Fast Quantum Methods for Financial Risk ManagementWe investigate how quantum simulation and optimization can deliver a fast, scalable, and secure computing platform for (1) assessing systemic market and credit enterprise risk, (2) pricing complex financial/insurance instruments for risk analysis/regulatory reporting, (3) executing portfolio analysis/robo-advising.
Causal Inference for Fairness and Explainability in Financial Decision
Properly explained and fairness adjusted algorithms are becoming trending demands in business decisions, from seemingly minor issues like same-day delivery eligibility to getting access to life-changing opportunities such as education, employment, housing, and creditworthiness. Interpreting decisions help to build trust with users, ultimately improving their public images and strengthening their market presence and influence.
Risky Business? Deep Dives into DeFi
CRDecentralized Finance (DeFi) is an emerging new financial ecosystem built on the back of blockchain technology. With over $2 trillion locked in cryptocurrencies and the rapid adoption of new DeFi products by retail users and institutions, we need to understand how the volatile DeFi ecosystem may potentially disrupt the traditional financial sector. We seek to investigate current patterns of usage in DeFi lending protocols and quantify risk and user behaviors across various protocols.
Explainable ML for Credit Risk Analytics
This research project aims to assess the validity of explainable AI/ML(XAI) tools and models in the context of credit analytics by comparing the time-series and cross-sectional stability of XAI algorithms (LIME, Shapley values, etc.) with regard to several common ML algorithms (including logistic regression, tree-based models, and deep neural networks) on real consumer credit data.
High-dimensional Portfolio Design and Optimization using an Explainable Ensemble Learning FrameworkOur research is going to deliver a framework and methodology to personalize portfolios. We propose a novel two-step approach to design an optimal portfolio by building an ensemble learning framework. Investment portfolio design requires combining various risky assets with appropriate weights to provide an acceptable trade-off between the portfolio return and risk to the investor, while satisfying policy and diversification requirements.
Risk Mitigation in Cross-Platform Decentralized Finance
The current leading solutions for blockchain interoperability in DeFI, such as Cosmos, Polkadot, and Chainlink, provide inter-blockchain communication to bridge various incompatible technologies, messaging, identity, and data formats. However, there are varying degrees of security, trust, and identity mechanisms utilized in these solutions, which has resulted in several cross-blockchain exploits in the recent past and hesitation by the financial industry in adopting blockchain solutions. The primary goal of this project is to see how cross-chain protocols can be strengthened to minimize such risks in DeFi.
Predictive Learning from Long Financial DocumentsThe aim of this project is to leverage the vast troves of textual data spanning disclosures, reports, news articles and reviews to extract insights and features for predictive learning. The focus is on effective textual models for prediction of quantitative performance indicators from long documents, which is a particularly challenging domain. Our goal is to develop novel language modeling techniques for the representation of long financial documents, advancing the state of pre-trained language models as well as graph neural networks.
CRAFT Previous Proposals
SHIFT: A Test Bed for Risk Assessment & Regulatory Compliance
SHIFT is not only a market replay tool but also allows trading algorithms to interact with each other. Trading agents implemented in the system drive the resulting price process. The system may be used for training and assessment of risk.
Explainable AI with Attention Networks for Credit/Loan Decisions
Reasonably explain the decisions suggested by the AI algorithms; using self-attention mechanisms and deep CNN and reinforcement learning as a way of interpreting the loan and trading decisions.
Blockchain Technologies for Cooperation and Risk Reduction of Green Energy Suppliers
The project will develop a framework of multilateral cooperation between green energy suppliers and ancillary service providers, to reduce their overall risk profile and be more competitive in bidding into energy markets. This will be enabled through the use of blockchain technologies and evaluated on NY grid data.
Dynamic Asset-Liability Management with Immunization and Risk Control
We intended to propose a notion of duration for a stochastic cash flow and to incorporate a duration constraint in a portfolio optimization problem, which is dedicated to matching (in a statistical sense) a random stream of liabilities. The question is particularly relevant when securities sensitive to changes in interest rates such as bonds are used but risky assets and other factors may be considered as well. The bonds may default within the given time horizon. We shall incorporate the statistical estimation of the duration into the portfolio optimization problem.
Knowledge Management Strategies in Financial Enterprises
Knowledge management strategy is critical to the success of financial enterprises, such as, asset management firms, Firms with organizational structures supporting coherent knowledge management strategies (talent recruitment, external/internal collaborations. thought leadership etc.) have a competitive advantage. Strategy design impacts the key performance indicators (KPIs) of an asset management firm.
Defining and Defying Decentralized Finance (DEFI)
There are many problems with existing and emerging Decentralised Finance (DeFi) products due to smart contract bugs, oracle manipulation, and governance based majority attacks. This project aims to characterised DeFi products, mine transaction graphs, and analyze smart contract code to detect, prevent, and recover from such problems.
A Model-Free Quantum Simulation Framework for Volatility Derivatives Valuation
We investigate how quantum engineered system can be combined with a model-free simulation framework to utilize the plethora of available financial data to design a rapid quantum enhanced general simulation platform for the valuation and real-time risk analysis of financial options.
AI-Driven Early Estimates of State for Predictive Robust Portfolio Allocations
State (market, sector, company) is stable as opposed to returns which are subject to volatility. We will build methods to predict state and robustly estimate state-dependent return distribution (HMM and deep network technologies). Our input features are flexible: financials technical indicators, macroeconomic indicators, etc. Given robust estimation of state, we use state to perform dynamic portfolio allocation.