Lhierarch/Big-Data-in-Finance-Asset-Management

We then move to asset management, in which the use of large unstructured datasets and new techniques is generating significant innovation. We begin with a review of standard techniques used for portfolio construction in time-series and cross-section, and then consider new techniques used to predict variation in asset prices in the time series and cross-section. These techniques are useful, for example, in quant hedge fund portfolio construction. We then recognize that asset managers need to consider their liability structure as well as their asset structure. This implies a deeper understanding of ultimate end-investors’ decision making models and learning techniques. Insights from this portion of the course are increasingly proving to be a critical input into roboadvising strategies.