Dividends and Tax-Optimal Investing

The previous post showed after-tax results of a hypothetical 8% return portfolio. The primary weakness in this analysis was a missing bifurcation of return: dividends versus capital gains.

The analysis in this post adds the missing bifurcation. It is instructive to compare the two results. This new analysis accounts for the qualified dividends and assumes that these dividends are reinvested. It is an easy mistake to assume that since the qualified dividend rate is identical to the capital gains rate, that dividends are equivalent to capital gains on a post-tax basis. This assumption is demonstrably false.

Tax Efficiency with dividends.

Tax Efficiency with dividends.

Though both scenarios model a net 8% annual pre-tax return, the “6+2” model (6% capital appreciation, 2% dividend) shows a lower 6.98% after-tax return for the most tax-efficient scenario versus a 7.20% after-tax return for the capital-appreciation-only model. (The “6+2” model assumes that all dividends are re-invested post-tax.)

This insight suggests an interesting strategy to potentially boost total after-tax returns. We can assume that our “6+2” model represents the expected 30-year average returns for a total US stock market index ETF like VTI, We can deconstruct VTI into a value half and a growth half. We then put the higher-dividend value half in a tax-sheltered account such as an IRA, while we leave the lower-dividend growth half in a taxable account.

This value/growth split only produces about 3% more return over 30 years, an additional future value of $2422 per $10,000 invested in this way.

While this value/growth split works, I suspect most investors would not find it to be worth the extra effort. The analysis above assumes that the growth half is “7+1” model.  In reality the split costs about 4 extra basis points of expense ratio — VTI has a 5 bps expense ratio, while the growth and value ETFs all have 9 bps expense ratios. This cuts the 10 bps per year after-tax boost to only 6 bps. Definitely not worth the hassle.

Now, consider the ETF Global X SuperDividend ETF (SDIV) which has a dividend yield of about 5.93%. Even if all of the dividends from this ETF receive qualified-dividend tax treatment, it is probably better to hold this ETF in a tax-sheltered account. All things equal it is better to hold higher yielding assets in a tax-sheltered account when possible.

Perhaps more important is to hold assets that you are likely to trade more frequently in a tax-sheltered account and assets that you are less likely to trade in a taxable account. The trick then is to be highly disciplined to not trade taxable assets that have appreciated (it is okay to sell taxable assets that have declined in value — tax loss harvesting).

The graph shows the benefits of long-term discipline on after-tax return, and the potential costs of a lack of trading discipline. Of course this whole analysis changes if capital gains tax rates are increased in the future — one hopes one will have sufficient advanced notice to take “evasive” action.  It is also possible that one could be blindsided by tax raising surprises that give no advanced notice or are even retroactive! Unfortunately there are many forms of tax risk including the very real possibility of future tax increases.


Beyond Numbers. The IQ and Qi of Investing.

Numbers, facts, analysis — this is how I am trained to evaluate and decide.  This approach works well in the world of engineering and physical science.  However, I’ve found that in the domain of investing, quantitative methods sometimes function best as a means keeping grounded and objective; subjective approaches can be extremely effective and profitable.

The  cognitive process of investing can roughly subdivided into 3 categories:

  1. Intuitive (‘I’).  This portion is essentially driven by emotion, feelings, hunches.   Thoughts and choices are arrived at, not by inductive nor deductive reasoning, but by other means.  Cognitive science suggests that this process is achieved by the brains massive neural nets weighing massive stores of perceived information and arriving at conclusions based on past experience and training.
  2. Quasi-objective reasoning (‘Q’) mixes intuitive thinking with top-down reasoning  — idea first.  It also encompasses bottom-up reasoning (guided by intuition) — data first.  This approach blends analytical and intuitive thinking.
  3. Numerical/analytical (‘N’).  This approach focuses on quantitative empirical data.  Numbers are crunched and conclusions are obvious.  The difficult part is in ensuring both the inputs to the “number crunching” and the mechanisms of the “number crunching” are accurate.

I’ll use the shorthand I,Q,N to denote the processes enumerated above.   In my experiences, most well-examined financial decisions follow the basic pattern I→Q→N→Q→I or I→Q→I.  In other words, most financial decisions begin and end with intuitive (A.K.A. gut-level) thinking.  If a “disciplined investing approach” is strictly employed, a truncated →Q (or →Q→N→Q)  is added to the end of the process, where pre-determined “rules” are used to vet investment elections against predetermined suitability criteria.

So far my highest-return investment decisions have been I1→Q1→I2Q2→N1→Q3 decisions.  The first red part is the time-consuming part (in aggregate) because many ideas are discarded during the Q1 and I2 steps.  The quicker green part is the sanity and scale check. Q2 frames the decision, N1 crunches the numbers, and Q3 evaluates the outcome.  If the investment is deemed sound it is then scaled appropriately based on risk and value-at-risk ratios, otherwise it is discarded, or in some cases revised and re-evaluated.

Sigma1 Financial software can play an important role in the Q and N steps of the investment decision process.  The Q process can be either data-first or idea-first.  Sigma1 Financial also excels in the N process, imposing objectivity and performing the numerical heavy lifting.  What Sigma1 Financial software cannot nor is ever likely to do is participate directly in the I steps.  ‘I’ steps remain solidly aligned with the human element of the investment decision process.

The choice of letters ‘I’ and ‘Q’ is deliberate.  The investment IQ of investors is one important component to successful investing.  Higher investment IQs tend to result in superior investment returns.  Similarly, Qi is also critical to long-term investing success.  ‘N’ is used because it is neutral and disconnected.  Numerical and disciplined analytical methods provide ballast against the classic investing emotions of fear and greed (as well as unbridled enthusiasm and despair).

Investing IQ and Qi are brought to the table by financial professionals, while financial software provides powerful enhancements to Q, N, and especially QN and NQ, the areas separate from IQ and Qi.

The IQN domain is continuous, not discrete.  ‘I’ defines one edge which begins to blend into ‘Q’.   ‘N’ defines the opposite edge which blends with the other side of ‘Q’.  Q resides in the middle, merging aspects of I and N.

When investors understand IQN concepts, it helps to remove emotion from investment decisions, while acknowledging the importance of intuition.  IQN concepts also help demonstrate where and how financial software and analysis tools integrate with the investing process.

The Business of Financial Business

Personally the easiest part of the financial software business is software development.  I have been involved with sales before and feel reasonably confident about this aspect of the business.  The primary challenge for me is marketing.

Sales is a face-to-face process.  Software development is either a solo process or a collaborative process usually involving a small group of developers.  Marketing is very different.  It is a one-to-many (or few-to-many) situation.  Striking a chord with the “many” is a perpetual challenge because the feedback is indirect and slow.  With marketing, I miss the face-to-face feedback and real-time personal interaction.

Knowing that marketing is not my strongest point, I have put extra effort into SEO, SEM, social media, and web marketing.  Over the past couple weeks I have purchased about 20 new domains.  Market and entrepreneurial research has shown me that a good idea, a good product, and a good domain name are not sufficient to achieve my business goals.  I realize that solid branding and trademarks are also important.

As a holder of 4 U.S. patents, I understand the importance of IP protection.  However, I am ideologically opposed to patents on software, algorithms, and “business processes.”   Therefore I feel that I must focus on branding, trademark protection, trade-secret protection, and copyright protection.

My redoubled marketing efforts have been exhausting and I hope they will pay off.  Next I plan to get back to software creation and refinement.

Portfolio Software: Day 8

Software development seems to inevitably take longer than scheduled.  I thought I’d have a working alpha model by “Day 4”, but it took me until “Day 7”.  Happily, yesterday my program produced its first algorithmically-generated portfolios.  These portfolios were generated from a small “universe” of stocks optimized using simple heuristics.  To test my new algorithm, I designed the two extremes of the search-space to have known solutions.  The solutions between the extremes along the test efficient frontier, however, have no obvious closed-form solutions.  One of the two trial portfolio heuristics is, by design, extremely non-linear as well as non-monotonic.

So far, on relatively small data sets, the run time is very good.  This is despite being coded in an interpreted language, and code that contains several known inefficiencies (such as repeating heuristic computations repeatedly on the same portfolio… caching will solve this particular issue).  I am now well positioned to begin refining the algorithms’ parameters and heuristics as well as to make run time improvement.

I have taken care, and extra time to build extensibility and testability (and of course revision control) into my Linux-based software development environment.  For example the portfolio software supports n-dimensions of analysis heuristics, not just 2 or 3.  Additionally the security selection space has no built-in limits.  Selecting from all listed, investable securities available on Earth is possible.  So long as the portfolio population is constrained (to say <= 100), the investable securities list can be very large.  Similarly, portfolios can contain many securities (1000+) without significant slow-down.

Regression testing can be a bit of a challenge with rand() being part of the of algorithm.  However srand() is very, very helpful in creating targeted software regression tests.  So far, I’ve been able to maintain regression-based testability for the entire program.

I also set aside some SEO, SEM, social media time on this project.  While the SEO and SEM efforts are very tedious, they are critical to building market awareness.  The social media aspect is somewhat more fun, and occasionally pays dividends that extend beyond the potential marketing benefits.

All in all I am relatively happy with the progress to date.  Sigma1 now has working, readable, extensible code for portfolio optimization.  The current software is pre-alpha, and very likely to have undiscovered bugs and numerous opportunities for efficiency and rate-of-convergence improvement.  At least I believe the code has arrived at the initial proof of concept stage.  This is only the “end of the beginning”.   Much work remains to improve the golden (Ruby) version of the software.  Once the Ruby code has sufficiently “gelled”, then begins the task of duplicating it with a C/C++ version.  I intend to refine both the Ruby and C/C++ versions so that they produce identical results in regression.   This will be a tedious process, but is extremely likely to find and squash subtle bugs.

Wall Street Interview

Years ago, a successful friend of mine was telling me stories about his early Wall Street interviews with a big-name investing house.   One stood out to me.   The question:

If you had to invest $1,000,000 for a client, and your had only two choices, which would you choose?   (A) “Invest” the whole $1,000,000 on red or black at the roulette wheel.  (B) “Invest” on red or black $1000 at a time, one thousand times.

My friend said he knew the right answer, to that question and most of the others.  I believe he was offered this particular job, but declined it in lieu of better offers elsewhere.  Anyhow, he asked what my answer would be.

I said (B).  If single zero roulette, the client can expect to lose on 1/37 (about 2.7%);  if double zero, 2/38 or about 5.3%.   My friend said, sorry, wrong answer.  If you lose money for a high-net-worth client, even 2.7%, they are likely to be disappointed and take their business elsewhere.  If you double their money, a roughly 50/50 proposition, you will have an ecstatic client who will stick their $2,000,000 with you for years.  If you lose their whole $1,000,000 they will be disappointed and walk away, but “them’s the breaks.”

This story resonates with me to this day.  This is an absurd question from a financial standpoint, but it is a powerful question on ethics.  The business rationale behind answer (A) is valid.  However, I chose to work for a company where the correct answer is (B).