Two-phase hybridisation using deep learning and evolutionary algorithms for stock market forecasting

Kumar, Yugal; Kumar, Pardeep; Kumar, Raghavendra

doi:10.1504/ijguc.2021.10042116

Cited by 1 publication

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“…Over the decades, many artificial intelligence algorithms have been developed and applied to financial market forecasting, for instance, artificial neural network [9]- [11], support vector machines [12]- [17], rough set theory [18]- [20], bayesian analysis [21]- [24] and evolutionary learning algorithms [25]- [28]. However, most of the past researches mainly focus on the accurate price forecast only.…”

Section: Introductionmentioning

confidence: 99%

Integrating Piecewise Linear Representation and Deep Learning for Trading Signals Forecasting

Chen

Zhu

2023

IEEE Access

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Trading signal forecasting is an interesting but challenging research topic in the field of financial investment, since the financial market is an nonlinearity and high volatility system influenced by too many factors, and a small improvement in forecasting performance can bring profits. To realize trading signals detection, this paper presents a novel method which integrates piecewise linear representation (PLR) with a deep learning learning framework to predict the financial trading points. Firstly, we utilize PLR to generate a number of turning points (valleys or peaks) from trading data and formulate the trading points prediction as a three-class classification problem. Then, the framework combined a convolutional neural network (CNN) for spatial features extraction and a long short-term memory (LSTM) network for temporal domain features extraction (CNN-LSTM) is used to learn the prediction model between the trading points and the financial time series data. Finally, we conduct a series of experiments among PLR-CNN-LSTM, PLR-CNN-TA and PLR-LSTM on companies of US , Turkey and daily Exchange-Traded Fund (ETFs) to test the performance of our established method. The experiment results show that our proposed method has better model performance and profitability with different investment strategies.

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