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

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

- 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

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

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

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

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 Create a Statistically Significant Fund of Funds from Balanced Mutual Funds

(Click on the image to enlarge)

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

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

The Missing Link between Fundamental and Technical Equity Analysis

(Click on the image to enlarge)

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

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

How to Create a Statistically Significant Fund of Funds from Balanced Mutual Funds

(Click on the image to enlarge)

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