A right kind of wrong: European equity market forecasting with custom feature engineering and loss functions
Study on European equity market forecasting using neural networks, custom feature engineering, and loss functions.
What it examines
This study aims to forecast future returns of the STOXX Europe 600 and German DAX using a diverse set of predictors and custom feature engineering. It introduces penalization factors in loss functions to adapt neural networks for equity forecasting, proposing convolutional neural network architectures.
What it concludes
The research demonstrates that neural network-based forecasts can outperform traditional methods in European equity markets. Potential applications include developing more robust trading strategies. Future research could explore different loss functions, longer time frames, and ensemble models to enhance prediction accuracy.
Evidence objects
The research demonstrates that neural network-based forecasts can outperform traditional methods in European equity markets. Potential applications include developing more robust trading strategies. Future research could explore different loss functions, longer time frames, and ensemble models to enhance prediction accuracy.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
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.
Source row: 80 · abstract type: unknown