Learn to understand and implement the latest machine learning innovations to improve your
investment performance Machine learning (ML) is changing virtually every aspect of our …

Order flow is toxic when it adversely selects market makers, who may be unaware they are
providing liquidity at a loss. We present a new procedure to estimate flow toxicity based on …

The rate of failure in quantitative finance is high, particularly in financial machine learning
applications. The few managers who succeed amass a large amount of assets and deliver …

… The inevitable consequence is that SR calculations are likely to be the subject of
substantial estimation errors (see Bailey and López de Prado [2] for a confidence band and …

Many investment firms and portfolio managers rely on backtests (ie, simulations of performance
based on historical market data) to select investment strategies and allocate capital. …

We solve a multi-period portfolio optimization problem using D-Wave Systems' quantum
annealer. We derive a formulation of the problem, discuss several possible integer encoding …

Understanding modern market microstructure phenomena requires large amounts of data
and advanced mathematical tools. We demonstrate how machine learning can be applied to …

Trustworthy Artificial Intelligence (AI) is based on seven technical requirements sustained over
three main pillars that should be met throughout the system’s entire life cycle: it should be …

We evaluate the probability that an estimated Sharpe ratio exceeds a given threshold in
presence of non-Normal returns. We show that this new uncertainty-adjusted investment skill …

How best to discern trading intentions from market data? We examine the accuracy of three
methods for classifying trade data: bulk volume classification (BVC), tick rule and aggregated …