FAANG – Review of 2019 Performance

** Intended for New Graduates **

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Wendy, a new college graduate with a major in Economics, is interviewing for the Market Analyst position.

Question # 1
Interviewer: Do you know what the acronym 'FAANG' stands for?

Wendy: Yes, it refers to five major stocks: Facebook, Amazon, Apple, Netflix and Google. In fact, I follow all of them very closely.

Question # 2
Interviewer: Okay, now take a look at the FAANG table and give us your interpretation of its overall performance.

Wendy: Obviously, Apple has hugely outperformed the other four components. While Facebook and Google produced fairly good returns, they were nonetheless below the S&P 500's 2019 return of 30%. Unfortunately, Amazon's return was sub-par while Netflix disappointed its investors. 

Question # 3
Interviewer: Why do you think we've added the Coefficient of Variation (COV)?

Wendy: COV is a very common metric used to demonstrate the volatility of asset classes and components. Amazon was the least volatile component while Apple was the most volatile in the FAANG complex.  

Question # 4
Interviewer: By simply glossing over the data series, would you have known that Apple was the most volatile in the complex?

Wendy: Yes, by simply looking at the data series I could have found it out. It has a much wider spread in the series than the rest, resulting in the highest COV and the volatility.


Question # 5
Interviewer: Was that the right use of the COV metric?

Wendy: It's perfectly fine in an interview setting. If I were compiling a report for a client, I would go back to the daily closing data, at least the weekly closing.

Question # 6
Interviewer: Okay, now switch to the Apple vs. Facebook chart and compare and contrast their performances. 

Wendy: They moved more or less in tandem during the first half of the year. Since then they produced significantly different performances. While Apple continued on a linear  growth path, Facebook moved sideways, remaining mostly range bound.

Question # 7
Interviewer: In terms of the market behavior, do you notice any similarity or dissimilarity between Amazon and Google?

Wendy: Yes, very dissimilar behavior. During the first half of the year, Amazon produced a nice run-up, while Google continued to decline. They however reversed courses in the second half.

Question # 8
Interviewer: Now let's move on to the Amazon chart. What is the point of overlaying the 2-month moving average trendline?

Wendy: To introduce a level of smoothing. It smooths out the noises that are inherent in month-over-month data series. For instance, the moving average trendline here is proving that the April and May data points are somewhat aberrations.   

Questions # 9
Interviewer: How would you graph all five components into one graph?

Wendy: By showing the month-over-month +/- growth rates, so they are apples-to-apples. 

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

Link to the Book

How did Dow Jones Industrial Average (DJIA) Fare in 2019?

** Intended for New Graduates **

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Jason is interviewing for Senior Research Analyst with a major brokerage house.

Interviewer
Question # 1: How would you interpret the 2019 DJIA trend?

Jason: Visually, it's a linear trend. It has been trending at 45 degree. 

Interviewer
Question # 2: When you say 'Visually,' do you mean there could be a better technical trend?

Jason: Yes, a polynomial trend would show better fit with significantly higher r-squared. Yet, I would stick to the linear trend as it would be easier to explain to the general clientele.  

Interviewer
Question # 3: What is the missing piece between these two graphs? Or, is there one?

Jason: Volatility. While the Weekly one depicts the market volatility, the Monthly one irons it out. 

Interviewer
Question # 4: Is there another way to depict the market volatility? If so, would that make the volatility case any stronger?

Jason: The Daily closing graph will show higher volatility but it won't make a better case than the Weekly one. As you know, a level of smoothing -- which the Weekly one incorporates -- is a better way to present volatility to the general clientele. 

Interviewer
Question # 5: What market event is common in these two graphs?

Jason: The precipitous market drop in May, bringing Dow under 25,000. Of course, by early July, Dow climbed to a new high, crossing the 27,000 mark. 

Interviewer
Question # 6: Do you think the May correction was driven by basic fundamentals of the market?

Jason: No. It was more news driven like trade, tariffs, etc. than pure market fundamentals. 

Interviewer
Question # 7: How did you come to the conclusion that it was news-driven rather than fundamentals-driven?

Jason: Had it been fundamentals driven, the correction would have lasted much longer. Instead, you can see the immediate V-shaped recovery leading to new market highs.

Interviewer
Question # 8: What is the difference between the 2-period and 3-period moving averages in the above graphs?

Jason: The 3-period in the Weekly graph points to 3-week moving average, whereas the 2-period in the Monthly refers to 2-month moving average.

Interviewer
Question # 9: Would you advise your clients to continue to pour in money in Dow in Q1-2020? 

Jason: Selectively. I will continue to recommend the Dow components with high dividend payouts. I will also urge them to keep some cash handy, just in case a correction occurs, leading to good buying opportunities.

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

Link to the Book

The Missing Link between Fundamental and Technical Equity Analysis

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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

Can Dow Jones Industrial Average (DJIA) Predict the Housing Market and vice versa?

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Suraj, a Harvard graduate with two years of experience as market strategist, is interviewing for Senior Quantitative Strategist.

Question # 1

Interviewer: Would you consider these two market segments predictive of each other?

Suraj: Absolutely. The Correlation Coefficient and Regression R-squared are showing they move in lockstep and in the same direction.

Question # 2

Interviewer: Is the linear trendline the best fit? Eyeball the scatter and use your best quantitative judgment.

Suraj: For a management presentation, the linear trendline is fine. For a technical presentation, I would use Polynomial trendline with 3rd order which would reduce the noise on the outer end of the curve.

Question # 3

Interviewer: By doing so, how much improvement do you expect to see? 

Suraj: I would expect the R-squared to move up in the vicinity of 0.96.

Interviewer: Okay, please give me a minute and let me find out. Yes, you are right. It's 0.956, so it's actually 0.96 rounded. I must say, you have developed an excellent eye for the data distribution.

Question # 4

Interviewer: Let's assume we are trying to hang our hat on this solution. Would you recommend this to our clients who enjoy short-term trading?

Suraj: No. This analysis is developed off the monthly data so it is not viable for the short-term traders. For the short-term housing traders, the analysis must be based off the local home sales data and for the short-term equity traders, it must be developed off the most recent 3-months of daily closing data or most recent 6-months of weekly closing data.  

Question # 5

Interviewer: Agreed, this is an analysis, not a solution. Either way, who would you recommend this analysis to? 

Suraj: Those who have much longer time horizon, like the Mutual and Pension Fund Managers, and other long-term investors.  

Question # 6

Interviewer: How would you improve upon this analysis in a very short period of time?

Suraj: I would try to study and isolate the seasonality in both data. For example, for the residential investors, Q1 might be better than Q3. Likewise, Q3 might be the best quarter to sell stocks to book profit. Analysis of seasonality is part and parcel of any long-term trend analysis.  

Question # 7

Interviewer: Would you stick to this data and time series to study the seasonality?

Suraj: No. The study of seasonality requires at least one full cycle of data, preferably more, so I would go back a few more years. Of course, this is a large enough sample to study the basic collinearity so I would expect the collinearity would still remain in the ballpark.

Question # 8

Interviewer: Don't you think the impact of new economic and fiscal policies and other major economic events would distort the seasonality analysis?

Suraj: No. Those impacts can be separated out. For instance, the new cap on SALT has been impacting the high-end residential market in high tax areas so the co-mingling of that sort of data would be imprudent. 

Question # 9

Interviewer: How would you (physically) separate out that data? Give me examples from both data series.

Suraj: In terms of the housing data, you are using the Case-Shiller Composite 20, meaning the largest 20 MSAs in the country. We know the pockets hit hardest by the SALT cap so they must be removed from the data. Similarly, I would not use the stretch of Dow Jones data post 9/11.     

  

* Case Shiller is a registered trademark of S&P CoreLogic.

-Sid Som, MBA, MIM
President, Homequant, Inc.

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 --

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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

To Evaluate Performance of a Major Stock, Compare it with the Average/Index it Belongs to

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-- Intended for New Graduates & Analysts --

To understand the performance of a major stock, compare it with the primary index/average it belongs to. Since Goldman Sachs (GS) is one of the 30 stocks that comprise the Dow Jones Industrial Average (DJIA), its performance should be compared with the DJIA, a priori.

The top chart shows the weekly closing prices of both between 7/01/17 and 6/30/18. Though GS outperformed the DJIA through 3/10/18, it completely fell apart ever since, leading to the retesting of the 7/3/17 price. The DJIA, on the other hand, registered a solid 13.34% price appreciation during this one-year period.

The DJIA (middle chart) shows the meteoric rise from 21,400 to 26,600 (24.30% gain) through 1/22/18, but gave back 11% since then. Nonetheless, the remaining annual gain was noteworthy.

GS (bottom chart) performed equally well through 3/5/18, moving up from 222 to 270, with a gain of 21.32%. Unfortunately, that was also the tipping point leading to a linear decline. The trendline confirms the continued awful decline.

FYI - since the weekly closing prices are already smooth, you do not need to add the moving average trendline. When you use the daily closing prices, you do. 

Disclaimer - The author is not advocating any of the stocks/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 you.

- Sid Som, MBA, MIM

President, Homequant, Inc.

homequant@gmail.com

Link to the Book