Machine Learning Methods for Equity Time Series Forecasting: A Compendium
CEUR Workshop Proceedings
Machine learning is a method of building predictive models using a vast amount of data from different sources, capturing non-linear relationships between different variables. As a result, financial markets in general and stock markets in particular, offer a promising ground for the application of such method. This survey examines machine learning methods for equity market forecasting, identifying gaps in current knowledge and suggesting potential avenues to pursue further research. Computer science-centred quantitative studies have focused mainly on algorithms, testing results mostly on US data on short time-frames, yet, feature engineering, and testing findings on different markets and different time horizons, appear to be under-explored. This study thus introduces the financial context for non-experts and moves to review different models and tools in the realm of statistical learning, and deep learning. We believe that this approach will prove to be effective in financial practice to an interested reader without much prior knowledge of the finance literature. We survey the end-to-end deployment of machine learning to help readers from industry and academia to understand the peculiarities of applying these methods to equity market forecasting.
Open Access Status
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