Machine learning (ML) is progressively reshaping the fields of quantitative finance and
algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers …

Building an investment portfolio is a problem that numerous researchers have addressed for
many years. The key goal has always been to balance risk and reward by optimally …

This study explores the effectiveness of Extreme Gradient Boosting (XGBoost) models in
predicting a stock's future Earnings Per Share (EPS). It utilizes preprocessed technical …

We propose an asset allocation strategy that engineers optimal weights before feeding them
to a supervised learning algorithm. In contrast to the traditional approaches, the machine is …

Machine learning (ML) is progressively reshaping the fields of quantitative finance and
algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers …

In this chapter, the authors explore the application of machine learning methods to build a
multifactor alpha model, a topic which has recently received considerable attention in …

In this article, we investigate the impact of truncating training data when fitting regression
trees. We argue that training times can be curtailed by reducing the training sample without …

We propose a linearization of rule-based algorithms that reveals the most important
interactions between characteristics and macroeconomic variables when explaining future …

Big data and computational technologies are increasingly important worldwide in asset and
investment management. Many investment management firms are adopting these data …

The main rationale of asset pricing theory is to identify the underlying pattern of the drivers
and establish their relationship with the financial performance of a firm. The proliferation of …