The business cycle is a pattern that captures changes in economic activity over time. The changes in the business cycle occur in a sequential or serial manner, moving through a predictable sequence of phases. These cycles are consistent but vary in both duration and intensity. The phases of the

The premise of using either time-series momentum or “trend-following” using moving averages is the same only the math differs very slightly (see Which Trend Is Your Friend? by AQR): using some fixed lookback you can time market cycles and capture more upside than downside and therefore improve

This post contains affiliate links. An affiliate link means CSSA may receive compensation if you make a purchase through the link, without any extra cost to you. CSSA strives to promote only products and services which provide value to my business and those which I believe could help you, the

In the last post I reviewed the Momentum Trading Strategies Course by Quantra (a division of QuantInsti) which I reviewed as part of a recent educational journey to improve my quantitative skill set. The next course that I will be reviewing is Mean-Reversion Strategies in Python which is taught by

This post contains affiliate links. An affiliate link means CSSA may receive compensation if you make a purchase through the link, without any extra cost to you. CSSA strives to promote only products and services which provide value to my business and those which I believe could help you, the

In the last post I described a relatively simply method to incorporate the VIX into the well-known AMA or Adaptive Moving Average framework. The alpha formula requires two separate parameters- a short and a long-term constant which requires greater specification by the user. Ideally the fewer

One of the challenges with technical or quantitative analysis is to identify strategies that can adapt to different market regimes. The most obvious is a change in the forecast or implied volatility as proxied by the VIX. During more volatile periods we would expect more signal noise and during less

For readers interested in getting signals and analytics on hundreds of ETFs and individual stocks our Investor IQ website is currently live and free during our beta-testing phase. We will be adding new data and analytics gradually over time as well as improving website functionality. The Economic

In the last post we showed that trend-following tends to be mean-reverting in the short-term. Data analysis also shows that trend-following has an even stronger mean-reverting effect using a 6-month or 120-day window using the same methodology. Take a look at the chart below using the BarclayHedge

In a recent post I showed that the momentum factor has been mean-reverting in the short-term, and that this effect can be used to trade both the factor and momentum strategies effectively. An obvious extension is to see whether trend-following as a factor is also mean-reverting. After all,

In the last post we used the data available for the momentum factor using an ETF (ticker: MOM) which seeks to replicate The Dow Jones Thematic Market Neutral Momentum Index to time when to be in or out of high momentum stocks. Alpha Architect recently did some interesting analysis of the

new concepts in quantitative research Home CSSA Investor IQ When Should You Buy Momentum? Mean-Reversion in The Momentum Factor September 12, 2019 by david varadi Recently there was a good post by Bespoke Research highlighting the “Momentum Massacre” that we recently witnessed in the market.

Both trend-following and absolute momentum are well established methods for managing risk. Another method for managing risk is to use volatility targeting. The former are superior for reducing large drawdowns in bear markets while the latter tends to reduce kurtosis by normalizing the daily bet

Its been a long time folks, but we have some shiny new toys in the works. Current trends in the industry and working with data scientists has made me a believer in the benefits of using a machine learning approach. I have always been a proponent of “theory-free” approaches on this blog as long

There is some interesting new content on the CSSA blog that will be very useful for readers. Investor IQ is currently a free tool that shows basic trend signals (Buy, Hold or Sell) for a wide range of US and Canadian ETFs as well as a relative strength ranking. The signals will be updated as of the

In the last post we showed how to use PCA to create Offense and Defense portfolios by focusing on the first principal component or “PC1.” After rotation has been completed it is possible to derive weights or portfolios for each principal component. Another good primer on using PCA for asset

Asset allocation is a complex problem that can be solved using endless variations of different approaches that range from theoretical like Mean-Variance to heuristic like Minimum Correlation or even “tactical strategies.” Another challenge is defining an appropriate asset class universe which

In the last post we introduced the concept of using adaptive volatility in order to have a flexible lookback as a function of market conditions. We used the R-squared of price as a proxy for the strength of the trend in the underlying market in order to vary the half-life in an exponential moving

One of the inherent challenges in designing strategies is the need to specify certain parameters. Volatility parameters tend to work fairly well regardless of lookback, but there are inherent trade-offs to using short-term versus longer-term volatility. The former is more responsive to current

There is a cool new website that tracks the performance of well-known tactical strategies. AllocateSmartly has collected an extensive list of strategies from well-known hedge fund managers like Ray Dalio along with several other portfolio managers and financial bloggers. The backtests for these

I recently read “Adaptive Asset Allocation” ( link to the book) by Butler, Philbrick and Gordillo of ReSolve Asset Management. The book is the culmination of research developed over the years by the ReSolve team towards the next generation approach of dynamic asset allocation. The core

I have spent many years toiling with creating different asset allocation methodologies including the application of traditional and non-traditional portfolio optimization. Given the recent flare of articles on this topic in the blogosphere, I felt it was worthwhile to share my two cents. Applying

In the last post I introduced the concept of “real momentum” which is a trend following signal based on real returns. In the post I used both expected inflation and risk-free returns to net out from the S&P500 to create a real excess return. This was done to make the hurdle for buy positions

“Time-Series Momentum” was introduced by Moskowitz and Pedersen of AQR circa 2011 and was popularized by Antonacci in 2013 as “Absolute Momentum.” Both measure the return of an asset in excess of the risk-free rate over some lookback window in order to determine whether to hold a long

Note: James Picerno of The Capital Spectator recently did an interesting piece evaluating the Self-Similarity Metric and provides some R code which is valuable for many of our readers. The principle of parsimony relates to being frugal with resources such as money or the use of computing time. It is

The Self-Similarity metric has been a popular series. Recently the original post was shared on Jeff Swanson’s popular site System Trader Success which covers a wide variety of thought provoking articles on trading system development and is worth reading. Jeff has also posted some TradeStation code

The images above are the famous Sierpinski Triangle and the Koch Snowflake. These objects are “self-similar” and this means that examination at finer levels of resolution will reveal the same shape. Both are examples of “fractal” geometry, and are characteristic of many phenomena in the

The code in R for Conditional Percentile Channels is now available on Michael Kapler’s Systematic Investor blog. The original code was contributed by long-time reader Pierre Chretien and subsequently verified by Michael. Pierre has been generous enough to share code from the blog material several

Ilya Kipnis at Quantstrat recently posted some R code attempting to replicate the ever-popular Percentile Channel Tactical Strategy. The results are similar but not exactly in line- which may have to do with the percentile function as Ilya has pointed out in the comments. In either case, the general

Michael Kapler of the always excellent Systematic Investor blog has moved his publishing to GitHub to make it easier to post code. This has flown under the radar (even to me), and we are all grateful that he is back to publishing. He was able to reproduce the “Simple Tactical Asset Allocation with

In the last several posts, I introduced some different methods for channel strategies including Percentile Channels. A simple way to potentially improve (or at least take a different approach) to a donchian channel strategy is to use a different price input to generate trading signals. As stated in

I actually received a large volume of what could best be chararcterized as “hate mail” for one of the previous posts on percentile channels. In reading these comments I was reminded of Jimmy Kimmel’s funny segments where celebrities read mean tweets about themselves. While I did not publish

James Picerno of Capital Spectator recently did a good review of Error-Adjusted Momentum in his post “A Momentum-Based Trading Signal with Strategic Value“. The Capital Spectator blog is rich with great content covering a diverse range of subjects from economics to asset allocation and

I prefer presenting new tools and concepts, but I know that there are a lot of readers that would like to see how they can be applied to creating strategies. So here is a very simple strategy that applies Percentile Channels from the last post to a tactical asset allocation strategy. The strategy

One of the most widely used trend-following approaches are Donchian Channels which were popularized by the famous “Turtle Traders.” In fact, it was the subject of Donchian Channels that started my collaboration with Corey Rittenhouse with the popular post Percent Exposure Donchian Channel