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
Investing isn’t about being mostly right. In fact you can be mostly wrong and beat portfolios that were mostly right! Today, we’ll explore how investors can potentially improve portfolio outcomes by targeting two seemingly contradictory but deeply complementary systems as outlined in the latest Mauboussin-Callahan paper, Probabilities & Payoffs: The Practicality and Psychology of Expected Value. But understanding this counterintuitive reality requires a shift in mindset—one that embraces uncertainty and focuses on the power of diversification.
This article examines and compares, from a digital signal processing (DSP) time domain perspective, several filters that are modeled on the assumption that the input follows a second order process, i.e., the input contains a linear trend. These filters are, by design, better able to track linear trends than some other more commonly-used filters, such as moving average, exponential smoothing, etc., which exhibit lag, or a time delay, in response to trends. Filters modeled on a second order process are commonly referred to in the technical analysis literature as “zero lag” filters.
"Buy the dip" (BTD) has become one of the most popular investment mantras of recent years, especially since the COVID-19 market recovery in 2020. The strategy seems intuitive: when markets fall, buy at a discount and wait for the inevitable rebound. However, BTD is not foolproof. By design, it performs well when market declines are brief, but poorly when declines mark the beginning of a prolonged drawdown. A new paper from AQR Capital Management, “Hold the Dip,” examines the empirical evidence and puts this popular strategy to the test.
Trend following is finally moving while U.S. stocks are flat. And so—like with most assets or strategies that post strong returns—investors may be eyeing this particular strategy and asking: Is it time to get in? The answer, while not surprising, is definitely nuanced.
Most trend-following research focuses on signal construction: how to detect trends better, faster, or earlier. The paper asks a different question, and arguably a more [...]
1. Introduction Two previous articles, “Trend-Following Filters – Part 7” [1] and “Trend-Following Filters – Part 9” [2], examined, from a digital signal processing (DSP) [...]
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