The Center currently carries out several research projects, revolving around asset selection and allocation.
Applying behavioral finance theory such as the cumulative prospect theory and regret theory to understand the behaviors of small investors, predict the impact of such behaviors on the market and mitigate behavioral risks.
Applying robust optimization techniques to construct data-driven portfolios without needing to estimate any parameters (especially the stocks expected return rates).
Applying stochastic automatic control theory to asset allocation in order to achieve different objectives such as return—risk efficiency, index tracking or maximizing the probability of reaching a goal.
Applying machine learning techniques such as supervised learning and reinforcement learning to train and develop evolutionally superior investment strategies.
Applying network clustering technique based on correlations to dramatically reduce the number of assets in a portfolio while still maintaining a sufficient level of diversification.
Inferring from data the probability weighting, which is a behavioral anomaly that exaggerates the tails of an asset return distribution, and taking advantage of its impact on prices to optimize asset selection and allocation.
Applying Gaussian exploration in reinforcement learning to dynamic portfolio selection and devising model-free, data-driven algorithms to make investment decisions.