COMPOSITE MACRO ETF WEEKLY ANALYTICS (1/16/2016)

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LAYOUT (Organized by Time Period): 

  1. Composite ETF Cumulative Returns Momentum Bar plot

  2. Composite ETF Cumulative Returns Line plot

  3. Composite ETF Risk-Adjusted Returns Scatter plot (Std vs Mean)

  4. Composite ETF Risk-Adjusted Return Correlations Heatmap (Clusterplot)

  5. Composite ETF Cumulative Return Tables

  6. Notable Trends and Observations

COMPOSITE ETF COMPONENTS:

LAST 252 TRADING DAYS

LAST 126 TRADING DAYS

LAST 63 TRADING DAYS

LAST 21 TRADING DAYS

LAST 10 TRADING DAYS

Cumulative Return Tables:

Notable Observations and Trends:

  • Investors have been extremely defensive in recent trading as evidenced by Utilities, Bonds, and Precious Metals making the top 3 performers over the last 21, and 10 day periods. 
  • Only one composite has had positive cumulative returns over the last 252 days - Treasury Yields. If you're a long investor there have been very few ways to escape the selling outside of moving to cash.
  • Flexible investors and traders are making a killing to the short side as selling has been very broad based across sectors.
  • We are in a binary risk-on, risk-off phase of investing as evidenced by the increased correlations across sectors and the very high negative correlations of traditional safe havens/crash investments. (Bonds, Precious Metals + Precious Metals Miners)

USING IMPLIED VOLATILITY TO PREDICT EQUITY/ETF RETURNS (1/11/16)

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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 Option Volatility Smirk they calculate is predictive 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. I will then track the results of the Long/Short portfolio, in equity returns, cumulatively for 4 weeks before rotating out of that portfolio. The ETF's are selected from the following groups:

 

With that said, it is now time to update Portfolio One's, one week results. 

PORTFOLIO ONE

Longs: XLF, EPI, VOX, XLI, XLP, XLV, HEDJ, IYT

Shorts: EZU, XLB, GDXJ, XRT, XHB, VGK, KRE, EWT

One Week Results: 

PORTFOLIO TWO

Longs: FEZ, VPU, INDA, IWB, HEDJ, IJR, IYT

Shorts: EEM, VDE, IAU, EWH, LQD, EWW, EWT

ETF SKEW LONGS

FEZ

VPU

INDA

IWB

HEDJ

IJR

IYT

ETF SKEW SHORTS

EEM

VDE

IAU

EWH

LQD

EWW

EWT

COMPOSITE MACRO ETF WEEKLY IMPLIED COST-OF-CAPITAL ESTIMATES VS. CUMULATIVE RETURNS (1/09/16)

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WHAT IS THE "IMPLIED COST OF CAPITAL (ICC)" MODEL?

“In accounting and finance the implied cost of equity capital (ICC)—defined as the internal rate of return that equates the current stock price to discounted expected future dividends—is an increasingly popular class of proxies for the expected rate of equity returns. ”

— CHARLES C. Y. WANG; an assistant professor of business administration in the Accounting and Management Unit at Harvard Business School

The basic concept of the ICC model is that it is a forward looking estimate of the implied earnings growth rate of stock given the current stock price. It is calculated using a combination of equity book value and earnings forecasts.

To see a more involved explanation of the previous model I used see here.  

In the past I used a Multi-Stage Residual Income Model. However, this time around I've decided to use a simpler Single-Stage Residual Income Model for these estimates. I chose this because I believe the additional complexity is not warranted given my purpose which I will elaborate on further.

The Single-Stage Residual Income Model as defined by the CFA Institute is the following:

source: CFA Institute

'V' is the stock price at time 0, 'B' is the book value of equity at time 0, 'ROE' is return on equity, 'g' is an assumed long term growth rate and 'r' is the cost of equity/capital. The ICC model essentially solves for 'r' given the other inputs. 

WHY USE THE IMPLIED COST OF CAPITAL MODEL?

There is ongoing debate regarding the ICC model's application and accuracy as a proxy for expected returns as quoted by Charles C. Y. Wang. As an investor/trader I'm less interested in the academic debate and more intrigued by the intuition behind the model and its practical application as a relative value tool. 

I use the ICC model as a relative value measure to identify analyst/institutional expectations and sentiment between different market sectors at a point in time. 

For this purpose I believe it provides great insight. 

COMPOSITE ETF COMPONENTS FOR ICC ESTIMATES

Z-SCORE ICC ESTIMATES AND CUMULATIVE RETURNS COMPARISON CHART

The below plot gives visual representation of the ICC estimates. I z-scored both year-to-date cumulative returns and the ICC estimates so we can view them on the same scale. Examining this chart allows investors to quickly determine which market sectors are outperforming (underperforming) their respective Implied Cost of Capital Estimates. 

The extreme cases show where there are disconnects between the analyst community's forward earnings expectations and actual market performance. The plot is sorted left to right by ascending ICC estimates.

LAST 252 TRADING DAYS 

Data Sources: YCharts.com, Yahoo Finance

Data Sources: YCharts.com, Yahoo Finance

LAST 126 TRADING DAYS

Data Sources: YCharts.com, Yahoo Finance

Data Sources: YCharts.com, Yahoo Finance

LAST 63 TRADING DAYS

Data Sources: YCharts.com, Yahoo Finance

Data Sources: YCharts.com, Yahoo Finance

LAST 21 TRADING DAYS

Data Sources: YCharts.com, Yahoo Finance

Data Sources: YCharts.com, Yahoo Finance

LAST 10 TRADING DAYS

Data Sources: YCharts.com, Yahoo Finance

Data Sources: YCharts.com, Yahoo Finance

CATEGORY AVERAGE ICC ESTIMATES

Long term growth rate (g) is assumed to be 2.5% reflective of our low growth high debt economic environment. 

ALL ETF ICC ESTIMATES BY CATEGORY

COMPOSITE MACRO ETF WEEKLY ANALYTICS (1/09/2016)

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LAYOUT (Organized by Time Period): 

  1. Composite ETF Cumulative Returns Momentum Bar plot

  2. Composite ETF Cumulative Returns Line plot

  3. Composite ETF Risk-Adjusted Returns Scatter plot (Std vs Mean)

  4. Composite ETF Risk-Adjusted Return Correlations Heatmap (Clusterplot)

  5. Composite ETF Cumulative Return Tables

  6. Notable Trends and Observations

COMPOSITE ETF COMPONENTS:

LAST 252 TRADING DAYS

LAST 126 TRADING DAYS

LAST 63 TRADING DAYS

LAST 21 TRADING DAYS

LAST 10 TRADING DAYS

Cumulative Return Tables:

Notable Observations and Trends:

  • It was a bloodbath in global markets to start 2016 with only 2 of the 23 composites registering gains over the last 10 days.
  • Risk off was in full force as Precious Metals, Bonds and Precious Metals Miners all gained over the last week (not shown) but were top performers over the last 10 days. 
  • There was precious little diversification offered across the composites during this selloff. Looking at the Risk-Adjusted Correlations Heatmap, the intensity of the red and the intensity of the blue suggest a very binary approach by investors. Again, Bonds, Precious Metals and Precious Metals Miners were the only composites providing any diversification benefits. 
  • It's unsurprising that, as market internals have weakened, we would see previous market leading sectors getting hit the hardest as investors lock in gains and reduce risk. (Technology, Healthcare)

Using Implied Volatility to Predict Equity/ETF Returns

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During a discussion with an knowledgeable options trader, I was told the significance of interpreting the "Implied Volatility Skew" for stocks and given a paper to read for homework. To get a basic understanding of Implied Volatility Skew see this link here. 

The paper I was told to read was "What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns?" by Yuhang Xing, Xiaoyan Zhang and Rui Zhao. In their paper they show empirically, using their SKEW measure, allowed one to predict future stock returns between 1-4 weeks out. Furthermore they also show that a long-short portfolio based on their SKEW measure can generate alphas of 10+% annualized. This their SKEW measure:

Source: What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns?

This idea was very intriguing. Using the Python/Pandas/Yahoo Finance API, I downloaded all the available options data for SPY holdings, and for a selected group of ETF's. 

I then used the paper's SKEW measure to sort the equities into deciles. I want to track the performance of some of the highlighted ETF's in the top and bottom deciles in real-time. Here are the resulting names for this week.

ETF SKEW LONGS

XLF

EPI

VOX

XLI

XLP

XLV

HEDJ

IYT

etf skew shorts

EZU

XLB

GDXJ

XRT

XHB

VGK

KRE

EWT