A LEARNING FRAMEWORK FOR WINNING LIKE A MULTI-MANAGER HEDGE FUND

Abstract

Stock prediction aims to forecast the future performance of publicly traded companies, which is a critical area of research and practice in financial markets. As investors seek to optimize their portfolios and maximize their returns, accurate predictions of stock prices and trends can provide a significant competitive advantage. Despite the complexity and volatility of financial markets, numerous approaches to stock prediction have been proposed and tested over the years, ranging from traditional econometric models to cutting-edge machine learning algorithms. This field is highly interdisciplinary, drawing on concepts and methods from finance and computer science. With the advent of big data and advances in computational power, stock prediction has entered a new era of innovation and discovery, opening up exciting opportunities for researchers and practitioners alike. In this context, the aim of this chapter is to provide an overview of the key challenges and proposed methods in stock prediction, with a particular focus on data-driven methods for diversified stock prediction.