Synthetic data generation with deep generative models to enhance predictive tasks in trading strategies

Daniel Carvajal-Patiño and Raul Ramos-Pollán

Research in International Business and Finance, 2022, vol. 62, issue C

Abstract: This work develops machine learning (ML) predictive models on price signals for financial instruments and their integration into trading strategies. In general, ML models have been shown powerful when trained with large amounts of data. In practice, the time-series nature of financial datasets limits the effective amount of data available to train, validate and retrain models since special care must be taken not to include future data in any way. In this setting, we develop deep generative models to produce synthetic time-series data, enhancing the amount of data available for training predictive models. Synthetic data obtained this way replicates the distribution properties of real historical data, leads to better performance, and enables thorough validation of predictive models for price signals. We leverage machine-generated predictive signals on synthetic data to build trading strategies. We show consistent improvement leading up to profits in our simulations for commodities and forex exchange markets.

Keywords: Trading strategies; Machine learning; Synthetic data; Deep generative models; Deep learning; Trading simulations (search for similar items in EconPapers)
JEL-codes: F37 G11 G15 G17 G40 O16 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:riibaf:v:62:y:2022:i:c:s0275531922001313

DOI: 10.1016/j.ribaf.2022.101747

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