How AI is changing content creation

There are a number of ways AI is changing the content creation landscape. Perhaps the most obvious way is in terms of cost and efficiency. Thanks to natural language processing (NLP) algorithms, businesses can now automatically generate blog posts, articles, social media posts, and even video scripts with little to no human involvement. This not only saves businesses money on content creation costs, but it also allows them to generate a larger volume of content in a shorter period of time.

Another way AI is changing content creation is in terms of quality. Thanks to advances in machine learning, AI-powered content generation tools are getting better at understanding context and generating more nuanced and insightful content as a result. For instance, OpenAI's GPT-3 algorithm is now able to generate realistic sounding poems and even convincing legal documents. This means that businesses no longer have to rely on human experts to generate high-quality content; they can now let AI do it for them.

AI is also changing how we index and search for content. Thanks to predictive analytics, businesses can now use AI to automatically surface the most relevant and interesting content for their audience. This not only saves users time, but it also helps businesses ensure that their audience always has access to the latest and greatest information.

What are the implications of AI-generated content?

There are a number of potential implications of the shift towards AI-generated content. Perhaps the most obvious one is that traditional content creators—such as writers, journalist, and photographers—could see their jobs replaced by machines. While this may sound like a dystopian nightmare to some, it's important to remember that this transition will likely take place over many years and that there will still be a need for humans to create higher-level conceptual pieces or manage complex projects.

Another potential implication of AI-generated content is that we could see an increase in low-quality or "clickbait" style content as businesses seek to capitalize on the fact that user attention span online is notoriously short. While this may be bad for users, it's important to remember that thanks to predictive analytics, there will also be an increasing number of tools available to help surface only the most relevant and interesting content for each individual user.

While there are still many unanswered questions about how artificial intelligence will change content creation, it's clear that the impact will be significant. As AI continues to evolve, content creators must keep key considerations in mind in order to stay ahead of the curve.By doing so, they'll be able to take advantage of all that machine learning has to offer while avoiding any potential pitfalls along the way.

1. How unique will the AI-generated content be?

In a world where AI is generating more and more content, it's important to consider just how unique that content will actually be. After all, if two pieces of content are indistinguishable from one another, then there's little incentive for readers to consume both. In order to stand out from the crowd, then, it's important to make sure that AI-generated content is truly original.

2. How insightful will the AI-generated content be?

As we all know, not all information is created equal. When it comes to consuming content, readers are primarily interested in pieces that offer new insights or perspectives on familiar topics. That's why, when evaluating AI-generated content, it's important to consider just how insightful the piece actually is. Is it merely regurgitating facts that can easily be found elsewhere? Or does it offer a fresh perspective that will leave readers wanting more?

3. How will indexing and search engines treat AI-generated content?

In addition to human readers, it's also important to consider how search engines will treat AI-generated content. After all, if a piece of content isn't properly indexed or ranked highly in search results, then it might as well not exist at all. Fortunately, most major search engines have announced that they're taking steps to ensure that AI-generated content will be properly indexed and ranked. However, it's still early days and things could always change further down the line.

4. What second-order effects will arise from AI-generated content?

Last but not least, it's important to consider the potential second-order effects of large-scale AI-generated content production. For example, what happens when copyright infringement becomes rampant? What legal protections will exist for authors of AI-generated works? And how will society react when confronted with seemingly endless streams of automated content? These are all questions that remain unanswered for now but will need to be addressed eventually. As more businesses begin using AI to generate marketing material or social media posts, there could be an increase in fraudulent or misleading information as unscrupulous actors seek to take advantage of gullible users. However, it's also possible that AI-generated content could help reduce fake news by providing users with easy access to accurate information from multiple reliable sources.

AI is changing the landscape of content creation in a number of ways—from reducing costs and increasing efficiency to improving quality and accuracy. While there are some potential downsides—such as job loss for traditional content creators—the overall impact is likely to be positive as businesses and consumers alike benefit from having access to better quality information more quickly and easily than ever before.

Surprise! This was was written by AI

With a total cumulative setup time of less than 2 minutes, this entire piece was written by AI. About 1,000 words and a unique image in less than the time than it takes to make a cup of coffee. It would have taken less time however I tweaked some of the inputs to obtain a higher quality of content. The image was generated using Open AI's DALL-E with the prompt: "a steampunk robot at a desk writing on a typewriter with a feather in the hat, digital art"

Using AI to generate content used to take a long longer - requiring an understanding of software tools, loading code libraries, and training models. The rise of SaaS platforms with simplified user interfaces is bringing AI content creation to the masses.

Disclaimer: Opinions are my own and not the views of my employer. Actually, the AI's opinions do not even reflect mine.

Maybe the media and government communication are to blame. Maybe more people are trying to gain the relevant knowledge because macroeconomics is increasingly affecting their lives on a daily basis - asset prices, inflation etc. That is certainly commendable but consider this a wake-up call if you didn't know it already - Macroeconomics is a social science and it is a complicated one.

It's complicated

Macroeconomics is intertwined with human behavior and follows very different processes in its development than that of regular science and mathematics. The most critical will say that the more you study macroeconomics the more you realize it is currently a mash of voodoo complex systems and political narratives. Unfortunately, I tend to agree here. They will also say that today's macroeconomics is a form of cargo cult built on pseudo mathematics and political science to the point that it is "corrupted". Macroeconomic is not scientific because critical variables aren’t measurable, complexity is impossible to simplify, and predictions aren’t falsifiable.

"If You Spend 13 Minutes a Year on Economics, You've Wasted 10 Minutes"
- Peter Lynch

In the past, I've written articles to share more about mainstream macroeconomics because it can help tangentially with investment decisions. Basically making sure you "Don't fight the Fed" and can clearly see through the Overton window. I stopped posting these types of articles because there are an increasing number of resources available outside what I have to say - which is good, I think. If you read anything I've written on these topics, they all still hold up. Outside of thinking about "how does this affect my portfolio?" - it still remains that discourse around macroeconomics needs some serious improvement to avoid the problems with the status quo.

Macroeconomics ≠ Microeconomics

“Microeconomics is what you do, macroeconomics is what you put up with”
- Charlie Munger

An important distinction to make here is that macroeconomics should not be confused with microeconomics. Microeconomics and game theory are fundamental. I don’t think you can be successful in business or even navigating through most of our modern capital society without an adequate understanding of supply and demand, labor versus capital and game theory and tit for tat and those kinds of things.

Discourse

None of the real discourse around advancement in macroeconomics is in the public eye nor does the majority even know that the current macroeconomic thinking is not the only way forward.

Let me give an example of a statement I've heard repeatedly recently:

"Well the only thing they can do is raise interest rates because inflation is out of control. They need to force a recession."

The main problem with this claim is that there is almost a complacent, deterministic view under the current frameworks. It is in fact not "the only thing they can do." In actuality there are other factors to consider including that inflation is not measured accurately, market participants set prices (it is not all the invisible hand), there are other ways to reduce inflation etc. Also, how did we get to this point? Was the reflationary policies during the Covid-19 downturn too broad? Historically, raising interest rates has had success in reducing inflation so a question should be - do we have similar conditions to the 1970s?

Let's take another look at a statement from someone with a deeper understanding:

"This weird mix of monetarism and MMT is causing too much short-term volatility and government manipulation."

This claim is slightly better but amounts to complaining that the current framework. The question becomes, what is a better solution?

"You never have the counterexample on the economy. You can never take the US economy and run two different experiments at the same time. Because there’s so much data, people kind of cherry-pick for whatever political narrative they’re trying to push. To the extent that people spend all their time watching the macroeconomy or the fed forecasts or which way the stocks are going to go the next year, is it going to be a good year or bad year, that’s all junk. It’s no better than astrology. In fact, it’s probably even worse because it’s less entertaining. It’s just more stress-inducing. I think of macroeconomics as a junk science. All apologies to macroeconomists." - Naval Ravikant

Where are the economists?

In the past century, we have had the rise of macroeconomics as serious field of study and some prominent and important economists - Keynes and Hayek through the 1930s leading up to the latter half of the century with Friedman, Robert Mundell, Samuelson etc. While I may not agree with Friedman on many points of view, I do enjoy how he creates an environment for discussing and (vigorously) defending his views. (YouTube - "Milton Friedman - Tyranny of the Status Quo - Beneficiaries w/ David Brook")

Where can we explore competing schools of thought today?

• Podcasts - This type of discourse is currently available in podcasts but the most popular podcasts are those which are most entertaining, not those with the most serious and credentialed participants. Podcasts are also dismissed by mainstream media as inconsequential to the public discussion except for when it agrees with the popular opinion.  
• Conferences and Interviews - The next best sources of discourse are conferences and interviews but where are the prominent economists with platforms to encourage public debate? Today it seems like the frontrunners would be Piketty, Krugman, Stiglitz etc. and it seems that their platforms are publications and panels like the one shared here (Youtube - "Thomas Piketty, Paul Krugman and Joseph Stiglitz: The Genius of Economics")

If we are to be more informed, it would seem natural that leading economists make better use of their platforms to promote discussion and critical thinking. Up until the 1980s, the prevailing macroeconomic school of thought was more fluid. Governments and policy makers experimented. Governments explained their plans to the populations with (just a little) more clarity than today. There was also more debate around which macroeconomic school to pursue where little choice is presented today.

Example: The Phillips Curve

Perhaps the most famous example where developing policies on mainstream economics has led to poor outcomes is the Phillips Curve. It is also the most relevant example today given the inflation that we are experiencing in 2022.

The Phillips Curve goes back to 1958 when Bill Phillips a former crocodile hunter from New Zealand and academic at the London School of Economics published an article titled "The Relation between Unemployment and the Rate of Change of Money Wage Rates in the United Kingdom, 1861-1957"

Looking at UK data between 1861 and 1957 it found an inverse relationship between the rate wages increased and the level of unemployment. Higher wage increases were associated with lower unemployment and a lower rate of wage increases were associated with higher unemployment - as increases in wages over this period were closely related to general price rises.

Ultimately what made the Phillips Curve so powerful and influential was that it came along at precisely the right time. Macroeconomists were developing new theories and models about how the economy worked and the evidence that Bill Phillips provided justified their theoretical musings. This was the heyday of Keynesianism in the 1950s when politicians encouraged by economists believe they could control the economy with monetary and fiscal policy. The Phillips Curve showed that the policy objectives of inflation and unemployment were in conflict or put another way - they had to make a choice low inflation or low unemployment.

By the 1970s though this relationship had broken down. Stagnant demand was causing high unemployment and at the same time there was high inflation - something the Phillips Curve did not foresee or predict. This combination of both high unemployment and high inflation became known as "stagflation." If the 1970s was about high inflation and high unemployment then by the mid 1990s the world was changing again - we started to see prolonged periods of low inflation and low unemployment. Further evidence that the relationship that Bill Phillips had spotted no longer existed.

So you may ask why do students still learn about the Phillips Curve - what's the point? Well it does remind us that although unemployment and inflation are related to the level of demand for goods and services in an economy there are many other factors that have a significant influence on both the rate of inflation and the level of unemployment.

Simplifications in economics are dangerous and rarely remain true forever. The Phillips Curve is nothing more than a loose statistical relationship on historical data. After all economics is a complicated social science it studies people and people can be unreliable and unpredictable.

Removing your delusion

Not everyone can devote a lot of time to macroeconomics, nor do I even recommend doing that. What is clear that we need to at least admit that mainstream macroeconomics is not the "only way" and promote public discourse and debate to hold leaders accountable. Professionals should be working to improving underlying data and rigor in macroeconomic research. In my opinion the latter won't happen without the former.

Engaging in conversation with at least these background ideas as starting points can help widen your view.

Disclaimer: This article is opinion and is for information purposes only.  It is not intended to be and should not be considered as investment advice.  Seek a duly licensed professional for investment advice. Opinions are my own and not the views of my employer.

“To understand the way financial assets are valued we must realize that, in essence, what is being traded are stories – competing imaginative accounts about the news and its impact on an uncertain future”
- David Tuckett, University College London

Admittedly, this is a rather obscure topic and focuses mostly on the psychology of investing. I will share my approach and tools that may help you think critically about your future investment decisions - especially when you are relying on the opinions of others.

Causal hypotheses

To get started you must first appreciate that most investing narratives take the form of causal hypotheses. A causal hypothesis is one that suggests that a cause-and-effect relationship exists between variables. A simple example would be "raising gas prices causes an increase in the number of people who carpool to work." On the surface it sounds like a reasonable cause-and-effect relationship but drivers may actually be insensitive to the gas prices because they value comfort, convenience and privacy more. While usually intuitive and logical, the causal relationship is not always accurate or predictive.

It is important to be aware of two serious problems that arise when using causal hypotheses in investing. One is that very little effort is made to seriously test the hypotheses. An even bigger problem is that people do not develop their own hypotheses - they act on those heard on the news, from friends, family or even strangers. It is important to be conscious of investing ideas that are not your own.

“Interest rates are low, so stocks only go up...”

“Inflation is coming because the fed is printing money...”

“The Yuan is the next global reserve currency because China is growing fast...”

Do any of the above investment narratives sound familiar to you? Do you believe any of them to be true and if so do you have any independent analysis to prove that they may be true?

Post-truth is not new

As of late, it has been fashionable to claim that we live in a “post-truth” era where the focus on “shared fictions” is a problem in society - the idea that objective facts are less influential in shaping public opinion than appeals to emotion and personal belief.

There is also the prevailing notion that this “post-truth” is a new phenomenon brought on by the internet - social media websites amplifying opinions and spreading them at a rate and in a form that has never been experienced before.“Post-truth” was even named word-of-the-year  in 2016 by Oxford Dictionary.

The word post-truth may be newer, but the core ideas behind it are not.

Philosophers and historians have long debated whether shared beliefs have helped or hindered humans as they cooperate and conquer the planet.^[1]  I’ve listed works below by prominent thinkers from over 100 years ago which readily prove that humans have lived in the post-truth long before the internet. (All of the works are freely accessible thanks to Project Gutenberg and I have linked each title.)

1. “The Will to Believe" (1896) by William James
2. “The Will to Doubt” (1907) by Alfred H. Lloyd built upon by Bertrand Russell in “Free Thought and Official Propaganda” (1922)

With this idea that post-truth is nothing new, it has a few loose synonyms going back further than 2016. One of these is “propaganda” however using that word openly is a faux-pas - it has an accusatory tone. It implies the information is fictional, self-serving and that someone or some institution is responsible for its purposeful distribution and mode of replication.

Instead, I regularly use the word “narrative,” which is a less egregious synonym. “Narrative” does not imply some devious third party and can arise innocently or spontaneously through word of mouth or some other method giving rise to its spread.

If post-truth has existed for so long and in so many parts of society, it seems reasonable that investing is not immune. What’s important to understand is that a narrative is just a story or an idea that can, but will not always be supported by rigorous analysis.

Memetic nature

I believe investment narratives primarily spread through memes. A meme acts as a unit for carrying cultural ideas, symbols, or practices, that can be transmitted from one mind to another through writing, speech, gestures, rituals, or other imitable phenomena with a mimicked theme.^[2] Put simply, a meme is a sticky idea that replicates and spreads.

In David Deutsch’s eccentric book “Beginning of Infinity”, he presents his two modes of meme replication, “rational” and “anti-rational”. While I am critical of many of Deutsch’s ideas, I have profound respect for his work in philosophy and quantum computing. I also find separating investment narratives by these two modes of replication to be a practical exercise that is incredibly useful.

Both rational and anti-rational memes are sticky but achieve their stickiness through opposite methods.

Anti-rational memes achieve their stickiness by depriving the holder of the ability to criticize them - an entrenched authority diverts away criticism. These memes thrive by disabling the capacities of their hosts (by means of fear, or an anxiety to conform, or the appearance of naturalness and inevitability, or in any number of other ways) to evaluate the truth. Because it is a meme, it also compels the holder to spread the meme to others. An important clarification here is that an anti-rational meme is not an idea to be considered harmful. Ie. an anti-rational memes are not inherently “bad”.

A rational meme is the opposite - it thrives in a critical environment because it is true and useful. it is one that survives criticism, not disables criticism. A rational meme reproduced faithfully because it is being criticized. The criticism itself is what makes the meme sticky.

The optimist in me likes to believe that an investment narrative that spreads as rational meme leads to a more successful investment in the long-run.

Three tests of investing narratives

Over time, I have developed a basic framework for identifying and evaluating investment narratives. I’ve simplified my framework into a series of tests as follows:

1. The “Tupperware Party” Test
2. The “Smart-speak” Test
3. The “Call to Arms” Test
   

1. The “Tupperware Party” test

The first test focuses on whether the one sharing the narrative stands to gain. I call this test the Tupperware test because we are often sold investing narratives in social interactions. Are you being sold the idea?

Tupperware is a home products line that includes mostly plastic storage products for the kitchen and home. Hosts and hostesses of Tupperware parties were rewarded with free products based on the level of sales made at their party.  They rose in popularity during the 1950s and parties took place in workplaces, schools, and other community groups. They of course were subsequently criticized as multi-level marketing programs amounting to a pyramid scheme.

This test is most important because the communication and spread of investment narratives are near impossible to regulate. Even if a disclaimer is shared ex. "this is not investment advice", the information that follows can lead an individual to generate an implied causal hypothesis.

When investment professionals or portfolio managers freely share information, it is wise to always assume they are "talking their book". Are they sharing the information because they genuinely believe in their analysis or are they sharing it to convince you to buy an investment they already own ie. already have in their "book". If they already own the investment, they stand to profit if you also buy it - demand increases and therefore price increases. Usually, really good investment ideas are proprietary and are not freely disseminated.

Tupperware is largely a fun and harmless exchange but some narratives are not.

3. The “Smart-speak” Test

Is the narrative understood, or does it just sound “smart”? Can you tell if someone is sharing an original idea or analysis of an existing idea? As previously explained, if investments are spread through memes, there will be individuals who spread ideas without understanding them. I genuinely don't have an explanation for this phenomenon but it happens often. Maybe individuals share investment narratives to carry conversations or to impress their colleagues - I am not sure.

View this post on Instagram

A post shared by Series Ayy lmao (@venturekaepital)

If the narrative is not within your domain, get a second opinion from an expert and get it explained to you simply. If you still don’t understand it, try to refrain from having a strong opinion on the matter or if you must, have an opinion that is very loosely held.

3. The “Call to Arms” Test

Does the narrative appeal to emotions? Is it presented in a way to incite a strong positive or negative reaction? This test also attempts to identify ant-rational memes - does an entrenched authority divert away criticism from the narrative? Anti-rational memes around investments are commonly perpetuated by large institutions for profits or governments to sustain macroeconomic goals. While they may be beneficial in aggregate and at scale, they may not be the best investment decision for you as an individual.

Putting it all together

All three tests lead to critically thinking about investing narratives. You may be seeing some red flags if someone stands to gain, doesn’t understand the idea they are perpetuating, or is trying to appeal to emotions from a position of authority.

After identifying a narrative, it is important to build your own thesis and test against it - similar to the “the scientific method”. The only difference is that the nature of investment narratives make them difficult to experiment on.

While I am a proponent of this approach, proving causality can be both (a) impractical, especially without sufficient data, and (b) time consuming. Investments usually involve large sums of money and your livelihood so striking a balance is key.

The goal is to determine whether the narrative is likely fiction. A nuanced point here is that the onus is on you to prove that the narrative is false. Seek disconfirming evidence. Before proceeding, you must see the validity in both sides or you will struggle to work with narratives.

“I never allow myself to have an opinion on anything that I don’t know the other side’s argument better than they do.”
- Charlie Munger

One added benefit that I’ve found using this method is that I am more confident with any investment decisions based on my thesis. If I have proven or disproven a narrative with my own research, I am less likely to change my mind based on anything other than my thesis being disproven. I find "panic selling" to be a less common occurrence leading to a strong portfolio in the long term.

Some caveats

I have proposed a framework for identifying investment narratives and assessing whether the underlying facts are strong enough to rely on.  The question that often arises: "Is investing is weak narratives a bad idea?" I feel the need to include a few caveats here because the answer is complicated and "it depends".

1. Riding waves

I am not suggesting that strong narratives be avoided, I am sharing why proving causality should be part of your process.

I would like to stress here that underlying facts just need to be “strong enough” based on your risk tolerance and the above noted factors (ie. practicality and time). In the end however, the wave can be so strong that the underlying facts don’t matter on the path to success. Investments with this characteristic are usually risky in nature - if they catch you off-guard it is because you did not assess the risks correctly upfront.

While it is possible to follow a narrative blindly, follow correlation or “ride the wave,” it then becomes a matter of timing and whether you can exit positions before any narrative shifts. I have noticed empirically that weak narratives are more likely to shift dramatically.  

2. Market microstructure and self-fulfilling prophecies

Narratives are not always factual just because some arbitrarily accepted metric for success is true. Most often, society associates over-bought assets as being a success because of price increases. A simple example is “Bitcoin is a success because the current price of an individual bitcoin is high”. Unfortunately, this is a fallacy.

Sometime narratives make the switch from being based in fiction to reality simply through the mechanisms of self-fulfilling prophecies. A potential example today is the ever-prevalent inflation narrative.

Disclaimer: This article is opinion and is for information purposes only.  It is not intended to be and should not be considered as investment advice.  Seek a duly licensed professional for investment advice. Opinions are my own and not the views of my employer.

Footnotes
[1] Yuval Noah Harari. Are we living in a post-truth era? Yes, but that’s because we’re a post-truth species. ideas.ted.com. 2018
[2] Graham, Gordon. Genes: a philosophical inquiry, New York: Routledge, p. 196, ISBN 978-0-415-25257-7. 2002

The public health officials are not entirely at fault here because although the datasets are helpful, they are not perfect. In an attempt to avoid any privacy concerns and protect commercial interests, both Apple and Google have gone to great lengths to obfuscate the data making it less useful.

While many parts of the United States and Canada are starting to get COVID-19 case numbers under control, my home of Ontario, Canada (with over 14 million people) is struggling. I will provide an example where the newly adopted mobility data analyzed by my local government is not clear. I will also share this with them in the hopes that they improve their analysis and communication going forward.

While reading this article, please know that I greatly appreciate the efforts of all of the hardworking people involved. The analysts, professionals, doctors, nurses, and front-line works are there to help keep us safe. Please follow the information, resources and advice from your local public health agency. I have followed and will continue to follow all instructions provided and I hope that you do the same.

The Updated Models

My local government has been providing regular updates around how they set policies related to COVID-19. As a citizen, I appreciate this disclosure and transparency. One of these updates was presented publicly yesterday by my local “COVID-19 Science Advisory Table” and included their updated modeling.

The Apple and Google data was used to explain the cause of the increase in COVID-19 cases and hospitalizations. Accompanying this presentation, was a declaration of state-of-emergency and stay-at-home order.

Here is the excerpt from the presentation on mobility data:

Problems with this analysis

Both Apple and Google provide raw mobility data therefore I have been able to attempt to recreate the charts provided. (The public health official did not state that data came from any other sources.) In doing so, I have noticed two main areas of concern:

1. Not understanding the “baseline” and not normalizing the data effectively

Instead of providing total number of visitors to each location category over time, Apple and Google normalize the data against a "baseline."  While Apple's data is less detailed, both Apple and Google provide an explanation of what that baseline is. To summarize it here for you, the baseline data is effectively from January 2020. This means that all of the raw data at each date is a percentage difference from January 2020.

Google provides clear guidance on how to read this data, and what should and shouldn’t be inferred from it.  Google even has a checklist called "Calibrate your region" which it is clear that this analysis did not follow.

"These baseline days also don't account for seasonality. For example, visitors to parks typically increase as the weather improves."

In January, the weather in my area is harsh and it is the coldest month of the year. An average of -5.5°C (22.1°F). People are generally outdoors and visiting parks less often. Comparing mobility data for March and April against a baseline of January without normalization does not seem reasonable. Also, parks have repeatedly been communicated as a safe place to travel outdoors. Should this mobility data be shown separately or have less of an influence? The public health official is not clear on this.

When factoring in the baseline, it may have been useful to further normalize the data for weather, holidays or even weighted by most populous regions. Maybe sprinkle in proprietary and more accurate data sources. At minimum, the public health officials should have stated the assumptions on the slides. To further compound the confusion, the y-axis is not well-defined (% of what?) and the width of the x-axis has a suspiciously short width.

2. The old correlation vs causation problem

It was repeatedly stated that the "public health measures" were leading to reduction in mobility at certain times in history. While it is obvious that reducing mobility and in-person interaction reduces the spread of the virus, I am not sure that the public health measures can take full or even most of the credit for reducing mobility during certain time periods.

The mobility drop-offs in December, February and April seem to be more likely caused by statutory holidays than public health measures. You can also see this is further corroborated by the government's chart for "Workplace" mobility. Citizens would more likely be in residential locations regardless of public health measures because most public locations are closed.

I am also not sure what the "mobility threshold" is and how it was determined. We are told it is the level of mobility that will lead to a reduction in virus spread but where does it come from? The threshold strongly correlates with the lowest new case numbers and lowest effective reproduction number (R_e) over the past six months. If this is true then I think there is too much reliance on this correlation. It will be interesting to see if the mobility threshold is hit in the future and whether that results in R_e below 1.00.

Data driving decisions

While the data and analysis from advisory groups inform decisions, it seems the policy-makers have the final say. One member of the local advisory group, professor Dr. David Fisman expressed his frustration with certain decisions. He said “The reason it’s so frustrating to hear stuff like this is [leadership]’s got the science absolutely upside-down,” and called the decision to restrict outdoor activities “absolute madness”.

Maybe if the data was better analyzed and presented, the policy-makers would make better decisions. It is also important to note that I have come to similar conclusions - there is an increase in mobility since January 2021. We should all aim to reduce this and do our part. While the conclusions are similar, I am disappointed by the level of analysis shared (and also by the local restrictions placed on outdoor activities).

I tend to have a Game-Theoretic perspective on the spread of COVID-19. Regardless of stricter public health measures, the few who don't follow the general guidelines will contribute to the spread and make it continuously difficult for the majority to endure. Fortunately, vaccine rollouts are well underway.

Disclaimer: The opinions presented are my own and do not reflect those of my partners or my employer.

To briefly summarize, there were three key take-aways from my original article:

1. Skepticism of investment experts should be at an all time high. I suggested looking at the effects of the pandemic as well as economic indicators then estimating the response from both policy makers and markets.
2. Hedging and diversification is key. Timing the market is a bad idea but attempting to separate pandemic winner/loser stocks would give you an advantage.
3. Lookout for new valuation methods. I was very interested to see how the market comes to a consensus on how to value financial assets and the impact policy makers would have.

Again, I think all of these still apply. However I do want to elaborate on the third point - specifically as it relates to government stimulus and financial asset values.

Reflation

"Reflation" (not to be confused with "inflation") is the growth of an economy by government stimulus, using either fiscal and/or monetary policy.

I previously explained the basics of these policies back in April. Without going into the details again, it is important to understand the intended outcome is always increased economic growth. Many struggle to see how execution of these policies leads to growth because it is not a direct cause-and-effect relationship. Policies may lead to spurred consumer spending or higher employment which then leads to growth. There are leading indicators for growth like interest rates, yield curves and financial asset prices but there are also as lagging indicators like GDP, inflation and employment numbers.

I also mentioned that these policies were and are continuing to be used at unprecedented levels - especially monetary policy. Building on this, it is useful to take an ongoing look at how these policies are affecting the investing environment.

Central banks

Traditionally, central banks put money on deposit. Banks come along and then borrow that money and lend it to those who they expect will pay it back. That money then passes through the credit system - all financial assets compete with each other and it spreads out. Central banks monitor this environment and then slightly loosen or tighten as needed.

Today, central banks are much more involved in trying to "jumpstart" the economy as opposed to gently tending to it. The purchases of financial assets and government securities by central banks (especially in the US) are the drivers of the market right now. These policy decisions are executed in coordination with central governments in attempt to increase productivity in the longer run.

The Economic pie

It can be argued that reflation from monetary policy in the short term is not genuine growth. It may provide the "jumpstart" but wealth cannot be created from producing debt and printing money alone. There needs to be a real increase in productivity leading to a bigger economic pie. Capitalism is great at increasing the size of the pie (artificially or otherwise) and dividing it efficiently but it's profit-seeking nature can make it a poor long-term divider of that pie.

The growth rate has always been higher for capital than it has been for wages. Similarly, there has also always been wealth gaps stemming from this. The interesting part is that you will not find a single economist that argues stronger growth comes from an environment with extreme wealth gaps.

Why is this relevant now?

This reflation environment can be confusing especially if you follow mainstream media. Having this broad understanding can help you make better investment decisions because many traditional views of markets are skewed or no longer apply right now. This information also serves as a warning that some of these policies are not sustainable or can have negative effects in the long term.

High equity prices (according to traditional metrics) seem to be a sign that the market is disconnected from reality and excessively driven by monetary policy. Equity indices such as the S&P500 are reaching all-time highs yet macroeconomic indicators are still weak - improving, but still weak. While it can be concerning, there is support and some possible explanations for these price levels. You will here people say equities are "overvalued" and I am here to tell you they are not. What's great about markets today is that they are very liquid. Unless you are in the middle of a panic, getting out of your positions is really easy before any unlikely correction.

Real economy vs Financial economy

It is important to understand that there's a real economy and then there's the financial economy. The real economy has the supply and demand of goods and services. The financial economy that has its supply and demand of money and credit. Using some basic theory, the price of a financial asset will equal the risk free return (which everyone knows is something close to zero) and a risk premium.

Risk free rates and risk premiums

We could start by looking at the risk free rate which represents the percentage that investors are getting for a risk-free return. Normally, this is a treasury bond with a rate of between 0-1% (depending on the country) or a high quality corporate bond at slightly more than 1%.

What will the premium be for equities then? The central bank has the capacity to put money in the system and the money competes for getting returns. The risk premium or the expected excess return for equities over cash could go from 4% to let's say 2%. Traditional equity valuations such as a PE ratio are just the inverse of that. By way of this example, you could make the PE go from 20-25 times up to 40-50 times.

Assuming a range of risk premiums we can easily create a discounted cash flow model that supports the S&P500 at its current levels. That may seem implausible but it is no less implausible than zero interest rates to begin with. The risk premium will be driven by the amount of liquidity put into the system and therefore traditional metrics such as multiples become a less useful frame of reference.

The capital markets drive the economy (and the risk premiums) more than the real economy drives the capital markets.  

Market microstructure

Looking at risk premiums is helpful but there are alternative and simpler explanations as to why prices of equities may increase in the absence of underlying performance. Market microstructure is the study of financial markets and how they operate. It has a fancy name but its just the study of how buying and selling on a market fundamentally works.

When more liquidity exists and capital flows to buy a particular financial asset - the buy volume increases. More buy volume usually increases prices. It is really that simple and can have nothing to do with whether a company is actually performing well or not.

An "order book" is the list of orders that an exchange uses to record the interest of buyers and sellers in a particular financial instrument. A matching engine uses the book to determine which orders can be fully or partially executed. It connects the buyers and sellers on the exchange.

Below is a visualization of an order book depth chart of a low volatility financial asset. The x-axis is the unit price, the y-axis is cumulative order depth. Bids (buyers) are on the left in green, asks (sellers) are on the right in red. The space between the buyers and the sellers is called the "spread" and the middle point is the quoted price.

The supply and demand for a particular stock and the mechanisms for trading can therefore affect the price. In the above example, you don't know which stock is being traded but you know that the price will increase if more buyers want it and are willing to slightly increase their bid to get it.

How far are central banks willing to go?

The consensus is that banks are willing to go and need to go as far as it takes in order to keep the system afloat. The alternative is a sinking ship. In absolute terms, this is the furthest monetary policy has ever been deployed. Because we're in this late stage, there is a lot of debt and central bank balance sheets are expanding.

Realistically, there are limits and they are extremely complex because they weave through the entire structure of society. Without attempting to identify the exact metrics, it seems to a be a mixture of maintaining the following (in order of importance)

1. certain economic measures such as inflation to avoid devaluing currency
2. political and geopolitical stability to avoid conflict
3. social order and confidence to avoid civil unrest

What if equity prices declined in this environment or the economy does not recover in the longer term? Even the most prominent economists don't know exactly but it will likely be creative and unlike anything we've seen before. In a post-COVID environment, I'm hoping that stronger fiscal policy is experimented with a little bit more.

I've had a running joke over the past few months which involves me throwing macroeconomics books in the trash. Many need to be rewritten to factor in the past decade. While it may be interesting to study, this is the world we live in right now and this short period can have a lasting effect on our financial futures.

Disclaimer: This article is opinion and is for information purposes only.  It is not intended to be and should not be considered as investment advice.  Seek a duly licensed professional for investment advice. Opinions are my own and not the views of my employer.

I want to recall the "origin stories" of a few consumer technology companies in their early days - specifically around their early users. Taking a look at how these users were acquired by those companies and in some cases forgotten over the years.

Everyone knows Amazon started with books and eBay focused first on collectables but there are a few that may surprise you.

Starting small

In the product adoption curve, "early adopters" and "early majority" form the basis on which tech companies are able to experience exponential growth. "Crossing the Chasm" was coined in the 1991 book by Geoffrey A Moore and identified the "chasm" as a rift between the enthusiast and pragmatist users of a product - which can have very different expectations. There are also some great lessons to gather here about how to successfully cross the chasm including product positioning, marketing, distribution, pricing etc.

"Counter-intuitively, the best way to get big in the future is to start small now in the area where you can dominate the most quickly. Look at any startup that achieved scale, the overwhelming odds are that it dominated a niche before going to the broader opportunity."
- Geoffrey Moore

There are some companies that stick to serving their core audience and it works as that audience grows. Though as certain companies reach critical mass they can focus less on their early users or even abandon them altogether to appeal to a mass market.

Jumping the Chasm

There are a few technology companies where the stories of their jumps from the "early market" to the "mainstream market" are not widely known. This could be in part because the companies look very different today or because their real origin stories don't make for the best PR narrative anymore. While I am not internet historian, I lived through these jumps and want to share a few of my favorites:

1. Instagram
2. Whatsapp
3. Paypal
4. Bitcoin
5. Gmail
6. Youtube
7. Google Android

1. Instagram - Sharing on Twitter and “Filters”

Within days of Instagram's release in October 2010, everyone's social networking feeds were inundated with shared photos from the app. Each shared photo was accompanied by a clean link to install Instagram - effectively acting as an advertisement to install the app. This proved to be an incredible growth hack that led to exponential user adoption. It spread through Twitter, Tumblr, and Facebook like wildfire.

At the time, Twitter did not have a photo uploading feature and Instagram filled that void perfectly. Instagram's astronomical growth was hinged to being the de-facto one-off photo sharing app for other social media websites. There were a few that made this connection while it was happening but it took a few years until people caught on.

In the middle of 2011, Twitter released photo uploading/sharing to users as a direct response to Instagram and other 3rd party services (e.g. TwitPic) popularity on the platform.

In Spring 2012, Instagram sold to Facebook for $1 Billion in cash and Facebook stock. Twitter co-founder Jack Dorsey was notably soured by the sale. Dorsey built a relationship with Instagram's team as they were growing and it was obvious to him where their growth came from. Widely considered a move of retaliation, Twitter actually disabled API access for Instagram and removed the image preview card within the app. This was part of Twitter's increasing hostility towards its developer community.

Filters

Instagram also succeeded in providing one click programmatic photo editing called "filters". Around the time Instagram was becoming popular, the iPhone 4/4S was the flagship iPhone featuring a 5 MP rear camera. This was a leap for its time but taking quality photos was still reserved for professionals with professional equipment (SLR cameras).

In 2011, to properly edit a photo from a professional camera took me about 5-20 minutes in Adobe Lightroom (which is still the software I use today). Editing photos with a mobile device required transferring the photo to a computer, using the editing software and then uploading to a photo storage and sharing service like "Flickr".

Instagram got around this workflow for amateur users by offering a range of pre-made "filters" that would automatically stylize a photo with one click. Based on observation there were four possible steps to the algorithm:

1. Color/Contrast - Perform an independent RGB color transformation on the original image pixel by pixel to increase contrast or make color cast.
2. Vignetting - Optional overlay a circle back-group png image.
3. Graining - Optional overlay a background png image as decorative texture.
4. Frame - Optional addition of a border or frame.

Easy editing of photos in addition to the sharing features was the primary usage of Instagram and is still part of the DNA in what makes the service popular today.

2. Whatsapp - Avoiding SMS charges

SMS (short message service) is a text messaging service component of most telephone, Internet, and mobile device systems. It uses standardized communication protocols to enable mobile devices to exchange short text messages.

When Whatsapp was released around 2009, over five trillion SMS text messages were sent earning over $100 billion globally annually. Also at that time, data plans for mobile phones were available but not everyone had one. I know - it's hard to imagine.

When data plans became more affordable and widespread there was a point at which paying for SMS texts just didn't make sense - it became cheaper to send messages using the data plan.  WhatsApp took a bite out of the SMS market by offering a cheaper solution using an entirely different infrastructure. The app allowed users to send a near infinite number of messages with no overage costs. WhatsApp did to SMS what Skype did to international phone calls.

What also really helped Whatsapp grow consistently was it's approach to developing as cross-platform application. Whatsapp started with iPhone and BlackBerry then eventually released on Android and even Nokia phones. No user was left behind because of the device they owned.

Inviting other users and adding users from your phone's directory was also really, really easy. Those who remember the popularity of "Blackberry Messenger (BBM)" will have no trouble understanding how Whatsapp came to dominate mobile messaging.

3. PayPal - Free money

PayPal gave their first users $10 for free when they opened an account and then $10 for every referral.

Initially users just had to sign up, confirm their email address, and add a (unique, authorized) credit card. The money was simply added to their account. This was real money. Users could send it to someone else or withdraw it. So it was a real cost to PayPal. We must have spent tens of millions in signup and referral bonuses the first year.
- David O Sacks, original COO of PayPal

Social media didn't really exist when Paypal started so referrals happened almost entirely through emails, blogs and instant messaging. The bonuses were gradually phased out, first by reducing them to $5, then by adding more verification hoops (like bank account verification) so they became more difficult to get. Then they were eliminated altogether.

I didn’t expect PayPal’s growth rate to be what it was. And that actually created major problems... After the first month or so of the website being active, we had 100,000 customers
- Elon Musk, original CEO of PayPal

PayPal acquired 1 million users by March 2000 and 5 million by summer 2000. In addition to the referral program, the platform also became the primary choice for eBay.com auction payments.

PayPal went public in early 2002 and was eventually sold in October 2002 to eBay for $1.5 billion, in a highly touted "marriage" between digital auction and online payment services.

4. Bitcoin - Illegal activity

Bitcoin's killer feature was that it was both a digital and untraceable store of value which made it ripe for use alongside illegal activity.

Keep in mind that the Bitcoin today looks very little like it once did. When it was released, there was really only one wallet application and online exchanges did not exist ie. you could not exchange fiat currency for Bitcoin. To get Bitcoin you either had to mine it, exchange on forums/chat or engage in a shady transaction with a third party (meet up to exchange cash or pay for it online - sometimes even using eBay).

"Silk Road" was a digital black market platform that was popular for hosting money laundering activities and illegal drug transactions using Bitcoin. Launched in 2011, it was widely regarded as the first popular "darknet" market and was eventually shut down by the FBI in 2013.

Yes, I was an early adopter of Bitcoin and to answer your second question - No I am not rich as a result. Also worth noting that I did not use Bitcoin for any illegal activity. I don't want to get into much detail here but I am asked about it a lot:

When not using my computer I "donated" my idle computing resources to a project called "Folding at Home".  It was a distributed computing network that combined individual computing power for scientific research and it still operates today. Back then, computers took much longer to boot so naturally I left my desktop on continuously (it's a bad habit I carry to today). The famous story of "buying a pizza with Bitcoin" made its rounds in my online network in 2010 and I was intrigued and fascinated by the released Whitepaper. I started really mining bitcoin in early 2011 right after the code was released to mine with GPUs and I'll leave it at that for now.

5. Gmail - Exclusivity

When Gmail was released, Google was probably the coolest tech company around. There were no privacy concerns and it was not the advertising juggernaut we now think of it as. In early 2004 there were widespread rumors that Google was developing a new free email platform. People were excited and almost didn't believe it was real when Gmail went live on April 1, 2004. Once it became clear that Gmail was not an April Fools' joke, invitations became highly desired.

Google wanted to roll out Gmail to the public in beta phases because the company lacked the infrastructure to handle the influx (possibly millions) of new users. Although the limited rollout was born of necessity, it created an aura of exclusivity.

The exclusivity is what contributed to its publicity windfall. Georges Harik, who was responsible for most of Google's new products at the time, acknowledged this:

“Everyone wanted it even more. It was hailed as one of the best marketing decisions in tech history, but it was a little bit unintentional”

While Gmail was desirable because it was exclusive, it was also better than the available alternatives. From the start, Gmail had several significant advantages over other services like Microsoft's Hotmail. Gmail offered 1GB of free storage which was 500 times as much storage as Hotmail. Gmail also had much better search and spam blocking, and it worked like an app rather than an HTML web page.

Google eventually spun out Gmail into a complete web-based productivity suite with offerings like Google Drive and Google Docs while simultaneously scaling to appeal to companies for commercial use - again directly competing with Microsoft.

6. YouTube - Pirated content

Launched publicly in late 2005, Youtube clings to "user-generated content" as part of their origin story but that really isn't the whole story. While it is true that you could upload your own created video, the reality is that the source of the website's popularity was not user created videos.

To be clear, there were many original viral videos (ex. "Evolution of Dance") but for the first few years, the platform was dominated by illegally uploaded and professionally produced content. This included clips from tv shows or movies as well as music videos. You could often find full tv episodes or movies with very little effort.

In-video ads and ad partner programs didn't even ramp up until early 2007. At that time, Youtube was also struggling to implement anti-piracy software. They admitted quite openly that their process for identifying copyrighted material was not automated - receiving hundreds of thousands of DMCA take-down requests from media companies per month.

For users, editing videos and uploading was a painfully time-consuming process and for some it still is. Phones did not ship with great cameras and internet speeds were much slower. The first iPhone was released in 2006 only a year after YouTube launched and it didn't even have an app store yet.

With time, YouTube has obviously been able to get ahead of the piracy problem - their backend software now programmatically filters pirated videos.

7. Google Android - Open source

In July 2005, Android Inc. had yet to publicly release a product but was acquired quietly by Google for an estimated $50 million. Very little information was shared publicly but it was rumored that Android Inc. was working on software for mobile phones.

Its key employees joined Google as part of the acquisition and continued to develop a mobile device platform built on top of the Linux kernel called "Android". Keeping in the spirit of linux, the platform was open-source. Nearing completion, Google marketed the platform to handset makers and carriers on the promise of providing a flexible, upgradeable system for new devices.

The first commercial Android phone was the HTC Dream (G1). Riding Google's rising popularity and open source promises, many were excited by device. I was a fan of Apple's iPhone and Motorola phones at the time but my brother bought the G1 right when it came out. I had the opportunity to try it out early and it was certainly impressive. A cult following rallied behind the new platform until Google quietly built walls around Android making the "open source" promises just a remnant of the past.

Unlike in the early days of the Android, the Google Now Launcher and most of Google’s apps have become closed source. Today, Android is both open source and closed source at the same time. For example, most of the software on Android devices is no longer open source but large pieces of of the core operating system is. The "Android" name and logo are trademarks of Google which impose standards to restrict "uncertified" devices outside their ecosystem to use Android branding. You cannot develop a new "Android" device that includes with Google Play app store without Google's permission.

Google slowly built a poor reputation with this "bait-and-switch" approach to product releases and it became disappointing to many (including me) over time. They would release great new platforms to developers with open-source promises or free tiers. Once you were embedded and reliant on the platform, high-volume usage pricing were jacked up or open-source usage started to have unreasonable restrictions.

How's my memory?

Is there anything you remember differently? If so, don't hesitate to reach out to me on twitter.

Long-Term Capital Management (LTCM) was a hedge fund founded in the early 1990s by former Salomon Brothers trader, John Meriwether and led by two Nobel Prize Laureates, Myron Scholes and Robert C. Merton. At LTCM, their team used complex mathematical models to derive trading profits from leveraged investments until their ultimate collapse in 1998.

You may have heard these names before - they are the same Scholes and Merton from the Black-Scholes-Merton model. Their models are still used today by millions of economists and financial professionals primarily to value "options" - a type of financial instrument with value derived from underlying securities (such as stocks). You may encounter options if you work at a startup or trade call or put options on a public exchange.

The story of LTCM is fascinating one and also a cautionary tale for applying mathematics in finance.

Gaining steam

Having a team of some of the most connected and brightest minds in finance meant LTCM was able to easily raise funds on Wall Street. LTCM's founders had stellar reputations and investors were honored to participate.  Most were already well versed in their contributions to finance. In a matter of months they were able to raise more than $3 billion and that grew substantially as LTCM outperformed the S&P500 consistently for the first three years. Much of LTCM's capital was composed of funds from the same financial professionals with whom it traded.

Working out of an office in Greenwich, Connecticut, LTCM kept their strategies secret. Investors paid $10 million or more to get into the fund. They were not permitted to take the money out for three years or inquire about LTCM investing strategies.

Most of LTCM's investment strategies were based upon hedging against a predictable range of volatility in foreign currencies and bonds. Due to the small spread in arbitrage opportunities, LTCM had to leverage itself highly to make money. At their peak, LTCM had more than $100 billion under management with leveraged investments rumored to be more than $1 trillion.

The Collapse and Bailout

In August of 1998, Russia's declining productivity, high fiscal deficit and unfavorable exchange rates reached a critical point. As a result, the Russian government and the Russian Central Bank devalued their currency and defaulted on its debt. This event and the effects that rippled across financial markets worldwide were well beyond the normal range that LTCM had estimated. LTCM's investments started to crumble.

In 1998, The Federal Reserve Bank of New York organized a bailout of just over $3.5 billion by major creditors to avoid a wider collapse in the financial markets. Since so many banks and pension funds had invested in LTCM, its problems threatened to push most of them to near bankruptcy.

The demise of LTCM was highly publicized and subsequently analyzed in detail. During bailout hearings, LTCM was criticized for reckless behavior which amounted to the equivalent of gambling. A small group of people's investments threatened global economic stability.

History does not always predict the future

Many of LTCM's many strategies were built on the premise that the price difference between two types of securities (mostly fixed income ex. bonds) would "converge" around its historical mean. Their swap spread strategy was believed to be "mean reverting" and would work unless the difference between two prices exceeded acceptable and unlikely thresholds. The 1998 Russian financial crisis obliterated LTCM's model thresholds and triggered rapid trading losses.

LTCM strategies were once compared to "vacuuming up nickels" from inefficiencies in markets. After LTCM went bust, these same strategies were compared to "picking up pennies in front of a steamroller". It is a very clear example of the classic lesson that history does not always predict the future.

Mimicking LTCM

It is easy to find additional reading material on LTCM including a few entertaining documentaries.  If you want to learn more about LTCM I can recommend "When Genius Failed: The Rise and Fall of Long-Term Capital Management" by Roger Lowenstein or the 1999 BBC Horizons Documentary "The Midas Formula".

I wanted to provide enough background to introduce an algorithmic trading strategy that mimics the same strategies used by LTCM.

Fair warning

Here is where things get technical. Some statistics knowledge is needed as well as familiarity with market microstructure and finance in general. A disclaimer is that I would not recommend deploying this strategy but you will also find that it is difficult to succeed in this without commercial discounts on trading fees and reduced slippage.

Mean reversion strategy: "Pairs" Trading

Because of LTCM, mean reversion strategies are well-known and infamous in algorithmic trading and trading in general. One of the more popular strategies is commonly referred to as "pairs" trading and is the one I will be sharing how to perform effectively while understanding what is happening.

A pairs trade is a market neutral trading strategy where trading profit can be obtained in any market condition - independent of whether a security's price trend is increasing, decreasing, or horizontal. This strategy is often categorized as statistical arbitrage and like with LTCM, it relies on "convergence" and "mean reverting" of price differences.

When you hear this it is common to think that this is the proverbial holy grail of trading strategies. "I can trade without estimating which direction a stock will go!" Just keep in mind the lessons from LTCM throughout.

Stationary time series

A stationary time series is one whose statistical properties such as mean, variance, autocorrelation, etc. are all constant over time. As part of this strategy, we want the price differences of two securities to revert around a mean. So naturally, the price differences over time should be stationary.

The default null hypothesis is that the time series is non-stationary. To test this null hypothesis you could use an "Augmented dickey fuller"(ADF) test. This tests the null hypothesis that a unit root is present in a time series sample. We can then use a cut-off p-value to reject the null hypothesis. ie. determine if the time series is "likely stationary".

If we can take one price series and some other price series with a stationary spread and then buy one and short the other this effectively acts as hedge and will create trading profit as the prices revert to the mean. For example, if we bought $100 dollars worth of a loser (L) and shorted $100 dollars worth of winner (W), and when their prices converged we could sell L for $110, and pay $90 for W. In the end, we made $20.

Stop losses are recommended for when the differences exceed a preset standard deviation σ (sigma). Lack of effective stop losses is they key reason that LTCM failed. It should become very clear that this type of strategy is particularly dangerous if the time series of price differences is non-stationary. If the price differences never return to the mean, profits will never be realized.

The goal then becomes finding two (or more) securities whose price differences are stationary over time. We will get into how to test for stationarity later. Although I mentioned that history does not predict the future, without it there is no evidence to suggest mean reversion.

Finding pairs

Unless there's a reasonable economic basis for why the randomness inherent in one time series would explain the randomness of another time series well then we have no reason to believe that it's going to continue to be so. Price differences need to remain stationary over time so trading profit can be realized over time.

Explaining the non stationarity of some time series with the non stationarity of some other time series is a very difficult relationship to explain and it's also a very difficult relationship to find. Maybe two companies that are publicly traded are dependent on each other or on some underlying economic indicator. Maybe there is a joint relationship in some sector or maybe one company is attempting to copy a competitor.

We want to start with some reasonable economic basis for the pair and in trying to find this relationship we can narrow the search space in hopes that we can actually find a legitimate relationship.

With the key assumption that returns are normally distributed, it's hard enough to find one stationary return time series unless you take a particular year so it's going to be significantly harder to find a pair of time series that are stationary together - that's a weird and sort of subtle relationship.

A good start is evaluating whether two securities are cointegrated.

Cointegration

This should not be confused with correlation. Correlation and cointegration, while theoretically similar, are not the same. Cointegration focuses on the spread between the two time series whereas correlation focuses on their direction in relation to each other. There are many ways to test for cointegration and it is convenient that with most tests, if a pair is likely cointegrated it is also likely stationary.

In practice a common way to do this for pairs of time series is to use linear regression to estimate  β  in the following model.

The idea is that if the two are cointegrated we can remove X2's depedency on X1, leaving behind stationary noise. The combination X2 − βX1 = α + ϵ should be stationary.

It is worth repeating that you should not assume that because some set of securities have passed a cointegration test historically, they will continue to remain cointegrated.

Putting it all together

This strategy is often deployed with diversification. A quant would normally identify many pairs of securities they hypothesize are pairs and as long as more than half of the pairs are successful, the strategy will work. As mentioned, stop losses are key and because spreads are small in today's markets it is difficult to realize substantial profits without significant capital and low fees/slippage. If you want to try it out programmatically using python I would recommend giving "Quantopian" a try. They have a built-in backtesting solution and some of the information gathered here is directly from that platform.

Rich is a curator of life principles and has shared words of wisdom throughout his career. During the COVID-19 pandemic, he shared the following "Ten Common Mistakes People Make During a Crisis". If you want to find more from Rich, I encourage you to follow him on Instagram @HandlerRich.

Ten Common Mistakes People Make During a Crisis

1. Thinking that it will never end - mentally and physically allowing yourself to become frozen or fall into the abyss.
2. Thinking it will end quickly - falsely encouraging you to over-react as an aggressive contrarian when you should really be focusing on assessing all and protecting yourself, loved ones, firm and liquidity.
3. Not anticipating how the world going forward will change for both the worse and better. It is too easy to expect everything will return to normal in time or be positive that everything going forward will be completely different. Things are rarely so obvious or predictable. Think.
4. Failing to keep perspective of all that is positive in your life and all the things that prior to the pain you may have taken for granted but now can truly appreciate. It is a great time to rid yourself of all the negative clutter (people, things, obligations etc...) that drag you down and identify and focus on all that lifts you up. When you eliminate the notice, what's left is precious time to make wonderful music.
5. Putting all decisions regarding personal improvement (health, relationships, career, friendships, family relationships, etc...) on hold because you "dont't need one more thing to worry about or change." "Justifiable procrastination" becomes your life theme. A crisis is a terrible thing to waste because it provides the perfect opportunity to begin the changes that are long over do. When the crisis passes, you will be on your way to a better path and besides, when things are good you know you will be "too busy" to prioritize tough decisions.
6. Not giving yourself the time and peace to put everything in perspective. If times of crisis, uncertainty and stress don't force you to step back and re-evaluate where you are going, when exactly do you expect it to happen?
7. Worrying constantly about things you cannot control is ridiculous, a waste of time and a distraction that will prevent you from focusing your energy on the things you can affect. You might not be able to have complete control, but if your actions, attitude, emotion and effort can at least partially affect the outcomes, time on these issues is well spent. Identify which things you can and can't affect and ignore or immerse yourself accordingly.
8. Sometimes people make major decisions in the heat of a crisis or panic because they think they finally are seeing things clearly and they cannot fight their overpowering unstoppable urge to take drastic action. Some drastic changes are good, but usually it is best to think about these over time and see how you truly feel as the circumstances evolve and the sunlight eventually returns.
9. It is easy to keep yourself in a "comfort zone" induced "personal rut" during a crisis. The same mindless activities can consume you and appear comforting because you know what the expected outcomes will be. The same can be said for talking regularly with only the same people because it is easy and requires little personal investment or risk of embarrassment or rejection. If you expand your horizons during a crisis it will be empowering and a personal growth experience. You may also find that others are more welcoming than ever during their crises, especially if you are helping them expand beyond their comfort zone.
10. People can convince themselves that it is not ok to smile, laugh, enjoy themselves or have happy times during a crisis. The seriousness and pain in the world is real, but if you can find some happiness in the midst of a crisis, you can find it anytime and that will serve you well through life's many ups and downs.

More from Rich and Jefferies:

• 100 Things I wish Someone Told Me Before I Went To College
• 30 Things I Have Learned During My First 30 Years At Jefferies
• 25 Secrets That Should Be Obvious About How To Value And Treat Clients
• 10 Years Later — 25 Lessons We Learned From The Financial Crisis

The Berkshire Hathaway Annual Shareholder Meeting occurred a few weeks ago. For the first time, the meeting was streamed live and remote due to the Covid-19 pandemic.

As a shareholder and long-time fan of Buffett, I enjoyed the unexpected long-form introduction and the candid responses during the Q&A. Buffett talked for well over 4 hours and didn't miss a beat at age 89.

Even more interesting is that Buffett spent the first 30 minutes sharing a history lesson on the progress of American economy going back to the 1700's. As a famous long-term value investor, his pervasive message is that America succeeds over time. He provided multiple examples from American history where the country persevered through short/medium term problems.

"...if you bet on America and sustain that position for decades, you’re going to do better than, in my view, far better than owning Treasury securities"

I encourage you to watch or listen to the entire meeting here.

Taking notes

I make notes from the meeting every year and wanted to share an interesting excerpt from the 2020 edition. It does not pertain specifically to Berkshire or Covid-19. Within the context of "value investing", Warren uses a "farm ownership" analogy to explain how to stick to your own valuation methods and ignore the short-term pricing noise. Having a liquid market with realtime pricing for your investments is advantageous but no one is forcing you to sell at any given time.

Value your investment like a farm

Imagine for a moment that you decided to invest money now, and you bought a farm.

And the farmland around here, let’s say you bought 160 acres, and you bought it at x dollars per acre. The farmer next to you had 160 identical acres, same contour, same quality, soil quality, so it was identical. That farmer next door to you was a very peculiar character because every day that farmer with the identical farm said, “I’ll sell you my farm, or I’ll buy your farm at a certain price,” which he would name.

Now, that’s a very obliging neighbor. I mean, that’s got to be a plus to have a fellow like that with the next farm. You don’t get that with farms, you get it with stocks. You want 100 shares of General Motors, and then on Monday morning, somebody will buy you 100 shares or sell you another 100 shares at exactly the same price. And that goes on, I don’t know, five days a week.

But just imagine if you had a farmer doing that. When you bought the farm, you looked at what the farm would produce. That was what went through your mind. You were saying to yourself, “I’m paying x dollars per acre. I think I’ll get so many bushels of corn or soybeans on average, some years good, some years bad. Some years the price will be good. Some years the price will be bad,” etc.

But you think about the potential of the farm.

Now we get this idiot that buys the farm next to you, and on top of that, he’s sort of a manic depressive and drinks, maybe smokes a little pot. So his numbers just go all over the place. Now, the only thing you have to do is to remember that this guy next door is there to serve you and not to instruct you. You bought the farm because you thought the farm had the potential. You don’t really need a quote on it. If you bought in with John D. Rockefeller or Andrew Carnegie, etc. there were never any quotes. Well, there were quotes later on, but basically you bought into the business.

And that’s what you’re doing when you buy stocks.

But you get this added advantage that you do have this neighbor who you’re not obliged to listen to at all who is going to give you a price every day. And he’s going to have his ups and downs. And maybe he’ll name a selling price that they’ll buy at, and in which case you sell if you want to.Or maybe he’ll name a very low price, and you’ll buy his farm from him. But you don’t have to. You don’t want to put yourself in a position to where you have to.

So stocks have this enormous inherent advantage of people yelling out prices all the time to you, and many people turn that into a disadvantage. And of course many people can profit in one way or another from telling you that they can tell you what this farmer’s going to yell out tomorrow or next… your neighboring farmer’s going to yell out tomorrow, or next week, or next month.

There’s huge money it. So people tell you that it’s important, and they know, and that you should pay a lot of attention to their thoughts about what price changes should be, or you tell yourself that there should be this great difference. But the truth is, if you owned the businesses you liked prior to the virus arriving, it changes prices, and it changes, but nobody’s forcing you to sell. And if you really like the business, and you like the management you’re in with, and the business hasn’t fundamentally changed, and I’ll get to that a little when I report on Berkshire, which I will soon, I promise, the stocks have an enormous advantage. And you can bet on America.

But you can’t bet unless you’re willing and have an outlook to independently decide that you want to own a cross section of America, because I don’t think most people are in a position to pick single stocks; a few may be, but on balance, I think people are much better off buying a cross section of America and just forgetting about it. If you’d done that, if I’d done that when I’d got out of college, it’s all I had to do to make 100 for 1 and then collect dividends on top of it, which increased, would increase substantially over time.

The American tailwind is marvelous. American business represents, and it’s going to have interruptions, and you’re not going to foresee the interruption, and you don’t want to get yourself in a position where those interruptions can affect you either because you’re leveraged or because you’re psychologically unable to handle looking at a bunch of numbers.

If you really had a farm, and you had this neighbor, and one day he offered you $2,000 an acre, and the next day he offers you $1,200 an acre, and maybe the day after that he offers you $800 an acre, are you really going to feel that at $2,000 an acre when you had evaluated what the farm would produce, are you going to let this guy drive you into thinking, “I better sell because this number keeps coming in lower all the time”? It’s a very, very, very important matter to bring the right psychological approach to owning common stocks.

More interesting excerpts

• 3:13:19 Warren Buffet should you buy stocks
• 2:07:13 Using leverage to invest
• 2:11:53 Stocks vs. treasury bills
• 2:14:15 Stocks being part of businesses
• 2:24:51 Buffet uncertain about the near future
• 2:38:21 Buffet sends thank you letter to the Fed
• 2:41:24 Warren Buffet on selling airline stocks
• 3:38:42 When Warren Buffet decides to sell
• 4:02:10 Why index funds are recommended
• 4:08:35 Selling investments more profitable than managing
• 4:09:02 Warren Buffet’s response to his under-performance
• 4:14:29 The size of Berkshire Hathaway
• 4:26:34 Risk in oil markets
• 4:28:42 Warren Buffet on selling put options
• 4:37:45 Negative interest rates
• 4:48:25 US government bonds and printing money
• 4:51:11 Stock buy-backs
• 5:09:36 Warren Buffet advice on credit card debt
• 5:21:53 Warren Buffet on capitalism

Disclaimer: I've edited some of the transcript to make it easier to read. Any edits, omissions and other changes are not intended to create confusion or misrepresent the words of Warren Buffett

I do want to share one killer feature in Sheets that you can quickly add to your workflow. Google "Apps Script" is the scripting platform for Sheets and was introduced way back in 2009. Until recently, I had never heard of it. Not a single colleague I know uses it and I discovered it by accident.

Microsoft Excel - the king of spreadsheets

In the battle for desktop productivity software, Microsoft Office is the undisputed leader and has been for a very, very long time.  Within Office, Excel is also the leader in spreadsheet software. For decades, analysts cited "first-mover" advantage, market share and learning curve as to the reasons why this is the case. With respect to "learning curve" - I don't think the majority of users use Excel correctly, but that is a discussion for another day.

There was a slight hiccup between 2006-2010 where I was skeptical that Office would survive the wave of software cloud-ification. Sure enough, Microsoft brute-forced its way into the cloud with Office 365. Although the transition was a little bumpy, the current offering is exceptional (sorry - Sharepoint is still awful). Files can be stored locally or in the cloud (Microsoft OneDrive) and you can choose between desktop or online versions of Office products. They work as expected and are reliable.

Google Sheets - the underdog

Google Sheets is often quoted as an alternative to Excel but has always lagged behind Excel's feature set. The convenience and cost (free) made it a hit when it was first released. While many praised Google Sheets for its collaboration features, Excel caught up within a few years. Sheets does the basics well and for amateur users it "just works" - Google knows this and has embraced it.

Google has carved out a niche where power users are not welcome.  The web-only interface is clunky, a lot of the expected features are missing and the Microsoft ".xlsx" filetype is not cross-compatible. The last point is probably not within Google's control.

Great - so why continue this discussion if Microsoft Excel is the best and Google Sheets is inferior? Well, the Google Sheets scripting environment (Apps Script) is so powerful and so easy to set up that it is a very attractive alternative for simple (and complex) tasks.

"Apps Script" Features

Google describes "Apps Script" as a "rapid application development platform that makes it fast and easy to create business applications that integrate with G Suite." You write code in modern JavaScript and have access to built-in libraries. There's nothing to install because Google gives you a code editor right in the browser, and your scripts run on Google's servers.

• Language: Javascript
• Libraries: Built in
• IDE: Built in or local
• Execution: Google servers

It uses a language I already know and requires zero setup time. There is really nothing more to ask for.

Okay, but Excel has that too!

Yes, this is partially true. Microsoft is making a lot of progress in this area.

For Microsoft Excel, VBA is the legacy programming and scripting language. It had an extremely painful learning curve and few used it for anything critical. Because of the learning curve, the average team could not use VBA effectively unless there were dedicated experts. It is important to note that any expert will always prefer a professional stack that doesn't include VBA ie. separate relational/object databases, custom server setups etc.

Fortunately, Microsoft embraced Javascript as the successor to VBA a few years ago with Office.js and now a suite of Office Add-in APIs built on Javascript. The problem is that the Add-in API is not for "scripting" and using the Microsoft Graph API takes time to set up.

Not convinced?

The key advantage for Apps Script that it is fast. I timed it and the setup time is < 5 seconds. I will be sharing specific examples in the future but in the mean time you should give it a try:

All you need to do is open a Google Sheet then navigate to "Tools > Script Editor" from the main menu. You should be presented with a screen that looks like this:

That's it, you are set up!

Still not convinced?

You should be. If you cannot wait for me to share examples, here is a simple one:

function myFunction() {
  var response = UrlFetchApp.fetch("https://api.exchangeratesapi.io/latest");
  var sheet = SpreadsheetApp.getActiveSpreadsheet().getSheetByName("Sheet1");
  var object = Utilities.jsonParse(response.getContentText());
  var objRates = object["rates"];
  for (var i in objRates) {
    if(i="USD") {
      sheet.getRange(1, 1).setValue(objRates[i]);
    }
  }
}

What does it do?

This script pulls the latest EUR/USD exchange rate and places it in cell A1.  This simple example is so powerful because it covers an HTTP GET request and manipulation of spreadsheet cell data in 11 lines of code using no library includes.

Note: If the above is not self explanatory then you might need to brush up on your programming knowledge before taking full advantage of this feature.

I guess the business model makes sense. Big companies looking to offload old servers may look to 3rd parties to decommission in bulk and resell them.

For $495, I managed to grab a 24TB server with a dual Xeon CPU and 48GB of ram. The storage server is rack-mountable 2U and came out of an IBM XIV Gen3? Storage system (shown below). Its origin and use is unknown but if had to ballpark it, the server is probably between 5 and 10 years old.  It may be old but it holds up really well. Here are the details:

HS-1235T 2U 12 Bay System

• Dual Intel® Xeon® Processor E5620 (12M Cache, 2.4 GHz, 5.86 GT/s Intel QPI
• 48 GB DDR3 Memory (6x 8GB Modules)
• 2x XYRATEX 850W Switching Power Supply p/n HS-PSU-850-AC-INT
• 1x Addonics SATA Internal Compact Flash Reader Adapter p/n ADSACF-7MS
• 1x Xyratex 1.8" Micro SATA to PCI-e Adapter Card p/n 0959303-07
• 1x IBM SAS9201-8i PCIe x8 6GB Dual SAS RAID Card p/n H3-25268-00D
• 2x LSI LSI7204EP-LC Dual Port Fibre Chanel Card +2 JDSU 4Gbps Transceivers
• 2x IBM 45W1959 Quad Port Gigabit PCI-E PRO/1000PT Server Network Interface Card
• 12x Seagate ST2000NM001 2TB 7.2K SAS 3.5" Hard Drive

eBay Source

All of the server sellers on eBay look shady. With a referral, I bought my server from ebay user "yournewgift" which I highly recommend. The server was shipped in perfect packaging and the inside the chassis - not a spec of dust. The eBay user also sells replacement 2TB Seagate drives meant for the system ($30 each). These can be hot-swapped into each drive bay within seconds. If you set up your RAID array correctly, you can be "resilvering" in no time.

Things to know

1. Without modification, it is very... very loud.

Firing it up for the first time was quite concerning. The best comparison would be that it sounds like a jet engine. I was planning to stick it in a closet with some other equipment so the noise was a serious problem. At this point I was regretting the decision.

The unit comes with 2 x 850W PSUs and 12 x fans sitting between the drive bays and the motherboard. I found that the power supplies were loud but the fans were the real problem. The fans have do not have variable RPM out of the box so they run at full speed - all the time. Fortunately, each fan comes with a hot-swappable enclosure so adding and removing them is really easy. To fix the noise, I soldered a resistor to the power wire in all twelve fans keeping the RPM to a middle range. This was a tedious and repetitive process but it was worth it.

It ended up running at a light hum similar to the average desktop computer! I do not intend to keep the server under consistent heavy load and through testing the drive temperatures consistently hovered around 30C which is completely acceptable.

2. It is big and heavy

As mentioned, the server is full size rack-mountable 2U and comes pre-filled with 12 hard drives so it is very heavy. There are a myriad of miscellaneous PCI-E, RAID and connectivity cards included. All in, it probably weighs around 80 pounds. This should be no problem at all if you already have a server rack but it is something to be aware of.

3. No software

You are on your own when it comes to software. The server did not come with anything pre-installed. I share the software implementation I went with below.

Software and Reliability

Even with a system that is 5+ years old, reliability is not an issue. Through using open an source NAS operating system (FreeNAS), virtual RAID (RAIDZ2) and encrypted cloud backups with Backblaze, I don't think I will have any problems. If a drive fails, it will be as easy as swapping in a new one - with no data loss!

Operating system

I originally tried to install OMV which is a NAS solution based on Debian but wasn't finding success. I am more familiar with Linux so I thought this was the solution for me. I ended up installing FreeNAS which is based on FreeBSD/OpenZFS and am finding it to be an absolute joy to work with.

RAIDZ

Even though the system comes with a hardware RAID controller, I just found it easier to use OpenZFS RAIDZ. I chose RAIDZ2 which works by taking the drives given to the pool (known in RAIDZ as a "vdev"), and using two of those to store redundant data for recovery purposes in case something goes wrong.

This means that I do sacrifice 2 drives for redundancy ie. 2 drives would have to fail at the same time for me to lose all of the data. If one drive fails I can hot-swap in a new one and let it resilver. If a drive fails during resilvering, which is common, I am still okay because of the dual parity.

Backblaze

For further redundancy on top of RAIDZ, I have scheduled encrypted backups to the cloud just in case. Scheduling these automatically in FeeNAS to Backblaze B2 only took a few minutes. Backblaze B2 provides unlimited data storage in the cloud at 1/4th the cost of Amazon S3, Microsoft Azure, and Google Cloud. I use Backblaze's services for desktop/laptop backups and love the "set it and forget it" approach.

Done!

It may not be the most convenient way to store personal data but it was cheap is turning out to be extremely reliable. I will likely share more about my operating system configuration in FreeNAS but if you want more details right now on how I was able to configure this system, give me a shout on twitter.

However, there is no denying that a hot topic is the current state of public equity markets and global economies. I am asked for my take on this constantly and wanted to share a little bit here in response.

Second-Order Effects

An unfortunate second-order consequence of the pandemic is decreased economic activity and job loss resulting from mandated "social distancing" and quarantine-like measures. Businesses with physical locations have been shut down, people are forced to work remotely or stop working altogether, consumers are forced to make purchases online, supply chains have been disrupted and the list goes on. Investors have panic sold and panic purchased all within the past few weeks. All of this can also result in some hefty unrealized losses in your traditional investments:

Risk tolerance and relative value

In addressing these effects, you can follow the news closely and do your own research or you can rely on your usual investment advisor. Anyone telling you they know what is happening is too confident. Your skepticism of experts should be at an all time high. In a lower risk portfolios, experts with hedging ideas are likely on the right path. I repeat: "hedging".

Hopefully, your portfolio risk profile allows you to whether the storm. The prevailing opinion amongst professionals is to ride out your current investments over the next few years while economies recover. This strategy is based entirely on the theory that the pandemic and the associated downturn is temporary. Long term value investors have never been wrong and I am not going to start disagreeing now. Just keep in mind that long term value investors like "Warren Buffet" are making moves.

With a lower risk tolerance I can say with certainty that attempting to "time the market" is a bad idea. Many argue that now is a great time to buy equities because everything is cheaper than it was 6-12 months ago. The classic problem of trying to find a bottom, "buying the dip" or as I like to call it: "catching the falling knife". I should clarify that within the context of long term value investing, buying now would be possible though using relative values as your only rationale would be foolish.

I believe that those relative values are important to consider but not everything in assessing where your future value and returns will come from. If you are going to attempt to get into the market at this point, you should have a more targeted and refined approach.

The Supposed Facts:

1. Temporary stoppage - The current economic "halt" is deemed to be temporary. The unknown is how long the halt will last. There are some well documented estimates that are correlated with projected "Epidemiology Curves" of the disease.
2. Government backstops - Governments everywhere have attempted to and succeeded so far in improving sentiment by guaranteeing aggressive monetary policy, fiscal policy and bailouts. Whether or not these programs will actually be effective has yet to be seen right now.
3. Macro sentiment - Current market movements are driven strongly by overall sentiment. The majority of equity prices are tightly correlated with indices which may or may not be driven by the recent ETF Boom. I will talk about "separating winners from losers" as one of my suggestions.
4. Specific industries - Some companies will feel the effects of the pandemic more than others. Brick and mortar retail, travel, tourism, restaurant and hospitality industries are front and center.
5. Job loss - There are new reports of high unemployment numbers. Whether or not these losses are temporary is still debatable.

My approach has been to: (1) estimate the unknown variables surrounding the facts and then also (2) attempt to assess the market's response. I believe this can also form the basis for your research and understanding.

Macroeconomic response

Macroeconomics focuses on national output, unemployment, and inflation. The two main policy makers are governments and central banks. The major tools of macroeconomic policy makers are "fiscal policy" (government spending and taxation) and "monetary policy" (central bank control of the money supply). These tools are used to achieve "macroeconomic equilibrium" - knowing what macroeconomic equilibrium looks like here isn't really all that important.

There is the unprecedented occurrence of adverse supply/demand shocks all compounded by an exogenous fall in spending on goods and services because of (a) increased uncertainty (b) reduced wealth and (c) quarantine measures.

Quantifying the recent decisions in monetary and fiscal policy has been challenging because both levers have been pulled to their extremes. For monetary policy, interest rates are zero or near zero and the purchasing/issuing of debt feels unlimited. The numbers involved and execution in fiscal policy is not clear and I don't think anyone expects the deployment of stimulus to be efficient. There is also limited basis for comparison.

The policy makers are prepared to spend big in order to keep this economy afloat. The good news is that the overarching immediate goal has been to improve investor confidence and sentiment. I believe this goal has been achieved - at least temporarily.

The looming "Spiral" effect

The longer the pandemic persists, the less effective these macroeconomic measures will be. Also, the longer the pandemic persists, the sharper the impact on secondary industries. There are media outlets and pundits who are focused on retail, travel, tourism, restaurant and hospitality industries as the only losers. On the flip-side, virtual productivity software, healthcare, and drug companies are the winners. This is too simplistic in grappling with what a post-COVID-19 economy might look like.

Businesses are interconnected where you might not expect. Any planned capital and operating expenditure tightening for a struggling company will ripple through seemingly unrelated industries. Ex. Software companies may not be affected directly by the pandemic, but their customers may be across many industries are less likely to purchase or renew.

Laissez Faire? Don't you dare

Corporate and individual bailouts appear guaranteed at this point however the amount of money and the recipients are both vague. The US government has guaranteed more than $1.8 trillion of relief with more on the horizon.

Free market capitalists have never disagreed with "helicopter money" in times of need and there are many who enjoy pointing out the hypocrisy there. I do not agree with Chamath Palihapitiya on a lot of his policy ideas however I found his recent CNBC  interview segment to sum up the alternative view point (it's also entertaining).

Chamath argues that "Corporate welfare" in the form of government's bestowal of grants, forgivable loans, tax incentives or other special favorable treatment for corporations is not needed. He points out that a finance version of natural selection will weed out unprepared businesses. Chamath further notes that employees will not bear the brunt of bankruptcy - speculators and investors will be the big losers, which is part of the "rules of the game".

Unemployment indicators

An animated graph titled “U.S. Weekly Initial Jobless Claims (thousands, seasonally adjusted)” went viral on social media and is shared below. Those numbers were real and originated with an April 3 2020 BLS report. However, they were not entirely reflective of reality in terms of genuine unemployment. Blanket stay-at-home orders and expansion of unemployment benefits to those temporarily furloughed or out-of-work were counted in, and an unknown number of people had yet to be counted.

It was further impossible to measure how many jobs would resume versus companies or businesses never to reopen, whether the scenario was stimulating or depressing the “gig economy,” and what the long term labor-related effects of the COVID-19 pandemic might be. Although the graph had underlying real numbers, they were hobbled by a myriad of immeasurable variables in an unprecedented situation.

Winners and losers

A somewhat less ambitious variation of the Efficient-market hypothesis is that all relevant public information is always reflected in market share prices and the market nearly instantly adjusts for any information once it becomes publicly known.

I am a strong believer that when the dust starts to settle, there will be more information about which companies perform better or worse than expected. The separation of winners or losers does not appear to be completely priced in to markets yet. As previously noted, current equity prices are tightly correlated to indices however certain sectors and businesses are affected more than others during this time. How can this be? Well markets can be irrational for long stretches of time.

Markets can stay irrational longer than you can stay solvent

• John Maynard Keynes

Building on this, it is obvious that certain Q1 and Q2 earnings calls for many companies will be terrible and yet their stock have not sold off. The current phase seems to be a repricing of general market "risk" but trading the news and information on individual companies is weak. This is where there is some opportunity to select a group of specific stocks and I suggest choosing wisely. I expect there to be extreme responses to Q1 and Q2 earnings calls. Reallocation of capital from weak to strong companies seems to be in its early stages.

New era of valuations?

In separating winners and losers, I am very interested to see how the market comes to a consensus on how to value companies. For the expense side, adjusted EBITDA is commonly accepted however how investors choose to model adjusted revenue will be key.

What about residential real estate?

The cost of borrowing for regular homebuyers or speculators has been low for a few years leading up to the pandemic causing increased sales and rising home prices. Borrowing costs are only decreasing with the pandemic response but it is interesting to note that prices in some major markets have leveled off at the end of Q1 2020. This could be an indicator or just a temporary supply problem caused by "social distancing". If unemployment causes a spike in supply relative to demand then real estate will only compound the problems markets are experiencing.

Slippery slopes and liquidity traps

Just want to leave a note here that there is serious risk to maintaining the current monetary policy after the pandemic has ended and companies/employment rates recover. Part of the reason monetary policy may not be as effective in this situation is because interest rates were arguably already too low heading in. Whether COVID-19 exposed a hidden "Liquidity trap" is a debate for another time.

Disclaimer: This article is opinion and is for information purposes only.  It is not intended to be and should not be considered as investment advice.  Seek a duly licensed professional for investment advice. Opinions are my own and not the views of my employer.

Thank you: Thank you to @mwood1089 for some very interesting and more candid (virtual) discussions around many of these topics. Also huge thank you to some friends and colleagues (you know who you are) for proofreads

The "Hackintosh"

The name for this type of custom built computer is a "Hackintosh". A Hackintosh, is a computer that runs Apple's Mac operating system on computer hardware not authorized by Apple. To get this out of the way - it is not authorized but calling it illegal would be pushing it a bit far. Also, I do not sell any software to enable this nor do I sell any preconfigured systems.

I will get into the details of my build including all of the components used. I will also share resources so you can build one yourself.

After discovering the "Hackintosh" community in 2013, I have continued to be a contributor and builder of these systems. The community is open, collaborative and has some of the most knowledgeable people in hardware and software. They have also been very helpful in building my Mac Pro (especially the AMD OSX Discord channel and users like Pavo)

I criticize Apple often and will do so here (especially as it relates to their desktops) but in general I am huge fan. I have never purchased a desktop from Apple but have purchased many MacBooks, iPads and iPhones over the years - almost too many. Over seven years I have built three stable hackintoshes and each one was my primary productivity desktop.

My MacOS history

I use MacOS as my primary operating system for productivity and I have done so for most of my life.  Because it is UNIX based I always thought of it (incorrectly) as the "prettier Linux". In terms of command line interface, I can barely tell them apart and that's really where the similarities end. It has and will likely continue to be my OS of choice for writing software and producing/editing media.

I have made use of five Apple laptops over the past decade.^[1]  Each had unrivalled build quality, form factors that were compact, the designs were elegant and screen quality was impeccable. The raw power of each laptop may have been underwhelming compared to competitors based on price but these aspects always made up for it.

It's a different story when it comes to Apple's desktop lineup.

Although being a fan of the laptop products, I could not and have not come to terms with the hardware prices for any desktop products. As a teen, I used a variety of iMacs in computer labs daily but my personal desktop was always dual booted Windows/Linux. After discovering the Hackintosh community I never found the need to buy a desktop from Apple.

The usual walled garden

Without turning this into a timeline of Apple's desktop product history, there have been conscious decisions made over the life of the company to deliver its products within the infamous "walled garden".

The "walled garden" is a closed ecosystem enabling Apple to regulate hardware, software, apps, and services on its devices to control the entire user experience and effectively create loose vertical integration. This has proved over time to have positive and negative effects to the consumer.

No modular components

Since the 1990's desktop computers have always had modular components meaning you could swap hardware in and out with relative ease. This also meant that you could repair and upgrade parts yourself over time.

Apple was never on board with this idea and still isn't. Except for memory, Apple desktops cannot be upgraded with standard computer components or taken apart with standard tools.

No install on non-Apple hardware

Windows and Linux operating systems could generally be installed on any arrangement of hardware components independent of manufacturer. The operating systems were never tied to any hardware.

Apple was never on board with this idea and still isn't. Mac OS can only be installed on Apple products

Competition

If you were inside Apple's "Walled garden" and managed to take a peak outside, all of a sudden the garden didn't look as pretty

Over time, the market for computer products outside the "walled garden" were very competitive. The result was that Apple customers often overpaid for equivalent hardware - this was especially true for Apple desktop products.

If you purchased any Apple desktop, the cost was high and the life was limited. Some desktops like the 2013 Mac Pro were obsolete within a year or two of release but Apple continued to sell it for six years unchanged.

Building the Mac Pro killer

The 2019 Mac Pro

This is the target build for the Mac Pro I am going to compare to. I upgraded the processor from the 8 core to the 12 core and the storage to 2TB. All in the cost is 7,800USD.

• 3.3GHz 12‑core Intel Xeon W processor, Turbo Boost up to 4.4GHz
• 32GB (4x8GB) of DDR4 ECC memory
• Radeon Pro 580X with 8GB of GDDR5 memory
• 2TB SSD storage
• Stainless steel frame with feet

My 2019 Hackintosh build

Below are the components and prices at the time of building for the Hackintosh that I built. The total cost for my custom build is around $2,100USD

• AMD Ryzen 9 3900X 3.8 GHz 12-Core Processor ($420)
• Corsair Vengeance RGB Pro 32 GB (2 x 16 GB) DDR4-3200 Memory ($165)
• Gigabyte Radeon RX 5700 XT 8 GB GAMING OC Video Card ($410)
• Samsung 970 EVO Plus 2 TB M.2-2280 NVME Solid State Drive ($400)
• MSI MPG X570 GAMING EDGE WIFI ATX AM4 Motherboard ($190)
• Dune Pro Case ($300) ^[2]
• SeaSonic PRIME Gold 1200 W 80+ Gold Certified Fully Modular ATX Power Supply ($150)^[3]

Performance comparison

Geekbench CPU and Compute

Geekbench is a cross-platform processor benchmark, with a scoring system that separates single-core and multi-core performance, and workloads that simulate real-world scenarios.

The CPU Benchmark scores for the Mac Pro (Late 2019) with an Intel Xeon W-3235 processor 3.3 GHz are:

• Single-core score: 1,121
• Multi-core score: 11,769

The scores for my Hackintosh with an AMD Ryzen 9 3900X 3.8 GHz are:

• Single-core score: 1,259
• Multi-core score: 10,515

As you will note, both CPUs are 12 cores and the benchmarking scores are very comparable. In this test, my hackintosh wins on single core performance but is edged out on multi-core scoring. It should be noted that the scores are so close that performance differences between them would be hard to notice during usage.

If you are wondering about the comparability of the graphics cards, The OpenCL compute score for my hackintosh's  Radeon RX 5700 XT is 72,451 where as the Mac Pro's Radeon Pro 580X is 40,813.

How to install MacOS

A Fair Warning

Building a Hackintosh is not easy. The expertise required to build and maintain a stable system is not extremely high but a slightly above average knowledge of computer systems is necessary.

Resources

If you are up for the challenge, here are a few resources to get you started:

1. AMD OSX Website - The best resource for detailed information

2. AMD OSX Discord - A chat channel for the AMD OSX community - everyone is friendly and helpful

3. Youtube - More and more content creators are sharing their builds and how to copy them. This video by SnazzyLabs is probably the most informative and most relevant here

Footnotes
[1] 2013 MacBook Air, 2015 Macbook Pro, 2019 15-inch MacBook Pro
[2] Currently awaiting delivery of the Dune Pro case, so I am using a temporary generic full tower chassis in the meantime
[3] Just a spare power supply I had laying around

Today Bill Gates announced his decision to step down from both of his public board positions which includes Berkshire Hathaway and more notably, Microsoft. This comes thirty four years, exactly to the day, after Microsoft went public and he took up his board seat.

I have become a follower of Gates more so in the past few years than in any of the years he ran Microsoft. Rarely do I agree with almost all of the ideas, observations and methods of a single person but the Bill Gates of 2020 is certainly one of the exceptions. Along with his wife, their philanthropic focus casts a wide net - energy, climate change, education, public health, poverty and more.

I really enjoy retirement-aged-Bill Gates' nonpartisan, pragmatic approach to humanitarianism. He often feels like the only adult left in the room or at least, the only adult that still has any interest at all in the well-being of the world at large.

He's certainly passionate... I think that as he's grown older that the ratio of shouting to non-shouting has tremendously decreased

• Microsoft employee (source: Bill Gates: How a Geek Changed the World)

As Gates has gotten older and put on the philanthropist hat, his conflict resolution style has switched starkly to using a collaborative (win-win) approach to resolving conflict and negotiating.

Early days as "Shrewd Businessman"

The story of Bill Gates, Microsoft and the rise of the personal computer is well documented. There is no need to revisit the story but it is important to acknowledge that building the first real software company in the 1970s and keeping it going strong for decades is certainly a testament to him and his colleagues. His public image of "super nerd" is well deserved and infamous reputation as shrewd businessman is also warranted. His leadership style at Microsoft was often described as being "confrontational", "passionate" and "authoritarian". It is unlikely that Gates would have been as successful as he was if he only ever adopted an authoritarian style but it is what he was known for.

During the 1990s Bill Gates became an unlikely celebrity and a reputation developed around Gates as a bully in the business world. The general consensus was Microsoft did not need to stay as aggressive as it did after it had reached market dominance. So much so that in 1998 the US government brought anti-trust charges against Microsoft for "unlawfully taking advantage of monopoly and undermining consumer choice"

Because of the anti-trust case, we will never know how Microsoft or Bill Gates would have continued unchecked. With hindsight, he probably could have handled those situations with more grace. His goal at Microsoft was “A computer on every desk, and in every home, running Microsoft software” and his core responsibility was to his company. He defended his responsibility pursued that goal with absolute vigour.

The Switch

Following his tenure at Microsoft, Gates' traits may have remained the same but his behaviour changed with his circumstances. Gates task-oriented behaviour began to model and move closer to relationship behaviour. His philanthropy efforts focus on the needs of others.

In public today, Gates tends to not challenge anyone directly. Personal attacks are not really his style. He will point out facts and the best course of action as opinion. His opinion is always supported by evidence that he has gathered and he does a fantastic job of clarifying his point of view. He will use sharp sarcasm at times but his current approach is one of collaborative discussion.

For instance, Gates is more bearish than most on the current state of solar as a viable alternative energy source and backs nuclear more than solar (he still invests quite heavily in solar). He has a well thought out "intermittency" argument for this. An easy person to target in this argument is Elon Musk yet he does not publicly criticize him. His emotional intelligence has transcended that of his younger self. I assume he understands that discouraging progress is a net negative. Elon Musk hasn't gotten there yet:

If he is pushed far enough he will respond more directly - "You are wrong and here is why". It almost feels like triggering latent program and maybe it's from his days leading Microsoft. Even then he usually leaves room for rebuttal and he expects responses that are as well thought out as his.  

Bill Gates still takes everything outside of work in a light-hearted way which is something that has never changed.

Vision and problem solving approach

It is well known that Bill Gates is an avid reader and he prides himself on this. By accumulating knowledge it makes him a better decision maker, philanthropist and global citizen.

When you spend your time thinking intensely about a field.... that's where you can do great things.

Solving problems at Microsoft came with brute force and passion. The passion is still there today but the force is more passive. For instance, he identified highly infectious viruses as one of the biggest risks to global catastrophe and was proven correct with the recent Covid-19 pandemic. No one can say that his approach to addressing this issue mirrored the force he used at Microsoft. (April 27, 2020 Update: I have been proven wrong - "Bill Gates details how his foundation shifted focus 'almost entirely' to addressing COVID-19")

What about the money?

Gates has acknowledged that having money is a privilege and has enabled him to do the things that he loves. He has a history of some extravagant purchases but has always made those purchases privately without flaunting his wealth. This hasn't changed.

Gates also cofounded "The Giving Pledge" in 2009 with Warren Buffet which is a campaign to encourage extremely wealthy people to contribute a majority of their wealth to philanthropic causes. This pledge is a tool for addressing income disparity and a mechanism for the redistribution of wealth.

He doesn't want to get political

It is clear that Bill Gates does not wish to become a political leader but wishes to remain a high profile advocate and investor in global issues. This should not be confused as Bill gates not understanding politics - he definitely does. He understands it so well that he picks his battles very carefully.

Many have argued that he is too old to take up such a position but I disagree. Gates is currently 64 while other prominent world leaders are much older. Donald Trump, the current sitting US President in 2020 is 73 years old. Instead, this is his retirement and this is how he wants his retirement to play out.

I won't be running for President because I am super committed to the work Melinda and I are doing at the Foundation and outside the Foundation. I agree it is important to have a President who thinks long term about the US role in the world and the research to solve disease burdens and costs and to tackle climate change and improve education.

I do think people are expecting too much from Government. Yes Government can do better but local groups can do a lot that government can't -- helping out in schools, reaching out to people in poverty. This is also true internationally. I would like to see this civil society sector step up a lot more. Some issues like abortion or even immigration we may never get a consensus on but there are things like better health and better education that we can achieve"

• Bill Gates (Source: Reddit 2018)

Gates does not want to run for US President. The context here is that Bill believes he can accomplish more as a private individual and I'm just fine with that.

For those who know me on a personal level I tend to share ideas, insights and stories any chance I get. I enjoy hearing the opinions of others and will solicit discussions regularly. With some very rare down-time over the past few days I decided to create this blog as a new platform for online sharing.

Current presence

My main public online presence is though Linkedin, Twitter and some guest articles in various publications. Despite developing a little bit of a viral following a few years ago on Twitter, I have not tweeted that much - precisely three times in 2019 actually. I have set up blogs before but I always found experimenting with the blogging technology to be more interesting than the writing part. A lot of my online activity is anonymous and it will likely stay that way.

What to expect?

If I feel I have anything interesting to share, I will certainly try to do so. I imagine the content will lean towards finance, technology and software with a mix of status updates on ongoing projects and hobbies. As a life-long learner, I will probably look back on what I share and laugh at how naive everything is but I think that's part of the process.

I would not expect any elegant prose. I do not intend to review any grammar, spelling etc. My writing style will likely be raw and conversational because I do not want to spend all my free time editing. I will do my best to write in a clear and concise way.

The Technical Details

Ghost platform

This blog is powered by the "Ghost" blogging platform. I followed the creation of Ghost from the very beginning in 2013. I am not proud to say this because I was sure the original idea would fizzle out. A former Wordpress employee John O'Nolan shared his idea online eventually leading to a successful Kickstarter raising over $300,000 in 2013.

The main value propositions of this Wordpress alternative were speed and simplicity. Despite the robust capabilities of Wordpress, it has and apparently always will be trendy to poke fun at its bloat.  I don't need bloat for something this simple so Ghost seemed like a good idea:

• Ghost is run as a non-profit that is very transparent
• They are still fiercely devoted to the original ethos
• Their roadmap and future looks promising

It's not all rainbows - there are some key shortfalls including lack of comments and search out of the box. Customizing the software is also a bit of a pain.

Hosting

Ghost offers a hosted option which is very attractive but the pricing is too steep at $36 per month. I already have (too many) servers worldwide and decided to host it myself. This puts me in complete control and avoids an additional server bill.

Custom Design

This blog runs a custom theme I hacked together on top of the free "Attila" theme. Get in touch with me if you would like to use the theme (MIT License) I will invite you to the github repo.

Disclaimer

Opinions expressed are solely my own and do not express the views or opinions of my employer