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)

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

-- Intended for Start-up 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 Define, Compute and Manage True Volatility of Major Stocks

(Click on the image to enlarge)

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. 

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 Start-up 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

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

- Intended for Start-up Analysts and Researchers -

The Correlation Matrix (top graphic) shows the correlation between 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 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 having to own and manage them physically. 

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

Correlation coefficients ranging between + 0.10 and -0.10 are considered uncorrelated. VNQ is the only one that falls outside of that range, showing 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% as compared to 3% to 4% for the equity ones.
     
The weekly graph (bottom graphic) is more telling. While 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 as well (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 having to physically own and manage 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, 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

(Click on the image to enlarge)

- Intended for Start-up Analysts and Researchers -

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

A Scatter Plot of Weekly Closing Prices is a Good Starting Point to Analyze Stocks and Indices

(Click on the image to enlarge)

-- Intended for Start-up Analysts and Researchers --

While there are many ways to learn to analyze stocks or the stock market as a whole, here is one simple way I generally propose:

1. Instead of starting with a Stock or ETF, consider a liquid Index/Average like Dow Jones Industrial Average (DJIA), which comprises the 30 largest cap stocks. You may look at it as the front-end of the stock market. This type of analysis is known as the top-down approach (analysis of individual stocks represents the bottom-up approach). Alternatively, you may use the S&P 500, a.k.a. the broader market.

2. Whether you decide to experiment with stocks or indices, the most common database will consist of these variables: Date, Open, High, Low, Close, Adj Close and Volume. In terms of frequency (time interval), the common choices are: daily, weekly and monthly. Some sites may offer yearly roll-up as well (yearly prices are used to study historical trends like Laureate Shiller's CAPE ratio, etc.).

3. Though the Daily Adj Closing Price is the most frequently used data (along with other variables) in defining trend and strategy, use the Weekly/Adj Price as part of your first attempt. As you can imagine, weekly prices are less noisy and much smoother (than the daily prices), leading to easier data visualization. Once you get into more advanced analysis and modeling, you will use the other variables either as ratios or as independent variables. 

4. The best way to get a good feel for the data, trend and outliers is to create a scatter plot. Eyeball the scatter and fit your trendline. Since you are dealing with weekly averages here, leave out the moving averages. As you learn to analyze the daily data, you will see the utility of 60 to 200-day moving averages which are standard metrics in this business. If you are unsure of the differences amongst linear, logarithmic, exponential, polynomial, power, etc. trendlines, go back to your text books and brush up your knowledge. 

5. One of the skills you must develop is to quickly identify the outliers (noise). If you are working on defining trends leading to business strategy, it is absolutely imperative to work with the data as outlier-free as possible. Look at the two scatter graphs above. The only difference between the top and the bottom is that the latter has three fewer data points (week of 1/7/18, 1/14/18 and 1/21/18), resulting in a much cleaner dataset with higher r-squared. If you remove two more data points (12/31/17 and 1/28/18), the r-squared jumps to 0.923 (not shown). Again, one of the skills (perhaps habits) you must develop is to be able to identify the outliers quickly; otherwise you will end up fitting wrong trendlines.

6. Once you have the data and trendlines under control, the first thing you will look for is the formation of supports. If the stock/index bounces off a price level repeatedly, a support is being buoyed. When the support extends out to form a double bottom (like W), any reversal tends to be bullish.

7. The next thing you need to learn is to identify the congestion level. If the stock/index makes an extended sideways move within a band, it is considered "stuck" within a congestion zone. For instance, if it remains range-bound between $40 and $45 for several weeks, it has developed a short-term congestion. Many professional traders take advantage of the congestion by "channeling" those stocks/indices.

8. Often, a stock/index makes a rally but falls apart quickly at a particular price point. For example, if the stock makes multiple attempts to cut through the $45 area but fails, it has developed a short-term resistance there. Traders who buy on strength tend to develop a watch list of such stocks/indices. Professional traders generally write covered calls when the stock fails to break out.

9. When a stock/index eclipses past the resistance and maintains the upward move, it is considered a breakout. Traders who buy on strength wait for a breakout to occur. As soon as the breakout is confirmed (closes above the breakout price), they start to initiate long positions (or buy calls, sell puts, etc.).

As you get started, these are some of the market basics you must be very comfortable with.

Good Luck!

- Sid Som, MBA, MIM

President, Homequant, Inc.

homequant@gmail.com

Link to the Book

How to Define, Compute and Manage True Volatility of Major Stocks

(Click on the image to enlarge)

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. 

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

The Missing Link between Fundamental and Technical Equity Analysis

(Click on the image to enlarge)

The missing link between fundamental and technical equity analyses is a meaningful 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

Consider these Additional Factors while Choosing High Dividend Stocks - for Long Haul

(Click on the image to enlarge)

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

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

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

(Click on the image to enlarge)

-- Intended for Start-up Analysts and Researchers --

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

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

(Click on the image to enlarge)

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

How to Define, Compute and Manage True Volatility of Major Stocks

(Click on the image to enlarge)

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. 

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

How to Use Sector ETFs to Create a Diversified Stock Portfolio

(Click on the image to enlarge)

The highly correlated sector ETFs -- XLB, XLF, XLI, XLK, XLV and XLY -- would make the portfolio an undiversified and aggressive one, while the addition of XLP (less correlated), GDX (uncorrelated) and XLE (negatively correlated) would help reduce risk and make it a more diversified one.

The graph demonstrates, while XLK and XLP moved in tandem initially, they significantly diverged later in the year, suggesting that the longer holding period is equally important in reaping the true benefits of diversification.

Ideally, in order to capture any meaningful shifts in ETF 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 ETFs listed here; instead, this is promoted as an alternative research in diversifying an equity portfolio, leading to a better asset allocation model.

Consult your Registered Rep, RIA or Financial Planner for an appropriate asset allocation model and the potential holdings therein.  

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

Link to the Book