Put Allocation Horse Before Product Cart

Just another example of continuous innovation at ActiveAllocator.com

ActiveAllocator’s proprietary technology searches, recognizes, classifies and instantly maps more than 4 million traded financial instruments to 50 granular asset sub-classes. By doing so, we shift focus from financial products to asset classes that drive portfolio performance. If you believe that a given financial product should belong in a different asset class, we provide the ability to change the mapping to your liking.

At the end of the mapping process, you have a comprehensive picture of your investments across multiple asset sub-classes.



Advisor Fees and Inefficient Asset Allocation to Destroy $400 bn in 2018

We estimate that retail investors lose around $400 billion annually in advisory fees and inefficient model portfolios. This value destruction is both explicit in typical 1% or higher fees charged and implicit in  further 0.5% or more model portfolio asset allocation inefficiency. This is in addition to other contracting frictions, product commissions and costs.

At ActiveAllocator we are driven by an authentic mission. To increase expected returns for millions of investors by eliminating the loss that’s built into the way the retail financial advice and investment management industry is currently structured. To remove the dead-weight costs of $130 billion from strategic asset allocation inefficiency and to drastically reduce the $260-$300 billion in advisory fees. One portfolio at a time, across 33 million individual portfolios.


Compare Your Own Mid-Year Capital Market Assumptions with Wall Street

Optimization for strategic asset allocation involves the forecasts of  at least three types of information:

  • Expected returns of different asset classes
  • Expected risk (volatility) of different asset classes
  • Correlations of different asset classes

We recommend that investors avoid recency bias. Since the expected returns of all assets classes vary over time, we estimate expected return of an asset class by combining the following information:

  • Long-term historical average return over multiple economic cycles
  • Key drivers of historical returns and market forecasts of future performance of such drivers
  • Consensus estimate of reputable wealth management companies

Investors may be inclined to take the long-term average return of an asset class as the expected future return. Such an approach likely will not work as the market environment mid 2017 is substantially different from the average of the past decades. Instead, historical returns give us information on the range of the possible returns. More importantly, historical returns help investors understand the key drivers that have determined realized historical long-term returns.

The following equation shows one way to decompose S&P returns:

  • S&P return = equity risk premium + long-term bond return + noise

The first two terms explain the expected return:

Expected S&P return = equity risk premium + long-term bond return

= equity risk premium + short-term interest rate + term premium

= equity risk premium + (inflation + real short-term interest rate) + term premium

The last term explains the volatility in S&P returns. It is a random variable centered around zero.

We expect the inflation rate to stay at low level in the future as it did in the past decade. The real short-term interest rate depends on the Federal Reserve’s policy. In recent years, Federal Reserve has kept short-term interest to be below inflation rate. We do not expect dramatic policy change. The term premium depends on investors’ long-term view on economic growth. Market consensus is that the U.S. economy will grow at an average of ~2% in the next decade. All these factors indicate long-term bond returns are likely to be below historical average. As a result, return of S&P is likely to be below historical average as well. Consensus view: Estimates from wealth management firms suggest an expected return of +6%.

You can now compare and contrast your own views across +50 asset sub-classes with others.





Conventional Firms May Destroy +30% of Retail Investor Returns Annually

In a low returns environment, private banks and conventional financial advice firms continue to charge 1% to 2% in fees, irrespective of portfolio performance. Common sense suggests that the lower the costs are for investors, the higher their share of an investment’s returns will be. In addition, what remains invisible is the implicit fees on returns caused by inefficient model portfolios and poor asset allocation decisions. This further detracts another 0.5% annually from investor returns, which investors don’t see.

A +1.5% cost over a typical 5% annualized return suddenly becomes a meaningful number. It suggests a +30% value destruction for the price of having a ‘personalized’ relationship with a friendly financial advisor.

Probably much too high a price to pay?



How Inefficient is Your Portfolio?

Here is how you figure out.

  • Aggregate your investment accounts.

We aggregate your held away assets by linking your investment accounts. A holistic view of your holdings is the place to begin to discover strategic asset allocation inefficiencies.

  • Discover your economic and market risks.

Our proprietary technology searches, recognizes, classifies and instantly maps more than four million traded financial instruments to over fifty asset sub-classes. This helps you cut through funds and securities and see the world in terms of asset classes.

  • Compare your market views with others.

We monitor research from wirehouses, investment banks, consulting firms to help you arrive at consensus long term capital market assumptions. This becomes a valuable starting point for you to validate and express your own market beliefs.

  • Express your investing preferences.

We help you impose your constraints on specific asset type exposure, aversion or preference for illiquidity and alternative investments, your investing horizon, time varying risk preferences and other choices. This creates personalization.

  • Analyze and optimize your portfolio.

We highlight strategic asset allocation inefficiencies to enable comparison between recommended choices with your current allocation. This provides detached objective portfolio analysis.

  • Implement your portfolio.

Our proprietary order management system securely routes trade orders to your broker to execute. We further provide real-time portfolio data from your brokerage firm. This provides the most current picture of your portfolio’s forward-facing characteristics.

Constructing Bespoke Allocations

It is our belief that in the digital age individual personalization is going to be a key weapon in the battle against irrelevance and disintermediation for financial advisors.

We provide, for the first time ever, a way to utilize disruptive technology driven personalization and mass-customization. Our proprietary Strategic Asset Allocation framework and technologies allow one to aggregate held away assets and diagnose existing portfolio holding. It maps fund products and securities to precise granular asset sub-classes, offers consensus capital market assumptions as a useful starting point, personalizes investor preferences, demonstrates inefficiency in portfolios, optimizes and allows for multi broker securities execution.

All in under 10 minutes and within 10 mouse clicks!

Consolidation and Aggregation

Our first step is to arrive at a complete picture of securities, fund holdings and other finances held in a client portfolio. With permission, we securely retrieve such data from thousands of financial institutions and brokerages on behalf of clients and their financial advisors.  We reduce repetitive data collection and data entry and provide our clients with a simple, interactive data-capturing experience.

Mapping  Financial Products to Asset Sub-classes

We, in seconds, analyze the forward-looking statistical properties and expected behavior of a client’s existing portfolio. This done, we recommend the best combination of asset subtypes that improves it on a variety of chosen metrics. Our, to be patented, proprietary methodology and scalable technology searches, recognizes, classifies and instantly maps more than four million traded financial instruments to over fifty asset sub-classes to improve strategic asset allocation and portfolio construction. Within equities ActiveAllocator scrutinizes and maps nearly all global stocks, depositary receipts, certificates, ETFs, mutual funds, investment trusts, preference shares, rights, royalty trusts and other equity-linked products. This universe is comprised of more than two million equity securities including, 665,000 stocks, 163,000 exchange traded products, 51,000 closed end funds, 31,000 fund of funds, 18,000 ADRs, 7,600 Unit Investment Trusts, 6,500 preferred stocks, as well as 1,600 private equity and 10,000 hedge funds. Within fixed income, ActiveAllocator’s technology encompasses over one million U.S. government and agency bonds, collateralized loan obligations, collateralized mortgage obligations and other types of commercial mortgage backed securities and structured products. Within money market instruments, our system recognizes more than 42,000 instruments across fifty categories of bankers’ acceptance, bills of exchange, call loans, certificates of deposit, commercial paper, time deposits, discount notes and monetary bills. Within municipal securities, we distinguish between 1.1 million instruments across original issue discount munis, fixed, adjustable, tax credit, floating, zero coupon, intermediate appreciation and consumer price index linked products, amongst others.

Capital Markets Assumptions

Each wealth management firm, each wire house, often each broker-dealer periodically arrives at their own future view of asset class expected risk and return. Sometimes clients too have strong viewpoints that they may want to discuss. Yet financial advisors have no easy way to test such comparative views within the specific context of their particular client’s portfolio. We allow advisors to do this this in minutes; our portal helps advisors quickly simulate such scenarios and allow them to quantify revision in views, triggering valuable dialog. We constantly monitor research from wirehouses, investment banks, consulting firms to arrive at consensus long term capital market assumptions such as  expected risk, return and correlation within asset classes. This becomes a useful starting point for an asset allocator or an investment committee to accept or over ride generally accepted market views. In addition to traditional liquid assets we also generate such assumptions for illiquid assets such as private equity, real estate and hedge funds. These serve as inputs into allocation optimization.


We go far beyond conventional risk and return tradeoffs, whilst personalizing for unique investor preferences including allowing for or aversion to alternatives or illiquidity, accommodating different investing horizons, time varying risk preferences as well imposing constraints on specific asset type exposure. We integrate passive and active, liquid & illiquid, traditional and alternative investments analysis personalized to particular investor preferences, constraints and situations. For example, each investor can override consensus expected future risks and returns in any asset class and generate personalized, forward looking portfolios.


Our new approach is ahead of that built around Modern Portfolio Theory. We allocate simultaneously across multidimensional return sources including alpha (skills), beta (marketexposure accessible inexpensively), downside risk (extreme but rare market movements) and illiquidity (whose time premiums can be purposively unlocked). We analyze the statistical properties of existing portfolios and improve strategic asset allocation by finding the best combinations of liquid and illiquid asset classes to improve existing portfolios. We enable strategic asset allocation across over 50 asset subclasses. We integrate passive and active, liquid & illiquid, traditional and alternative investments analysis personalized to particular investor preferences, constraints and situations. We allocate to non-traded assets. We account for non-tradability, illiquidity premium as well as marked to market risk. We also allocate to actively managed structures while a) appropriately calibrating returns; b) accounting for factor exposures and their implications for returns in the future; c) removing survivorship and selection bias in historical returns; d) appropriately estimating risk including skewness, kurtosis gleaned from historic returns; e) incorporating pricing distortions, serial correlation and the impact on risk; f) managing strategy drift and unstable histories; and g) optimizing downside risk.

Trade Execution

To implement  the recommended portfolio and associated changes in security holdings we utilize a proprietary order management system which securely routes trade orders to brokers. This enables investors to optimize their existing portfolios on ActiveAllocator by linking their brokerage account and sending order messages to their broker directly from ActiveAllocator’s platform. We further provide real-time portfolio data from the user’s brokerage firm, which is imported into ActiveAllocator to provide the most current picture of their portfolio’s forward facing characteristics.



Instant Multi-Asset Portfolio Diagnosis

ActiveAllocator’s proprietary technology now enables multi-asset portfolio diagnosis in less than three minutes.

New proprietary methodology and scalable technology searches, recognizes, classifies and instantly maps more than four million financial instruments to fifty asset sub-classes to improve strategic asset allocation and portfolio construction.

The movement to multi-asset investing is a dominant trend. This allows investors to quickly analyze and invest their capital in the full panoply of assets that exist today: public securities, private securities, commodities, real estate and other hard assets, loans, leases, insurance and other types of contingent claims and intellectual property.

Traditionally, each of these classes of assets and fund products have been the province of a different manager at a distinct shop. A firm either ran a hedge fund that held predominantly public securities, a private equity fund that acquired control or strategic positions in businesses, a commodity pool that traded commodities futures, or a real estate fund that invested in real property. Often, clients own their assets in different firms and have no way of knowing what their expected portfolio characteristics are likely to be. Now, they can.

Everything changes with ActiveAllocator.com


Configure Bespoke Portfolios in Minutes. Model Portfolios are Passé.


custom–made (also bespoken), customcustomizedcustom-tailoredmade-to-ordertailoredtailor-made

Related Words


Every investor has preferences such as tolerance for risk, investment horizon, liquidity requirements, aspirations for a targeted return, choice of alternative assets as well as levels of annual portfolio turnover for tax planning purposes amongst others. A bespoke asset allocation approach takes these into account. ActiveAllocator allows for:

Selecting portfolio risk target range – whether one has a low, medium or high risk tolerance or one can specify a clear number.

Specifying maximum levels of illiquidity – the level of illiquidity a client is comfortable with.

Setting limits on alternative investments and types – some investors believe in passive management while many others see value in actively managed Alternative Investments as well as actively managed funds are valuable complements to traditional stock and bond holdings for affluent investors. However, including them in portfolios is extremely difficult. One reason is their heterogeneity and complexity. Another is that Modern Portfolio Theory or MPT, does not work in holistic asset allocation. But simply excluding them in model portfolios curtails investor choice and their potential benefits.

Providing for maximum turnover – some prefer to own allocations that remain largely static while others are comfortable with higher levels of turnover.

Targeting returns – clients often have a general expectation of expected target return. Most clients see historical realized returns but wish they had a way to automatically calculate the target return of their existing holdings as a useful starting point.

Setting an investment horizon – clients have different investing horizons which can heavily influence choices in both asset allocation and portfolio construction.

Asset class exposure constraints – the maximum or a minimum amount of exposure to any asset class.


Model Portfolios. One Size Fits All. Bad Bad Idea.

The notion of putting retail investors in cookie-cutter mass-produced standardized buckets of allocations, referred to as model portfolios, is a remnant of antiquated ‘90s thinking. In 2017, it is akin to paying a doctor expensive fees for an over-the-counter pain medicine prescription.

Departing from model portfolios has potential to foster greater transparency. It raises the quality of discourse an advisor has with her client. It encourages a conversation not on historical, but around forward looking risk- return expectations, probabilities of loss or those of exceeding a target. It brings nuance in investment decision making, conspicuous by absence in model portfolios.

Rather than persist with the historical notion of putting clients in coarse model portfolios, a far better approach is to begin by analyzing the forward-looking statistical properties and expected behavior of a client’s existing portfolio. This helps an advisor recommend the best combination of asset sub-types that improve existing allocations on a variety of chosen metrics.

Bespoke allocation goes far beyond conventional passive risk and return tradeoff found in model portfolios. It personalizes for unique investor preferences including accommodating a desire or aversion to alternative investments, expressing preferences for desired levels of illiquidity, considering different investing horizons, incorporating time varying risk preferences as well imposing constraints on specific asset classes to reflect unique investor circumstances.

ActiveAllocator.com lets you create bespoke portfolios in minutes.


WireHouse ‘Model Portfolios’ are Often Inefficient and Sub-optimal

Tens of thousands of Financial Advisors trust an investment committee’s house view and place Trillions of $ of client money in so called ‘model portfolios’. At ActiveAllocator, we analyzed and modeled many firms’ published views using their own long term Strategic Asset Class assumptions. We can now quantitatively demonstrate that such allocations are flat out wrong – this is bad advice.

Trust but verify.

Best of all, any Financial Advisor can now do this on her own in minutes at ActiveAllocator.

Better Financial Advice.

Expand Your Efficient Frontier- Include New Asset Classes

We believe that intelligent, personalized, comprehensive and unbiased long term asset allocation is the foundation of high quality financial advice. One that seamlessly integrates traditional, illiquid and alternative investments to build better portfolios.

Active Allocator’s advanced algorithms further help you intelligently search from over 50 asset sub-classes that you may not already own, but may benefit from allocating to. In seconds, you identify such assets and figure out how much to allocate to them. This expands your choices in a quantitative, rational, fact based manner.


Implement Multi-Alpha Investing Thesis

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:

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

(ii)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.

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