How to Use Sector ETFs to Create a Diversified Stock Portfolio

(Click on the image to enlarge)

The highly correlated sector ETFs -- XLB, XLF, XLI, XLK, XLV and XLY -- would make the portfolio an undiversified and aggressive one, while the addition of XLP (less correlated), GDX (uncorrelated) and XLE (negatively correlated) would help reduce risk and make it a more diversified one.

The graph demonstrates, while XLK and XLP moved in tandem initially, they significantly diverged later in the year, suggesting that the longer holding period is equally important in reaping the true benefits of diversification.

Ideally, in order to capture any meaningful shifts in ETF relationships, researchers should run this matrix in three phases: short-term (recent 30 days), medium-term (6 months) and long-term (9-12 months).

Disclaimer - The author is not advocating any of the ETFs listed here; instead, this is promoted as an alternative research in diversifying an equity portfolio, leading to a better asset allocation model.

Consult your Registered Rep, RIA or Financial Planner for an appropriate asset allocation model and the potential holdings therein.  

--Sid Som, MBA, MIM
President, Homequant, Inc.
homequant@gmail.com

Link to the Book

A Diversified REIT ETF may Proxy Physical Real Estates in an Asset Allocation Model

- Intended for Start-up Analysts and Researchers -

The Correlation Matrix (top graphic) shows the correlation between S&P 500 and five publicly traded Real Estate Investment Trust (REIT) ETFs. While MORT is a mortgage REIT, the other four are diversified equity (Real Estate) REITs. 

The Correlation Matrix shows almost negligible correlations between S&P 500 and the REITs. This lack of correlation entices investors to own REITs as a separate asset class in their asset allocation model, proxying a portfolio of diversified real estates (residential, commercial and industrial), without having to own and manage them physically. 

In order to maintain the tax advantage status, REITs have to pay out at least 90% of their income as dividend. Since REITs are designed to yield higher dividends, they tend to complement the fixed income (asset) class in the asset allocation model as well.

Correlation coefficients ranging between + 0.10 and -0.10 are considered uncorrelated. VNQ is the only one that falls outside of that range, showing slightly negative correlation. Save MORT, the other four equity REITs are moving in lockstep, considering their top holdings (accounting for at least 35% of the portfolio) are virtually alike (e.g., American Tower, Simon Property, Crown Castle, Prologis, Public Storage, Avalon Bay, Equinix, Equity Residential, Digital Realty, etc.).

Though Mortgage REITs tend to generate much higher yields than their equity (real estate) counterparts, they are inherently more volatile as they are more prone to interest rate fluctuations. MORT currently has a yield of 7.77% as compared to 3% to 4% for the equity ones.
     
The weekly graph (bottom graphic) is more telling. While S&P 500 moved from 2,400 to 2,800 (between 8/1/17 and 7/31/18), both REITs (IYR and VNQ) remained range bound between $74 and $82. As a result, the diversified equity REITs have low beta as well (usually between 0.5 and 0.7). 

Again, a diversified equity REIT ETF could be an excellent way to own this asset class (a wide variety of real estates) without having to physically own and manage them.

Disclaimer - The author is not advocating any of the ETFs/indices listed here. Consult your Registered Rep, RIA or Financial Planner for an appropriate asset allocation model and the suitability of stocks and other holdings for your portfolio.

- Sid Som, MBA, MIM

President, Homequant, Inc.

homequant@gmail.com

Link to the Book

A Diversified REIT ETF may Proxy Physical Real Estates in an Asset Allocation Model

- Intended for Start-up Analysts and Researchers -

The Correlation Matrix (top graphic) shows the correlation between S&P 500 and five publicly traded Real Estate Investment Trust (REIT) ETFs. While MORT is a mortgage REIT, the other four are diversified equity (Real Estate) REITs. 

The Correlation Matrix shows almost negligible correlations between S&P 500 and the REITs. This lack of correlation entices investors to own REITs as a separate asset class in their asset allocation model, proxying a portfolio of diversified real estates (residential, commercial and industrial), without having to own and manage them physically. 

In order to maintain the tax advantage status, REITs have to pay out at least 90% of their income as dividend. Since REITs are designed to yield higher dividends, they tend to complement the fixed income (asset) class in the asset allocation model as well.

Correlation coefficients ranging between + 0.10 and -0.10 are considered uncorrelated. VNQ is the only one that falls outside of that range, showing slightly negative correlation. Save MORT, the other four equity REITs are moving in lockstep, considering their top holdings (accounting for at least 35% of the portfolio) are virtually alike (e.g., American Tower, Simon Property, Crown Castle, Prologis, Public Storage, Avalon Bay, Equinix, Equity Residential, Digital Realty, etc.).

Though Mortgage REITs tend to generate much higher yields than their equity (real estate) counterparts, they are inherently more volatile as they are more prone to interest rate fluctuations. MORT currently has a yield of 7.77% as compared to 3% to 4% for the equity ones.
     
The weekly graph (bottom graphic) is more telling. While S&P 500 moved from 2,400 to 2,800 (between 8/1/17 and 7/31/18), both REITs (IYR and VNQ) remained range bound between $74 and $82. As a result, the diversified equity REITs have low beta as well (usually between 0.5 and 0.7). 

Again, a diversified equity REIT ETF could be an excellent way to own this asset class (a wide variety of real estates) without having to physically own and manage them.

Disclaimer - The author is not advocating any of the ETFs/indices listed here. Consult your Registered Rep, RIA or Financial Planner for an appropriate asset allocation model and the suitability of stocks and other holdings for your portfolio.

- Sid Som, MBA, MIM

President, Homequant, Inc.

homequant@gmail.com

Link to the Book

Additional Factors to be Considered in Choosing High Dividend Stocks - for Long Haul

(Click on the image to enlarge)

In choosing a set of high dividend stocks for the long haul, data savvy investors need to additionally consider, at a minimum, price-earnings ratio and volatility. Of course, equity research analysts would consider a slew of other factors including book, cash, reserve, growth, liquidity, debt, etc.  

A composite combining PE and Beta (or V-factor) is critical. The two composites - Beta-adj and Vfact-adj - have been used (the graphic above) to make the case. While the Beta-adj composite points to Verizon (VZ), P & G (PG), IBM (IBM Corp.), XOM (Exxon Mobil), GE and JNJ (J & J) as the best (< 50 as acceptable scale value) high dividend stocks, Vfact-adj picks PG, VZ, XOM, IBM and MRK (Merck). 

Despite high dividend yields, CVX (Chevron) and KO (Coca Cola) didn't make either cut due to high PEs. Likewise, BA (Boeing) didn't fare well either due to the high volatility.

Disclaimer - The author is not advocating any of the stocks listed here; instead, this is promoted as an alternative research in creating a statistically significant and more predictive volatility factor for individual stocks. Consult your Registered Rep, RIA or Financial Planner for an appropriate asset allocation model and the suitability of stocks and other holdings.  

-Sid Som, MBA, MIM
President, Homequant, Inc.

homequant@gmail.com

Link to the Book

How to Define, Compute and Manage True Volatility of Major Stocks

(Click on the image to enlarge)

The most widely-used metric to determine the volatility of a given stock is known as the Beta which shows the volatility of a stock relative to the overall market (generally S&P 500). When the stock moves in perfect tandem with the market, the Beta is 1. Likewise, when the stock is more volatile, Beta > 1 and vice versa. In the above example, Cisco (CSCO) an Intel (INTC) are the two most volatile stocks while Procter & Gamble (PG) and Coca-Cola (KO) are the least volatile ones.

While Beta is an external metric, an internal metric in the form of a Coefficient of Variation (COV=Std Dev/Mean) may be computed using the daily closing prices. Then, the combination of the external and internal metrics would help create a more efficient and predictive volatility factor (V-factor). FYI - COV is a better metric than Std Dev as it is normalized.

Here is why the aforesaid V-factor is more efficient and predictive than the Beta: Though CSCO has the highest Beta, it has low internal volatility (daily movement of prices) as reflected in the low COV, thus lowering the overall V-factor significantly (down to 6.21), even lower than GE's which tends to move almost in lockstep with the market.

Of course, there are other methods to capture the volatility including modeling the daily swings. 

Disclaimer - The author is not advocating any of the stocks listed here; instead, this is promoted as an alternative research in creating a statistically significant and more predictive volatility factor for individual stocks. Consult your Registered Rep, RIA or Financial Planner for an appropriate asset allocation model and the suitability of stocks and other holdings.  

--Sid Som, MBA, MIM
President, Homequant, Inc.

homequant@gmail.com

Link to the Book

How to Use Sector ETFs to Create a Diversified Stock Portfolio

(Click on the image to enlarge)

The highly correlated sector ETFs -- XLB, XLF, XLI, XLK, XLV and XLY -- would make the portfolio an undiversified and aggressive one, while the addition of XLP (less correlated), GDX (uncorrelated) and XLE (negatively correlated) would help reduce risk and make it a more diversified one.

The graph demonstrates, while XLK and XLP moved in tandem initially, they significantly diverged later in the year, suggesting that the longer holding period is equally important in reaping the true benefits of diversification.

Ideally, in order to capture any meaningful shifts in ETF relationships, researchers should run this matrix in three phases: short-term (recent 30 days), medium-term (6 months) and long-term (9-12 months). 

Disclaimer - The author is not advocating any of the ETFs listed here; instead, this is promoted as an alternative research in diversifying an equity portfolio, leading to a better asset allocation model.

Consult your Registered Rep, RIA or Financial Planner for an appropriate asset allocation model and the potential holdings therein.  

--Sid Som, MBA, MIM
President, Homequant, Inc.
homequant@gmail.com

Link to the Book

How to Define, Compute and Manage True Volatility of Stocks

(Click on the image to enlarge)

The most widely-used metric to determine the volatility of a given stock is known as the Beta which shows the volatility of a stock relative to the overall market (generally S&P 500). When the stock moves in perfect tandem with the market, the Beta is 1. Likewise, when the stock is more volatile, Beta > 1 and vice versa. In the above example, Cisco (CSCO) an Intel (INTC) are the two most volatile stocks while Procter & Gamble (PG) and Coca-Cola (KO) are the least volatile ones.

While Beta is an external metric, an internal metric in the form of a Coefficient of Variation (COV=Std Dev/Mean) may be computed using the daily closing prices. Then, the combination of the external and internal metrics would help create a more efficient and predictive volatility factor (V-factor). FYI - COV is a better metric than Std Dev as it is normalized.

Here is why the aforesaid V-factor is more efficient and predictive than the Beta: Though CSCO has the highest Beta, it has low internal volatility (daily movement of prices) as reflected in the low COV, thus lowering the overall V-factor significantly (down to 6.21), even lower than GE's which tends to move almost in lockstep with the market.

Of course, there are other methods to capture the volatility including modeling the daily swings. 

Data - The above matrix is compiled off of the aforesaid closing prices between 01-03-2017 and 11-03-2017.

Disclaimer - The author is not advocating any of the stocks listed here; instead, this is promoted as an alternative research in creating a statistically significant and more predictive volatility factor for individual stocks. Consult your Registered Rep, RIA or Financial Planner for an appropriate asset allocation model and the suitability of stocks and other holdings.  

--Sid Som

sidsom1@gmail.com

How to Use Sector ETFs to Create a Diversified Stock Portfolio

(Click on the image to enlarge)

The highly correlated sector ETFs -- XLB, XLF, XLI, XLK, XLV and XLY -- would make the portfolio an undiversified and aggressive one, while the addition of XLP (less correlated), GDX (uncorrelated) and XLE (negatively correlated) would help reduce risk and make it a more diversified one. 

The graph demonstrates, while XLK and XLP moved in tandem initially, they significantly diverged later in the year, suggesting that the longer holding period is equally important in reaping the true benefits of diversification.

Ideally, in order to capture any meaningful shifts in ETF relationships, researchers should run this matrix in three phases: short-term (recent 30 days), medium-term (6 months) and long-term (9-12 months). 

Data - The above matrix is compiled off of the closing prices between 01-03-17 and 10-25-17.

Disclaimer - The author is not advocating any of the ETFs listed here; instead, this is promoted an alternative research in diversifying an equity portfolio, leading to a better asset allocation model. 

Consult your Registered Rep, RIA or Financial Planner for an appropriate asset allocation model and the potential holdings therein.  

--Sid Som

sidsom1@gmail.com