Consider these Additional Factors while 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 Create a Statistically Significant Fund of Funds from Balanced Mutual Funds

(Click on the image to enlarge)

1. Screening Funds: It's important to select funds with very similar attributes which, in turn, will enhance collinearity of the portfolio. In selecting the above funds, the following set of criteria has been used: NAV > $7B; Morningstar Rating = 4 to 5; Track > 10 years; Yield = Positive; YTD Return > 8%.

2. Balanced Funds: Balanced Mutual Funds are inherently diversified (40-60% in stable/dividend stocks, 30-40% in fixed incomes and balance in Cash, Precious metals and other debt instruments). Since these funds are self-hedged by design, meaning stocks hedged by bonds etc., no additional hedge component is needed.

3. Fund of Funds: In order to create a statistically significant Fund of Funds from a group of Balanced Mutual Funds, it is imperative to draw them from a highly correlated group, as shown in the correlation matrix above. Thus, while reducing the number of funds, the "least" collinearity must be adhered to. For instance, since Dodge and Cox shows lower collinearity than its peers, it must be removed first from this line-up.

4. Risk Mitigation: A Fund of Funds  is more prudent from the investment point of view as it helps reduce the general risk embedded in a single balanced fund (risk scenarios: merger, change of ownership, departure of a veteran portfolio manager, etc.). 

Therefore, instead of investing $100K in one balanced fund, it's better to spread the sum over a group of highly correlated balanced funds (again, the highly correlated funds tend to project very similar attributes).

Disclaimer - The author is not advocating any of the funds listed here; instead, this is promoted as an alternative research in creating a statistical fund of funds. Consult your Registered Rep, RIA or Financial Planner for an appropriate asset allocation model and the suitability of mutual funds and other instruments.  

-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

The Missing Link between Fundamental and Technical Equity Analysis

(Click on the image to enlarge)

The missing link between the fundamental and technical equity analysis is a market-based statistical Correlation Matrix.

Analysis of the above Correlation Matrix

1. The correlation among Apple (AAPL), Amazon (AMZN), Facebook (FB) and Google (GOOG) is very (positively) high (> 0.80), meaning they will move in tandem. A portfolio comprising exclusively of such highly correlated stocks would be considered an 'Ultra Aggressive' portfolio.

2. Twitter (TWTR) however adds a low-to-moderate positive correlation to the aforesaid four, meaning there are days TWTR will not necessarily move in lockstep with the other four stocks. A portfolio constructed as such would, nonetheless, be 'Very Aggressive.'

3. IBM, on the other hand, shows negative correlations with all five and obviously very high negative correlations with the first four, thus providing an excellent hedge. The inclusion of the IBM hedge would lower the overall risk, paving the way for an 'Aggressive' portfolio.

Ideally, in order to capture any meaningful shifts in 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 stocks listed here; instead, this is just a research piece  - often overlooked - connecting fundamental and technical analyses. Consult your Registered Rep, RIA or Financial Planner for an appropriate asset allocation model and the holdings therein.  

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

Link to the Book