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 can be found hereWant to read our summaries of academic finance [...]
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 Architect (see here for my last post). We will search [...]
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 subject that summarized the results of a paper introducing my [...]
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 decision related to implementing basic trend-following strategies on either a [...]
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 to the current crisis atmosphere. These posts focus on research [...]
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 following it's worth your time to review Alpha Architects white [...]
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 summaries of academic finance papers? Check out our Academic Research [...]
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 La LagunaA version of this paper can be found here [...]
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 last 12 months of returns excluding the most recent month [...]
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 this paper can be found hereWant to read our summaries of [...]
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 least for a short period of time. Initial research on [...]
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 had recent positive returns and short assets that have had [...]
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 to “buy low” and “sell high”. A trend is considered [...]
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 illustrate their properties and limitations. To briefly recap, a time [...]
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 a digital signal processing (DSP) perspective[ref]For background information on DSP, [...]
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 are negatively correlated. This relationship results in the tendency to [...]
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, and Stephen ThomasWorking Paper, Cass Business School, London and University [...]
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.
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.
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.