Prof. Andrew B. Whinston, The University of Texas at Austin, USA
Speech Title: We will announce soon.
BIO: Prof. Andrew B. Whinston is currently a professor at The University of Texas at Austin where he holds the Hugh Roy Cullen Centennial Chair in Business Administration. He is also the director of the Center for Research in Electronic Commerce in the McCombs School of Business. He has published extensively on resource allocation issues and am currently working on Internet security. He has completed numerous research projects that investigate economics, Internet technology, and operations research in the study of information systems issues. In 2011, He was rated as the most influential scholar in the Information Systems field by the h-index which measures scholarly influence. For more information you can view at: https://www.mccombs.utexas.edu/faculty-and-research/faculty-directory/andrew-whinston/
Prof. Erik Cambria, IEEE Fellow, Nanyang Technological University, Singapore
Speech Title: Explainable AI for Fully Interpretable Financial Insights
BIO: Erik Cambria is a Professor at Nanyang Technological University, where he also holds the appointment of Provost Chair in Computer Science and Engineering, and Founder of several AI companies, such as SenticNet, offering B2B sentiment analysis services, and finaXai, providing fully explainable financial insights. Prior to moving to Singapore, he worked at Microsoft Research Asia (Beijing) and HP Labs India (Bangalore), after earning his PhD through a joint program between the University of Stirling (UK) and MIT Media Lab (USA). Today, his research focuses on neurosymbolic AI for interpretable, trustworthy, and explainable affective computing in domains like social media monitoring, financial forecasting, and AI for social good. He is ranked in Clarivate's Highly Cited Researchers List of World's Top 1% Scientists, is recipient of many awards, e.g., IEEE Outstanding Early Career, was listed among the AI's 10 to Watch, and was featured in Forbes as one of the 5 People Building Our AI Future. He is an IEEE Fellow, Associate Editor of various top-tier AI journals, e.g., Information Fusion and IEEE Transactions on Affective Computing, and is involved in several international conferences as keynote speaker, program chair and committee member. For more information you can view at: https://dr.ntu.edu.sg/cris/rp/rp00927
Dr. Ruidan Su, Shanghai Jiao Tong University, China
Speech Title: Deep Reinforcement Learning Approaches for Portfolio Management.
Abstract: Reinforcement learning (RL) has been applied to financial portfolio management in recent years. Current studies mostly focus on profit accumulation without much consideration of risk. Some risk-return balanced studies extract features from price and volume data only, which is highly correlated and missing representation of risk features. To tackle these problems, we propose a weight control unit (WCU) to effectively manage the position of portfolio management in different market statuses. A loss penalty term is also designed in the reward function to prevent sharp drawdown during trading. Moreover, stock spatial interrelation representing the correlation between two different stocks is captured by a graph convolution network based on fundamental data. Temporal interrelation is also captured by a temporal convolutional network based on new factors designed with price and volume data. Both spatial and temporal interrelation work for better feature extraction from historical data and also make the model more interpretable. Finally, a deep deterministic policy gradient actor–critic RL is applied to explore optimal policy in portfolio management. We conduct our approach in a challenging non-short-selling market, and the experiment results show that our method outperforms the state-of-the-art methods in both profit and risk criteria. Specifically, with 6.72% improvement on an annualized rate of return, 7.72% decrease in maximum drawdown, and a better annualized Sharpe ratio of 0.112. Also, the loss penalty and WCU provide new aspects for future work in risk control.
BIO: Dr. Ruidan Su received his MSc in Software Engineering from Northeastern University, China in 2010, and his Ph.D degree in Computer Application Technology from Northeastern University, China in 2014. He was an assistant professor of Shanghai Advanced Research Institute, Chinese Academy of Sciences during 2015–2020. He is currently a research assistant professor with Department of Computer Sciences and Engineering, Shanghai Jiao Tong University. Dr. Ruidan Su is an IEEE Senior Member. His field of science is artificial intelligence, system optimization, AI applications in medical image and finance.
Prof. SIAU Keng Leng, Singapore Management University, Singapore
Speech Title: AI and Metaverse: The Good, The Bad, and The Unknown.
Abstract: Artificial Intelligence (AI) and Metaverse are transforming our businesses, societies, and lives. On the one hand, AI and Metaverse can drive productivity enhancement. On the other hand, AI will transform work, replace workers, and change the workplace. Metaverse provides a virtual environment that changes the way we work, play, and interact with one another. In this talk, we will look at the good and the bad of these technologies. The unknown future created by these digital advancements will also be discussed.
BIO: Professor Siau is the Lee Kong Chian Professor of Information Systems at the School of Computing and Information Systems, Singapore Management University. From June 2021 to June 2024, he was the Head of the Department of Information Systems and Chair Professor of Information Systems at the City University of Hong Kong (CityU). He was also an Affiliated Chair Professor of the School of Data Science at CityU and an Affiliated Professor of the CityU Academy of Innovation. From 2012 to 2021, he was Head (“Business Dean” equivalent) of the AACSB accredited Business Program at the Missouri University of Science and Technology (Missouri S&T). Before joining Missouri S&T, he was the Edwin J. Faulkner Chair Professor and Full Professor of Management at the University of Nebraska-Lincoln. Professor Siau received his Ph.D. in Business Administration with a specialization in Management Information Systems from the University of British Columbia. Professor Siau has more than 350 academic publications. According to Google Scholar, he has a citation count of more than 24,000. His h-index and i10-index, according to Google Scholar, are 77 and 205 respectively. He is also on the Stanford University list of the world’s top 2% most-cited scientists. Professor Siau is an AIS Distinguished Member–Cum Laude. He is a recipient of the prestigious International Federation for Information Processing (IFIP) Outstanding Service Award in 2006, IBM Faculty Awards in 2006 and 2008, IBM Faculty Innovation Award in 2010, AIS Sandra Slaughter Service Award in 2019, AIS Award for Outstanding Contribution to IS Education in 2019, AIS Fellow Award in 2022, and AMCIS Outstanding Leader Award in 2023.
Assoc. Prof. Qi Zhang, Antai College of Economics and Management, Shanghai Jiaotong University, China
Speech Title: Better than Human? Experiments with AI Debt Collectors
Abstract: How good is artificial intelligence (AI) at persuading humans to perform personally
costly actions that carry a moral valence? We study the effectiveness of phone calls made to
persuade delinquent consumer borrowers to repay their debt. Both a regression discontinuity
design and a randomized experiment reveal that AI is substantially less able than human callers to
get borrowers to repay. Substituting human callers for AI six days into delinquency closes much
of the collection gap, but one year later, borrowers initially assigned to AI and then switched to
humans have repaid 1% less than borrowers who were called by humans from the beginning. Even
accounting for wage costs and assuming zero costs for AI, using AI is less profitable (with the
caveat that we do not observe non-wage costs of labor). AI’s lesser ability to handle complex
situations and extract payment promises that feel binding may contribute to the performance gap.
BIO: Qi Zhang is an Associate Professor of Finance at Finance Department, Antai College of Economics and Management, Shanghai Jiaotong University. Prior to this, he taught at University of Leeds and University of Durham. He gained his BA and MA in Economics from the School of Economics and Management at Tsinghua University, China. His PhD was awarded by University of Leeds.
Qi’s research interests are in the areas of empirical asset pricing, behavioural finance, banking and emerging markets. He has had papers published in the Review of Financial Studies, Journal of Accounting and Economics, Journal of Financial and Quantitative Analysis etc.
Dr. Thierry H. Brutman (CEO), EDDA Stock Finance, France
Speech Title: AI & Options: Unlocking the Potential of a Self-Managed Company through New Knowledge Management, including Machine Learning & Blockchain.
Abstract: Fintech have every day a larger weight in global economy. We know all the
figures, and we just keep our eyes on the July 2023 market value of public traded company
it is an impressive beginning value of 550 billion USD capitalization, 2 times the 20219
value, and it is not finish even if 2022 supported a correction. A market correction is not
a revenue correction… However, if we analyze the Fintech Market looks like an addition
of different periphery markets that focused more on making faster and in a cheaper way
more transaction. A new Fintech market is not only that and with new models in
association with existing classic Fintech is the pure management and optimization of
decisions of a company that can be now a Self-Managed Company with existing tools
that now exist through Artificial Intelligence, Machine Learning, Knowledge Management
and Options models that prepare a new revolution for all economy, its organization and
even financial markets.
BIO: Thierry Brutman, a seasoned Financial Analyst, embarked on his career at IBM,
where he spearheaded financing risk methodology and credit risk management for the
Dealer Channel. His expertise extended to orchestrating the financial rescue of one of
the largest listed Computer Dealers in France and Europe, as well as overseeing the SpinOff of the PC Company. Since 2005, he launched new mathematic risk investment
methods for the Financial sector and manage himself a Fintech: EDDA Stock Finance”
Dr. Gary Farrow (IET Fellow), Consulting Architect, Bank of Ireland, UK
Speech Title: SEPA Instant Payments - Short Term Challenges : Long Term Benefits for Cross Border Payments
Abstract: Recent EU regulation[ Proposal for a REGULATION OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL amending Regulations (EU) No 260/2012 and (EU) 2021/1230 as regards instant credit transfers in euro, European Commission, 2022] has mandated that Payment Service Providers in the Single European Payments Area (SEPA) provide Instant Payments to their customers. This paper explores the challenges in implementing the SEPA Instant payments scheme for a large European Bank. The key characteristics of the scheme are presented and the critical success factors for its implementation are highlighted. The technical challenges in meeting the demanding scheme SLA’s are first discussed. The paper then explores the architectural challenges of integrating legacy banking systems that, fundamentally, have not been designed for realtime transactions. In a further analysis, the ability to support the requirements of realtime compliance checking and FX conversion is discussed. It concludes with some practical limitations these may place on Instant Payments processing. Finally, the paper explores longer term perspectives of Instant Payments and highlights their potential use in enabling the modernisation of cross border payments. Two future usage scenarios are discussed; (i) connecting local Instant Payment systems globally via a Hub, (ii) Instant Payments role in supporting cross border payments made via a multi-CBDC platform. In both these contexts, the significance of the critical success factors for Instant Payments implementation and the relevance of the practical limitations highlighted in this paper are examined.
BIO: Dr. Gary Farrow is the Director of Triari Consulting, a consultancy specialising in regulatory technical consulting and provider of IT architecture advisory services for the financial sector. As a lead architect on many successful programmes, he has undertaken senior roles at major banks and financial services firms in the UK and was the Chief Architect on the UK’s Open Banking initiative. Gary has deep domain specialism in payments systems architectures and cash/liquidity management solutions and broad experience across multiple financial ser¬vices domains. Recent interests relate to the application of intelligent automation and AI to banking operational processing. His work on payments systems architecture has been widely published and is a regular contributor and reviewer for the Journal of Payment Strategy and Systems. His professional associations include Fellow IET, Chartered Engineer and is an Open Group Master Certified Architect.
Assoc. Prof. Ke-Wei Huang, National University of Singapore, Singapore
Speech Title: Advancing Industry Classification: From Traditional Methods to AI-Powered Approaches with Large Language Models
Abstract: Traditional industry classification systems like SIC and NAICS, while
foundational for business research and investment, often fail to keep
pace with the rapidly evolving nature of modern businesses. Recent
research has proposed alternative methods, such as TF-IDF and topic
modeling, to create more dynamic classifications by analyzing textual
data from sources like 10-K filings. While approaches like the
text-based industry classification by Hoberg and Phillips (2016) have
advanced this field, challenges remain in maintaining relevance and
accuracy over time. This paper reviews the progress and limitations of
these earlier methods and explores current advances through deep
learning techniques. We introduce a novel Multi-View Graph Clustering
approach that combines traditional domain expertise with
state-of-the-art natural language processing (NLP) methods. By
leveraging large language models (LLMs) like GPT-4, our method extracts
richer, more nuanced information for industry classification. We also
discuss future directions, including the use of LLMs to enhance
adaptability and precision in industry classifications, ultimately
making them more reflective of the complexities of today's business
environment.
BIO: Dr. Ke-Wei Huang is an Associate Professor in the Department of Information Systems and Analytics at the National University of Singapore (NUS). Dr Huang joined NUS in July 2007. He received his PhD (2007), MPhil (2005), and MSc (2002) degrees in Information Systems from the Stern School of Business at New York University, and his MBA in Finance (1997) and BSc in Electrical Engineering (1995) from National Taiwan University. Dr Huang's research interests are in the economics of information systems and data mining for financial applications. Currently, he focuses on various topics of pricing digital goods, labor economics of IT professionals, and data mining or econometrics issues for topics in accounting or finance.
Assoc. Prof. Jaber Jemai, Higher Colleges of Technology (HCT), United Arab Emirates
Speech Title: Financial advising: AI and ML leverage.
BIO: Dr. Jaber Jemai is an associate professor of computer information systems at the Higher Colleges of Technology (HCT) in the UAE. He holds master's and doctoral degrees in computing and information systems from the University of Tunis, as well as a postgraduate diploma in data science and business analytics from the University of Texas in Austin. His research interests include the use of artificial intelligence to solve a variety of business problems, including green transportation and logistics, healthcare operations, and Fintech. Dr. Jaber has won various research funds from HCT Research Council (UAE), Imam University (KSA), and SNDP (Tunisia), among others. He has an extensive publication record in refereed international journals and conferences. Further to his research work, Dr. Jemai occupied teaching, managerial, and quality assurance roles.
Prof. Steven Li, RMIT University, Melbourne, Australia
Speech Title: Do Investor Protection Measures Matter for Equity Crowdfunding Portals?
Abstract: In this paper we examine the impact of various investor protection measures on the performance of equity crowdfunding platforms, as denoted by the amount raised on the platform. These measures include extra due diligence checks, educational resources for investors, a communication channel between the investors and issuer, and selection criteria implemented by the platform. The overall findings reveal that extra due diligence processes, educational resources, communication channels facilitated by a portal and selection criteria all have a significantly positive influence on the total amount raised, while the number of investor protection measures show an opposite effect. The number of employees also has a positive association with the total amount raised. Our findings suggest that equity crowdfunding platforms should consider taking the initiative to conduct additional due diligence checks as it can have a desirable effect on the performance, reputation and credibility of the platform and attract future users. However, portals should put in careful consideration for the number of measures, as too many measures can be unappealing for issuers and reduce the portal’s competitiveness in attracting more crowdfunding campaigns.
BIO: Prof. Steven Li is a professor of finance at RMIT University, Melbourne, Australia. He holds a PhD from Delft University of Technology, the Netherlands, an MBA from University of Melbourne, Australia and a Bachelor of Science degree from Tsinghua University, China. He has worked previously as a mathematician specialised in PDE. After his MBA, he embarked on his academic career in finance. His current research interests are mainly in quantitative finance and financial management. He is best known for his high-quality, impactful, and interdisciplinary research works in applying mathematics in finance, economics, and engineering problems. He has published consistently in top tier journals including Energy Economics, Journal of International Financial Markets, Institutions and Money, European Journal of Finance, International Review of Economics and Finance, Applied Energy, Energy, Applied Soft Computing, Water Resources Research etc. To date, he has more than 100 peer reviewed publications with >4,500 citations in total and his i10-index is 61 (Google Scholar: https://scholar.google.com.au/citations?user=5ZGIymQAAAAJ&hl=en&oi=ao )
Assoc. Prof. Chi Seng Pun, Nanyang Technological University, Singapore
Speech Title: Distributional Reinforcement Learning and Cumulative Prospect Theory.
Abstract: Distributional reinforcement learning (RL) emerges as a powerful tool for modeling risk-sensitive sequential decisions, where leveraging distribution functions in place of scalar value functions has allowed for the flexible incorporation of risk measures. However, due to the inherent time inconsistency (TIC) in the use of numerous risk measures in sequential decision making, the nature of controls under distributional RL has remained a mystery. For its use in the risk-sensitive problems in mathematical finance, this paper seeks to fill the research gap by building on the cumulative prospect theory (CPT)-based analysis of human gambling behavior and the emergence of three policy classes under TIC: precommitment, equilibrium, and dynamically optimal. We focus on the prevailing quantile-based distributional RL (QDRL) for CPT risk measures. Our theoretical results extend some results from the risk-insensitive QDRL theory to CPT prediction, from which we derive the characterization of QDRL control as an approximate equilibrium of an intrapersonal game. We empirically demonstrate the efficacy of our CPT QDRL algorithm in approaching the equilibrium. Finally, by further exploring the economic interpretation of the three policy classes in their handling of TIC, we devise some metrics and instances relevant for driving interesting patterns of interactions between these policies, including when and how the equilibrium may be more desirable than the precommitment.
BIO: Patrick Pun is currently a tenured Associate Professor, Assistant Chair (MSc Programmes), and the Programme Director of Master of Science in Financial Technology (MSc in FinTech) at School of Physical and Mathematics Sciences, Nanyang Technological University, Singapore. In 2018, Patrick founded the MSc in FinTech programme to cultivate the FinTech talents. The programme has been well-received in Asia. Prior to NTU, Patrick obtained his Ph.D. in Statistics at the Chinese University of Hong Kong (CUHK) in 2016. His Ph.D. thesis on “Robust Stochastic Control and High-Dimensional Statistics with Applications in Finance” won numerous awards, including Nicola Bruti Liberati Prize 2016 (Best Ph.D. Thesis in Quantitative Finance, worldwide) and the Young Scholars Thesis Award 2016 (Best Ph.D. Thesis from Faculty of Science, CUHK). His research paper on high-dimensional portfolio selection won Best Student Research Paper (First Place) in INFORMS Financial Section in 2015. Patrick also won Best Teaching Assistant Award in 2014. Patrick obtained his M.Phil. in Risk Management Science from CUHK in 2013 and obtained his B.Sc. in Statistics, from Nankai University in 2011. He also passed the Financial Risk Manager (FRM) qualification examinations in 2012. Patrick has strong research interests in Financial / Actuarial Mathematics, Big Data Analytics, and AI applications in Finance, as evidenced by his numerous top-tier publications in these fields. He has received several distinguished grants, namely from MOE, NRF, QEP, DSAIR, and NTU, to further his research work alongside his tertiary teaching responsibilities.