Invited Talks

Time: Tuesday, July 18, 2023, 09:10 - 09:50 (SGT), via Zoom

Tucker Balch, Managing Director, J.P. Morgan AI Research

Bio: Tucker Balch is a computer scientist, researcher, and educator specializing in AI, Robotics, and Finance. He earned his Bachelor's degree and Ph.D. in Computer Science from the Georgia Institute of Technology. Dr. Balch has published over 120 peer-reviewed research papers in Robotics, AI, multi-agent systems, and Finance. He has held research and teaching positions at Carnegie Mellon University and Georgia Institute of Technology, where he instructed AI, Robotics, and Computational Finance courses. In the financial industry, Dr. Balch co-founded Lucena Research, a data-driven investment technology company utilizing Machine Learning, big data, and quantitative analysis to offer actionable insights for investment professionals. He currently serves as a managing director at J.P. Morgan AI Research, leading teams exploring ML and Cryptography, multi-agent system simulations, high-frequency electronic markets, and Synthetic Data for Finance. Dr. Balch is also an online education pioneer, developing and teaching MOOCs like "Computational Investing" and "Machine Learning for Trading," which have reached over 170,000 students worldwide. Before his research career, Balch served as an F-15 pilot in the U.S. Air Force.

Topic: Synthetic Data in Finance

Time: Tuesday, July 18, 2023, 09:50 - 10:30 (SGT), via Zoom

Dacheng Xiu, Professor of Econometrics and Statistics, Booth School of Business, University of Chicago

Bio: Dacheng Xiu is a Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. His current research focuses on developing machine learning solutions to big-data problems in empirical finance. Xiu’s work has appeared in the Journal of Finance, Review of Financial Studies, Econometrica, Journal of Political Economy, the Journal of the American Statistical Association, and the Annals of Statistics. He is a Co-Editor for the Journal of Financial Econometrics, an Associate Editor for the Review of Financial Studies, Journal of the American Statistical Association, Journal of Econometrics, Management Science, etc. He has received several recognitions for his research, including the Fellow of the Society for Financial Econometrics, Fellow of the Journal of Econometrics, AQR Insight Award, EFA Best Paper Prize, and Swiss Finance Institute Outstanding Paper Award. At Booth, he teaches a variety of courses related to FinTech, Big Data, and Statistical Inference to MBA, college, and PhD students. Xiu earned his PhD and MA in applied mathematics from Princeton University. 

Topic: Expected Returns and Large Language Models

Abstract: We extract contextualized representations of news text to predict returns using the state-of-the-art large language models in natural language processing. Unlike the traditional word-based methods, e.g., bag-of-words or word vectors, the contextualized representation captures both the syntax and semantics of text, thus providing a more comprehensive understanding of its meaning. Notably, word-based approaches are more susceptible to errors when negation words are present in news articles. Our study includes data from 16 international equity markets and news articles in 13 different languages, providing polyglot evidence of news-induced return predictability. We observe that information in newswires is incorporated into prices with an inefficient delay that aligns with the limits-to-arbitrage, yet can still be exploited in real-time trading strategies. Additionally, we find that a trading strategy that capitalizes on fresh news alerts results in even higher Sharpe ratios. 

Time: Tuesday, July 18, 2023, 11:00 - 11:40 (SGT)

Pingping Chen,  APAC Head of Applied AI, Goldman Sachs

Bio: Pingping Chen currently serves as the APAC Head of Applied AI at Goldman Sachs, where she leads the integration of artificial intelligence to address complex financial challenges and steer strategic decision-making. Her responsibilities are wide-ranging and include supervising significant projects related to deep pricing, time series forecasting, as well as the application of generative AI in sales, trading, and asset management. With over 15 years of experience in the field of machine learning and artificial intelligence, Pingping has carved a unique niche for herself at the intersection of finance and technology. Before her tenure at Goldman Sachs, she held key roles at several leading global organizations, including Microsoft, Yahoo, Baidu, and HSBC. In these positions, she was instrumental in driving their machine learning strategies and pioneering AI-driven initiatives.

Topic: Generative AI for Financial Time Series Forecasting

Time: Tuesday, July 18, 2023, 14:00 - 14:30 (SGT) and 16:00 - 16:30 (SGT)

Ling Cheng

Feida ZHU,  Associate Professor of Computer Science, SMU; Associate Dean at SMU

Yong Wang,  Assistant Professor of Computer Science, SMU; Coordinator, BSc (CS) Cyber-Physical Systems Track; Lee Kong Chian Fellow

Topic:  Crypto Asset Analytics and Visualization - A Brief Introduction and Recent Advances (Part I & II)

Time: Tuesday, July 18, 2023, 16:30 - 17:10 (SGT), via Zoom

Tomaso Aste, Professor of complexity science in the Computer Science Department at University College London (UCL), Founder and director of the UCL Centre for Blockchain Technologies (UCL CBT), Head of UCL's Financial Computing and Analytics Group

Topic:  Deep Learning Financial Markets

Abstract: Markets are word-wide information processing systems where humans and machines interact dynamically searching for an agreement on the price of things. This is a formidable test bed for artificial intelligence systems because markets are changeable and adapt when new information and new insights emerge preventing arbitrage. I will discuss the application of reinforcement learning applied to the limit order book to transaction price forecast and the use of topological deep learning techniques for forecasting with time series data. I will discuss general perspectives about the increasingly complex implications of the use of artificial intelligence for the automation of trade and the services industry.

Time: Wednesday, July 19, 2023, 09:10 - 09:50 (SGT)

Bin Ke, Professor of Accounting and Provost’s Chair at the NUS Business School

Bio: Dr. Bin KE is Provost’s Chair Professor at the NUS Business School. He received his bachelor's degree from Beijing Institute of International Relations, master's degree from Pennsylvania State University, and PhD from Michigan State University. He was a faculty member at Pennsylvania State University and Nanyang Technological University. He was the President of the Chinese Accounting Professors Association of North America (www.capana.net), a leading academic organization that promotes high-quality accounting research on China, the Asia Pacific region, and other emerging market economies. He has served on the editorial board of multiple global academic journals, including the Journal of American Taxation Association, The Accounting Review, and The International Journal of Accounting. He was an advisor to the Accounting Research in China published by China Accounting Society and an editor for The Accounting Review, one of the top three accounting journals in the world. He is the consulting editor of China Journal of Accounting Research and a senior editor of China Accounting and Finance Review. Dr. Ke’s primary research interests focus on the production and use of accounting information in business decisions. He is interested in using interdisciplinary approaches to tackle today’s complex business problems. His recent research focuses on financial reporting, investor protection and digital transformation in emerging markets with a particular focus on China. His research has been published in all major accounting journals, including The Accounting Review, Journal of Accounting and Economics, and Journal of Accounting Research. One important area of his recent research examines the roles of big data and new technologies (including blockchain) in organizations’ digital transformation. His research has received numerous funding support from various governments including Hong Kong and Singapore. In recent years he has been invited by numerous academic and industry conferences to give keynote speeches on the challenges and opportunities in the ongoing digital transformation of businesses, governments and ecosystems, including blockchain-based ecosystems. Currently, he is working with various financial institutions, traditional businesses, government agencies, and big data companies to conduct research on a variety of management challenges in businesses and societies’ digital transformation journey.

Topic: Measuring Firm Quality Using Machine Learning

Abstract: Firm quality is a foundational construct in the fundamental analysis literature. Asness et al. (2019), a recent representative example of this literature, measures firm quality based on 19 fundamental ratios guided by valuation theory (referred to as Asness’ Q score). We examine whether it is possible to leverage the power of machine learning to construct a better measure of firm quality using the same 19 fundamental signals. We show that an advanced machine learning model called XGBoost based on the 19 ratios can outperform a linear OLS regression model based on Asness’ Q score (our benchmark) by 27%. However, we fail to find economically significant evidence that adding more raw accounting data items identified by the prior literature or financial statements or using alternative ratios derived from the DuPont decomposition can yield stronger prediction models. We show that our measure of firm quality based on XGBoost and the 19 ratios can better explain contemporaneous stock prices than Asness’ Q score. In addition, a value investing trading strategy using our XGBoost model outperforms the same trading strategy based on Asness’ Q score by an economically significant margin.

Time: Wednesday, July 19, 2023, 09:50 - 10:30 (SGT)

Alvin Chia, Senior Vice President, Head of Digital Assets Innovation (APAC), Northern Trust

Krishan Dave, Head of Investment Risk & Analytical Services (Asia)

Topic: Investment Management in 2030: How Will Generative Artificial Intelligence (AI) Transform the Way Portfolio Managers Invest?

Abstract: While ChatGPT and its underlying generative AI technology has only been in the public conscience for a few months, the world has since entered a rapid state of flux trying to understand how best to harness this revolutionary technology while dealing with the number of significant security and ethical question that it raises. It has the power to change the face of every industry on the planet. But we believe that in the short term, the role of generative AI will be limited to supporting existing research and data gathering and not the primary driver of decisions. Our current knowledge of generative AI does not allow us to discern false positive with actual data, resulting in additional risk for portfolio managers. But that does not deter us from taking a longer term view of how generative AI can shape the investment management industry. Join us in our journey to reimagine how portfolio managers could invest in the year 2030.