Protecting the Downside of Trend When It Is Not Your Friend: Part 2/2
Protecting the Downside of Trend When It Is Not Your Friend Kun Yang, Edward Qian, and Bran BeltonJournal of Portfolio ManagementA version of this paper [...]
Visualization Sector Trends with R Code
Welcome to a year-end installment of Reproducible Finance with R, a series posts that will be a little bit different from the norm on Alpha [...]
Smart Money Indicator Rebuttal
In February 2019, Wes asked that I share my research on what I call the "Smart Money Indicator." I did a guest post on the [...]
Daily vs. Monthly Trend-Following Rules…Plus Some DIY Tools!
Trend-following strategies are a lot like stock-picking strategies -- there are endless approaches and varying levels of complexity. In this short piece, we explore the [...]
Volatility, Risk Management, and Market Chaos: Research that Might Help
Given the recent market decline, we thought it would be helpful to review some of our blog posts from the past that may be relevant [...]
Trend Following is Everywhere
Similar to some better-known factors, such as size and value, time-series momentum (TSMOM) historically has demonstrated abnormal excess returns. For the less familiar with trend [...]
How Trend Following Strategies Shape Return Distributions
Some Observations on Trend Following: A Binomial Perspective David M. ModestWorking Paper, QLS Partners LPA version of this paper can be found hereWant to read our [...]
Time Series Momentum: Theory and Evidence
Time Series Momentum in the US Stock Market: Empirical Evidence and Theoretical Implications Valeriy Zakamulin and Javier GinerWorking paper, University of Agder and University of [...]
Combining Momentum with Long-Term Reversal
Two of most documented anomalies in the asset pricing literature are the momentum effect and the long-term reversal effect. Momentum is typically defined as the [...]
What is Sequence Risk and Can Trend Following Help Reduce It?
Reducing Sequence Risk Using Trend Following and the CAPE Ratio Andrew Clare, James Seaton, Peter N. Smith, and Stephen ThomasFinancial Analysts Journal A version of [...]
Trend Following Research: Breaking Bad Trends
Momentum is the tendency for assets that have performed well (poorly) in the recent past to continue to perform well (poorly) in the future, at [...]
Equity Trend Following Performance Around the Globe
Time-series momentum (TSMOM) historically has demonstrated abnormal excess returns. Also called trend following, it is measured by a portfolio that is long assets that have [...]
Trend-Following Filters: Part 1/2
1. Introduction Many traders use strategies based on trends that occur in stock, bond, currency, commodity, and other financial asset price time series in order [...]
Trend-Following Filters – Part 2/2
1. Introduction Part 1 of this analysis, which is available here, examines filters modeled on second-order processes from a digital signal processing (DSP) perspective to [...]
Trend-Following Filters – Part 3
Introduction This is the third article in a series of three, the first two are available here and here. Those articles focus on examining from [...]
The Performance of Volatility-Managed Portfolios
As far back as 1976, with the publication of Fischer Black’s “Studies of Stock Price Volatility Changes” financial economists have known that volatility and returns [...]
Trend Following the S&P 500? Some Practical Advice
BREAKING INTO THE BLACKBOX: Trend Following, Stop Losses, and the Frequency of Trading: the case of the S&P500 Andrew Clare, James Seaton, Peter N. Smith, [...]
Trend-Following Filters – Part 4
This article considers a different type of filter called the Kalman filter. The Kalman filter is a statistics-based algorithm used to perform the estimation of random processes. Our research will explain what Kalman Filters are and utilize them with financial time series data for trend following purposes.
The Best Strategies for Dealing with Inflation? Factors and Trend-Following
Inflation -- what's that? ... It has been quite a while since inflation has been considered a problem. Today, however, the angst surrounding the possibility of a resurgence in inflation is real and “top of mind” for investors. If the current fear becomes a reality, how should investors react? What strategies and asset classes perform well in a rising inflationary environment? If inflation does resurge beyond a temporary phase, how should investors restructure or reposition their portfolios? The purpose of this article is to provide context for those decisions.
Trend-Following Filters – Part 5
There are two general types of Kalman filter models: steady-state and adaptive. A steady-state filter assumes that the statistics of the process under consideration are constant over time, resulting in fixed, time-invariant filter gains. The gains of an adaptive filter, on the other hand, are able to adjust to processes that have time-varying dynamics, such as financial time series which typically display volatility and non-stationarity.