Forecasting based on spectral time series analysis: prediction of the Aurubis stock price
The attempt to predict stock price movements has occupied investors ever since. Reliable forecasts are a basis for investment management, and improved forecasting results lead to enhanced portfolio performance and sound risk management. While forecasting using the Wiener process has received great a...
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| Format: | Article |
| Language: | English |
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LLC "CPC "Business Perspectives"
2020-12-01
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| Series: | Investment Management & Financial Innovations |
| Subjects: | |
| Online Access: | https://businessperspectives.org/images/pdf/applications/publishing/templates/article/assets/14324/IMFI_2020_04_Babirath.pdf |
| _version_ | 1818394073539543040 |
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| author | Julia Babirath Karel Malec Rainer Schmitl Kamil Maitah Mansoor Maitah |
| author_facet | Julia Babirath Karel Malec Rainer Schmitl Kamil Maitah Mansoor Maitah |
| author_sort | Julia Babirath |
| collection | DOAJ |
| description | The attempt to predict stock price movements has occupied investors ever since. Reliable forecasts are a basis for investment management, and improved forecasting results lead to enhanced portfolio performance and sound risk management. While forecasting using the Wiener process has received great attention in the literature, spectral time series analysis has been disregarded in this respect. The paper’s main objective is to evaluate whether spectral time series analysis can produce reliable forecasts of the Aurubis stock price. Aurubis poses a suitable candidate for an investor’s portfolio due to its sound economic and financial situation and the steady dividend policy. Additionally, reliable management contributes to making Aurubis an investment opportunity. To judge if the achieved forecast results can be considered satisfactory, they are compared against the simulation results of a Wiener process. After de-trending the time series using an Augmented Dickey-Fuller test, the residuals were compartmentalized into sine and cosine functions. The frequencies, amplitude, and phase were obtained using the Fast Fourier transform. The mean absolute percentage error measured the accuracy of the stock price prediction, and the results showed that the spectral analysis was able to deliver superior results when comparing the simulation using a Wiener process. Hence, spectral time series can enhance stock price forecasts and consequently improve risk management. |
| first_indexed | 2024-12-14T05:55:25Z |
| format | Article |
| id | doaj.art-73a3363b348d4e3485a0dfa557c8e04d |
| institution | Directory Open Access Journal |
| issn | 1810-4967 1812-9358 |
| language | English |
| last_indexed | 2024-12-14T05:55:25Z |
| publishDate | 2020-12-01 |
| publisher | LLC "CPC "Business Perspectives" |
| record_format | Article |
| series | Investment Management & Financial Innovations |
| spelling | doaj.art-73a3363b348d4e3485a0dfa557c8e04d2022-12-21T23:14:36ZengLLC "CPC "Business Perspectives"Investment Management & Financial Innovations1810-49671812-93582020-12-0117421522710.21511/imfi.17(4).2020.2014324Forecasting based on spectral time series analysis: prediction of the Aurubis stock priceJulia Babirath0Karel Malec1https://orcid.org/0000-0002-3395-018XRainer Schmitl2Kamil Maitah3Mansoor Maitah4https://orcid.org/0000-0003-2797-3155Ph.D. Candidate, University of Applied Sciences in EisenstadtPh.D., Assistant Professor, Faculty of Economics and Management, Dept. of Economics, Czech University of Life Sciences PraguePh.D. Candidate, University of Applied Sciences in EisenstadtPh.D. Candidate, Faculty of Economics and Management, Dept. of Finance and Trade, Czech University of Life Sciences PragueProfessor, Faculty of Economics and Management, Dept. of Economics, Czech University of Life Sciences PragueThe attempt to predict stock price movements has occupied investors ever since. Reliable forecasts are a basis for investment management, and improved forecasting results lead to enhanced portfolio performance and sound risk management. While forecasting using the Wiener process has received great attention in the literature, spectral time series analysis has been disregarded in this respect. The paper’s main objective is to evaluate whether spectral time series analysis can produce reliable forecasts of the Aurubis stock price. Aurubis poses a suitable candidate for an investor’s portfolio due to its sound economic and financial situation and the steady dividend policy. Additionally, reliable management contributes to making Aurubis an investment opportunity. To judge if the achieved forecast results can be considered satisfactory, they are compared against the simulation results of a Wiener process. After de-trending the time series using an Augmented Dickey-Fuller test, the residuals were compartmentalized into sine and cosine functions. The frequencies, amplitude, and phase were obtained using the Fast Fourier transform. The mean absolute percentage error measured the accuracy of the stock price prediction, and the results showed that the spectral analysis was able to deliver superior results when comparing the simulation using a Wiener process. Hence, spectral time series can enhance stock price forecasts and consequently improve risk management.https://businessperspectives.org/images/pdf/applications/publishing/templates/article/assets/14324/IMFI_2020_04_Babirath.pdfinvestment managementportfolio performancestock price simulation |
| spellingShingle | Julia Babirath Karel Malec Rainer Schmitl Kamil Maitah Mansoor Maitah Forecasting based on spectral time series analysis: prediction of the Aurubis stock price Investment Management & Financial Innovations investment management portfolio performance stock price simulation |
| title | Forecasting based on spectral time series analysis: prediction of the Aurubis stock price |
| title_full | Forecasting based on spectral time series analysis: prediction of the Aurubis stock price |
| title_fullStr | Forecasting based on spectral time series analysis: prediction of the Aurubis stock price |
| title_full_unstemmed | Forecasting based on spectral time series analysis: prediction of the Aurubis stock price |
| title_short | Forecasting based on spectral time series analysis: prediction of the Aurubis stock price |
| title_sort | forecasting based on spectral time series analysis prediction of the aurubis stock price |
| topic | investment management portfolio performance stock price simulation |
| url | https://businessperspectives.org/images/pdf/applications/publishing/templates/article/assets/14324/IMFI_2020_04_Babirath.pdf |
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