Planning stock portfolios for long-term investments is a well-known financial problem. Many data mining and machine learning strategies have been proposed to automatically predict the set of uncorrelated stocks maximizing long-term portfolio returns. Among others, the use of scalable itemset-based strategies has recently been studied. Potentially, they can analyze large sets of historical prices corresponding to thousands of stocks in the worldwide market indexes. However, the current studies are still limited to single markets. This paper investigates the applicability of itemset-based strategies for planning stock portfolios in a multi-market context. Scaling the analyses towards multi-market scenarios poses a number of research questions, among which the choice of the diversification strategy, the influence of inter-market correlations among stock prices, and the profitability of multi-market strategies compared to single-market ones. This paper aims at answering to the aforesaid questions by considering a state-of-the-art itemset-based approach. The experimental results show that itemset-based strategies focus the generated portfolios on the outperforming markets. Furthermore, the performance of multi-market strategies with sector-based diversification is on average superior or comparable to single-market ones.

Study of the applicability of an itemset-based portfolio planner in a multi-market context / Cagliero, Luca; Garza, Paolo. - STAMPA. - 2083:(2018), pp. 50-55. (Intervento presentato al convegno 2018 Workshops of the International Conference on Extending Database Technology and the International Conference on Database Theory, EDBT/ICDT-WS 2018 tenutosi a aut nel 2018).

Study of the applicability of an itemset-based portfolio planner in a multi-market context

Cagliero, Luca;Garza, Paolo
2018

Abstract

Planning stock portfolios for long-term investments is a well-known financial problem. Many data mining and machine learning strategies have been proposed to automatically predict the set of uncorrelated stocks maximizing long-term portfolio returns. Among others, the use of scalable itemset-based strategies has recently been studied. Potentially, they can analyze large sets of historical prices corresponding to thousands of stocks in the worldwide market indexes. However, the current studies are still limited to single markets. This paper investigates the applicability of itemset-based strategies for planning stock portfolios in a multi-market context. Scaling the analyses towards multi-market scenarios poses a number of research questions, among which the choice of the diversification strategy, the influence of inter-market correlations among stock prices, and the profitability of multi-market strategies compared to single-market ones. This paper aims at answering to the aforesaid questions by considering a state-of-the-art itemset-based approach. The experimental results show that itemset-based strategies focus the generated portfolios on the outperforming markets. Furthermore, the performance of multi-market strategies with sector-based diversification is on average superior or comparable to single-market ones.
2018
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2723034
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