Multiple STL decomposition in discovering a multi-seasonality of intraday trading volume
The seasonal and trend decomposition of a univariate time-series based on Loess (STL) has several advantages over traditional methods. It deals with any periodicity length, enables seasonality change over time, allows missing values, and is robust to outliers. However, it does not handle trading day...
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| Format: | Article |
| Language: | English |
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Croatian Operational Research Society
2021-01-01
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| Series: | Croatian Operational Research Review |
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| Online Access: | https://hrcak.srce.hr/file/377389 |
| _version_ | 1827282261479784448 |
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| author | Josip Arnerić |
| author_facet | Josip Arnerić |
| author_sort | Josip Arnerić |
| collection | DOAJ |
| description | The seasonal and trend decomposition of a univariate time-series based on Loess (STL) has several advantages over traditional methods. It deals with any periodicity length, enables seasonality change over time, allows missing values, and is robust to outliers. However, it does not handle trading day variation by default. This study offers how to deal with this drawback. By applying multiple STL decompositions of 15-minute trading volume observations, three seasonal patterns were discovered: hourly, daily, and monthly. The research objective was not only to discover if multi-seasonality exists
in trading volume by employing high-frequency data but also to determine which seasonal component is most time-varying, and which seasonal components are the strongest or weakest when comparing the variation in the magnitude between them. The results indicate that hourly seasonality is the strongest, while daily seasonality changes the most. A better understanding of trading volume multiple patterns can be very helpful in improving the performance of trading algorithms. |
| first_indexed | 2024-04-24T09:15:10Z |
| format | Article |
| id | doaj.art-bb978be9f3cf48938a8e7dcd8dfe5a9a |
| institution | Directory Open Access Journal |
| issn | 1848-0225 1848-9931 |
| language | English |
| last_indexed | 2024-04-24T09:15:10Z |
| publishDate | 2021-01-01 |
| publisher | Croatian Operational Research Society |
| record_format | Article |
| series | Croatian Operational Research Review |
| spelling | doaj.art-bb978be9f3cf48938a8e7dcd8dfe5a9a2024-04-15T17:00:58ZengCroatian Operational Research SocietyCroatian Operational Research Review1848-02251848-99312021-01-01121617410.17535/crorr.2021.0006Multiple STL decomposition in discovering a multi-seasonality of intraday trading volumeJosip Arnerić0Faculty of Economics and Business, University of ZagrebThe seasonal and trend decomposition of a univariate time-series based on Loess (STL) has several advantages over traditional methods. It deals with any periodicity length, enables seasonality change over time, allows missing values, and is robust to outliers. However, it does not handle trading day variation by default. This study offers how to deal with this drawback. By applying multiple STL decompositions of 15-minute trading volume observations, three seasonal patterns were discovered: hourly, daily, and monthly. The research objective was not only to discover if multi-seasonality exists in trading volume by employing high-frequency data but also to determine which seasonal component is most time-varying, and which seasonal components are the strongest or weakest when comparing the variation in the magnitude between them. The results indicate that hourly seasonality is the strongest, while daily seasonality changes the most. A better understanding of trading volume multiple patterns can be very helpful in improving the performance of trading algorithms.https://hrcak.srce.hr/file/377389hourly seasonalityintraday volumeLoessmultiple seasonal patternsSTL decomposition |
| spellingShingle | Josip Arnerić Multiple STL decomposition in discovering a multi-seasonality of intraday trading volume Croatian Operational Research Review hourly seasonality intraday volume Loess multiple seasonal patterns STL decomposition |
| title | Multiple STL decomposition in discovering a multi-seasonality of intraday trading volume |
| title_full | Multiple STL decomposition in discovering a multi-seasonality of intraday trading volume |
| title_fullStr | Multiple STL decomposition in discovering a multi-seasonality of intraday trading volume |
| title_full_unstemmed | Multiple STL decomposition in discovering a multi-seasonality of intraday trading volume |
| title_short | Multiple STL decomposition in discovering a multi-seasonality of intraday trading volume |
| title_sort | multiple stl decomposition in discovering a multi seasonality of intraday trading volume |
| topic | hourly seasonality intraday volume Loess multiple seasonal patterns STL decomposition |
| url | https://hrcak.srce.hr/file/377389 |
| work_keys_str_mv | AT josiparneric multiplestldecompositionindiscoveringamultiseasonalityofintradaytradingvolume |