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

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The Correlation Matrix (top graphic) shows the correlation between the 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 the 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 physically owning and managing them. 

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

Correlation coefficients ranging between + 0.10 and -0.10 are considered uncorrelated. VNQ is the only one that falls outside of that range, showing a 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% compared to 3% to 4% for the equity ones.

   

The weekly graph (bottom graphic) is more telling. While the 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 (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 physically owning and managing 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

homequant@gmail.com

Link to the Book

Did Russell 2000 Outperform S&P 500 in 2019?

        Intended for New Graduates

(Click on the image to enlarge)

Emily, a new graduate with co-concentrations (Econ & Finance), is interviewing for Equity Analyst position. 

Question # 1
Interviewer: Explain to us the basic difference between these two indices. 

Emily: While the S&P 500 index measures the performance of 500 large-cap stocks, the Russell 2000 index measures the performance of 2000 small-cap stocks. S&P 500 is the most widely followed stock market index.

Question # 2
Interviewer: Is there a market definition of large-cap stock? Also, can you name a few large caps?

Emily: Typically, a large-cap company has a market value of at least $10 Billion. Microsoft, Apple, Amazon, Google and Facebook are examples of large-caps. 

Question # 3
Interviewer: Can S&P 500 include one such large-cap stock that is listed on Nikkei only?

Emily: No. S&P 500 comprises large-cap stocks that are listed on US Exchanges.     

Question # 4
Interviewer: The data table shows S&P 500 has higher volatility than Russell's. What "quick" metric did we use to arrive at these volatility figures? And why?

Emily: I believe the quick metric you used is the Coefficient of Variation (commonly known by its short form COV). COV is the ratio of standard deviation to mean. Since you are making inter-index comparisons, you used the "normalized" metric. 

Question # 5
Interviewer: By glossing over these two graphs, do you notice any similarity?

Emily: Yes, between August and December, they both produced linear growth. Spectacular growth, indeed!

Question # 6
Interviewer: Any striking dissimilarity, per se?

Emily: Yes, the correction in August was way more pronounced for Russell than that of S&P's. 

Question # 7
Interviewer: By looking at the data table, can you tell us how S&P outperformed Russell in terms of overall growth?

Emily: Because S&P produced 8% growth between January and August, whereas Russell remained on a slippery slope, failing to hang on to its gains. 

Question # 8
Interviewer: To take advantage of these indices, what investment vehicles would you recommend to our clients?

Emily: Index Funds, Index ETFs, S&P Futures and Options, etc.

Question # 9
Interviewer: Of these two indices, which one would you recommend to our conservative clients? Or, would you recommend both?

Emily: Russell 2000 would not be appropriate for them.

Good Luck!

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

                     Link to the Book

Consider these Additional Factors while 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.

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

High-Low Ratio is a Good Way to Measure Volatility of Stock Market Averages and Indices

-- Intended for New Analysts and Researchers --

(Click on the image to enlarge)

Stock market Volatility, particularly highly liquid individual stocks, averages and indices can be defined by the ratio of their Daily Highs and Lows. Simply put, the higher ratio represents higher volatility and vice versa. 

The Daily Volatility chart shows an elevated volatility in February through early April, gradually tapering in May, June and July. While the median ratio during this 7-month period was 1.01, it exceeded 1.04 on four occasions in February and 1.03 on five occasions thereafter. Obviously, the February standard deviation was significantly higher than the overall (Feb 0.0158 vs. Overall 0.0093).

As expected, the Weekly Volatility chart shows more extreme volatility as it depicts the weekly highs and lows. For instance, the median ratio and standard deviation were 1.0244 and 0.0180, respectively. The volatility peaked at 1.0925 (week of February 5th), keying off the weekly high of 25,521 and low of 23,360. Additionally, it exceeded 1.04 on six occasions - a wow feat indeed! The volatility waned in May-July.

If you decide to present one chart, the Weekly one is more meaningful as it cuts through the daily noise and hones in on true extremes. In that case, add the trendline. You may also normalize it by Closing Prices, making it more predictive.

Good Luck! 

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

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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

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

The Correlation Matrix (top graphic) shows the correlation between the 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 the 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 physically owning and managing them. 

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

Correlation coefficients ranging between + 0.10 and -0.10 are considered uncorrelated. VNQ is the only one that falls outside of that range, showing a 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% compared to 3% to 4% for the equity ones.

   

The weekly graph (bottom graphic) is more telling. While the 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 (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 physically owning and managing 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

homequant@gmail.com

Link to the Book

Can FAANG Stocks Predict Dow Jones Industrial Average (DJIA)?

(Click on any image to enlarge)

John, a new college graduate with co-concentrations (Econ and Math) is interviewing for Equity Analyst with a major Hedge Fund. 

Interviewer: Thanks for interviewing with us, John. On that corner laptop you'll find a spreadsheet containing one year of daily closing prices - between 7/1/2018 and 6/30/2019 - pertaining to the Dow Jones Industrial Average (DJIA) and FAANG (Facebook, Amazon, Apple, Netflix and Google) stocks. Please analyze the data and give us your conclusions as to:

a) The FAANG stock that is most predictive of the DJIA so, from time to time, we could recommend it to our clients in place of the Dow ETF.

b) The FAANG stock that best represents as a hedge to the DJIA so it could be recommended as the DJIA becomes over-valued.

c) Finally, the FAANG stock that is highly predictive of the DJIA but has low multi-collinearity within the mix.

Once you are ready, just press 201 on this dial and I'll be back to talk to you.

___________________________________________________

As the interviewer returns, John presents his conclusions:

1. John -- The above correlation matrix clearly demonstrates that Amazon (AMZN) is the FAANG stock that is most predictive of the DJIA. The regression output, with DJIA as the dependent variable in the equation, further confirms it via its smallest standard error.

Interviewer -- But Netflix (NFLX) has better t-stat and lower p-value? Doesn't it contradict your conclusion?

John -- No. The correlation coefficient, which is the primary metric here, makes Amazon a far better (DJIA) predictive choice than Netflix.

2. John -- Of the FAANG components, Facebook (FB) is best hedge as it has the lowest correlation with DJIA. The regression out also confirms it via its negative coefficient.

Interviewer -- Would it be okay to recommend Facebook as a DJIA hedge to our clients?

John -- If the choice is limited to the FAANG complex only, yes. But there are other ETFs with much lower correlations with DJIA. I'd rather research and recommend one from the outside universe.     

3. John -- Apple (AAPL) is the FAANG stock that is highly predictive of the DJIA, but has lower multi-collinearity with the other components. The graph shows how Apple diverges from Facebook (which is the inside hedge component) with very low r-squared.

Interviewer -- Would you play the FAANG complex? If so, how?

John -- Each component has its own contributory properties so the complex as a whole makes a good investment vehicle. I would play it via a liquid FAANG ETF and as the DJIA becomes over-valued I would introduce an ETF with good hedging property.

   

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


Good Luck!

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

Link to the Book

How to Solve Complex Data Problems in a Job Interview (20 Live Simulations with actual Market Data)

How to define Options Strategy from a Technical Chart

(Click on the image to enlarge)

Question #1
Interviewer: Would you consider this a strong technical chart? What sort of trend do you see here?

Candidate: Yes, it is a good technical chart with a strong linear trend.

Question #2
Interviewer: Why do you think it's a strong linear trend?

Candidate: When the R-squared is approaching 0.90, it is considered strong, if not very strong.

Question #3
Interviewer: Do you think the weekly closing prices would have made the chart more meaningful?

Candidate: No. Since you are using only 6 months of data here, daily closing prices are better. Weekly would be better if you were using at least 12 months of data. 

Question #4
Interviewer: What is the best inflection point on the chart?

Candidate: Around the 90th trading day when the stock reversed its direction from under $10 and started making sharp upward move.  

Question #5
Interviewer: Would you still consider that stretch linear?

Candidate: No. That particular stretch of data shows more of an exponential trend than linear trend. Of course, the overall trend is still linear.

Question #6
Interviewer: If you were analyzing that stretch of data only, would you have seen any difference in stats?

Candidate: Yes. The R-squared would be higher, perhaps around 0.90 (it's actually 0.90, though not shown).

Question #7
Interviewer: As one of our market analysts, would you advise our clients to sell covered calls now?

Candidate: No. When a stock keeps making higher highs everyday, I would not advise selling covered calls. I will let it continue its run, for now.

Question #8
Interviewer: When do you think it's appropriate to sell covered calls?

Candidate: When the stock breaches a major support like the 120-day moving average.   

Question #9
Interviewer: How would you decide what kind of covered call to sell? 

Candidate: If the decline is really sharp, I will sell in the money or deep in the money. If it is just trending down, I will sell at the money anticipating a short consolidation and then a quick reversal.  

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

Link to the Book

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

Crude, Gold, Treasury Yields and VIX – Which one is most Predictive of Dow Jones Industrial Average?

(Click on the image to enlarge)

Julie is interviewing for an Equity Analyst position with a Wall Street Brokerage firm.

Question # 1
Interviewer: Julie, we used 13-months (i.e., 07/01/2018 thru 07/31/2019) worth of daily closing prices to compile this correlation matrix and the regression graph. Now, by looking at them, can you tell me what our objective here is?

Julie: You are trying to see if Gold, 10 and 30-year Treasury Yields, Crude and VIX collectively can predict Dow Jones Industrial Average (DJIA).

Question # 2
Interviewer: Why did we use ETFs like GLD and XOP instead of the actual futures data?

Julie: Futures contracts have different expiration dates so combining such data from different contract periods would be discontinuous. ETFs, instead, would be much better proxies.

Question # 3
Interviewer: In this example, is VIX the most un-correlated with DJIA? Qualify your answer with the underlying theory.

Julie: No. It's the most correlated of the five independent variables. Correlation can be positive or negative, hence the correlation coefficient varies between +1 and -1. VIX is negatively correlated with DJIA here.

Question # 4
Interviewer: In that case, which one is the least correlated independent variable here?

Julie: It's the crude ETF, that is the XOP variable in the equation.

Question # 5
Interviewer: Based on this correlation matrix, would you use all of the five independent variables in the regression equation? Qualify your answer with the underlying theory.    

Julie: No. I would remove GLD and 30-year Treasury Yield right off the top because they are failing the test of multi-collinearity. GLD is highly correlated with three others, while the 30-year Yield is moving in lockstep with the 10-year Yield.

Question # 6
Interviewer: Why did you choose 10-year Yield over 30-year Yield? Aren't they interchangeable here?

Julie: 10-year has better predictive relationship with the DJIA and lesser correlation with the VIX, while 30-year has only one positive, that is lesser correlation with XOP. Out of three, two positives here are better than one positive. Therefore, they are not necessarily interchangeable here.

Question # 7
Interviewer: The regression line shows a r-squared of 0.7633. What r-squared would the actual regression output show?

Julie: The same 0.7633. The regression value here represents all five independent variables against the same DJIA dependent variable so the r-squared would be identical. You are basically graphing the outcome of the actual regression.

Question # 8
Interviewer: If you are asked to fine-tune the model with an improved r-squared, what would you do? Qualify your answer with the underlying theory.

Julie: I would remove some outliers systematically from both ends of the curve. Unlike weekly closing prices, daily closing prices are inherently very volatile, so removing some outliers would be reasonable.

Question # 9
Interviewer: If you are forced to run a simple regression, rather than a multiple regression comprising these five variables, which one would you choose? And, what type of regression coefficient would you expect to see?

Julie: VIX, because it has the best predictive relationship with the DJIA. The regression coefficient would be negative as well, in line with the correlation coefficient.

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

Good Luck!

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

Link to the Book

Can Sector ETFs be used to construct Funds?

(Click on the image to enlarge)

ETF Sectors:
SPY=S&P 500; XLE=Energy; XLF=Financial; XLI=Industrial; XLK=Technology; XLP=Consumer Staples: XLU=Utilities; XLV=Healthcare; XLY=Consumer Discretionary

Laura is interviewing for the Hedge Fund Analyst position.

Question # 1

Interviewer: These graphics have been compiled off Standard and Poor's Exchange Traded Funds (ETF), reflecting daily closing prices between 07/01/2018 and 07/31/2019. Are you familiar with these ETFs?

Laura: Yes, I track and analyze them quite frequently. While SPY tracks the S&P 500 stock market index, the other ones are individual sector ETFs.

Question # 2

Interviewer: Use 5 sector ETFs to construct an aggressive (long only) fund. Weighting factors can range between 10% and 30%.

Laura: I would use XLF, XLI, XLK, XLP and XLY, equally weighted at 20% each. They are all highly correlated so they would move in tandem.     

Question # 3

Interviewer: How come you didn't select a hedge component while constructing the portfolio?

Laura: Because I was asked to construct an aggressive (long only) fund. An aggressive (long only) fund generally excludes hedges or negatively correlated components. 

Question # 4

Interviewer: In continuation of the prior fund construction, develop a weighted balanced fund where the dividend yields proxy fixed income assets. 

Laura: I would select the three equally-weighted stock ETFs, i.e., XLF, XLK and XLV with low multi-collinearity and the two equally-weighted high yield ones, XLP and XLU, surrogating fixed incomes. 

Question # 5

Interviewer: Why did you skip XLE despite yielding the highest dividend?

Laura: Since it has the highest beta, it's the most volatile one in the mix. Ideally, the balanced funds should try to minimize the use of highly volatile asset classes and components.  

Question # 6

Interviewer: Now, construct an income fund, with minimum volatility and maximum income. 

Laura: In constructing the income fund, I would use variable weights. My fund would include 30% XLU, 25% XLP, 20% XLF, 15% XLV and 10% XLK, respectively.  Again, though XLE has the highest yield, it is also the most volatile, hence skipped.

Question # 7

Interviewer: Is there an alternate use of these 3 funds?

Laura: Yes, as Fund of Funds; for example, for a low risk investor, the income and balance funds could be heavily weighted while the aggressive fund could contribute marginally. Similarly, for someone without any appetite for risk, the aggressive fund could be avoided altogether.

Question # 8

Interviewer: So, what's the use of these sector ETFs when SPY can represent them all?

Laura: SPY represents all the major sectors of the economy, so it's more or less an all of all index. The fund managers cannot use it to address clients' specific investment objectives or levels of risk tolerance. The sector ETFs can help achieve those goals.    

Question # 9

Interviewer: Finally, do you think ETFs have any special advantages over the competing Mutual Funds?

Laura: Yes, ETFs provide a number of advantages over the competing Mutual Funds. Here are the three most important ones: (a) ETFs have significantly lower expense ratios, e.g., all of these sector ETFs have under 0.15% expense ratios as compared to the usual 1-3% for Mutual Funds; (b) ETFs can be self-directed, while Mutual Funds are managed by dedicated managers; and (c) ETFs have no additional sales commissions, while all actively managed funds (generally sold by brokers and private managers) carry loads, making them quite expensive.

    

Interviewer: Did you learn all these at school?

Laura: No. My mom taught me. She is a consulting Economist.

"Well, that says it all."

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

Good Luck!

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

Link to the Book