I Robo: The Rise of the Robo Advisor

Think Ahead About Your Role in a Robo Advisory World

Financial innovation is here and it is here to stay.  Financial advisors, broker/dealers, hybrids, and even financial planners should be thinking about how to adapt to inevitable changes launched by disruptive investing technologies.

Robo Design — Chip designers have been using it for decades

I have an unique perspective on technological disruption.  For over ten years, my job was to develop software to make microchip designers more productive. Another way of describing my work was to replace microchip design tasks done by humans with software. In essence, my job was to put some chip designers out of work. My role was called (digital circuit) design automation, or DA.

In reality my work and the work of software design automation engineers like myself resulted in making designers faster and more productive — able to develop larger chips with roughly the same number of design engineers.

Robo Advisors: Infancy now, but growing very fast!

“The robos are coming, the robos are coming!” It’s true. Data though the end of 2014 shows that robo advisors managed $19 billion in assets with a 65% growth rate in just eight short months. This is essentially triple-digit growth, annual doubling.  $19 billion (likely $30 billion now), is just a drop in the bucket now… but with firms like Vanguard and Schwab already developing and rolling out robo advising option of their own these crazy growth rates are sustainable for a while.

With total US assets under management (AUM) exceeding $34 trillion, an estimated $30 billion for robo advisors represents less than 0.1% of managed assets.  If, however, robo advisors grow double their managed assets annually for the next five years that amounts to about 3% of total AUM management by robo advisors. If in the second five years the robo advisory annual grow rate slows to 50% that still mean that robo advisors will control in the neighborhood of 20% of managed assets by 2025.

“Robo-Shields” and Robo Friends

Deborah Fox was clever enough to coin and trademark the term “robo-shield.” The basic idea is for traditional (human) investment advisors to protect their business by offering robo-like services ranging from client access to their online data to tax harvesting. I call this the half-robo defense

Another route to explore is the “robo friends”, or “full robo-hybrid” approach. This is partnering with an internal or external robo advisor.  As an investment advisor, the robo advisor is subservient to you, and provides portfolio allocation and tax-loss harvesting, while you focus on the client relationship.  I believe that the “robo friends” model will win over the pure robo advising model — most people prefer to have someone to call when they have investment questions or concerns, and they like to have relationships with their human advisors. We shall see.

What matters most is staying abreast of the robo advisor revolution and having a plan for finding a place in the brave new world of robo advising.

 

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Quant Cross-Training

A very astute professor of finance told our graduate finance class that the best way to become a bona fide quant is NOT to get a Ph.D. in Finance!  It is better, he said, to get a Ph.D. in statistics, applied mathematics, or even physics. Why? Because a Ph.D in Finance is generally not sufficiently quantitative. A quant needs a strong background in Stochastic Calculus.

“Quants for Hire?”

Our company has been described as a “quants for hire” firm. That is flattering. While we currently have 4 folks with master’s of science degrees (and one close to finishing a master’s) what we do is probably more accurately described as “quant-like” or “quant-lite” software and services. However “Quants for Hire” definitely has a nice succinct ring to it.

Quant-like Tangents to Financial Learning

Most of our quant-like work has been fairly vanilla — back testing trading strategies in Excel, Monte Carlo simulations (also in Excel), factor analysis, options strategy analysis. So far our clients like Excel and are not very interested in R. The main application of R has been to double-check our Excel back tests!

We have attracted fairly sophisticated clients.  They seem reasonably comfortable about talking about viewing portfolios as unit vectors that can be linearly combined.  They tend to understand correlation matrices, Sortino ratios, and in some cases even relate to partial derivatives and gradients. But they tend to push back on explanations involving geometric Brownian motion, Ito’s lemma, and the finer points of  Black-Scholes-Merton. They do, however, appear to appreciate that we “know our stuff.”

I’ve got a decent set of R skills, but I’m looking to take them to the next level. I’m taking a page from my professor in tackling non-financial quantitative problems. My current problem du jour image compression. I came up with an R script that achieves very high compression levels for lossy compression.  It is shorter than 200 lines commented and shorter than 100 lines when stripped of comments and blank (formatting) lines.

It can easily achieve 20X or greater compression, albeit with a loss in quality. In my initial tests my R algorithm (IC_DXB1.1) was somewhat comparable to JPEG (GIMP 2.8) at 20X compression, though I the JPEG clearly looks better in general. I also found an elegant R compressor that is extremely compact R code… the kernel is about 5 lines! Let’s call this SVD (singular value decomposition) for reference. So here’s the bake off results (all ~20X compressed to ~1.5KB):

JPEG:                                                             IC_DXB1.1:

JPEG 20X Compressed

IC_DXB1.1 20X Compressed

SVD 20X Compressed

 

 

 

 

 

 

 

 

 

What’s interesting to me is that each algorithm uses radically different approaches. JPEG uses DCT (discrete cosine transform) plus a frequency “mask” or filter that reduces more and more high-frequency components to achieve compression. My ic_dxb1.1 algorithm uses a variant of B-splines. The SVD approach uses singular value decomposition from linear algebra.

Obviously tens of thousands of hours have been invested in JPEG encoding. And, unfortunately, 99%+ of JPEG images are not as compact as they could be due to a series of patent disputes around arithmetic coding. Even thought the patents have all (to the best of my knowledge) expired, there is simply too much inertia behind the alternative Huffman coding at the present. It is worth noting that my analysis of all 3 algorithms is based on Huffman coding for consistency.  All three approaches could ultimately use either Huffman or arithmetic coding.

 

So this Image Stuff Relates to Finance How?

Another of my professors explained that, fundamentally, finance is about information. One set of financial interview questions start with the premise that you have immediate (light-speed, real-time) access to all public information. Generally how would you make use of this information to make money trading? Alternatively you are to assume (correctly) that information costs money… how would your prioritize your firm’s information access?  How important is frequency and latency?

Having boat loads of real-time data and knowing what to do with it are two different things. I use R to back test strategies, because it easy to write readable R code with a low bug rate. If I had to implement those strategies in a high-frequency trading environment, I would not use R, I would likely use C or C++. R is fast compared to Excel (maybe 5X faster), but is slow compared to good C/C++ implementations (often 100X slower).

My thinking is that while knowledge is important, so is creativity. By dabbling in areas outside of my “realm of expertise”, I improve my knowledge while simultaneously exercising my creativity.

Both signal processing and quant finance can reasonably be viewed as signal processing problems. Signal processing and information theory are closely related. So I would argue that developing skills in one area is cross-training skills in the other… and with greater opportunity for developing creativity. Finance is inextricably linked to information.

The Future of Finance Requires Disruptive (Software) Technology

It aint gonna be pretty for traditional financial advisors, hybrid advisors, broker/dealers, etc. Not with the rapid market acceptance of robo advisors.

Robo advising will have at least three important disruptive impacts:

1. Accelerating downward pressure on advisory fees
2. Taking of market share and AUM
3. Increasing market demand for investment tax management services such as tax-loss harvesting

Are you ready for the rise of the bots? We at Sigma1 are, and we are looking forward to it. That is because we believe we have the software and skills to make robo advisors work better. And we are not resting on our laurels — we are focusing our professional development on software, computer science, advanced mathematics, information theory, and the like.

How to Write a Mean-Variance Optimizer (Part III)… In R

Parts 1 and 2 left a trail of breadcrumbs to follow.  Now I provide a full-color map, a GPS, and local guide.  In other words the complete solution in the R statistical language.

Recall that the fast way to compute portfolio variance is:

The companion equation is r_p = w^Trtn, where rtn is a column vector of expected returns (or historic returns) for each asset.  The first goal is to find find w₀ and w_n. w₀ minimizes variance regardless of return, while w_n maximizes return regardless of variance.  The goal is to then create the set of vectors {w₀,w₁,…w_n} that minimizes variance for a given level of expected return.

I just discovered that someone already wrote an excellent post that shows exactly how to write an MVO optimizer completely in R. Very convenient!  Enjoy…

http://economistatlarge.com/portfolio-theory/r-optimized-portfolio

The Equation Everyone in Finance Should Know (MV Optimization: How To, Part 2)

As the previous post shows, it all starts with…

In order get close to bare-metal access to your compute hardware, use C.  In order to utilize powerful, tested, convex optimization methods use CVXGEN.  You can start with this CVXGEN code, but you’ll have to retool it…

• Discard the (m,m) matrix for an (n,n) matrix. I prefer to still call it V, but Sigma is fine too.  Just note that there is a major difference between Sigma (the covariance-variance matrix) and sigma (individual asset-return variances matrix; the diagonal of Sigma).
• Go meta for the efficient frontier (EF).  We’re going to iteratively generate/call CVXGEN with multiple scripts. The differences will be w.r.t the E(R_p).
• Computing Max: E(R_p)  is easy, given α.  [I’d strongly recommend renaming this to something like expect_ret comprised of (r₁, r₂, … r_n). Alpha has too much overloaded meaning in finance].
• [Rmax] The first computation is simple.  Maximize E(R_p) s.t constraints.  This is trivial and can be done w/o CVXGEN.
• [Rmin] The first CVXGEN call is the simplest.  Minimize σ_p² s.t. constraints, but ignoring E(R_p)
• Using Rmin and Rmax, iteratively call CVXGEN q times (i=1 to q) using the additional constraint s.t. R_{p_i}= Rmin + (i/(q+1)*(Rmax-Rmin). This will produce q+2 portfolios on the EF [including Rmin and Rmax].  [Think of each step (1/(q+1))*(Rmax-Rmin) as a quantization of intermediate returns.]
• Present, as you see fit, the following data…
  • (w₀, w_1, …w_q+1)
  • [ E(R_{p_0}), …E(R_{p_(q+1)}) ]
  • [ σ(R_{p_0}), …σ(R_{p_(q+1)}) ]

My point is that —  in two short blog posts — I’ve hopefully shown how easily-accessible advanced MVO portfolio optimization has become.  In essence, you can do it for “free”… and stop paying for simple MVO optimization… so long as you “roll your own” in house.

I do this for the following reasons:

• To spread MVO to the “masses”
• To highlight that if “anyone with a master’s in finance and computer can do MVO for free” to consider their quantitative portfolio-optimization differentiation (AKA portfolio risk management differentiation), if any
• To emphasize that this and the previous blog will not greatly help with semi-variance portfolio optimization

I ask you to consider that you, as one of the few that read this blog, have a potential advantage.  You know who to contact for advanced, relatively-inexpensive SVO software. Will you use that advantage?

How to Write a Mean-Variance Optimizer: Part 1

The Equation Everyone in Finance Show Know, but Many Probably Don’t!

Here it is:

… With thanks to codecogs.com which makes it really easy to write equations for the web.

This simple matrix equation is extremely powerful.  This is really two equations.  The first is all you really need.  The second is just merely there for illustrative purposes.

This formula says how the variance of a portfolio can be computed from the position weights w^T = [w₁ w₂ … w_n] and the covariance matrix V.

• σ_ii ≡ σ_i² = Var(Ri)
• σ_ij ≡ Cov(Ri, Rj) for i ≠ j

The second equation is actually rather limiting.  It represents the smallest possible example to clarify the first equation — a two-asset portfolio.  Once you understand it for 2 assets, it is relatively easy to extrapolate to 3-asset portfolios, 4-asset portfolios, and before you know it, n-asset portfolios.

Now I show the truly powerful “naked” general form equation:

This is really all you need to know!  It works for 50-asset portfolios. For 100 assets. For 1000.  You get the point. It works in general. And it is exact. It is the E = mc² of Modern Portfolio Theory (MPT).  It at least about 55 years old (2014 – 1959), while E = mc² is about 99 years old (2014 – 1915).  Harry Markowitz, the Father of (M)PT simply called it “Portfolio Theory” because:

There’s nothing modern about it.

 

Yes, I’m calling Markowitz the Einstein of Portfolio Theory AND of finance!  (Now there are several other “post”-Einstein geniuses… Bohr, Heisenberg, Feynman… just as there are Sharpe, Scholes, Black, Merton, Fama, French, Shiller, [Graham?, Buffet?]…)   I’m saying that a physicist who doesn’t know E = mc² is not much of a physicist. You can read between the lines for what I’m saying about those that dabble in portfolio theory… with other people’s money… without really knowing (or using) the financial analog.

Why Markowitz is Still “The Einstein” of Finance (Even if He was “Wrong”)

Markowitz said that “downside semi-variance” would be better.  Sharpe said “In light of the formidable
computational problems…[he] bases his analysis on the variance and standard deviation.”

Today we have no such excuse.  We have more than sufficient computational power on our laptops to optimize for downside semi-variance, σd. There is no such tidy, efficient equation for downside semi-variance.  (At least not that anyone can agree on… and none that that is exact in any sense of any reasonable mathematical definition of the word ‘exact’.)

Fama and French improve upon Markowitz (M)PT [I say that if M is used in MPT, it should mean “Markowitz,” not “modern”, but I digress.] Shiller, however, decimates it.  As does Buffet, in his own applied way.  I use the word decimate in its strict sense… killing one in ten.  (M)PT is not dead; it is still useful.  Diversification still works; rational investors are still risk-averse; and certain low-beta investments (bonds, gold, commodities…) are still poor very-long-term (20+ year) investments in isolation and relative to stocks, though they still can serve a role as Markowitz Portfolio Theory suggests.

Wanna Build your Own Optimizer (for Mean-Return Variance)?

This blog post tells you most of the important bits.  I don’t really need to write part 2, do I?   Not if you can answer these relatively easy questions…

• What is the matrix expression for computing E(Rp) based on w?
• What simple constraint is w subject to?
• How does the general σ_p² equation relate to the efficient frontier?
• How might you adapt the general equation to efficiently compute the effects of a Δw event where w_i increases and wj decreases?  (Hint “cache” the wx terms that don’t change,)
• What other constraints may be imposed on w or subsets (asset categories within w)?  How will you efficiently deal with these constraints?
• Is short-selling allowed?  What if it is?
• OK… this one’s a bit tricky:  How can convex optimization methods be applied?

If you can answer these questions, a Part 2 really isn’t necessary is it?

Binary Options and Test Taking

Most of the important financial industry tests (Series 6, 7, 24, 26, CFA I, CFA II, CFA III, etc) only have two possible binary outcomes: PASS or FAIL. Failure is a waste of time and money. Over-studying, however, can also waste time. (Studying for a PASS/FAIL test is investing in a binary “real option.”)

All of the material is worth knowing for someone, but some information is simply not relevant to everyone. For example, investment advisor reps don’t necessarily need to know all of the rules for broker dealer agents (and vise versa). Knowing the stuff that is relevant to you is more valuable than simply allowing you to pass a test.

That said, the goal is to PASS. And you’ve got a million other things to do. So what’s a quant to do? Get quantitative of course!

Quantitative Test Prep

Step 1: Find representative sample tests. All else hinges on this. Obtaining sample tests from multiple independent sources may help.

Step 2: Determine your average score on practice tests.

Step 3: Determine the standard deviation of your scores.

Step 4: Calculate the probability of achieving a passing score given your mean score and standard deviation.

Step 5: Decide the risk/reward and whether more study provides sufficient ROI.

Assuming normal distributions, I use the 68/95/99.7 rule. Regardless of the standard deviation, if your practice average is the same as the minimum score your chance of success is only 50%. Naturally, if your mean practice score is 1-sigma above the threshold for passing, your chance on the real test is 84% [1-(1-0.68)/2]. If your mean score is plus 2 sigma, your chance of passing is almost 98% [1-(1-0.95)/2].

This little exercise shows two possible ways to improve your expected pass rate. The obvious way is getting better with the material. The less obvious way is reducing your standard deviation. Can this second way be achieved? If so how?

Keeping in mind the four-answer multiple-choice format, the mean deviation is:

MD = 2*p*(1-p)

Where p is the probability of answering a particular question correctly. Per-question deviation (PQD) is highest for p=0.5 at 0.5. PQD is lowest when p=1 at 0. For random guessing, PQD is 0.375.

Increasing your p_q to from random guess 0.25 to 0.5 for a given category q will increase your expected score, but will also increase sigma. Taking the first derivative of MD with respect to p gives: 2-4p. Because the range of p is [0,1] (arguably [0.25,1)) the best incremental decrease in MD is greatest near p=1.

Now, the test candidate must decide what the the d/dt(p_qc(t)) is for each question category (where t is time spent studying that category).  Studying the categories (qc) with the highest d/dt(p_qc(t)) will most efficiently improve the expected score. Further studying the categories with the maximum d/dt(p_qc(t))*(4p-2) will reduce PQD and hence reduce test standard deviation.

Deeper Analysis of the Meta Problem

Naturally, this analysis only scratches the tactical surface of the “binary-test optimization meta problem.” [The test itself is the problem, the tactics are part of the meta-problem of optimizing generalized multiple-choice test prep]. Improving from p=0.8 to p=0.9 is clearly better than improving from p=0.4 to p=0.5 in terms of PQD reduction, and equal in terms of increase of expected score.

Also of relevance is PQD (modified) downside semi-deviation, which I will call PQDd. I’ll spare you the derivation; it turns out that:

PQDd = p*sqrt(2*(1-p))

This value peaks at p=2/3 with a value of 0.5443. PQDd slowly ascends as p goes from 0.25 up to 0.667, then falls pretty rapidly for values of p>0.8.

We care about the random variable S which represents the actual test score. S is a function of the mean expected score μ and standard deviation σ… in a normal distribution. What we really care about is Pr(S>=Threshold), the probability that our score meets or exceeds the minimum passing score.

PQD = PQDd only when p = 0, 0.5, or 1.  For p in (0,0.5) PQDd<PQD and for p in (0.5,1) PDQd>PQD. Even though it seems a bit strange for discrete binary distribution, p in (0,0.5) has positive skewness and p in (0.5,1) negative skewness.

In the “final” analysis the chance of passing, pr(S=>Threshold), depends on score mean, μ, and downside deviation, σd.  In turn σd depends on PQD and PQDd.

Summary and Conclusions

Theoretically, one’s best course of action is to 1) increase the average expected score and 2) reduce σd. If practical, the best and most efficient way to achieve both objectives simultaneously is to improve areas that are in the 60-75% range (p=0.6 to 0.75) to the mid to high 90% range (p>=0.95).  This may seem counter-intuitive, but the math is solid.

Caveats: This analysis is mostly an exercise in showing the value of statics, variance, and downside variance in an area outside of finance.  It shows that there is more than one way to approach to a goal; in this case passing a standardized test.

 

Clover Patterns Show How Portfolios Manage Risk

Illustration of Classic Covariance.

The red and green “clover” pattern illustrates how traditional risk can be modeled.  The red “leaves” are triggered when both the portfolio and the “other asset” move together in concert.  The green leaves are triggered when the portfolio and asset move in opposite directions.

Each event represents a moment in time, say the closing price for each asset (the portfolio or the new asset).  A common time period is 3-years of total-return data [37 months of price and dividend data reduced to 36 monthly returns.]

Plain English

When a portfolio manager considers adding a new asset to an existing portfolio, she may wish to see how that asset’s returns would have interacted with the rest of the portfolio.  Would this new asset have made the portfolio more or less volatile?  Risk can be measured by looking at the time-series return data.  Each time the asset and the portfolio are in the red, risk is added. Each time they are in the green, risk is subtracted.  When all the reds and greens are summed up there is a “mathy” term for this sum: covariance.  “Variance” as in change, and “co” as in together. Covariance means the degree to which two items move together.

If there are mostly red events, the two assets move together most of the time.  Another way of saying this is that the assets are highly correlated. Again, that is “co” as in together and “related” as in relationship between their movements. If, however, the portfolio and asset move in opposite directions most of the time, the green areas, then the covariance is lower, and can even be negative.

Covariance Details

It is not only the whether the two assets move together or apart; it is also the degree to which they move.  Larger movements in the red region result in larger covariance than smaller movements.  Similarly, larger movements in the green region reduce covariance.  In fact it is the product of movements that affects how much the sum of covariance is moved up and down.  Notice how the clover-leaf leaves move to the center, (0,0) if either the asset or the portfolio doesn’t move at all.  This is because the product of zero times anything must be zero.

Getting Technical: The clover-leaf pattern relates to the angle between each pair of asset movements.  It does not show the affect of the magnitude of their positions.

If the incremental covariance of the asset to the portfolio is less than the variance of the portfolio, a portfolio that adds the asset would have had lower overall variance (historically).  Since there is a tenancy (but no guarantee!) for asset’s correlations to remain somewhat similar over time, the portfolio manager might use the covariance analysis to decide whether or not to add the new asset to the portfolio.

Semi-Variance: Another Way to Measure Risk

 

Semi-variance Visualization

After staring at the covariance visualization, something may strike you as odd — The fact that when the portfolio and the asset move UP together this increases the variance. Since variance is used as a measure of risk, that’s like saying the risk of positive returns.

Most ordinary investors would not consider the two assets going up together to be a bad thing.  In general they would consider this to be a good thing.

So why do many (most?) risk measures use a risk model that resembles the red and green cloverleaf?  Two reasons: 1) It makes the math easier, 2) history and inertia. Many (most?) textbooks today still define risk in terms of variance, or its related cousin standard deviation.

There is an alternative risk measure: semi-variance. The multi-colored cloverleaf, which I will call the yellow-grey cloverleaf, is a visualization of how semi-variance is computed. The grey leaf indicates that events that occur in that quadrant are ignored (multiplied by zero).  So far this is where most academics agree on how to measure semi-variance.

Variants on the Semi-Variance Theme

However differences exist on how to weight the other three clover leaves.  It is well-known that for measuring covariance each leaf is weighted equally, with a weight of 1. When it comes to quantifying semi-covariance, methods and opinions differ. Some favor a (0, 0.5, 0.5, 1) weighting scheme where the order is weights for quadrants 1, 2, 3, and 4 respectively. [As a decoder ring Q1 = grey leaf, Q2 = green leaf, Q3 = red leaf, Q4 = yellow leaf].

Personally, I favor weights (0, 3, 2, -1) for the asset versus portfolio semi-covariance calculation.  For asset vs asset semi-covariance matrices, I favor a (0, 1, 2, 1) weighting.  Notice that in both cases my weighting scheme results in an average weight per quadrant of 1.0, just like for regular covariance calculations.

 

Financial Industry Moving toward Semi-Variance (Gradually)

Semi-variance more closely resembles how ordinary investors view risk. Moreover it also mirrors a concept economists call “utility.” In general, losing $10,000 is more painful than gaining $10,000 is pleasurable. Additionally, losing $10,000 is more likely to adversely affect a person’s lifestyle than gaining $10,000 is to help improve it.  This is the concept of utility in a nutshell: losses and gains have an asymmetrical impact on investors. Losses have a bigger impact than gains of the same size.

Semi-variance optimization software is generally much more expensive than variance-based (MVO mean-variance optimization) software.  This creates an environment where larger investment companies are better equipped to afford and use semi-variance optimization for their investment portfolios.  This too is gradually changing as more competition enters the semi-variance optimization space.  My guestimate is that currently about 20% of professionally-managed U.S. portfolios (as measured by total assets under management, AUM) are using some form of semi-variance in their risk management process.  I predict that that percentage will exceed 50% by 2018.

 

Choosing your Crystal Ball for Risk

Choose Your “Perfect” Risk Model

I start with a hypothetical.  You are considering between three portfolios A, B, and C.  If you could know with certainty one of the following annual risk measures, which would you choose:

1. Variance
2. Semi-variance
3. Max Drawdown

For me the choice is obvious: max drawdown. Variance and semi-variance are deliberately decoupled from return.  In fact, we often say variance as short-hand for mean-return variance. Similarly, semi-variance is short-hand for mean-return semi-variance. For each variance flavor, mean-returns — average returns — are subtracted from the risk formula.  The mathematical bifurcation of risk and return is deliberate.

Max drawdown blends return and risk. This is mathematically untidy — max drawdown and return are non-orthogonal. However, the crystal ball of max drawdown allows choosing the “best” portfolio because it puts a floor on loss.  Tautologically the annual loss cannot exceed the annual max drawdown.

Cheating Risk

My revised answer stretches the rules.  If all three portfolios have future max drawdowns of less than 5 percent, then I’d like to know the semi-variances.

Of course there are no infallible crystal balls.  Such choices are only hypothetical.

Past variance tends to be reasonably predictive of future variance; past semi-variance tends to predict future semi-variance to a similar degree.  However, I have not seen data about the relationship between past and future drawdowns.

Research Opportunities Regarding Max Drawdown

It turns out that there are complications unique to max drawdown minimization that are not present with MVO or semi-variance optimization. However, at Sigma1, we have found some intriguing ways around those early obstacles.

That said, there are other interesting observations about max drawdown optimization:

1) Max drawdown only considers the worst drawdown period; all other risk data is ignored.

2) Unlike V or SV optimization, longer historical periods increase the max drawdown percentage.

3) There is a scarcity of evidence of the degree (or lack) of relationship between past max drawdowns and future.

(#1) can possibly be addressed by using hybrid risk measures such as combined semi-variance and max drawdown measures. (#2) can be addressed by standardizing max drawdowns… a simple standardization would be DD_norm = DD/num_years.  Another possibility is DD_norm = DD/sqrt(num_years). (#3) Requires research. Research across different time periods, different countries, different market caps, etc.

Also note that drawdown has many alternative flavors — cumulative drawdown, weighted cumulative drawdown (WCDD), weighted cumulative drawdown over threshold — just to name three.

Semi-Variance Risk Measure Reaching Critical Mass?

The bottom line is that early adopters have embraced semi-variance based optimization and the trend appears to be snowballing.  For instance, Morningstar now calculates risk “with an emphasis on downward variation.”  I believe that drawdown measures, either stand-alone or hybridized with semi-variance, are the future of post post modern portfolio theory.

Bye PMPT. Time for a Better Name! Contemporary Portfolio Theory?

I recommend starting with the the acronym first.  I propose CPT or CAPT.  Either could be pronounced as “Capped”. However, CAPT could also be pronounced “Cap T” as distinct from CAPM (“Cap M”). “C” could stand for either Contemporary or Current.  And the “A” — Advanced, Alternative — with the first being a bit pretentious, and the latter being more diplomatic. I put my two cents behind CAPT, pronounced “Cap T”; You can figure out what you want the letters to represent.  What is your 2 cents?  Please leave a comment!

Back to (Contemporary) Risk Measures

I see semi-variance beginning to transition from the early-adopter phase to the early-majority phase. However, my observations may be skewed by the types of interactions Sigma1 Financial invites. I believe that semi-variance optimization will be mainstream in 5 years or less. That is plenty of time for semi-variance optimization companies to flourish. However, we’re also looking for the next next big thing in finance.

 

Semi-variance: Choosing the Best Formula

Unlike variance, there a several different formulas for semivariance (SV).  If you are a college student looking to get the “right” answer on test or quiz, the formula you are looking for is most likely:

Classic Semi-Variance Formula

The question-mark-colon syntax simply means if the expression before the “?” is true then the term before the “:” is used, otherwise the term after the “:” is used.  So a?b:c simply means chose b if a is true, else chose c.  This syntax is widely used in computer science, but less often in the math department.  However, I find it more concise than other formulations.

Another common semivariance formula involves comparing returns to a required minimum threshold r_t.  This is simply:

Min Return Threshold SV

Classic mean-return semivariance should not be directly compared to mean-return variance.  However a slight modification makes direct comparison more meaningful.  In general approximately half of mean-adjusted returns are positive and half are negative (exactly zero is a relatively rare event and has no impact to either formula).  While mean-variance always has n terms, semi-variance only uses a subset which is typically of size n/2.  Thus including a factor of 2 in the formula makes intuitive sense:

Modified Semi-Variance

Finally, another useful formulation is one I call “Modified Drawdown Only” (MDO) semivariance.  The name is self-explanatory… only drawdown events are counted.  SV_mdo does not require r_avg (r bar) nor r_t.  It produces nearly identical values to SV_mod for rapid sampling (say for anything more frequent than daily data).  For high-speed trading it also has the advantage of not requiring all of the return data a priori, meaning it can be computed as each return data point becomes available, rather than retrospectively.

Modified Drawdown-Only Semi-variance

Why might  SV_mdo be useful in high-speed trading?  One use may be in put/call option pricing arbitrage strategies.  Black–Scholes, to my knowledge, makes no distinction between “up-side” and “down-side” variance, and simply uses plain variance. [Please shout a comment at me if I am mistaken!]    However if put and call options are “correctly” priced according to Black–Scholes, but the data shows a pattern of, say, greater downside variance than normal variance on the underlying security, put options may be undervalued.  This is just an off-the-cuff example, but it illustrates a potential situation for which SV_mdo is best suited.

Pick Your Favorite Risk Measure

Personally, I slightly favor SV_mdo over SV_mod for computational reasons. They are often quite similar in practice, especially when used to rank risk profiles of a set of candidate portfolios. (The fact that both are anagrams of each other is deliberate.)

I realize that the inclusion of the factor 2 is really just a semantic choice.  Since V and (classic) SV, amortized over many data sets, are expected to differ by a factor of 2, standard deviation, σ,  and semideviation, σ_d, can be expected to differ by the square root of 2.  I consider this mathematically untidy.  Conversely, I consider SV_mod to be the most elegant formulation.

Pursuing Alpha with Antivariance

A simple and marginally-effective strategy to reduce portfolio variance is by constructing an asset correlation matrix, selecting assets with low (preferably negative) correlations, and building a portfolio of low-correlation assets.  This basic strategy involves creating a set of assets whose cross-correlations (covariances) are minimized.

One reason this basic  strategy is only somewhat effective is that a correlation matrix (or covariance matrix) only provides a partial picture of the chosen investment landscape.  Some fundamental limitations include non-normal distributions, skewness, and kurtosis to name a few.  To most readers these are fancy words with varying degrees of meaning.

Personally, I often find the mathematics of the work I do seductive like a Siren’s song.  I endeavor to strike a balance between exploring tangential mathematical constructs, and keeping most of my math applied. One mental antidote to the Siren’s song of pure mathematics is to think more conceptually than mathematically by asking questions like:

What are the goals of portfolio optimization?  What elements of the investing landscape allow these goals to be achieved?

I then attempt to answer these questions with explanations that a person with a college degree but without a mathematically background beyond algebra could understand.  This approach lets me define the concept first, and develop the math later.  In essence I can temporarily free my mind of the slow, system 2 thinking generally required for math.

Recently, I came up with the concept of antivariance.  I’m sure others have had similar ideas and a cursory web search reveals that as profession poker player’s nickname.  I will layout my concept of antivariance as it relates to porfolio theory in particular and the broader concept in general.

By convention, one of the key objective of modern portfolio theory is the reduction of portfolio return variance.  The mathematical concept is the idea that by combining assets with correlations of less than 1.0, the return variance is less than the weighted sums of each asset’s individual variance.

Antivariance assumes that there are underlying patterns explain why two or more assets should be somewhat less correlated (independent), but at times negatively correlated.  Consider the affects of major hurricanes like Andrew or Katrina.  Their effects were negative for insurance companies with large exposures, but were arguably positive for companies that manufactured and supplied building materials used in the subsequent rebuilds.  I mention Andrew because there was much more and more rapid rebuilding following Andrew than Katrina.  The disparate groups of stocks of (regional) insurance versus construction companies can be considered to exhibit paired antivariance to devastating weather events.

Nicholas Nassim Taleb coined the the term antifragile, because terms such as robust simply don’t convey the exact mental connections.  I am beginning to use the term antivariance because it conveys concepts not well captured by terms like “negatively correlated”, “less correlated”, “semi-independent”, etc.   In many respects antifragile systems should exhibit antivariance characteristics, and vice versa.

The concept of antivariance can be extended to related concepts such as anticovariance and anticorrelation.