A new hybrid approach for forecasting of daily stock market time series data
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | A new hybrid approach for forecasting of daily stock market time series data |
| 2. | Creator | Author's name, affiliation, country | Ahmad M. Awajan; Al-Hussein Bin Talal University; Jordan |
| 3. | Subject | Discipline(s) | |
| 3. | Subject | Keyword(s) | EMD, Forecasting, Nonstationary time series |
| 4. | Description | Abstract | In recent years, many researchers have focused on forecasting financial time series data, especially stock market data. Stock market data possesses so many features that forecasting may be very challenging. In the present study, a hybrid of two methodologies is proposed, which is the Empirical Mode Decomposition (EMD) and the Random Walk (RW) in order to enhance the stock market forecasting performance, denoted by (EMD-RW). The advantage of EMD-RW is its ability to forecast nonlinear and nonstationary stock market data without the need to use some transformation method or differencing a time series technique. Moreover, the new proposed EMD-RW produced high-accuracy results. Ten stock market time series for ten different countries are used in this study to demonstrate the forecasting accuracy of the EMD-RW. Results using four forecasting accuracy functions display that EMD-RW forecasting accuracy is better than the four compared methods. |
| 5. | Publisher | Organizing agency, location | Coordinamento SIBA - Università del Salento |
| 6. | Contributor | Sponsor(s) | |
| 7. | Date | (YYYY-MM-DD) | 2024-03-15 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | http://siba-ese.unisalento.it/index.php/ejasa/article/view/27483 |
| 10. | Identifier | Digital Object Identifier | 10.1285/i20705948v17n1p162 |
| 11. | Source | Publication/conference title; vol., no. (year) | Electronic Journal of Applied Statistical Analysis; Vol 17, No 1 (2024): Electronic Journal of Applied Statistical Analysis (Special Issue: Big Data in Economics' Applications) |
| 12. | Language | English=en | en |
| 13. | Relation | Supp. Files | |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions | Authors who publish with EJASA agree to the Creative Commons Attribuzione - Non commerciale - Non opere derivate 3.0 Italia License. |