Stock Price Forecasting Based on Wavelet Filtering and Ensembled Machine Learning Model

Financial data are not only characterized by time-domain correlations but also heavily influenced by numerous market factors. In stock price analysis, the prediction of short-term movements is of much interest to investors and traders. In this paper, we consider forecasting price movements based on...

Full description

Saved in:
Bibliographic Details
Published in:Mathematical problems in engineering 2022-06, Vol.2022, p.1-12
Main Authors: Wang, Pengyue, Li, Xuesheng, Qin, Zhiliang, Qu, Yuanyuan, Zhang, Zhongkai
Format: Article
Language:English
Subjects:
Algorithms
Classification
Decomposition
Forecasting
Machine learning
Mathematical models
Prices
Securities markets
Signal processing
Stock exchanges
Trends
Wavelet transforms
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Financial data are not only characterized by time-domain correlations but also heavily influenced by numerous market factors. In stock price analysis, the prediction of short-term movements is of much interest to investors and traders. In this paper, we consider forecasting price movements based on ensembled machine learning models, which is generally viewed as a challenging task due to noise components inherent in the data and uncertainties in various forms of financial information related to stock prices. To enhance the accuracy of trend predictions, we propose to use wavelet packet decomposition (WPD) and kernel-based smoothing techniques to remove high-frequency noise from the data, based on which we further perform feature engineering to obtain a comprehensive list of multidimensional technical features. Subsequently, we employ the light gradient boosting machine (lightGBM) algorithm to classify the change in the direction of the price trend that occurs in ten trading days. Numerical results on the Shanghai composite index show that the proposed approach has noticeable advantages over traditional statistical and machine learning methods when predicting near term price trends. Index terms—ensembled machine learning, feature correlation, financial data, LGBM, and wavelet denoising.
ISSN:1024-123X
1563-5147
DOI:10.1155/2022/4024953