We design an adaptive framework for the detection of illegal trading behavior. Its keycomponent is an extension of a pattern recognition tool, originating from the field of signalprocessing and adapted to modern electronic systems of securities trading. The new methodcombines the flexibility of...

Data driven companies effectively use regression machine learning methods for making predictions in many sectors. Cloud-based Azure Machine Learning Studio (MLS) has a potential of expediting machine learning experiments by offering a convenient and powerful integrated development environment....

This article is the second of two articles by the authors on the construction of CDS proxy rates. In the first article, the authors proposed a machine learning (ML) -based proxy-rate construction technique which uses classification to construct so-called Proxy-Names whose liquidly quoted CDS...

We examine machine learning and factor-based portfolio optimization. We find that factors based on autoencoder neural networks exhibit a weaker relationship with commonly used characteristic-sorted portfolios than popular dimensionality reduction techniques. Machine learning methods also lead to...

To price and risk-manage OTC derivatives, financial institutions have to estimate counterparty default risks based on liquidly quoted CDS rates. For the vast majority of counterparties, liquid CDS quotes are not available and proxy CDS-rates need to be constructed. Existing methods ignore...

A presentation was given on 7 March 2018 as the Call for Paper winner for Risk's Quant Summit Europe 2018 Conference based on an original paper titled CDS Rate Construction Methods by Machine Learning Techniques jointly by Raymond Brummelhuis and Zhongmin Luo available...

We introduce the XPER (eXplainable PERformance) methodology to measure the specific contribution of the input features to the predictive or economic performance of a model. Our methodology offers several advantages. First, it is both model-agnostic and performance metric-agnostic. Second, XPER...

We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance--in terms of SDF Sharpe ratio and test asset pricing errors--is improving in model parameterization (or "complexity"). Our empirical findings verify the...

We propose a statistical model of differences in beliefs in which heterogeneous investors are represented as different machine learning model specifications. Each investor forms return forecasts from their own specific model using data inputs that are available to all investors. We measure...

We study the market performance of Chinese companies listed in the U.S. stock exchanges using machine learning methods. Predicting the market performance of U.S. listed Chinese firms is a challenging task due to the scarcity of data and the large set of unknown predictors involved in the...