ICAIF

Successful predictive models for financial applications often require harnessing complementary information from multiple datasets. Incorporating data from different sources into a single model can be challenging as they vary in structure, dimensions, ...

Many finance time-series analyses often track a matrix of variables at each time and study their co-evolution over a long time. The matrix time series is overly sparse, involves complex interactions among latent matrix factors, and demands advanced ...

We develop a simple and computationally efficient significance test for the features of a predictive model. Our forward-selection approach applies to any model specification, learning task and variable type. The test is non-asymptotic, straightforward ...

Understanding bond (debt) valuation and predicting future prices are of great importance in finance. Bonds are a major source of long-term capital in U.S. financial markets along with stocks. However, compared with stocks, bonds are understudied. One ...

How can banks recommend relevant banking products such as debit, credit cards or term deposits, as well as learn a rich user representation for segmentation and user profiling, all via a single model? We present a sequence-to-item recommendation ...