Stock price prediction and modeling demonstrate high economic value in the financial
market. Due to the non-linearity and volatility of stock prices and the unique nature of …

The emerging literature suggests that machine learning (ML) is beneficial in many asset
pricing applications because of its ability to detect and exploit nonlinearities and interaction …

We analyze the performance of investable portfolios built using predicted stock returns from
machine learning methods and attribute their performance to linear, marginal non-linear and …

Abstract Machine learning models are increasingly used in a wide variety of financial
settings. The difficulty of understanding the inner workings of these systems, combined with …

The global financial crisis not only highlights the important role of financial network
connectedness, but also urges regulators pay more attention to systemic risk. Here, we study …

The authors propose three principles for evaluating the practical efficacy of machine
learning for stock selection, and they compare the performance of various models and …

This study reflects the assessment of the largest European buyside companies concerning
new order execution opportunities like Direct Market Access, Smart Order Routing or …

Meta-labeling is a machine learning (ML) layer that sits on top of a base primary strategy to
help size positions, filter out false-positive signals, and improve metrics such as the Sharpe …

This article applies formal risk management methodologies to optimization of a portfolio of
hedge funds (fund of funds). We compare recently developed risk management …

In the present economic scenario, money plays a vital role as edge money is essential to
meet out the crisis in the future. The main focus of this study is to evaluate the role of Artificial …