Predicting Trend of High Frequency CSI 300 Index Using Adaptive Input Selection and Machine Learning Techniques

Kong Ao () and Zhu Hongliang ()
Additional contact information
Kong Ao: School of Finance, Nanjing University of Finance and Economics, Nanjing, 210023, China
Zhu Hongliang: School of Management and Engineering, Nanjing University, Nanjing, 210093, China

Journal of Systems Science and Information, 2018, vol. 6, issue 2, 120-133

Abstract: High-frequency stock trend prediction using machine learners has raised substantial interest in literature. Nevertheless, there is no gold standard to select the inputs for the learners. This paper investigates the approach of adaptive input selection (AIS) for the trend prediction of high-frequency stock index price and compares it with the commonly used deterministic input setting (DIS) approach. The DIS approach is implemented through computation of technical indicator values on deterministic period parameters. The AIS approach selects the most suitable indicators and their parameters for the time-varying dataset using feature selection methods. Two state-of-the-art machine learners, support vector machine (SVM) and artificial neural network (ANN), are adopted as learning models. Accuracy and F-measure of SVM and ANN models with both the approaches are computed based on the high-frequency data of CSI 300 index. The results suggest that the AIS approach using t-statistics, information gain and ROC methods can achieve better prediction performance than the DIS approach. Also, the investment performance evaluation shows that the AIS approach with the same three feature selection methods provides significantly higher returns than the DIS approach.

Keywords: high-frequency data; index trend prediction; machine learning; technical indicators; feature selection (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.21078/JSSI-2018-120-14 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:jossai:v:6:y:2018:i:2:p:120-133:n:2

DOI: 10.21078/JSSI-2018-120-14

Access Statistics for this article

Journal of Systems Science and Information is currently edited by Shouyang Wang

More articles in Journal of Systems Science and Information from De Gruyter
Bibliographic data for series maintained by Peter Golla ().