Deep learning-based feature engineering for stock price movement prediction

By Wen Long, Zhichen Lu, Lingxiao Cui. Knowledge-Based Systems, 2019

401 citations recorded by the source corpus.

Abstract Stock price modeling and prediction have been challenging objectives for researchers and speculators because of noisy and non-stationary characteristics of samples. With the growth in deep learning, the task of feature learning can be performed more effectively by purposely designed network. In this paper, we propose a novel end-to-end model named multi-filters neural network (MFNN) specifically for feature extraction on financial time series samples and price movement prediction task. Both convolutional and recurrent neurons are integrated to build the multi-filters structure, so that the information from different feature spaces and market views can be obtained. We apply our MFNN for extreme market prediction and signal-based trading simulation tasks on Chinese stock market index CSI 300. Experimental results show that our network outperforms traditional machine learning models, statistical models, and single-structure(convolutional, recurrent, and LSTM) networks in terms of the accuracy, profitability, and stability.

Subfields: Electrical and Electronic Engineering, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/5abe3b28efd07efb72d343ebb0f14f7754011459

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Pooyan Nayyeri
2. Hamed Jabani
3. Ze Hu
4. Amir Mosavi
5. Matloob Khushi
6. Yiqi Zhao
7. Mojtaba Nabipour

In Finance

1. Ze Hu
2. Matloob Khushi
3. Yiqi Zhao

In Management Science and Operations Research

1. Pooyan Nayyeri
2. Hamed Jabani
3. Ze Hu
4. Amir Mosavi
5. Matloob Khushi
6. Yiqi Zhao
7. Mojtaba Nabipour

Citations in the corpus

• Applications of deep learning in stock market prediction: recent progress (2020, neutral): “Both bi-directional LSTM (BiLSTM) and bi-directional GRU (BGRU) have been used for stock market prediction.”
• Deep Learning for Stock Market Prediction (2020, background): “[37] suggested an innovative end-to-end model named multi-filters neural network (MFNN) specifically for price prediction task and feature extraction on financial time series data.”
• Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis (2020, background): “Employing deep learning was the next phase in improving prediction models with better performance [3], [4].”
• A Survey of Forex and Stock Price Prediction Using Deep Learning (2021, methodology): “The results showed that the proposed model is better than the RNN, CNN, and traditional machine learning methods [84].”