Abstract
Over the past ten years, applications of machine learning research have steadily shown mastery of broader and more complex domains, and the tools to create advanced models have become ubiquitous and more democratized. During that same time, the resources for studying algorithmic finance have also greatly expanded with populous open source communities and more granular time series available to researchers. However, progress in applying machine learning to finance has mostly stagnated, and in some cases regressed. We not only see similar, routine mistakes to those made ten years ago, but the complexity of cutting-edge models and frameworks can easily hide those mistakes. Drawing on both referee reports and published papers, this editorial series will review frequent problems that occur throughout the ML research pipeline which lead to invalid results and unwarranted conclusions.1 The pipeline refers to the series of research stages that are common to all ML projects and independent of any specific machine learning technique or architecture.
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