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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-investorsβ decision making models and learning techniques. Insights from this portion of the course are increasingly proving to be a critical input into roboadvising strategies.Big-data-in-Finance-Credit-Analytics-Part
This is the first part assignemnt from course Big Data in Finance I, ICBS. Professor of Financial Economics: Tarun Ramadorai. In the credit analytics part of the module we will consider how a range of techniques (including machine learning) can be used to predict default in both corporate (firms) and retail (credit cards and peer to peer lending) credit markets. We then turn to perhaps the largest retail credit market β the mortgage market β and examine theoretical, and primarily empirical, models of mortgage choice, mortgage refinancing, and mortgage default. Our attempt will be to keep up with the rapid pace of innovation in B2C lending markets in this part of the module.Love Open Source and this site? Check out how you can help us