Factor Momentum

Doing some work on Factor momentum.


So basically, academics found out that factors (value, momentum and stuff) not only make money (e.g. outperform)
in a portfolio that rank those factors and rebalances, but that they in itself have autocorrelation, e.g., if for example high beta (one of the best trending factors)
has a 1-3 Month outperformance it is significant (e.g., it will trend further up).
Important to state that some factors trend much better than others.

Factor Ab. Does the factor trend well? Author of original factor paper

Betting against beta BAB 13.68 Frazzini and Pedersen (2014)
Residual variance IVOL 10.34 Ang et al. (2006
Size SMB 6.88 Banz (1981)
Long-term reversals LREV 6.03 De Bondt and Thaler (1985)
Short-term reversals SREV 4.67 Jegadeesh (1990)
Quality minus junk QMJ 4.62 Asness et al. (2019)
Earnings to price EP 4.24 Basu (1983)
Cash flow to price CP 3.87 Barr Rosenberg and Lanstein (1984)
Profitability RMW 3.8 Novy-Marx (2013)
Liquidity LIQ 3.6 Pastor and Stambaugh (2003)
Value HML 3.13 Barr Rosenberg and Lanstein (1984)
Investment CMA 2.9 Titman et al. (2004)
Net share issues NI 1.56 Loughran and Ritter (1995)
Accruals AC 1.1 Sloan (1996)

So what I am now doing discretionary is to look at the cap curve of about 25 “clean” systems (with one to two factors) in order to see what trends and what not and being long the factors that are trending up and hedging my book as soon certain factors get into a downtrend.

What I found out is, that my small my book of small cap value momentum, quality, profitability, EPS Estimates trending up, value etc. are doing very well
in almost all market regimes but have big DDs when specific factors are not doing well.

For example, if high beta or micro-cap value momentum is not doing well, risks that my small cap value momentum stuff gets a DD is higher as when high beta is doing well, etc.

First of all, I wanted to publish this here.

Secondly my question:

Is there a chance that P123 could implement a trending back testing system of different capital curves.
It would work the following way:

a) You define let’s say 3 Systems as a signal. When they are in an uptrend (driven by formulas we put in, above 20 Day MA, 4 weeks low, 4 week high etc.)
then the trading book is not hedged. If 1 of the signal systems is in a downtrend, hedge with x. etc.

Is that possible now, e.g., is it possible to access a cap curve of a system?

b) You system book gets put together automatically based on criteria like be only long the strongest systems (strongest systems of the last 4 weeks, 3 months etc.) and weight the book dynamically.

I know something could be done by using the API, but my programmer knowledge is just not strong enough to do this.

Its just an idea, but it might open p123 up to hedgefunds, individuals that are interested in trading factor momentum.

Best Regards


My whole current framework https://maxquantstrike.substack.com/p/current-framework?s=w

To quote from the paper:

“we find that factor momentum, although exhibiting statistically significant positive returns, does not outperform a simple buy-and-hold strategy based on the same set of factors.” In other words, switching from one factor to another based on factor momentum isn’t better than simply applying the same factors and buying and holding.

Is it then worth the trouble to implement a factor-momentum switching policy?

By the way, the betting-against-beta factor is to go long leveraged low-beta assets and short high-beta assets. You seem to be doing it the other way around on your “framework” website.

yes, but as seen in above table some factors (top 6) do trend very well (same thing with high beta, good luck shorting it from April 2020-Nov 2021)…

Since the transition to international data seems to make a lot of work, lets postpone this discussion…