Wobbles in the private credit market in the fourth quarter of 2025 spooked those retail investors with investments in private credit funds – a significant segment of the growing shadow banking sector. These funds use investors’ money to finance lending to businesses and individuals who struggle to, or do not want to, access credit from banks and the public market. Therefore, the risks are higher.
The failures of two auto parts suppliers in the USA last year have highlighted the risks involved. Retail investors are exiting such funds in significant numbers. Bcred, Blackstone’s $82 billion private credit fund, saw money equivalent to 8% of its net asset value (NAV) withdrawn. The firm, and employees, put $400m in to maintain confidence.
Blue Owl, another credit manager, closed investors’ usual quarterly redemption window, largely due to unprecedented demand. The fund’s managers have decided that they will wind down the fund and return money back to investors over time, whether that want it or not.
Several other listed funds run by big names, such as Blackrock and KKR, have slashed dividends and written down asset values. This week, both Morgan Stanley and Cliffwater limited withdrawals from their credit funds.
So, what has happened? In recent years, there has been a big growth in private credit funds in the USA aimed at individual retail investors. With interest margins low and fees from public investment products diminishing due to the shift to passive investing, financial institutions spied an opportunity for chunky fees by offering private credit investment to retail investors.
The liquidity–return trade-off
Such investors are attracted by the potential for higher returns that private credit funds offered compared to public funds. The need to provide higher returns was related partly to the higher credit risk associated with the lending, but also to the illiquidity of the private credit assets that the funds invested in.
While much attention in the financial media has focused on the heightened credit risk in private funds, less attention has been given to the liquidity issue. At the heart of the private credit business model is a level of illiquidity that individual retail investors would not be comfortable with. The liquidity–return trade-off is one of the fundamental concepts in finance. Investors must be prepared to trade-off liquidity for higher returns, and vice versa. They cannot have both.
This blog will discuss that trade-off in the context of private credit funds and its lessons for retail investors, particularly in Europe where institutions are gearing up to offer such investment products.
Liquidity preference
One of the fundamental concepts in finance is the maturity mismatch between the preferences of ultimate lenders (typically households) and the requirements of ultimate borrowers (typically firms, but also households and governments too). Typically, lenders want to ‘lend short’ while borrowers want to ‘borrow long’. The financial system reconciles this mismatch by providing two important economic functions – maturity transformation and liquidity provision.
Banks offer maturity transformation by offering current and other accounts to individuals where deposits can be redeemed at short notice. These institutions use the deposits to finance long-term lending for a variety of purposes; examples include property, investment in capital or day-to-day spending. Their effective management of this process is important economically for the smooth running of the payments mechanism and for economic growth.
But, to fulfil this, banks have to hold a mixture of assets with varying degrees of liquidity – some highly liquid, such as cash and short-term government debt instruments, and some illiquid, such as long-term loans. Liquidity is such an important issue for banks that their assets are listed on their balance sheet in order of liquidity – from most liquid to least liquid.
However, there is an inverse relationship between liquidity and expected return. Banks and their customers have to sacrifice return if they want higher liquidity. Therefore, liquid assets tend to offer a low rate of return and illiquid assets a higher rate of return. Consequently, in order to retain sufficient liquidity, the overall return banks can generate is limited compared to a situation where they invest wholly in illiquid assets.
If individuals want to invest directly in long-term financial assets, such as debt and equity, there must be a secondary market where these can be bought and sold – the stock market. Without this mechanism providing liquidity, individuals are less likely to invest in these assets in the first place. Few would want to wait for a debt security to mature or hold a share in perpetuity. Secondary markets mean they don’t have to.
Liquidity and private credit
Private credit funds have existed for a long time as part of the shadow banking sector and have grown in scale. Such funds invest in non-tradable, long-term illiquid loans as a parallel to the better-known private equity sector. Traditionally they have been targeted at institutional investors, who are more comfortable with the higher credit risk and illiquidity involved.
However, while institutions are prepared to forgo liquidity for many years in expectation of higher returns, individual retail investors are not – they have a higher liquidity preference. Funds tailoring private credit funds acknowledged that individual investors required a liquidity incentive to invest. Since there is no liquid secondary market to facilitate liquidation, private funds aimed at such retail investors offered quarterly redemption opportunities. The industry standard settled on around 5% of a fund’s value.
However, offering these ‘liquidity windows’ creates a tension in the private credit business model. Private credit operates on the basis of illiquidity in return for higher returns. This includes borrowers prepared to pay a higher interest rate on debt to avoid exposure to the glare of public market scrutiny.
Further, the prices of private loans are not ‘marked-to-the-market’ like publicly traded debt, so they are not correlated with public markets. This enables fund managers to work out credit problems over time rather than be forced into fire sales to meet the liquidity needs of investors.
Offering liquidity confounds that. To do so, private credit funds end up operating like quasi-public funds. They have to hold sufficient liquid assets to cover redemptions. Indeed, regulations for such funds in Europe are proposing a minimum of 20% of assets in liquid investments so there is a reserve to meet redemptions. But, by doing so, funds will not be able to generate the promised returns. Indeed, returns may be not much higher that that offered by public traded funds.
Further, providing quarterly redemption windows requires fair and timely valuations of the fund. Irrespective of perceptions around credit risk, if investors feel that the valuation is generous then many will want to take advantage of the liquidity window to redeem and no limit on withdrawals, be it 5%, 10% or whatever, is sufficient. However, with no secondary market mechanism to remove the excess demand, those told they cannot redeem their investment will only increase their demands for liquidity further and exit at the next available opportunity.
This irreconcilable tension in offering private credit funds to retail investors is being recognised. Not only are funds like Blue Owl being wound up, but the share prices of providers in the USA have fallen sharply as markets realise that the anticipated returns from selling private credit to retail investors are unlikely to be realised. Blackstone’s market capitalisation has halved from $250 billion at the end of 2024 to $134 billion on 11 March 2026.
But this is the moment when private credit funds are being offered to retail investors in Europe. The lesson for European retail investors from the US experience is that you can’t have high liquidity and high return. As with most allocation decisions, there is a trade-off.
Articles
Questions
- What is maturity transformation? Explain how banks conduct maturity transformation.
- What is liquidity provision? Explain how secondary financial markets provide liquidity.
- Explain why private credit funds offer a higher expected return than public ones?
- Analyse the pressures on profit margins in public markets which led financial institutions to offer private credit funds. In doing so, consider the ethics around offering such a product to retail investors.
- Explain why offering such funds to individual (retail) investors has not worked.
With businesses increasing their use of AI, this is likely to have significant effects on employment. But how will this affect the distribution of income, both within countries and between countries?
In some ways, AI is likely to increase inequality within countries as it displaces low-skilled workers and enhances the productivity of higher-skilled workers. In other ways, it could reduce inequality by allowing lower-skilled workers to increase their productivity, while displacing some higher-skilled workers and managers through the increased adoption of automated processes.
The effect of AI on the distribution of income between countries will depend crucially on its accessibility. If it is widely available to low-income countries, it could significantly enhance the productivity of small businesses and workers in such countries and help to reduce the income gap with the richer world. If the gains in such countries, however, are largely experienced by multinational companies, whether in mines and plantations, or in labour-intensive industries, such as garment production, few of the gains may accrue to workers and global inequality may increase.
Redistribution within a country
The deployment of AI may result in labour displacement. AI is likely to replace both manual and white-collar jobs that involve straightforward and repetitive tasks. These include: routine clerical work, such as data entry, filing and scheduling; paralegal work, contract drafting and legal research; consulting, business research and market analysis; accounting and bookkeeping; financial trading; proofreading, copy mark-up and translation; graphic design; machine operation; warehouse work, where AI-enabled warehouse robots do many receiving, sorting, stacking, retrieval, carrying and loading tasks (e.g. Amazon’s Sequoia robotic system); basic coding or document sifting; market research and advertising design; call-centre work, such as enquiry handling, sales, telemarketing and customer service; hospitality reception; sales cashiers in supermarkets and stores; analysis of health data and diagnosis. Such jobs can all be performed by AI assistants, AI assisted robots or chat bots.
Women are likely to be disproportionately affected because they perform a higher share of the administrative and service roles most exposed to AI.
Workers displaced by AI may find that they can find employment only in lower-paid jobs. Examples include direct customer-facing roles, such as bar staff, shop assistants, hairdressers and nail and beauty consultants.
Such job displacement by AI is likely to redistribute income from relatively low-skilled labour to capital: a redistribution from wages to profits. This will tend to lead to greater inequality.
AI is also likely to lead to a redistribution of income towards certain types of high-skilled labour that are difficult to replace with AI but which could be enhanced by it. Take the case of skilled traders, such as plumbers, electricians and carpenters. They might be able to use AI in their work to enhance their productivity, through diagnosis, planning, problem-solving, measurement, etc. but the AI would not displace them. Instead, it could increase their incomes by allowing them to do their work more efficiently or effectively and thus increase their output per hour and enhance their hourly reward. Another example is architecture, where AI can automate repetitive tasks and open up new design possibilities, allowing architects to focus on creativity, flexibility, aesthetics, empathy with clients and ethical decision-making.
An important distinction is between disembodied and embodied AI investment. Disembodied AI investment could include AI ‘assistants’, such as ChatGPT and other software that can be used in existing jobs to enhance productivity. Such investment can usually be rolled out relatively quickly. Although the extra productivity may allow some reduction in the number of workers, disembodied AI investment is likely to be less disruptive than embodied AI investment. The latter includes robotics and automation, where workers are replaced by machines. This would require more investment and may be slower to be adopted.
Then there are jobs that will be created by AI. These include prompt engineers, who develop questions and prompt techniques to optimise AI output; health tech experts, who help organisations implement new medical AI products; AI educators, who train people in the uses of AI in the workplace; ethics advisors, who help companies ensure that their uses of AI are aligned with their values, responsibilities and goals; and cybersecurity experts who put systems in place to prevent AI stealing sensitive information. Such jobs may be relatively highly paid.
In other cases, the gains from AI in employment are likely to accrue mainly to the consumer, with probably little change in the incomes of the workers themselves. This is particularly the case in parts of the public sector where wages/salaries are only very loosely related to productivity and where a large part of the work involves providing a personal service. For example, health professionals’ productivity could be enhanced by AI, which could allow faster and more accurate diagnosis, more efficient monitoring and greater accuracy in surgery. The main gainers would be the patients, with probably little change in the incomes of the health professionals themselves. Teachers’ productivity could be improved by allowing more rapid and efficient marking, preparation of materials and record keeping, allowing more time to be spent with students. Again, the main gainers would be the students, with little change in teachers’ incomes. Other jobs in this category include social workers, therapists, solicitors and barristers, HR specialists, senior managers and musicians.
Thus there is likely to be a distribution away from lower-skilled workers to both capital and higher-skilled workers who can use AI, to people who work in new jobs created by AI and to the consumers of certain services.
AI will accelerate productivity growth and, with it, GDP growth, but will probably displace workers faster than new roles emerge. This is likely to increase inequality and be a major challenge for society. Can the labour market adapt? Could the effects be modified if people moved to a four- or three-day week? Will governments introduce statutory limits to weekly working hours? Will training and education adapt to the new demands of employers?
Redistribution between countries
AI threatens to widen the global rich–poor divide. It will give wealthier nations a productivity and innovation edge, which could displace low-skilled jobs in low-income nations. Labour-intensive production could be replaced by automated production, with the capital owned by the multinational companies of just a few countries, such as the USA and China, which between them account for 40% of global corporate AI R&D spending. For some companies, it would make sense to relocate production to rich countries, or certain wealthier developing countries, with better digital infrastructure, advanced data systems and more reliable power supply.
For other companies, however, production might still be based in low-income countries to take advantage of low-cost local materials. But there would still be a redistribution from wages in such countries to the profits of multinationals.
But it is not just in manufacturing where low-income countries are vulnerable to the integration of AI. Several countries, such as India, the Philippines, Mexico and Egypt have seen considerable investment in call centres and IT services for business process outsourcing and customer services. AI now poses a threat to employment in this industry as it has the potential to replace large numbers of workers.
AI-related job losses could exacerbate unemployment and deepen poverty in poorer countries, which, with limited resources, limited training and underdeveloped social protection systems, are less equipped to absorb economic and social shocks. This will further widen the global divide. In the case of embodied AI investment, it may only be possible in low-income countries through multinational investment and could displace many traditional jobs, with much of the benefit going in additional multinational profit.
But it is not all bad news for low-income countries. AI-driven innovations in healthcare, education, and agriculture, if adopted in poor countries, can make a significant contribution to raising living standards and can slow, or even reverse, the widening gap between rich and poor nations. Some of the greatest potential is in small-scale agriculture. Smallholders can boost crop yields though precision farming powered by AI; AI tools can help farmers buy seeds, fertilisers and animals and sell their produce at optimum times and prices; AI-enabled education tools can help farmers learn new techniques.
Articles
- New Skills and AI Are Reshaping the Future of Work
IMF Blog, Kristalina Georgieva (14/1/26)
- Generative AI: degenerative for jobs?
Bank Underground, Bank of England blog, Edward Egan (22/1/26)
- Artificial intelligence (AI) and employment
UK Parliament Research Briefing Lydia Harriss and Sam Money-Kyrle (23/12/25)
- Is Your Job AI-Proof? What to Know About AI Taking Over Jobs
Built In, Matthew Urwin (27/8/25)
- AI likely to displace jobs, says Bank of England governor
BBC News, Michael Race (19/12/25)
- These Jobs Will Fall First as AI Takes Over the Workplace
Forbes, Jack Kelly (30/4/25)
- Disrupted or displaced? How AI is shaking up jobs
exec-appointments.com, Anjli Raval (9/7/25)
- Navigate the economic risks and challenges of generative AI
EY-Parthenon, Lydia Boussour (25/6/24)
- AI Isn’t Increasing Inequality; It’s Revealing the Gaps We Haven’t Wanted to See
HR News, Mark Abbott (18/12/25)
- AI promises efficiency, but it’s also amplifying labour inequality
The Conversation, Mehnaz Rafi (3/12/25)
- 10 Jobs AI Will Replace in 2025
Live Career, Marta Bongilaj (29/12/25)
- From steam to Silicon: Why inequality persists
Aik News HD (Pakistan), Ahmed Fawad Farooq (27/12/25)
- Rethinking AI’s role in income inequality
PwC: The Leadership Agenda (4/9/25)
- How Europe Can Capture the AI Growth Dividend
IMF Blog, Florian Misch, Ben Park, Carlo Pizzinelli and Galen Sher (20/11/25)
- The Next Great Divergence
UNDP: Asia and the Pacific (2/12/25)
- AI risks sparking a new era of divergence as development gaps between countries widen, UNDP report finds
UNDP Press Release (2/12/25)
- AI threatens to widen inequality among states: UN
Aljazeera (2/12/25)
- AI risks deepening inequality, says head of world’s largest SWF
Financial Times, James Fontanella-Khan and Sun Yu (23/11/25)
- Three Reasons Why AI May Widen Global Inequality
Center for Global Development, Philip Schellekens and David Skilling (17/10/24)
- AI Will Transform the Global Economy. Let’s Make Sure It Benefits Humanity
IMF Blog, Kristalina Georgieva (14/1/24)
- AI’s $4.8 trillion future: UN Trade and Development alerts on divides, urges action
UNCTAD Press Release (7/4/25)
- AI could affect 40% of jobs and widen inequality between nations, UN warns
CNBC, Dylan Butts (4/4/25)
Questions
- What types of job are most vulnerable to AI?
- How will AI change the comparative advantage of low-income countries and what effect will it be likely to have on the pattern of global trade?
- Assess alternative policies that governments in high-income countries can adopt to offset the growth in inequality caused by the increasing use of AI.
- What policies can governments in low-income countries or aid agencies adopt to offset the growth in inequality within low-income countries and between high- and low-income countries?
- How might the growth of AI affect your own approach to career development?
- Is AI likely to increase or decrease economic power? Explain.
The share prices of various AI-related companies have soared in this past year. Recently, however, they have fallen – in some cases dramatically. Is this a classic case of a bubble that is bursting, or at least deflating?
Take the case of NVIDIA, the world’s most valuable company, with a market capitalisation of around $4.2 trillion (at current share prices). It designs and produces graphics cards and is a major player in AI. From a low of $86.62 April this year, its share price rose to a peak of $212.19 on 29 October. But then began falling as talk grew of an AI bubble. Despite news on 19 November that its 2025 Q3 earnings were up 62% to $57.0bn, beating estimates by 4%, its share price, after a temporary rise, began falling again. By 21 November, it was trading at around $180.
Other AI-related stocks have seen much bigger rises and falls. One of the biggest requirements for an AI revolution is data processing, which uses huge amounts of electricity. Massive data centres are being set up around the world. Several AI-related companies have been building such data centres. Some were initially focused largely on ‘mining’ bitcoin and other cryptocurrencies (see the blog, Trump and the market for crypto). But many are now changing focus to providing processing power for AI.
Take the case of the Canadian company, Bitfarms Ltd. As it says on its site: ‘With access to multiple energy sources and strategic locations, our U.S. data centers support both mining and high-performance computing growth opportunities’. Bitfarms’ share price was around CAD1.78 in early August this year. By 15 October, it had reached CAD9.27 – a 421% increase. It then began falling and by 24 November was CAD3.42 – a decline of over 63%.
Data centres do have huge profit potential as the demand for AI increases. Many analysts are arguing that the current share price of data centres undervalues their potential. But current profits of such companies are still relatively low, or they are currently loss making. This then raises the question of how much the demand for shares, and hence their price, depends on current profits or future potential. And a lot here depends on sentiment.
If people are optimistic, they will buy and this will lead to speculation that drives up the share price. If sentiment then turns and people believe that the share price is overvalued, with future profits too uncertain or less than previously thought, or if they simply believe that the share price has overshot the value that reflects a realistic profit potential, they will sell and this will lead to speculation that drives down the share price
The dot.com bubble of the late 1990s/early 2000s is a case in point. There was a stock market bubble from roughly 1995 to 2001, where speculative investment in internet-based companies caused their stock values to surge, peaking in late 1999/early 2000. There was then a dramatic crash. But then years later, many of these companies’ share prices had risen well above their peak in 2000.
Take the case of Amazon. In June 1997, its share price was $0.08. By mid-December 1999, it had reached $5.65. It then fell, bottoming out at $0.30 in September 2001. The dot-com bubble had burst.
But the potential foreseen in many of these new internet companies was not wrong. After 2001, Amazon’s share price began rising once more. Today, Amazon’s shares are trading at over $200 – the precise value again being driven largely by the company’s performance and potential and by sentiment.
So is the boom in AI-related stock a bubble? Given that the demand for AI is likely to continue growing rapidly, it is likely that the share price of companies providing components and infrastructure for AI is likely to continue growing in the long term. But just how far their share prices will fall in the short term is hard to call. Sentiment is a fickle thing.
Articles
Questions
- Using a supply and demand diagram, illustrate how speculation can drive up the share price of a company and then result in it falling.
- What is meant by overshooting in a market? What is the role of speculation in this process?
- Does a rapid rise in the price of an asset always indicate a bubble? Explain.
- What are the arguments for suggesting that markets are/are not experiencing an AI share price bubble? Does it depend of what part of the AI market is being considered?
- What is meant by the market capitalisation of a company? Is it a good basis for deciding whether or not a company’s share price is a true reflection of the company’s worth? What other information would you require?
- Find out what has been happening to the price of Bitcoin. What factors determine the price of Bitcoin? Do these factors make the price inherently unstable?
The UK’s poor record on productivity since the 2008 financial crisis is well documented, not least in this blog series. Output per worker has flatlined over the 17 years since the crisis. As was noted in the blog, The UK’s poor productivity record, low UK productivity is caused by a number of factors, including the lack of investment in training, the poor motivation of many workers and the feeling of being overworked, short-termism among politicians and management, and generally poor management practices.
One of the most significant issues identified by analysts and commentators is the lack of investment in physical capital, both by private companies and by the government in infrastructure. Gross fixed capital formation (a measure of investment) has been much lower in the UK compared to international competitors.
From Figure 1 it can be observed that, since the mid-1990s, the UK has consistently had lower investment as a percentage of GDP compared to other significant developed market economies. The cumulative effect of this gap has contributed to lower productivity and lower economic growth.
Interestingly, since the financial crisis, UK firms have had high profitability and associated high cash holdings. This suggests that firms have had a lot of financial resources to reinvest. However, data from the OECD suggests that reinvestment rates in the UK, typically 40–50% of profit, are much lower than in many other OECD countries. In the USA the rate is 50%, in Germany 60–70% and in Japan 70%+. There is much greater emphasis in the UK on returning funds to shareholders through dividends and share buybacks. However, the reinvestment of much of this cash within firms could have gone some way to addressing the UK’s investment gap – but, it hasn’t been done.
Analysis by the OECD suggest that, while the cost of financing investment has declined since the financial crisis, the gap between this and the hurdle rate used to appraise investments has widened. Between 2010 and 2021 the difference nearly doubled to 4%. This increase in the hurdle rate can be related to increases in the expected rate of return by UK companies and their investors.
In this blog we will analyse (re)investment decisions by firms, discussing how increases in the expected rate of return in the UK raise the hurdle rate used to appraise investments. This reduces the incentive to engage in long-term investment. We also discuss policy prescriptions to improve reinvestment rates in the UK.
Investment and the expected rate of return
Investment involves the commitment of funds today to reap rewards in the future. This includes spending on tangible and intangible resources to improve the productive capacity of firms. Firms must decide whether the commitment of funds is worthwhile. To do so, economic theory suggests that they need to consider the compensation required by their provider of finance – namely, investors.
What rewards do investors require to keep their funds invested with the firm?
When conducting investment appraisal, firms compare the estimated rate of return from an investment with the minimum return investors are prepared to receive (termed the ‘expected return’). Normally this is expressed as a percentage of the initial outlay. Firms have to offer returns to investors which are equal to or greater than the minimum expected return – the return that is sufficient to keep funds invested in the firm. Therefore, returns above this minimum expected level are termed ‘excess returns’.
When firms conduct appraisals of potential investments, be it in tangible or intangible capital, they need to take into account the fact that net benefits, expressed as cash flows, will accrue over the life of the investment, not all at once. To do this, they use discounted cash flow (DCF) analysis. This converts future values of the net benefits to their present value. This is expressed as follows:
Where:
NPV = Net present value (discounted net cash flows);
K = Capital outlay (incurred at the present time);
C = Net cash flows (occur through the life of the investment project);
r = Minimum expected rate of return.
In this scenario, the investment involves an initial cash outlay (K), followed in subsequent periods by net cash inflows each period over the life of the investment, which in this case is 25 years. All the cash flows are discounted back to the present so that they can be compared at the same point in time.
The discount rate (r) used in appraisals to determine the present value of net cash flows is determined by the minimum expected return demanded by investors. If at that hurdle rate there are positive net cash flows (+NPV), the investment is worthwhile and should be pursued. Conversely, if at that hurdle rate there are negative net cash flows (–NPV), the investment is not worthwhile and should not be pursued.
According to economic theory, if a firm cannot find any investment projects that produce a positive NPV, and therefore satisfy the minimum expected return, it should return funds to shareholders through dividends or share buybacks so that they can invest the finance more productively.
Firm-level data from the OECD suggest that UK firms have had higher profits and this has been associated with increased cash holdings. But, due to the higher hurdle rate, less investment is perceived to be viable and thus firms distribute more of their profits through dividends and share buybacks. These payouts represent lost potential investment and cumulatively produce a significant dent in the potential output of the UK economy.
Why are expected rates of return higher in the UK?
This higher minimum rate of expected return can be explained by factors influencing its determinants; opportunity cost and risk/uncertainty.
Higher opportunity cost. Opportunity cost relates to the rate of return offered by alternatives. Investors and, by implication firms, will have to consider the rate of return offered by alternative investment opportunities. Typically, investors have focused on interest rates as a measure of opportunity cost. Higher interest rates raise the opportunity cost of an investment and increase the minimum expected rate of return (and vice versa with lower interest rates).
However, it is not interest rates that have increased the opportunity cost, and hence the minimum expected rate of return associated with investment, in the UK since the financial crisis. For most of the period since 2008, interest rates have been extremely low, sitting at below 1%, only rising significantly during the post-pandemic inflationary surge in 2022. This indicates that this source of opportunity cost for the commitment of business investment has been extremely low.
However, there may be alternative sources of opportunity cost which are pushing up the expected rate of return. UK investors are not restricted to investing in the UK and can move their funds between international markets determined by the rate of return offered. The following table illustrates the returns (in terms of percentage stock market index gain) from investing in a sample of UK, US, French and German stock markets between August 2010 and August 2025.
When expressed in sterling, returns offered by UK-listed companies are lower across the whole period and in most of the five-yearly sub-periods. Indeed, the annual equivalent rate of return (AER) for the FTSE 100 index across the whole period is less than half that of the S&P 500. The index offered a paltry annual return of 2.57% between 2015 and 2020, while the US index offered a return of 16.48%. Both the French and German indices offered higher rates of return, in the latter part of the period particularly. This represents a higher opportunity cost for UK investors and may have increased their expectations about the return they require for UK investments.
Greater perceived risk/uncertainty. Expected rates of return are also determined by perceptions of risk and uncertainty – the compensation investors need to bear the perceived risk associated with an investment. Investors are risk averse. They demand higher expected return as compensation for higher perceived risk. Higher levels of risk aversion increase the expected rate of return and related investment hurdle rates.
There has been much discussion of increased uncertainty and risk aversion among global investors and firms (see the blogs Rising global uncertainty and its effects, World Uncertainty Index, The Chancellor’s fiscal dilemma and Investment set to fall as business is baffled by Trump). The COVID-19 pandemic, inflation shocks, the war in Ukraine, events across the Middle East and the trade policies adopted by the USA in 2025 have combined to produce a very uncertain business environment.
While these have been relatively recent factors influencing world-wide business uncertainty, perceptions of risk and uncertainty concerning the UK economy seem to be longer established. To measure policy-related economic uncertainty in the UK, Baker, Bloom and Davis at www.PolicyUncertainty.com construct an index based on the content analysis of newspaper articles mentioning terms reflecting policy uncertainty.
Figure 2 illustrates the monthly index from 1998 to July 2025. The series is normalised to standard deviation 1 prior to 2011 and then summed across papers, by month. Then, the series is normalised to mean 100 prior to 2011.
Some of the notable spikes in uncertainty in the UK since 2008 have been labelled. Beginning with the global financial crisis, investors and firms became much more uncertain. This was exacerbated by a series of economic shocks that hit the economy, one of which, the narrow vote to leave the European Union in 2016, was specific to the UK. This led to political turmoil and protracted negotiations over the terms of the trade deal after the UK left. This uncertainty has been exacerbated recently by the series of global shocks highlighted above and also the budget uncertainty of Liz Truss’s short-lived premiership and now the growing pressure to reduce government borrowing.
While spikes in uncertainty occurred before the financial crises, the average level of uncertainty, as measured by the index, has been much higher since the crisis. From 1998 to 2008, the average value was 89. Since 2008, the average value has been 163. Since the Brexit vote, the average value has been 185. This indicates a much higher perception of risk and uncertainty over the past 15 year and this translates into higher minimum expected return as compensation. Consequently, this makes many long-term investment projects less viable because of higher hurdle rates. This produces less productive investment in capital, contributing significantly to lower productivity.
Policy proposals
There has been much debate in the UK about promoting greater long-term investment. Reforms have been proposed to improve public participation in long-term investment through the stock market. To boost investment, this would require the investing public to be prepared to accept lower expected returns for a given level of risk or accept higher risk for a given level of returns.
Evidence suggests that the appetite for this may be very low. UK savers tend to favour less risky and more liquid cash deposits. It may be difficult to encourage them to accept higher levels of risk. In any case, even if they did, many may invest outside the UK where the risk-return trade-off is more favourable.
Over the past 10 years, policy uncertainty has played a significant role in deterring investment. So, if there is greater continuity, this may then promote higher levels of investment.
The Labour government has proposed policies which aim to share or reduce the risk/uncertainty around long-term investment for UK businesses. For instance, a National Wealth Fund (NWF) has been established to finance strategic investment in areas such as clean energy, gigafactories and carbon capture. Unfortunately, the Fund is financed by borrowing through financial markets and the amount expected to be committed over the life of the current Parliament is only £29 billion, assuming that private capital matches public commitments in the ratio expected. It is questionable whether the Fund’s commitment will be sufficient to attract private capital.
Alternatively, Invest 2035 is a proposal to create a stable, long-term policy environment for business investment. It aims to establish an Industrial Strategy Council for policy continuity and to tackle issues like improving infrastructure, reducing energy costs and addressing skills gaps. Unfortunately, even if there is some attempt at domestic policy stability, the benefits may be more than offset by perceptions around global uncertainty, which may mean that UK investors’ minimum expected rates of return remain high and long-term investment low for the foreseeable future.
Articles
Data
Questions
- Use the marginal efficiency of capital framework to illustrate the ‘lost’ investment spending in the UK due to the investment hurdle rate being higher than the cost of capital.
- Explain the arbitrage process which produces the differences in valuations of UK securities and foreign ones due to differences in the expected rate of return.
- Sketch an indifference curve for a risk-averse investor, treating expected return and risk as two characteristics of a financial instrument.
- How does higher uncertainty affect the slope of an indifference curve for such an investor? How does this affect their investment hurdle rate?
- Analyse the extent to which the proposed polices can reduce the investment hurdle rate for UK companies and encourage greater levels of investment.
In a blog in October 2024, we looked at global uncertainty and how it can be captured in a World Uncertainty Index. The blog stated that ‘We continue to live through incredibly turbulent times. In the past decade or so we have experienced a global financial crisis, a global health emergency, seen the UK’s departure from the European Union, and witnessed increasing levels of geopolitical tension and conflict’.
Since then, Donald Trump has been elected for a second term and has introduced sweeping tariffs. What is more, the tariffs announced on so-called ‘Liberation Day‘ have not remained fixed, but have fluctuated with negotiations and threatened retaliation. The resulting uncertainty makes it very hard for businesses to plan and many have been unwilling to commit to investment decisions. The uncertainty has been compounded by geopolitical events, such as the continuing war in Ukraine, the war in Gaza and the June 13 Israeli attack on Iran.
The World Uncertainty Index (WUI) tracks uncertainty around the world by applying a form of text mining known as ‘term frequency’ to the country reports produced by the Economist Intelligence Unit (EIU). The words searched for are ‘uncertain’, ‘uncertainty’ and ‘uncertainties’ and the number of times they occur as percentage of the total words is recorded. To produce the WUI this figure is then multiplied by 1m. A higher WUI number indicates a greater level of uncertainty.
The monthly global average WUI is shown in Chart 1 (click here for a PowerPoint). It is based on 71 countries. Since 2008 the WUI has averaged a little over 23 000: i.e. 2.3 per cent of the text in EIU reports contains the word ‘uncertainty’ or a close variant. In May 2025, it was almost 79 000 – the highest since the index was first complied in 2008. The previous highest was in March 2020, at the start of the COVID-19 outbreak, when the index rose to just over 56 000.
The second chart shows the World Trade Uncertainty Index (WTUI), published on the same site as the WUI (click here for a PowerPoint). The method adopted in its construction therefore mirrors that for the WUI but counts the number of times in EIU country reports ‘uncertainty’ is mentioned within proximity to a word related to trade, such as ‘protectionism’, ‘NAFTA’, ‘tariff’, ‘trade’, ‘UNCTAD’ or ‘WTO.’
The chart shows that in May 2025, the WTUI had risen to just over 23 000 – the second highest since December 2019, when President Trump imposed a new round of tariffs on Chinese imports and announced that he would restore steel tariffs on Brazil and Argentina. Since 2008, the WTUI has averaged just 2228.
It remains to be seen whether more stability in trade relations and geopolitics will allow WUI and WUTI to decline once more, or whether greater instability will simply lead to greater uncertainty, with damaging consequences for investment and also for consumption and employment.
Articles
- IMF World Economic Outlook: economic uncertainty is now higher than it ever was during COVID
The Conversation, Sergi Basco (23/4/25)
- Economic uncertainty hits new high
McKinsey, Sven Smit et al. (29/5/25)
- Trade tensions and rising uncertainty drag global economy towards recession
UNCTAD News (25/4/25)
- IMF Warns Global Economic Uncertainty Surpasses Pandemic Levels
The Global Treasurer (24/4/25)
- Britons ‘hoarding cash amid economic uncertainty and fear of outages’
The Guardian, Phillip Inman (10/6/25)
- America’s Brexit Phase
Foreign Affairs, Jonathan Haskel and Matthew J. Slaughter (10/6/25)
- Goldman Sachs’ CEO on the ‘Big, Beautiful Bill,’ Trump’s Tariffs and Economic Volatility
Politico, Sam Sutton (13/6/25)
- The Countries Where Economic Uncertainty Is Rising Fastest
24/7 Wall St., Evan Comen (9/6/25)
- Trump’s tariffs have finally kicked in, so what happens next?
The Conversation, Maha Rafi Atal (8/8/25)
Uncertainty Indices
Questions
- Explain what is meant by ‘text mining’. What are its strengths and weaknesses in assessing business, consumer and trade uncertainty?
- Explain how the UK Monthly EPU Index is derived.
- Why has uncertainty increased so dramatically since the start of 2025?
- Compare indices based on text mining with confidence indices.
- Plot consumer and business/industry confidence indicators for the past 24 months, using EC data. Do they correspond with the WUI?
- How may uncertainty affect consumers’ decisions?
