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Algorithmic Trading Using Long Short-Term Memory Network and Portfolio Optimization

Lucato, Riccardo; Jimenez, Edgar; Rocha, Eduardo Salvador; Qi, Yang; Gavrilyuk, Marina; Drumond, Rafael Rêgo


Investors typically rely on a mix of experience, intuition, knowledge of economic fundamentals and real-time information to make informed choices and try to get as high a rate of return as possible. Their decisions are customarily more instinct-driven than methodical. Propelled by the need for numerically inspired judgments, ever stronger within the financial community, in recent years the usage of computational and mathematical tools has been taking root. In this work we used a Long Short-Term Memory (LSTM) Network trained on historical prices to predict future daily closing prices of several stocks listed on the Standard & Poor 500 (S&P500) index. We compared the predictions of our LSTM network with those produced by another state-of-the-art approach, the Hidden Markov Model (HMM), in order to validate our findings. We then fed our forecasts into aMarkowitz Portfolio Optimization (PO) procedure to identify the best trading strategy. The purpose of PO, which allows for simultaneous and optimal trading of multiple stocks, is to compute a set of daily weights representing the portion of initial capital to be invested in each company. ... mehr

Verlagsausgabe §
DOI: 10.5445/KSP/1000098012/13
Veröffentlicht am 29.09.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 09.2020
Sprache Englisch
Identifikator ISSN: 2363-9881
KITopen-ID: 1000138284
Erschienen in Archives of Data Science, Series A
Band 6
Heft 2
Seiten P13, 17 S. online
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