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.co

Coming soon ... Sid's New Book: Modern Interviewing Techniques and Skills - Live Simulations with actual Market Data

Nikkei 225 vs. S&P 500 – Are They Correlated?

(Click on the image to enlarge)

Muhammad is interviewing for the Market Data Analyst position. 

Question # 1
Interviewer: The above graphics comprise the daily closing data between July 1, 2018 and July 31, 2019. Are you familiar with these two indices?

Muhammad: Yes, I work with them quite frequently. S&P 500 is our broader market index, while the Nikkei 225 is the Japanese counterpart. 

Question # 2
Interviewer: Would you say these two indices are highly correlated? Qualify your answer with the appropriate statistic.

Muhammad: No. They have low to moderate correlation depending on the statistic you consider. Based on the correlation coefficient, they have moderate correlation, whereas the two regression r-squared(s) are demonstrating lower correlations. 

Question # 3
Interviewer: Considering Nikkei's significantly higher standard deviation, would you say it is more volatile than the S&P 500?

Muhammad: No. The standard deviations are not directly comparable because the underlying data values are significantly different. In fact, the graph axes show how different they are.

Question # 4
Interviewer: Given these statistics, how would you characterize the relative volatility here? 

Muhammad: Since the coefficient of variation is a normalized statistic (standard deviation divided by average), it is a better statistical indicator of the market volatility. Thus, Nikkei was slightly less volatile than the S&P 500 during this period.

Question # 5
Interviewer: If you are asked to establish a better correlation between these two markets, what would you do?

Muhammad: Instead of 13 months' worth of data, I would use a more extended data series, perhaps going back five to six years, thus smoothing out the scatter, resulting in more meaningful correlation statistics.

Question # 6
Interviewer: How did you decide on five to six years, rather than a longer series?

Muhammad: I used five to six years, to avoid having to pick any data from the bottom of the last recession. The real recovery started about six years ago so the last five to six years would provide more normal data.

Question # 7
Interviewer: By extending the series to five to six years, you will be introducing more noise and volatility. How is that statistically more prudent?

Muhammad: I will switch from the daily closings to weekly closings which are inherently smoother and less volatile. Weekly closings are more modelable as well.
  
Question # 8
Interviewer: The left header of the top graphic says "Statistics." Is that accurate?

Muhammad: Yes. Statistics are derived from samples, while parameters are extracted from the entire population. In this case, you are working out of a 13-month sample.

Question # 9
Interviewer: We have openings in both stock fund and index fund units. If you are allowed to choose, which one would you opt for and why?

Muhammad: Definitely the stock fund. It would be lot more challenging. I will get to research the entire sector, narrow my choices down and make recommendations on my final selections. I am looking forward to a challenging job like that.


Disclaimer - The author is not advocating the 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)

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

-- Intended for New College Graduates --

(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

Coming soon: Sid's New Book: Modern Interviewing Techniques and Skills - Live Simulations with Actual Market Data

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

-- Intended for New College Graduates --

(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

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

Does VIX Really Move in Tandem with S&P 500?

-- Intended for New Graduates --

VIX is the implied volatility index derived off the S&P 500, so it has become one of most widely watched and followed market metrics in the financial world, since its very inception. 

Many professional traders still define their market entry and exit based largely on the movement of the VIX and their  primary stock trading mantra continues to be:

"When VIX is high, it's time to buy.

When VIX is low, it's time to go."

So, let's use some recent market data (July 1, 2018 thru June 30, 2019) to examine if the S&P 500 index and VIX truly move in tandem and, if so, to what extent (meaning the extent of their statistical relationship).

As a student, you might have used the weekly closing data to establish such relationships, but now that you are ready to enter the corporate world, start making your case more emphatically with both daily and weekly closing data, where the daily serves as the "Champ" while the weekly "Challenges."

Though the correlation coefficient is the primary metric to derive and demonstrate such a relationship, a scatter plot with the trendline and the R-squared is as important considering it offers a more compelling visual case showing the line of best fit relative to the datapoints. 

In this instance, the correlation matrix of daily closings shows a high inverse correlation (-0.828) between them, meaning they move in tandem (not necessarily in lockstep) but in opposite directions. The daily scatter plot also confirms the same inverse relationship, with a fairly high R-squared (0.716). 

Therefore, the professional traders tend to use the VIX Options to hedge the S&P 500 Index (loosely, a long-short strategy).

As you expect, the correlation matrix of weekly closings would show a similar but smoother inverse relationship, and it certainly does (-0.842).

Likewise, the weekly scatter plot shows the same inverse relationship, as well as a negative trendline, predictably with a slightly tighter fit and a higher R-squared (rising to 0.747). 


PART-2


Now, let's simulate a job interview and frame a few meaningful questions out of the above presentation (remember, in a job interview they are not going to ask you some straight-forward questions like the definition of VIX or the S&P 500 Index, etc.)...

1. Interviewer: Megan, look at this Daily Scatter Plot and tell me if X and Y have been correctly graphed.

Megan: Yes, because VIX is a derivative of the S&P 500 index, and not the other way around. Using the basic math construct of Y is a function of X, VIX has been corrected depicted on the Y-axis.

2. Interviewer: Other than the two slightly different R-squared values, do you see any other difference between the Daily and Weekly Scatter Plots?

Megan: Yes, two basic differences: (a) obviously, the Daily plot has roughly five times more datapoints than the Weekly one, and (b) as expected, the Weekly Plot is smoother.

3. Interviewer: Do you see any technical inconsistency between the two scatters?

Megan: Yes, one. The X-scales are slightly different. They should have been held constant. 

4. Interviewer: Why do you think the Daily Correlation Coefficient is different from the Daily R-squared?

Megan: They are not apples-to-apples. The underlying maths are different. The Correlation Coefficient shows the overall statistical relationship between two variables, while R-squared shows to what extent (as a %) the independent variable explains the variations in the dependent variable.

5. Interviewer: As a follow-up to the prior question, why is the Daily Correlation Coefficient negative while the R-squared is positive?

Megan: Adding to my prior answer, the Correlation Coefficient can vary between +1 and -1, while the R-squared varies between 0 and 1 (cannot be negative), hence the difference. 

6. Interviewer: Why do you think we didn't show you any Regression output(s)?

Megan: Because it's a simple regression construct here, meaning one independent variable to the dependent variable. Had it been a multi-variate event, you would have produced a multiple regression output with the respective parameter estimates and the associated statistics.

7. Interviewer: You just indicated that had it been a multiple regression, we would have produced the respective parameter estimates with associated statistics. What sort of associated statistics would you have expected to see?

Megan: At a minimum, Standard Errors, T-stats and P-values.
   
8. Interviewer: Take a look at the two Scatter Plots and try to explain why a non-linear Trendline has been forced in.

Megan: Because of the slight tilt-up in the data at the outer end; I mean the most recent data seems to be bucking the trend a bit.  

9. Interviewer: Any guess as to the type of the Trendline?

Megan: Looks like, it's a 2nd or 3rd degree Polynomial.

Interviewer: Megan, we've a few more interviews this week so expect to hear back from us sometime next week. By the way, did you learn all this at school?

"No. My mom taught me."

Good Luck!

Disclaimer - The author is not advocating VIX or 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.


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

Write Covered Calls to Create Cash-flows during Market Corrections

Pros often use advanced options as one of their market strategies to manage portfolios. While professional options strategies require advanced knowledge of quantitative sciences, simple options hardly require any such knowledge. In fact, buying some calls to take advantage of the rising markets or buying some puts to hedge downturns is quite straightforward.

More importantly, the practice of writing covered calls, meaning selling calls against existing positions, to create cash-flows when market gets overbought or is ready for an imminent correction is considered an excellent market strategy even for the individual investors in stocks, bonds, commodities, foreign exchanges and real estate.

Considering the safety of writing covered calls, it is even allowed in IRA accounts.

Buying vs. Selling Calls

While options-approved individual and professional investors often buy calls to take advantage of the rising markets, buying calls carries an inherent risk if the market suddenly turns negative or moves sideways, thus making those calls worthless or at least significantly eroding their time value. Of course, if the market behaves as expected those calls gain in value. Therefore, buying calls is a speculative strategy, if not a total gamble.  

On the other hand, writing covered calls could be a very sound investment strategy to hedge market downturns or overbought conditions. For example, if you bought 1,000 X stocks at $30 (cost basis) at the bottom of the last correction and the same stock is now trading at $45, you may consider writing up to 10 covered calls (each option covers 100 shares) to create some temporary cash-flows, without having to liquidate the position. 

Of course, the mere fact that your stock has made a decent run-up should not force you to sell some calls. Make sure your research shows that the market is ready to correct or is way overbought, or at least, your stock is way ahead of the market and is showing clear signs of an overbought condition. One such sign could be the breach of a statistically significant trend-line, e.g., the 200-day moving average. In such a changed market situation, writing some covered calls is an excellent way to create some meaningful cash-flows.

Ideally, calls should be written against 50% of the covered positions, positioning the rest to ride out the market or to take advantage of the further upside potential in the market just in case your research turns out somewhat ill-timed. Of course, any such options strategy must always be reached in consultation with a registered investment professional to minimize speculation.

Again, while I am opposed to buying options – calls or puts – I am always in favor of writing limited calls as long as the aforesaid market conditions are met and proper professional help is part and parcel of the decision-making process.

In the money vs. At the money vs. Out of the money

Options have two value attributes – intrinsic value and time value. Option contracts that are expiring shortly, say in six weeks, will have lesser time value than those expiring in six months. Therefore, while buying options it is always advisable to buy with adequate time, preferably six to nine months remaining on the contract.

Likewise, while selling options, immediate contract months are preferred as market conditions are more predictable. Therefore, if your research shows the market could decline or remain range-bound and choppy in next 3 months, consider writing your covered calls keeping the option’s expiration in mind. Of course, the equally important question you would face is: Should you write those calls in the money, at the money, or out of the money? 

Since your stock is now trading at $45, the $45 strike price would be at the money, while $40 would be in the money and $50 would be out of the money. In other words, in the money options have higher intrinsic value than their counterparts. Again, if your research shows your particular stock has recently made a significant move – well ahead of the competition – and is therefore expected to retrace more than the overall market and the competition, consider writing the covered calls in the money, factoring in the potentially bigger pull-back. On the other hand, if you are expecting a pull-back in line with the market as well as the competition, stay with at the money or out of the money covered calls.

Either way, as market trends lower dragging the time value down, you can always cover (buy back) your position at a fraction of your original selling price. You can repeat this process again at the top of the next bull-run. Conversely, if your research proves wrong and the market continues to trend up subsequent to the writing of the covered calls, your other unencumbered 50% position will participate in the market.

Always consult a licensed investment advisor before engaging in any options activity as it involves significant risks.

- Sid Som MBA, MIM

President, Homequant, Inc.

homequant@gmail.com

Link to the Book