ThinkorSwim just released a 7 minute video tutorial on Portfolio Margin. I've seen a few video overviews of PM and this one is probably the best I've seen so far. It's short, but the video presents the main concepts of Portfolio Margin and is reasonably accurate in its content. Kudos to TOS for their effort.
I've updated the list of broker margin rates. A notable surprise was the substantial increase in TradeMonster's margin borrowing rates. I'm not sure exactly what their strategy is here (besides making more money that is), but such a significant change in the margin rate can't be good for Portfolio Margin investors.
I've spent a lot of time talking about the margin rates offered by various on-line brokers and the impact it can have on a trading strategy. Unfortunately, I have yet to find a decent comparison of margin rates across various brokers. While a handful of sites compile this data they are almost always lacking in some critical way (updated infrequently or incomplete in their list of brokers).
So, I've created a new page on The Margin Investor site with an updated comparison of broker margin rates.
The chart below illustrates margin rates for a small brokerage account. It continues to surprise me just how how much variability there is between different brokers.
See here for the full list.
Dividend Yield focused strategies have become increasingly popular among investors seeking to enhance the cashflow and income generated by their portfolio. Much of the interest in this strategy is driven by a lack of sufficient investment opportunities in bond markets. Simply put, bond yields are far too low to warrant meaningful attention from income driven investors.
Typically, a portfolio with such large dividend yield comes with substantial risk. In order to mitigate this risk we'll use both long and short positions. This allows us to hedge out a significant portfion of this risk.
We utilize the following assumptions for constructing our portfolio:
Below you will find an analysis of the long/short portfolio, as well as the composition of the portfolio itself. Figure 1 shows that the portfolio has roughly the same risk as the S&P 500. You can also see how the portfolio compares to other portfolios of interest.
Figure 2 shows that there are 168 positions in the portfolio: 92 long and 76 short. The total risk is shown and is also decomposed across the long and short sides. The risk displayed here is quoted as the annualized standard deviation of returns. Note that this risk in the "Longs" and "Shorts" columns is additive. That is, the risk on the long side plus the risk on the short side equals the Risk in the "Total" column. If you look at the "Shorts" column, you will notice that many of the numbers are negative. This tells out the short positions are acting as a hedge to the long positions and therefore reduce risk.
Risk is also attributed to "Common Factor" risk and "Asset Specific" risk. Common Factors drive returns across multiple assets and thus are the source of correlation between securities (think sector/industry risk and the so called "style" factor risk such as value/growth and size). In contrast, asset specific risk emanates from a the operations of an individual company and is diversifiable within limits (i.e. asset specific risk decreases as we add positions to the portfolio). As we can see, Common Factor risk dominates the portfolio at 14.63%, while Asset Specific Risk is 7.74%. To get to the Portfolio Total Risk, we add the square of the Common Factor Risk to the square of the Asset Specific Risk and then take the square root of this number.
Figure 3 shows the exposure and risk attributable to the common factors. Focus your attention on the last column which represents the amount of risk contributed by exposure to the given factor. As we can see, most of the risk is driven by exposure to three of the common factors: Utilities Sector factor, Services Sector factor, and the Growth/Value factor. The table also shows that some of the factors act as a hedge and thus end up reducing risk. For example, exposure to the Health Care factor reduces risk by 1.442%
Below is a table containing the portfolio's positions assuming a portfolio net equity value of $1,000,000. The share quantity can be scaled up or down to accommodate the actual amount available to be invested. For example, if you only have $100,000 to invest then scale down the positions by a factor of 10.
Most of the columns in the table are self explanatory, however, the "Component Total Risk" deserves further commentary. The Component Total Risk represents the risk emanating from that specific position considering the portfolio context. A positive value means the position adds to risk. A negative value means the position decreases (i.e. hedges out) risk. The sum of these values is equal to the "Portfolio Total Risk" show in Figure 2.
Portfolio Composition - Long/Short with 14.3% Dividend Yield
The last two blog posts focused on quantifying news stories to shed light on Earnings news and M&A news. Today, we'll carry on with this theme to look at "Recession" news. Again, the idea here is to collect news articles on S&P 500 stocks and then compute the percentage of that overall news flow that has to do with "recessions".
The charts below plot recession news (see top chart) and the SPY ETF (i.e. S&P 500 ETF) on the lower chart.
News articles for S&P 500 stocks: Recession News
(percent of overall news flow)
SPY (S&P 500 ETF)
The most salient point we can deduce from these graphs is that recession news spiked in Aug 2011 to around 0.35% of the overall news flow. This massive spike corresponded to the sharp market sell-off. Then, as recession fears mitigated, recession news flow dissipated, and the market began it's 2012 rally.
As investors, we have some obvious questions about this metric:
Looking at the more recent data, it appears that recession news is nearing a post spike low. This seemingly should bode well for the market going forward.
Apropos of my previous blog post discussing quantifying news stories, I present an additional news metric that provides food for thought.
The graph below represents M&A news chatter activity through time, with the Y-axis representing M&A news as a percentage of the overall news flow. The graph shows that M&A news peaked in Q1/Q2 2011 (at around 1.3%) and has been declining ever since.
News articles for S&P 500 stocks: M&A News
(percent of overall news flow)
What trader doesn't love CNBC, right?
Like many traders I watch CNBC and Bloomberg TV religiously. Keeping abreast of breaking news, market happenings and listening to the talking heads espouse and debate their opinions - what could be better, right? While you may not always agree with the viewpoint being promulgated, understanding the merits of the argument has value in and of itself.
Fast forward a few hours later and the market had taken a sharp turn to the negative. Once again, I tuned to CNBC to see what had changed to warrant this new market direction. Lo and behold, the only thing that had changed was their conclusion! The same narrative, the same compelling arguments, the same supporting data, but this time used to sustain the exact opposite hypothesis - that the market was surely destined to be headed into negative territory.
Now, I'm not a prosecutor and the financial markets are certainly no court room. Market prognosticators and commentators have the right to change their opinion should they find themselves in err. What I find so irksome however is the depth of their conviction. The situation certainly called for an iota of humility given how incorrect the previous assessment was.
The Lesson - Get the unfiltered view of the News
What's the lesson to be learned here? Is it reasonable to castigate the story tellers when - after all - it's their job to tell us a story. No, what I lacked were the tools to independently determine the value of news information. I needed to attain a clearer, more unfiltered view of what the news (including opinions and editorials) was saying. So, I did what seemed logical to a programmer like myself, I decided to build something. The next few weeks were spent coding software that would absorb news information from across the web and perform semantic analysis to garner meaning from the jumble of words.
Was there a larger story to the news flow then simply what could be determined by reading a handful of articles? Could my software divine the meaning of this news - if not at the individual story level then at least at the aggregate level? If so, would this information be of value?
Quantifying News Information
Several months after I finished the project the financial crisis settled down and I all but forgot about my news analysis program. Luckily, the code has been running continuously on one of my servers, dutifully pulling news data from across the web and crunching numbers. I recently checked in with the program to see what it had to say.
One of the metrics tracked by the program is news related to earnings. Figure 1 show the ratio of "good earnings news" to "bad earnings news", computed by classifying earnings news as either "good" or "bad" and then computing the ratio of the two. A simple example of this classification method is as follows: if the news story was about a company beating its earnings guidance then the article would be classified as "good earnings". In contrast, an earnings miss would result in a "bad earnings" classification.
News articles for S&P 500 stocks: Good Earnings vs. Bad Earnings
This "Good vs. Bad" earnings metric can be viewed as a guide to what type of earnings information dominates the news flow. The day-to-day metric shown in Figure 1 is quite volatile and so the moving averages show in Figure 2 provide for easier interpretation.
What's the key piece of information being communicated here?
Well, the chart tells us that positive earnings news peaked in Q3/Q4 2010 and has been declining ever since. In fact, we're close to a three year low in S&P 500 earnings as far as the news chatter is concerned.
The earnings metric is but one of many metrics currently being tracked by my news program. While the value of this information is still to be determined, it certainly does communicate novel information and sheds a quantitative light on the qualitative world of news. While I haven't given up my CNBC just yet, I'm now able to check a commentator's opinion of the news against the quantitative reality.
As an investor that weathered the recent financial crises and the tech bust back in 2000, there's something extraordinarily reassuring about dividend focused strategies. Having cash hit your brokerage account every month just feels good. Luckily, it turns out it's made for good investing too.
The Leveraged HYG Strategy - has the risk been worth it?
Our previous two posts discussed how to create a portfolio with a dividend yield of 17% by establishing a leveraged position in HYG – a high yield corporate bond ETF. We used risk analytics to show that the leveraged HYG portfolio comes with about twice the overall risk as an S&P 500 portfolio (i.e. SPY) – not bad considering the dividend yield, though certainly not great either.
Figure 1 below shows the performance of the 3-to-1 leveraged HYG portfolio over the past two years (assuming dividends are reinvested). The total return on the portfolio is 51%, representing an average return of 1.68% per month.
Clearly, the returns of HYG have been quite attractive. Both the return from dividends, as well as the returns from capital appreciation have made this portfolio a big winner during the past two years. Undoubtedly, this is a result of the excellent performance of corporate bonds: contracting credit spreads coupled with low default rates. It's been a great time to be a corporate bond investor indeed.
In the previous post we examined the use of bond ETFs such as the iShares iBoxx HY Corp Bond ETF (ticker: HYG) to create a portfolio with an annualized dividend yield of over 17% net of margin borrowing costs. This involved taking on a leveraged position in HYG. The amount of leverage discussed was 3-to-1 and, as a result, a Portfolio Margin account was recommended.
We concluded the previous post by warning about the risks involved with this type of highly leveraged fixed income ETF position. In today's post, we look at the risk profile of this strategy from a scientific standpoint. The tables below are taken from our risk system which evaluates portfolio risk using a multi-factor risk model.
Table 1 above shows the risk of the leveraged HYG portfolio in comparison to the risk of other well known assets. As can be discerned from table 1, the 3 times leveraged HYG portfolio is twice as risky (where risk is measured in terms of volatility of future returns) as the S&P 500 portfolio (ticker: SPY) but has only a little more risk than a portfolio consisting of a Crude Oil ETF (ticker: USO).
On row three of Table 2 we can see that the Portfolio Total Risk is 37.71%. This means that the expected annualized standard deviation of the portfolio's return is 37.71%.
We can also investigate the sources of this risk. Table 3 above displays the Common Factors the Portfolio is exposed to (column 1), the exposure level of the portfolio to these common factors (column 2), and the decomposition of the risk across the common factors (last column). As the table indicates, the majority of the portfolio's risk emanates from the exposure of this fund to the High Yield Credit Spread factor. This is evident by looking at the column on the far right of table 3, which lists this factor's contribution to the overall portfolio risk. The numbers in the last column are additive (i.e. sum to the the Portfolio's Total Risk). Interestingly, the portfolio's exposure to changes in the yield curve actually decrease the portfolio's overall risk since interest rates and credit spreads are negatively correlated.
So, what can we glean from the above analysis? There are three key takeaways:
One of the questions most frequently posed by readers of The Margin Investor is whether (and how) Portfolio Margin can be used to build portfolios on the higher cash flow yielding side; undoubtedly a germane question given the combination of robust yields on dividend paying stocks/corporate bonds, coupled with exceedingly low margin rates.
An attractive approach to constructing a yield focused portfolio is through the judicious use of a high yield corporate bond ETF. A good example of this is iShares iBoxx HY Corp Bond ETF (ticker: HYG) which currently yields around 7%. This ETF tracks the performance of a diversified basket of US corporate non-investment grade bonds. Without further ado, let's walk through an example.
Fist, assume we have an Interactive Brokers account such that our margin borrowing rate is 1.7%. Under Portfolio Margin, we can leverage this up to 3 to 1 (or more depending on risk appetite) in order to obtain a gross yield of 21%. To calculate the net yield, we minus the IB margin rate cost of 3.4% (1.7% * 2), which results in a net yield of 17.6%. By using leverage, we've converted a 7% yielding portfolio to a portfolio yielding 17.6%. That's 1.47% per month flowing directly into our brokerage account.
What's the catch? Well, there's always a catch. The added yield comes with additional risk. Specifically, the fund is exposed to the following two core risk factors:
So, the bottom line here is this strategy is only for you if you believe the Fed is committed to continuing its policy of low interest rates and provided you don't expect to see any problems in credit markets. We'll leave it to the reader to decide if this is a prudent strategy. Regardless, this portfolio will pay you handsomely while you wait.
In the next article, we will examine the risk of this portfolio in more detail.