You cannot escape physics. The value of every investment starts at zero. Entropy is our natural state (thank you to the Second Law of Thermodynamics) meaning that we are constantly fighting the destruction of value. There is always a force, equivalent to gravity, pushing an investment down. Value is created by the efficient use of capital and the created, sustainable competitive advantage. Consistent investment in a thoughtful portfolio will create sustained value, but it is work, and you will always be fighting natural physical forces. One recent example is the financial crisis of 2008 to 2009. 40% of the average equity value was destroyed in this time. However, if one invested consistently at the height of the market and continue to invest through the crash and then ultimate recovery, and investor still earned over 9% annually. Thoughtfulness, consistency, patience, and determination is the most effective way to fight gravity and thermodynamics. The most important way to fight physics and the ultimate effect of gravity is to determine what you are looking for first. Highlight growth, disruption, sustainability – what will have a long-term value creating effect. What sectors make the most impactful difference? Recently, as we look at technology, biotechnology, and other important sectors, we see above average returns because of the impactful nature of the sectors. But technology is also permeating finance (Fintech) and entertainment (streaming services) that are disrupting incumbents and creating disproportionate value to the new entrants. Is this sustainable? Will the disruptors capture value, or will more established companies ultimately win? Observation, questioning assumptions, testing models, and assuming no knowledge regardless of historical experience are the only cures for gravity.
When to sell is more important than what to buy. One of the biggest mistakes investors make is thinking that their purchase decision is the most important decision they will make. This is misguided because most losses are lost opportunities. They may be buying decisions that were never made, but most likely, they are selling decisions where the decision to sell was made too soon. When Warren Buffett owned 5% of Disney in the 1960s, he made a 50% return. He happily sold the stock. But investment decisions should not be made based on historical returns. Once again, all investments are predictions for the future. Regardless of whether the investment you currently hold has generated a great return or lost you money, what will it do from this point on?
Buying or selling is a crucial investment decision because you are always either buying or selling. There is no such thing as “holding.” If you own something you have bought it. If you would not buy it today but continue to own it simply because you bought it in the past, and not making an investment decision, just simply being inactive. If you are an investor who is buying or selling. Selling decisions tend to be inefficient. One does not need to be active, but one does need to think like an owner. If you own a great company, there is little reason to do anything else other than stay on top of developments within that company and industry to make sure they can remain a great company. Eventually, they will revert to the mean. More than anything, that is an investor’s job – figure out when the company will revert to the mean. That means they will either be losses or tremendous gains in the future as this trend occurs.
Nothing stays above average.
Look at the facts not the opinion about the facts. Anyone holding themselves out as an expert has, a very deep but narrow knowledge base that is rarely universally applicable. Fundamentally, listening to opinions rarely give useful insight. Often, it assumes looking backward but does not apply to the current situation. Global commerce, trade (and trade wars) tariffs, flexible manufacturing, and global markets, along with technological innovation and automation create significant pressures against inflation, regardless of employment levels. These are the set of facts to be considered, not an assumed economic model where few people understand the actual inputs from 50 years ago.Another example looks at revenue projections based on historical business models. But what happens when those business models are changing? We discussed the example of the metamorphosis from Blockbuster to Netflix where a fundamental change in the business model made revenue projections from the historical model meaningless. Then, Netflix had to change their business model again to one of the original production and international expansion – once again obviating existing models for revenue. Facts are what happened. Specific and verifiable. Knowledge is the appropriate combination of facts. Knowledge comes from understanding the facts that matter. Wisdom is the insight that leads to prediction. At its core, any investment strategy predicts the future. To predict the future effectively one needs the wisdom to grasp what will happen. Of course, this cannot be known, and there are many random events that can affect the future (see Anti-fragile and Fooled by Randomness by Naseem Taleb), and uncertainty should always be factored into any investment decisions or predictions.
“Assume no knowledge” (Socrates)
No successful company can create or sustain its competitive strength without constantly examining its First Principles. It means defining a problem effectively, understanding the actions needed, and then implementing those plans. This requires a unique combination of perspective, talent, drive, and organizational flexibility. It is rare, but when discovered, it is where the most valuable investments are found. Defining a problem in its most basic form is essential for the most effective solutions. Most thinking stays at a superficial level because not enough thought is given to the problem one is trying to solve. There are many examples of this but mostly it comes from an attitude that says “it can’t be done that way” until, of course, someone does it.
This is the true source of any disruption. It is not simply doing things differently but looking at how the same thing can be done in a more basic and fundamental way. Assumptions about how things work (“assuming knowledge”) impedes innovation. “We’ve always done it that way” is usually the death knell of creative thinking. Many examples exist but some fundamental and obvious examples range from several well-known developments and innovations that were generated more from asking simple, basic questions instead of coming from some ill-defined inspiration.
Obvious answers only come from asking the right questions.
The current low interest rate environment increases the discounted present value of future cash flows and reduces the return demanded for every investment. In other words, when the Fed funds rate is zero, 6% bonds become disproportionately attractive. Buyers have now bid bond prices up until yields are now significantly less. What does it mean if the prices of stocks and listed credit instruments are at levels not driven primarily by fundamentals reasons (i.e. current earnings and the outlook for future growth), but in large part because of the Fed’s buying, it’s injection of liquidity, and the resultant low cost of capital and the market’s lower demanded returns on financial instruments? My conclusions are limited by inadequate foresight and influenced by my optimistic and pessimistic biases. Experience teaches it is hard to get the answer right. Or, as Charlie Munger has said, “it’s not supposed to be easy. Anyone who finds it easy is stupid.” At the risk of being stupid, equity investments in companies, including the five largest, with the unique competitive positions and “closed-loop” business models will remain excellent investments. In addition, certain fixed income securities paying high yields with attractive long-term risk-adjusted safety are extremely attractive and are being ignored in this unique interest-rate environment. This combination will create substantial value. Unique macro forces created by the central banks, unprecedented and sustainably low interest rates combined with pandemic-tested sustainable business models have created truly unique opportunities.
The history of AI shows that attempts to build human understanding into computers rarely work. Instead, most of the field’s progress has come from the combination of ever-increasing computer power and exponential growth in available data. Essentially, the ability to bring ever more brute computational force to bear on a problem-focused on larger data sets have given increasing usefulness. But, it’s limitations are also magnified in sharp relief more than ever. The bitter lesson is that the actual contents of human minds are tremendously, irredeemably complex…They are not what should be built into machines. Machine learning doesn’t live up to the hype. These systems are fundamentally brittle, and always break down at the edges where performance is essential and consequences much direr. There are many potential applications that can be effective and useful tools. They are simply much less ambitious than the current hype would indicate, but they are also far more realistic.
Productivity, expansion, and entrepreneurship were enabled through the adoption of new technology. Undeniably, the net economic benefit was substantial. But lives were disrupted, jobs were lost, and what would be seen with a historical perspective as an obvious beneficial choice, was anything but obvious to those so immediately and negatively impacted. Technological advancements produce net benefits for society. But for every advancement, there is a cost. Leadership and subsequent public policy must address this shortfall. As in the past, the solution has been training and education leading to economic inclusion and prosperous lives. and subsequent public policy must address this shortfall. As in the past, the solution has been training and education leading to economic inclusion and prosperous lives. History has taught us the net benefit of technological advancement, the turmoil it brings, and the solution required.
Medical Intelligence is a new discipline, converging human and artificial intelligence. Artificial intelligence will not replace human intelligence, especially in medicine. Diagnosis and treatment will remain a human endeavor. But AI will be an indispensable tool helping human intelligence effectively deliver better quality healthcare. The overwhelming benefit is that it raises the bar for all practitioners. A minimum level of quality medical care can available globally. The higher standard for diagnostic accuracy, therapeutic recommendations, and overall care from this mass of data gathering will improve overall health and wellness everywhere. Applied effectively, these tools also drive down overall healthcare costs, diagnostic errors, and unnecessary procedures. Greater accuracy eliminates needless testing and procedures significantly and delivers effective care more quickly. Diagnosis is more immediate, recovery times faster, care more available, and overall expenses reduced.
Instead of “internet time” we now have “pandemic time.” The need for advanced systems to keep society functioning, manufacturing moving, and give consumers some sense of safety is immediate. Driving innovations – whether those innovations are in health care, technology or other areas of production and manufacturing – is essential to not only offset the impact of the global pandemic but stay competitive and sustainable long after the current health crisis has subsided. Technological advancements, especially machine learning and other powerful software tools, combined with developments in nanotechnology, monitoring, and global communication networks will accelerate a profound change that will permeate all aspects of business and manufacturing. Advanced technologies were set to indelibly affect all aspects of industry in about five years. The curve to successfully implement the best tools and make processes more efficient, informative, and effective has been accelerated by the pandemic. The need for automation and systematic tools to keep society functioning, keep manufacturing moving, and give consumers some sense of safety and confidence is immediate. More than anything, driving innovations – whether those innovations are in health care and life science, technology or other areas of production and manufacturing – is now seen as essential to not only offset the impact of the global pandemic but stay competitive and sustainable long after the current health crisis has subsided. Technological advancements, especially machine learning and other powerful software tools, combined with developments in nanotechnology, monitoring, and global communication networks will accelerate a profound change that will permeate all aspects of business and manufacturing.
Since major disruptions and market discontinuities occur on a regular basis (every 7 to 10 years is regular enough for this definition), understanding that these opportunities will arise and to be clearheaded about how to best take advantage of them, invest in the long-term, and capture disproportionate returns should be the rule – not the exception. The world may seem riskier, but risk-adjusted returns are much more favorable. Market modulation will interrupt rational pricing. We are having a moment of extreme downward pricing pressure on assets that are perceived as riskier, and upward pressure on prices for safer assets. This can be easily represented by the pricing differential between government securities and lower investment grade fixed income securities. One security has rallied substantially, while spreads between government securities and high-yield debt have widened dramatically.
Distributed learning can enable machine learning for health care. With its unique privacy approach, it can very effectively overcome the greatest obstacle facing AI adoption in health care today. We no longer need to choose between patient privacy and the utility of the data to society. We can now achieve privacy and utility simultaneously.
A new technique, inspired by quantum cryptography, allows large medical databases to be tapped for causal links. This is a fundamental breakthrough in thinking, and this perspective has the potential to spot cause-and-effect, supercharge medical diagnoses, and use AI effectively.