ActiveAllocator Business Overview 2020, is seeking to enter into an equity swap, revenue sharing or a partnering arrangement with an established financial services company. We now have the world’s first portal that seamlessly integrates traditional, illiquid and alternative investments within portfolios. We help investors analyze existing allocations, discover inefficiencies and create bespoke portfolios in minutes. If you see complementary synergies and understand the Fintech space well, have decision steering authority, do email me in confidence at or call me on +1 312 498 1903, NY.
I explain our business in this short video.


Our Latest Regional Equity Valuation Model Captures Coronacrisis Recovery Differences

regional model 1

#ActiveAllocator develops state of art algorithms to capture different rates of coronacrisis recovery within countries and international equity markets. Here is the general design idea. We naturally also include multiple other proprietary dynamic factor drivers specific to coronavirus trackers, which are not shown here.

The objective is to rank the attractiveness of regional equity markets over a 12-month time frame

  • The ranks are based on a composite score
  • The composite score is a weighted average of individual ranks for various factors:
  • Valuation factors include Forward PE, PEG, Price to Book and Yield Gap
  • For each valuation factor we
    • Generate for each region a z-score (current level – 15 year historical average / standard deviation over that 15 year period) .
    • Rank the regions based on their z-scores, in other words based on how far the current valuation is from the historical norm (for most factors a low score ranks highest, but for Yield Gap a larger number ranks highest)
    • Momentum factors include earnings revision breadth and earnings revisions depth. Larger, positive numbers rank highest
    • Use different factors and different weights for developed markets and emerging markets

Asia Real Estate, Especially China, has Come a Long Way Since 2008

The Wall Street Journal today Friday, July 17, 2020  has an article “The $52 Trillion Bubble : China Grapples With Epic Property Boom”

Asian Real Estate markets have come a long long way over the last decade plus. I remember tracking them way back in 2008 when they were beginning to take off just around the time of the global financial crisis.

Here is my research note of 2008 for download :

Asia Real Estate – CAI_Journal_Summer2008

Recommended Changes in Strategic Asset Allocation Demonstrate Potential to Add Substantial Value

#ActiveAllocator Research Case Study – We successfully demonstrated to the world’s perhaps most sophisticated Government Investment Fund that our proposed changes to their strategic portfolio could increase annual returns by over 60 bps, while holding risk constant. That’s a non-trivial returns enhancement when you are speaking about hundreds of billions of dollars. Never underestimate the power of Strategic Asset Allocation done correctly. Here is a snapshot of one such portfolio sleeve by way of illustration (NDA prohibits us from disclosing specifics).

3- SAA case study

ActiveAllocator Research Drives Locating and Generating Alpha

2- incremental alpha

#ActiveAllocator Research – I was in essence recently asked “Why does ActiveAllocator create public goods by publishing its proprietary research?”. I guess an answer is we are in the business of creating incremental alpha for our clients, and holding on to ideas in a fast moving world is seldom optimal. Here is a visual on how we recently helped a very large public fund – where strategic asset allocation was a small part of our engagement.

Case Study:

Client has an objective to:

– increase returns by 100bps

– lower volatility

– Sharpe ratio of 0.5

Client has made substantial changes to its strategic asset allocation

These changes have the potential to add a great deal of value, although in and of themselves, they will not necessarily meet the above objectives

– Based on ActiveAllocator’s proprietary models, “index-like” returns in each asset  category will achieve approximately 60% of the return objective

To fully meet its objectives, Client will need to

– enhance performance through long-run value-added activities (“offense”)

– minimize ‘slippage’ in the portfolio during the transition period (“defense”)

While accomplishing these objectives will involve connecting a myriad set of puzzle pieces, Client should focus intensively on what may be the most important implementation areas

– enhancing internal alpha generation capabilities

– implementing an alternative investment strategy which maximizes value

– effective portfolio management

Bringing an Investing Mindset to Active Funds Due Diligence

In seeking risk transparency without seeking position level specifics of a hedge fund we need to know, at minimum, a few things usually captured in a risk report. Risk reports are a useful starting point, but they are of course a static snapshot and do not tell us the entire story. For each hedge fund we can develop a better story using the understated approach and reasons for the approach:

  • Position level information: Provides a static picture of where the fund stands. These measures are most relevant for short term shocks where the actual position held matters much more than a manager’s behavior or trading strategy to define performance. These are especially useful during volatile markets as well as periods of market stress.

For medium term horizons this needs to be coupled with a fund’s trading strategy.

  • Trading strategy information: This helps us understand how (magnitude and direction) the previous static position will change over time when external market factors change i.e. trading strategy should explain sensitivity of the fund to market rises and falls.

In addition, we need to be clear about a fund’s exposure, leverage and counter party risk depending on the specifics of the hedge fund. Exposure is generic risk proxy and specifics are important.

  • Exposure as sensitivity to ‘what’ i.e. to which few important factors?

Is it net exposure? : i.e. the sum of – Short positions and + Long positions; but we should use this measure for tightly correlated positions with similar volatility for this gives a sense of sensitivity to market factors i.e. how much we stand to lose or win when a market factor moves.

Is it gross exposure: Sum of absolute positions; we should use this measure for loosely or uncorrelated positions with dissimilar volatility such as for example global equities.

  • If the hedge fund trading strategy employs futures and derivatives it helps us to see a future’s contract as a +Long position in the underlying with -Short position in cash/funding and variation margin. The “exposure” to a future’s contract is equivalent position in the underlying funding position. Therefore, we must separate the funding position from the future’s position in a risk report. Likewise, for derivatives, we can see it as a case of ‘futures’ i.e. a +Long position in underlying with -Short position in cash/funding but with the underlying changing dynamically.


  • Long Short: If +Longed and -Shorted assets are correlated (as often happens in relative value trades) we should look at net exposure as measure of leverage. If less correlated, we should look at gross exposure.

The key point here is to answer the question exposure to ‘what?’ for there is usually no one single exposure measure to characterize a position’s risk. For the same position we may be interested in different exposures and we may meaningfully aggregate the same sensitivities only

  • Leverage which is the ratio of fund assets to equity contributed where leverage may be explicit from borrowing or embedded from derivatives. Leverage provides us with a simple relative indication of risk to answer how large will losses or gains be to an un-leveraged portfolio. It is important to bear in mind that higher leverage does not automatically imply greater risk. It is a relative measure for a twice leveraged portfolio is twice as risky as an un-levered portfolio provided the two portfolios invest in exactly same underlying assets.

The way to analyze leverage is to decompose positions to make implicit borrowing (from derivatives) explicit. One can for example replicate a forward contract by borrowing the present value of future fixed price X at X^e-rt + F(today) and buying the asset. The futures position contributes to leverage, but when matched with an equivalent cash position, may serve as an alternative to buying the underlying. When so coupled we cannot say that just because a fund uses futures or derivatives to gain exposure, we are increasing leverage. Therefore, we need to know the risky underlying asset position and the cash positions together. It is for this reason a risk report should separate the two.

  • Risk of total loss to fund equity capital; This is an important measure that every risk report should continuously monitor. i.e. at what loss will equity get wiped out.


  • Risk of a fund, including from its explicit loans and from its implicit derivatives or leverage producing positions, should be less than or equal to X times ( as mentioned in the fund documents usually) the risk of the fund’s unleveraged counterpart/risk benchmark. A fund’s performance benchmark may be used as risk benchmark as a rule of thumb.


  • Exposure to credit risk/counterparty. This is the cost to replace the contract or a set of positions if the counterparty defaults; it may be a loan equivalent amount measure. One can develop a forecast of distribution of future exposures in which case qualitative questions range around long term forecasts of underlying risk factors, accounting for collateral, netting, and credit risk mitigation techniques etc.

We should ask for a risk report with the above as basics so that we can have; (i) a consistent set of scenarios side by side; (ii) effect of scenarios as short term shocks based on position level information and; (iii)effect of scenarios over medium horizons based on factor models and a fund’s stated strategy.

It is important to be cognizant of some issues that surround active strategies.

  • Since hedge funds hold non-linear instruments they trade dynamically and produce non- linear payoffs. So, we can say that while funds are certainly exposed to markets, the exposure is non-linear. Using multi factor models here is akin to using mis-specified linear models and inevitably leads to erroneous conclusions. So how do we then capture non linearity through appropriate factor construction? The literature is replete with suggestions including; (i) perfect trend follower replicated through a look back straddle options;  (ii) momentum trades; (iii) use of factor regressions over rolling windows to see if a hedge fund has significantly altered its strategy or compare the regression results on either side of a market event. In interpreting such we need to examine both position level results and factor sensitivities.

We can use current price information and pricing model and not need historic data dependency for:

  • Sensitivity measures: The effect of a small movement in spreads on the present value of a position. this is typically at the security level and not at portfolio level.


  • Stress test or scenario analysis: Modeling ‘what happens if spreads widen by X with variants such as parallel spread stress test or historic volatility of spread moves.


  • Relative value investors: Buying a bond and simultaneously buying credit protection through CDS (i. if the basis between them is historically very wide and investor will profit if basis returns to normal typical. Or if basis is negative and investor receives difference between bond spread and cost of CDS protection without taking any credit risk.


For relative value fixed income hedge funds, we need to be aware of special issues around performance and risk forecasting.

  • Forecast the overall portfolio risk: an example is to isolate interest rate risk where hold credit spreads constant, while allowing the base interest rate curve to change. And similarly, to isolate credit risk, we should hold interest rate curve constant while allowing credit spreads to change.


  • When credit spreads are strongly correlated to the interest rate base curve, the distinction between interest rate risk and credit spread risk is not relevant. Therefore, it is important to decompose risk across factors that are independent of each other. Spreads on CDS often exhibit little correlation with base rates and are a good mechanism to decompose risk.


  • We may also decompose the risk and estimate the portion from spread movements in which case we need historical data that has statistical properties amenable to forecasting. So, we cannot use Yield Spread to treasury as the benchmark T Bill rate will be changed and this change should not reflect in change in creditworthiness of a corporate bond. Also, spread volatility tends to be greater for longer maturity bonds and it is better to create spread curves on each day and apply stats on data for a constant maturity point.


  • Series to be analyzed: Whether we use OAS (OAS added to the base curve gives us a discount curve for cashflows promised by a bond issuer) or CDS spreads the objective is the same i.e. to ascertain how much compensation an investor should receive for bearing credit risk. The two are not interchangeable as for a CDS , upon a credit event, the receiver gets par-amount irrespective of the prevailing interest rate; to make them strictly comparable we may need a fixed to floating IRS which cancels at the event of default . ((CDS and Bonds risk free value: discount all cash flows by base curve)) – (Default probability * loss given default at the same time points)); can give us a ‘bond implied CDS’.


  • A single risk measure or a single risk factor to describe all positions on a single issuer is almost never relevant. For example, if the mark to market value of negative basis trade moves against investor, a previously predictable ‘zero risk’ position would suddenly become risky. Therefore, it is important to separate the two distinct sources of risk – bond and CDS market spread where simplification to a single source of risk for relative value trades would be inappropriate.


  • Since volatility changes there is a limit to how much historic data we need to use; using more history will not improve forecasts.


  • Qualitative questions too can lead to better understanding of relative value / fixed income hedge funds: What type of pricing model do you use to arrive at the NAV? How do you mark your positions to market? Is it to a model? Have you stress tested the value of your portfolio against alternative methods for marking to market? What did you conclude? How long would it take to liquidate your portfolio and what will be the incremental effect on NAV? Why would you not? What is your data source for volatility? How do you deal with correlation assumptions during these stressful times? How do you build your volatility curve?  Give us a breakdown of your trades (at the position level) by major strategy types.

While VAR alone is a reasonable measure of market risk for some portfolios, the risks of many arbitrage type strategies are better represented by stress tests and scenario analysis. Stress tests should be chosen based on the nature of the portfolio, but might include:

  • Large market shocks
  • Changes in the level of volatility, the shape of the yield curve or the volatility curve, sector definitions, correlations
  • Changes in liquidity
  • Some variables, given a small move, cause a large move in price or risk valuation
  • Some variables important to a portfolio that have a high likelihood of change
  • Those variables or exposures that offset each other

Is 2020 Going to be More Profitable for VIX Shorts – Recovery Hope Fuel Bets on Lower Volatility?

The WSJ  Friday July 3, B11 article “Recovery Hope Fuel Bets on Lower Volatility” says that traders are projecting calmer markets as the VIX drops to a low 28. We analyzed variance swaps for the last 14 years and our take on payouts is presented here.

3- vol shorts