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    Machine learning methods for Equity Time Series forecasting: a compendium

    Matuozzo, Alberto and Yoo, Paul and Provetti, Alessandro and Kim, Maria (2022) Machine learning methods for Equity Time Series forecasting: a compendium. In: 31st ACM International Conference on Information and Knowledge Management, Oct 17-21 2022, Atlanta, U.S.. (Unpublished)

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    Abstract

    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 mar- kets 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, identify- ing 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 finan- cial 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.

    Metadata

    Item Type: Conference or Workshop Item (Paper)
    Additional Information: Applied Machine Learning Methods for Time Series Forecasting (AMLTS) - Workshop held in conjunction with CIKM 2022
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Depositing User: Paul Yoo
    Date Deposited: 16 Mar 2023 14:09
    Last Modified: 09 Aug 2023 12:53
    URI: https://eprints.bbk.ac.uk/id/eprint/49176

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