IMF update

 

Dear MARIA, We just published a new blog—please find the full text below.  Artificial Intelligence Can Make Markets More Efficient—and More Volatile (Credit: IMF Photo/Tuane Fernandes) By Nassira Abbas, Charles Cohen, Dirk Jan Grolleman, and Benjamin Mosk More efficient or more volatile? The adoption of the latest iterations of artificial intelligence by financial markets can improve risk management and deepen liquidity; but it could also make markets opaque, harder to monitor, and more vulnerable to cyber-attacks and manipulation risks. The new Global Financial Stability Report looks at new market data to understand where this technology might be taking us. IMF staff conducted extensive outreach across various stakeholders—from investors to technology providers to market regulators—to show how financial institutions are harnessing advances in AI for capital market activities, and the potential impact of its adoption. Hedge funds, investment banks, and others have been using quantitative trading strategies for decades. Automated trading algorithms have helped markets move faster and digest large trades more efficiently in major asset classes such as US equities. But they have also contributed to “flash crash” events when market prices have swung wildly in very short periods of time—such as in May 2010 when US stock prices collapsed only to rebound minutes later—and there are fears they could destabilize markets in times of severe stress and uncertainty. Artificial intelligence, through its ability to almost instantly process large amounts of data and even text for use by traders, is poised to take these kinds of changes to another level. However, while generative AI and other recent breakthroughs are attracting attention in both the popular press and in financial markets, today they are used in only limited ways by actual investors. So, if we are only at the beginning of an AI-led transformation, where might we be headed? Patent filings are a good way to understand this, given what is often a long lead time between filings and actual production-ready technology. Since large language models, or LLMs, started to appear in 2017, the share of AI content in patent applications related to algorithmic trading has risen from 19 percent in 2017 to over 50 percent each year since 2020, suggesting a wave of innovation is coming in this area. These new innovations will likely further AI's ability to quickly rebalance investment portfolios, which will in turn lead to higher trading volumes. Market participants we surveyed concur that high-frequency, AI-driven trading is expected to become more prevalent, particularly in liquid asset classes like equities, government bonds, and listed derivatives. They foresee greater integration of sophisticated AI in investment and trading decisions within three to five years, although a “human in the loop” approach is expected to persist, especially for large capital allocation decisions. Evidence of these changes is already being seen in the exchange-traded fund market. Although currently small, AI-driven ETFs show a significantly higher turnover compared to other ETFs. While a typical actively managed equity ETF turns over its holdings much less than once a year, AI-driven ETFs do so about once a month. If widespread, such strategies may in the future mean deeper, more liquid markets which are good for investors. But they could also contribute to market instability: several AI-driven ETFs saw increased turnover during the March 2020 market turmoil, suggesting the potential for increased herd-like selling during times of stress. Prices may react much more quickly in an AI-driven market. Investors mentioned the release of the complex and lengthy minutes of Federal Reserve meetings as an example where AI could provide a trading signal faster than any human trader could, and this may already be happening. Since 2017 and the introduction of LLMs, the movement of US equity prices 15 seconds after the release of the Fed minutes seem to be more consistently in the direction of the longer-lasting movement seen after 15 minutes, in contrast to the apparently uncorrelated movements in the pre-LLM period. Who will be able to take advantage of these new technologies? AI might lead to a further migration of investment to hedge funds, proprietary trading firms, and other nonbank financial intermediaries, which would make markets less transparent and harder to monitor. Nonbanks have a structural advantage in the adoption of AI. They are generally more agile and subject to fewer regulatory constraints than large commercial and investment banks, which often must deal with legacy infrastructure and may be subject to more stringent requirements, including ensuring the explainability of complex AI models. Policy recommendations How should regulators and supervisors prepare for this new world? In a faster reacting market where nonbanks may continue to rise in importance, various aspects of regulation and supervision in AI-related areas should be enhanced. Financial sector authorities and trading venues should determine if they need to design new volatility response mechanisms—or modify the existing ones appropriately—to respond to “flash crash” events potentially originated in AI-driven-trading. These include margin requirements, circuit breakers, and the resilience of central counterparties. Similarly, financial sector authorities should continue to strengthen oversight and regulation of nonbank financial intermediaries by requiring them to identify themselves and disclose AI-relevant information; as well as require financial institutions to regularly map interdependencies between data, models, and technological infrastructure supporting AI models. A close monitoring and oversight of this rapidly changing market lays the foundation for an opportune and balanced regulatory response that may allow financial sector participants to benefit from AI while mitigating its risks. —This blog is based on Chapter 3 of the October 2024 Global Financial Stability Report, “Advances in Artificial Intelligence: Implications for Capital Market Activities.”

Global Public Debt Is Probably Worse Than it Looks (Credit: IMF Photo/Tuane Fernandes) By Era Dabla-Norris, Davide Furceri, Raphael Lam, and Jeta Menkulasi Global public debt is very high. It is expected to exceed $100 trillion, or about 93 percent of global gross domestic product by the end of this year and will approach 100 percent of GDP by 2030. This is 10 percentage points of GDP above 2019, that is, before the pandemic. While the picture is not homogeneous—public debt is expected to stabilize or decline for two thirds of countries—the October 2024 Fiscal Monitor shows that future debt levels could be even higher than projected, and much larger fiscal adjustments than currently projected are required to stabilize or reduce it with a high probability. The report argues that countries should confront debt risks now with carefully designed fiscal policies that protect growth and vulnerable households, while taking advantage of the monetary policy easing cycle. Worse than expected The fiscal outlook of many countries might be worse than expected for three reasons: large spending pressures, optimism bias of debt projections, and sizable unidentified debt. Previous IMF research has shown that fiscal discourse across the political spectrum has increasingly tilted toward higher spending. And countries will need to increasingly spend more to cope with aging and healthcare; with the green transition and climate adaptation; and with defense and energy security, due to growing geopolitical tensions. On the other side, past experience suggests that debt projections tend to underestimate actual outcomes by a sizable margin. Realized debt-to-GDP ratios five-years ahead can be 10 percentage points of GDP higher than projected on average. The Fiscal Monitor presents a novel “debt-at-risk” framework linking current macro-financial and political conditions to the entire spectrum of possible future debt outcomes. This approach goes beyond the typical focus on the point estimates of debt forecasts and helps policymakers quantify risks to the debt outlook and identify their sources. This framework shows that in a severely adverse scenario global public debt could reach 115 percent of GDP in three years—nearly 20 percentage points higher than currently projected. This could be due to several reasons: weaker growth, tighter financing conditions, fiscal slippages, and greater economic and policy uncertainty. Importantly, countries are increasingly vulnerable to global factors affecting their borrowing costs, including spillovers from greater policy uncertainty in systematically important countries, such as the United States. Sizable unidentified debt is another reason for public debt to end up being significantly higher than projected. An analysis of more than 30 countries finds that 40 percent of unidentified debt stems from contingent liabilities and fiscal risks governments face, of which most are related to losses in state-owned enterprises. Historically, unidentified debt has been large, ranging from 1 to 1.5 percent of GDP on average, and it increases sharply during periods of financial stress. Larger fiscal consolidation If public debt is higher than it looks, current fiscal efforts are likely smaller than needed. Fiscal adjustment plays a crucial role in containing debt risks. With inflation moderating and central banks lowering policy rates, economies are better positioned now to absorb the economic effects of fiscal tightening. Delaying would be both costly and risky, as the required correction grows as time goes by; and experience shows that high debt and lack of credible fiscal plans can trigger adverse market reaction, constraining room to maneuver in the face of turbulence. Our analysis, accounting for country-specific risks surrounding the debt outlook, suggests that current fiscal adjustments—on average, of 1 percent of GDP over six years by 2029—even if implemented in full, are not enough to significantly reduce or stabilize debt with a high probability. A cumulative tightening of about 3.8 percent of GDP over the same period would be needed for an average economy to ensure a high likelihood of debt stabilization. In countries where debt is not projected to stabilize, such as China and the United States, the required effort is substantially greater. But these two largest economies have a much richer set of policy choices than other countries. Focus on people Such large fiscal adjustments, if not well calibrated, will entail large output losses as aggregate demand falls and can harm vulnerable groups and lead to higher inequality. A careful design is thus needed to mitigate the costs of the adjustment and to garner public support for needed fiscal adjustment. The choice of fiscal measures matters because the impacts are not alike and involve trade-offs. For example, cuts in public investment have the largest output losses and hurt long-term growth prospects, while reducing social transfers hurts vulnerable households and raises inequality. A judicious mix of people- and growth-focused fiscal measures is needed and will vary across countries. Advanced economies should advance entitlement reforms, reprioritize expenditures, and increase revenues where taxation is low. Emerging market and developing economies have greater potential to mobilize tax revenues—by broadening tax bases and enhancing revenue administration capacity—while strengthening social safety nets and safeguarding public investment to support long-term growth. Speed also matters. Our analysis suggests that a measured and sustained pace of adjustment would alleviate fiscal risks, while limiting the negative impact on output and inequality by about 40 percent less than a more abrupt tightening. That said, some countries with high risk of debt distress will need front-loaded adjustments. Adjustments need to be accompanied by stronger fiscal governance, including credible medium-term frameworks, independent fiscal councils, and sound risk management. Enhancing fiscal risk assessment, monitoring closely contingent liabilities in state-owned enterprises, and publishing granular and timely debt statistics can reduce unidentified debt. High public debt is a concern. Even for some countries where the public debt levels seem manageable, the Fiscal Monitor argues that risks are elevated, and actual debt outcomes in coming years may be worse than projected. Current adjustment plans are not enough to stabilize or reduce debt confidently. The report also shows that well-designed fiscal adjustments can help reduce debt risks, improve public debt outlooks, and mitigate the adverse impact on society.   Jeff Kearns Managing Editor IMF Blog jkearns@IMF.org   Thank you again for your interest in IMF Blog. Read more of our latest content here. Take good care!

(Photo: Courtesy of Yuval Noah Harari ) Stories can unify or divide but our ability to imagine them is uniquely human. Cooperation and trust, built through shared stories and narratives, are the foundation of human societies and economies. So what happens when humans no longer hold the pen? Yuval Noah Harari is a historian, philosopher, and author of several books on human evolution, including Sapiens, and Nexus: A brief history of Information Networks from the Stone Age to AI. In this podcast, Harari says artificial intelligence is a risk to humankind’s most valuable resource, trust.    Listen to the podcast on: IMF.org | Apple Podcasts | Spotify | Soundcloud | YouTube   Read the transcript   Thanks for listening to the podcast. We're always looking to improve your experience so let us know if you have any suggestions! Send your comments to me at bedwards2@IMF.org.   Bruce Edwards Producer, IMF Podcasts

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