Algorithms in future capital markets: A survey on AI, ML and associated algorithms in capital markets

This paper reviews Artificial Intelligence (AI), Machine Learning (ML) and associated algorithms in future Capital Markets. New AI algorithms are constantly emerging, with each 'strain' mimicking a new form of human learning, reasoning, knowledge, and decisionmaking. The current main disrupting forms of learning include Deep Learning, Adversarial Learning, Transfer and Meta Learning. Albeit these modes of learning have been in the AI/ML field more than a decade, they now are more applicable due to the availability of data, computing power and infrastructure. These forms of learning have produced new models (e.g., Long Short-Term Memory, Generative Adversarial Networks) and leverage important applications (e.g., Natural Language Processing, Adversarial Examples, Deep Fakes, etc.). These new models and applications will drive changes in future Capital Markets, so it is important to understand their computational strengths and weaknesses. Since ML algorithms effectively self-program and evolve dynamically, financial institutions and regulators are becoming increasingly concerned with ensuring there remains a modicum of human control, focusing on Algorithmic Interpretability/Explainability, Robustness and Legality. For example, the concern is that, in the future, an ecology of trading algorithms across different institutions may 'conspire' and become unintentionally fraudulent (cf. LIBOR) or subject to subversion through compromised datasets (e.g. Microsoft Tay). New and unique forms of systemic risks can emerge, potentially coming from excessive algorithmic complexity. The contribution of this paper is to review AI, ML and associated algorithms, their computational strengths and weaknesses, and discuss their future impact on the Capital Markets

Social and Governance Implications of Improved Data Efficiency

Many researchers work on improving the data efficiency of machine learning. What would happen if they succeed? This paper explores the social-economic impact of increased data efficiency. Specifically, we examine the intuition that data efficiency will erode the barriers to entry protecting incumbent data-rich AI firms, exposing them to more competition from data-poor firms. We find that this intuition is only partially correct: data efficiency makes it easier to create ML applications, but large AI firms may have more to gain from higher performing AI systems. Further, we find that the effect on privacy, data markets, robustness, and misuse are complex. For example, while it seems intuitive that misuse risk would increase along with data efficiency -- as more actors gain access to any level of capability -- the net effect crucially depends on how much defensive measures are improved. More investigation into data efficiency, as well as research into the "AI production function", will be key to understanding the development of the AI industry and its societal impacts.Comment: 7 pages, 2 figures, accepted to Artificial Intelligence Ethics and Society 202

Application of Artificial intelligence to high education: empowerment of flipped classroom with just-in-time teaching

[EN] In the so-called society 4.0, Artificial Intelligence (AI) is being widely used in many areas of life. Machine learning uses mathematical algorithms based on "training data", which are able to make predictions or take decisions with the ability to change their behavior through a self-training approach. Furthermore, thanks to AI, a large volume of data can be now processed with the overall goal to extract patterns and transform the information into a comprehensible structure for further utilization, which manually done by humans would easily take several years. In this framework, this article explores the potential of AI and machine learning to empower flipped classroom with just-in-time teaching (JiTT). JiTT is a pedagogical method that can be easily combined with the reverse teaching. It allows professors to receive feedback from students before class, so they may be able to adapt the lesson flow, as well as preparing strategies and activities focused on the student deficiencies. This research explores the application of AI in high education as a tool to analyze the key variables involved in the learning process of students and to integrate JiTT within the flipped classroom. Finally, a case of application of this methodology is presented, applied to the course of Energy Markets taught at the Polytechnic University of Valencia.This work was supported in part by the regional public administration of Valencia under the grant ACIF/2018/106.Montuori, L.; Alcázar Ortega, M.; Bastida Molina, P.; Vargas Salgado, CA. (2021). Application of Artificial intelligence to high education: empowerment of flipped classroom with just-in-time teaching. En Proceedings INNODOCT/20. International Conference on Innovation, Documentation and Education. Editorial Universitat Politècnica de València. 223-231. https://doi.org/10.4995/INN2020.2020.11896OCS22323

Statistical arbitrage powered by Explainable Artificial Intelligence

Machine learning techniques have recently become the norm for detecting patterns in financial markets. However, relying solely on machine learning algorithms for decision-making can have negative consequences, especially in a critical domain such as the financial one. On the other hand, it is well-known that transforming data into actionable insights can pose a challenge even for seasoned practitioners, particularly in the financial world. Given these compelling reasons, this work proposes a machine learning approach powered by eXplainable Artificial Intelligence techniques integrated into a statistical arbitrage trading pipeline. Specifically, we propose three methods to discard irrelevant features for the prediction task. We evaluate the approaches on historical data of component stocks of the S&P500 index and aim at improving not only the prediction performance at the stock level but also overall at the stock set level. Our analysis shows that our trading strategies that include such feature selection methods improve the portfolio performances by providing predictive signals whose information content suffices and is less noisy than the one embedded in the whole feature set. By performing an in-depth risk-return analysis, we show that the proposed trading strategies powered by explainable AI outperform highly competitive trading strategies considered as baselines

Incorporating Machine Learning with Satellite Data to Support Critical Infrastructure Measurement and Sustainable Development

Under the umbrella concept of Artificial Intelligence (AI) for good, recent advances in machine learning and large-scale data analysis have opened new opportunities to solve humanity’s most pressing challenges. Improvements in computation complexity and advances in AI (e.g., Vision Transformers) have led to faster and more effective techniques for extracting high-dimensional patterns from large-scale heterogeneous datasets (big data). Further, as satellite data become increasingly available at varying temporal-spatial resolutions, AI tools are helping us to better understand the underlying causes of environmental and socioeconomic changes at an unprecedented scale, ushering in an era of data-driven decision-making to support sustainable and equitable development. Based on these, we propose data-driven methods and techniques for critical infrastructure measurement and sustainable development. Using machine learning and remotely sensed data, we show that we can exploit knowledge and temporal-spatial characteristics learned from data-rich regions to improve data-driven predictions in regions with scant to no data. Specifically, we focus on three critical infrastructures: rivers, roads, and electricity access. Knowledge rivers, particularly their discharge, can help us understand how climate change is evolving, its manifestation on global water resources, and its impact on critical sectors like agriculture and renewable energy generation. On the other hand, better roads facilitate societal development, enabling access to local and global markets and socioeconomic opportunities, leading to better equality in service provision, faster socioeconomic development, and, ultimately, better human outcomes. Finally, we develop tools to support sustainable development, focusing on supporting electricity demand stimulation to improve energy access in rural communities. These methodologies and techniques can help emerging economies achieve their primary sustainable development goals (SDGs) by 2030

Energy System Digitization in the Era of AI: A Three-Layered Approach towards Carbon Neutrality

The transition towards carbon-neutral electricity is one of the biggest game changers in addressing climate change since it addresses the dual challenges of removing carbon emissions from the two largest sectors of emitters: electricity and transportation. The transition to a carbon-neutral electric grid poses significant challenges to conventional paradigms of modern grid planning and operation. Much of the challenge arises from the scale of the decision making and the uncertainty associated with the energy supply and demand. Artificial Intelligence (AI) could potentially have a transformative impact on accelerating the speed and scale of carbon-neutral transition, as many decision making processes in the power grid can be cast as classic, though challenging, machine learning tasks. We point out that to amplify AI's impact on carbon-neutral transition of the electric energy systems, the AI algorithms originally developed for other applications should be tailored in three layers of technology, markets, and policy.Comment: To be published in Patterns (Cell Press

An Ethical proposal for a flourishing digitalised financial system

The UK financial services industry is undergoing significant transformative digitalisation through the development of information technology, increased internet communications, computer speed and programming capacity, and application of big data to traditional financial services. In particular, the use of Artificial Intelligence (AI), i.e. intelligence simulated by technological means, and ‘machine learning’, i.e. automatic learning by machines and software based on a computational and statistical process, is becoming increasingly and rapidly prevalent in financial services. The purpose of this thesis is to examine whether, in the light of the potential risks that AI and machine learning will pose upon society, our current ethical, legal and regulatory standards are satisfactory. Primarily, this thesis observes that the separation of ethics as an academic discipline from economic theory and modern finance theory has undermined the efficacy of the ethical, legal and regulatory standards to regulate the financial system. Therefore, it proposes an integrated ethical approach to UK financial regulation, which seeks to regulate the relationship between action, character of the actor, and the consequences of action. The proposed integrated ethical approach is anchored in the ‘social licence for financial markets’, which helps us to focus on the purpose of financial activity to serve the human good, and, ultimately, to improve the well-being of society. This thesis argues that, further to adopting an integrated ethical approach, we should refine our current ethical standards, and introduce new ethical standards. This thesis demonstrates that while an integrated ethical approach may be applied to programme AI and machine learning technology to behave ethically using, overall responsibility for AI and machine learning should remain with humans. In addition, in light of potential responsibility gaps, specially designed liability rules are required. Finally, this thesis will recommend a series of legal and regulatory reforms with the ultimate goal of cultivating a flourishing digitalised financial system