We establish the out-of-sample predictability of monthly exchange rate changes via machine learning techniques based on 70 predictors capturing country characteristics, global variables, and their interactions. To guard against overfitting, we use the elastic net to estimate a high-dimensional...

Deep learning is a powerful tool, which is becoming increasingly popular in financial modeling. However, model validation requirements such as SR 11-7 pose a significant obstacle to the deployment of neural networks in a bank's production system. Their typically high number of (hyper-)parameters...

We propose a data-driven Neural Network (NN) optimization framework to determine the optimal multi-period dynamic asset allocation strategy for outperforming a general stochastic target. We formulate the problem as an optimal stochastic control with an asymmetric, distribution shaping, objective...

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...

In this paper, we show empirically that Active Risk Budgeting is a superior portfolio construction methodology to the tangency portfolio method postulated by Mean Variance Optimization. We compare the performance of Active Risk Budgeting and Tangency Portfolio in a series of systematic...

We present a portfolio construction methodology for futures strategies that incorporates active trading and also borrows salient features from the risk-parity methodology. We document the evolution of expected risk and return based portfolio construction methodologies and propose a new...

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,...

Along with the advance of opinion mining techniques, public mood has been found to be a key element for stock market prediction. However, how market participants' behavior is affected by public mood has been rarely discussed. Consequently, there has been little progress in leveraging public mood...

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...