Kings Portfolio Newsletter Q1 2020 – Now That’s What I Call A Quarter

Economy & Capital Markets
The S&P500 fell 35% and the U.S. Federal Reserve Bank cut effected funds rate from 1.59% to 0.25% in 24 trading days. This followed a reversal of Western Governments response to airborne COVID-19 (first globally reported on new years eve), initially failing to impose “cheap” measures pre-boarder, then enforcing delayed internal draconian ones. Government treasuries price rose then moderated, as the size of fiscal expansion further elevated borrowing levels on a diminishing or slowing income tax base. Although an opportunity to move to fiscal surplus and improve savings rates were missed during the previous decade, we maintain a lack of inflation accommodates both monetary and fiscal easing to combat the immediate slowdown in economic growth.

Multiple companies have identified potential vaccines for COVID-19 expected by year end and doctors are already sharing best “non-approved” remedies. Evidence from Japan and Korea support (as the secondary strategy) local authority social distancing orders to protect and bolster health service facilities and internally develop supply for testing, remedial and protective equipment. After which the spreadability can be embraced so non-vulnerable and non-infected citizens are released from quarantine back to normal activities. From mid-April individual economies can stagger start to accelerate through the end of the year. It is likely there will be no significant (5%) variance in 2020 from the death rate in 2019 (2.8m Americans in 2018).

Remodelling supply lines, to reduce exposure to China in favour of its low cost neighbours and home production, is anticipated to accelerate. International travel is expect to have health prescreening enforced similar to terrorism after 9/11, adding to cost and duration. Communication and broadcasting providers are likely beneficiaries. A large number of previously loss making business are not expected to reopen.

In the quarter OPEC and Russia could not agree production limits while Brent crude oil prices fell sixty percent.

As we expressed during the 2008 recession (, economic disruption takes the form of an absence of liquidity and moral hazard brought about by the inability of commercial financial institutions to distribute funds made available to them by central banks. If central banks are unable to LEND directly to the general economy, governments should provide generous loan facilities underwritten by central banks and administered by the commercial banks, based on tax registration of LOW INCOME individuals and previously PROFITABLE business taxed entities. The moral hazard of bail-out and need for massive fiscal stimulus could then also be avoided. Once the liquidity crisis is over commercial banks should then be forced to adopt these loans.

Over the last 12 months our net exposure to equities fluctuated around the zero mark due to excessive valuations. As an investing portfolio the minimum holding period for any position is three months, therefore when western governments preparations for the epidemic proved to be “the Emperors New Clothes” we were unable to react, in response we have accelerated development of AI market sentiment tools. During the quarter we had overexposure to the transport sectors and underexposure to energy sectors that balanced out, with a marginal exposure to equities. The net performance of the strategy over the last three months was -3.5% providing a 39 month performance of +32% with 0.8 sharp ratio, compared to the S&P500 performance of -20%, +15% and 0.3 respectively.

Year 2019 in review

• Successes.
– Developed and implemented first deep-learning applications providing a massive improvement in analytical power by leveraging previous years hardware purchases
– Acceleration in pipeline of third party cutting edge quantitative research to implement
– Impressive performance from improved security selection criteria
– Further improved the fundamental and technical criteria for security selection
– Corrected and refined machine learning methods in asset allocation
– Further improved econometric criteria in asset allocation
– Developed new software to access better data sources
– Now accepting client defined portfolio universes (asset allocation, ESG, religious etc.)
– First venture capital investment
• Fails.
– Single digit net return for year (annualised double digit return over three years)
– Poor asset allocation due to not applying correct procedures for machine learning
– Natural language processing sentiment analysis not developed further

A.I. Stock-Picking October Gross Realised Results: 0.83%

Jan17- Oct19 net cumulative return 39% & sharp ratio 1.03 against it’s ACWI universe 35% & 0.81.

Trade, FX, Ticker, Holding Period, Position Return, Portfolio Contribution

Short, CAD, CPG, 16Jul-10Oct, -18.20%, -0.20%

Short, GBP, DRX, 16Jul-10Oct, 4.40%, 0.04%

Short, GBP, INTU, 16Jul-10Oct, 52.40%, 0.53%

Short, GBP, TED, 16Jul-10Oct, 41.75%, 0.46%

Immediate transparency, 3 day full liquidity, 3rd party custodian.

The A.I. assesses Econometric, Fundamental, Qualitative, Quantitative, Technical and Traditional data and, sentiment from documentation and news flow.

Our A.I.’s Stock-Picking September Realised Results

Trade, FX, Ticker, Dates, P&L, Contribution

Short, EUR, APAM, Jun27-Sep24, 6.29%, 0.21%

Short, EUR, VAS, Jun27-Sep24, 17.89, 0.46%

Long, HKD, 3968, Jun27-Sep24, -9.16%, -0.07%

Long, HKD, 688, Jun27-Sep24, -13.32%, -0.24%

Long, USD, EBR, Jun27-Sep24, 11.21%, 0.09%

Long, USD, LKOD, Mar22-Sep24, -6.32%, -0.04%

Gross Month Realised Return: 0.41%

Net cumulative return 36% (Jan17-Sep19) and Sharp Ratio 0.98 against it’s ACWI universe 31% and 0.74.

Immediate transparency, 3 day full liquidity, 3rd party custodian.

The A.I. assesses Econometric, Fundamental, Qualitative, Quantitative, Technical and Traditional data and, sentiment from documentation and news flow.


Between Wednesday 28th August and Wednesday 11th September (the last 10 bars in the charts below) the S&P 500 Index had a mild recovery to near record highs on light volume.

However over the same period the DJ US Thematic Market Neutral Momentum index fell steeply, in it’s most violent move ever.

Whilst the DJ US Thematic Market Neutral Value index recovered strongly.

Further during the period we incurred several security specific news items that were detrimental to our positions.

Securities in our AI selected global equity strategy have a minimum holding period of three months and profitable positions suffered from this rapid reversal. The AI does however assesses both momentum and value and will adapt (over time) to changes in thematic rotation whilst not suffering behavioural issues of short term misfortunes.

Broker statements are always available for inspection.

Our A.I.’s Stock-Picking August Realised Results

The A.I. assesses Econometric, Fundamental, Qualitative, Quantitative, Technical and Traditional data and, sentiment from documentation and news flow.

Trade, FX, Ticker, Dates, P&L, Contribution

Long, USD, AMZN, May28-Aug9, -1.18%, -0.03%

Long, USD, ESNT, May28-Aug9, -2.1%, -0.02%

Long, USD, LRCX, May28-Aug9, 8.73%, 0.09%

Short, USD, MAC, May28-Aug9, 23.57% 0.69%

Total Month Realised Return: 0.73%

Net cumulative return 39% (Jan17-Aug19) and Sharp Ratio 1.1, MSCI ACWI universe 29% and 0.69.

Immediate transparency, 3 day full liquidity, 3rd party custodian.


Bitcoin has a market capitalisation of approximately US $200bn with 24 hour trading 7 days a week, making it the perfect TRADING instrument.

At present the value of mining a bitcoin, is sustained by the price of bitcoin, which is sustained by net new money into the market, which is sustained by……(insert your own ideas).

When all 21m bitcoins have been mined or by 2140, miners who verify transactions will no longer be rewarded for mining but only by the transaction fee they can charge.

However the cost in electricity to do one transaction in bitcoin is the same as approximately 400,000 Visa transactions.

For bitcoin use as a transaction medium to be valid when the last coin is mined requires huge productivity falls from Visa to equate the two systems.

Or the protocols of bitcoin can be changed by 95% support from the last 2,016 miners, to lower the cost of validation. Either by increasing the amount of bitcoins, i.e. a reduction in the value of a bitcoin or, they could change the method for validation (the highly praised algorithm).

Further approximately 80% of mining pools are in China, making bitcoin protocol susceptible to that countries political system.

Whilst we have been a huge successful INVESTOR in the Chinese economy it has been with companies that ADR list, therefore providing transparent and recognised reporting. Unfortunately for us, bitcoin remains an instrument too far.

Steven J Cohen CFA, August 26th 2019.

How AI will help you live forever then eat your lunch.

First Published In FOTT Family Office Magazine Beijing – June 2019

The Turing machine that broke the enigma code during WWII was the birth of Artificial Intelligence (AI). However since then, the adoption of AI has mainly been confined to the manufacturing, transportation and distribution sectors; with the rise of robots that build Japanese cars and stack Amazon warehouses. But now AI is moving leaps and bounds into the service sector including: game playing, self driving vehicles, facial recognition, customer service chat bots, language translation and even medical surgery.

AI consists of two strains: machine learning and deep learning. Deep learning seeks a statistical inference from a small part of the data and then apply all that it has “learnt” upto that stage to the next small part until all parts have been examined, similar to the way a human will adapt from experience. Machine learning seeks to extract a statistical inference intermediately from the whole data. Your preference would depend on whether you believe the way humans learn is superior, although there are statistical methods of measurements that provide a guide as to which gives the best results based on the PREVIOUS data. The resources to develop both deep and machine learning are: free, open source and available on-line to all who wish to utilise.

The benefit of AI is that it can provide: repeatable results, irrespective of the behavioural bias of humans and with unimaginable productivity. Repeatable results allow an amount of certainty as to performance in the future. A lack of behavioural bias provides results without human failings such as: hangovers, partner disputes, career pressure, saving face and emotional attachment to incorrect decisions. However where AI comes into its own is handling exponential growing amounts of data and choices, that our 10,000 year old brain design is not equipped for.

An early adopter of AI in investment management was the Medallion Fund of Renaissance Technologies with spectacular results for over 30 years. However the majority of adoption has been on the sell side with automated customer service departments and robo-advisors that provide asset allocation portfolios to retail markets. The downside of the recent adoption being, data scientists and computer programmers extract the most perfect result from the available data without any understanding as to whether the input data is correct or relevant to the result (“garbage in garbage out”) or, if the model has any intellectual rigour. Leading to failure once the program goes “live”.

Market participants can either accept or reject the relevance of AI on the investment industry, I can only give you our story……

Way back in autumn 2016 as CIO of a Swiss based multi-family office it was out of scientific curiosity that I attended a seminar on AI coding (computer programming). It was given by a former CERN employee, who discussed how they discovered “The God Particle” or “Higgs-Boson”, the smallest sub-atomic particle for which I still have a still have an early edition of the same named book. However as someone steeped in investment education and not quantum physics it appeared easier to utilised these techniques in my chosen professional field, any by the end of that year we were ready to go live.

Initially we had a macro model for the US economy that provided basic asset allocation. Our model accessed the limitless information provided by Federal Reserve Banks and the rest of the internet. The trick was knowing which information was relevant to use. In the last thirty years we already had two “new paradigms” called by the economic community. And as someone who skipped his undergraduate econometric exam due to never getting beyond being asked for a password on the university computer, this could have been a show stopper. However in 2008 our office had already delivered a 23% investment return against the 45% fall in the S&P 500, that was the result of: experience in several previous economic cycles, admitting and learning from mistakes, strong outperformance of managing a multi sector portfolio for a big four UK bank, and actually attending and getting some decent grades in international, monetary and fiscal economic undergraduate exams.

Our US economic model actually gives us an eighteen month forward forecast, which allows us adjust the portfolios in a timely manner, and back in early 2017 we were still very bullish. Due to our very strong stock picking abilities (long and short) the next stage was to utilise the AI to assist in this task. We took the universe of stocks with a US, ADR, Canadian or UK listing and a market capitalisation of over $1billion, that is over 6000 stocks. Again the challenge was to choose the relevant input data, be it: fundamental, technical, industry, company or independent news, as well as the multiple other sources, for example, outperformance against Google “usual customer attendance” matrix. Our limiting factor was the intel i5 core processor that our AI was using (we wish to avoid the cloud due to security issues), which again meant that we could only choose a few most relevant inputs. This resulted in the very strong appearance of Chinese stocks, some of which produced returns of nearly 200% over the year. It was an obvious step to develop an AI macro model of the Chinese economy, which by coincidence was the second largest. However during 2017 our US economic model started to flash recession meaning we would soon need to move to fixed income and so also developed an AI model for the US 10 treasury.

Despite our massive risk adjusted performance of 2017, in 2018 we had a little underperformance as we transitioned to the cautious portfolio, caused mainly by the poor performance of our individual stock positions. We reprogrammed our stock picking AI to allow it to make better decisions given the stage in the economic cycle and this year it is producing absolute returns on a monthly basis whilst being net short the market. Our biggest problem is what to do with all our cash, especially given our AI US 10 treasury view is not positive (in contrast to our traditional methods), so we are investing in 3 month corporate bonds.

After the initial success we had an obvious capital requirement to invest in internal hardware, purchasing the top of the range “gaming” computers with the best CPU and GPU. In AI the CPU is used for the machine learning software and the GPU is used for the deep learning software. We have further developed, “natural language processing” AI to provide sentiment analysis on news flow especially transcripts, reading and deciphering in a matter of seconds as opposed to the hours a human would take.

So what is my job as the CIO of an AI driven MFO? It is both master and servant; as servant it is to ensure that the data the AI receives is not garbage and, as master it is to reprogram the AI to take account of changing computing, economic and investment circumstances. Can the AI do these spare roles? As servant certainly, for us it’s a matter of cost effectiveness, will the time taken writing and testing the code be recouped by using the code, at this moment we are happy just to run our human eye over the data to ensure accuracy and suitability. Can the AI learn from its own mistakes and take over the master role? The beauty of deep learning is that it is unsupervised (it can choose its own inputs) take for example the champion Go program developed by Deep Mind (also UCL alumni) after given the rules it taught itself strategy or, Google language translation that no longer needs a Rosetta Stone, it can understand just by word structure. Unfortunately we don’t have the resources to develop these high level creative solutions, whilst undoubtedly some of the larger houses do, their decision makers are steeped the fear of losing their jobs and, their indecisiveness provides us with an alpha to harvest.

So what have I done, I’ve cloned myself as a senior level analyst to live forever but instead of eating my lunch it is eating the lunch of the 2000 other analysts I would have had to employ to get the same level of productivity.

Whilst it will be very difficult for AI to capture the creativity of genius (“thinking outside the box”) it can certainly do the hard-work.

Suffice to say there is very little genius in the top professions: law, medicine, engineering, architecture, hairdressing etc. so the service sector is not immune to disruption.

In fact, where the is enough data on any successful person decision making in an professional industry, AI can isolate the factors that made them successful, and then apply these factors to opportunities for eternity.

Steven J Cohen CFA is the principal and CIO of a Zurich based multi-family office. He gained his BSc Econ from UCL, spent several years in Chartered Accountancy and had established a retail clothing firm in central London. He was in-house counsel to the wealthiest family in the UK and managed a multi billion dollar portfolio for a big 4 UK bank. After a very successful 2007 & 2008 he established his family office and today develops artificial intelligence generated investment strategies for an increasing Asian client base.