A comprehensive high pure momentum equity timing framework using the Kalman filter and ARIMA forecasting - Tsumbedzo Mashamba; Modisane Seitshiro; Isaac Takaidza (Data Science in Finance and Economics)
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.