Making Asset Allocation ‘Great Again’!


Through interactions with over 300 broker dealer systems and meetings with over 5000 Financial Advisors (FA), we discovered, first hand that Alternative Investments (AI), at just around 4% of typical retail client portfolios, remain grossly under allocated.

This is in part because FAs lack sound means to quantify and demonstrate AI’s accretive portfolio benefits. Given mind boggling heterogeneity across 40 sub-types within real estate, private equity, hedge funds, liquid alternatives, managed futures, oil & gas, credit structures and fixed income substitutes, AI is poorly understood.

Moreover, AI given unique issues of inconsistent returns measurement, opacity, special risks, variable degrees of liquidity and usage of active management techniques within inefficient markets does not lend itself to conventional asset allocation methods. In order to place AI within client portfolios, FAs need a simple way to understand and to explain its benefits within the context of their particular clients; to allocate appropriately, as well as to access portfolio exposure by investing with the right set of manager funds.


ActiveAllocator Calculates Impact of Alternative Investments to Portfolio


We provide the ability to include soft qualitative views from a firm’s due diligence function and incorporate a degree of confidence within a quantitative manager allocation framework.

We help financial advisors decide the amount to allocate to expensive skilled managers versus simply accessing inexpensive passive benchmark indices.

We further help them show their clients the appropriate relative allocations across skilled fund managers, calculate the impact of adding a fund manager to a portfolio…

Asset Allocation Before Portfolio Construction

“Alice: Would you tell me, please, which way I ought to go from here?

The Cheshire Cat: That depends a good deal on where you want to get to.

Alice: I don’t much care where.

The Cheshire Cat: Then it doesn’t much matter which way you go.

Alice: …So long as I get somewhere.

The Cheshire Cat: Oh, you’re sure to do that, if only you walk long enough.”

— Lewis Carroll, Alice in Wonderland

Not to be too facetious, but very often this is how a conversation between a financial advisor and a client goes. If one doesn’t quite know where one is going, one is likely to take meandering paths and walk for a very very long time. Too often, we put the cart before the horse.

It is widely recognized that the majority of investment returns come from taking long term strategic asset allocation decisions. The how much to allocate to different asset sub-classes question. This simple, boring, yet extremely powerful insight is unfortunately often glossed over in obsessive search for ‘best investment products’ or the next hot fleeting investment theme.

The long term strategic allocation is about figuring out WHERE to go. The roads that get one there then become subsequent choices between a myriad of investment products.

The asset allocation module within helps set direction, analyzes and then optimizes on what a client already owns. In seconds it dissects a client’s aggregate portfolio, does a risk-return analysis, generates forward looking numbers and metrics and makes recommendations to reallocate asset classes to improve portfolio characteristics. In other cases, it suggests the addition of alternative and illiquid investments (when permissible) to complement and enhance existing holdings.

Only after getting the long term strategic asset allocation right,  ought one to worry about portfolio construction and fund manager selection.

Put the horse before the cart with

Alice would approve. As would the Cheshire cat!


Analyze and Rebalance Portfolios To Improve Strategic Asset Allocation

Active Allocator brings a fact based approach to investment decision making – one largely conspicuous by its absence when it comes to integrating liquid and illiquid investments in portfolios.

In seconds, can dissect and analyze the forward looking statistical properties and expected behavior of an existing portfolio. This done, it finds the best combination of asset subtypes (including illiquid alternative investments and actively managed funds) that improves it on a variety of chosen metrics.

ActiveAllocator goes far beyond conventional risk and return tradeoffs, whilst personalizing for unique investor preferences including allowing for desire or aversion to alternatives or illiquidity, accommodating different investing horizons, time varying risk preferences as well imposing constraints on specific asset type exposure.

ActiveAllocator Lets You Specify Turnover

Depending on portfolio turnover, execution costs often have significant negative impact on a portfolio’s or a trading strategy’s profitability. There is often a trade-off between paper profits from a trading strategy and portfolio turnover: lower-frequency strategies with lower turnover tend to have lower paper profits (without considering costs) and higher-frequency strategies with higher turnover tend to have higher paper profits. For example, traditional value signals such as book to price ratio have very low turnover—often less than 25% annually—and short-term mean reversion strategies have much higher turnover—often as high as 25% daily. For strategies with high turnover, reducing trading costs is crucial. A strategy with a daily turnover of 25% and an assumed trading cost of 10 basis points requires 12.6% annualized before-cost return to break even. High trading costs are the reason why some apparent anomalies are hard to monetize and continue to exist.

Our overall approach has been to choose factors that have good performance and consistency in predicting portfolio returns balanced against turnover.

By design, some factors change slowly from one period to the next, whereas others change rapidly. For example, the size factor stays stable over months or even years. Therefore, the single-factor portfolio constructed using size factor has low turnover. In contrast, values of a short-term reversal factor change rapidly from day to day (if the signal works, recent losers become winners and recent winners become losers). Therefore, a single-factor portfolio constructed using a short-term reversal factor has high turnover. Since the turnover depends on how we use a factor to construct a portfolio, there is no standard approach to estimate turnover. One approach is to directly look at a zero-investment portfolio, e.g., the top quintile minus the bottom quintile, to calculate portfolio turnover. A different approach, independent of portfolio construction, measures the serial correlation of factor scores: the correlation between factor scores of the stocks at t and the factor scores of the same stocks at t + 1. Higher correlation means that factor scores are more stable over time and indicates lower factor turnover.

Factors with high turnover need to be, and sometimes are, compensated with higher predictive power. If we have a reasonable estimation of trading costs, they can be directly incorporated to estimate after-cost returns and information ratios. Mediocre predictable power and high turnover, however, do not automatically make the factor a poor signal. Investors usually use multiple factors in the portfolio construction and the final turnover depends on the interaction of the selected factors.

The hysteresis approach is a valuable tool for reducing turnover as well. After we enter a long position when the stock moves into the top quintile, we do not necessarily exit the position when it moves out of the top quintile. Instead, we only exit the position if the stock falls below the 60th percentile. Although the expected alpha is lower when the stock falls from the top quintile to the fourth quintile, holding the existing long position does not require extra trading and does not incur trading costs. Similarly, after we enter a short position when the stock moves into the bottom quintile, we only exit the position if the stock moves above the 40th percentile. By using different thresholds for entry and exit, we reduce the turnover of fast-moving signals and achieve a better trade-off between raw returns and trading costs.

This is just one of hundreds of active management approaches we use at to create better portfolios.

ActiveAllocator Lets You Specify Desired Illiquidity

Liquidity is defined as the ability of an asset to be converted into cash quickly and without any price discount. It is a risk factor because illiquidity requires a return premium. Even investors without immediate liquidity needs prefer to hold liquid assets rather than illiquid assets, assuming equal return and risk. As a corollary, in order to entice investors into illiquid assets, it will be necessary to offer return enhancement relative to liquid assets. Since different measures are used to capture different aspects of liquidity, there is no consensus on the best proxy

Regardless of the proxy used, researchers have mostly demonstrated a negative relationship between stock returns and liquidity. For example, stocks with higher stock turnover have had lower expected returns. Expected stock returns have been positively correlated with both across stocks and over time. Thus, it is generally accepted that stocks with lower liquidity command a risk premium. There is significant interaction between size and liquidity factors. In fact, market cap is used as a proxy for liquidity as well. Large-cap stocks have much higher trading volumes than small-cap stocks. As a result, large-cap names naturally have lower illiquidity ratios. Nevertheless, liquidity risk adds value beyond the size effect. After controlling for size, stocks with higher turnover still tend to have lower expected returns. There is also interaction between liquidity and the price-to-book ratio. Growth stocks with high price-to-book ratios tend to have higher turnover than value stocks with low price-to-book ratios. However, when the stock universe is restricted to large cap stocks, liquidity premiums disappear. Compared with small cap stocks, large cap stocks are relatively liquid, which is why trading volume differences may have limited impact on the liquidity premium. Instead, large trading volume and high turnover may reflect momentum trading and information trading. allows you to specify the amount of illiquidity you prefer across your entire portfolio. It allows you to extract a time varying illiquidity premium which is especially valuable in privately held asset classes and actively managed structures.

Digital Disruption in Alternative Investments Advice

Forrestor Research suggests that “the financial advice, retirement and wealth management industries have been spared convulsive change to date. They are insulated by an older demographic, regulatory barrier, and high capital requirements. The forces of digital disruption that have ripped through the news, music, and travel industries are now amassing an assault on wealth management. As financial institutions continue to spend on technology, a growing proportion of that spending will shift from outdated internally developed or custom built enterprise software to cloud based solutions, whose deployment happens in digital disruptive companies”.

While there is a fair amount of innovation happening in digital money management, digital investment and traditional digital financial advice there has almost none happening in alternative investments. Financial advisors lack sound means to quantify and demonstrate accretive portfolio benefits of alternative investments. Given mind boggling heterogeneity across over forty subtypes within real estate, private equity, hedge funds, liquid alternatives, managed futures, real assets, credit structures and fixed income substitutes, these investments are poorly understood.

Financial advisors need a simple way to allocate appropriately, as well as to access portfolio exposure by investing with the right set of active manager funds. However, allocating to alternative investments is extremely hard to do, for simply extending the widely prevalent Modern Portfolio Theory to this domain just does not work.

There are multiple financial software firms providing archaic asset allocation systems for traditional investing but none for blending alternative investments with traditional, Extending simplistic allocation and portfolio construction views to alternative investments have led to a proliferation of vendors creating commoditized tools and a new genre of undifferentiated robo advisors who decry benefits of active management. We submit that existing tools, often using arbitrary ‘rules of thumb’ which lack any scientific rigor, create false precision and indeed harm investor portfolios.

We differ from digital money managers, digital investment managers and digital financial advice providers; we differentiate ourselves from emerging robo advisors that create simple, low-fee, diversified, automated ETF portfolios largely targeting naïve mass affluent investors.

We are born of digital disruption. We are a beneficiary of major technology shifts including reliance on cloud-based services, open APIs and the new agile software development paradigm. Consumers are ready, industry conditions favor innovation, and the technologies required to transform these industries now finally work. We also believe that as the number of affluent households increase, many consumers will be outgrowing their current financial advisors. Affluent buyers who are beginning to value digital advice will be disappointed when the human financial advisor experience does not meet their expectations.

Our disruptive approach accounts for, amongst other things, alternative investment specific considerations such as stale pricing in returns measurement, unique risks that differ from traditional investing, the effects of illiquidity on portfolios, the special issues that come with investing in inefficient markets, resolving greater strategy heterogeneity in complex sub-strategies as well as special manager evaluation considerations.

ActiveAllocator Invites Product Dev Collaboration is the world’s first and only online asset allocation and portfolio construction portal for advisors to seamlessly include traditional, illiquid and alternative investments in portfolios. Our approach accounts for, amongst other things, alternative investment specific considerations such as stale pricing in returns measurement, unique risks that differ from traditional investing, the effects of illiquidity on portfolios, the special issues that come with investing in inefficient markets, resolving greater strategy heterogeneity in complex sub-strategies as well as special manager evaluation considerations.

We seek to partner with others who share our vision. Our product development direction includes codifying new approaches to arriving at (i) robust correlation; (ii) better risk forecasts; (iii) incorporating extreme events; (iv) tax optimized allocation; (v) tactical allocation; (vi) technology improvements.


Is your Financial Advisor Still Stuck on MPT?

Most folks acknowledge that long term Strategic Asset Allocation creates the bulk of investment returns. Yet, hundreds of software vendors, tens of consulting firms and robo advisors, thousands of RIAs and Broker Dealer systems still doggedly cling to and defend outdated notions encapsulated in sixty years old Modern Portfolio Theory (MPT).

MPT no longer serves affluent investors well. It is also responsible, in part at least, for disillusionment  with a Financial Advisor’s value add. This has to change. At we are changing the quality of discourse by blending the digital with the human.

Challenge your advisor with some questions:

  • MPT assumes returns and risks are measured comparably and accurately across all asset classes. Reality is that they are not. Our optimization algorithms account for the fact that it is not possible to observe market prices (and hence returns), (and hence by extension volatility of returns = risk ) for illiquid investments such as in private equity, private debt or private real estate.


  •  MPT assumes all investors and fund managers possess comparable information. The reality on the ground is often fund managers know more than investors do –  asymmetric information. We attend to this in our portfolio construction part using a probabilistic Bayesian construct with assigned confidence levels.


  • MPT assumes that liquidity in each asset class is similar. However, we know that many alternative investments contain significant tradability restrictions affecting rebalancing. We account for disparate and time varying illiquidity premiums in our optimization methods. We calculate how far and for how long an illiquid portfolio is from a theoretical efficient frontier and extract a dynamic illiquidity premium to compensate.


  • MPT assumes markets are efficient and new information is priced immediately. The reality is that Active managers seek restricted markets to exploit inefficiencies. We separate between market exposure and manager skills when we implement portfolios to fulfill the strategic asset allocation.


  • MPT assumes volatility accurately reflects risk, and investors only worry about variance of returns. The reality is that returns are not distributed symmetrically (or as in high school we learnt ‘normally’ around their mean). Volatility understates downside risk and that’s why we account for downside risk by taking into account skewness and kurtosis.< N.B. we do not optimize across these moments as it is hard to calculate co-skewness and co-kurtosis as implementing extreme value theory is very difficult>.


  • MPT suggests investing passively at the broad asset class level using inexpensive indices is a good idea. The reality is that solving for complexities of alternative investments— illiquidity, asymmetric return distributions and active management techniques— and purposively including into the portfolio construction process is hugely beneficial. In our approach returns come from fundamentals, manager skills, unlocking illiquidity premiums & managing downside risks.


I had learnt this the hard way at CAI and ActiveAllocator brings clarity in thought process to all investors now.

Special Considerations in Allocating to Alternative Investments

Unlike traditional investing forecasting risk and return becomes challenging when dealing with alternative investments. These have shorter data histories, expose investors to new kinds of risk and, in some cases, offer limited liquidity. A method is to attempt to purposely remove the differences between alternative and traditional asset classes so that investors can evaluate them in a like-for-like fashion. The approach is threefold. First, to modify the historical characteristics of each alternative asset class to ensure that they are comparable to fixed income and equity. Second, to develop an integrated set of return forecasts using historical relationships among alternatives and traditional asset classes. Third, to add constraints to capture preferences of typical investors and account for issues such as shorter data histories for alternative classes.

Risk Forecasts

Alternative asset classes differ from traditional asset classes in several ways, making it difficult to properly characterize risk. The differences, as well as our approach, built off our previous work at CAI, to correcting them, include:

• Risk in non-traded, illiquid assets. One of the most difficult problems investors face is how to compare the risk in traded assets, such as fixed income and public equities, to the risk in non-traded assets, such as real estate and private equity. The fundamental challenge is that estimating risk from non-traded assets requires an investor first to “mark to market” the valuations. ActiveAllocator has refined dated methodology that incorporates information of various kinds in order to estimate risk on a basis comparable to that of liquid assets. This methodology indicates that both risk-adjusted returns and diversification benefits from these asset classes are less than previously believed.

• Restrictions on tradability. Many alternative assets set restrictions on when the investors can exit the investment—what is called “tradability.” These restrictions affect the ability to re-balance, respond to new investment opportunities and meet unforeseen cash requirements. In exchange for these limitations, investors are paid a premium for locking up their assets in addition to the premium they can expect in the long run for taking risks. The failure to appropriately account for this premium, however, can lead investors to inappropriate portfolios. Therefore, a portfolio construction approach that accounts for these restrictions by incorporating tradability into the traditional risk/return framework is usually better.

• Reporting biases. Because industry returns are usually reported for a sample of funds, the performance of many funds—which often, but not exclusively, turn out to be those funds that perform poorly—do not appear in published indices. This reporting bias has the potential to artificially inflate reported performance. ActiveAllocator’s approach allows investment advisers to correct for these biases to returns.

• Serial correlation in hedge fund returns. As is the case with the highly illiquid assets such as real estate and private equity, certain hedge fund strategies such as distressed debt are characterized by pricing distortions in reported returns. This can lead to an understatement of risk. Our methodology corrects for serial correlation which refers to correlation of returns over time, and typically occurs when assets are traded infrequently. We, as others have found, too discover that removing the effect of serial correlation increases historical risk compared with reported values.

• Strategy drift. The fact that alternative investments are often unregulated, active strategies means that fund managers may change their investment focus over time to seek higher returns, introducing potential risks that investors may not have known that they were taking. This risk can be mitigated by aggregating hedge funds into more stable units, thus attempting to limit strategy drift at the portfolio construction stage.

• Tail risk. Unlike traditional investments, some alternative investment strategies are peculiarly subject to the risk that returns will be significantly below their average. For most investors who are particularly concerned about limiting losses, this potential for significantly below-average returns means that traditional measures of risk, such as volatility, might be inappropriate. We build on a framework that allows investors to construct portfolios of alternatives that explicitly account for downside, or tail risk.

• Fees. Reported index returns are net of manager fees. However, many investors access alternative investment products, such as hedge funds or private equity, through a fund of funds. This introduces a second layer of fees that the reported returns do not capture. As a result, reported returns actually overstate the value captured by most investors. We account for this by stripping out fund of fund fees from the historical data.

• Appropriate time frame. Over short time periods, returns for an asset class can depend heavily on that asset class’s stage in the investment cycle. For example, equity returns look very different in bull markets than in bear markets. In order to properly characterize the risks and returns of an asset class, investors must analyze these characteristics over a full investment cycle. However, cycles for certain alternative investment classes, such as real estate or managed futures, are longer than those for equity and fixed income. We adjust the length of historical data used for each alternative asset class depending on the length of the investment cycle.

After modifying the historical data to account for the issues listed above, ActiveAllocator uses the modified data to compute the risk characteristics – standard deviations and correlations – of each asset class. The differences can be fairly dramatic.

Return Forecasts

Using historical return forecasts to construct portfolios is problematic, because return characteristics are much less stable than risk characteristics. This problem can be circumvented, however, by recognizing that relative returns among asset classes are far more stable. For example, while equity returns may vary substantially over different time periods, the spread between equity and cash is far more consistent over time.

Our approach starts with the forecasts for traditional asset classes described in the previous section. From these basic building blocks, listed above, ActiveAllocator constructs forecasts of average returns for alternatives. For each of the alternative asset classes, we begin with a spread-based method, mirroring the approach taken on the traditional side, which calculates the differences between returns in alternative asset classes and related traditional assets.

For example, to calculate a forward-looking return for each hedge fund strategy, we begin with the spread to cash as a basis. These spreads, as it turns out, have been more stable over the period for which hedge fund return data have been collected. We then estimate the relationship between this spread and other factors that drive hedge fund returns, in particular exposure to equities and exposure to credit. The results of this analysis allow us to tie nominal hedge fund return estimates to our forecasts of the other asset classes by incorporating the forward looking forecast for cash, equities and credit. Following our earlier discussion, we then adjust these forecast spreads for a number of other factors—such as survivorship and selection bias, operating expenses which might be incurred at a fund of funds level and indicative fund of funds fees.

For managed futures, we also use the spread to cash as a basis for forecasting. Here, again, we must adjust for the fees incurred by various investors. Managed futures return however are often already net of fees. As separate accounts, they are generally reported net of the fees a fund of funds manager would take, for example. As a result, it might seem as if no additional adjustment for fees is necessary. Notwithstanding that point, however, managed futures fees will still differ across various investor types. We therefore make an additional adjustment to the spread-based forecast to account for fees that might be incurred by investors who invest in smaller lot sizes.

For the private equity classes, including leveraged buyouts and venture capital, we calculate the spread between returns in each of these categories to the public equity market. We then add this spread to the forecast public equity return to arrive at the forecast for venture capital and leveraged buyouts. Finally, for real estate, we apply a similar methodology that not only accounts for interest rate movements, but also for vacancy rates and net operating income. This assembly of return forecasts creates a complete set of parameters for use in constructing portfolios: risk, return and correlation.


As in any asset allocation exercise, it is necessary to consider constraints on particular asset categories. This is because models are only approximations of reality, and do not capture the full set of criteria on which investors base decisions. Some of these criteria include:

Tradability – As described earlier, many alternative assets set restrictions on when the investors can exit the investment, which imposes a cost on the portfolio. Therefore, we allow investors to constrain the amount of illiquidity in their portfolios based on their cash flow requirements.

Tail risk – Although, in our earlier discussion we have attempted to remove the unaccounted for tail risk, some residual tail risk might still exist. All things being equal, investors should constrain allocations to asset classes with significant tail risk, as tail risk increases the probability of significant losses. Therefore, our optimization algorithms take negative skewness and excess positive kurtosis effects into account.

Confidence – Even after modeling each asset class, investors will have more confidence in the forecasts of some asset classes relative to others. This can be due to the length of data with which our research methodology operates. Whereas, at most, alternative asset classes tend to have approximately twenty years of data, data at least four times that length are available for many publicly traded assets. Furthermore, some asset classes experience more rapid change in their structure than others. For example, the hedge fund industry has been characterized in the last few years by a decline in returns and more recently an outflow of capital. In contrast, equities and fixed income have been much more stable. Therefore, our optimization algorithms apply levels of confidence to different forecasts.

Market Capitalization – Another indicator that is generally used as a basis for ensuring diversification is the market capitalization of the various asset classes. This is because an asset class’s relative market capitalization reflects the market’s consensus regarding an appropriate allocation. If the market capitalization of a particular asset class is small relative to other asset classes, investors should constrain allocations to this class, unless their beliefs regarding future performance differ significantly from the market consensus. Therefore, our default constraints take this into account, but we allow investors to override this.

Risk Tolerance – In general, investors who are more willing to take risks will be more willing to bear unaccounted for downside risk and uncertainty about forecasts. These investors may generally place less restrictive constraints on their portfolio. Therefore, our defaults allow investors to specify upto three bands of risk tolerance.


A pre-requisite for constructing any great portfolio is identifying skilled managers that an investor believes can consistently generate alpha. Although investors intuitively recognize the value of alpha, they have historically lacked the tools required to build portfolios that include active managers. ActiveAllocator is trying to address newer challenges by designing a rigorous, integrated and, perhaps most importantly, practical framework for active investing. Our extensive testing provides evidence that this framework can significantly improve upon more traditional portfolio construction methods. The approach is unique in that it:

  1. More accurately measures manager alpha and beta on historical basis
  2. Explicitly forecasts manager performance by combining historical data with other information
  3. Quantifies unique risks of active managers
  4. Accounts for these unique risks when constructing portfolios

While the details of this approach are outside the scope of this note we have tried to summarize the general principles here. We hope they benefit investors even if they use other qualitative portfolio construction techniques.

Map Millions of Securities to Asset Sub-classes in Seconds now incorporates over 50 asset sub-classes to precisely map your funds and securities holdings. This allows for much better allocation decisions. Of course we do not suggest that an investor should include all asset classes in their allocation. Many of the asset classes have significant overlaps and some classes are subsets of other classes. For example, U.S. large cap value is a subset of U.S. large cap, which is a subset of U.S. Equity, which is a subset of Equity. Since some investors have preference for certain regions, (e.g. U.S. over Japan), certain market caps (e.g. small-cap over large-cap), or certain styles (e.g. value over growth), the inclusions of subsets give investors the flexibility to target specific types of assets they prefer to invest in.

Overall, we recommend that an investor specify 6 to 15 asset classes in making asset allocation decisions. If the allocation includes more than 15 asset classes, many of these classes will have allocations below 5% of the total portfolio value. Such small allocations have little impact on the overall portfolio’s performance. Besides, the large number of asset classes also requires investors to estimate more expected returns, risks, and correlations as well as set more minimum and maximum constraints. The estimation errors of all these inputs may lead to a worse portfolio in practice. On the other hand, allocation with fewer than 6 asset classes my not provide sufficient diversification as the final portfolio is often concentrated in two or three asset classes.

All Asset Allocation is Not Created Equal

We hold these truths to be self-evident: that all strategic asset allocation is not created equal; that they are endowed by their Creator with certain unalienable characteristics ; that among these are returns that come from Market Exposure, Skills, Illiquidity Premium and taking on rare Downside Risk.

Strategic Asset Allocation is responsible for the majority of long term variation in portfolio returns. Yet, it is glossed over and subsumed to making tactical calls, products and fund manager selection. Extending the not so modern portfolio theory and variants of mean variance optimization to create ‘model portfolios’ is undifferentiated and suboptimal; moreover, it just does not work in the real world.

Make Asset Allocation Great Again!

1.           All portfolio problems—strategic asset allocation, tactical asset allocation, manager selection, portable alpha, asset-liability management—are subtle variations of the same problem; they need to be approached:

–             in holistic and integrated manner.

–             from a practical perspective analyzed sequentially.

–             understood as being driven by choices on two dimensions: (i) how does one define risk? (ii) choice of constraints to arrive at tradeoffs?

2.           All asset allocation problems boil down to one of comparison—making valid comparisons between often hard-to-compare choices.

3.           There are four primary sources of returns that investors choose from: fundamental market exposure, skill based excess returns, unlocking illiquidity premium and taking on rare, but potentially large, sudden downside risks:

–             strategic asset allocation is about picking the right mix within and between each of these.

–             each investment out to be evaluated for its degree of exposure to each of these.

4.           Risks in many asset classes are understated; getting this right is the key to successful asset allocation:

–             valuation risk (volatility of valuations) is generally higher than reported within illiquid and alternative assets.

–             forecast risk (“the risk that you are wrong”) is generally higher in skill-based investments than fundamental-based ones (this includes sampling problems,    instability of characteristics, wrong modeling choices).

–             downside risk is generally higher in non-traditional assets.

5.           Using a broad set of investments (asset classes) is generally better—even if they are all equally efficient—than a more restricted set.

6.           Non-traded assets should and do attract a premium over the long-run:

–             this suggests that liquidity tolerance, and not risk tolerance, should be the first place one starts for asset allocation.

7.           Investors are generally overly wary of illiquidity:

–             diversification in various risks of illiquid investments (eg uncertainty about cash inflows, outflows, valuations, spending requirements, rebalancing      requirements, new investment opportunities) reduces the total risk.

–             volatility of portfolio weights from illiquidity should be offset by the non-tradability premium.

8.           Skill-based returns are attractive because of diversification:

–             many sources, given thousands of managers, relative to few fundamental returns sources ( equity, bonds, real assets..)

–             low correlation.

9.           Market exposure based returns are common and accessible cheaply using ETFs; skills based returns are expensive and rare.

–             Timeo Danaos et dona ferentes – “Beware of Greeks Bearing Gifts”:

–             Distinguish between market exposure (beta) masquerading as skill (alpha).

10.         Resolve to re-invent how you have traditionally approached strategic asset allocation in 2017;

–             calculate active risk adjusted returns and measure an active manager’s skill.

–             identify and isolate characteristics of a manager’s skill (alpha); calculate average alpha of managers, correlation among alphas, the effect of changes in the  number of managers and use Bayesian statistics to arrive at degrees of confidence in future performance.

–             forecast the probability distribution of degree of uncertainty in return, volatility, correlations of a fund manager.

–             select fund managers who produce high skills-based returns and optimally combine such managers into a portfolio to diversify active risk.

–             generate relative allocations across skilled fund managers.

–             include soft qualitative views and a degree of confidence within a quantitative manager allocation framework.

–             decide on appropriate allocations to expensive skilled managers versus to inexpensive passive benchmarks.

–             calculate the marginal impact of adding a fund manager to an existing portfolio.

–             compare manager performance in a like-for-like manner.

Taxes and Strategic Asset Allocation


Taxes are clearly an important part of any investment strategy for taxable investors. Harvesting losses, deferring gains can add value to a portfolio. But what impact do taxes have on arriving at the long term strategic asset allocation decision?

Conclusion: taxes have an impact not only on return but also on risk. This fact mediates the impact of taxes on asset weights, as the relative efficiency of assets is roughly the same before and after tax.

II.                 Asset Allocation: The Basics

  1. Asset allocation is complicated but essentially consists of 2 steps:

(i)                 formulating views about asset class performance in the future

(ii)               constructing portfolios based on those views

  1. Taxes affect the first step, but how much do they affect the second?
  2. Notably, in all of the complicated portfolio construction process, 4 things matter:

(i)                 investor objectives: how much risk am I willing to bear? (post-tax)

(ii)               asset risk: how risky is each asset on the margin and what is its risk ranking?

(iii)              risk-adjusted returns: what is the level and ranking of risk-adjusted returns? In the classic mean variance problem, this can be expressed as efficiency (excess return over risk)

(iv)              portfolio risk: how do the assets fit together in terms of risk and return? Here correlation is a critical input.

III.               Taxes: An overview

  1. In the US, three types of taxes are considered:

(i)                 taxed as ordinary income (either interest/yield or short-term gains)

(ii)               taxed as dividends or long-term gains

(iii)              state taxes

  1. The other consideration is how much positive and negative returns can be offset in each category.

IV.               Tax Impact on Returns

  1. Three key factors in determining the “effective tax rate” for a particular asset class:

(i)                 turnover of the portfolio

(ii)               yield and dividend rates

(iii)              tax rates

  1. The impact of turnover

(i)                 Definition

(ii)               Does turnover imply the proportion of cap gains which is short-term? No.

(iii)              If turnover is independent of tax considerations, then short-term gains will approximately = Turnover^2

(iv)              Note: in practice managers might be more than simply random in managing tax implications of trading strategies which will reduce this further

  1. Putting it together

(i)                 Based on the three assumptions above, we can calculate an effective tax rate

(ii)               In this case, returns will be (1-Effective Tax Rate) Pre-tax Return

(iii)              This is the conventional stopping point: some assets are more heavily taxed than others and therefore should be penalized in the portfolio construction process

V.                 Risk: The Forgotten Quantity

  1. Two important components of risk: volatility and correlation
  2. Volatility

(i)                 Key point is taxes “shrink” not only returns but also volatility of returns

(ii)               Key assumptions: losses can be roughly offset with gains

(iii)              Result is that volatility shrinks at the same rate as returns

(iv)              This in turn means that the efficiency of a particular asset class is held roughly constant before and after tax

(v)               Only impact on asset allocation therefore will be the ranking of assets in terms of risk but not in terms of efficiency (in other words, it can potentially move the place on the frontier that the asset sits, but not the average level (ranking versus penalization)

(vi)              Possible exception: if sources of volatility are not proportional to sources of return. As an example, consider bonds. Yield is a large component of return but a very small component of marked-to-market and trading volatility. If this is true, then volatility might change at a different rate than return.

  1. Correlation

(i)                 Key point: correlation is unaffected by taxes as well

(ii)               Why? Correlation is unaffected by multiplying

VI.               Conclusion

Key thing missing in conventional wisdom and the classic approaches is that taxes affect risk as well as return. This mitigates the impact of taxes on choice of portfolio weights.

How is ActiveAllocator Different?

I was recently asked ‘Yes, yes but how are ya’ll any different?”. Here is an attempt – albeit somewhat self serving – to expatiate 🙂

New opportunities draw market participants

The evolving FinTech landscape brings new opportunities: (i) self-directed investors struggling to effectively manage their portfolios; (ii) RIAs with challenges to engage and retain their customers; (iii) emerging internet-based financial services companies who are paving the path of innovation; (iv) cloud-based platforms that are simplifying software delivery; (v) open platforms and application-level developer ecosystems which are driving innovation forward; (vi) new technology platforms that are leveraging big data; and (vi) a large addressable financial advice and fulfillment market through technology driven mass-customization being created.

The landscape for online integrated asset allocation and portfolio construction which combines traditional and alternative investments provides a unique opportunity for a new entrant such as us. To the best of our knowledge there is no firm, other than that brings liquid and illiquid, active and passive, traditional and alternative investments holistically together – though multiple software vendors compete closely in the traditional asset allocation space. In absence of a player that has managed to create a positive externality, or content based differentiation, our proposition offers a low switching cost to the user. Our business, with truly superior content, combined with ease of use and access, faces an opportunity to stand differentiated and emerge as a leader.

We compete on the basis of several factors, including the breadth and quality of our long term strategic asset allocation, tactical allocation and portfolio construction services. Additional factors include the number of RIAs, independent broker-dealers and custodians that are connected through application programming interfaces (APIs) to our portal, the price of our investment solutions and services, the ease of use of our platform and the nature and scope of services that each client believes are necessary to address their needs. In our opinion none of existing market participants bring in the narrow specialized integrated holistic traditional investing and alternative investments focus that we do.

Some broad ‘competitor’ types are described below:

  1. Financial software firms. Many software firms, as they have morphed and grown have become all things to all people. Their offerings are most often geared to providing the applications and services advisors use to manage their practices. These include workstation based deployment for data aggregation and analytics; financial planning; portfolio management, trading and re-balancing; multi-custodial aggregated performance reporting; portfolio accounting, re-balancing and; client relationship management and billing calculation and administration. Within this category, we also include the traditional asset allocation software firms that provide software packages for conventional stocks and bonds using mean variance optimization approaches. Some also offer basic research and due diligence on traditional investment managers and funds. In general, most of them compete closely without a clear source of differentiation in content.

Our differentiator: In doing all of the above, many firms have lost sight of the single most reason that a client reaches out to a financial advisor in the first place – which is to create better investment outcomes. Our offering, by contrast, is intended for high-end sophisticated advisors focused on bringing cutting edge finance theory and computer science in a system that blends liquid and illiquid investing, as well as of anyone else doing this in the manner that we are.

2. Robo advisors. Robo advisors are part of the growing cluttered category of digital investment managers. Forrester Research notes that thanks to the rise of low-cost indexed ETFs, digital investment managers have the building blocks they need to assemble low-fee, diversified, and automated portfolios. Software startups for robo advice have been formed over the past three years and many have accepted tens of millions of dollars in venture capital funding to challenge the traditional financial advisor model. Their proposition is that by leveraging technology they can deliver financial advice to millions of people (the mass affluent segment) at lower prices and lower account minimums than incumbent firms. Examples include: (i) Digital money managers like to help users budget and pay back debt; (ii) Digital investment managers like Nutmeg, Wealthfront, Betterment, Ellevest, Riskalyze, Personal Capital, Future Advisor and a dozen others offer simple diversified portfolios to individual mass affluent investors; (iii) Digital financial advice platforms like SigFig deliver actionable recommendations based on an understanding of the mass affluent consumer’s financial position; (iv) Digital retirement plan advice platforms like Financial Engines guide users on how to get the most out of their retirement investments; and (v) Comparison engines have ended information asymmetry as consumers no longer have to rely on commission-driven agents, brokers, and advisors to find the best financial products and services for their needs. Firms such as Bankrate aggregate product information in personal finance, letting consumers specify their requirements to find the product that is right for them.

Our differentiator: These, unlike us, are meant for very simple traditional passive investing with access to very limited equity and bond asset classes (typically fewer than 15 versus over 50 that we offer). Not one of them address alternative investments or bring together asset allocation and manager selection for active managers. Not one of them is remotely as sophisticated as that what is codified in our algorithms and methodology. Moreover, they cater to aggregating small investors with a few thousand-dollar savings, unlike us whose offering finds traction with large RIAs who may have already aggregated hundreds of millions of dollars in client capital. Unlike robo advice we intend to create new value for individual affluent investors by democratizing strategies that have, to date, been available only to institutional or ultra-high-net-worth investors.

3. Private banks. Outside the independent broker-dealer and RIA channels some private bank platforms have set up in-house consulting divisions for managing high-net-worth portfolios, offering proprietary products and delivering advice (often, in our view biased) while charging large fees. The non-captive affluent investor does not have the attention of such full service firms.

Our differentiator: Most of these firms provide bundled asset management, wealth planning, investments, fiduciary and risk management services and are one stop shops. We believe that unlike them our advice is independent and objective for we do not draw revenue from soft dollars, commissions, captive broker-dealers, or referrals. Often these firms are self-focused driven by firm profitability, legacy operations/approaches with process limited to their firm’s products or deriving revenue from the third-party products they distribute. They have a standardized product-sales orientation, while we are product agnostic. Most of them have a lack of comfort with non-traditional asset classes. Our entire business model by contrast is built around bringing illiquid and traditional investing together to exceed client goals.

4. Asset management platform providers. These typically provide financial advisors with one or more types of products and services. However, they generally offer fewer choices in terms of asset classes to allocate to as well as restrict the usage of active management techniques.

Our differentiator: We are not wedded to products for our approach is driven by allocation and portfolio construction advice. We may elect to use multiple asset management platform providers for fulfillment, thus bringing benefits of multi-asset, multi-product inclusion to portfolios.

5. Custodians. A number of leading asset custodians have expanded beyond their custodial businesses to also offer rudimentary asset allocation tools that target the mass affluent segment.

Our differentiator: We do not think that they compete with our solution. Their entire approach is built around volume driven profitability, operational inefficiencies and scale. Our approach by contrast is about personalization, mass-customization and finding inefficient investment clusters that can provide skills based returns. Our value comes from intelligent active management derived from arriving at and taking purposive active investment decisions.

Implement Multi-Alpha Investing Thesis With Active Allocator

Active Allocator can be a great way to build active investing portfolios and programs. We have built cutting edge models to support fund manager selection, portfolio allocation, and market exposure hedging.

We follow a simple three-step process.

  • First, we measure alpha ( in other words, skills) and beta ( in other words, market exposure, when a rising market tide lifts all ships) by removing biases in fund manager returns.
  • Second, we integrate multiple information sources in creating a forecast alpha.
  • Third we account for unique forms of risk by penalizing managers for downside risk and integrating forecast risk into the portfolio construction process.

In short Active Allocator provides you with a mechanism to implement an active investment management strategy. We have tested for around 30 classes of Alpha already. We help create core portfolios that combine various alpha sources across a broad range of asset classes and strategies including: Equities, fixed income, foreign exchange, real estate securities, commodities, and special opportunities.

Our approach is closely tied to active investment management, which separates passive risk (beta) from active risk (alpha). The central principles of active investment management that we use are:

  •  Risk and return can be separated into passive and active components.
  • Passive return, or beta, comes from systematic, undiversifiable risk. Examples of these are returns from indexes such as the S&P 500. An investor can gain beta exposure very cheaply and therefore should not pay active management fees for these returns.
  • Active returns, or alphas, are generated through information asymmetry, structural inefficiency, security selection, and market timing. These returns are uncorrelated with beta.
  • In constructing portfolios, an investor should obtain beta exposure in the cheapest manner possible, and then add alpha exposure in proportion to one’s risk tolerance.

At Active Allocator, we unearth and calculate alphas properly. Then we rank them. We figure out correlations, or lack of correlation, across alphas.

We may, for example, compute correlations on a pairwise basis. In order to reduce the estimation error in these correlations, we group fund managers by strategy. Our portfolio construction system then determines the allocations by running an optimization to identify the portfolio with the highest information ratio – in other words the highest alpha per unit of risk; Not on a historical basis, but on a projected basis. In addition, we also account for the downside risk and forecast risk of each strategy when determining allocations to multiple alpha sources.

Our approach is finding the most efficient sources of excess returns and transferring them onto a benchmark. The benchmark itself can be cheaply accessed by investing in say tracker ETFs for example. If properly implemented, this strategy can substantially improve returns over a benchmark.

The questions we ask are:

•How does an alpha strategy create value?

•How should alpha be measured?

•What types of instruments are best suited to an alpha strategy?

•How can one implement an alpha strategy in practice?

By identifying and measuring changing market exposures — or dynamic Betas — of fund managers on a real-time basis, we are able to break down a manager’s returns and risks over the long run into three components:

Beta: the portion created by the passive average long-run market exposure .

Timing Alpha: the portion created by proactive variations in market exposure around the passive average exposure over time. If on average the manager increases his exposure to markets when they go up, and decreases exposure to these markets when they go down, the manager will generate positive returns through market timing. Comparing the returns from a manager’s average market exposures to the returns from the manager’s time varying market exposures can therefore help determine value added from market timing.

Security Selection Alpha: the portion remaining, or residual, which is due to security selection and stock picking skills

Wealth Management Firms ‘Model Portfolios’​ are Inefficient and Sub-optimal

300,000 financial advisers in captive channels, with independent RIAs and Broker Dealers, in private banks and wealth platforms blindly trust their firm’s investment committee’s  generated house view. They place trillions of $ of client money in so called ‘model portfolios’.  We analyzed and modeled 10 firms’ published views using their own capital market assumptions to see how good a job such firms are doing. We can now quantitatively, and with detached objectivity open to third party scrutiny, demonstrate that such allocations are typically flat out wrong –  often leading to a +20% value destruction in expected returns. 

This is giving bad financial advice. Clients should Trust but Verify.

Best of all, any Financial Advisor or their client can now do this in ten minutes at