In this research study, we show how existing approaches of using generative adversarial
networks (GANs) as economic scenario generators (ESG) can be extended to an entire …

There are three fundamental ways of testing the validity of an investment algorithm against
historical evidence: a) the walk-forward method; b) the resampling method; and c) the Monte …

Simulating financial time series (FTS) data consistent with non-stationary, empirical market
behaviour is difficult, but it has valuable applications for financial risk management. A better …

Training deep learning models that generalize well to live deployment is a challenging
problem in the financial markets. The challenge arises because of high dimensionality …

Overfitting is an inevitable phenomenon when applying deep learning techniques to
financial data, given the relative scarcity of available historical data and the ever-changing …

The following sections are included: Learning Objectives Introduction A Taxonomy of
Transaction Costs Liquidity and Transaction Costs Market Impact Measurements and …

This paper presents an evaluation framework that attempts to quantify the" degree of
realism" of simulated financial time series, whatever the simulation method could be, with …

The main issue at the heart of this dissertation is the improvement of ML financial prediction
systems through the use of augmented training data. An overview and assessment of …

The original M-test used for testing equality of several independent samples covariance
matrices is developed based on likelihood ratio test under assumption of multivariate …