We address the detection of emission reduction goals in corporate reports, an important task for monitoring companies' progress in addressing climate change. Specifically, we focus on the issue of integrating expert feedback in the form of labeled example passages into LLM-based pipelines, and compare the two strategies of (1) a dynamic selection of few-shot examples and (2) the automatic optimization of the prompt by the LLM itself. Our findings on a public dataset of 769 climate-related passages from real-world business reports indicate that automatic prompt optimization is the superior approach, while combining both methods provides only limited benefit. Qualitative results indicate that optimized prompts do indeed capture many intricacies of the targeted emission goal extraction task.

Trend following and momentum investing are common strategies employed by asset managers. Even though they can be helpful in the proper situations, they are limited in the sense that they work just by looking at past, as if we were driving with our focus on the rearview mirror. In this paper, we advocate for the use of Artificial Intelligence and Machine Learning techniques to predict future market trends. These predictions, when done properly, can improve the performance of asset managers by increasing returns and reducing drawdowns.

There is growing recognition among financial institutions, financial regulators and policy makers of the importance of addressing nature-related risks and opportunities. Evaluating and assessing nature-related risks for financial institutions is challenging due to the large volume of heterogeneous data available on nature and the complexity of investment value chains and the various components' relationship to nature. The dual problem of scaling data analytics and analysing complex systems can be addressed using Artificial Intelligence (AI). We address issues such as plugging existing data gaps with discovered data, data estimation under uncertainty, time series analysis and (near) real-time updates. This report presents potential AI solutions for models of two distinct use cases, the Brazil Beef Supply Use Case and the Water Utility Use Case. Our two use cases cover a broad perspective within sustainable finance. The Brazilian cattle farming use case is an example of greening finance - integrating nature-related considerations into mainstream financial decision-making to transition investments away from sectors with poor historical track records and unsustainable operations. The deployment of nature-based solutions in the UK water utility use case is an example of financing green - driving investment to nature-positive outcomes. The two use cases also cover different sectors, geographies, financial assets and AI modelling techniques, providing an overview on how AI could be applied to different challenges relating to nature's integration into finance. This report is primarily aimed at financial institutions but is also of interest to ESG data providers, TNFD, systems modellers, and, of course, AI practitioners.

Developing learning-based methods for navigation of aerial robots is an intensive data-driven process that requires highly parallelized simulation. The full utilization of such simulators is hindered by the lack of parallelized high-level control methods that imitate the real-world robot interface. Responding to this need, we develop the Aerial Gym simulator that can simulate millions of multirotor vehicles parallelly with nonlinear geometric controllers for the Special Euclidean Group SE(3) for attitude, velocity and position tracking. We also develop functionalities for managing a large number of obstacles in the environment, enabling rapid randomization for learning of navigation tasks. In addition, we also provide sample environments having robots with simulated cameras capable of capturing RGB, depth, segmentation and optical flow data in obstacle-rich environments. This simulator is a step towards developing a - currently missing - highly parallelized aerial robot simulation with geometric controllers at a large scale, while also providing a customizable obstacle randomization functionality for navigation tasks. We provide training scripts with compatible reinforcement learning frameworks to navigate the robot to a goal setpoint based on attitude and velocity command interfaces. Finally, we open source the simulator and aim to develop it further to speed up rendering using alternate kernel-based frameworks in order to parallelize ray-casting for depth images thus supporting a larger number of robots.

The use of artificial intelligence in asset management is rapidly increasing -- or at least that's what asset managers want you to believe. I've evaluated scores of managers claiming to use AI. Although some are genuine in their adoption, many are guilty of what I call AI-washing -- professing to use AI when in fact they are merely employing traditional quantitative techniques, such as simple linear regressions, that technically qualify as "machine learning." These dubious claims largely target asset owners who are "eager" to invest in AI-driven funds, according to a recent CFA Institute Investor Trust Study. The survey found that 84 percent of institutional investors want to invest in funds that use artificial intelligence and 78 percent "believe that the use of AI in investment decision making will lead to better investor outcomes."

Investing is one of the most quantitatively intensive fields there is. Still, it is cluttered with old-school models that are simple and heuristic-based. The new-age millennial investors recognize the power of artificial intelligence. They are increasingly looking to utilize the power of AI to democratize the world of investing and get access to tools to invest, like professional Wall Street investors. Artificial Intelligence has had an enormous impact and has surpassed humans in many fields, from gaming to computer vision to self-driving cars.

Briefly describe your firm, and your own professional background? Quantology Capital Management is a leading French asset manager specializing in quantitative finance. We manage three listed equity-based strategies; our investment philosophy is focused on capturing outperforming stocks by analyzing investors' decision-making processes. Our aim is to exploit behavioral biases (over/under price reactions on corporate events) in a systematic way, in order to generate alpha. Our trading/R&D desk is composed of four experienced people with engineering and actuarial science backgrounds. I am a fellow at the French Institute of Actuaries and I run the R&D/trading team at Quantology.

After years of complaints that there were no rules to determine what constitutes a "sustainable" investment, investors are now fretting that there will soon be too many to navigate easily. More than 30 taxonomies outlining what is and isn't a green investment are being compiled by governments across Asia, Europe and Latin America, each one reflecting national economic idiosyncrasies that can jar with a global capital market that has seen trillions pour into sustainable funds. The European Union will introduce its green investment taxonomy, or common framework, in January to help asset managers inside the bloc and make green activities more visible and attractive to investors. The rules also aim to stamp out "green washing," whereby organizations overstate their environmental credentials. The U.K., which hosts the COP26 climate change conference from Oct. 31, is set to finalize its own taxonomy next year but has already signaled it will not just replicate what is drawn up across the channel.

Arabesque has unveiled its Autonomous Asset Management offering for the creation of highly customised and sustainable active investment strategies, powered by an artificial intelligence technology that can generate and operate millions of active equity strategies. Developed by Arabesque AI, 'AutoCIO' enables asset managers and investment professionals to configure and build hyper-customised active strategies that can be tailored to each investor through more than a thousand different personalised investment options. The launch comes as the asset management industry increasingly looks to leverage technologies like automation and AI for cost-efficient product development, alpha generation and delivering a customised and differentiated client experience. With over USD 400 million currently powered by Arabesque's AutoCIO, the platform offers investors an unprecedented degree of customisation through a streamlined web app that can generate a vast range of bespoke strategies, with AI used to forecast stock performance across 25,000 equities daily. Speaking about today's announcement, Georg Kell, Chairman of the Arabesque Group, said: "Artificial intelligence will play a pivotal role in the customisation of active investing in the coming years, with pressure growing to innovate both in terms of technology and client centricity. "Whilst the market is increasingly demanding sustainable products that align with the objectives and values of investors, asset managers are currently unable to offer customisable, active solutions at scale.

Investment in technology and data infrastructure sit at the top of asset managers' priorities as they position themselves to deliver business growth in the recovery from the COVID-19 pandemic. These are the findings of a report by Funds Europe. The survey of global investment professionals across the asset management sector also reveals COVID-19 has pushed firms to review their IT strategies and transition to the public/hybrid cloud. To improve levels of operational efficiency, firms are seeking seamless interconnection between functions along the investment value chain. In the survey, 83% of asset managers say they will extend their strategic alliances with asset servicing and tech partners, enabling connection of middle- and back-office services straight to their front office tools and investment book of record (IBOR).