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Alpha Theory Blog - News and Insights

August 17, 2018

Signs of Seasonality

 

One of the members of our Customer Success team was wondering about the difficulty of getting client attention at the end of August. We ran an analysis to try and answer the question, “how active are our clients by month?” We used price target updates, logins, and trades per month as a proxy for investor activity.

 

Signs of seasonality1

 

August was definitely the softest month, but clients weren’t as “checked out” as we expected. We hypothesized that the peak periods would be during earnings season and troughs will be after earnings. Here’s the rub, they’re in the same month. The end of second-quarter earnings season and the before school vacation season are in the same month.

 

To remedy this fact, we created periods starting on the 15th of each month (i.e. August 15th to September 15th). This allows us to catch each earnings season as its own isolated period. Here are the results:

 

Signs of seasonality2(final)

 

There is clear seasonality. The post Q2 earnings season is 2.5 standard deviations from the norm. I suspect that if we broke this down into two-week tranches, we would have seen even more pronounced deviation from August 15th to August 31st.

 

As expected, the Post Earnings Season cohort’s activity was light at 0.7 standard deviations below normal activity, while the During Earnings cohort was busy (+0.8).

 

One of my favorite parts of working at Alpha Theory is that we have a long series of robust, structured data that allows us to ask and answer interesting questions. If you would like to be able to do the same, the first step is collecting and maintaining well-structured data. Then you can ask interesting questions like “what season do we make our most money?”, “who is the best forecaster on my team?”, “how often do stocks go below our risk targets?”, etc.

 

If you would like to learn more about how we can help. Contact us at

 

(866)-482-2177  

sales@alphatheory.com  

 

July 05, 2018

More Evidence of Manager Skill – Concentration Manifesto Continued

In preparation for a webinar we hosted about the Concentration Manifesto on June 21st we had a client question using batting average (win percentage) as a way of measuring skill. Their contention was that high batting averages do not always result in great returns, because a low hit rate with high asymmetry (lots of upside with little downside) can be even more profitable than predictable low returners.

 

Screen Shot 2018-07-05 at 12.25.30 PM

 

To analyze that point, we looked at the Return on Invested Capital (ROIC) by the same buckets we analyzed batting average.

 

Chart2

 

You can see that there is a similar correlation. Assets that are sized the largest had the highest return on invested capital. Said another way, the Top 5 positions went up an average of 12.1% while the portfolio as a whole went up 8.4% (for shorts, went down 8.4%).  That’s 50% better!

Chart3

 

We then analyzed the distribution of returns by bucket.

 

Again, you can see a predictive quality in manager position sizing. Stocks that have smaller positions have a wider distribution of returns (and more downside). The smallest positions had the most upside, but what we see in the data is that managers can forecast more volatile positions and size accordingly.

 

To finish the point, I’ll pull up a chart from the original Concentration Manifesto where we use our clients’ forecasted returns (Expected Return) and created two portfolios. One with the 20 best forecasted returns and then the rest. In the graph below, you can see that managers can forecast which assets will have the best returns. This shows skill not associated just with positions sizing, but on forecasting price return.

Chart4

 

There is very little question that our clients demonstrate skill. There is also very little question that they have mitigated a substantial portion of their skill by having too many positions.