18 March 2024


Speaker: Professor Lin William Cong,
Founding Faculty Director, FinTech  Initiative @ Cornell
Abstract: I provide a brief overview of how goal-oriented search as a core driver in the success of modern AI can be utilized to answer fundamental questions in finance. I briefly introduce deep reinforcement learning (e.g., Transformer-based RL) as a heuristic search for the solutions to portfolio management and managerial decision-making as stochastic control problems without pre-specified probabilities of state-transitions and rewards. I then focus on a new class of tree-based models as economically guided, goal-oriented greedy search for panel data analysis, with specific applications to clustering/sorting assets, providing basis portfolios, and constructing pricing kernels. In particular, we introduce the Bayesian Clustering Model (BCM), a novel and interpretable framework combining decision tree and Bayesian variable selection, to identify and model grouped heterogeneity in panel data, especially for asset returns. Utilizing marginal likelihood that accounts for parameter and model uncertainties, BCM detects time-series breaks using macroeconomic information and splits the cross section based on high-dimensional characteristics. We find strong evidence of structural breaks linked to market variance and valuation, and differential factor exposure and potential segmentation of assets primarily associated with idiosyncratic volatility, size, and value. We also identify MKTRF and SMB as common factors and multiple uncommon factors in different characteristics-managed clusters and macroeconomic regimes. BCM delivers outstanding asset pricing performance and informs the priceability of assets by well-established factors, achieving out-of-sample cross-sectional R2 exceeding 25% for some clusters. Moreover, a tangency portfolio built from leaf clusters delivers exceptional investment performance, including tripling the out-of-sample Sharpe ratio of that built from the Fama-French double-sorted portfolios.

10 February 2023


Speaker: Professor Lin William CONG, Associate Professor of Finance at the Johnson Graduate School of Management at Cornell University SC Johnson College of Business
Abstract:Blockchains, DeFi, and Web3, innovations for greater financial inclusion and democratization, currently fall short of the advocated benefits and present new risks and challenges. For example, while cryptocurrencies and digital assets hold promise for offering cheap, quick, and secure transfer of value, they also create payment channels for cybercrimes. The lack of disclosure and regulation also lead to vertical integration of centralized intermediaries that manipulate the market and commit frauds, as seen in the collapse of FTX. I discuss several projects assembling diverse sets of public, proprietary, and hand-collected data, and using large-scale computation and big data, both on-chain and off-chain, to investigate issues including wash trading, tax manipulation, ransomware, scams, mining concentration, network wealth inequality, and financial exclusion. Whereas blanket restrictions may prove ineffective and hinder innovations, blockchain forensics, statistical and behavioral principles, and appropriate tokenomics policies potentially enable the tracking, monitoring, and penalizing cybercriminals, market manipulators, and facilitate digital inclusion.

26 October 2022


Speaker: Professor Kwok-Yan LAM, School of Computer Science and Engineering at Nanyang Technological University
Abstract: The rapid adoption of digitalization in almost all aspects of economic activities has led to serious concerns in security, privacy, transparency, and fairness issues of digitalized systems. These issues will result in negative impacts on people’s trust in digitalization, which need to be addressed in order for organizations to reap the benefits of digitalization. The typical value proposition of digitalization such as elevated operational efficiency through automation and enhanced customer services through customer analytics require the collection, storage, and processing of massive amounts of user data, which are a typical cause for data governance issues and concerns on cybersecurity, privacy, and data misuses. AI-enabled processing and decision-making also lead to concerns about algorithm bias and distrust in digitalization. In this talk, we will briefly review the motivation for digitalization, discuss the trust issues in digitalization, and introduce the emerging areas of Trust Technology which is a key enabler in developing and growing the digital economy.