A comprehensive high pure momentum equity timing framework using the Kalman filter and ARIMA forecasting
The pursuit of higher returns has led to a growing interest in factor timing as a strategy to enhance portfolio returns. Momentum is a popular factor, which involves buying securities that have shown consistent price appreciation over the past 3 to 12 months or past few years, with the expectation t...
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| Format: | Article |
| Language: | English |
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AIMS Press
2024-11-01
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| Series: | Data Science in Finance and Economics |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/DSFE.2024023 |
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| author | Tsumbedzo Mashamba Modisane Seitshiro Isaac Takaidza |
| author_facet | Tsumbedzo Mashamba Modisane Seitshiro Isaac Takaidza |
| author_sort | Tsumbedzo Mashamba |
| collection | DOAJ |
| description | The pursuit of higher returns has led to a growing interest in factor timing as a strategy to enhance portfolio returns. Momentum is a popular factor, which involves buying securities that have shown consistent price appreciation over the past 3 to 12 months or past few years, with the expectation that the trend will continue and reducing exposure to those that consistently declined. An important part of a factor timing strategy is in the portfolio optimization process. This article aimed to first construct a large capitalization pure momentum portfolio, which included a dynamic stringent portfolio construction process criteria for selecting stocks estimated from historical data. Second, as a part of the portfolio's risk management strategy, the Kalman filter was applied to the historical performance of this portfolio. Lastly, the ARIMA forecast was used to estimate expected performance and the confidence intervals. The empirical results showed that this pure equity momentum factor timing framework with the Kalman filter together with the ARIMA (autoregressive integrated moving average) forecasting methodology was iterative and incorporated new information as it became available and further enhanced the monitoring and rebalancing process. This adaptive approach enabled the portfolio to capitalize on time-varying return anomalies as they occured. |
| format | Article |
| id | doaj-art-aa0beb46c52b4148bcf36ca915e03032 |
| institution | Kabale University |
| issn | 2769-2140 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Data Science in Finance and Economics |
| spelling | doaj-art-aa0beb46c52b4148bcf36ca915e030322025-01-24T01:03:03ZengAIMS PressData Science in Finance and Economics2769-21402024-11-014454856910.3934/DSFE.2024023A comprehensive high pure momentum equity timing framework using the Kalman filter and ARIMA forecastingTsumbedzo Mashamba0Modisane Seitshiro1Isaac Takaidza2School of Mathematical and Statistical Sciences, North-West University, Hendrik Van Eck Boulevard, Vanderbijlpark, 1900, South AfricaCentre for Business Mathematics and Informatics, North-West University, Potchefstroom, 2531, South AfricaSchool of Mathematical and Statistical Sciences, North-West University, Hendrik Van Eck Boulevard, Vanderbijlpark, 1900, South AfricaThe pursuit of higher returns has led to a growing interest in factor timing as a strategy to enhance portfolio returns. Momentum is a popular factor, which involves buying securities that have shown consistent price appreciation over the past 3 to 12 months or past few years, with the expectation that the trend will continue and reducing exposure to those that consistently declined. An important part of a factor timing strategy is in the portfolio optimization process. This article aimed to first construct a large capitalization pure momentum portfolio, which included a dynamic stringent portfolio construction process criteria for selecting stocks estimated from historical data. Second, as a part of the portfolio's risk management strategy, the Kalman filter was applied to the historical performance of this portfolio. Lastly, the ARIMA forecast was used to estimate expected performance and the confidence intervals. The empirical results showed that this pure equity momentum factor timing framework with the Kalman filter together with the ARIMA (autoregressive integrated moving average) forecasting methodology was iterative and incorporated new information as it became available and further enhanced the monitoring and rebalancing process. This adaptive approach enabled the portfolio to capitalize on time-varying return anomalies as they occured.https://www.aimspress.com/article/doi/10.3934/DSFE.2024023momentumfactor timingkalman filterreturn predictabilityfactor investinginvestment style factors |
| spellingShingle | Tsumbedzo Mashamba Modisane Seitshiro Isaac Takaidza A comprehensive high pure momentum equity timing framework using the Kalman filter and ARIMA forecasting Data Science in Finance and Economics momentum factor timing kalman filter return predictability factor investing investment style factors |
| title | A comprehensive high pure momentum equity timing framework using the Kalman filter and ARIMA forecasting |
| title_full | A comprehensive high pure momentum equity timing framework using the Kalman filter and ARIMA forecasting |
| title_fullStr | A comprehensive high pure momentum equity timing framework using the Kalman filter and ARIMA forecasting |
| title_full_unstemmed | A comprehensive high pure momentum equity timing framework using the Kalman filter and ARIMA forecasting |
| title_short | A comprehensive high pure momentum equity timing framework using the Kalman filter and ARIMA forecasting |
| title_sort | comprehensive high pure momentum equity timing framework using the kalman filter and arima forecasting |
| topic | momentum factor timing kalman filter return predictability factor investing investment style factors |
| url | https://www.aimspress.com/article/doi/10.3934/DSFE.2024023 |
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