A dynamic predictor selection algorithm for predicting stock market movement

Although training a deep network with financial time series is not hard, the important issue is, how much the prediction for the truly new data can be trusted with a trained network. In this study, we propose a dynamic predictor selection algorithm (DPSA) that dynamically evaluates and selects the p...

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Bibliographic Details
Published in:Expert systems with applications Vol. 186; p. 115836
Main Authors: Dong, Shuting, Wang, Jianxin, Luo, Hongze, Wang, Haodong, Wu, Fang-Xiang
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 30.12.2021
Elsevier BV
Subjects:
Algorithms
Clustering
ConvLSTM
Deep learning
Dynamic prediction
Financial time series
Improved KFCM algorithm
Kernels
Machine learning
Performance prediction
Prediction models
Stock exchanges
Time series
Time-weighted
Training
Deep learning
Time-weighted
Improved KFCM algorithm
Financial time series
ConvLSTM
Dynamic prediction
ISSN:0957-4174, 1873-6793
Online Access:Get full text
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Summary:Although training a deep network with financial time series is not hard, the important issue is, how much the prediction for the truly new data can be trusted with a trained network. In this study, we propose a dynamic predictor selection algorithm (DPSA) that dynamically evaluates and selects the prediction model (predictor) for stock daily movement trend prediction. We first build an initial set of potential candidate predictors based on the convolutional long short-term memory networks (ConvLSTMs) by using different values of parameters. To evaluate the candidate predictors, we propose a kernel time-weighted fuzzy c-means clustering algorithm (KTFCM), which improves the kernel FCM algorithm (KFCM), to organize the historical samples according to their relevance to the target sample, which makes the historical samples that are closely related to the target sample have more influence on the predictors. Then, we use the well-organized historical samples to evaluate the candidate predictors. The predictor that yields the best accuracy is selected to predict the target sample. The proposed DPSA algorithm takes less than one minute in total for training the networks, evaluating and selecting the predictors, and performing prediction, which greatly shortens the time of the deep learning prediction. We perform the comparative experiments for the proposed DPSA algorithm and seven popular methods. These experiments test a large real-life financial time series data of various stock markets. The experiment results show that DPSA achieves the best accuracy and the highest return compared to the seven other popular methods. •A dynamic predictor selection model for financial prediction is proposed.•The model organizes the historical samples according to their relevance.•A clustering algorithm is proposed to improve prediction performance.•The model needs a few iterations to differentiate each of the predictor.•The training time of this model is greatly shorter than in deep learning methods.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2021.115836