Investments in power generation assets are risky due to high construction costs and long asset lifetimes. Technology diversification in generation portfolios represents one option to reduce long-term investment risks for risk-averse decision makers. In this article, we analyze the impact of...

Predictions of asset returns and volatilities are heavily discussed and analyzed in the finance research literature. In this paper, we compare linear and nonlinear predictions for stock- and bond index returns and their covariance matrix. We show in-sample and out-of-sample prediction accuracy...

One of the main principles to build portfolios of financial assets is to achieve stable long-term performance and avoid large drawdowns. This article describes how a method of Machine Learning, Kohonen's Self-Organising Maps (SOM), can be applied to visualise risk and to build robust portfolios...

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...

Modeling counterparty risk is computationally challenging because it requires the simultaneous evaluation of all the trades with each counterparty under both market and credit risk. We present a multi-Gaussian process regression approach, which is well suited for OTC derivative portfolio...

We show how one can use deep neural networks with macro-economic data in conjunction with price-volume data in a walk-forward setting to do tactical asset allocation. Low cost publicly traded ETFs corresponding to major asset classes (equities, fixed income, real estate) and geographies (US,...

We propose to solve large scale Markowitz mean-variance (MV) portfolio allocation problem using reinforcement learning (RL). By adopting the recently developed continuous-time exploratory control framework, we formulate the exploratory MV problem in high dimensions. We further show the...

Cross-sectional predictability of returns by past prices, or momentum, is a lasting market anomaly. Previous research reports numerous ways to measure momentum and establishes a multitude of factors predicting its performance. The emerging machine learning asset pricing literature further...

In this work, we have found a risk model that improves the performance of Risk Targeting. Risk Targeting in portfolio construction is implemented to improve capital utilization in growing markets and systematically step away from risk scenarios. However, the performance of risk targeting varies...