957-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-86957-1_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86956-4
Online ISBN: 978-3-030-86957-1
eBook Packages: Computer ScienceComputer Science (R0)