We introduce an ensemble learning method based on Gaussian Process Regression (GPR) for predicting conditional expected stock returns given stock-level and macro-economic information. Our ensemble learning approach significantly reduces the computational complexity inherent in GPR inference and...

We classify the sentiment of a large sample of StockTwits messages as bullish,bearish or neutral, and create a stock-aggregate daily sentiment polarity measure.Polarity is positively associated with contemporaneous stock returns. On average,polarity is not able to predict next-day stock returns....

We introduce a simulation method for dynamic portfolio valuation and risk management building on machine learning with kernels. We learn the dynamic value process of a portfolio from a finite sample of its cumulative cash flow. The learned value process is given in closed form thanks to a...

We present a general framework for portfolio risk management in discrete time, based on a replicating martingale. This martingale is learned from a finite sample in a supervised setting. The model learns the features necessary for an effective low-dimensional representation, overcoming the curse...

The replicating portfolio (RP) approach to the calculation of capital for life insurance portfolios is an industry standard. The RP is obtained from projecting the terminal loss of discounted asset-liability cash flows on a set of factors generated by a family of financial instruments that can...

This is a summary of the main topics and findings from the Swiss Risk and Insurance Forum 2017. That event gathered experts from academia, insurance industry, regulatory bodies, and consulting companies to discuss past and current developments as well as future perspectives in dealing with...