Stock market and economic commentary June 2023

“I can calculate the motion of heavenly bodies, but not the madness of people.”

Isaac Newton 1642-1726; Mathematician, physicist, astronomer, alchemist & theologian. The godfather of physics.

Economic Update

Advances in Technology

The release of generative AI models, loosely defined as problem solving using computers and big datasets, like ChatGPT has caused huge excitement and generated predictions of mass job losses and large gains in productivity.

New technologies create excitement. The invention of the railroad led to “Railway Mania” in the 1840s which saw investors pile into railway stocks. In the 1920s the radio similarly captured the imagination. More recently, euphoria over Internet adoption saw the NASDAQ share index rise fivefold between 1995 and 2000. The hysteria stems in part from high expectations over how far and how fast innovation can boost human well-being and productivity. But in each case, the initial bubble burst as reality caught up with expectations.

The rise of generative AI, particularly large language models like Open AI Inc’s ChatGPT has sparked a similar frenzy. Since Chat GPT’s launch last November, glowing reports on the potential economic impact of the technology which can automate tasks from writing essays to generating code, have been coming thick and fast. Goldman Sachs has estimated that it could drive gains in productivity that could raise global GDP by as much as 10% over a 10-year period.

But estimating the benefits is largely a guessing game. Most experts agree it will take time to pay off. Indeed, the impact of prior technologies has often been conceptualised using the “J curve” effect, where productivity may initially fall as it is adopted, before rising steeply. Will generative AI follow a similar path?

Electricity, railways, and computers all took decades before they generated productivity booms. In comparison, AI technology is far less capital-intensive and does not require the wide-scale development of new infrastructure. ChatGPT can be accessed at the click of a button and over 100 million people have already done so. Generative AI systems will however need vast computing power which is not cheap. Companies will need to retrain staff and adjust their business models. This will take time, though user-friendliness will make adoption easier than prior technologies.

Goldman Sachs estimates that two-thirds of jobs in the US are exposed to some degree of automation due to AI and 25% of those exposed occupations could have as much as half of their workload replaced. Economists at the University of Pennsylvania have given similar estimates.

It will take years for such effects to become apparent at a macro level. For now, trying to assess the effects of AI relies more on individual studies, cases and stories, most of which highlight the potential rather than the limitations of the technology.

One of the more striking studies was published earlier this month by the US National Bureau of Economic Research. It involved a control trial with 5,000 customer service employees in a major US software company. Some employees were provided with an AI tool that offered potential replies to customer queries which the agent could ignore, adapt, or use. The AI tool increased productivity by 13.8%. Less experienced workers achieved a 35% rise in productivity while the most skilled and experienced workers saw fewer gains from using the tool. In what read like a glowing endorsement of this new technology, the study found that AI-assisted interactions helped improve customer satisfaction and reduced employee attrition by 8.6%.

In a similar experiment, the chief executive of Octopus Energy in the UK said that AI was doing the work of 250 customer service workers and writing emails that delivered 80% customer satisfaction, well above the 65% achieved by skilled, trained people.

Other stories also speak of the change brought by AI. The CEO of IBM recently said that the firm would pause hiring for roles that could be replaced by AI in the coming years. Last month the share price of Chegg, an online education provider, fell by 50% after it said that students were increasingly turning to ChatGPT for tuition.

These studies underscore the potential of AI, but there are significant barriers to realising this potential across the economy. New technologies endlessly reshape individual sectors. But these sorts of sector-specific changes are quite different from those wrought across the whole economy by a general-purpose technology, such as electrification or the personal computer. AI demonstrates capacity in specific tasks, such as coding or customer relations. The question is whether it has the potential to be deployed much more widely across the economy in a far wider range of roles.

History shows that it often takes a long time for work to be redesigned to harness the full benefit of new technologies. It took decades for personal computers to impact measured US productivity. No wonder that, in 1987, the US economist and Nobel laureate, Robert Solow, famously lamented that “You can see the computer age everywhere except in the productivity statistics”. Productivity did eventually respond, in the 1990s, but getting there took time, involved major disruption, the deployment of large amounts of capital and a great deal of trial and error.

Businesses also need to be alert to the limitations of and risks from AI. Current chatbots, for instance, have a tendency to ‘hallucinate,’ producing plausible but incorrect answers. Concerns about AI generating discriminatory outcomes or ownership and copyright questions loom large. Regulation is trying to catch up but, inevitably, it is behind the curve. The EU AI Act, for instance, is unlikely to come into force until early next year.

A recent Economist article observed that train drivers on the London underground are paid close to double the national median wage, even though the technology to partially or wholly replace them has existed for decades. In San Francisco, the global centre of AI, police are still employed to direct traffic during rush hour.

It could be different this time. The spread of ChatGPT suggests a potential for rapid take-up. But the true driver of change seems more likely to be AI tools made for particular uses, such as report writing, customer enquiries or analysing data. AI is already incorporated into functions such as predictive texting and there seems to be significant scope for it to be used in existing systems. Microsoft is currently evaluating its AI assistant, 365 Copilot, which is incorporated into programs such as Word, Excel, and Outlook.

But what of the potential for AI to render large swathes of the workforce redundant? It seems more likely that the pattern of the past, where those displaced by technology are redeployed in newly created roles, will hold. A recent report by David Author of the Massachusetts Institute of Technology estimates that 60% of US workers are employed in occupations that did not exist in 1940, implying that over 85% of employment growth over the last 80 years is explained by the technology-driven job creation.

Previous predictions of mass unemployment driven by technology have tended to be wide of the mark. In 2013 two Oxford academics estimated that automation could wipe out almost half of all US jobs over the subsequent “decade or two”. And yet here we are, in 2023, with US employment at 156m, up by 26% or 31m jobs since 2013.

A detailed analysis of the US labour market published by the US Bureau of Labor Statistics (BLS) last year found little support for the idea of automation, robotics or AI causing a general acceleration in job losses. Even in sectors thought by economists and technologists to be at most risk of automation, the authors found few signs of rapid job losses. As the paper notes, “Part of the reason new technology does not produce sharper changes in employment is the diversity of jobs within occupations and the diversity of tasks within jobs, not all of which are equally susceptible to technological substitution. Automation of some tasks may also alter the task composition of jobs, rather than simply reducing the number of jobs.”

The BLS also finds that an array of new technologies, from robot lawnmowers and vacuum cleaners to robot surgery and translation software, have failed to put a measurable dent in employment in the affected sectors in the US.

On balance, AI seems likely to do what technology has always done – to reshape work, replace some jobs, create many more and raise productivity.

Businesses are convinced of the opportunities. In a recent CFO survey by Deloitte, respondents said they expect to see a wave of AI-related capex driving UK productivity. It seems reasonable to take an optimistic view but equally, it is very likely that it will take time to see the effects of AI in the productivity numbers.

Stock Markets

Fear, greed and desperation

Fear matters in stock markets. Indeed, prices are often determined by the balance between fear and greed. Between them, these two instincts will prompt investors to back exciting growth opportunities but avoid foolish risks. One of the many ways to explain the volatile behaviour of markets of the last few years is by making one substitution. People are still greedy, because they’re human, but what has been playing a big part in driving investment decisions in recent years is not so much greed as desperation. And despair can overcome fear.

If you’re a long way under water, then your interest in a patient investing approach or a buy-and-hold stock-picking strategy will be limited. You’re drowning and you need to get to the surface to survive. If you come by some money and you are desperate, you will deploy it to give yourself the best chance of a spectacular return, even if there is also a real chance that it will go to zero.

In real life, people with nothing to lose are dangerous. Think of suicide bombers who are happy to die, or the goalkeeper charging into the opposition penalty area in the last few seconds of a game in a desperate attempt to score the winning goal. Or, in financial terms, rogue traders whose losses mean they are going to be fired, and so they double down on big risks that might just save them. Normally, these would be wildly stupid and irresponsible things to do; but when driven by desperation they can make sense. They also create danger for others.

Where do we see desperate investing? Two years ago, the meme stock phenomenon was fuelled by anger and desperation, rather than greed. The excitement around GameStop and other companies in part reflected a desire to hurt short sellers, who were widely seen as villains. But it also showed the incentives facing small investors with pandemic stimulus windfalls to invest. The younger generations face a difficult future as it is, lacking the generous pension guarantees of their parents, weighed down by student loans, and having little prospect of being able to buy a house. In this situation, it might make more sense to put a windfall to use in a risky way that just might hit the jackpot. A safe index fund or multi-asset approach is not going to move the dial on such a person’s life opportunities; a big win squeezing the shorts just might.

People who have nothing to lose are dangerous and create volatility in stock markets. That’s true even in the apparently humdrum world of pension management. Low interest rates brought despair and some seriously bad decisions on where to allocate capital. This was demonstrated last September when the Liability Driven Investment strategies deployed by some Trustees went badly wrong.

A fog has descended on the investment landscape, clouding judgment, leaving many investors grappling at what is behind them instead of focusing on what might lie ahead. Fear, greed, and desperation are all playing a part.

It’s times like these that investors tend to move in herds desperately pursuing short-term strategies that lead to mostly similar outcomes. It is also times like these that present opportunities to pursue long-term strategies that can create significant value for the patient investor. The Clarion portfolio funds, and model portfolios are constructed with that in mind. We avoid being distracted by short-term noise because we know that fundamentals will reassert themselves in time.

Long-term, active investing can be likened to a game of golf… winning is often less about driving the furthest off the tee than about avoiding the bunkers.

Advances in technology are changing the world and stock markets

One debate about investing is, do share prices drive narratives, or do narratives drive share prices? Do we observe market movements and find a story to attach to them, or is it the stories themselves which drive market movements?

Markets themselves are significantly over-analysed, as the media and investors seek to explain every share price move, every day. There are very few durable and long-lasting drivers of share prices and markets, with a very high proportion of day-to-day ‘news’ being nothing but noise. Most of the time if the market is up, the simple explanation is that there are more buyers than sellers, and it is nothing more than that.

The narrative driving markets has been unremittingly negative over the last 24 months. A war in Europe, inflation is too high, interest rates are moving higher at a rapid pace, a recession is near etc. All these narratives are valid of course, but they neglect other more positive developments. One of these is the development of artificial intelligence (AI) into Generative Artificial Intelligence (GAI).

GAI is the next stage in development of the broader area of AI. It is a term used to describe any AI system whose main role is to generate content, which can be text, images or sound. Previous forms of AI systems performed functions that were more data orientated, which included classifying, extracting and decision making. GAI will impact, a broader range of industries than AI has previously.

At the core of GAI remains many of the same principles of more traditional AI; a huge amount of information being crunched by immense computing power. Despite concerns, it is unlikely to replace humans, but it will make them more productive and allow time to be spent on more value-added tasks. For anyone worried that AI is becoming too powerful, it is still not close to being able to deliver autonomous driving despite years of development and millions of hours of training. A 17-year-old can learn to drive in roughly 20 hours. The human brain is a wonderful thing!

Is GAI enough to offset the negative concerns around markets? It could be if it improves human productivity rapidly and provides downward pressure on inflation and interest rates by reducing labour shortages. It could also improve corporate growth and profitability. Overall, it seems more sensible to view it not in a pessimistic light but rather as another, positive, variable to consider in the investment environment and a reminder that innovation and optimism can source investment returns.

Charting an epic shock for markets

There is no question that the current buzz in financial markets now centres around Artificial Intelligence. It’s impossible to ignore the blistering rally last month that propelled Nvidia Corp to the cusp of the trillion-dollar club. What is now the world’s biggest chipmaker by market value reported a blowout revenue forecast that caught even the most bullish Wall Street analysts by surprise. It has now become the world’s fifth-largest publicly traded company.

The Santa Clara, California-based group gained as much as 30% after saying it expects $11 billion revenue in the next quarter. That’s 52% higher than the $7.2 billion that analysts had estimated, and it’s hard to imagine that any company has ever administered a surprise on such a scale. Many analysts upgraded the stock which is now more than six times the size of Intel Corp – even though that company still has almost twice Nvidia’s annual revenue

The chipmaker, which was founded three decades ago, made one of the biggest one-day gains in market cap ever thanks almost entirely to the frenzy around artificial intelligence. AI is not new. It has been around for a while. But hype around AI has rocketed since the launch of OpenAI Inc’s ChatGPT last year sparked a race to capitalise on the phenomenon. The rally – which added more than $180 billion to Nvidia’s market cap in one day — left it bigger than its two largest US competitors put together.

The shares of Nvidia Inc are very highly rated and now trade at a price of 35 x Gross Revenue, note, not 35 x profit but 35 x Gross Revenue. From which, of course, the company must pay employees and operational expenses, purchase equipment, buy goods, invest in research, and pay taxes.

By comparison, the shares in Astra Zeneca, one of the world’s leading pharmaceutical companies, which makes stuff that actually saves lives, trade on a much more modest multiple of about 18 x net profit, a market rating which is many, many times less than that of Nvidia Corporation.

Fear (of missing out), greed or desperation or maybe all three…. or just plain madness? Only time will tell.

We thank you for your continued support and we look forward to updating you regularly throughout 2023. Please get in touch if you have any questions.

Keith W Thompson

Clarion Group Chairman

June 2023

Creating better lives now and in the future for our clients, their families and those who are important to them.

Risk Warnings

The content of this article does not constitute financial advice and you may wish to seek professional advice based on your individual circumstances before making any financial decisions.

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