Abstract
This paper investigates relevant features for the prediction of intraday S&P 500 returns. In contrast to most previous research, the problem is approached as a four class classification problem to account for the magnitude of the returns and not only the direction of price movements. A novel framework for feature selection using a hybrid approach is developed that combines correlation as a fast filter method, with the wrapper method differential evolution feature selection (DEFS) that deploys distance-based classifiers (k-nearest neighbor, fuzzy k-nearest neighbor, and multi-local power mean fuzzy k-nearest neighbor) as evaluation criterion. The experimental results show that feature selection successfully discarded features for this application to improve the test set accuracies or, at a minimum, lead to similar accuracies than using the entire feature subset. Moreover, all setups in this study ranked technical indicators such as 5-day simple moving average as the most relevant features in this application. In contrast, the features based on other stock indices, commodities, and simple price and volume information were a minority within the top 10 and top 50 features. The prediction accuracies for the positive return class considerably higher than the negative class predictions with over \(70\%\) accuracy compared to \(30\%\).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The MATLAB code of the updated MLPM-FKNN algorithm can be found from https://github.com/MahindaMK/Multi-local-Power-means-based-fuzzy-k-nearest-neighbor-algorithm-MLPM-FKNN.
References
Kazem, A., Sharifia, E., Hussainb, F.K., Saberic, M., Hussain, O.K.: Support vector regression with chaos-based firefly algorithm for stock market price forecasting. Appl. Soft Comput. 13, 947–958 (2013). https://doi.org/10.1016/j.asoc.2012.09.024
Zhang, X., Hu, Y., Xie, K., Wang, S., Ngai, E.W.T., Liu, M.: A causal feature selection algorithm for stock prediction modeling. Neurocomputing 142, 48–59 (2014). https://doi.org/10.1016/j.neucom.2014.01.057
Tsai, C.F., Hsiao, Y.C.: Combining multiple feature selection methods for stock prediction: union, intersection, and multi-intersection approaches. Decis. Support Syst. 50(1), 258–269 (2010). https://doi.org/10.1016/j.dss.2010.08.028
Lohrmann, C., Luukka, P.: Classification of intraday S&P500 returns with a random forest. Int. J. Forecast. 35, 390–407 (2019). https://doi.org/10.1016/j.ijforecast.2018.08.004
Kittler, J., Mardia, K.V.: Statistical pattern recognition in image analysis. J. Appl. Stat. 21, 61–75 (1994)
Liang, J., Yang, S., Winstanley, A.: Invariant optimal feature selection: a distance discriminant and feature ranking based solution. Pattern Recogn. 41, 1429–1439 (2008). https://doi.org/10.1016/j.patcog.2007.10.018
Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. 40, 16–28 (2014)
Kumbure, M.M., Luukka, P., Collan, M.: An enhancement of fuzzy K-nearest neighbor classifier using multi-local power means. In: Proceeding of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), pp. 83–90, Atlantis Press (2019)
Zhang, N., Lin, A., Shang, P.: Multidimensional k-nearest neighbor model based on EEMD for financial time series forecasting. IPhysica A Stat. Mech. Appl. 477, 161–173 (2017)
Cao, H., Lin, T., Li, Y., Zhang, H.: Stock price pattern prediction based on complex network and machine learning. Complexity 2019 (2019)
Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theor. 13(1), 21–27 (1967). https://doi.org/10.1109/TIT.1967.1053964
Keller, J.M., Gray, M.R., Givens, J.A.: A fuzzy k-nearest neighbor algorithm. IEEE Trans. Syst. Man Cybern. 15(4), 580–585 (1985). https://doi.org/10.1109/TSMC.1985.6313426
Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution - a Practical Approach to Global Optimization. Springer, Heidelberg (2005)
Yang, F., Chen, Z., Li, J., Tang, L.: A novel hybrid stock selection method with stock prediction. Appl. Soft Comput. J. 142, 820–831 (2019)
Khushaba, R.N., Al-Ani, A., Al-Jumaily, A.: Feature subset selection using differential evolution and a statistical repair mechanism. Expert Syst. Appl. 38, 11515–11526 (2011). https://doi.org/10.1016/j.eswa.2011.03.028
Bisoi, R., Dash, P.K., Parida, A.K.: Hybrid variational mode decomposition and evolutionary robust kernel extreme learning machine for stock price and movement prediction on daily basis. Appl. Soft Comput. 74, 652–676 (2019)
Yang, F., Chen, Z., Li, J., Tang, L.: A novel hybrid stock selection method with stock prediction. Appl. Soft Comput. 80, 820–831 (2019)
Nabipour, M., Nayyeri, P., Jabani, H., Shahab, S., Mosavi, A.: Predicting stock market trends using machine learning and deep learning algorithms via continuous and binary data; a comparative analysis. IEEE Access 8, 150199–150212 (2020). https://doi.org/10.1109/ACCESS.2020.3015966
Patel, J., Shah, S., Thakkar, P., Kotecha, K.: Predicting stock market index using fusion of machine learning techniques. Expert Syst. Appl. 42(4), 2162–2172 (2015). https://doi.org/10.1016/j.eswa.2014.10.031
Yahoo Finance. https://finance.yahoo.com/. Accessed 22 Oct 2020
FRED Economic Data. https://fred.stlouisfed.org. Accessed 25 Oct 2020
Kumbure, M. M., Lohrmann, C., Luukka, P., Porras, J.: Machine learning techniques and data for stock market forecasting: a literature review. Expert Syst. Appl. (2021, Submitted)
Kumbure, M.M., Luukka, P., Collan, M.: A new fuzzy k-nearest neighbor classifier based on the Bonferroni mean. Pattern Recogn. Lett. 140, 172–178 (2020). https://doi.org/10.1016/j.patrec.2020.10.005
Teixeira, L.A., De Oliveira, A.L.I.: A method for automatic stock trading combining technical analysis and nearest neighbor classification. Expert Syst. Appl. 37(10), 6885–6890 (2010). https://doi.org/10.1016/j.eswa.2010.03.033
Acknowledgment
This research was supported by the Finnish Foundation for Share Promotion (Pörssisäätiö).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Mailagaha Kumbure, M., Lohrmann, C., Luukka, P. (2022). A Study on Relevant Features for Intraday S&P 500 Prediction Using a Hybrid Feature Selection Approach. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2021. Lecture Notes in Computer Science(), vol 13163. Springer, Cham. https://doi.org/10.1007/978-3-030-95467-3_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-95467-3_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-95466-6
Online ISBN: 978-3-030-95467-3
eBook Packages: Computer ScienceComputer Science (R0)