Proceedings of the Sixth International Workshop on Data Science for Macro-Modeling最新文献

Leveraging the explainability of associative classifiers to support quantitative stock trading 利用关联分类器的可解释性来支持定量股票交易

Proceedings of the Sixth International Workshop on Data Science for Macro-Modeling Pub Date : 2020-06-14 DOI: 10.1145/3401832.3402679

Giuseppe Attanasio, Luca Cagliero, Elena Baralis

{"title":"Leveraging the explainability of associative classifiers to support quantitative stock trading","authors":"Giuseppe Attanasio, Luca Cagliero, Elena Baralis","doi":"10.1145/3401832.3402679","DOIUrl":"https://doi.org/10.1145/3401832.3402679","url":null,"abstract":"Forecasting 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.","PeriodicalId":336159,"journal":{"name":"Proceedings of the Sixth International Workshop on Data Science for Macro-Modeling","volume":"98 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114126393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Ontology mediated information extraction in financial domain with Mastro System-T 基于master System-T的金融领域本体信息提取

Proceedings of the Sixth International Workshop on Data Science for Macro-Modeling Pub Date : 2020-06-14 DOI: 10.1145/3401832.3402681

D. Lembo, Yunyao Li, Lucian Popa, Federico Maria Scafoglieri

引用次数: 6

Price series cross-correlation analysis to enhance the diversification of itemset-based stock portfolios 价格序列相互关联分析增强基于项目集的股票投资组合的多样化

Proceedings of the Sixth International Workshop on Data Science for Macro-Modeling Pub Date : 2020-06-14 DOI: 10.1145/3401832.3402680

Jacopo Fior, Luca Cagliero, P. Garza

引用次数: 3

Proceedings of the Sixth International Workshop on Data Science for Macro-Modeling 第六届宏观建模数据科学国际研讨会论文集

Proceedings of the Sixth International Workshop on Data Science for Macro-Modeling Pub Date : 1900-01-01 DOI: 10.1145/3401832

引用次数: 0