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EnglishForecasting the stock market is particularly challenging due to the presence of a variety of inter-related economic and political factors. In recent years, the application of Machine Learning algorithms in quantitative stock trading systems has become established, as it enables a data-driven approach to investing in the financial markets. However, most professional traders still look for an explanation of automatically generated signals to verify their adherence to technical and fundamental rules. This paper presents an explainable approach to stock trading. It investigates the use of classification rules, which represent reliable associations between a set of discrete indicator values and the target class, to address next-day stock price prediction. Adopting associative classifiers in short-term stock trading not only provides reliable signals but also allows domain experts to understand the rationale behind signal generation. The backtesting of a state-of-the-art associative classifier, relying on a lazy pruning strategy, has shown promising performance in terms of equity appreciation and robustness of the trading system to market drawdowns.
Leveraging the explainability of associative classifiers to support quantitative stock trading / Attanasio, Giuseppe; Cagliero, Luca; Baralis, Elena. - ELETTRONICO. - (2020), pp. 1-6. ((Intervento presentato al convegno Sixth International Workshop on Data Science for Macro-Modeling tenutosi a Portland, OR, USA nel June 14, 2020.
| Titolo: | Leveraging the explainability of associative classifiers to support quantitative stock trading |
| Autori: | |
| Data di pubblicazione: | 2020 |
| Abstract: | Forecasting the stock market is particularly challenging due to the presence of a variety of inte...r-related economic and political factors. In recent years, the application of Machine Learning algorithms in quantitative stock trading systems has become established, as it enables a data-driven approach to investing in the financial markets. However, most professional traders still look for an explanation of automatically generated signals to verify their adherence to technical and fundamental rules. This paper presents an explainable approach to stock trading. It investigates the use of classification rules, which represent reliable associations between a set of discrete indicator values and the target class, to address next-day stock price prediction. Adopting associative classifiers in short-term stock trading not only provides reliable signals but also allows domain experts to understand the rationale behind signal generation. The backtesting of a state-of-the-art associative classifier, relying on a lazy pruning strategy, has shown promising performance in terms of equity appreciation and robustness of the trading system to market drawdowns. |
| ISBN: | 9781450380300 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| cameraready_DSMM2020___Leveraging_the_Explainability_of_Associative_Classifiers_to_Support_Quantitative_Stock_Trading.pdf | Articolo principale (postprint editoriale) | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
| DSMM20_openaccess.pdf | Articolo principale (postprint referato) | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri |
http://hdl.handle.net/11583/2845459
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