A comprehensive high pure momentum equity timing framework using the Kalman filter and ARIMA forecasting

Tsumbedzo Mashamba; Modisane Seitshiro; Isaac Takaidza; Tsumbedzo Mashamba; Modisane Seitshiro; Isaac Takaidza

doi:10.3934/DSFE.2024023

Data Science in Finance and Economics

2024, Volume 4, Issue 4: 548-569. doi: 10.3934/DSFE.2024023

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Research article

A comprehensive high pure momentum equity timing framework using the Kalman filter and ARIMA forecasting

1.
School of Mathematical and Statistical Sciences, North-West University, Hendrik Van Eck Boulevard, Vanderbijlpark, 1900, South Africa
2.
Centre for Business Mathematics and Informatics, North-West University, Potchefstroom, 2531, South Africa
3.
National Institute of Theorectical and Computational Sciences (NITheCS), Stellenbosch, South Africa

Received: 28 June 2024 Revised: 30 October 2024 Accepted: 27 November 2024 Published: 29 November 2024
JEL Codes: G11, G12, G17, C22

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.
- momentum,
- factor timing,
- Kalman filter,
- return predictability,
- factor investing,
- investment style factors
Citation: Tsumbedzo Mashamba, Modisane Seitshiro, Isaac Takaidza. A comprehensive high pure momentum equity timing framework using the Kalman filter and ARIMA forecasting[J]. Data Science in Finance and Economics, 2024, 4(4): 548-569. doi: 10.3934/DSFE.2024023

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Abstract

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.

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