2024 will be a pivotal year

1. Emergence of TARA, after the end of TINA

The inflationary shock of 2021-22 encouraged the emergence of a correlation between equities and bonds in the wrong direction (decline in both equities and bonds). The disinflation of 2023 has already moderated this correlation, and fixed income and equity products have once again become diversified. We have gradually moved from an end-of-TINA (There Is No Alternatives) rationale, with the attractiveness of money market products, to a TARA (There Are Some Reasonnable Alternatives) rationale.

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Bonds are (finally) on a roll

The situation on the euro bond market has changed completely since March. As can be seen from the chart below, while most yields on the various credit qualities and maturities were below 3%, these levels have now completely disappeared. In most cases, yields are between 3% and 5%, and even exceed 5% for ratings below BB+.

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The sun should shine in Japan

Looking back to 2023

The restructuring of Japanese companies, which began a decade ago with the aim of improving profitability, cash flow and returns to shareholders, accelerated last year under the impetus of a wave of management buy-outs, hostile takeovers and interventions by activist shareholders. In 2023, for example, activist shareholders forced companies to make payments to their shareholders on sixty occasions.

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Clean Energy: our 4 convictions for 2024

2023 will go down in history as a particularly difficult year for the renewable energy sector. The geopolitical situation caused companies involved in fossil fuels, the only ones capable of responding quickly to shortages, to take a dive. Between virtue and the urgent need to find energy sources to replace Russian exports, the market’s choice was quickly made.

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Small & Mid caps: Beware of complacency

Despite some optimism among market participants, the macroeconomic environment remains rather weak and growth is set to slow in 2024. The big question is whether this will be a soft landing or a harder one. Caution therefore seems to be the order of the day in the first half of the year, even if price falls will provide opportunities to add to positions.

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Convertible bonds: the comeback

Key points for 2024

We are sailing through particularly troubled waters at the moment, with economic pitfalls but also major geopolitical whirlwinds, between the war in Ukraine, unrest in the Middle East and tensions between China and Taiwan. On top of this, there are important electoral deadlines this year, not only the US presidential election, but also elections in the European Union, Taiwan, Russia and India.

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Infrastructure: two themes for 2024

The rise in interest rates in 2023 has had a negative impact on the infrastructure sector, whose valuation is often closely linked to the discount rate used to assess its value. As a result, the indices concerned underperformed, with the S&P Utilities falling by 10% over the year, compared with a rise of the same order for the Dow Jones Industrials. For its part, the global infrastructure index only just managed to end the year in positive territory (+1%). We are therefore satisfied with the performance of our infrastructure fund, which posted a result of +5.1% for the year 2023, overperforming its benchmark* by 2.9 percentage point.

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Generative AI: what is the real potential for macro and equity markets?

The emergence of generative AI has technological, macroeconomic, societal (social, spatial and generational inequalities), managerial, demographic (life expectancy), political and geopolitical dimensions.

Of course, we don’t have the time in this column to go into precise and extensive detail on generative AI, and we will obviously return to the subject in greater detail in subsequent publications.

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The sustainability challenges raised by AI

While a primitive concept of artificial intelligence (AI) can be traced back to the Bell Laboratories in the 1950s, it was not until this year that many of us experienced its potential for the first time, with the launch of OpenAI’s ChatGPT and other large language models. In simple terms, the simulation of human intelligence by machines requires computer programs that are trained on large amounts of data to infer or problem solve with minimal human intervention. As we embark upon the next wave of technological innovation, we reflect upon the balance of ESG risks and opportunities on the horizon.

Climate Change

Artificial intelligence may be the next frontier for fighting climate change. A recent study from the World Economic Forum[1] concludes that digital technologies, such as AI, can reduce greenhouse gas emissions by up to 20% by 2050 in the three highest-emitting sectors: energy, mobility and materials. In brief, AI can be used to better track and report greenhouse gas emissions, improve circularity and reduce emissions. A tangible example of this would be Johnson Controls International (JCI), which can deploy AI across its heating, ventilation, and air conditioning (HVAC) control systems to deliver significant improvement in building energy efficiency. The company’s suite of OpenBlue digital solutions allows customers to reduce operating costs and carbon emissions and improve indoor air quality. Using AI, HVAC systems can respond to data from internal sensors monitoring temperature and humidity and combine that with additional data, such as weather forecasts, occupancy levels, energy source and energy cost to optimize equipment efficiency.

Energy efficiency is especially relevant when considering that data centers running AI training and inference models are expected to consume an increasing amount of energy, not just for processing data but also for cooling equipment. Indeed, a peer-reviewed analysis[2] published last month lays out a range of scenarios around the growing energy footprint of artificial intelligence. In a base-case scenario, AI servers could use between 85 to 134 terawatt hours of energy annually by 2027, which represents around 0.5% of the world’s current electricity use and approximates what Argentina, the Netherlands and Sweden each use in a year.

 Jobs and Workforce

While initially disruptive, industrial or technology revolutions have historically translated into an overall growth in employment opportunities. As evidence, a recent study by MIT economist David Autor indicates that 60% of today’s workers are employed in occupations that did not exist in 1940 – implying that 85% of employment growth over the last 80 years can be explained by technology-led creation of new jobs. Viewed differently, AI could be the new demographic that supplies the labor shortage caused by slower population growth and aging populations around the world. Unlike prior technological innovations, which have historically disrupted blue-collar jobs, AI is likely to impact white-collar jobs – several studies have identified knowledge roles in the administrative, computer, mathematical, business, design and media domains as most likely to be impacted. As noted earlier, history would suggest that AI is unlikely to result in a reduction of aggregate employment, but rather lead to the creation of new roles. As such, what we are focused on is the corporate response to these workforce changes with respect to upskilling resources for displaced workers.


The responsible phasing out of human judgment in favor of AI models carries significant risks associated with biased inputs, inaccurate results (model hallucinations), accessibility, data security and privacy and cybersecurity. Since AI models are trained on human-generated data, we run the risk of perpetuating biases that could have meaningful ramifications for a range of end-use applications, such as financial (i.e. biasing lending practices) or legal (i.e. biasing legal outcomes). Furthermore, the costs associated with training AI models today are prohibitively expensive, which concentrates access to this powerful resource into the hands of large and resourceful developers that may broaden access to these tools in limited ways. Looking ahead, we await clarity on the regulatory framework in the United States, where several government agencies are working on finding a balance between encouraging innovation and mitigating the potential harms of the use of AI without guardrails, including political polarization, privacy violations and social inequity.

[1] https://www.weforum.org/press/2022/05/digital-tech-can-reduce-emissions-by-up-to-20-in-high-emitting-industries/

[2] Joule, The Growing Energy Footprint of Artificial Intelligence, Alex de Vries, October 10, 2023