With the resurgence of machine learning and artificial intelligence, never has it been easier to implement predictive algorithms both new and old. With just a few lines of code, state of the art models can be readily accessible at the fingertips of the budding data enthusiast, ready to conquer whatever insurmountable digital task may lay at hand. But a little bit of knowledge can be a dangerous thing. While much of machine learning can be attributed to statistics and programming what is equally important, but often skipped over in favor of instant gratification, is domain knowledge.
Nowhere is this more true than in investing. While the environment is rich with stock price, and fundamental data that is both accessible and free, indiscriminate application of pre-processing techniques and machine learning algorithms will produce indiscriminate results. Financial time series data are incredibly nuanced with the signal to noise ratio systemically low, practitioners spend their careers trying to achieve the elusive aim of generating consistent outperformance, with only a few succeeding. Thus the need for a more intimate understanding of the data is pertinent to achieving some semblance of success. As such, this article aims to shed light on some common reasons why stock prediction projects may fail once put into production.