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