AI in Finance

Leveraging the research efforts of more than 60 experts in the area, the book Machine Learning and Data Sciences for Financial Markets, co-edited by Prof. Agostino Capponi and Dr. Charles-Albert Lehalle, reviews cutting-edge practices in machine learning for financial markets. Instead of seeing machine learning as a new field, the authors explore the connection between knowledge developed in quantitative finance over the past 40 years and modern techniques generated by the current revolution in data sciences and artificial intelligence. The text is structured around three main areas: “Interacting with investors and asset owners,” which covers robo-advisors and price formation; “Towards better risk intermediation,” which discusses derivative hedging, portfolio construction, and machine learning for dynamic optimization; and “Connections with the real economy,” which explores nowcasting, alternative data, and ethics of algorithms. Accessible to a wide audience, this invaluable resource will allow practitioners to include machine learning-driven techniques in their day-to-day quantitative practices, while students will build intuition and come to appreciate the technical tools and motivation behind the theory.

Robo-advising has grown enormously over the last decade, offering a large range of financial services to investors, ranging from retirement planning to managing checking and saving accounts to meet investment goals. Robo-advisors democratize access to financial services by reducing barriers to entry through the imposition of low fees for assets under management and minimum required investment amounts. Increased adoption will depend on whether the robo-algorithm is able to personalize its recommendations to the risk preferences of users, and whether it can learn the needs and preferences of the clients served. See Robo-Advising: Learning Investors’ Risk Preferences via Portfolio Choices and Personalized Robo-Advising: Enhancing Investment Through Client Interaction.

Empirical and behavioral finance research shows that clients associate the notion of “risk” with the likelihood of not attaining their goals. For example, clients who want to pay for college expenses in five years, purchase a house in ten years, and have enough in their retirement accounts in thirty years worry about the likelihood of falling short of these goals. Robo-advisers are then confronted with the challenges of investing in risky assets to make sure that the goals are well funded by their deadlines. Robo-advisers trade off the immediate consumption of wealth to satisfy an upcoming goal versus saving and maintaining wealth in the portfolio to satisfy future goals of higher priority. See Goal Based Investment Management.

Big data challenges in modern market microstructure study require new empirical tools to analyze trading dynamics, market liquidity, and price formation. We explore machine learning models and their applications in high-frequency, fragmented trading environments. In particular, we focus on ML models designed for time-series data such as long short-term memory neural networks (LSTM) and Transformers. We show that they perform well in predicting market price dynamics. In addition, we demonstrate using ML models to identify the origination of price information. See here for details.