Thread by @KoyfinCharts

Guess what ladies & gents it's...Tutorial Thursday

This week, were going to introduce a new twist on screening for stocks

- by demoing how you can decompose factor performance by sector!

No idea what this means? Don't worry, we're jargon free and the concept is approachable

What is a factor?

A factor is a rules-based way of categorizing cos

By grouping cos w/ similar traits (val metric, mkt cap, etc) you can:

1. See mkt rotations occurring
2. Invest/Trade w/ a certain trait in mind (ie focusing on value or momo etc)

more: msci.com/msci-factor-in…

An example

There's many on the Factors Page (koyfin.com/fact), but lets explore one in particular

Namely, in 4Q 2018, the Low Vol factor (cos bucketed based on lowest vol of returns) outperformed Momentum (cos based on highest 6 & 12 mo returns)

This just changed...

Several analysts @HumbleStudent and @BruniCharting / @Allstarcharts recently highlighted this with a Momo:Low Vol ratio chart

Lets add a second layer to this observation in the next tweet

Track here: koyfin.com/charts/g/MTUM%…

We can examine Mom. group's Sector exposure. This offers a detailed look at what changed / caused this

1. load ETF holdings by typing MTUM -> enter on keyboard -> HDS -> enter

Not surprisingly, 55% of the Momo ETF is weighted to Info Tech & Healthcare

koyfin.com/snapshot/hds/M…

In the above screenshot lets focus on the Sector Breakdown

columns:
- Sectors in Momo ETF
- % weight
- Return in the past yr (can select other timeframes)
- How much the Weight * Perf = Contribution to Total Return

in the lower left, same this breakdown on a co specific level

Beyond the analytical benefits to doing this (what is helping / hurting the market etc), we also have a great screening tool.

If we want to find momo opportunities, we have narrowed the pool into stocks that are leading the momo pack - a great way to analyze and filter markets

We can go further:

1. Load the ETF holdings into Market Scatter
2. Filter to Info Tech & HC only
3. Examine hundreds of different fundie & technical datapoints on the individual cos themselves

In this ex, well use short interest vs. price return

here: koyfin.com/s/2ffcbaf3

Take $SQ ... hover over the dot

1. we get a better data visualization of the impact of Short Interest relative to not just peers, but specifically to Momo *and* relative to its own history

Thats a powerful information advantage compared just (only) running screens

Now we can examine the fundamental aspects of the company, the payment industry, or jump into technical analysis to justify any further decision

Or we can draw a conclusion on which companies are more likely to squeeze if momentum continues. Possibilities are endless

Believe it or not, a major focus of the quantitative investment field is precisely this exercise! Properly classifying & explaining companies based on traits

Of course, the next step is to extract a premium for this effort over market performance... but this gets you started!

That’s it! Thanks for reading!

As always feedback, ideas, and contributions are welcome

(unless the suggestion you have is bad, in which we still want to hear it, just please leave it with our secretary who can be reached at 867-5309)