COMPOSITE MACRO ETF WEEKLY ANALYTICS (4/09/2016)

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COMPOSITE MACRO ETF WEEKLY ANALYTICS (4/09/2016)

Notable Observations and Trends:

  • L/252 days the top 4 performing composites have a risk-off/defensive 'tilt': (1) Utilities (2) Telecom (3) T-Bond (4) Precious Metals Miners (PMM).
  • L/252 and L/126 the Large Cap composite is almost unchanged at ~1% and ~2% respectively. 
  • L/252 the correlation clustermap (dendrogram) groups T-Bond, Bonds, Precious Metals (PM), and PMM as most closely correlated. Based on the data this grouping has offered the most diversification vs the remaining composites.
  • YTD L/71 days the top 3 performers are PMM, PM, and Utilities. PMM is trending strongly over the period gaining over 43%.
  • L/21 and L/10 days Healthcare, PMM, and real estate have been the strongest performers. 
  • Financials appear to be trending negatively over the L/71, L/21 and L/10 days. The composite has been a bottom 3 performer across timeframes. This is likely related to the Fed signaling the pace of interest rate increases should be slower than expected. 
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A Dead Simple 2-Asset Portfolio that Crushes the S&P500 (Part 1)

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A Dead Simple 2-Asset Portfolio that Crushes the S&P500 (Part 1)

I'm going to share a portfolio with you that has absolutely annihilated the performance of the market (as proxied by SPY) since the recovery began in 2009*. The strategy has not had a down year since. This portfolio maintains constant exposure, has 1 un-optimized parameter and wins on a risk-adjusted basis even after considering reasonable transaction costs.

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USING IMPLIED VOLATILITY TO PREDICT ETF RETURNS (4/02/16)

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USING IMPLIED VOLATILITY TO PREDICT ETF RETURNS (4/02/16)

To see the origin of this series click here

In the paper that inspired this series "What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns?" the authors' research shows that their calculation of the Option Volatility Smirk is predictive of equity returns up to 4 weeks. Therefore, each week, I will calculate the Long/Short legs of a portfolio constructed by following their criteria as closely as possible. However this study will focus on ETF's as opposed to single name equities. I will track the results of the Long/Short portfolio, in equity returns, cumulatively for 4 weeks before rotating out of that portfolio.

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COMPOSITE MACRO ETF WEEKLY ANALYTICS (4/02/2016)

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COMPOSITE MACRO ETF WEEKLY ANALYTICS (4/02/2016)

Notable Observations and Trends:

  • The Precious Metals Miners composite has exploded over the last 126 and 66 days gaining ~+33, ~+37% respectively. That nearly doubles the next best performer for L/126 days and is just over 2.5x the second best performer over L/66 days. 
  • Precious Metals + Miners finally took a breath over the last 21 days as they lost ~ -3% and ~-2% respectively. 
  • Healthcare looks interesting again. It has been the third worst performer L/252 days losing investors almost -20%. Looking at the Best/Worst line plot L/66 days, Healthcare returns appear to have formed a base. Performance is positive since mid February 2016. Healthcare was the top performer L/10 days gaining ~+5%. 
  • Telecom has notable potential tailwinds. On a momentum basis the Telecom composite has been a top 3 performer for the L/252, L/126, L/10 days. On a fundamental basis, the ICC's of IXP and VOX are both >7% putting them in the upper half of investor's expected returns, compared to all ETFs.
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Using ETF Internal Analytics to Identify Mean Reversion Opportunities (python)

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Using ETF Internal Analytics to Identify Mean Reversion Opportunities (python)

Since I started producing the following graphic for the ETF Internal Analytics product, I found the weekly return bin information compelling. I became curious about whether there was an opportunity to be exploited in the distribution patterns. I distilled all the questions I had into two: 

  1. Does the percentage of ETF component stocks at various return levels provide actionable information?
  2. Can a long-short market-neutral strategy be constructed by analyzing the relative return dispersion of each ETF's stock components?

To answer these questions I used a combination of tools/data sources including State Street's SPDR Holdings data, the Yahoo Finance API, and Python. 

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