Abstract
Forecasting the stock markets is among the most popular research challenges in finance. Several quantitative trading systems based on supervised machine learning approaches have been presented in literature. Recently proposed solutions train classification models on historical stock-related datasets. Training data include a variety of features related to different facets (e.g., stock price trends, exchange volumes, price volatility, news and public mood). To increase the accuracy of the predictions, multiple models are often combined together using ensemble methods. However, understanding which models should be combined together and how to effectively handle features related to different facets within different models are still open research questions. In this paper we investigate the use of ensemble methods to combine faceted classification models for supporting stock trading. To this aim, separate classification models are trained on each subset of features belonging to the same facet. They produce trading signals tailored to a specific facet. Signals are then combined together and filtered to generate a unified, multi-faceted recommendation. The experimental validation, performed on different markets and in different conditions, shows that, in many cases, some of the faceted models perform as good as or better than models trained on a mix of different features. An ensemble of the faceted recommendations makes the generated trading signals more profitable yet robust to draw-down periods.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
For the sake of simplicity, throughout the paper we have considered yearly periods.
Recommended configuration settings: SVC (Rbf kernel. \(\text {C}=1\). \(\text {Gamma}=\frac{1}{|D|}\)), MNB (\(\alpha =1.0\)), K-NN (\(K=5\)), RFC (\(\text {Criterion}=Gini\), \(\text {Max}\_\text {depth}=none\), \(\text {num}\_\text {estimators}=100\)), MLP (\(\text {hidden}\_\text {layer}\_\text {sizes}=20\), \(\text {solver}=lbfgs\), \(\text {n}\_\text {iter}\_\text {no}\_\text {change}=2\)).
Due to the lack of space, the detailed results are given as additional material.
References
Baralis E, Cagliero L, Cerquitelli T, Garza P, Pulvirenti F (2017) Discovering profitable stocks for intraday trading. Inf Sci 405:91–106
Chan E (2013) Algorithmic trading: winning strategies and their rationale, 1st edn. Wiley, Hoboken
Chen Y, Hao Y (2017) A feature weighted support vector machine and k-nearest neighbor algorithm for stock market indices prediction. Expert Syst Appl 80:340–355
Chiang WC, Enke D, Wu T, Wang R (2016) An adaptive stock index trading decision support system. Expert Syst Appl 59:195–207
Enke D, Thawornwong S (2005) The use of data mining and neural networks for forecasting stock market returns. Expert Syst Appl 29(4):927–940
Gaaken M, Afezaalaca M, Boru A, Dosdoayru AT (2016) Integrating metaheuristics and artificial neural networks for improved stock price prediction. Expert Syst Appl 44:320–331
Kim MJ, Min SH, Han I (2006) An evolutionary approach to the combination of multiple classifiers to predict a stock price index. Expert Syst Appl 31:241–247
Kim Y, Ahn W, Oh KJ, Enke D (2017) An intelligent hybrid trading system for discovering trading rules for the futures market using rough sets and genetic algorithms. Appl Soft Comput 55:127–140
Kwon Y, Moon B (2007) A hybrid neurogenetic approach for stock forecasting. IEEE Trans Neural Netw 18(3):851–864
Li Q, Chen Y, Jiang LL, Li P, Chen H (2016) A tensor-based information framework for predicting the stock market. ACM Trans Inf Syst 34(2):11:1–11:30
Loper E, Bird S (2002) Nltk: the natural language toolkit. In: In Proceedings of the ACL workshop on effective tools and methodologies for teaching natural language processing and computational linguistics. Association for Computational Linguistics, Philadelphia
Murphy J (1999) Technical analysis of the financial markets: a comprehensive guide to trading methods and applications. New York Institute of Finance Series. New York Institute of Finance. https://books.google.it/books?id=5zhXEqdr_IcC
Naranjo R, Arroyo J, Santos M (2018) Fuzzy modeling of stock trading with fuzzy candlesticks. Expert Syst Appl 93:15–27
Nayak RK, Mishra D, Rath AK (2015) A naive svm-knn based stock market trend reversal analysis for Indian benchmark indices. Appl Soft Comput 35:670–680
Nelson DMQ, Pereira ACM, de Oliveira RA (2017) Stock market’s price movement prediction with lstm neural networks. In: 2017 International joint conference on neural networks (IJCNN), pp 1419–1426
Patel J, Shah S, Thakkar P, Kotecha K (2015) Predicting stock market index using fusion of machine learning techniques. Expert Syst Appl 42(4):2162–2172
Malagrino LS, Roman NT, Monteiro AM (2018) Forecasting stock market index daily direction: a Bayesian network approach. Expert Syst Appl 105:11–22
Selvin S, Vinayakumar R, Gopalakrishnan EA, Menon VK, Soman KP (2017) Stock price prediction using lstm, RNN and cnn-sliding window model. In: ICACCI. IEEE, pp 1643–1647
Tan PN, Steinbach M, Karpatne A, Kumar V (2018) Introduction to data mining, 2nd edn. Pearson, London
Tsai CF, Lin YC, Yen DC, Chen YM (2011) Predicting stock returns by classifier ensembles. Appl Soft Comput 11(2):2452–2459
Zhong X, Enke D (2017) Forecasting daily stock market return using dimensionality reduction. Expert Syst Appl 67:126–139
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Cagliero, L., Garza, P., Attanasio, G. et al. Training ensembles of faceted classification models for quantitative stock trading. Computing 102, 1213–1225 (2020). https://doi.org/10.1007/s00607-019-00776-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00607-019-00776-7