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    A right kind of wrong: European equity market forecasting with custom feature engineering and loss functions

    Matuozzo, Alberto and Yoo, Paul and Provetti, Alessandro (2023) A right kind of wrong: European equity market forecasting with custom feature engineering and loss functions. Expert Systems with Applications , ISSN 0957-4174.

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    This study makes time-series-based predictions on future returns of the STOXX Europe 600 and the German DAX by adopting (in addition to a lagged and transformed version of the target series) a diversified set of predictors. Feature engineering expands further — from the initial raw group of variables, to extract knowledge of market conditions and demand for hedging. A penalisation factor is introduced with loss functions to learn a model from neural networks, in order to adapt a traditional machine learning regression framework to solve the equity forecasting problems in question. Architectures based on convolutional neural network are proposed, treating the obtained feature map similarly to an image. Experiments over different time periods demonstrate that trading strategies derived from the forecasts are more profitable than models based on efficient market assumptions. The temporal, non-stationary structure of financial data has a significant impact on the out of sample success of any model. It thus can be seen that different architectures exhibit different resilience to changing market conditions.


    Item Type: Article
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Depositing User: Paul Yoo
    Date Deposited: 16 Mar 2023 14:35
    Last Modified: 09 Aug 2023 12:54


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