Sage Advice

You expect things to go badly

And they don’t

That you call good luck

You expect things to go well

And they don’t

That you call bad luck

Because you lack an understanding of uncertainty

You put your trust in luck

Rather than reason

Mulla Nasrudin

The article below is what I have learned about managing uncertainty.  Begin by attempting to describe it.  You cannot manage what you cannot describe.

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Sortino & Forsey Give Away

This is the last vestige of the software we have developed over the last half-century. To download the Sortino-Metrics Software click on the link below:

https://drive.google.com/open?id=1j-lL2Y33dqHFBLdXfvNuKHZXtPvV7RxM

Right click on the Sortino-Matrix folder. A window will open. Click on Download.  The Readme word file will explain how to set it up.

The motivation for providing the executable software, Sortino-Metrics.jar, is to provide an easy way for practitioners to use these metrics to help their clients. The reason for including the source code is to encourage academics to improve on the work that Professor Hal Forsey and I have worked on for the last 50 years. We believe the metrics found in this software are an improvement on the standard metrics, particularly those in the A-B-C form shown below:

(A – B) / C

A is the average return on some portfolio of securities, B is the return on a benchmark and C is a measure of risk.  If B is the return on the 90-day T-bill, a common surrogate for the risk-free rate of return, then the numerator is that of the Sharpe ratio.  Many people also use the 90-day T-bill rate when calculating the Sortino Ratio, implying that only the risk measure, C, separates the Sortino ratio from the Sharpe ratio. Since downside risk would then be measured as deviations below the T-bill rate, the denominator of the Sortino ratio would have to be much smaller than the denominator of the Sharpe ratio.  The result is:  The Sortino ratio would always make the performance of the portfolio look better than does the Sharpe ratio.  Small wonder that many portfolio managers prefer the Sortino ratio to the Sharpe ratio when showing their performance.

Sortino first published his version of this ratio in the Journal of Risk Management in 1981.  That was a long time ago and the Sortino ratio should be replaced by a much-improved ratio, called, The Upside Potential Ratio, developed with my colleagues at Groningen University, Robert van der Meer and Auke Plantinga.  This ratio was first published in Pensions and Investments Magazine and then The Journal of Portfolio Management, both in 1999.  Hmmm, this is a long time ago also. So why is some ratio that doesn’t work as well, live on, while this superior ratio is ignored? Possibly because the Upside Potential ratio is based on a much different theory than any other.

The focus for all other asset management theory is on A, the average return on the asset.  Our theory*, focuses on B, the return on some Benchmark return required in order to achieve an investment objective that will accomplish a financial goal.  For a pension fund, B is the return required in order to meet or exceed all the promised payouts in a defined benefit plan or, meet or exceed all the payouts that would maintain the standard of living for a 401k beneficiary.  B separates good outcomes from bad outcomes.  C is the downside risk below B, a measure of the risk of not achieving the investment objective that will accomplish the goal. Mathematically it looks like this:

Visually it looks something like this to me

 

*Brian Rohm named it, Post Modern Portfolio Theory, in an effort to market the optimizer developed at PRI

Thus, it requires a different mindset just as MPT required a different mindset from the financial accounting approach of Graham and Dodd.

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It Has Gotten Worse

In the previous posting we called attention to the call for 40% fixed, 60% equity in July.  What does a 60/40 allocation look like as of Jan 1?

It now projects 20% more downside risk than upside potential if only passive indexes are used.  Even selecting a combination of active ETF’s has 10% more downside risk than upside potential.

What if we go to 50% fixed?

Now the UP Ratio is 14 times more upside potential than downside risk with a 15% allocation to Vanguard Growth Technology, 8.9% allocation to the Hong Kong ETF, 7.8% to the MidCap Growth iShare ETF and 4.7% allocation to the iShares Asia X Japan ETF.  This is not a well diversified portfolio, but it is close to mine.  I overweight China Technology and the Spyder, which over weights Technology.  I use the model as a guide and my brain as the decision maker.  I am currently at 50% Equity and I plan to move to 40% fixed in the very near future.

This is how I recommend professionals use this model.  Amateurs should not use it.

 

 

 

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The Roller Coaster Market Ain’t Over

                                      Where did PMPT say we were in July 2018?

The graph below shows the effect of increasing equity above 60% (left graph).  The upside potential ratio for the active portfolio with a 6.2% allocation to VGT, the Vanguard Growth Technology ETF becomes negative, i.e., there would be more downside risk than upside potential. By increasing equity 10% you would incur 30% more downside risk than upside potential.  The passive portfolio (no active managers) would fair even worse, it would drop from an even trade off to 50% more downside risk than upside potential.

The question is, does the theory developed at PRI, before the tech bull market and the Trump election, still provide useful information? Has risk increased or decreased in the past decade?  PMPT said you are not getting paid to take more risk than the 60/40 market mix.  If you have exceeded that magic mix of bygone years, put some stop loss orders in to reinstate a 60% equity position.

It turns out, that was good advice then.

What Now?

As stated in the  Article below I started investing in equities again in November and increased equity to 90% in late December.  I now have a 3.5% profit in my portfolio after   I got stopped out of NVDIA for a small profit and sold CRM for a 20% profit.

What Comes Next?

Next week’s meeting with China should provide a continued rally.  I will raise my stop loss orders accordingly.  Why? the Federal debt, the corporate debt, the junk bond debt, the BBB debt that is really junk, the money market debt that is full of junk.  Proceeds have gone and will continue to go into short term treasuries.  It’s a beautiful day for a family drive in our Tesla to enjoy the good life.

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UPDATE

 

I have now sold my stock in Tesla at a ridiculous profit.  Still have the car and love it!

 

It has been a long time since my last posting on Elon Musk.  Since then I have bought a model S Tesla and Tesla stock.

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On Embracing Complexity

Recently deceased scientist, Stephen Hawking, said to a graduating class, “The next century [21st] will be the century of complexity” and encouraged them to embrace it.  Yes, we should not fear it, but embrace it by gaining knowledge of a new science, Artificial Intelligence (AI).  It began at Dartmouth University in 1956 when John McCarthy used the term (AI) as the title of a conference. To help us operate successfully in an increasingly complex world, we need to learn the language of complexity.

Why have so many experts who forecasted a market top in the past few years been wrong? Perhaps it is because they have been using 20th century methodologies in a 21st century world of increasing complexity.  This is no longer like us playing a game of chess against a few experts and a lot of amateurs, as existed in the 50’s and 60’s.  We are playing against computer programs that have learned to reason from massive amounts of data derived from a very complex system, and are making better decisions than us about everything from driving cars to investing in the stock market.  That means we need to know something about complex systems and the computer programs that understand them better than we do.

The evidence:

The 1st shot across the bow was when IBM’s Deep Blue beat the world champion chess player, Gary Kasperov, because it knew all the games he and every other expert had ever played and could access them in a flash.  That was the earliest form of AI  which involved 3 steps:

  1. Training the model on a massive amount of data shoveled into computers run in parallel on processing platforms.
  2. Testing the model to see if it learned how to win in the past.
  3. Validating the model to see if it can uncover relationships from the data to predict future outcomes, then, learn from and adjust to future events better than human experts.

Another breakthrough came in March of 2016.  The Google program, named AlphaGo, was taught to play the game of Go that has 10 to the 127th power possible moves.  AlphaGo scored a decisive win over Lee Sedol, a top-ranked international Go player, winning 4 out of a 5-game series in South Korea.  Then, in 2017 it won the first two games of Go in a three game championship game with the world’s champion, Ke Jie. The Google AI program appeared to think and reason better than human experts. Below is an example of a supervised learning approach which requires a massive amount of historic data to teach the model how to achieve the goal, for example, beat the S&P 500.  This involves some of the language you need to be familiar with.

Neural Network (Supervised Learning)

The Xi are data sets affecting the thing you are trying to forecast, like, turning points in the stock market.  The Wi are the initial weights assigned by experts based on historical results. For example, X1 could be the 3 year moving average return on the S&P 500.  X2 could be the 3 year moving average standard deviation.  X3 could be the upside potential ratio calculated from the first two Xi with the aid of the Forsey-Sortino model.

The tree search in AlphaGo then evaluates positions using statistical methods like Logistic Regression and Random Forests to adjust the weights. The model then makes a forecast and learns from the results how the weights should be adjusted further. This is called reinforcement learning.

The Break Through (Unsupervised Learning)

In 2018, Google updated the AlphaGo model to AlphaGo Zero, which uses an algorithm based solely on reinforcement learning, without human data, guidance, or domain knowledge beyond game rules . This is known as Unsupervised Learning. AlphaGo Zero became a neural network trained to predict AlphaGo’s own moves without requiring massive amounts of historic data. It learned as things changed in real time. How did it do? In 4 days the Zero version learned how to beat the original AlphaGo model in 100 out of 100 games.

What might somebody using an AI model see that you and I don’t see?

Similar to the AlphaGo Zero model mentioned above, AlgoDynamix utilizes an unsupervised machine learning algorithm to process limit buy and sell orders from financial exchanges to identify patterns as shown above. It seems there are certain patterns of buyers and sellers that the AI model has identified in clusters which have predictive power, the rest are noise.  To be sure, I know very little about this company or the methodology they are using.  However, the result shown below leads me to want to know more.

For more information visit www.algodynamix.com. The few graphs in this paper were selected from an introductory course in AI at Stanford University and do not begin to explain the revolution akin to the industrial revolution that is taking place.  I for one intend to find out more about the research being done in AI so I can comfortably surrender my 20th century driving skills to my new Tesla Model 3 when it rolls off the assembly line.  I encourage you to go to your nearest university to take some introductory classes.  You may want to start by downloading:  Kaplan, Jerry. “Artificial Intelligence: What Everyone Needs to Know”. Oxford University Press. Kindle Edition.

 Conclusion:

It is estimated that AI will have replaced 13 million U.S. workers by the end of 2018 and 1 billion workers worldwide by 2022. Most vulnerable: Truck & Uber drivers, retail workers and… investment advisors ?

“Last year, On March 28, the CEO of BlackRock sacked seven fund managers and shifted billions of dollars to an AI product called Systematic Active Equities (SAE). SAE now employs 80 portfolio managers and researchers, who include more than 30 PhDs in computer science, physics and engineering. This is one of the greatest opportunities in active management that I have ever seen, said Ron Kahn, head of research at SAE.”

Is BlackRock going to need RIA’s to sell their product? Are RIA’s going to need firms like BlackRock to keep their clients? How do they do that if they don’t understand what BlackRock is doing vis-a- vis AlgoDynamix?

The information technology revolution is not coming, it’s here! The winners will be shareholders in high tech stocks, college grads with science degrees and Businesses that adapt to 21st century technology.

 

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Who Knew?

 

Figure 1

 

Who knew last November that the market would crash in February? That was the beginning of the Trump rally which just collapsed (Figure 1). In June of last year Kal Salama and I posted an article on Linkedin discussing the merits of the Upside Potential ratio as a predictive performance ratio and offering a link to download the software (Forsey-Sortino Model).

In September we called attention to the work of one of my former research assistants, Bernardo Kuan (deceased) who tested the efficacy of the PRI methodology and noted the methodology was “now warning investors to seek liquidity”(see “The Cost of Assuming Liquidity”) on this website.

I also called attention to a book by Joshua Cooper Ramo, Co-CEO of The Kissenger Institute. In 2016 Ramo wrote: The Seventh Sense: Power, Fortune, and Survival in the Age of Networks.  In it, he warned that we are living in the age of “collapse and construction.” He forecasted the rise of “Gatelands” controlled by a few companies with big data and massive computer power, “each, essential in some way, to national or economic security. Trade, finance, education, cybersafety, artificial intelligence, and military affairs will move from unconnected to connected.”  And, if you are not connected you are Ausgespielt (English translation SOL).  This theme was echoed by Professor Yuval Harari at the recent Davos opening ceremony.  True, they are not trained in economics or finance. If you want mathematical rigor, read “Scale, The Universal Laws of Growth, Innovation, Sustainability” by Geoffrey West, physicist and president of the Santa Fe Institute and read about “Finite Time Singularity” leading to “Entropy”.

Figure 2

Yes, the blue chart in both graphics above is the S&P 500. West also posited a new theory disputing the old economic theory of diminishing returns to size.  West explains why firms like Google and Amazon have increasing returns to size, which supports Ramo’s view of the future.

Why look outside the disciplines of economics and finance? We don’t have all the answers, and perhaps, “You won’t find a forecast of the future by sifting through the ashes of the past” (author unknown). The future may belong to big data driven by AI.

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