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

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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.

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 Define, Compute and Manage True Volatility of Stocks

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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 Create a Statistically Significant Fund of Funds from Balanced Mutual Funds

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1. Screening Funds: It's important to select funds with very similar attributes which, in turn, enhances 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 (highly correlated funds tend to project very similar attributes).

Data - The above matrix is compiled off of the aforesaid Fund NAV's between 01-03-2017 and 11-02-2017.

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

sidsom1@gmail.com