1st R42 AI Fund Launched

In 1981, Dr. Hal Forsey and I launched Post Modern Portfolio Theory. Now I have taken a quantum leap forward to join Dr. Ronjon Nag and Dr. Jeremy Sosabowski in launching this new framework for constructing and managing portfolios.

Frank A. Sortino, Professor Emeritus

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Portfolio Theory for the 21st Century will be AI Driven.

Begin by unlearning the false assumptions of CAPM that led to these false conclusions:

  1. No one can beat the market on a risk adjusted basis.
  2. No one can time the market more often than not.
  3. No one can actively manage asset allocations.
  4. The best strategy is to buy and hold.

Our research refutes all of the above.

Who are we:



Forget about alpha and beta and focus on the Tipping point (T) that separates good outcomes from bad outcomes. Maximize the Upside Potential above T relative to the downside risk below T and forget the Sortino and Sharpe ratios. TPR is the New Sortino Ratio.


Of course, past results from back tests do not guarantee future results anymore than Big Blue’s results guaranteed it would beat the world chess champion, or that Google’s AI model would beat Big Blue, or that another Google AI model would beat the world’s GO champion.

For more details contact The R42 Institute, Palo Alto, California

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PMPT Marries AI

Yesterday I posted a brief note to let you know I got back in the market due to some Flags posted by an AI firm. The company is AlgoDynamics in London and they are sponsoring a webinar in New York. Here is the link if you wish to attend: https://www.algodynamix.com/upcomingwebinars/. I have been working closely with them and Dr. Ronjon Nag, director of the R42 institute. Our findings call into question some of the basic tenets of Portfolio Theory. It is my pleasure and privilege to be working with them on this research project.

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Flags are Up

This morning I received notice that Flags were Up for SPY and QQQ and I invested 30% of the 37% cash position I took at the time of my last posting. I will be speaking at a program by the people who issued these flags on the 24th of this month. I will ask them if I can post a link for you to that event. The gist of my speech is: the buy-hold strategy on a market index, is dead and the culprit’s initials are, AI.

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Stop Loss Time in America

As of today I have entered stop loss orders on all my US equities and a couple of Chinese stocks, but not CQQQ. Why? I have seen no evidence that America has changed course in its Foreign policy and I believe that is our only way out of the mess we are in. Also, the people making headlines with their foolish gambles that paid off, for the time being, are not people I respect. Also, there is the matter of the worst cyber attack since ours (remember the Iran nuclear melt down?) from Trump’s friend Putin. What are the chances that Putin will find a way for America to sidestep crashing networks throughout our military and industrial system?

So who will be the major beneficiary of our probable correction? The country who is eating our lunch economically. The country all others, except probably us, will be turning to for aid. The country whose people Pompeo and associates demonize and blame for every ill from inventing Chinese Flu to sending hoards of spy’s to America posing as students and scientists.

When I do return to the market it will be with a new investment framework that is AI driven.

Best wishes,

Frank Sortino

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Back to the Future

A few years ago (seems like a few to me (2012/01/25), I posted a paper comparing the body of work Hal Forsey and I developed (Post Modern Portfolio Theory) with Prospect Theory, developed by Daniel Kahneman and Amos Tversky. An excerpt from that posting is reprinted below because parts of that article have now become an integral part of a new theory I am developing with Dr Jeremy Sosabowski, CEO of AlgoDynamics and Dr. Ronjon Nag at the R42 AI Institute at Stanford. It is tentatively called, The “Tipping Point Theory”.


In chapter 26 of “Thinking Fast and Slow” Daniel Kahneman points out some of the flaws in Prospect Theory, starting with when the reference point is assumed to be zero   He goes on to say,  “Prospect theory has flaws of its own, and theory induced blindness to these flaws has contributed to its acceptance.”  That led him to posit a two system approach to understanding the decision making process of human beings.  System 1 deals with intuitive responses that are automatically and effortlessly arrived at.  System 2 is concerned with the deliberate and effortful mental activities associated with quantitative reasoning.  I like to think of our approach to investing as being associated with System 2.

The purpose here is to point out how we attempt to incorporate some of the findings of K&T in our strategy.  K&T pointed out that classical utility theory lacks a reference point from which to evaluate gains and losses.  They said the reference point may be an outcome to which one feels entitled (e.g., retirement income).  We call this reference point the Desired Target Return® or DTR®. Returns below the DTR incur risk of not accomplishing the goal.  Returns above the DTR are the gains in excess of what is needed.  Prospect Theory implies losses loom larger than gains.  We agree (see Figure 2).

The S shaped utility function of prospect theory in Figure 1 indicates investors are risk averse to returns above the DTR and become risk takers for returns far below the DTR.

Figure 1

This may well be the way people behave but it is contrary to one of the oldest strategies on Wall Street, “Ride your gains and cut short your losses.”  People like Bernard Baruch used this simple rule of thumb to overcome some of the System 1 responses that Khaneman uncovered.   The foolishness of ignoring a small probability of a disastrous outcome can be illustrated by considering the small probability of a parachute not opening.  Something akin to considering only the probability of a mechanical failure while ignoring the magnitude factor of breaking every bone in your body after plummeting to earth is foolhardy at best and should never be offered to clients as a low risk strategy.

Peter Fishburn proposed the utility function in Figure 2 .  We view it as a System 2 type theory of choice.

Figure 2

 Figure 2 seems a more sensible way to behave than Figure 1.  Investors are viewed as risk neutral for achieving returns above the DTR.   They like the notion of acquiring greater wealth than they need; yet they are risk averse to losses that result in failure to achieve their goal.  

Then Hal and I worked with two professors at Groningen University to develop a performance measure that captured that concept and corrected the “faulty parachute” type of reasoning Khaneman cited above with the ratio shown in Figure 3


Figure 3

Recent findings in neuroscience provide evidence that a System 2 approach that captures what investors intuitively want but don’t know how to quantify should be superior to asking them to fill out a risk tolerance questionnaire they don’t understand.  It seems that the part of the brain just inside our temples, called the dorsolateral prefrontal cortex is involved with events in the outside world that require explicit, analytic and rule based reasoning.  The part of the brain behind our forehead deals with emotions, whether something is good or bad, and is called the medial prefrontal cortex.  Preference questionnaires require the wrong part of the brain, the emotional part, to deal with events in the outside world (like portfolio management) instead of the rational part of the brain.

END of Excerpt

What do you know when you know that? People behave foolishly when making investment decisions and they don’t learn from their mistakes. So, what do you do with that information? When and what do you buy or sell?? These are questions that behavioral finance does not answer…but Tipping Point Theory does.

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Upside Potential Ratio Revisited

I have reposted this for the benefit of some people at R42 Institute:

The Cost of Assuming Symmetry in a Skewed World

     Frank Sortino & Kal Salama

 In an Uncertain World

If you believe the uncertainty associated with investing in the indexes shown in Figure 1 is bell shaped, and it is not, all your metrics for performance measurement and asset allocation will be wrong. Well, the law of large numbers may be right in the long run, but we all live and operate in the often skewed world of the here and now.  This paper offers a tool for recognizing those times when there is more downside risk than upside potential and vise versa and how to manage it better by formally recognizing a different shape to uncertainty that is better suited to the task at hand.

That “better shape” is the brain child of two professors at Cambridge University, Aitchison and Brown, who developed a form of the lognormal distribution that can use negative as well as positive returns to skew the distribution either left or right. Fortunately, you don’t need a PhD in mathematics to calculate the shapes shown in Figure 1.  All you need is the Forsey-Sortino model; a simplified version of software that Professor Emeritus in Mathematics, Hal Forsey and Professor Emeritus in Finance Frank Sortino developed at the Pension Research Institute (PRI) at San Francisco State University.  And all you need to know as inputs to the model is what you already know: the average return and standard deviation of each index, plus one more return that we call: the Desired Target Return®, or DTR®.  Given the better shape, the model can then calculate better statistics.  Better, in that one could have beat the market more often than not over the past 26 years.  Of course, the more people who use this model, the less inefficiencies one would expect to find in the stock market.  History indicates that will not be a problem.

                                                         Figure 1

     The picture on the left is the positively skewed large core equity index (LC) and the picture on the right is the negatively skewed small cap growth index (SG). If you are forced to viewing both of these indexes as symmetric, bell shaped distributions, you may well conclude that you should just buy the market index because in the long term, the market is bell shaped, and everyone knows, you can’t beat the market, right?  Maybe not.  The key forecasting element in Figure 1 is the Upside Potential ratio (UP ratio)[1], which was first mentioned in an article published with my Dutch friends’ way back in 1991.  PRI has repeatedly recommended replacing the Sortino ratio with the UP ratio, but to no avail.  We will conduct three tests to indicate the superiority of the UP ratio.

How it works:

Imagine two investment committees with two different investment objectives. The first investment committee is a foundation that needs to earn 6% annually to meet their scheduled philanthropic payouts. The second investment committee has a defined benefit plan and their Desired Target Return (DTR®) is the actuarial rate of return of 8.5%.  Both committees use the Forsey-Sortino model (F-S model) shown in Figure 1 that can calculate the UP ratio on data provided by their consultant.  Beginning in 1990 they both use the average return and standard deviation for the last three years as input to the F-S model.  Figure 2 shows how the foundation would key in the historic data and DTR of 6%.

                                                     Figure 2

The output from the F-S model is shown in the right hand side of Figure 2.  The UP ratio of 3.59 means this large value index they own has 3.59 times more upside potential than downside risk. The committee intuitively understands that upside potential is good and downside risk is bad.[2] They don’t know how these terms are calculated, nor do they care.  They decide to see if some combination of the nine style indexes they own could beat the market the following year by using the UP ratio.  The pension committee acted the same as the foundation, only they used the actuarial return of 8.5% as their DTR. Based on this simple framework, both committees performed three tests:

First Test: Can an Asset Allocation based on the UP ratio perform better than the market mix of 60% S&P 500 and 40% Barclays A+ Aggregate bond index? Any negative UP ratios will be allocated to the bond index.

Second test: Holding the 60/40 asset allocation constant, can the UP ratio weights select style indexes that beat the 60/40 market mix?

Third test: Can the UP ratio select equity style Indexes, remaining 100% in equities, that beat the S&P 500?

Figure 3 presents the results they would have seen on the first test year after year.

                                                            Figure 3

In 15 of the 26 years the UP ratio 6% strategy (UPR) beat the market mix. In one year it beat the market mix by less than 100 basis points so they called that a tie.  The market mix beat the UPR 6% strategy 10 times; so, using the UP ratio won  50% more often than the 60/40 market mix.  That is astounding. The pension committee, using a DTR of 8.5%, found the UP strategy beat the market mix as often as it lost but earned a higher cumulative return.  Importantly, this shows the sensitivity of the UP ratio to the return the user needs to earn in order to accomplish their investment objective.  When the committees held the asset allocation mix constant to test the ability of the UP ratio to add value, by identifying sectors of the market that would perform better than the S&P 500, they got the second test results shown in Figure 4. 

                                                                Figure 4

 Once again both UP ratio strategies beat the market mix even when the asset allocations were held to a 60/40 mix. So, it wasn’t just the asset allocation decision that was adding value. It was something buried in the calculus used to generate the picture of uncertainty shown in the F-S model output.

Third test: Can the UP ratio weighted equity style Indexes beat the S&P 500?

The all equity 6% UP ratio strategy averaged 9.63% versus 7.64% for the S&P 500 over 26 years. The all equity 8.5% UP ratio averaged 9.10% over 26 years.

The S&P 500 was only able to beat the UP ratio strategies 6 out of the 26 years and two of those times it was by less than 100 basis pts.

                                                         Figure 5

Figure 5 shows using the UP ratio weights to invest in the indexes while remaining 100% in equity performed better than the S&P 500 most of the time over the past 26 years, in spite of the fact that the three year interval following the 2008 market collapse resulted in negative UP ratios for all indexes. To ensure we were measuring 100% equity in both strategies, we assumed all funds were invested in the S&P 500 for these three years.

                                                           Figure 6

The tests above were performed with no constraints on the asset allocations. The result shown in Figure 6 identifies years when excessive allocations (100% Stock or 100% Bond) were made that could be considered outside of acceptable investment policy guidelines due to lack of diversification.  The F-S model was developed as a tool to be used by financial professionals to make better portfolio decisions. The authors caution that the model should not be the decision maker.

We now return to Figure 1, repeated below, for a more detailed explanation of the otherwise unavailable information disclosed in the F-S model output.

                                                            Figure 1

On the left is the forecast for the Surz large centrix index (LC) which had the highest UP ratio of the nine Surz Style Pure indexes®.  LC is all the stocks that are neither growth nor value.  This is different than all other core style indexes which are a mish-mosh of everything (For more details see www.ppca-inc.com.).  The UP ratio of 1.66 indicates It has 66% more upside potential than downside risk.  Because the mean is positive it is anchored at -25% causing the shape of uncertainty to be positively skewed, not symmetric and not a normal or bell shape.

The small cap growth index shown on the right has a negative mean of -3.01% so we anchored it at +25% causing it to be negatively skewed with below target risk of 23.3% as opposed to 4% for the LC index. This obviously had the worst UP ratio.  While there is a 41% chance returns could be above 6%, the UP ratio of .12 indicates this index has 88% more downside risk than upside potential.  If one doesn’t believe the inputs, you can play,” what if” games by changing them.  This is unique information one should want to know.  We don’t believe any other model has this information with only these 3 inputs.  How did these projections do so far?  As of June 1st the LC index was up 8.4% and SG was up 2%.

The Traditional Picture

                                                      Figure 7

Traditional mean-standard deviation risk measures would make the all equity UP ratio strategies appear to be the most risky over the last 26 years; highest return and highest risk. Nobody would choose the S&P 500 risk-return trade-off and everybody would choose the 8.5% UPR, really? If someone measured risk relative to their DTR and most of the volatility was above the DTR?  Risk for the committee members in this study is not a bumpy ride, it is that they don’t accomplish their investment objective, causing a failure to accomplish their goal of a specified pay out.  For them, beating the market is not the goal and is not the investment objective.  For them, the traditional framework of performance measurement is misleading.  For them, performance should be measured relative to their DTR, as shown in the Journal of Performance Measurement, Summer 2016.

To download the software: Control click on the highlighted link below, install the VBruntime.exe file to ensure your computer can run software written in Visual Basic. Then install and run the F-S model.

The Forsey-Sortino Model-1 software 

This dropbox link may ask you to sign in but at the bottom you can skip this step. This zip file contains two files: Forsey-Sortino.exe and a Microsoft VBRuntime.exe.  If you get an error message with F-S Sortino.exe you must install the enclosed VB runtime.exe.


Please direct all responses to:

Kal Salama, CFA

Chief Investment Officer

The Headlands Group, Inc.







[1] Sortino, F., van der Meer, R.A.H and Plantinga, A (1999) The Dutch Triangle, Journal of Portfolio Management, Fall

[2] Researchers at Groningen University in The Netherlands found that people they interviewed intuitively understood these terms without having to explain exactly what upside potential or downside risk were or how they were calculated.

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The Other Side of the Coin

In an attempt to maintain a balanced approach to the conflict between China and the US, I recommend “Ill Winds” by Larry Diamond. He provides a well documented, scathing expose of China’s threat to the world as opposed to the views expressed in: “Has China Won”.


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Bad and Worst Case Scenarios

I have a 20.79? gain in my portfolio for the year and I’m taking it. Will the students riot in Hong Kong? Will China take control?  Is there a steer in Texas? Cash is an asset category!

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The Inmates are Running the Asylum

As of the open I am 37% in equity, 63% cash.

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