Monthly Archives: January 2017

Portfolio Changes 2017

A new year is always a time for reflection and planning for the future. This is especially true for one’s investments. My investment policy statement (IPS) allows for a once-a-year plan review and gradual changes in my passive allocation. Those changes don’t have to be implemented right away and can subject to a range of dates or pre-conditions. The important thing is to keep a record of them and hold myself accountable.

2016 Results

I was quite happy with the portfolio level gains in 2016: 10.87%. It was calculated with the Simple Dietz method which meant contributions were properly accounted for. One of my pet peeves for many personal finance blogs is the co-mingling of contributions and investment returns. Another widely used formula is (end value – start value – contributions)/start value. It’s generally fine except when the contribution is large. The simple Dietz method adds half of the contribution to the denominator to approximate the time-weighted return. The 10.87% figure was calculated on an annual basis, more accurate would be to chain link monthly figures. Unfortunately the official record for this blog was only started in August. I’ll have much more data to work with in the future.

AllocateStartly had a summary of various allocation strategies for 2016. The Golden Butterfly portfolio which I drew inspirations from was the top dog at 10.79%, while the benchmark 60/40 portfolio returned 7.71%. So it doesn’t seem I have much to complain about. Though in all fairness I took on more risk — I have silver/miners in my PM sector, my equities are higher and cash position lower. Conversely, the equity slice-and-dice to include international hurt my returns. The timing of the start of this blog was unfortunate as July was the high watermark in terms of percentage gains. I gave back more than 3 points in the 2nd half of the year while the S&P was going gang-busters, so the results from August look rather poor. I’m an unabashed market timer, so that’s definitely something to improve on.

A New X

I have no plans to disclose actual dollar amounts — I hope it doesn’t detract from the ideas discussed here. At times I have spoken about the portfolio value in terms of X, where X is my non-inflation-adjusted, no-mortgage, target annual pre-tax retirement income. More recently, after some thought about desired life-style and future medical expenses, I’ve decided to increase X by about 10%. I don’t foresee any further changes to this figure.

Current investible assets stand at 17X after a contribution of 1.3X and a gain of 1.6X in 2016. I define “financial independence” as 25X plus a paid-for primary residence. There is still 4.5X left on the mortgage. Being naturally conservative I’ll probably keep working until reaching 30-35X. This amount will also include any future financial support for my daughters. There’s definitely some margin of safety in X, such that I call investible assets at 20X “financial independence lite” even without paying off the mortgage. It’s tantalizingly close, with luck may even be reached in 2017.

Passive Allocation

My guiding assumptions for the next couple of years are based on an equity pricing model I have been following. So the plan is to increase the equity allocation slightly after a drop in the market in the first quarter.

I’ll maintain the 50/50 split between US and international and increase the overall equity allocation by 5% which comes out of TBM. No changes in PMs.

Active Portfolio

The active and overall portfolios don’t follow a set allocation, although I do check it for risk management purposes. The overall equity allocation may grow to 55% by the end of 2017 from 50%. I expect the DGI portfolio that is heavy in consumer staples to under perform the broader market but don’t plan to make any major changes. Additions to the DGI will likely be old tech (MSFT, QCOM, CSCO), or a high-growth, low payout name like V. I plan to add more to growth stocks, currently at 7% of the active portfolio, and bring it up to 10%.

Option writing was a reasonably successful endeavor last year but my activity tapered off as job responsibilities increased. It’s still something I plan to continue this year, although I don’t have a target in mind. It is reassuring to know that if I ever lose my job I can generate some income this way. I do plan to use more synthetic equities (buy call, sell put) as a means to increase leverage. More details will follow when I open such positions.

Closed End Funds

My current asset allocation has about 35% in fixed income/cash, which is about age – 10. Within the passive portion, 40% is fixed income (FI), split between stable value (SV) funds and the total bond market (TBM) 35/5. The interest rate is about 2% for the SV funds in my 401K and an outstanding 3+% in my wife’s 401K. They compare favorable to current bond yields such that I’m considering getting out of TBM altogether. Views on my approach to FI in my passive accounts may vary from mainstream to conservative.

The opposite is true in my active accounts where I use leveraged closed end funds (CEFs) for the FI allocation. In open-end mutual funds, investors transact with the mutual fund company. In closed-end funds, investors buy or sell fund shares with other investors on an exchange. CEFs operate just like an ETF except usually they’re actively managed. ETFs have a fund sponsor who can create/destroy fund units in response to demand so that fund price tracks closely its net asset value (NAV). In contrast, CEFs can have market values that deviate substantially from NAV. Such discount/premium is a key evaluation criterion for CEFs.

One outstanding feature of many CEFs is their high current yield. For example, I own PCI, PDI and PTY in my tax-advantaged accounts. PCI/PDI are multi-sector funds, whereas PTY is a corporate bond fund. All are managed by Pimco. Yields in the trailing 12 months were 12-14%. Double digit yields were made possible by employing leverage, typically around 40% (achieved by issuing lower-yielding preferred shares). Leverage works both ways so you can say my overall approach to FI is a “bar-bell” in terms of risk. CEF management fees are on par with (the more expensive) active mutual funds but obviously the after-fees return is the primary consideration. Pimco funds are known to employ derivatives to hedge interest rate risk which makes them particularly valuable in this environment.

In my taxable account, I also own leveraged muni CEFs, PCQ and PCK, both CA muni funds managed by Pimco (yes I think they are the best in this business). They’re yielding 5.5-6% which is close to double digit pre-tax yields depending on your tax bracket. They dropped quite a bit post election since certain anticipated tax changes will make them less attractive. I’m less worried about these tax changes than the longer term fiscal situation in California. For now they are medium term (~2 years) holds. I will not add to them, but rather will seek opportunities to sell especially if I can create more space in my tax-advantaged accounts.

The backdrop of any FI discussion is of course the direction of interest rates. I believe rates have in fact bottomed. Not all is lost for FI though. Hedging with interest rate derivatives is one approach. For now, MBS (mortgage backed securities) should do quite well as pre-payments stop. Floating rate loans should do quite well, too. In fact, I’ve already picked out a CEF in the latter category for my watch list.

There is far more diversity in FI as I pointed out in Beyond 60/40. A recent post from Newfound Research did an excellent job decomposing risk factors in various FI instruments:

Most individual investors have an FI allocation heavy in treasuries and investment grade corporates which is a lot of rate exposure (see LQD and TLT in the graph). I find it ill-advised giving my outlook on rates. At any rate, individual investors tend not to pay much attention to FI, since they tend to be overweighted in equities anyway. Readers of this blog though should not be surprised by where I stand. I don’t think a TBM index fund works nearly as well as its equity counterpart. One reason being the index is weighted by issuance. That there are non-economic, state actors with heavy footprints is another. Not to mention it doesn’t cover SV funds, CDs, and bank loans if one considers all the FI options available.

Supplementing DGI for Retirement Income

A common criticism for DGI for retirement income is that it requires a higher portfolio value due to current low yields, thus over-saving and longer working years. That is a valid point. The canonical approach for retirement income generation is to withdraw a fixed percentage, e.g. 4% from a $1M portfolio for an initial annual income of $40K. The S&P 500 yields just over 2% today. A 4% yielding portfolio will force one into high yielding but low growth sectors such as utilities, telecoms, and REITs, etc. This naturally increases portfolio risk.

My solution is: supplementing a well-rounded, high quality DGI portfolio with high-yielding CEFs. Let’s do some quick math. Assuming the DGI portfolio yields 2.5% and has a 7% annual growth rate. We can also construct a CEF portfolio with 8% yield and assume no principle growth. Then the combination of $727K in the DGI portfolio and $273K in CEFs will generate $40K initially. The weighted portfolio growth will be 0.727 x 7% > 5%, more than enough to overcome inflation. All of the assumed numbers are quite conservative and can easily be constructed from securities available today.

Readers interested in investing in CEFs should do their due diligence. Two resources I find tremendously helpful are CEFConnect and the MorningStar discussion forum.

Performance Tracking December 2016

For calculation methodology see earlier post

2016 is finally in the books! This blog was launched in August which was a high in terms of portfolio percentage gains at around mid teens. PMs, dividend paying stocks and closed end funds all performed well upto that point. Since then, PMs retraced almost all the yearly gains and muni CEFs had the most severe setback as interest rates started to rise. The overall portfolio still managed a 10.87% yearly gain (using simplified Dietz method), but it’s not an “official” number since tracking here officially started in August. For the year-end summary I also provided the “chained” results based on monthly data.

Passive Portfolio

The passive portfolio without PMs managed a gain of 1.04% vs. 1.23% for the 60/40 bench mark. International equities, both developed and EM were sources of drag. PMs faired poorly. Gold closed the year at $1151, above last year’s closing but further weakness is expected into the New Year, with a low below last year’s low of $1045 possible. I would consider adding to my PM positions if that were to realize. I have not changed my target allocations, but I have rebalanced out of my small/mid cap blend into stable value funds recently. My IPS allows annual allocation reviews and small tweaks. My current plan is to re-evaluate when and if we see weakness in the first quarter. Major planned actions are to be out of bond funds entirely (use stable value funds only), and to increase my equity allocation by 5%.

Active Portfolio

The DGI portfolio outperformed SPY for once, 2.52% vs. 2.04%. Otherwise the relative performance since August stands at -3%. As part of the interest rate re-alignment, rate-sensitive stocks took a hit. It affected my DGI portfolio to a degree. That said I’m optimistic of its chances against SPY in the coming years since I tend to focus on high-quality dividend growers. My total gain from options activities (mostly premium selling) was over $2500 but I wasn’t very active in the latter part of the year. That’s something I’ll pick up again in 2017 with a goal of generating 1-2% of the account value at Interactive Brokers.

One of my main objectives for 2017 is to continue to grow my DGI portfolios, where most of additional funding will come from vesting of the RSUs from my employer. My long term goal is to assemble 50 stocks at around $20K each. In December, I picked up some Gilead ($GILD) based on valuations and its cash position. Pharma, financials and tech are the sectors I’m paying most attention to right now.

Plan and Forecast

I laid out my S&P predictions here. The year-end rally did materialize although stalled at Dow 20K. Lots of capitals gains probably have been deferred to 2017 to take advantage of potential lower tax rates. A 10% correction is not out of the question. I have set aside amounts for Roth IRA and 529 contributions that will be deployed at the end of January/early February or whenever the correction materialize. Other than that, I actually don’t have a lot of spare capital and will be using option strategies such as synthetic equity to take advantage of what I believe will be a two-year long rip-roaring bull market.

The Arithmetic of Prudence, Part 2

In the second installment of this series I want to further expound on the point made at the end of Part 1: that when projecting into the future, the expected value is the mean which for a normal distribution is the same as the median or the 50 percentile value. A more comprehensive approach is to examine the distribution of all possible outcomes. Keeping in mind the asymmetry between the extra pain from missing the target number and the more subdued joy from exceeding it, a prudent planner may want to optimize a lower percentile outcome, e.g. 10 or 25 percentile.

To illustrate the point I present the probability density functions (PDFs) of the ending values of two hypothetical portfolios . For simplicity, the outcomes are assumed to follow standard Gaussian distributions [the actual parameters are mean = 115 and standard deviation (SD) = 20 for Portfolio 1, mean = 100 and SD = 5 for Portfolio 2]. The target portfolio value is indicated by the red dash line. I intentionally left out the context where for this exercise to make it as general as possible. It could be about reaching the number at the end of accumulation phase to support a certain retirement lifestyle; or it could be about the end of the distribution phase where success means portfolio value is positive.

The majority outcome from both portfolios are to the right of the red dash line: both portfolios are likely to meet the financial goals, a very good thing. Now let’s delve a little deeper. The expected ending values are at the center of the respective distributions and clearly Portfolio 1 has a higher value. On the other hand, Portfolio 1 has a wider spread or larger standard deviation. Note that the areas below the two curves both equals 1 by definition. The areas under the curves to the left of the red dash line represent the probability of failure. Portfolio 1, despite having a higher expected value, actually has a higher probability of failure! As you can see from the diagram, the two PDFs cross at some point, the exact location depends on the relative mean and SDs. Mathematically, when we examine the left side of the distribution we’re looking at lower percentile outcomes. For example, in calculating withdrawal scenarios we regularly look at the 95% confidence level, in other words, the 5 percentile outcome. It is my contention that a more conservative planner should pay more attention to the lower percentile outcomes (the “sure thing”) than the mean expected value.

While it is often the case that a portfolio with higher expected rated of return will also have a larger standard deviation, the two PDFs above were specifically chosen to illustrate a point. We should rightly ask how real portfolios behave. To that end, we can turn to the excellent tools available at

Step 1. Determine sample portfolio CAGR and SD from backtesting

First we establish three sample portfolios: portfolio 1, 100% equities with a 50/50 US/international split; portfolio 2, a 60/40 3-fund portfolio, again with a 50/50 US/international split in equities, and the total bond market for the fixed income portion; and lastly the Permanent portfolio with equal weights in US equities, long and short term treasuries, and gold. Note that the backtest was from 1987, the earliest time data on all asset classes involved was available. The CAGRs were 7.79%, 7.53% and 7.11% respectively. The SDs were 15.53%, 9.35% and 7.11% respectively. There was a clear reduction in volatility from adding bonds, while the Permanent Portfolio exhibited even less volatility as expected. The difference in CAGR appeared minor but would be appreciable over a multi-decade compounding period. My personal view in portfolio construction is that the closer to the end of the projection period the greater impact of volatility and lesser of CAGR.

Step 2. Use Monte Carlo simulation to project portfolio value at the end of distribution phase

For illustration purposes I’ll use Porfoliovisualizer’s Monte Carlo simulation tool for a standard 30-year withdrawal at 4% of portfolio value per year, matching the recommendations from the famous Trinity study. The starting portfolio value is $1MM, the parameters of the Monte Carlo study was set up as follows: statistical returns from 1987-2015 was used (only slightly different from the numbers from 1987-2016), assume returns follow normal distributions, and a 3% inflation with a volatility of 1.5%.

The screen capture above shows the simulation the 100% equity portfolio. Results are for 25/50/75 percentile ending portfolio values. The same simulations were run for the 60/40 3-fund portfolio and the Permanent portfolio and summarized below.

For both the 50 and 75 percentile results, the 100% equity portfolio has by far the highest expected value, followed by the 60/40 portfolio and then the permanent portfolio. However, when looking at the 25 percentile results, the order is exactly reversed with the Permanent Portfolio on top and the 100% equity portfolio on the bottom. Here we have a situation similar to the very first, entirely hypothetical diagram: the portfolio with lower expected value but tighter spread wins when examining lower percentile outcomes. So the take-away is that for conservative planners who want to adopt a maximin approach (maximize the guaranteed minimum) looking at the expected value alone can give misleading results.

A couple footnotes to the study above: 1) the start date of 1987 missed one of the biggest bull markets in gold so should have hurt the Permanent Portfolio, 2) in the Monte Carlo simulations, normal (Gaussian) return distributions was assumed instead of fat-tailed distributions which probably helped equity-rich portfolios, 3) my assumption of 3% inflation was based on current economic conditions and didn’t seem to make a big impact on the results, 4) note the study is NOT a forecast, backtesting was used to establish historically realistic CAGRs and SDs which under a standard withdrawal plan provided historically realistic portfolio value probability distributions. These probability distributions proved that the PDFs shown in the first diagram were qualitatively correct.

Edit: added the assumption that the distribution is normal, thus the mean and median are the same.