Tag: quant

Theme Recap 15.12.2018

A quick look at the results of some of our most popular investment strategies over the last 200-days.

Static Momentum Themes

Static Momentum Themes 200-day performance

Dynamic Momentum Themes

Dynamic Momentum Themes 200-day performance

Value Themes

Value Themes 200-day performance

Machine Learning Themes

Machine Learning Themes 200-day performance

Neural Network Themes

Neural Network Themes 200-day performance

*BRK denotes returns after assuming a brokerage of 0.1% and an STT of 0.1%

A complete list of Themes and investment strategies can be found here.

Theme Recap 08.12.2018

A quick look at the results of some of our most popular investment strategies over the last 200-days.

Static Momentum Themes

Static Momentum Themes 200-day performance

Dynamic Momentum Themes

Dynamic Momentum Themes 200-day performance

Value Themes

Value Themes 200-day performance

Machine Learning Themes

Machine Learning Themes 200-day performance

Neural Network Themes

Neural Network Themes 200-day performance

*BRK denotes returns after assuming a brokerage of 0.1% and an STT of 0.1%

A complete list of Themes and investment strategies can be found here.

S&P 500 SMA Regimes

In the post Mixture model over S&P 500 returns, we looked at how mixture models can be used to classify returns as belonging to “bull” or “bear” regimes. Unfortunately, we found that using it to trade the index itself was a losing proposition. This lead us to ask ourselves whether a mixture model was any better than a simple moving average based classifier.

Daily returns

If we split returns that occur over different moving averages (50-, 100-, 200-days) and plot their densities, we can see how losses are more frequent when the index is trading below some moving average:
S&P 500 simple moving average returns density plot

Avoiding being long the index when it is trading below a moving average seems to be a good idea. And a quick back-test shows the 200-day average is the one to watch:
S&P 500 long-only SMA returns

All the moving-average “systems” above out-performed the mixture-model based system.

Take-away

Simple beats complex, most of the time.

Code and charts are on github.

Mixture model over S&P 500 returns

Market returns have different characteristics depending on whether they are in a “bull” phase or a “bear” phase. Daily returns can be labeled as either belonging to the “bull” camp or the “bear” camp using mixture models. This post extends Eran Raviv’s idea described here.

Rolling vs. whole period analysis

The density plot of daily returns of the distributions fit by the mixture model using the entire data-set of daily returns looked promising. There was a very visible difference in the way returns behaved under the two regimes. However, rolling period analysis hews closer to the real world. And the densities don’t look as pretty:
density plot of returns in stable and unstable regimes

Sure, “unstable” or “bear” regimes have slightly fatter tails but the densities are not as different as they appeared to be in the whole-period analysis.

Another gotcha is that when the regimes are superimposed on the S&P 500 price index, it looks like it could be good idea to use this system to trade it:
S&P 500 regimes
1 (blue) => stable

Timing signal?

It looks like the model helped escape most of the 2008 carnage and the “stable” regime looks to be long most of the up-trends. However, the overall return profile is sub-par when used in a systematic trading strategy:
S&P 500 cumulative returns

Take-away

Mixture models are an interesting tool in the quant tool-box. However, like how using skew as a timing signal appeared to be a good idea on the face of it, it turns out that using mixture models to time trades in a linear fashion is not such a good idea.

Code and charts on github.

Theme Recap 01.12.2018

A quick look at the results of some of our most popular investment strategies over the last 200-days.

Static Momentum Themes

Static Momentum Themes 200-day performance

Dynamic Momentum Themes

Dynamic Momentum Themes 200-day performance

Value Themes

Value Themes 200-day performance

Machine Learning Themes

Machine Learning Themes 200-day performance

Neural Network Themes

Neural Network Themes 200-day performance

*BRK denotes returns after assuming a brokerage of 0.1% and an STT of 0.1%

A complete list of Themes and investment strategies can be found here.