You Can't Always Trend When You Want Abhilash Babu, Brendan Hoffman, Ari Levine, Yao Hua Ooi, Sarah Schroeder and Erik Stamelos The Journal of Portfolio Management, March 2020A version of this [...]
For investors that use trend-following strategies, Avramov, Kaplanski, and Subrhmanyam provided new evidence supporting momentum strategies and showed that the distance between short- and longer-term momentum signals provides additional explanatory power in the cross-section of equity returns.
This article describes digital filters derived from time series regression models that can be used as technical analysis tools. The filters are analyzed from a digital signal processing (DSP) frequency domain perspective to illustrate their properties. Example charts of the filters applied to the S&P 500 index are also included.
Allocation to trend following can further improve the efficiency of their portfolio by also adding allocations to the other uncorrelated strategies, further reducing tail risks by reducing the dispersion of potential outcomes.
Trend following, at its core, is a strategy where investors buy an asset when it's going up and sell when it’s going down. But unlike panic-driven investors who sell at the worst possible moment, trend followers adhere to a rules-based approach in an attempt to remove emotion from the equation.
Original: August, 2020 Updated: March, 2025 Overview Digital signal processing (DSP), specifically the use of digital filters, is embedded in many indicators used by technical [...]
This article examines four digital filters commonly used for trend-following: moving average linear weighted moving average exponential smoothing time series momentum
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