The Application of Stock Index Price Prediction with Neural Network
Stock index price prediction is prevalent in both academic and economic fields. The index price is hard to forecast due to its uncertain noise. With the development of computer science, neural networks are applied in kinds of industrial fields. In this paper, we introduce four different methods in m...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-08-01
|
| Series: | Mathematical and Computational Applications |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2297-8747/25/3/53 |
| _version_ | 1827709000416755712 |
|---|---|
| author | Penglei Gao Rui Zhang Xi Yang |
| author_facet | Penglei Gao Rui Zhang Xi Yang |
| author_sort | Penglei Gao |
| collection | DOAJ |
| description | Stock index price prediction is prevalent in both academic and economic fields. The index price is hard to forecast due to its uncertain noise. With the development of computer science, neural networks are applied in kinds of industrial fields. In this paper, we introduce four different methods in machine learning including three typical machine learning models: Multilayer Perceptron (MLP), Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) and one attention-based neural network. The main task is to predict the next day’s index price according to the historical data. The dataset consists of the SP500 index, CSI300 index and Nikkei225 index from three different financial markets representing the most developed market, the less developed market and the developing market respectively. Seven variables are chosen as the inputs containing the daily trading data, technical indicators and macroeconomic variables. The results show that the attention-based model has the best performance among the alternative models. Furthermore, all the introduced models have better accuracy in the developed financial market than developing ones. |
| first_indexed | 2024-03-10T17:13:46Z |
| format | Article |
| id | doaj.art-025937e1b3c94b8c85b753781f5cb029 |
| institution | Directory Open Access Journal |
| issn | 1300-686X 2297-8747 |
| language | English |
| last_indexed | 2024-03-10T17:13:46Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematical and Computational Applications |
| spelling | doaj.art-025937e1b3c94b8c85b753781f5cb0292023-11-20T10:32:47ZengMDPI AGMathematical and Computational Applications1300-686X2297-87472020-08-012535310.3390/mca25030053The Application of Stock Index Price Prediction with Neural NetworkPenglei Gao0Rui Zhang1Xi Yang2Department of Mathematical Sciences, Xi’an Jiaotong-Liverpool University, Suzhou 215123, ChinaDepartment of Mathematical Sciences, Xi’an Jiaotong-Liverpool University, Suzhou 215123, ChinaDepartment of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, ChinaStock index price prediction is prevalent in both academic and economic fields. The index price is hard to forecast due to its uncertain noise. With the development of computer science, neural networks are applied in kinds of industrial fields. In this paper, we introduce four different methods in machine learning including three typical machine learning models: Multilayer Perceptron (MLP), Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) and one attention-based neural network. The main task is to predict the next day’s index price according to the historical data. The dataset consists of the SP500 index, CSI300 index and Nikkei225 index from three different financial markets representing the most developed market, the less developed market and the developing market respectively. Seven variables are chosen as the inputs containing the daily trading data, technical indicators and macroeconomic variables. The results show that the attention-based model has the best performance among the alternative models. Furthermore, all the introduced models have better accuracy in the developed financial market than developing ones.https://www.mdpi.com/2297-8747/25/3/53stock index predictionmachine learningneural networkattention-based model |
| spellingShingle | Penglei Gao Rui Zhang Xi Yang The Application of Stock Index Price Prediction with Neural Network Mathematical and Computational Applications stock index prediction machine learning neural network attention-based model |
| title | The Application of Stock Index Price Prediction with Neural Network |
| title_full | The Application of Stock Index Price Prediction with Neural Network |
| title_fullStr | The Application of Stock Index Price Prediction with Neural Network |
| title_full_unstemmed | The Application of Stock Index Price Prediction with Neural Network |
| title_short | The Application of Stock Index Price Prediction with Neural Network |
| title_sort | application of stock index price prediction with neural network |
| topic | stock index prediction machine learning neural network attention-based model |
| url | https://www.mdpi.com/2297-8747/25/3/53 |
| work_keys_str_mv | AT pengleigao theapplicationofstockindexpricepredictionwithneuralnetwork AT ruizhang theapplicationofstockindexpricepredictionwithneuralnetwork AT xiyang theapplicationofstockindexpricepredictionwithneuralnetwork AT pengleigao applicationofstockindexpricepredictionwithneuralnetwork AT ruizhang applicationofstockindexpricepredictionwithneuralnetwork AT xiyang applicationofstockindexpricepredictionwithneuralnetwork |